P156 Multiscale modeling of ischemic stroke with the NEURON reaction-diffusion module
Adam J. H. Newton1,2, Alexandra H. Seidenstein2,3, Robert A. McDougal1, William W.
Lytton2,4
1Department of Neuroscience, Yale University, New Haven, CT 06520, USA; 2Department
Physiology & Pharmacology, SUNY Downstate, Brooklyn, NY 11203, USA; 3NYU School of
Engineering, 6 MetroTech Center, Brooklyn, NY 11201, USA; 4Kings County Hospital Center,
Brooklyn, NY 11203, USA
Correspondence: Adam J. H. Newton (adam.newton@yale.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P156
Ischemic stroke is fundamentally a multiscale phenomenon [1]. Occlusion of blood vessels
in the brain triggers a cascade of changes including: 1. synaptic glutamate release,
related to excitotoxicity; 2. elevated extracellular potassium, leading to spreading
depression; 3. cell swelling, reducing the extracellular volume and diffusion; 4.
production of reactive oxygen species, which give rise to inflammation. These cascades
occur over multiple time-scales, with the initial rapid changes in cell metabolism
and ionic concentrations trigging several damaging agents that may ultimately leads
to cell death. Tissue affected by ischemic stroke is divided into three regions; 1.
a core where cells suffer irreparable damage and death, 2. a penumbra where cells
may recover with reperfusion, 3. a further region of edema where spontaneous recovery
is expected. Multiscale modeling and multiphysics modeling is essential to capture
this cascade. Such modeling requires coupling complex intracellular molecular alterations
with electrophysiology, and consideration of network properties in the context of
bulk tissue alterations mediated by extracellular diffusion.
Spreading depression is a wave of depolarization that propagates through tissue and
causes cells in the penumbra to expend energy by repolarization, increasing their
vulnerability to cell death. We modeled the spreading depression seen in ischemic
stroke by coupling a detailed biophysical model of cortical pyramidal neurons equipped
with Na+/K+-ATPase pumps with reaction-diffusion of ions in the extracellular space
(ECS). A macroscopic view of the ECS is characterised by its tortuosity (a reduction
in the diffusion coefficient due to obstructions) and its free volume fraction (typically
~20%). The addition of reactions allows the ECS be modeled as an active medium glial
buffering of K+. Ischemia impedes ATP production which results in a failure of the
Na+/K+-ATPase pump and a rise in extracellular K+. Once extracellular K+ exceeds a
threshold it will cause neurons to depolarize, further increasing extracellular K+.
NEURON’s reaction-diffusion module NRxD [2] provides a platform where detailed neurons
models can be embedded in a macroscopic model of tissue. This is demonstrated with
a multiscale biophysical model of ischemic stroke where the rapid intracellular changes
are coupled with the slower diffusive signaling.
Acknowledgements
Research supported by NIH grant 5R01MH086638
References
1. Newton, AJH, and Lytton, WW: Computer modeling of ischemic stroke. Drug Discovery
Today: Disease Models. 2017.
2. McDougal RA, Hines ML, Lytton WW: Reaction-diffusion in the NEURON simulator. Frontiers
in neuroinformatics. 2013, 7(28).
P157 Accelerating NEURON reaction-diffusion simulations
Robert A. McDougal1, William W. Lytton2,3
1Neuroscience, Yale University, New Haven, CT 06520, USA; 2Physiology & Pharmacology,
SUNY Downstate Medical Center, Brooklyn, NY 11203, USA; 3Kings County Hospital, Brooklyn,
NY 11203, USA
Correspondence: Robert A. McDougal (robert.mcdougal@yale.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P157
A neuron’s electrical activity is governed not just by presynaptic activity, but also
by its internal state. This state is a function of history including prior synaptic
input (e.g. cytosolic calcium concentration, protein expression in SCN neurons), cellular
health, and routine biological processes. The NEURON simulator [1], like much of computational
neuroscience, has traditionally focused on electrophysiology. NEURON has included
NRxD to give standardized support for reaction-diffusion (i.e. intracellular) modeling
for the past 5 years [2], facilitating studies into the role of electrical-chemical
interactions. The original reaction-diffusion support was written in vectorized Python,
which offered limited performance, but ongoing improvements have now significantly
reduced run-times, making larger-scale studies more practical.
New accelerated reaction-diffusion methods are being developed as part of a separate
NEURON module, crxd. This new module will ultimately be a fully compatible replacement
for the existing NRxD module (rxd). Developing it as a separate module allows us to
make it available to the community before it supports the full functionality of NRxD.
The interface code for crxd remains in Python, but it now transfers model structure
to C code via ctypes, which performs all run-time calculations; Python is no longer
invoked during simulation. Dynamic code generation allows arbitrary reaction schemes
to run at full compiled speed. Thread-based parallelization accelerates extracellular
reaction-diffusion simulations.
Preliminary tests suggest an approximately 10x reduction in 1D run-time using crxd
instead of the Python-based rxd. Like rxd, crxd uses the Hines method [3] for O(n)
1D reaction-diffusion simulations. Using 4 cores for extracellular diffusion currently
reduces the runtime by a factor of 2.3. Additionally, using the crxd module simplifies
setup relative to rxd-based simulations since it does not require installing scipy.
Once crxd supports the entire documented NRxD interface and has been thoroughly tested,
it will replace the rxd module and thus become NEURON’s default module for specifying
reaction-diffusion kinetics.
Acknowledgements
Research supported by NIH R01 MH086638.
References
1. NEURON | for empirically based simulations of neurons and networks of neurons [http://neuron.yale.edu]
2. McDougal RA, Hines ML, Lytton WW: Reaction-diffusion in the NEURON simulator. Front.
Neuroinform 2013, 7:28.
3. Hines M: Efficient computation of branched nerve equations. Int. J. Bio-Medical
Computing 1984, 15:69–76.
P158 Computation of invariant objects in the analysis of periodically forced neural
oscillators
Alberto Pérez-Cervera, Gemma Huguet, Tere M-Seara
Departament de Matemàtica Aplicada, Universitat Politècnica de Catalunya, Barcelona,
E-08028, Spain
Correspondence: Alberto Pérez-Cervera (alberto.perez@upc.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P158
Background oscillations, reflecting the excitability of neurons, are ubiquitous in
the brain. Some studies have conjectured that when spikes sent by one population reach
the other population in the peaks of excitability, then information transmission between
two oscillating neuronal groups is more effective [1]. In this context, the phase
relationship between oscillating neuronal populations may have implications in neuronal
communication between brain areas [2, 3]. The Phase Response Curve (PRC) of a neural
oscillator measures the phase-shift resulting from perturbing the oscillator at different
phases of the cycle. It provides useful information to understand how phase-locking
relationships between neural oscillators emerge but only when perturbations are weak
and amplitude is not taken into account.
In this work, we consider a population rate model [4] and perturb it with a time-dependent
input. In order to study the phase-locking relationships that emerge, we use the stroboscopic
map to perform a bifurcation analysis as a function of the amplitude and frequency
of the perturbation. We observe the existence of bistable solutions for some regions
of the parameters space, suggesting that, for a given input, populations may operate
in different regimes. Furthermore, we apply powerful computational methods [5] to
compute the invariant objects for the stroboscopic map, providing a framework that
enlarges the PRC comprehension of the perturbative effects in the phase dynamics.
References
1. Fries P: A mechanism for cognitive dynamics: neuronal communication through neuronal
coherence. Trends in cognitive sciences 2005, 9(10):474–48
2. Tiesinga PH, Sejnowski TJ: Mechanisms for phase shifting in cortical networks and
their role in communication through coherence. Frontiers in human neuroscience 2010,
4:196.
3. Canavier CC: Phase-resetting as a tool of information transmission. Current opinion
in neurobiology 2015, 31: 206–213.
4. Wilson HR, Cowan JD: Excitatory and inhibitory interactions in localized populations
of model neurons. Biophysical journal 1972, 12.1:1–24.
5. Haro À, Canadell M, Figueras JL, Luque A, Mondelo JM: The Parameterization Method
for Invariant Manifolds 2016. Springer.
P159 Computational model of spatio-temporal coding in CA3 with speed-dependent theta
oscillation
Caroline Haimerl1,2, David Angulo-Garcia1,3, Alessandro Torcini1,3,4, Rosa Cossart1,
Arnaud Malvache1
1Institut de Neurobiologie de la Méditerrannée (INMED), INSERM, UMR901, Aix-Marseille
Univ, Marseille, France; 2Center of Neural Science, New York University, New York,
NY, USA; 3Aix-Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France;
4Laboratoire de Physique Théorique et Modélisation, CNRS UMR 8089, Université de Cergy-Pontoise,
F-95300 Cergy-Pontoise Cedex, France
Correspondence: Caroline Haimerl (david.angulo-garcia@univ-amu.fr)
BMC Neuroscience 2017, 18 (Suppl 1):P159
Recent studies have demonstrated the capacity of hippocampal sequences associated
with theta oscillation, to encode spatio-temporal information. In particular, cells
in CA1 become active sequentially in a stable unidirectional order during spontaneous
run periods and under minimal external cues [1]. This sequential activity seems to
integrate either the distance that the animal has run or the time that has elapsed,
two related coding states that can be separated through the change in cellular dynamics
with the animals’ speed. Other studies indicate that these cell sequences depend on
theta oscillation from the medial septum and may reflect input from CA3 [2–4].
Running speed of the animal has also shown to influence theta oscillation frequency
and amplitude. This oscillation could thereby carry the spatio-temporal information
input required to determine distance/time coding. Inspired by [2], we modeled a circular
recurrent network of excitatory cells with short-term synaptic plasticity [5] and
global inhibition. By applying speed-dependent theta oscillation, we reproduced the
dynamics of spatio-temporal coding observed in experimental data and propose a mechanism
of switching between the two coding states through a change in integration of theta
input. In particular, our firing rate model reproduces the sequence properties (recurrence,
unidirectionality, sparse activity, memory) based on the network characteristics of
CA3 and allows exploring the dynamics of the sequential activity. Simulations with
this model show a non-trivial relationship between sequence slope and the frequency/amplitude
of the oscillatory input: depending on the amplitude range of the theta oscillation,
sequence dynamics can either be independent of speed (time coding) or linearly dependent
on speed (distance coding). Therefore, the model proposes a network structure that
could give rise to two basic and possibly default, self-referenced coding states observed
in the hippocampus.
This model provides insights into how a recurrent network operates in the absence
of spatially specific input, but still allows for such input to modulate sequential
activity towards place field representation [2]. We will next explore further the
mechanisms of sequence generation and coding correlates in both theoretical and experimental
work.
References
1. Villete V, Malvache A, Tressard T, Dupuy N, Cossart R: Internally Recurring Hippocampal
Sequences as a Population Template of Spatiotemporal Information. Neuron 2015, 88(2):357–366.
2. Wang Y, Romani S, Lustig B, Leonardo A, Pastalkova E: Theta sequences are essential
for internally generated hippocampal firing fields. Nature Neuroscience 2015 18(2):282–290.
3. Salz DM., Tigany Z, Khasnabish S, Kohley A, Sheehan D, Howard MW, Eichenbaum H:
Time Cells in Hippocampal Area CA3. J. Neurosci. 2016, 36:7476–7484.
4. Guzman SJ, Schlögl A, Frotscher M, Jonas P: Synaptic mechanisms of pattern completion
in the hippocampal CA3 network. Science 2016, 353:1117–1123.
5. Mongillo G, Barak, O, Tsodyks M: Synaptic theory of working memory. Science 2008,
319:1543–1546.
P160 The effect of progressive degradation of connectivity between brain areas on
the brain network structure
Kaoutar Skiker, Mounir Maouene
Department of mathematics and computer science, ENSAT, Abdelmalek Essaadi’s University,
Tangier, Morocco
Correspondence: Kaoutar Skiker (skiker.kaoutar85@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P160
Neurodegenerative diseases such as Alzheimer and Schizophrenia are characterized by
the progressive decline of cognitive functions such as memory, language and consciousness
with take the form of memory loss, deficits in verbal and non-verbal communication
and so on. Cognitive deficits are interpreted in terms of damage in the network of
brain areas, instead of damage to specific brain areas [1]. Many studies combining
network theory and neuroimaging data have shown that brain networks, known to have
a small world structure [2], are disorganized in people with neurodegenerative diseases
indicating that the connectivity between brain areas is altered by the disease [1].
The disorganization of brain networks can be a consequence of the vulnerability of
hub areas to diseases or from the abnormal connectivity between brain areas.
In this paper, we assess how the progressive degradation of connectivity between brain
areas affects the brain network structure. We propose an algorithm building on the
idea that the connections between brain areas are weakened as the disease progress
in time. We apply the algorithm on a functional connectivity matrix freely available
for download from the Brain Connectivity Toolbox consisting of nodes representing
brain areas and edges representing the functional links between two brain areas [3].
The network is weighted, with weights wij reflect the correlations between two brain
areas Ai and Aj. At a given threshold t, the new weights are given by wij-t; with
t indicates the progression of disease in time. The structure of the new network is
analyzed using graph theoretical measures including clustering coefficient and path
length. After damage, the functional brain network shows the properties of high clustering
and low path length indicting that the network presents a small world structure necessary
for the proper cognitive functioning. The progressive degradation of links doesn’t
change the network’s properties dramatically, clustering coefficient are slightly
modified until t = 0.25 (see Figure 1 for clustering coefficient). At this stage,
the functional network shifts from high organization to randomness.
In sum, cognitive deficits in neurodegenerative diseases can be understood in the
scope of the progressive degradation of the connectivity between brain areas within
the network.
Figure 1. The average clustering coefficient of the network decreases following the
progressive degradation of the connectivity between brain areas
References
1. DS Bassett, ET Bullmore: Human Brain Networks in Health and Disease. Current Opinion
in Neurology 2009, 22: 340–47.
2. O Sporns: Network Attributes for Segregation and Integration in the Human Brain.
Current Opinion in Neurobiology 2013, 23: 162–71.
3. M Rubinov, O Sporns: Complex network measures of brain connectivity: Uses and interpretations.
Neuroimage 2010, 52:1059–1069.
P161 A network architecture for comparing the behavior of a neurocomputational model
of reward-based learning with human
Gianmarco Ragognetti1, Letizia Lorusso2, Andrea Viggiano2 and Angelo Marcelli1
1Laboratory of Natural Computation, Department of Information and Electrical Engineering
and Applied Mathematics, University of Salerno, 84084 Fisciano (SA), Italy; 2Department
of Medicine, University of Salerno, 84083 Lancusi (SA), Italy
Correspondence: Gianmarco Ragognetti (gragognetti@unisa.it)
BMC Neuroscience 2017, 18 (Suppl 1):P161
Neuro computational models represent a powerful tool for bridging the gap between
functions of the neural circuits and observable behaviors [1]. Once the model has
been built, its output is compared with the observations either to validate the model
itself or to propose new hypotheses. This approach has led to building a multi-scale
model of the sensorimotor system from muscles, proprioceptors to skeletal joints,
spinal regulating centers and central control circuits [2–6].
In this framework, we propose a neural network architecture to simulate the selection
of actions performed by the motor cortex in response to a sensory input during a reward-based
movement learning. The network has as many input nodes as the number of different
stimuli, each node being a combination of the sensory inputs, and as many output nodes
as the number of different actions that can be performed, each node being a combination
of the motor commands. The network is fully connected, so that each stimulus concurs
to the selection of each action and each action is selected concurrently by all the
stimuli. The weights are updated by taking into account both the expected reward and
the actual reward, as suggested in [7]. By adopting this architecture, the percept
is represented by a combination of sensory inputs, while the action is represented
by a combination of motor commands. Thus, it reproduces faithfully the condition of
experiments of motor learning when a set of sensory inputs, such as semantically neutral
visual stimuli, are presented to the subject whose response is merely a motor action,
such as pushing a button. Under such conditions, it then becomes possible to fit the
data provided by the experiments with the model to both estimate the validity of the
model and to infer the role of the parameter on behavioral traits.
The simulations were compared to the behaviors of human subjects while learning which
out of two buttons to press in response to a collection of visual stimuli containing
edges and geometric shapes in a reward based setting. The results showed that the
behavior of the complete system is the one expected under the hypothesis that the
reward acts by modulating the action selection triggered by the input stimuli during
motor learning. Moreover, differently from most literature models, the learning rate
varies with the complexity of the task, i.e. the number of input stimuli. It can be
argued that the decrease in learning rate seen in humans learning large set of stimuli
could be due to an attenuation of memory traces in real synapses over time. In our
future investigations, we will work to improve the model by adding such an effect
in our network.
References
1. Lan, N., Cheung, V. and Gandevia, S.C.: EDITORIAL - Neural and Computational Modeling
of Movement Control. Front. in Comp. Neurosc. 2016, 10: 1–5.
2. Cheng, E. J., Brown, I.E., and Loeb, G. E.: Virtual muscle: a computational approach
to understanding the effects of muscle properties on motor control. J. Neurosci. Methods
2000, 101: 117–130.
3. Mileusnic, M. P., Brown, I.E., Lan, N., and Loeb, G. E.: Mathematical models of
proprioceptors. I. Control and transduction in the muscle spindle. J. Neurophysiol.
2006, 96: 1772–1788.
4. Song, D., Raphael, G., Lan, N., and Loeb, G. E.: Computationally efficient models
of neuromuscular recruitment and mechanics. J. Neural Eng. 2008, 5: 175–184.
5. Song, D., Lan, N., Loeb, G. E., and Gordon, J.: Model-based sensorimotor integration
for multi-joint control, development of a virtual arm model. Ann. Biomed. Eng. 2008,
36: 1033–1048.
6. He, X., Du, Y. F., and Lan, N.: Evaluation of feedforward and feedback contributions
to hand stiffness and variability in multi joint arm control. IEEE Trans. Neural Syst.
Rehabil. Eng. 2013, 21: 634–647.
7. Sutton, R. S., and Barto A.G.: Reinforcement learning: An introduction. Cambridge:
MIT press, 1998.
P162 Distributed plasticity in the cerebellum: how do cerebellar cortex and nuclei
plasticity cooperate for learning?
Rosa Senatore, Antonio Parziale, Angelo Marcelli
Laboratory of Natural Computation, Department of Information and Electrical Engineering
and Applied Mathematics, University of Salerno, 84084 Fisciano (SA), Italy
Correspondence: Rosa Senatore (rsenatore@unisa.it)
BMC Neuroscience 2017, 18 (Suppl 1):P162
Different forms of synaptic plasticity have been revealed within the cerebellum (CB),
and many hypothesis about their role have been proposed [1]. We used a model-based
analysis for investigating the role of these forms of plasticity in three behaviors:
phase reversal of the vestibule-ocular reflex, acquisition of conditioned responses
and learning a novel limb movement. We investigated these behaviors since they involve
different forms of learning: phase reversal requires to modify a preexistent stimulus-response
(S-R) association according to the feedback signal provided by climbing fibers (CFs);
conditioning involves learning a new S-R association according to a preexistent one
between the stimulus coming from the CFs and a motor response; learning novel motor
behaviors corresponds to create new S-R associations according to the CF feedback.
The analysis was carried through a CB model that incorporates plasticity mechanisms
at different stages of the CB processing, both in cortex and nuclei [2]. Synaptic
plasticity has been simulated in both granular (Gr) and Purkinje (PC) network: granule
cells show intrinsic plasticity depending on mossy fibers (MFs) activity, and MF-Gr
synapses undergo both Long Term Depression (LTD) and Long Term Potentiation (LTP)[3];
PF-PC synapses undergo both LTD and LTP, depending on PF and CF activity [4]. The
model also includes synaptic plasticity involving the molecular interneurons (MLI)
at PF-MLI synapses [5] and Rebound potentiation at MLI-PC synapses [6]. Within the
CB nuclei, LTD occurs in MF-NC synapses during inhibition from PCs, whereas LTP occurs
during release from inhibition [7]. Our results suggest that the main contribution
to CB learning is provided by the synaptic plasticity at PF-PC and MF-NC synapses.
Indeed, excluding the plasticity at PF–PC site caused strong impairment in learning
all the considered behaviors, while excluding the plasticity at MF–NC site induced
mild impairment in acquiring conditioned responses and novel limb movements, and strong
impairment was observed in phase reversal and motor adaptation. Removal of other forms
of synaptic plasticity only induced slower learning. Our results also suggest that
LTP at PF-PC underlies the extinction phenomenon observed in conditioning, and that
saving phenomenon could be ascribed to a residual plasticity within the CB cortex
rather than within the CB nucleus, since saving was observed even after removal of
MF-NC plasticity before reconditioning. Finally, model simulations support the view
that learned associations are transferred from the CB cortex to the CB nuclei, due
to the combined effect of plasticity at PF-PC synapses in early stage of learning,
and MF-NC synapses in late learning. Indeed, lesions at PCs layer or removal of PF-PC
synaptic plasticity in late learning stage did not induced any impairment in the behavior
of the model, whereas removal of PF-PC synaptic plasticity in early learning impaired
learning capabilities of the model.
References
1. Gao Z, van Beugen BJ, De Zeeuw CI: Distributed synergistic plasticity and cerebellar
learning. Nat Rev Neurosci 2012, 13:619–635.
2. Senatore R, Parziale A, Marcelli A: A computational model for investigating the
role of cerebellum in acquisition and retention of motor behavior. 25th Annual Computational
Neuroscience Meeting: CNS-2016. BCM Neurosci 2016, 17: 64–64.
3. Gall D, Prestori F, Sola E, D’Errico A, Roussel C, Forti L, Rossi P, D’Angelo E:
Intracellular calcium regulation by burst discharge determines bidirectional long-term
synaptic plasticity at the cerebellum input stage. J Neurosci 2005, 25:4813–4822.
4. Coesmans M, Weber JT, De Zeeuw CI, Hansel C: Bidirectional parallel fiber plasticity
in the cerebellum under climbing fiber control. Neuron 2004, 44:691–700.
5. Rancillac A, Crépel F: Synapses between parallel fibres and stellate cells express
long-term changes in synaptic efficacy in rat cerebellum. J Physiol 2004, 554:707–720.
6. Kano M, Rexhausen U, Dreessen J, Konnerth A: Synaptic excitation produces a long-lasting
rebound potentiation of inhibitory synaptic signals in cerebellar Purkinje cells.
Nature 1992, 356:601–604.
7. Aizenman CD, Linden DJ: Rapid, synaptically driven increases in the intrinsic excitability
of cerebellar deep nuclear neurons. Nat Neurosci 2000, 3:109–111.
P163 Ising Model with conserved magnetization on the Human Connectome: implications
on the relation structure-function in wakefulness and anesthesia
S. Stramaglia1, M. Pellicoro1, L. Angelini1, E. Amico2,3, H. Aerts2, J. Cortés4, S.
Laureys3, D. Marinazzo2
1Dipartimento di Fisica, Università degli Studi Aldo Moro, Bari, and INFN, Sezione
di Bari, Italy; 2Data Analysis Department, Ghent University, Ghent, Belgium; 3Coma
Science Group, University of Liège, Liège, Belgium; 4Cruces Hospital and Ikerbasque
Research Center, Bilbao, Spain
Correspondence: S. Stramaglia (sebastiano.stramaglia@ba.infn.it)
BMC Neuroscience 2017, 18 (Suppl 1):P163
Dynamical models implemented on the large-scale architecture of the human brain may
shed light on how function arises from the underlying structure. This is the case
notably for simple abstract models, such as the Ising one. We compare the spin correlations
of the Ising model and the empirical functional brain correlations, both at the single
link level and at the modular level, and show that the prediction is better in anesthesia
than in wakefulness, in agreement with recent experiments. We show that conserving
the magnetization in the Ising model dynamics (Kawasaki dynamics) leads to an improved
prediction of the empirical correlations in anesthetised brains, see Figure 1. Moreover,
we show that at the peak of the specific heat (the critical state) the spin correlations
are minimally shaped by the underlying structural network, explaining how the best
match between structure and function is obtained at the onset of criticality, as previously
observed.
These findings could open the way to novel perspectives when the conserved magnetization
is interpreted in terms of a homeostatic principle imposed to neural activity.
Figure 1. A. Mean Squared Error in Wakefulness and Anesthesia between the empirical
connectivity and the one simulated by Glauber and Kawasaki dynamics. B. Mutual Information
between the modular partitions of the empirical and modelled functional networks.
These quantities are depicted as a function of the inverse temperature β
Conclusions: In agreement with recent theoretical frameworks [1], our results suggest
that a wide range of temperatures correspond to criticality of the dynamical Ising
system on the connectome, rather than a narrow interval centered in a critical state.
In such conditions, the correlational pattern is minimally shaped by the underlying
structural network. It follows that, assuming that the human brain operates close
to a critical regime [2], there is an intrinsic limitation in the relationship between
structure and function that can be observed in data. We show that empirical correlations
among brain areas are better reproduced at the modular level using a model which conserves
the global magnetization. The most suitable way to compare functional and structural
patterns is to contrast them at the network level, using, e.g., the mutual information
between partitions like in the present work.
References
1. Moretti P. and Muñoz M.A.: Griffiths phases and the stretching of criticality in
brain networks, Nature communications 2013, 4: 2521.
2. Chialvo D.: Emergent complex neural dynamics, Nature Physics 2010, 6: 744–750.
P164 Multiscale Granger causality analysis by à trous wavelet transform
S. Stramaglia1, I. Bassez2, L. Faes3, D. Marinazzo2
1Dipartimento di Fisica, Università degli Studi Aldo Moro, Bari, and INFN, Sezione
di Bari, Italy; 2Data Analysis Department, Ghent University, Ghent, Belgium; 3BIOtech,
Dept. of Industrial Engineering, University of Trento, and IRCS-PAT FBK, Trento, Italy
Correspondence: S. Stramaglia (sebastiano.stramaglia@ba.infn.it)
BMC Neuroscience 2017, 18 (Suppl 1):P164
Great attention has been devoted in the last years to the identification of information
flows in human brains. Since interactions occur across multiple temporal scales, it
is likely that information flow will exhibit a multiscale structure: high-frequency
activity, reflecting local domains of cortical processing, and low-frequency activity
dynamically spread across the brain regions by both external sensory input and internal
cognitive events. In order to detect information flow at multiple scale the decomposition
of the signals in the wavelet space has been proposed in [1]; an analytical frame
for linear multivariate stochastic processes explored at different time scales has
been proposed in [2]. However, the computation of multiscale measures of information
dynamics may be complicated by theoretical and practical issues such as filtering
and undersampling: to overcome this problems, we propose here another wavelet-based
approach for multiscale causality analysis, which is characterized by the following
properties: (i) only the candidate driver variable is wavelet transformed (ii) the
decomposition is performed using the à trous wavelet transform with cubic B-spline
filter [3]. The use of the à trous transform is suggested by its interesting properties,
indeed it satisfies the shift invariance, and its coefficients at time t are a linear
combination of the time series values; no decimation of the time series, as in the
discrete wavelet transform, is done. Granger causality examines how much the predictability
of the target from its past improves when the driver variables’ past values are included
in the regression, where m is the order of the model. We propose here to measure the
causality at scale s by including w(t-1,s), w(t-2,s),…,w(t-m,s) in the regression
model of the target, where w(t,s) are the à trous wavelet coefficients of the driver.
In figure 1 we depict the multiscale causality evaluated by the proposed approach
on a simulated two-dimensional linear system unidirectionally coupled with lag equal
to 8 and strength a: it increases with the strength and peaks in correspondence of
the lag. We have applied the proposed algorithm to scalp EEG signals [4], and we found
that the global amount of causality among signals is significantly decreasing as the
scale s is increased. Furthermore, comparing signals corresponding to resting conditions
with closed eyes and with open eyes, we found that at large scales the effective connectivity,
in terms of the proposed measure, is significantly lower with eyes open.
Figure 1. A. Granger causality in an unidirectionally coupled system is depicted as
a function of the scale for several values of the coupling. B. GC values for eyes
open and closed conditions from regular time series. C. GC values in the same conditions
from wavelet coefficients (scale 4)
References
1. Lungarella M, Pitti A, Kuniyoshi K: Information transfer at multiple scales. Phys.
Rev. E 2007, 76: 056117
2. Faes, L., Montalto, A., Stramaglia, S., Nollo, G., Marinazzo, D.: Multiscale analysis
of information dynamics for linear multivariate processes, Proceedings of the Annual
International Conference of the IEEE Engineering in Medicine and Biology Society,
EMBS 2016.
3. Renaud O, Starck, J-L, Murtagh, F: Wavelet-Based Combined Signal Filtering and
Prediction. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics.
2005, vol. 35, no. 6, p. 1241–1251
4. http://www.physionet.org/pn4/eegmmidb
P165 New (spectral) dynamic causal modeling scheme improves effective connectivity
estimation within resting state networks in longitudinal data
Hannes Almgren1, Frederik Van De Steen1, Adeel Razi2,3, Daniele Marinazzo1
1Department of Data Analysis, Ghent University, Ghent, 9000, Belgium; 2The Wellcome
Trust Centre for Neuroimaging, University College London, London, WC1 N 3BG, UK; 3Department
of Electronic Engineering, NED University of Engineering and Technology, Karachi,
Pakistan
Correspondence: Hannes Almgren (Hannes.Almgren@ugent.be)
BMC Neuroscience 2017, 18 (Suppl 1):P165
Effective connectivity within resting state networks has been estimated using spectral
dynamic causal modeling (spDCM) [1]. Since its initial release, spDCM has been updated
to improve performance and to render it applicable to larger networks. The objective
of the present study is to assess the impact of these changes on parameter estimates
and stability. We therefore compared performance between an early version of DCM (v6303)
and a newer version of DCM (v6801) in combination with the parametric empirical Bayesian
(PEB) framework [2]. Both were compared regarding (1) ability to explain observed
cross spectral densities (CSD), (2) estimated network structure, and (3) stability
of parameter estimates. An extensive single-subject longitudinal dataset, including
101 resting state fMRI sessions, was analyzed (myconnectome.org/wp) [3]. Eight resting
state sessions were chosen for our analyses: occipital and lateral visual, auditory,
somatomotor, left and right frontoparietal, default mode, and executive control network.
Results showed that the newer spDCM-PEB combination explained the data (i.e., CSDs)
far better than the older spDCM (95.31% versus 68.31% explained variance, respectively).
Furthermore, the older version often failed to yield proper estimates (i.e., because
of low proportion explained variance or estimated connection strengths near zero)
in networks consisting of two- or three regions, while the newer version showed less
such problems. Concerning average network structure across sessions, the newer spDCM-PEB
combination detected asymmetric influences within networks consisting of two regions
(see Figure 1). Furthermore, regions located in the medial part of the brain showed
larger in- versus out-connectivity. For the default mode network, consisting of four
regions in the present study, both versions yielded largely similar network structures
(i.e., reciprocal influences between bilateral parietal cortices, and larger in- versus
out-connectivity for medial areas). However, the older version of spDCM showed a positive
influence (0.21 Hz) from precuneus to medial prefrontal cortex, which was much smaller
(0.05 Hz) for the newer DCM-PEB combination. Stability depended profoundly on the
size of the network: parameter estimates showed higher stability in two-region networks
than in larger networks for both versions.
Figure 1. Comparison of posterior parameter estimates within the auditory network.
A. median posterior parameter estimates for the older version (shown in red) and the
newer spDCM-PEB combination (shown in black). B and C. distribution of these parameter
estimates over sessions, together with the bootstrapped high density intervals, for
both the older and newer scheme
References
1. Friston KJ, Kahan J, Biswal B, Razi, A: A DCM for resting state fMRI. NeuroImage
2014, 94:396–407.
2. Friston KJ, Litvak V, Oswal A, Razi A, Stephan KE, van Wijk BC, Ziegler G, Zeidman
P: Bayesian model reduction and empirical Bayes for group (DCM) studies. NeuroImage
2016, 128:413–431.
3. Laumann TO, Gordon EM, Adeyemo B, Snyder AZ, Joo SJ, Chen MY, Gilmore AW, McDermott
KB, Nelson SM, Dosenbach NU, et al.: Functional system and areal organization of a
highly sampled individual human brain. Neuron 2015, 87(3):657–670.
P166 Effective connectivity modulations of win-and loss feedback: A dynamic causal
modeling study of the human connectome gambling task
Frederik Van de Steen1, Ruth Krebs2, Daniele Marinazzo1
1Department of data analysis, Ghent University, Ghent, 9000, Belgium; 2Department
of experimental psychology, Ghent University, Ghent, 9000, Belgium
Correspondence: Frederik Van de Steen (frederik.vandesteen@ugent.be)
BMC Neuroscience 2017, 18 (Suppl 1):P166
The main goal of this study was to investigate changes in effective connectivity associated
with reward and punishment. More specifically, changes in connectivity between the
ventral striatum (VS), anterior insula (aI), anterior cingulate cortex (ACC) and occipital
cortex (OCC) that are related to win- and loss- feedback were studied.
Here, fMRI data from the human connectome project [1] was used for our study purposes.
Data from 369 unrelated subjects performing a gambling task was analyzed. In short,
participants played a card game where they had to guess whether the upcoming card
would be higher or less than 5 (range was between 1 and 9). After the gamble, feedback
was provided indicating a reward, punishment or neutral trial. The minimally preprocessed
data was used and extra spatially smoothed with a 5-mm FWHM Gaussian kernel. The images
were then entered in a first level general linear model (GLM) and summary statistic
images of the first level GLM were entered in a second level GLM. The following two
contrasts were used to identify the relevant brain regions at the group level: [Win
- Neut] AND [Loss-Neut] (i.e. conjunction), and [Win-neut]. Based on the group level
results, time-series of VS, aI, ACC and OCC were extracted for every subject and used
in further dynamic causal modeling (DCM, [2]) analysis. We specified a fully connected
model (i.e. all nodes are reciprocally connected) where the win and loss events were
allowed to modulate all connections. The driving input consisted of all feedback events
(win, loss and neutral events) and entered the DCM’s via OCC. The fully connected
model was estimated for every subject and then used in the recently proposed parametric
empirical Bayesian (PEB, [3]) framework for estimating DCM parameters at the group
level. Finally, we used Bayesian model reduction to obtain the best 255 nested models.
Since there was no clear winning model, Bayesian model averaging (BMA) of the 256
model (full + 255 nested models) parameters was performed. Figure 1. shows the group
level BMA modulatory parameters with a posterior probability >.95.
Conclusion: Overall, both win- and loss- feedback have a general increasing effect
on effective connectivity. The main difference between win and loss can be observed
for the connection from aI and OCC with loss-feedback having a decreased effect. In
addition, only win-feedback increases the connection from VS to aI. Overall, the VS
appears as a key region in conveying loss and win information across the network.
Figure 1. BMA modulatory parameters at the group level are shown for A. loss feedback;
B. win feedback
Acknowledgements
This research was supported by the Fund for Scientific Research-Flanders (FWO-V),
Grant FWO16/ASP_H/255.
References
1. Van Essen, D. et al. The WU-Minn Human Connectome Project: An overview. NeuroImage,
2013, 80: 62–79.
2. Friston, Karl J., Lee Harrison, and Will Penny. Dynamic causal modelling. Neuroimage,
2003, 19(4): 1273–1302.
3. Friston, Karl J., et al. Bayesian model reduction and empirical Bayes for group
(DCM) studies. Neuroimage
P167 Modeling global brain dynamics in brain tumor patients using the Virtual Brain
Hannelore Aerts, Daniele Marinazzo
Department of Data Analysis, Ghent University, Ghent, Belgium
Correspondence: Hannelore Aerts (hannelore.aerts@ugent.be)
BMC Neuroscience 2017, 18 (Suppl 1):P167
Increasingly, computational models of brain activity are applied to investigate the
relation between structure and function. In addition, biologically interpretable dynamical
models may be used as unique predictive tools to investigate the impact of structural
connectivity damage on brain dynamics. That is, individually modeled biophysical parameters
could inform on alterations in patients’ local and large-scale brain dynamics, which
are invisible to brain-imaging devices. In this study, we compared global biophysical
model parameters between brain tumor patients and healthy controls. To this end, we
used The Virtual Brain (TVB; [1]), a neuroinformatics platform that utilizes empirical
structural connectivity data to create dynamic models of an individual’s brain.
Ten glioma patients (WHO grade II and III, mean age 41.1yo, 4 females; 5 from open
access dataset [2]), 13 meningioma patients (mean age 60.23y, 11 females), three pseudo-meningioma
patients (subtentorial brain tumors, mean age 58yo, 2 females) and 11 healthy partners
(mean age 58.6y, 4 females) were included in this study. From all participants, diffusion
MRI, resting-state fMRI and T1-weighted MRI data were acquired. Data were preprocessed
and converted to a subject-specific structural and functional connectivity matrix
using a modified version of the TVB preprocessing pipeline [3].
In order to simulate brain dynamics, the reduced Wong-Wang model [4] was used. This
is a dynamical mean field model that consistently summarizes the realistic dynamics
of a detailed spiking and conductance-based synaptic large-scale network. A subject-specific
parameter space exploration was conducted to obtain an optimal correspondence between
the individual’s simulated and empirical functional connectivity matrix. To this end,
values of the global scaling factor G and the local feedback inhibitory synaptic coupling
J
i
were varied. Values of G and J
i
yielding optimal correspondence were then compared between the brain tumor patient
groups and healthy controls.
The distribution of optimal values for G and J
i
per group is depicted in Figure 1. Visually, no clear group differences are apparent.
In future studies, larger sample sizes will be utilized, as data collection is still
ongoing and more efforts to data sharing across labs are undertaken. In addition,
local model parameter alterations in the vicinity of the lesion will be examined,
since global model parameters might not be sufficiently sensitive to capture local
lesion effects.
Figure 1. Distribution of optimal model parameter values per group: control subjects
(CON), pseudo control subjects with subtentorial brain tumor (pCON), meningioma patients
(MEN), and glioma WHO grade II and III patients (GLI). A. Global scaling factor (G);
B. Local feedback inhibitory synaptic coupling (J
i
)
References
1. P Sanz Leon, S A Knock, M M Woodman, L Domide, J Mersmann, A R McIntosh, V K Jirsa.
The Virtual Brain: A simulator of primate brain network dynamics. Frontiers in Neuroinformatics
2013, 7:1–23.
2. C Pernet, K Gorgolewski, I Whittle. UK Data Archive. [http://dx.doi.org/10.5255/UKDA-SN-851861]
3. M Schirner, S Rothmeier, V K Jirsa, A R McIntosh, P Ritter. An automated pipeline
for constructing personalized virtual brains from multimodal neuroimaging data. NeuroImage
2015, 117:343–357.
4. G Deco, A Ponce-Alvarez, P Hagmann, G L Romani, D Martini, M Corbetta. How local
excitation-inhibition ratio impacts the whole brain dynamics. The Journal of Neuroscience
2014, 34:7886–7898.
P168 Representation of Neuronal Morphologies
Lida Kanari1, Pawel Dlotko2, Martina Scolamiero3, Ran Levi4, Julian Shillcock1, Christiaan
P.J. de Kock5, Kathryn Hess3 and Henry Markram1
1Blue Brain Project, École polytechnique fédérale de Lausanne, Lausanne, Switzerland;
2Departement of Mathematics, Swansea University, Swansea, Wales, UK; 3Laboratory for
Topology and Neuroscience at the Brain Mind Institute, École polytechnique fédérale
de Lausanne, Lausanne, Switzerland; 4Institute of Mathematics, University of Aberdeen,
Aberdeen, Scotland, UK; 5Department of Integrative Neurophysiology, Center for Neurogenomics
and Cognitive Research, VU Universiteit Amsterdam, Amsterdam, the Netherlands
Correspondence: Lida Kanari (lida.kanari@epfl.ch)
BMC Neuroscience 2017, 18 (Suppl 1):P168
The shape of neuronal arborizations defines amongst other aspects their physical connectivity
and functionality. Yet an efficient method for quantitatively analyzing the spatial
structure of such trees has been difficult to establish. The wide diversity of neuronal
morphologies in the brain, even for cells identified by experts as of the same type,
renders an objective classification scheme a challenging task.
We propose a Topological Morphology Descriptor [1], inspired by Topological Data Analysis,
to quantitatively analyze the branching shapes of neurons, which overcomes the limitations
of existing techniques. The TMD algorithm maps the branches of a tree (Fig 1A) into
a “barcode” (Fig 1B). The TMD encodes the morphology of the tree into a simplified
topological representation that preserves sufficient information to be useful for
the comparison and the distinction of different branching patterns.
Figure 1. Topological morphology descriptor. A. The neuronal tree is mapped into a
barcode. B. Each bar represents the lifetime of a branch; its start and end distance
from the soma
This method is applicable to any tree-like structure, and we demonstrate its generality
by applying it to groups of mathematical random trees and neuronal morphologies. We
identify the structural differences between known morphological types [2-3] as well
as subtypes for human temporal cortex L2/3 pyramidal cells [4]. Our results show that
the TMD of tree shapes reliably and efficiently distinguishes different shapes of
trees and neurons. Therefore, the TMD provides an objective benchmark test of the
quality of any grouping of branching trees into discrete morphological classes. Our
results demonstrate that the TMD can enhance our understanding of the anatomy of neuronal
morphologies.
References
1. Kanari L, Dłotko P, Scolamiero M, Levi R, Shillcock J, Hess K, Markram H, Quantifying
topological invariants of neuronal morphologies, arxiv.org 2016, [https://arxiv.org/abs/1603.08432]
2. Ascoli G.A., Donohue D.E. and Halavi M., NeuroMorpho.Org: A Central Resource for
Neuronal Morphologies, J. Neurosc. 2007, 27 (35): 9247–9251.
3. Markram H. Muller E., Ramaswamy S., Reimann M.W. et al., Reconstruction and Simulation
of Neocortical Microcircuitry, Cell 2015, 163 (2): 456–492.
4. Mohan H., de Kock C.P.J., et al. Dendritic and Axonal Architecture of Individual
Pyramidal Neurons across Layers of Adult Human Neocortex, Cereb Cortex 2015, 25 (12):
4839–4853.
P169 Firing Rate Heterogeneity and Consequences for Stimulus Estimation in the Electrosensory
System
Cheng Ly1, Gary Marsat2
1Department of Statistical Sciences and Operations Research, Virginia Commonwealth
University, Richmond, VA 23284, USA; 2Biology Department, West Virginia University,
Morgantown, WV 26506, USA
Correspondence: Cheng Ly (CLy@vcu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P169
Heterogeneity of neural attributes is recognized as a crucial feature in neural processing.
Thus, we have developed theoretical methods (based on [1]) to characterize the firing
rate distribution of spiking neural networks with intrinsic and network heterogeneity
[2], both of which have been widely reported in experiments. This relationship (intrinsic
and network) can lead to various levels of firing rate heterogeneity, depending on
regime.
Next we adapt our theory to a delayed feedforward spiking network model of the electrosensory
system of the weakly electric fish. Experimental recordings indicate that feedforward
network input can mediate response heterogeneity of pyramidal cells [3]. We demonstrate
that structured connectivity rules, derived from our theory, can lead to qualitatively
similar statistics as the experimental data. Thus, the model demonstrates that intrinsic
and network attributes do not interact in a linear manner but rather in a complex
stimulus-dependent fashion to increase or decrease neural heterogeneity and thus shape
population codes.
As evidence for heterogeneity shaping population codes, we also present some preliminary
work using recordings from electric fish subject to noisy stimuli. We use a GLM model
for each neuron, fit the parameters to the data using standard maximum likelihood
methods, and perform Bayesian estimation of the stimuli. We find that firing rate
heterogeneity is a signature of optimal (Bayesian) stimulus estimation of noisy stimuli.
Interestingly, the firing rate correlation is not an indicator of decoding performance
for a given population of neurons.
References
1. W. Nicola, C. Ly, S.A. Campbell: One-Dimensional Population Density Approaches
to Recurrently Coupled Networks of Neurons with Noise. SIAM Journal on Applied Mathematics
2015, 75:2333–2360.
2. C. Ly: Firing Rate Dynamics in Recurrent Spiking Neural Networks with Intrinsic
and Network Heterogeneity. Journal of Computational Neuroscience 2015, 39:311–327.
3. G. Marsat, G.J. Hupe, K.M. Allen: Heterogeneous response properties in a population
of sensory neurons are structured to efficiently code naturalistic stimuli. Program
# 181.20 Neuroscience Meeting Planner 2014.
P170 Knowledge Space: a community encyclopedia linking brain research concepts to
data, models and literature
Tom Gillespie3, Willy Wong3, Malin Sandström1, Mathew Abrams1, Jeffrey S. Grethe3,
Maryann Martone4
1INCF Secretariat, Karolinska Institute, Nobels väg 15A, 17177 Stockholm, Sweden;
2Campus Biotech, EPFL, CH-1202 Genève, Switzerland; 3Center for Research in Biological
Systems, UCSD, La Jolla 92093, CA, USA; 4Neurosciences, UCSD, La Jolla 92093, CA,
USA
Correspondence: Malin Sandström (malin.sandstrom@incf.org)
BMC Neuroscience 2017, 18 (Suppl 1):P170
KnowledgeSpace [1] is a community encyclopedia platform currently under development
where neuroscience data and knowledge are synthesized. KnowledgeSpace aims to provide
a global interface between current brain research concepts and the data, models and
literature about them. It is an open project that welcomes participation and contributions
from members of the global research community.
KnowledgeSpace version 1.0 was launched at Neuroscience 2016 in San Diego, November
12-16, with three modes of search - keyword, category and atlas-based (so far only
for mouse brain). During the pre-launch phase, work focused on linking concepts to
data, models, and literature from existing community resources. Current data sources
include NeuroLex, Allen Institute for Brain Sciences, The Blue Brain Project, NeuroMorpho,
NeuroElectro, Cell Image Library, NIF Integrated Connectivity, Ion Channel Genealogy,
ModelDB, Open Source Brain, GenSat, BrainMaps, NeuronDB, The Human Brain Atlas, and
PubMed. Initial content included in KnowledgeSpace covers ion channels, neuron types,
and microcircuitry. For each content type, physiology, gene expression, anatomy, models,
and morphology data sources are available.
Going forward we will enhance atlas representations of the mouse brain linking concepts
to data, models, and literature, and an atlas representation of the human brain that
links to available data, models, and literature will be implemented. Links to analysis
tools will also be integrated into the KnowledgeSpace data section. The project will
also develop protocols, standards, and mechanisms that allow the community to add
data, analysis tools, and model content to KnowledgeSpace.
The initial development of KnowledgeSpace has been driven and supported by the International
Neuroinformatics Coordinating Facility (INCF; incf.org), the Neuroscience Information
Framework (NIF; neuinfo.org) and the Blue Brain Project (BBP; bluebrain.epfl.ch).
The KnowledgeSpace also represents an important component of the Neuroinformatics
Platform being deployed in the Human Brain Project web portal. KnowledgeSpace is currently
transitioning to a shared governance model, with a Governing Board composed of members
of the neuroscience community who are currently funded to generate or share data and/or
code as part of a lab, project or organization, and who will rotate off the board
when their project ends.
Reference
1. KnowledgeSpace website [https://knowledge-space.org/index.html]
P171 Evaluating the computational capacity of a cerebellum model
Robin De Gernier1, Sergio Solinas2, Christian Rössert3, Marc Haelterman1, Serge Massar1
1École polytechnique de Bruxelles, Université libre de Bruxelles, Brussels, Belgium,
1050; 2Department of Biomedical Science, University of Sassari, Sassari, Italia, 07100;
3Blue Brain Project, École polytechnique fédérale de Lausanne, Geneva, CH-1202, Switzerland
Correspondence: Robin De Gernier (rdegerni@ulb.ac.be)
BMC Neuroscience 2017, 18 (Suppl 1):P171
The cerebellum plays an essential role in tasks ranging from motor control to higher
cognitive functions (such as language processing) and receives input from many brain
areas. A general framework for understanding cerebellar function is to view it as
an adaptive-filter [1]. Within this framework, understanding, from computational and
experimental studies, how the cerebellum processes information and what kind of computations
it performs is a complex task, yet to be fully accomplished. In the case of computational
studies, this reflects a need for new systematic methods to characterize the computational
capacities of cerebellum models. In the present work, to fulfill this need, we apply
a method borrowed from the field of machine learning to evaluate the computational
capacity of a prototypical model of the cerebellum cortical network. Using this method,
we find that the model can perform both linear operations on input signals –which
is expected from previous work-, and –more surprisingly- highly nonlinear operations
on input signals.
The model that we study is a simple rate model of the cerebellar granular layer in
which granule cells inhibit each other via a single-exponential synaptic connection.
The resulting recurrent inhibition is an abstraction of the inhibitory feedback circuit
composed of granule and Golgi cells. Purkinje cells are modelled as linear trainable
readout neurons. The model was originally introduced in [2, 3] to demonstrate that
models of the cerebellum that include recurrence in the granular layer are suited
for timing-related tasks. Further studies carried out in [4] showed how the recurrent
dynamics of the network can provide the basis for constructing temporal filters.
The method, described in detail in [5], and developed in the context of the artificial
intelligence algorithm known as reservoir computing [6], consists in feeding the network
model with a random time dependent input signal and then quantifying how well a complete
set of functions (each function representing a different type of computation) of the
input signal can be reconstructed by taking a linear combination of the neuronal activations.
The result is a quantitative estimate of the number of different computations that
can be carried out by the model. We conducted simulations with 1000 granule cells.
Our results show that the cerebellum prototypical model has the capability to compute
both linear and highly nonlinear functions of its input. Specifically, the model is
able to reconstruct Legendre polynomial functions up to the 10th degree. Moreover,
the model can internally maintain a delayed representation of the input with delays
of up to 100 ms, and perform operations on that delayed representation. Despite their
abstract nature, these two properties are essential to perform typical cerebellar
functions, such as learning the timing of conditioned reflexes or fine-tuning nonlinear
motor control tasks or, we believe, even higher cognitive functions.
In future work, we hope to confirm these abstract results by applying our cerebellum
model to typical cerebellar tasks. Additionally, we will compare our results with
a very recent work which studied how a model of the cerebellum could solve several
machine learning tasks [7].
References
1. Dean P, Porril J: The cerebellar microcircuit as an adaptive filter: experimental
and computational evidence. Nat Rev Neurosci 2010, 11(1): 30–43.
2. Yamazaki T, Tanaka S: Neural Modeling of an Internal Clock. Neural Comput 2005,
17(5): 1032–1058.
3. Yamazaki T, Tanaka S: The cerebellum as a liquid state machine. Neural Netw 2007,
20(3): 290–297.
4. Rössert C, Dean P, Porrill J: At the Edge of Chaos: How Cerebellar Granular Layer
Network Dynamics Can Provide the Basis for Temporal Filters. PLOS Comput Biol 2015,
11(10):e1004515.
5. Dambre J, Verstraeten D, Schrauwen B, Massar S: Information processing capacity
of dynamical systems. Sci Rep 2012, 2:514.
6. Lukoševičius M, Jaeger H: Reservoir computing approaches to recurrent neural network
training. Computer Science Review 2009, 3:127–149.
7. Hausknecht M, Li WK, Mauk M, Stone P: Machine Learning Capabilities of a Simulated
Cerebellum. IEEE Trans Neural Netw Learn Syst 2017, 28(3):510–522.
P172 Complexity of cortical connectivity promotes self-organized criticality
Valentina Pasquale1, Vito Paolo Pastore2, Sergio Martinoia2, Paolo Massobrio2
1Neuroscience and Brain Technologies Department, Istituto Italiano di Tecnologia (IIT),
Genova, Italy; 2Department of Informatics, Bioengineering, Robotics, System Engineering
(DIBRIS), University of Genova, Genova, Italy
Correspondence: Valentina Pasquale (valentina.pasquale@iit.it)
BMC Neuroscience 2017, 18 (Suppl 1):P172
Large-scale in vitro cortical networks spontaneously exhibit recurrent events of propagating
spiking and bursting activity, usually termed as neuronal avalanches, since their
size (and lifetime) distribution can be approximated by a power law, as in critical
sand pile models [1, 2] (Figure 1). However, neuronal avalanches in cultures of dissociated
cortical neurons can distribute according to three different dynamic states, namely
sub-critical, critical, or super-critical, depending on several factors like developmental
stage, excitation/inhibition balance, cell density, etc. [3]. In this work, we investigated
the role of connectivity in driving spontaneous activity towards critical, sub-critical
or super-critical regimes, by combining both experimental and computational investigations.
Our experimental model consists of mature networks (third week of in vitro development)
of cortical dissociated neurons coupled to High-Density Micro-Electrode Arrays (HD-MEAs)
(3Brain, Wadenswill, Switzerland). These devices, containing 4’096 microelectrodes,
81 µm-spaced, allow to follow the emergence and propagation of neuronal avalanches
with high spatio-temporal resolution. We estimated the functional connectivity of
cortical networks by using cross-correlation based methods, collected in the software
ToolConnect [4]. In particular, our cross-correlation algorithm is able to reliably
and accurately infer functional and effective excitatory and inhibitory links in ex
vivo neuronal networks, while guaranteeing high computational performances necessary
to process large-scale population recordings. To support our experimental investigations,
we also developed a computational model of neuronal network, made up of Izhikevich
neurons [5] structurally connected by following well defined topologies of connectivity
(e.g., random, scale-free, small-world).
Simulations of the model demonstrated that the presence of hubs, the physiological
balance between excitation and inhibition, and the concurrent presence of scale-free
and small-world features are necessary to induce critical dynamics. We then confirmed
the predictions of the model by analyzing medium/high density cortical cultures coupled
to HD-MEAs, finding that networks featuring both scale-free and small-world properties
(as computed from functional connectivity graphs) display critical behavior.
Figure 1. Example of electrophysiological activity of a cortical network coupled to
a High-Density Micro-Electrode Arrays (HD-MEAs)
References
1. Beggs JM, Plenz D: Neuronal avalanches in neocortical circuits. J Neurosci 2003,
23(35):11167–11177.
2. Bak P: How nature works. Oxford (UK): Oxford University Press; 1997.
3. Pasquale V, Massobrio P, Bologna LL, Chiappalone M, Martinoia S: Self-organization
and neuronal avalanches in networks of dissociated cortical neurons. Neuroscience
2008, 153(4):1354–1369.
4. Pastore VP, Poli D, Godjoski A, Martinoia S, Massobrio P: ToolConnect: a functional
connectivity toolbox for in vitro networks. Front Neuroinform 2016, 10(13).
5. Izhikevich EM: Simple model of spiking neurons. IEEE Trans Neur Net 2003, 14:1569–1572.
P173 Attractor dynamics of cortical assemblies underlying brain awakening from deep
anesthesia
Cristiano Capone1,2, Núria Tort-Colet3, Maria V. Sanchez-Vives3,4, Maurizio Mattia1
1Istituto Superiore di Sanità (ISS), 00161 Rome, Italy; 2PhD Program in Physics, Sapienza
University, 00185 Rome, Italy; 3Institut d’Investigacions Biomèdiques August Pi i
Sunyer (IDIBAPS), 08036 Barcelona, Spain; 4Institució Catalana de Recerca i Estudis
Avançats (ICREA), 08010 Barcelona, Spain
Correspondence: Cristiano Capone (cristiano0capone@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P173
Slow rhythms of activity (~1 Hz) and slow-wave activity [1, 2] are a remarkably reproducible
dynamical activity pattern with a low degree of complexity which opens a window on
the brain multiscale organization, on top of which cognitive functions emerge during
wakefulness. Understanding how such transition takes place might shade light on the
emergence of the rich repertoire of neuronal dynamics underlying brain computation.
Sleep-wake transition is a widely-studied phenomenon ranging in experimental, computational
and theoretical frameworks [3–5], however it is still debated how brain state changes
occur. In our previous work [6] we showed from intracortical recordings in anesthetized
rats, that sleep-like rhythms fade out when wakefulness is approached giving rise
to an alternation between slow Up/Down oscillations and awake-like (AL) activity periods.
We also shown how this phase of activity pattern bistability is captured by a mean-field
rate-based model of a cortical column. Guided by this mean-field model, spiking neuron
networks are devised to reproduce the electrophysiological changes displayed during
the transition. Also, the model gave us hints on the mechanistic and dynamical nature
of the patterns of activity observed, suggesting that the AL periods appearance is
due to a Hopf-like transition from a limit cycle to a stable fixed point at a high
level of activity, and that AL-SO alternation is related to the presence of a slow
oscillating (∼ 0.2 Hz) level of excitation probably due to populations of neurons
in deeper regions of the brain.
We extended our previous findings by performing a stability analysis of the competing
attractors, observing a modulation of their stability, that affect the dynamics of
the Down-to-AL transition and the residence dynamics within the AL state. Moreover,
we found that the mean-field model remarkably matches the stability modulation observed
in experiments. This match between theory and experiments further strengthens our
claim that cortical assemblies of neurons display a Hopf bifurcation when anesthesia
fades out.
Such observation gives important information on intrinsic dynamical properties of
the system, suggesting that it does not respond in a passive way but rather it is
a strongly nonlinear component, capable to drastically change its dynamics under small
changes of relevant parameters. This can provide a computational advantage in terms
of the capability of producing a rich repertoire of network states during wakefulness.
Acknowledgements
Supported by EC FET Flagship HBP SGA1 (720270) to MM and MVSV
References
1. Sanchez-Vives MV, & Mattia M: Slow wave activity as the default mode of the cerebral
cortex. Arch Ital Biol 2014, 152:147–155.
2. Capone Cristiano, Mattia Maurizio: Speed hysteresis and noise shaping of traveling
fronts in neural fields: role of local circuitry and nonlocal connectivity. Scientific
Reports 2016, 7:39611 doi: 10.1038/srep39611
3. Bettinardi RG, Tort-Colet N, Ruiz-Mejias M, Sanchez-Vives MV, & Deco G: Gradual
emergence of spontaneous correlated brain activity during fading of general anesthesia
in rats: evidences from fMRI and local field potentials. Neuroimage 2015, 114:185–198.
4. G. Deco, P. Hagmann, A. G. Hudetz, and G. Tononi: Modeling resting-stat state functional
networks when the cortex falls asleep: local and global changes., Cereb. Cortex 2014,
vol. 24, no. 12, pp. 3180–3194.
5. Steyn-Ross ML, Steyn-Ross DA, and Sleigh JW: Interacting Turing-Hopf instabilities
drive symmetry-breaking transitions in a mean-field model of the cortex: a mechanism
for the slow oscillation, Phys. Rev. X, vol. 3, no. 2, p. 21005, 2013.
6. Capone C, Tort-Colet N, Mattia M, Sanchez-Vives MV (2016) Multistable attractor
dynamics in columnar cortical networks transitioning from deep anesthesia to wakefulness.
Bernstein Conference 2016.
P174 Are receptive fields in visual cortex quantitatively consistent with efficient
coding?
Ali Almasi1,2, Shaun L. Cloherty4, David B. Grayden2, Yan T. Wong3,4, Michael R. Ibbotson1,5,
Hamish Meffin1,5
1National Vision Research Institute, Australian College of Optometry, Melbourne, Australia;
2NeuroEngineering Laboratory, Dept. Biomedical Eng., University of Melbourne, Melbourne,
Australia; 3Dept. of Physiology, Monash University, Melbourne, Australia; 4Dept. of
Electrical & Computer Systems Eng., Monash University, Melbourne, Australia; 5ARC
Centre of Excellence for Integrative Brain Function, University of Melbourne, Melbourne,
Australia
Correspondence: Hamish Meffin (hmeffin@unimelb.edu.au)
BMC Neuroscience 2017, 18 (Suppl 1):P174
Numerous studies, across different sensory modalities, suggest that the neural code
employed in early stages of the cortical hierarchy can be explained in terms of Efficient
Coding. This principle states that information is represented in a neural population
so as to minimize redundancy. This is achieved when the features to which neurons
are tuned occur in a statistically independent fashion in the sensory environment.
The “statistically independent features” can be rigorously identified through methods
of statistical inference, and can be associated with a cell’s receptive field (RF).
Several studies using these methods have shown a qualitative similarity between predicted
RFs and those found in primary visual cortex, for simple and complex cells (with linear
and non-linear RF structures, respectively).
Recent methods allow direct experimental estimation of RFs. Using these methods, we
report on the first quantitative evaluation of the Efficient Coding Hypothesis at
the level of RF structures, including both simple and complex cells.
Experimental RF structures were estimated from recordings of single-units in the primary
visual cortex of anaesthetized cats in response to presentation of Gaussian white
noise. RFs were estimated from recordings assuming a General Quadratic Model for spike
rate and performing maximum likelihood estimation on the response given the stimulus.
Theoretical Efficient Coding RF structures were inferred by performing unsupervised
learning on a set of natural images, under the assumption of Efficient Coding that
evoked spike rates were statistically independent and sparsely distributed, and using
the same General Quadratic Model as for the experimental RFs.
We recovered spatial RF structures from 94 well isolated single-units in 3 cats, of
which 26 were classified as simple cells, 38 as complex cells and 30 as a mixed cell
class.
The results confirmed the qualitatively similarity of theoretical RF structures from
Efficient Coding with those estimated experimentally. However, quantitatively a number
of discrepancies were observed as well as similarities. (1) RF orientation tuning
was wider experimentally than theoretically (bandwidth was most frequently between
60° and 90° experimentally, while theoretically, it was mostly between 30° and 60°).
(2) Spatial frequency tuning was wider experimentally than theoretically (bandwidth
was most frequently 2 ± 0.5 octaves experimentally, but only 1 ± 0.5 octaves theoretically).
(3) For cells with more than one sub-RF it was possible to compare the tuning to orientation
and spatial frequency between different sub-RFs. The difference in orientation tuning
between sub-RFs showed that experimentally around 60% cells had precisely matched
orientation preferences (<15°), while in the theoretical population this proportion
dropped to around 40%. (4) Experimentally, the spatial frequency preference of sub-RFs
in the same cell were also tightly matched for the majority of cells (<0.5 octaves),
with a similar result in the theoretical population (<0.5 octaves). (5) Finally, the
spatial phase relationships of sub-RFs were compared: experimentally a large majority
(80%) of cells that had two quadratic sub-RFs that were 90° ± 15° out of phase. In
the theoretical population, this spatial phase relationship was common but less prevalent
(50%).
The quantitative discrepancies we found were robust to changes in meta-parameters,
such as the degree of image compression in pre-processing or the source of natural
images. The results suggest that the experimental RFs are sub-optimal in terms of
coding efficiency. However, it is important to note that we used a deterministic model
of spike rate in response to an image stimulus: a stochastic model is more realistic
and may limit the coding efficiency of the theoretical result, bringing it in closer
quantitative agreement with experiment.
Acknowledgements
AA acknowledges a Melbourne University Postgraduate Research Award. HM and MI acknowledge
support from the Australian Research Council Centre of Excellence for Integrative
Brain function.
P175 Cholinergic Modulation of DG-CA3 microcircuit dynamics and function
Luke Y. Prince1, Krasimira Tsaneva-Atanasova2,3, Jack R. Mellor1
1Centre for Synaptic Plasticity, School of Physiology, Pharmacology, and Neuroscience,
University of Bristol, Bristol, BS8 1TD, UK; 2Department of Mathematic, College of
Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK,
EX4 4QF; 3EPRSC Centre for Predictive Modelling in Healthcare, University of Exeter,
Exeter, UK, EX4 4QJ
Correspondence: Luke Y. Prince (l.y.prince@bristol.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P175
Dentate gyrus granule cells provide powerful feedforward excitatory drive onto a local
circuit of CA3 pyramidal cells and inhibitory interneurons, and is believed to selectively
activate subsets of pyramidal cells in the CA3 recurrent network for encoding and
recall of memories. Cholinergic receptors provide a key means to modulate this circuit,
increasing cellular excitability and altering synaptic release, but the combined action
of these changes on information processing between the dentate gyrus and CA3 remains
unknown. We recorded evoked monosynaptic EPSCs and disynaptic IPSCs in CA3 pyramidal
cells in response to a range of frequencies and stimulation patterns and in the presence
and absence of the cholinergic receptor agonist carbachol (5 μM). We found that carbachol
strongly reduced IPSC amplitudes but only mildly reduced EPSC amplitudes. The short-term
plasticity dynamics of these responses were used to constrain a computational model
of mossy fibre driven transmission across a range of stimulation patterns. This model
was then used to analyse how aceytlcholine influences encoding and recall in a spiking
neural network model of CA3 to study encoding and recall of neuronal ensembles driven
by mossy fibre input. We found that acetylcholine lowers the requirements for encoding
neuronal ensembles and increases memory storage in CA3.
P176 Subthalamic nucleus low frequency fluctuations carry information about future
economic decisions in parkinsonian gamblers
Alberto Mazzoni1†, Manuela Rosa2†, Jacopo Carpaneto1, Luigi M. Romito3, Alberto Priori2,4,
Silvestro Micera1,5
1Translational Neural Engineering, The Biorobotics Institute, Scuola Superiore Sant’Anna,
Pontedera, 56025, Italy; 2Clinical Center for Neurostimulation, Neurotechnology and
Movement Disorders Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan,
20122, Italy; 3Movement Disorders Department, Neurological Institute Carlo Besta,
Milan, 20133, Italy; 4Department of Health Sciences, University of Milan & ASST Santi
Paolo e Carlo, Milan, 20142, Italy; 5Bertarelli Foundation Chair in Translational
NeuroEngineering, Institute of Bioengineering and Center for Neuroprosthetics, Ecole
Polytechnique Federale De Lausanne, Lausanne, CH-1015, Switzerland
Correspondence: Alberto Mazzoni (alberto.mazzoni@santannapisa.it)
†equal first author contribution
BMC Neuroscience 2017, 18 (Suppl 1):P176
Dopamine replacement therapy for the treatment for Parkinson Disease (PD) has been
related to an increased risk of occurrence of Impulse Control Disorders (ICD), such
as Gambling Disorder (GD) [1]. Previous experimental and modeling studies [2] have
shown a link between ICD and specific activity of the subthalamic nucleus (STN), a
standard target for Deep Brain Stimulation (DBS) therapy for advanced PD. Several
brain areas involved in decision making, impulsivity and reward valuation, such as
the prefrontal cortex and striatum, are interconnected to the STN, and activity in
these areas might be modulated by STN DBS. Understanding the relationship between
STN functioning and ICD would help developing better therapies for PD while shedding
light on the mechanisms of human decision making.
To study how STN activity is modulated by gambling, we analyzed low-frequency ([1–12]
Hz) fluctuations of STN LFP recorded by DBS electrodes from PD patients during an
economic decision making task. All patients were under dopamine replacement therapy,
and half of them were affected by GD. In the task patients were asked to decide between
a high risk (HR) and low risk (LR) option, the first being associated to a negative
expected value, but to a high reward in case of win. Reaction times were strongly
affected by trial type, with GD patients and non-GD patients quicker in taking HR
and LR decisions respectively, suggesting that decision is actually determined before
options presentation. Analyzing low frequency STN LFP we found that amplitude of fluctuations,
recorded during specific intervals preceding option presentation, carried significant
information about future choices on single trials in patients affected by GD but not
in those not affected.
These results complement previous studies about the role of inhibiting impulsive behavior
displayed by the STN activity. Beta-range STN fluctuations were found to be modulated
by the level of conflict in decisions [3], while our results suggest that the lower
frequencies, which are functionally correlated with different cortical areas [4],
play instead a role to prevent pathological risk attraction.
Acknowledgements
This work was supported by institutional funds from Scuola Superiore Sant’Anna, by
the Italian Ministry of Health (GR-2009-1594645 grant), by the Aldo Ravelli Donation
for Research on Parkinson Disease, by the Bertarelli Foundation, and by institutional
funds from École Polytechnique Federale de Lausanne.
References
1. Weintraub D, David AS, Evans AH, Grant JE, Stacy M: Clinical spectrum of impulse
control disorders in Parkinson’s disease. Mov. Disord. 2015 30: 121–127.
2. Frank MJ, Samanta J, Moustafa AA, Sherman SJ: Hold Your Horses: Impulsivity, Deep
Brain Stimulation, and Medication in Parkinsonism. Science 2007 318: 1309–1312.
3. Brittain JS, Watkins KE, Joundi RA, Ray NJ, Holland P, Green AL, Aziz TZ, Jenkinson
N A Role for the Subthalamic Nucleus in Response Inhibition during Conflict. J. Neurosci.
2012 32: 13396–13401.
4. Herz DM, Tan H, Brittain JS, Fischer P, Cheeran B, Green AL, FitzGerald J, Aziz
TZ, Ashkan K, Little S, et al. Distinct mechanisms mediate speed-accuracy adjustments
in cortico-subthalamic networks. eLife 2017 6: 10.7554/eLife.21481
P177 Data-driven computational modeling of CA1 hippocampal principal cells and interneurons
Rosanna Migliore1, Carmen Alina Lupascu1, Francesco Franchina1, Luca Leonardo Bologna1,
Armando Romani2, Christian Rössert2, Sára Saray3, Jean-Denis Courcol2, Werner Van
Geit2, Szabolcs Káli3, Alex Thomson4, Audrey Mercer4, Sigrun Lange4,5, Joanne Falck4,
Eilif Muller2, Felix Schürmann2, and Michele Migliore1
1Institute of Biophysics, National Research Council, Palermo, Italy; 2Blue Brain Project,
École Polytechnique Fédérale de Lausanne Biotech Campus, Geneva, Switzerland; 3Institute
of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary; 4University
College London, London, United Kingdom; 5University of Westminster, London, United
Kingdom
Correspondence: Rosanna Migliore (rosanna.migliore@cnr.it)
BMC Neuroscience 2017, 18 (Suppl 1):P177
We present and discuss data-driven models of biophysically detailed hippocampal CA1
pyramidal cells and interneurons of a rat. The results have been obtained by using
the Brain Simulation Platform (BSP) of the Human Brain Project and two open-source
packages, the Electrophys Feature Extraction Library (eFEL, https://github.com/BlueBrain/eFEL)
and the Blue Brain Python Optimization Library (BluePyOpt) [1]. They have been integrated
into the BSP in an intuitive graphical user interface guiding the user through all
steps, from selecting experimental data to constrain the model, to run the optimization
generating a model template and, finally, to explore the model with in silico experiments.
Electrophysiological features were extracted from somatic traces obtained from intracellular
paired recordings performed using sharp electrodes on CA1 principal cells and interneurons
with classical accommodating (cAC), bursting accommodating (bAC) and classical non-accommodating
(cNAC) firing patterns. The extracted features, together with user selections for
realistic morphological reconstructions and ion channel kinetics, were then used to
automatically configure and run the BluePyOpt on the Neuroscience Gateway and/or on
one of the HPC systems supporting the BSP operations, such as CINECA (Bologna, Italy)
and JSC (Jülich, Germany) in this case. The resulting optimized ensembles of peak
conductances for the ionic currents, were used to explore and validate the model behavior
during interactive in silico experiments carried out within the HBP Collaboratory.
Such a modelling effort has been undertaken in the context of the Human Brain Project
and constitutes one of the major steps in the workflow that is being used to build
a cellular level model of a rodent hippocampus.
Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 720270
Reference
1. Van Geit W, Gevaert M, Chindemi G, Rössert C, Courcol J-D, Muller EB, Schürmann
F, Segev I and Markram H (2016) BluePyOpt: Leveraging Open Source Software and Cloud
Infrastructure to Optimise Model Parameters in Neuroscience. Front. Neuroinform. 10:17.
doi: 10.3389/fninf.2016.00017
P178 The interplay between basal ganglia and cerebellum in motor adaptation
Dmitrii Todorov, Robert Capps, William Barnett, Yaroslav Molkov
Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia
30303-3083, USA
Correspondence: Dmitrii Todorov (dtodorov@gsu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P178
It is widely accepted that the cerebellum and basal ganglia (BG) and play key roles
in motor adaptation (in error based and non-error based one, respectively) [1]. However,
despite considerable number of studies, the interactions between BG and cerebellum
are not completely understood [1]. In particular, in the experiments it is difficult
to dissociate the adaptation performed by cerebellum and by BG. To do so, some studies
[2] introduced perception perturbations that were suggested to impair cerebellum’s
ability to adapt to errors, and, thus, promoted the BG-based mechanisms. To our knowledge
no mathematical model exists that explains the conditions in which visual perturbations
make reinforcement learning in the BG the main mechanism of motor adaptation.
We have developed a model that integrates a phenomenological representation of the
cerebellum and a previously published firing rate-based description of BG network
[3], and mimics the trial-to-trial motor adaptation in 2D reaching arm movements.
Cerebellum is implemented as an artificial neural network performing corrections of
the motor program, descending from motor cortex to spinal cord, via supervised learning.
Figure 1 below shows the model architecture. Stimulus signal comes from prefrontal
cortex (PFC) and is sent to direct and indirect pathways of BG. The strength of PFC → BG
connections changes due to reinforcement learning mediated by substantia nigra pars
compacta (SNc) dopaminergic input, whose activity is defined by the reward prediction
error (RPE) signal. Direct and indirect pathways converge at globus pallidus internus
(GPi)/substantia nigra pars reticulata (SNr), which together project to premotor cortex
(PMC)/Thalamus to perform action selection. There are also direct PFC → PMC connections
representing habitual cue-action associations. The PMC/Thalamus then project to the
motor cortex (MC) and to the cerebellum. Cerebellum output represents a correction,
which adds to the motor command descending from the MC to the spinal cord. This correction
is calculated as a linear transformation of the motor command. The transformation
matrix is updated by the supervised learning algorithm, accounting for the vector
error provided by the visual feedback. The corrected signal goes to the spinal cord
neuron network that controls a two-joint arm to perform center-out reaching movements.
The perceived movement endpoint of the is used to compute the vector error and/or
the reward.
Figure 1. Model architecture
Our model simulations suggest that when the perception of the vector error provided
to the cerebellum is significantly perturbed, the faulty cerebellar corrections adversely
affect or even completely destroy motor adaptation. We speculate and show via simulations
that error-based learning in cerebellum has an adaptive critic component which effectively
suppresses error-based mechanisms to enable reinforcement-based motor adaptation.
References
1. Izawa J, Shadmehr R. Learning from sensory and reward prediction errors during
motor adaptation. PLoS Comput Biol. 2011; 7(3):e1002012.
2. Gutierrez‐Garralda JM, Moreno‐Briseño P, Boll MC, Morgado‐Valle C, Campos‐Romo
A, Diaz R, Fernandez‐Ruiz J. The effect of Parkinson’s disease and Huntington’s disease
on human visuomotor learning. European Journal of Neuroscience 2013;38(6):2933–40.4.
3. Kim T, Hamade KC, Todorov D, Barnett WH, Capps RA, Latash EM, Markin SN, Rybak
IA, Molkov YI. Reward based motor adaptation mediated by basal ganglia. Frontiers
in Computational Neuroscience. 2017;11.
P179 Microscopic and macroscopic dynamics of neural populations with delays
Federico Devalle1,2, Diego Pazó3, Ernest Montbrió1
1Center for Brain and Cognition, Universitat Pompeu Fabra, 08018 Barcelona, Spain;
2Department of Physics, Lancaster University, LA1 4YB Lancaster, UK; 3Instituto de
Fisica de Cantabria (IFCA), CSIC-Universidad de Cantabria, 39005 Santander, Spain
Correspondence: Federico Devalle (federico.devalle@upf.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P179
Bridging descriptions of brain activity across different scales is a major challenge
for theoretical neuroscience. Numerous experimental techniques are available to measure
brain activity, ranging from single cells recordings to population measurements of
the average activity of large ensembles of neurons. It is often in these population-level
recordings (e.g. EEG, MEG…), that important phenomena are observed. A particularly
relevant example are gamma oscillations, a temporal coherent activity with frequency
between 30 and 100 Hz. A large body of experimental and computational works indicates
that the interplay between synaptic processing and recurrent inhibition is the key
ingredient to generate such oscillations, in a mechanism commonly referred to as Interneuronal
Gamma oscillations (ING) [1, 2]. Here, we analyse the dynamics of a network of quadratic
integrate-and-fire neurons with time-delayed synaptic interactions, both in their
excitable and self-oscillatory regime. Time delays have been indeed shown to approximate
the effect of synaptic kinetics [3]. Using the so-called Lorentzian ansatz [4, 5],
we derive a set of two delayed firing rate equations (FREs). Due to their analytical
tractability, the FREs allow us to find exact boundaries of stability for the parameters
regions of oscillatory (collective synchrony-CS) and asynchronous dynamics. Moreover,
for inhibitory coupling, we observe a more complex oscillatory state, the so-called
quasiperiodic partially synchronized state (QPS). Here, neurons are quasiperiodic,
and have a mean frequency different from the global frequency of the entire population,
which corresponds to fast brain oscillations (f ~ 80 Hz). Interestingly, macroscopically
this state strongly resembles the sparsely synchronized state observed in networks
of leaky integrate-and-fire neurons subjected to strong recurrent inhibition and noise
[6]. However, microscopically, these two states have qualitatively different dynamics,
suggesting a dichotomy between microscopic and macroscopic dynamics. For a certain
region of parameters, the QPS coexists also with the CS. Moreover, sufficiently increasing
inhibition, the QPS undergoes a series of period doubling bifurcation that eventually
leads to chaos. Notably, only the collective dynamics is chaotic, while microscopically
neurons are non-chaotic. Finally, we find that while excitation always leads to collective
synchronous oscillations, inhibition fails to synchronize neural activity when a precise
degree of heterogeneity is exceeded, consistently with previous numerical studies
of heterogeneous, inhibitory spiking neural networks [7].
Acknowledgements
We acknowledge support by MINECO (Spain) under project No. ~FIS2014-59462-P, and the
project COSMOS of the European Union’s Horizon 2020 research and innovation programme
under the Marie Sklodowska-Curie grant agreement No.642563.
References
1. Whittington MA, Traub RD, Jefferys JG: Synchronized oscillations in interneuron
networks driven by metabotropic glutamate receptor activation. Nature 1995, 373:612–615.
2. Whittington MA, Traub RD, Kopell N, Ermentrout B, Buhl EH: Inhibition-based rhythms:
experimental and mathematical observations on network dynamics. Int J Psychophysiol
2000, 38:315–336
3. Roxin A, Montbrió E: How effective delays shape oscillatory dynamics in neuronal
networks. Physica D 2011, 240: 323–345.
4. Montbrió E, Pazó D, Roxin A: Macroscopic description for Networks of Spiking Neurons.
Phys Rev X 2015, 5: 021028
5. Pazó D, Montbrió E: From Quasiperiodic Partial Synchronization to Collective Chaos
in Populations of Inhibitory Neurons with Delay. Phys Rev Lett 2016, 116: 238101
6. Brunel N, Hakim V: Fast global oscillations in networks of integrate-and-fire neurons
with low firing rates. Neural Comput 1999, 11:1621.
7. Wang XJ, Buzsáki G: Gamma Oscillations by Synaptic inhibition in a Hippocampal
Interneuronal Network Model. J Neurosci 1996, 16(20):6402–6413.
P180 Motivation signal in anterior cingulate cortex during economic decisions
Gabriela Mochol1, Habiba Azab2, Benjamin Y. Hayden2, Rubén Moreno-Bote1
1Center for Brain and Cognition and Department of Information and Communications Technologies,
University Pompeu Fabra, Barcelona, 08005, Spain; 2Department of Brain and Cognitive
Sciences and Center for Visual Sciences, University of Rochester, Rochester, NY 14618,
USA
Correspondence: Gabriela Mochol (gabriela.mochol@upf.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P180
Anterior cingulate cortex (ACC) plays regulatory and cognitive roles. Its functions
are associated with conflict and performance monitoring, regulation of strategy and
response selection, all of which depend on reward monitoring and its anticipation
[1]. It has been shown previously that in the condition when the reward was certain
and its proximity was cued, animal’s error rate decreases together with the number
of trial remaining to the reward [2]. Concurrently, the firing rate of ACC neurons
gradually increased or decreased along with reward expectancy. It happened when the
reward was certain and correct decisions could only bring animal closer to the reward.
However, when certainty about outcome was removed and no notion of reward proximity
was provided the progressive modulation of behavior and ACC activity disappeared.
Here we tested whether such motivation signal can be also found in the circumstances
when the reward is no longer certain and the animal choices brings reward closer or
further away but the information about reward closeness reminds - the situation more
common in the economic decisions of everyday life. We recorded single unit activity
from dorsal ACC while monkey performed token gambling task. On each trial, monkeys
gambled to gain certain number of tokens, but they could also lose tokens. The collection
of six tokens resulted in a jackpot reward delivery. The number of collected tokens
was displayed on the monitor and was known to the animal. The animal learnt the task
and exhibited risk seeking behavior as previously reported [3]. The analysis of behavioral
data revealed that animal performance (percent of correct responses) depended on the
number of previously collected tokens. The relation was not monotonic with the drop
of performance after reward administration. At the same time, the significant fraction
of recorded neurons exhibited tuning towards the number of previously collected tokens.
Our preliminary results suggest that ACC monitors rewards in risky conditions, and
that neuronal signals could be directly related to the motivation of the animal.
Acknowledgements
The Spanish Ministry of Economy and Competitiveness IJCI-2014-21937 grant (to G. M.);
the Marie Curie FP7-PEOPLE-2010-IRG grant PIRG08-GA - 2010-276795, and the Spanish
Ministry of Economy and Competitiveness PSI2013-44811-P grant (to R. M. B.)
References
1. Heilbronner SR, Hayden BY: Dorsal Anterior Cingulate Cortex: A Bottom-Up View.
Annu Rev Neurosci 2016, 39: 149–170.
2. Shidara M, Richmond BJ: Anterior Cingulate: Single Neuronal Signals Related to
Degree of Reward Expectancy. Science 2002, 296(5573):1483–1490.
3. Azab H, Hayden BY: Shared roles of dorsal and subgenual anterior cingulate cortices
in economic decisions. bioRxiv 2016. [http://biorxiv.org/content/early/2016/09/09/074484].
P181 A simple computational model of altered neuromodulation in cortico-basal ganglia
dynamics underlying bipolar disorder
Pragathi Priyadharsini Balasubramani1, Srinivasa V. Chakravarthy2, Vignayanandam R.
Muddapu2
1Brain and Cognitive Sciences, University of Rochester, Rochester, New York 14627,
USA; 2Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, IIT-
Madras, Chennai, TN, India
Correspondence: Srinivasa V. Chakravarthy (schakra@iitm.ac.in)
BMC Neuroscience 2017, 18 (Suppl 1):P181
Bipolar disorder (BPD) is characterized by oscillations alternating between manic
and depressive episodes causing swings in moods. The length of an episode in a patient’s
mood cycle (time period) can vary from hours to years. Some medications popularly
used for stabilizing mood include selective serotonin reuptake inhibitors and lithium
therapy. This computational study focuses on the serotonergic system dysfunction,
and particularly, understanding their contribution to cortico-basal ganglia network
(CGBN) dynamics for stability and recurrence of moods. To this end, we try to model
the disorder in a decision-making framework that tries to choose between actions of
positive or negative affects. We propose a computational model that explores the effects
of impaired serotonergic neuromodulation on the dynamics of CBGN and relate this impairment
to the manic and depressive episodes of BPD. The proposed model of BPD is derived
from an earlier model, that describes the roles of dopamine and serotonin in the action
selection dynamics of CBGN. In that model, rewarding actions are selected based on
the Utility function, which combines Value and Risk functions as follows (eqn. 1).
1
\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$ U_{t} (s_{t} ,a_{t} ) = Q_{t} (s_{t} ,a_{t} ) - \alpha \;sign(Q_{t}
(s_{t} ,a_{t} ))\;\sqrt {h_{t} (s_{t} ,a_{t} )} $$\end{document}
U
t
(
s
t
,
a
t
)
=
Q
t
(
s
t
,
a
t
)
-
α
s
i
g
n
(
Q
t
(
s
t
,
a
t
)
)
h
t
(
s
t
,
a
t
)
where U, Q and h represent Utility, Value and Risk respectively, for a given state,
s, and action, a, at time, t. The parameter α, which represents risk preference, is
associated with serotonin action in CBGN. Value and Risk are trained by Reinforcement
Learning using the Temporal Difference (TD) error, which represents dopamine in CBGN.
The lumped model was later extended to a detailed network model of BG. In those models,
α was a constant, whereas in the current model it varies as per the following dynamics:
2
\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$ \dot{\alpha } = \tau_{\alpha } ( - \alpha + A_{r} \bar{r} +
\alpha_{k} ) $$\end{document}
α
˙
=
τ
α
(
-
α
+
A
r
r
¯
+
α
k
)
3
\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$ \dot{\bar{r}} = \tau_{r} \left( {r - \bar{r}} \right) $$\end{document}
r
¯
˙
=
τ
r
r
-
r
¯
The variable r-bar tracks the average rewards ‘r’ gained through time, and α-dot defines
serotonin dynamics with α
k constant (eqns. 2, 3) indicating basal risk sensitivity levels. The parameter A
r denotes the amplitude of reward sensitivity, and thus the reward history is proposed
to modulate α dynamics. When the model is run in a simple two arm bandit task - one
rewarding (+ve reward) and the other punitive (-ve reward) with probability 0.5, under
normal conditions the network shows high preference for rewarding actions. But for
certain ranges of reward sensitivity (A
r) and basal risk sensitivity (α
k) the model exhibits oscillations reminiscent of BPD mood oscillations (Fig. 1).
There exists clinical and experimental evidence supporting abnormality in serotonin
levels and reward sensitivity in case of BPD. Specifically, high reward sensitivity
with medium levels of risk sensitivity (serotonin activity correlate, as tonic/basal
levels or that induced by medication), can trigger bipolar mood oscillations. This
preliminary model can be extended to a detailed network model. Future work will include
expanding CBGN with neural models of limbic system, and predicting plausible treatment
strategies for effectively dealing with the onset and progression of BPD symptoms.
Figure 1. Action (positive or negative affect) selection in CBGN model: Yellow: rewarding
(+ve) action selection as in healthy controls; Green: Oscillations between +ve and
–ve actions as in BPD; Blue: -ve action selection as in depression
P182 Theta/alpha coordination of pre-motor and parietal networks during free behavior
in rats
Medorian D. Gheorghiu1, Bartul Mimica2, Jonathan Withlock2, Raul C. Mureșan1
1Romanian Institute of Science and Technology, Cluj-Napoca, Cluj 400552, Romania;
2Centre for Neural Computation, Kavli Institute for Systems Neuroscience, Trondheim,
NO-7491, Norway
Correspondence: Medorian D. Gheorghiu (medorian@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P182
Activity of posterior parietal cortex (PPC) neurons exhibits self-motion tuning to
both ongoing and impeding movements, which may reflect behavioral planning [1]. A
major input to PPC originates from the frontal medial agranular cortex (AGm), which
is believed to be involved in complex motor planning. In the monkey, Pesaran and colleagues
[2] showed that fronto-parietal coherence is stronger in free-choice tasks than in
instructed trials, probably activating different decision-related circuits in these
areas. Therefore, we hypothesize that in the rat the interaction between AGm and PPC
may be instrumental in coordinating decision making and motor planning. Here, we are
investigating the coupling strength between PPC and AGm in the theta/alpha frequency
band by computing pairwise spectral coherence and phase delays across the two areas
(see Figure 1) during goal-directed spatial navigation in rats. Two tasks were implemented:
an instructed or “known” task where the rat had to run straight to a fixed well named
“Home”; an “exploratory” task where the rat had to search for reward delivered in
“Target” wells located randomly across the arena and then run back to the Home well.
Results: As the rat stopped running and started licking at the target well, there
was an increase in theta coupling strength accompanied by a gradual decrease in frequency
(Figure 1A). Using the phase information, we computed the delay of PPC relative to
AGm. The delay decreased sharply from ~5.5 to ~2.5 ms when the rat arrived at the
target location (see Figure 1B), and it was gradually resetting in the last 5 s that
the rat spent at that location (see Figure 1D). As suggested by anatomical evidence,
AGm was leading PPC indicating a causal interaction where AGm coordinates the activity
in PPC.
Conclusions: Our results indicate a complex regulation of oscillatory behavior in
PPC and AGm during free behavior in rats. In particular, a pronounced ongoing oscillation
in the theta/alpha band is expressed throughout the task and seems to be coordinated
across the two areas. AGm leads PPC and both the frequency of the oscillation and
the time delay between the two areas change as a function of behavioral events.
Figure 1. A and C. Time-resolved spectral coherence between PPC and AGm in the 6-10 Hz
frequency band, aligned to the initiation (A) and cessation (C) of licking at the
target well. B and D. phase delays in ms between PPC and AGm aligned to the initiation
(B) and cessation (D) of licking at the target well
Acknowledgements
This work was supported by CNCS - UEFISCDI (PN-II-RU-TE-2014-4-0406 and PN-III-P3-3.6-H2020-2016-0012).
References
1. Withlock J., Robert J. Sutherland, Menno P. Witter, May-Britt Moser, Edvard I.
Moser: Navigating from hippocampus to parietal cortex. PNAS 2008 vol. 105.39: 14755–14762;
2. Pesaran B, Nelson MJ, Andersen RA: Free choice activates a decision circuit between
frontal and parietal cortex. Nature 2008: 406–409.
P183 Information theoretic approach towards identifying changes in cellular-level
functional connectivity and synchrony across animal models of schizophrenia
Jennifer L. Zick1,2, Kelsey Schultz4, Rachael K. Blackman1,2,3, Matthew V. Chafee1,3,
Theoden I. Netoff1,4
1Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN 55455
USA; 2Medical Scientist Training Program (MD/PhD), University of Minnesota, Minneapolis,
MN 55455 USA; 3Brain Sciences Center, VA Medical Center, Minneapolis, MN 55417 USA;
4Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455
USA
Correspondence: Jennifer L. Zick (zick@umn.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P183
Schizophrenia has long been described as a syndrome of disordered connectivity in
the brain. While originally based on clinical symptomatology, neurophysiological evidence
for this concept has been found in imaging studies in humans with schizophrenia. It
has also been found that cortical pyramidal neurons have a reduced density of the
synaptic spines necessary for cellular communication in postmortem brain tissue recovered
from people with schizophrenia. However, functional evidence for disconnectivity at
the level of local neuronal circuits is limited. To address this question, we characterized
neuronal dynamics between groups of simultaneously recorded cortical neurons in data
obtained from both primate and mouse models of schizophrenia. Neural data were obtained
from multielectrode recording arrays inserted into the parietal and prefrontal cortices
of macaque monkeys while the animals performed a cognitive control task that measures
a specific cognitive impairment in human patients with schizophrenia. Phencyclidine,
an NMDA receptor (NMDAR) antagonist that has long been used as a pharmacological model
of psychosis, was administered systemically on alternating days with injections of
saline. In the mouse experiments, analogous data were obtained from medial prefrontal
cortex in awake head-fixed mice during locomotion. Data from Nestin-promoted Dgcr8+/−
mutant mice (DiGeorge syndrome critical region 8; a gene strongly associated with
schizophrenia in humans and shown to produce schizophrenia-like symptomatology in
mice) is compared with that obtained from wildtype littermate controls.
Cross-correlation analysis was performed on spike trains from pairs of simultaneously
recorded neurons to characterize changes in synchrony between conditions. In the primate
neural data, cross correlations frequently displayed a prominent “zero-lag” peak representing
a large number of coincident action potentials between cells in the control condition
that could be a result of common input. In the phencyclidine condition, there was
a reduction in synchronous firing between pairs of cells. A similar rate-independent
reduction in precise synchrony was also found in medial prefrontal cortical neuronal
ensemble recordings obtained from Dgcr8 mice as compared to controls, suggesting that
this is may be a consistent finding related to the root pathophysiology of schizophrenic
processes.
To characterize deficits in synaptic communication between neurons in the disease
state, we employed higher-order transfer entropy (TE) metrics to identify pairs of
cells that exhibited effective connectivity (Ito et al, 2011, PLOS One). Consistent
with the disconnection hypothesis of schizophrenia, we found that acute administration
of PCP resulted in a reduction in the percent of cell pairs identified as significantly
functionally connected by TE analysis, as well as a reduction in the overall distribution
of population shared information. This result suggests a cellular basis for the reduced
information-processing capabilities seen in schizophrenics performing prefrontal cortex-dependent
tasks, as well as synaptic disconnection. Furthermore, this result is supported by
a similar reduction in both number of functionally connected cell pairs and overall
shared information in prefrontal cortex in the Dgcr8+/− mouse genetic model of schizophrenia.
In summary, these results display a reduction in both zero-lag synchrony and cellular-level
functional connectivity in two very distinct animal models of schizophrenia. It is
well known that coincident firing of action potentials facilitates connectivity between
neurons, and asynchrony results in disconnection. Thus, the results presented here
support the notion that alterations in precise spike timing may be an underlying driving
factor towards reduced functional connectivity in schizophrenia, providing a new mechanistic
model for disease pathophysiology.
Acknowledgements
This material is based upon work supported by the NIH (R01 MH1107491; Chafee); NRSA
F30 MH108205-01A1 (Zick); NSF Career Award (TIN); Medical Scientist Training Program
NIH T32-008244
P184 Neural Suppression with Deep Brain Stimulation using a Linear Quadratic Regulator
Nicholas Roberts1, Vivek Nagaraj2,, Andrew Lamperski3, Theoden I. Netoff1
1Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455,
USA; 2Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN 55455,
USA; 3Department of Electrical and Computer Engineering, University of Minnesota,
Minneapolis, MN 55455, USA
Correspondence: Nicholas Roberts (robe1521@umn.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P184
Current neuromodulation techniques for seizure suppression, such as vagus nerve or
deep brain stimulation, have shown some clinical efficacy. Yet their application is
complicated by the large parameter space of electrical stimulation settings inherent
to these systems. A physician must skillfully choose stimulation parameters such as
frequency, amplitude, and pulse width for each individual patient in order to effectively
reduce their incidence of seizures. We demonstrate an algorithm capable of automatically
generating a continuous stimulation waveform to suppress neural activity and minimize
total stimulation energy.
We treat the suppression of neural activity as a linear-quadratic-Gaussian (LQG) control
problem. The resulting optimal controller consists of a Kalman filter and a linear-quadratic
regulator (LQR). The effectiveness of the LQG controller in suppressing seizure biomarkers
was first verified in a computational model of epilepsy called Epileptor [1], which
simulates local field potential (LFP) recordings within a seizure focus. We built
a model of the generated LFPs using the Ho-Kalman algorithm [2] for subspace system
identification. The Kalman filter estimated the state of the system and a feedback
control signal provided by the LQR successfully prevented seizures during stimulation,
even while varying the Epileptor model parameters.
We then implemented the LQG controller in an in vivo rodent model. We stimulated the
ventral hippocampal commissure while recording in the hippocampus. The Ho-Kalman algorithm
was again used to build a dynamical systems model of the LFP activity based on the
evoked response to Gaussian white noise stimulation. We used a three-phase experiment
to test the LQG controller: 2 min of baseline activity; 2 min of closed-loop neural
stimulation; and 2 min post-stimulation to check if LFPs return to baseline levels.
This same stimulation waveform was then replayed in “open-loop,” without state estimation
from the Kalman filter. The LFP power from 1-100 Hz was used to measure performance.
Our results show a significant decrease in LFP power during closed-loop stimulation.
Open-loop stimulation produced negligible change in LFP power. The LQG controller
was confirmed to be an effective tool for minimizing LFP activity within a selected
frequency band. The mathematical models of neural dynamics it uses are subject specific
and determine stimulation waveforms based on state to suppress neural activity.
References
1. Jirsa VK, Stacey WC, Quilichini PP, Ivanov AI, Bernard C: On the nature of seizure
dynamics. Brain 2014, 137 (pt. 8):2210–2230.
2. Miller DN, & de Callafon RA: Identification of linear time-invariant systems via
constrained step-based realization. IFAC Proceedings Volumes 2012,
45
(16): 1155–1160.
P185 Reinforcement learning for phasic disruption of pathological oscillations in
a computational model of Parkinson’s disease
Logan L. Grado1, Matthew D. Johnson1,2, Theoden I. Netoff1
1Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455,
United States; 2Institute for Translational Neuroscience, University of Minnesota,
Minneapolis, MN, 55455, United States
Correspondence: Logan L. Grado (grado@umn.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P185
Deep brain stimulation (DBS) is an effective therapy for motor symptoms of PD, and
is often used as a complement to medication in patients who have progressed to severe
stages of PD. However, programming these devices is difficult and time consuming,
and DBS therapy is limited by side effects and partial efficacy [1]. Furthermore,
traditional continuous DBS (cDBS) does not account for fluctuations in motor symptoms
caused by factors such as sleep, attention, stress, cognitive and motor load, and
current drug therapy [2], and as the patient’s state changes, so does the need for
stimulation. Current cDBS strategies are incapable of adapting to the needs of patients:
once the clinician sets the parameters, they do not change until the next programming
visit. In this study, we have created a reinforcement learning (RL) algorithm capable
of learning online how best to stimulate to reduce pathological oscillations in silico.
We have developed the reinforcement learning DBS (RL-DBS) algorithm for tuning DBS
parameters, and have tested it on a biophysically realistic mean-field model of the
basal ganglia-thalamocortical system (BGTCs) [3], simulating parkinsonian neural activity.
The RL-DBS algorithm decides when to deliver stimulus pulses based upon the real-time
amplitude and phase of the pathological oscillation in order to reduce the amplitude
of that oscillation. The algorithm learns which actions lead to the highest cumulative
reward (i.e. reduction of oscillation amplitude). After training on the model, the
RL-DBS algorithm is able to learn both phase and amplitude selectivity to optimally
reduce the pathological oscillation. The algorithm learns the expected reward for
both actions (not stimulating and stimulating) as a function of the phase/amplitude
of the oscillation (Figure. 1A, Figure. 1B). The algorithm then decides which action
to execute based upon the action difference (Figure. 1C). Additionally, the algorithm
learns to deliver bursts of stimulation phase-locked to the oscillation.
We created an adaptive RL-DBS algorithm capable of learning on-line how to reduce
the power of a pathological oscillation in a computation model of PD. The algorithm
has the potential to deliver individualized, adaptive DBS therapy that can improve
the quality of life for PD patients.
Figure. 1. Learned reward maps A, B and action difference C as a function of the phase
and amplitude of the oscillation. A and B show the learned reward for no stimulation
and stimulation respectively, while C shows the action difference. The algorithm selects
the action that with the highest expected reward. The action difference reveals that
the algorithm learns both phase- and amplitude-selective stimulation
Acknowledgements
Research supported by the Systems Neuroengineering NSF IGERT Program (DGE-1069104),
NIH R01-NS094206, NIH P50-NS098573, and NSF CBET-1264432.
References
1. G. Deuschl, S. Paschen, and K. Witt: Clinical outcome of deep brain stimulation
for Parkinson’s disease. Handb. Clin. Neurol., vol. 116, pp. 107–128, 2013.
2. J. a Obeso, M. C. Rodríguez-Oroz, M. Rodríguez, J. L. Lanciego, J. Artieda, N.
Gonzalo, and C. W. Olanow: Pathophysiology of the basal ganglia in Parkinson’s disease.
Trends Neurosci., vol. 23, no. 10 Suppl, pp. S8–S19, 2000.
3. S. J. van Albada and P. a Robinson. Mean-field modeling of the basal ganglia-thalamocortical
system. I Firing rates in healthy and parkinsonian states. J. Theor. Biol., vol. 257,
no. 4, pp. 642–63, Apr. 2009.
P186 Metrics for detection of delayed and directed coupling
David P. Darrow1, Theoden I. Netoff2
1Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, USA;
2Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455,
USA
Correspondence: David P. Darrow (Darro015@umn.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P186
Detecting delayed coupling in dynamical systems remains a challenging frontier in
Neuroscience. Frequently used tools such as cross-correlation have been shown to be
robust against measurement noise but fail to identify coupling direction. [1] More
recently developed tools such as multivariate granger causality and various forms
of transfer entropy provide methods of detecting direction of coupling but may be
less resilient to measurement noise and require more substantial quantities of data
depending on the signal to noise ratio. With widespread use of these tools, it is
important to have a complete understanding of the limitations of each metric and the
circumstances of optimal use in experimental design.
To test these metrics over a salient parameter space, a linear, delayed vector autoregressive
model was created with probabilistic and complex coupling over probabilistic time
delays. The model was run with various measurement noise strengths, numbers of nodes,
and number of available data points. Correlation, cross-correlation, mutual information,
multivariate granger causality (MVGC), and transfer entropy (TE) were computed and
compared to true coupling adjacency matrices using an L-2 metric.
Significant differences were found between reconstruction results between metrics.
MVGC was found to outperform all other metrics when the signal to noise ratio exceeded
0.23. Transfer entropy and correlation fared worse than maximum cross-correlation
and mutual information, as summarized in Figure 1. Reconstruction error was found
to be minimally affected by number of nodes for metrics other than MVGC and TE, where
MVGC outperformed all others. Similarly, MVGC and TE required a minimum number of
samples to converge, and the required number of points was found to be a function
of the number of nodes.
Figure 1. Reconstruction error of time-lagged coupling as a function of measurement
noise with standard deviations
Conclusions: Based on this work, significant disparity exists between the performance
of existing methods to detect delayed coupling. Many common tools fail to detect delayed
coupling. However, even with a minimal density of time points to number of nodes,
MVGC efficiently recovers complex and delayed coupling. Careful consideration should
be given to metrics used in experiments where coupling may be delayed or spread out
over time. Measurement noise and data sample density requirements may affect experimental
design.
References
1. Netoff TI, Carroll TL, Pecora LM, Schiff SJ. 11 detecting coupling in the presence
of noise and nonlinearity. Handbook of Time Series Analysis: Recent Theoretical Developments
and Applications. John Wiley & Sons; 2006;
2. Barnett L, Seth AK. The MVGC multivariate Granger causality toolbox: a new approach
to Granger-causal inference. J. Neurosci. Methods. Elsevier; 2014;223:50–68.
3. Lindner M, Vicente R, Priesemann V, Wibral M. TRENTOOL: a Matlab open source toolbox
to analyse information flow in time series data with transfer entropy. BMC Neurosci.
BioMed Central Ltd; 2011;12:119.
4. Barnett L, Barrett AB, Seth AK. Granger causality and transfer entropy Are equivalent
for Gaussian variables. Phys. Rev. Lett. 2009;103:2–5.
P187 Insurgence of network bursting events in formed neuronal culture networks: a
computational approach
Davide Lonardoni1, Hayder Amin1, Stefano Di Marco2, Alessandro Maccione1, Luca Berdondini1†,
Thierry Nieus1,3†
1Neuroscience and Brain Technology Department, Fondazione Istituto Italiano di Tecnologia,
Genova, Italy, 16163; 2Scienze cliniche applicate e biotecnologiche, Università dell’Aquila,
L’Aquila, Italy, 67100; 3Dept. of Biomedical and Clinical Sciences “Luigi Sacco”,
University of Milan, Milan, Italy
Correspondence: Davide Lonardoni (davide.lonardoni@iit.it)
†co-senior authors
BMC Neuroscience 2017, 18 (Suppl 1):P187
A common property of developing neuronal systems is their intrinsic ability to generate
spatiotemporally propagating spiking activity involving a large number of highly synchronously
firing neurons. Primary neuronal cultures are among the experimental preparations
that allow the investigation of the principles underlying the generation of such spontaneous
coordinated spiking activity: cell cultures self-organize during development up to
the stage where they elicit stereotyped network-wide spiking activity, called network
bursts. The high spatial resolution of the high-density CMOS multi-electrode arrays
revealed that network bursts correspond to a coordinated propagation of action potentials
throughout the network [1]. Specifically, these propagations could be well clustered
into few groups differing for their ignition sites (i.e. the starting point) and propagation
paths (i.e. the mean trajectory followed by the spiking activity) [2]. This finding
suggests the presence of regions in charge of triggering such spontaneous events.
Following this direction, we investigated what were the main determinants underlying
the generation of network bursts in cell cultures at the mature stage. To this end,
we implemented a network model made of principal cells (excitatory) and fast spiking
(inhibitory) neurons endowed with the proper synaptic currents (AMPA, NMDA, GABA).
With minimal topological constraints on the coupling between neuronal pairs (i.e.
a network structure based on the reciprocal distance among neurons), the model expressed
realistic spontaneous activities that mimicked the experimental findings.
The results obtained in this study, by combining experimental datasets with our neural
network computational model, shows that while the synaptic contribution is mainly
involved in shaping the network burst, the key player in the generation of network
bursts could be found in the local properties of the neuronal network.
Specifically, with functional connectivity analysis, we found and detected, both in
simulation and in experiments, a few and specific ‘hot spots’ of the networks that
matched with the ignition sites of the propagations. In particular, in the model,
the neurons of to the hot spots were much more responsive than any other region to
mild stimulations delivered to these regions. Although the connectivity was truly
uniform by design we found that the ‘hot spots’ were characterized by local graph
properties (i.e. higher clustering, lower path length respect to the remaining network)
that favor the amplification of asynchronous firing and determine the onset of a network
event. Our modeling study suggests that the ‘hot spots’ might naturally result from
the simple constraints on the network topology and the sparseness of the network.
Acknowledgements
We acknowledge the financial support of the Future and Emerging Technologies (FET)
programme within the Seventh Framework Programme for Research of The European Commission
for the SICODE project, under FET-Open grant number: FP7-284553 and for the NAMASEN
Marie-Curie Initial Training Network, under FET-Open grant number: FP7-264872.
References
1. Berdondini L, Imfeld K, Maccione A, Tedesco M, Neukom S, Koudelka-Hep M, Martinoia
S: Active pixel sensor array for high spatio-temporal resolution electrophysiological
recordings from single cell to large scale neuronal networks. Lab Chip 2009, 9(18):2644–2651.
2. Gandolfo M, Maccione A, Tedesco M, Martinoia S, Berdondini L: Tracking burst patterns
in hippocampal cultures with high-density CMOS-MEAs. J Neural Eng 2010, 7(5):056001.
P188 Brian2GeNN: Free GPU Acceleration for Brian 2 Users
Marcel Stimberg1, Dan F. M. Goodman2, Thomas Nowotny3
1Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut de la Vision, Paris,
France; 2Department of Electrical and Electronic Engineering, Imperial College, London,
UK; 3School of Engineering and Informatics, University of Sussex, Brighton, UK
Correspondence: Thomas Nowotny (t.nowotny@sussex.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P188
Over the last decade graphics processing units (GPUs) have evolved into powerful,
massively parallel co-processors that are increasingly used for scientific computing
and machine learning. But it has also become quite clear that writing efficient code
for GPU accelerators is difficult even with APIs designed for general purpose computing,
such as CUDA and OpenCL. As a consequence, frameworks are being developed for making
GPU acceleration available for specific applications without complex parallel code
design. Examples include Matlab GPU extensions [1], TensorFlow GPU support [2], Theano
GPU extensions [3] and so on. Here we present the first public release of Brian2GeNN
[4], a software package that connects the popular Brian 2 simulator [5] to the GPU
enhanced neuronal networks (GeNN) framework [6] to provide effortless GPU support
for computational Neuroscience investigations to Brian 2 users.
Brian2GeNN was first announced at CNS*2014 and has undergone a long phase of maturation
and development until its first public release this year. It is a Python based package
that allows users to deploy their Brian 2 models to a device named “genn”, using the
simple command “set_device(‘genn’)”. This triggers the use of Brian2GeNN, which generates
code that can be executed on GPUs using GeNN. Brian2GeNN supports all common features
of Brian 2 with few exceptions such as multi-compartment models, multiple networks
or heterogeneous delays.
On this poster, we present the basic principles of how Brian2GeNN works and benchmark
examples of its performance with a number of different benchmark models and using
a number of diverse GPU accelerators. We can demonstrate that depending on the model
and the accelerator, achieved speedups can vary considerably. Brian2genn is Open Source
and freely available on GitHub under GPL v2.
Acknowledgements
The development of Brian2GeNN was partially supported by EPSRC, grant EP/J019690/1.
References
1. Mathworks web pages [https://uk.mathworks.com/company/newsletters/articles/gpu-programming-in-matlab.html],
accessed 03-03-2017.
2. TensorFlow web pages [https://www.tensorflow.org/tutorials/using_gpu], accessed
03-03-2017.
3. Theano documentation [http://theano.readthedocs.io/en/latest/tutorial/using_gpu.html],
accessed 03-03-2017.
4. Brian2genn repository [https://github.com/brian-team/brian2genn], accessed 03-03-2017
5. Stimberg M, Goodman DFM, Benichoux V, Brette R: Equation-oriented specification
of neural models for simulations. Front. Neuroinf. 2014, doi: 10.3389/fninf.2014.00006.
6. E. Yavuz, J. Turner and T. Nowotny (2016). GeNN: a code generation framework for
accelerated brain simulations. Scientific Reports 2016, 6:18854. doi: 10.1038/srep18854.
P189 Spike counts in the visual cortex consistently encode both stimuli and behavioral
choices in a change-detection task
Veronika Koren1,2, Valentin Dragoi3, Klaus Obermayer1,2
1Neural Information Processing Group, Institute of Software Engineering and Theoretical
Computer Science, Technische Universität Berlin, Berlin, 10587, Germany; 2Bernstein
Center for Computational Neuroscience Berlin, Berlin, Germany; 3Department of Neurobiology
and Anatomy, University of Texas Medical School, Houston, Texas, 77030, US
Correspondence: Veronika Koren (veronika.koren@ni.tu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P189
In visual discrimination tasks, the subject collects information about sensory stimuli
and makes behavioral decisions accordingly. In this study, we are searching for coding
strategies in visual cortices of the macaque (macaca mulatta) that relate to both
stimuli and behavior. Multi-units within a single cortical column are recorded in
V1 and V4 areas simultaneously while the subject is performing a change detection
task with matching and non-matching stimuli. We assess systematic differences in distribution
of spike counts for matching vs. non-matching stimuli (detection probability) and
for correct vs. incorrect behavioral performance (choice probability, [1]) on the
single cell and on the population level. In addition, we estimate pair-wise correlations
of spike counts. The spiking signal is weakly but significantly predictive on the
type of stimulus (matching vs. non-matching stimuli with correct behavioral responses)
as well as on different behavioral choices with correct and incorrect behavioral performance
(correct vs. incorrect behavioral responses on non-matching stimuli). In both areas,
the effect is limited to the superficial layers of the cortical column. Detection
and choice probability are consistent, the behavioral choice “match” being characterized
by higher spike counts in both cases. In V1, but not in V4, the signal corresponding
to the choice”match” is even statistically invariant with changes in both the type
of the stimulus and the behavioral performance. In incorrect trials, neural activity
in V1 is in addition characterized by a systematic bias in spike counts already at
the beginning of the trial. The bias is consistent with the future behavioral choice
and is only present in the deep cortical layers. Comparing the distribution of correlation
coefficients across pairs of neurons with matching and non-matching stimuli, distribution
of coefficients in V4 is less variable with matching stimuli, in particular for short
(0-0.5 mm) and middle-range (0.5-1 mm) inter-neuron distances. This effect could be
interpreted as a fast adaptation of neural responses to two consecutive presentations
of the same stimuli [2]. A change in long-range (>1 mm) correlations in V4 is observed
when comparing trials with correct and incorrect behavioral performance, correlations
in incorrect trials showing higher variability. In V1, we did not observe any systematic
changes in spike-count correlations with different stimuli. However, correlations
are significantly more variable in trials with incorrect compared to correct behavioral
performance. This effect is once again limited to deep cortical layers. Higher variability
of correlations in V1 might be a signature of spontaneously generated network state
that is more likely leading to incorrect behavioral performance. Finally, we test
the interactions between choice probabilities and spike-count correlations. Choice
probabilities and correlations do not interact in V1, but weakly interact in the V4
area, where cells with similar choice probabilities tend to be more strongly correlated.
In summary, we observe various differences in the first and second order statistics
of spike counts in both V1 and V4 areas. The first order statistics is related to
coding of both stimuli and behavioral choices while correlations would rather modulate
the efficacy of encoded signals.
Acknowledgements
This work was supported by the Deutsche Forschungsgemeinschaft (GRK1589/2).
References
1. Britten KH, Newsome WT, Shadlen MN, Celebrini S, Movshon JA: A relationship between
behavioral choice and the visual responses of neurons in macaque MT. Visual Neurosci
1996, 13(1):87–100.
2. Gutnisky DA, Dragoi V: Adaptive coding of visual information in neural populations.
Nature 2008, 452(7184): 220–224.
3. Hansen BJ, Chelaru MI, Dragoi V: Correlated variability in laminar cortical circuits.
Neuron 2012, 76(3): 590–602.
4. Nienborg H, Cumming BG: Decision-related activity in sensory neurons may depend
on the columnar architecture of cerebral cortex. J.Neurosci. 2014, 34(10): 3579–85.
P190 Local topology of connectome stabilizes critical points in mean field model
Samy Castro1,2, Mariano Fernandez3, Wael El-Deredy4, Patricio Orio1,5
1Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso,
Valparaíso, 2360102, Chile; 2Programa de Doctorado en Ciencias, mención en Neurociencia,
Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, 2360102, Chile; 3Laboratorio
de Electrónica Industrial, Control e Instrumentación, Universidad Nacional de La Plata,
La Plata, Argentina; 4Escuela de Ingeniería Biomédica, Universidad de Valparaíso,
2362905, Valparaíso, Chile; 5Instituto de Neurociencia, Universidad de Valparaíso,
Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, 2360102, Chile
Correspondence: Samy Castro (samy.castro@cinv.cl)
BMC Neuroscience 2017, 18 (Suppl 1):P190
The interplay between structural connectivity (SC) and neural dynamics is still not
yet fully understood. Applying topological analysis, the connectome approach links
this anatomical network to brain function. Here we adopt a computational approach
to find topology features related to the stability on global neural dynamics. A previous
study of a mean field model based on the human cortex network, shows at least 2 global
neural states, with either a low or high firing rate pattern [1, 3]. These 2 possible
states, or bistability, emerge in the model within a range of the global coupling
parameter G, limited by critical values G
- and G
+[1, 3]. Also, at this bistable range, this model achieves the highest correlations
with empirical resting state fMRI data. How the network connectivity pattern shapes
the critical G values has not been yet investigated. Our aim is to identify local
or global topology features related to the critical G values. We studied 4 different
SC networks: a cortical parcellation of human brain [2], a human binary equivalent,
a Random Network (RN) having the same degree distribution as human SC, and an equivalent
Watts & Strogatz Small World (SW) network. For each of the analyzed networks, values
in their critical G points have small or null variability. Then, we selectively prune
the edges of the networks and calculate their critical G values to show the effect
of structure pattern in maintaining the bistable dynamics. The edges were pruned selectively
based on either the degree or the k core decomposition measure; interpreted as a local
or global topology feature, respectively. Also, the pruning procedure is applied to
the edges on one of 3 specific ways: i) high degree/k core nodes, ii) random cuts,
and iii) low degree/no k core nodes. The highest shifts in critical G values are achieved
when the edges of high degree or k core nodes are pruned. In contrast, when we prune
those edges belong to low degree or no k core nodes, the shifts in the critical G
points are irrelevant. We interpret this as that the model can use either local or
global connectivity pattern in order to stabilize the critical G points. Furthermore,
our study show that shifts in the critical G points are statistically equivalent when
the degree distribution (but not k core structure) is shared, such as in the binary
human SC compared to the RN. Therefore, in our simulation the degree distribution,
interpreted as a local connectivity feature, determines the critical G points for
bistability, capturing the essential structural pattern of the network. We also show
that it is possible to obtain bistability in other types of networks, suggesting that
structure dynamic relationships may obey a topological principle.
Acknowledgements
SC is recipient of a Ph.D. fellowship from CONICYT. PO is partially funded by the
Advanced Center for Electronic Engineering (FB0008 CONICYT, Chile). The Centro Interdisciplinario
de Neurociencia de Valparaíso (CINV) is a Millennium Institute supported by the Millennium
Scientific Initiative of the Ministerio de Economía (Chile).
References
1. Deco G, McIntosh AR, Shen K, Hutchison RM, Menon RS, Everling S, Hagmann P, Jirsa
VK: Identification of optimal structural connectivity using functional connectivity
and neural modeling. J Neurosci. 2014, 34(23):7910–7916.
2. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Van Wedeen J, Sporns O: Mapping
the structural core of human cerebral cortex. PLoS Biol. 2008, 6(7):1479–1493.
3. Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta M: Resting-state
functional connectivity emerges from structurally and dynamically shaped slow linear
fluctuations. J Neurosci. 2013, 33(27): 11239–11252.
P191 How chaos in neural oscillators determine network behavior
Kesheng Xu1, Jean Paul Maidana1, Patricio Orio1,2
1Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso,
Valparaíso, Chile; 2Facultad de Ciencias, Instituto de Neurociencia, Universidad de
Valparaíso, Valparaíso, Chile
Correspondence: Patricio Orio (patricio.orio@uv.cl)
BMC Neuroscience 2017, 18 (Suppl 1):P191
Chaotic dynamics of neural oscillations has been shown at the single neuron and network
levels, both in experimental data and numerical simulations. Theoretical works suggest
that chaotic dynamics enrich the behavior of neural systems, by providing multiple
attractors in a system. However, the contribution of chaotic neural oscillators to
relevant network behavior has not been systematically studied yet. We investigated
the synchronization of neural networks composed of conductance-based neural models
that display subthreshold oscillations with regular and burst firing [1]. In this
model, oscillations are driven by a combination of persistent Sodium current, a hyperpolarization-activated
current (Ih) and a calcium-activated potassium current, very common currents in the
CNS. By small changes in conductance densities, the model can be turned into either
chaotic or non-chaotic modes [2]. We study synchronization of heterogeneous networks
where conductance densities are drawn from either chaotic or non-chaotic regions of
the parameter space. Measuring mean phase synchronization in a small-world network
with electrical synapses, we characterize the transition from unsynchronized to synchronized
state as the connectivity strength is increased. First, we draw densities from fixed-size
regions of the parameter space and find the transition to synchronized oscillations
is always smooth for chaotic oscillators but not always smooth for the nonchaotic
ones. However, non-smooth transitions were found to be associated to a change in firing
pattern from tonic to bursting. Nevertheless, we noticed that chaotic oscillators
display a wider distribution of firing frequencies than non-chaotic oscillators, thus
making more heterogeneous networks. Next, we draw the conductance densities from the
parameter space in a way that maintained the same distribution of firing frequencies
(hence the heterogeneity of the network) for both chaotic and non-chaotic. In this
case, synchronization curves are very similar, being second order phase transition
for both cases. However, we cannot discard that non-chaotic oscillators become chaotic
(or vice versa) when in a network, because of the extra parameter associated to the
electrical synapse. Finally, when the chaos-inducing Ih current is removed, the transition
to synchrony occurs at a lower value of connectivity strength but with a similar slope.
Our results suggest that the chaotic nature of the individual oscillators may be of
minor importance to the synchronization behavior of the network. Ongoing work is being
conducted to measure the chaotic nature of the whole network, and how it is related
to the synchrony behavior.
Acknowledgements
KX is funded by Proyecto Fondecyt 3170342. PO is partially funded by the Advanced
Center for Electrical and Electronic Engineering (FB0008 Conicyt, Chile). The Centro
Interdisciplinario de Neurociencia de Valparaíso (CINV) is a Millennium Institute
supported by the Millennium Scientific Initiative of the Ministerio de Economía (Chile).
References
1. Orio P., Parra A., Madrid R., González O., Belmonte C., Viana F. Role of Ih in
the Firing Pattern of Mammalian Cold Thermoreceptors. J Neurophysiol 2012, 108:3009–3023
2. Xu K., Maidana JP, Caviedes M, Quero D, Aguirre P and Orio P. Hyperpolarization-activated
current induces period-doubling cascades and chaos in a cold thermoreceptor model.
Front Comput Neurosci 2017, 11:12. doi: 10.3389/fncom.2017.00012.
P192 STEPS 3: integrating stochastic molecular and electrophysiological neuron models
in parallel simulation
Weiliang Chen1, Iain Hepburn1, Francesco Casalegno2, Adrien Devresse2, Aleksandr Ovcharenko2,
Fernando Pereira2, Fabien Delalondre2, Erik De Schutter1
1Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate
University, Okinawa, Japan; 2Blue Brain Project, École Polytechnique Fédérale de Lausanne,
Lausanne, Switzerland
Correspondence: Weiliang Chen (w.chen@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P192
Stochastic spatial molecular reaction-diffusion simulators, such as STEPS (STochastic
Engine for Pathway Simulation) [1], often face great challenges when simulating large
scale complex neuronal pathways, due to the massive computation required by the models.
This issue becomes even more critical when combining with cellular electrophysiological
simulation, one of the main focuses in computational neuroscience research. One example
is our previous research on stochastic calcium dynamics in Purkinje cells [2], where
a biophysical calcium burst model was simulated on approximate ¼ of a Purkinje cell
dendritic tree morphology using the serial implementation of spatial Gillespie SSA
and electric field (EField) solver in STEPS 2.0. Even with a state-of-the-art desktop
computer, it still took months to finish the simulation, significantly slowing down
research progress.
One possible, yet not trivial approach to speedup such simulation is parallelization.
In CNS2016 we reported our early parallel implementation of an Operator-Splitting
solution for reaction-diffusion systems, which achieved super-linear speedup in simulation
of the buffer components of the above published model on full Purkinje cell morphology.
While the performance of our parallel implementation was promising, the test model
had no calcium presented in the system and only buffers were simulated. Since buffers
were uniformly distributed in the geometry, the loading of each computing process
was relatively balanced, resulting in a close to ideal scenario for parallel computation.
The membrane potential computation, as well as voltage-dependent reactions in the
published model, were omitted due to the lack of a parallel EField solver at the time.
In a recent publication [3], we further extended the model by applying a dynamically
updated calcium influx profile extracted from the published calcium burst simulation.
Our result shown that in a realistic scenario with dynamic calcium influx, data recording,
and without special load balancing, our parallel reaction-diffusion solution can still
achieve more than 500 times of speedup with 1000 computing processes comparing to
the conventional serial SSA solution.
STEPS 3 is the first public release out of the collaboration between the CNS Unit
of OIST and the Blue Brain Project of EPFL. The ongoing collaboration aims to deliver
a scalable parallel solution for future integrated stochastic molecular and electrophysiological
neuron modelling. Combining the parallel TetOpSplit molecular solver developed by
OIST and EPFL’s parallel EField solver based upon the PETSc library, our new release
addresses the limitations of above test cases, and allows full scale parallel simulation
of the complete Purkinje cell calcium burst model. It also contains new changes that
are essential to parallel STEPS modelling and simulation pipeline, such as the improved
python binding using Cython technology. In this poster, we will use this model as
an example to showcase the general procedure of converting a serial STEPS simulation
to its parallel counterpart using these new changes. We will also analyze the performance
and scalability of our integrated solution, and discuss the direction of future STEPS
development.
References
1. Hepburn, I., Chen, W., Wils, S., and De Schutter, E. (2012). STEPS: efficient simulation
of stochastic reaction–diffusion models in realistic morphologies. BMC Systems Biology
6, 36. doi:10.1186/1752-0509-6-36.
2. Anwar, H., Hepburn, I., Nedelescu, H., Chen, W., and De Schutter, E. (2013). Stochastic
calcium mechanisms cause dendritic calcium spike variability. J. Neurosci. 33, 15848–15867.
doi:10.1523/JNEUROSCI.1722-13.2013.
3. Chen, W., and De Schutter, E. (2017). Parallel STEPS: Large Scale Stochastic Spatial
Reaction-Diffusion Simulation with High Performance Computers. Front. Neuroinform.
11, 137–15. doi:10.3389/fninf.2017.00013.
P193 A conductance-based model of cerebellar molecular layer interneurons
Peter Bratby1, Erik de Schutter1
1Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son,
Kunigami-gun, Okinawa 904-0495, Japan
Correspondence: Peter Bratby (peter.bratby@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P193
The cortex of the cerebellum is one of the most well-characterized regions of the
brain, comprising three distinct layers whose connectivity is well understood. Numerical
simulations of parts of the cerebellar cortex, including the granular layer and Purkinje
cell layer, have been instrumental in revealing the computational properties of the
cerebellum. However, one important part of the cortex - the molecular layer - has
yet to be modeled in detail.
The molecular layer is comprised of many thousands of parallel fibers (the long unmyelinated
axons of granule cells), Purkinje cell dendrites and a network of inhibitory interneurons
termed stellate cells and basket cells. The inhibitory interneurons were originally
classified according to their morphology, although modern molecular techniques have
indicated that they are likely to belong to a single class of neuron, the molecular
layer interneuron (MLI). As well as forming excitatory connections onto Purkinje cells,
parallel fibers make disynaptic connections via MLIs. Furthermore, MLIs form chemical
and electrical connections with each other via GABAergic synapses and gap junctions.
Thus, the MLIs form a sophisticated inhibitory network whose properties are important
in shaping the output of the cerebellum itself.
We develop a detailed conductance-based model of an MLI, and present the results of
a simulation of a small MLI network. The neuron model, developed using NEURON simulation
software, comprises somatic and dendritic compartments containing distinct voltage-
and calcium-dependent ion channels. Two types of synapse are simulated, representing
chemical synapses and gap junctions. The connectivity and cellular geometry of the
network model conforms with morphological reconstructions, and the model parameters
were tuned in order to reproduce known electrophysiological properties of MLIs, including
spontaneous spiking activity, modest spike frequency adaptation and the presence of
a slow depolarization wave.
P194 An Ultrasensitive ON/OFF Switch Mechanism Controls the Early Phase of Cerebellar
Plasticity
Andrew R. Gallimore, Erik De Schutter
Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate
University, Onna-son, Okinawa, Japan
Correspondence: Andrew R. Gallimore (andrew.gallimore@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P194
The expression of postsynaptic long-term depression (LTD) and long-term potentiation
(LTP) in cerebellar Purkinje cells results from the internalisation or insertion,
respectively, of postsynaptic AMPA receptors (AMPAR) [1]. LTD is induced by concurrent
parallel fiber and climbing fiber stimulation of Purkinje cells, and is regulated
by a complex intracellular signaling network that suppresses phosphatase activity
leading to activation of a positive feedback loop that maintains PKC activity for
at least 30 min [2]. LTP is dependent on nitric oxide [3], produced during parallel
fiber stimulation [4], which nitrosylates N-ethylmaleimide-sensitive factor (NSF)
and promotes exocytosis of AMPARs by actively disrupting the interaction between AMPAR-GluR2
and protein interacting with C-kinase 1 (PICK-1) [5, 6].
We report the largest and most sophisticated model of bidirectional synaptic plasticity
to date at the PF-PC synapse. Our unified molecular model replicates both PF-PC LTD
and NO/NSF-dependent LTP, as well as the sharp calcium threshold separating them.
The importance of the positive feedback loop in LTD expression is now well-established.
However, the control of feedback loop activation and deactivation has, until now,
remained obscure. Model simulations reveal that the feedback loop is activated by
an ultrasensitive ‘on-switch’ controlled by CaMKII activation. Furthermore, as predicted
by experiments showing that the feedback loop is not required once the early phase
of LTD induction is complete [2, 7], our model reveals a rapid and automatic ‘switch-off’
mechanism controlled by phosphatase activity. We are also able to replicate several
experimental observations that have so far remained unexplained. These include reconciling
conflicting data regarding the importance of nitric oxide in LTD induction: nitric
oxide supports loop activation by augmenting phosphatase inhibition, but is not required
when the calcium signal is high or sustained [4]. In addition, experiment has shown
that selective inhibition of the cytosolic phosphatase, PP2A, elicits robust LTD,
whereas inhibition of other phosphatases does not [8]. We show that only PP2A inhibition
causes CaMKII-independent activation of the feedback loop and thus LTD induction,
revealing the importance of PP2A in suppressing spontaneous loop activation under
basal conditions.
References
1. Wang YT, Linden DJ: Expression of cerebellar long-term depression requires postsynaptic
clathrin-mediated endocytosis. Neuron 2000, 25(3):635–647.
2. Tanaka K, Augustine GJ: A positive feedback signal transduction loop determines
timing of cerebellar long-term depression. Neuron 2008, 59(4):608–620.
3. Lev-Ram V, Wong ST, Storm DR, Tsien RY: A new form of cerebellar long-term potentiation
is postsynaptic and depends on nitric oxide but not cAMP. Proceedings of the National
Academy of Sciences of the United States of America 2002, 99(12):8389–8393.
4. Bouvier G, Higgins D, Spolidoro M, Carrel D, Mathieu B, Lena C, Dieudonne S, Barbour
B, Brunel N, Casado M: Burst-Dependent Bidirectional Plasticity in the Cerebellum
Is Driven by Presynaptic NMDA Receptors. Cell Reports 2016, 15(1):104–116.
5. Huang Y, Man HY, Sekine-Aizawa Y, Han YF, Juluri K, Luo HB, Cheah J, Lowenstein
C, Huganir RL, Snyder SH: S-nitrosylation of N-ethylmaleimide sensitive factor mediates
surface expression of AMPA receptors. Neuron 2005, 46(4):533–540.
6. Hanley JG, Khatri L, Hanson PI, Ziff EB: NSF ATPase and alpha-/beta-SNAPs disassemble
the AMPA receptor-PICK1 complex. Neuron 2002, 34(1):53–67.
7. Tsuruno S, Hirano T: Persistent activation of protein kinase C alpha is not necessary
for expression of cerebellar long-term depression. Molecular and Cellular Neuroscience
2007, 35(1):38–48.
8. Launey T, Endo S, Sakai R, Harano J, Ito M: Protein phosphatase 2A inhibition induces
cerebellar long-term depression and declustering of synaptic AMPA receptor. Proceedings
of the National Academy of Sciences of the United States of America 2004, 101(2):676–681.
P195 The use of hardware accelerators in the STochastic Engine for Pathway Simulation
(STEPS)
Guido Klingbeil, Erik de Schutter
Computational Neuroscience Unit, Okinawa Institute of Science and Technology, 1919-1
Tancha, Onna-son, Kunigami-gun, Okinawa 904-0495, Japan
Correspondence: Guido Klingbeil (guido-klingbeil@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P195
STEPS is a stochastic reaction-diffusion simulator. Its emphasis is on accurately
simulating signaling pathways [1].
The Human Brain Project (HBP) is a European Project set out to gain long-sought insights
into our brain and the processes that fundamentally make us human. A parallelised
version of STEPS will be part of the Brain Simulation Platform of the Human Brain
Project by efficiently simulating reaction-diffusion models in realistic morphologies
[2]. The HPB will model the brain at unprecedented detail. It is becoming apparent
that such large scale and computationally expensive models are required to either
capture more realistic morphologies or to simulate more complex systems [3].
Hardware accelerators such as NVidia’s graphics processing units (GPU) or Intel’s
Xeon Phi are one approach to mitigate the high computational cost of such models.
They are, in general, massively parallel multicore co-processors and have become a
cornerstone of modern high performance computing [4].
The hardware architecture of these two accelerator families differ significantly and
thus require different software approaches. While both are programmable via the common
programming interface OpenCL, important features such as unified memory or remote
direct memory access (RDMA) are often only supported in the native hardware architecture
specific programming frameworks [5, 6]. These not only need to be integrated into
an overall parallel software system performing a coherent spatial simulation but also
need to scale well over several accelerators and compute nodes.
Previous research has shown that we can exploit the computational power of accelerators
to improve spatially homogenous stochastic simulations by two orders of magnitude
while avoiding the limitation imposed to the size of the reaction system to be simulated
by the small fast memory space [7].
STEPS implements a spatial version of Gillespie’s stochastic simulation algorithm
computing reaction-diffusion systems on a mesh of tetrahedral sub-volumes [1, 8].
Operator splitting techniques allow to separate the reaction of molecules within a
sub-volume from the diffusion of molecules between them.
We develop a layered hybrid software architecture using classic central processing
units as well as multiple accelerators, integrated into STEPS. Multiple sub-volumes
are assigned to an accelerator. To accommodate the different hardware characteristics,
NVidia GPUs are applied within a sub-volume and the Intel Xeon Phi at the level of
the operator splittings. Furthermore, due to differences in the performance characteristics
of the accelerators the use of load balancing at the tetrahedral mesh level will be
important.
Our architecture will be a plug-in solution to STEPS not requiring any changes to
the interfaces towards the user or other software systems of STEPS itself.
References
1. Hepburn et al.: STEPS: efficient simulation of stochastic reaction-diffusion models
in realistic morphologies. BMC Syst Bio 2012, 6:36.
2. The Human Brain Project Brain Simulation Platform [https://www.humanbrainproject.eu/brain-simulation-platform1].
3. Anwar et al.: Stochastic Calcium Mechanisms Cause Dendritic Calcium Spike Variability.
J Neurosci 2013, 33(40):15848–15867.
4. TOP500 Supercomputer Site [http://www.top500.orgError! Hyperlink reference not
valid.].
5. Khronos OpenCL Working Group: The OpenCL Specification, V 2.1, 2015.
6. NVidia: CUDA C programming guide, V 8.0, 2017, [https://developer.nvidia.com/cuda-toolkit].
7. Klingbeil et al.: Stochastic simulation of chemical reactions with cooperating
threads on GPUs. (in preparation).
8. Gillespie: Exact stochastic simulation of coupled chemical reactions. J Phys Chem
1977, 81(25):2340–2361.
P196 A model of CaMKII sensitivity to the frequency of Ca2+ oscillations in Cerebellar
Long Term Depression
Criseida Zamora and Erik De Schutter
Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate
University, Okinawa 904-0895, Japan
Correspondence: Criseida Zamora (criseida.chimal@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P196
Cerebellar Long Term Depression (LTD) is a form of synaptic plasticity involved in
motor learning. The LTD signaling network includes a PKC-ERK-cPLA2 positive feedback
loop and mechanisms of AMPAR receptor trafficking. Experimental studies suggest that
Ca2+/calmodulin-dependent protein kinase II (CaMKII) is required for the LTD induction
[1]. Additionally, theoretical and experimental work has shown that CaMKII is sensitive
to the frequency of Ca2+ oscillations [2, 3]. The activation and autophosphorylation
of CaMKII by Ca2+ and calmodulin (CaM) are thought to influence its ability to decode
Ca2+ oscillations. However, the molecular mechanism by which this sensitivity contributes
to LTD is not fully understood.
The CaMKII enzyme is a multimeric complex conformed by 12 subunits, each of which
contains a catalytic domain, a regulatory domain, and a carboxyl-terminal association
domain. Due to the combinatorial complexity of activation of this enzyme, we chose
to model four-subunits. We propose a model for the activation of CaMKII by Ca2+ in
LTD signaling network. These reactions include: activation of the enzyme by Ca2+/CaM
binding, intersubunit autophosphorylation at threonine residue Thr286, Ca2+-independent
activation state through autophosphorylation and secondary intersubunit autophosphorylation
at threonine residue Thr305/306. Noise in the signaling networks plays an important
role in cellular processes. CaMKII models including its activation have been developed
[3], but they have not included the intrinsic stochasticity of molecular interactions.
Our lab recently developed a stochastic model of the LTD signaling network including
a PKC-ERK-cPLA2 feedback loop, Raf-RKIP-MEK interactions and AMPAR trafficking [4].
We have extended this model by adding the molecular network regulating CaMKII activity
and its activation. This new model was solved stochastically by STEPS (STochastic
Engine for Pathway Simulation) [5] to simulate the influence of noise on the LTD signaling
network.
Through stochastic modeling we observed that CaMKII can decode the frequency of Ca2+
spikes into different amounts of kinase activity during LTD induction. This result
is congruent with previous studies of CaMKII sensitivity to Ca2+ oscillations [2].
Furthermore, we observed that PKC activity is highly sensitive to the frequency, amplitude,
duration and the number of Ca2+ oscillations and consequently has an important effect
on LTD activation. The LTD signaling network involves phosphatases and phosphodiesterases
related with CaMKII activity, such as PP2A and PDE1. Our stochastic model may be useful
in understanding the role of these enzymes in the CaMKII sensitivity to the frequency
of Ca2+ oscillations.
References
1. Hansel C, de Jeu M, Belmeguenai A, Houtman SH, Buitendijk GH, Andreev D, De Zeeuw
CI, Elgersma Y: αCaMKII is essential for cerebellar LTD and motor learning. Neuron
2006, 51:835–843.
2. Paul De Koninck and Howard Schulman: Sensitivity of CaM Kinase II to the Frequency
of Ca2 + Oscillations. Science 1998, 279: 227–230.
3. Geneviève Dupont, Gerald Houart, Paul De Koninck: Sensitivity of CaM Kinase II
to the Frequency of Ca2 + Oscillations: a simple model. Cell Calcium 2003, 34: 485–497
4. Iain Hepburn, Anant Jain, Himanshu Gangal, Yukio Yamamoto, Keiko Tanaka-Yamamoto
and Erik de Schutter. A Model of Induction of Cerebellar Long-Term Depression Including
RKIP Inactivation of Raf and MEK. Front Mol Neurosci 2017, 10: 19.
5. Hepburn I, Chen W, Wils S, De Schutter E: STEPS: efficient simulation of stochastic
reaction-diffusion models in realistic morphologies. BMC Syst Biol 2012, 6:36.
P197 Exploring the response to climbing fiber input in Purkinje neurons by a new experimental
data based model
Yunliang Zang, Erik De Schutter
Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate
University, Onna-son, Okinawa, Japan
Correspondence: Yunliang Zang (yunliang.zang@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P197
Purkinje neurons receive powerful climbing fiber (CF) input from Inferior Olive (IO)
neurons to provide an instructive signal for cerebellar learning. The initial observation
that CF input causes all or none responses has been questioned in recent years. However,
the mechanisms of initiation and propagation of dendritic calcium spikes evoked by
CF input are still poorly understood. Here, we build a new Purkinje cell model based
on available experimental data to explore dendritic and somatic responses to CF input
in the Purkinje cell under different conditions. All the ionic current models are
well constrained according to the experimental data.
Model ionic currents regulate the electrophysiological properties of the Purkinje
cell consistent with experimental observations. Our model reproduces a plethora of
experimental observations, properties that are critical for the model to be able to
predict responses to excitatory and inhibitory inputs. Both simple spike and complex
spikes initiate first in the axonal initial segment (AIS). The first derivative and
second derivative of the somatic simple spike are in agreement with experimental data.
Using this model, we can explain the discrepancies between experimental observations
from different groups about the spatial propagation range of dendritic calcium spikes.
Dendritic spikelets can initiate and propagate in a branch-specific manner and depolarization
of dendrites can cause secondary spikelets. We find that the timing of occurrence
of a spikelet is critical to determine whether it can affect somatic firing or not.
The branch-specific dendritic spikelets can combine with contaminant excitatory input
and inhibitory inputs to affect somatic firing output more efficiently. Our results
indicate that voltage-dependent and branch specific spikelets may enrich CF instructive
signals for cerebellar learning.
P198 Effects of network topology perturbations on memory capacity in a hippocampal
place cell model
Patrick Crotty, Eric Palmerduca
Department of Physics and Astronomy, Colgate University, Hamilton, NY 13346, USA
Correspondence: Patrick Crotty (pcrotty@colgate.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P198
The relationship between the structure, or topology, of a neural network and its dynamics
remains largely unexplored. This relationship may be particularly significant for
the place cell network in region CA3 of the hippocampus. Place cells are believed
to encode position by firing when the animal is in a specific spatial location [1].
Multiple “charts” mapping place cells to locations for several different environments
may be stored simultaneously in the network [2]. Given hippocampal neurogenesis and
synaptic plasticity, the place cell network should be robust to small perturbations
in its topology: it shouldn’t “forget” charts if the pattern of synaptic connections
changes slightly. Conversely, if Alzheimer’s or another neurodegenerative disease
attacks the place cell network, declines in the chart capacity could provide clues
about the presence and progression of the disease. Using a computational model based
on a place cell network model published by Azizi et al. [3], we investigated the effects
that random removal of synapses in the network had on chart capacity. When small numbers
of synapses were removed, the chart capacity was not measurably affected, but larger
numbers removed caused the chart capacity to decline (see the Figure 1). Moreover,
the decline in the chart capacity depended on how the synapses were selected. If they
were selected with uniform probability, the chart capacity remained unaffected out
to about 10% removed and then fell sharply. But if neurons, rather than synapses,
were first selected with uniform probability, and then synapses randomly removed from
the selected neurons, the chart capacity began to fall linearly at about 5% removed.
These results suggest that the place cell network chart capacity is indeed stable
to small perturbations in its topology, and that the effects of larger disruptions
depend on the underlying mechanisms, i.e., whether it is the synapses or the cells
themselves that are targeted by a disease.
Figure 1. The chart capacity (M) as a function of the fraction of the synapses removed
from the network (p), using two different synapse-removal algorithms. For the blue
curve, synapses are selected and removed with equal probability. For the red curve,
neurons are selected with equal probability, and then a random synapse is removed
from the selected neuron. The dashed line is a linear fit to the random-neuron (red)
curve, with slope -0.27
Acknowledgements
We thank A. Azizi and S. Cheng for helpful discussions.
References
1. O’Keefe J, Dostrovsky J: The hippocampus as a spatial map: preliminary evidence
from unit activity in the freely-moving rat. Brain Research 1971, 34:171–175.
2. Alme CB, Miao C, Jezek K, Treves A, Moser EI, and Moser M: Place cells in the hippocampus:
eleven maps for eleven rooms. Proceedings of the National Academy of Sciences 2015,
111(52):18428–18435.
3. Azizi A, Wiskott L, Cheng S: A computational model for preplay in the hippocampus.
Frontiers in Computational Neuroscience 2013, 7(161):1–15.
P199 A NEST-simulated cerebellar spiking neural network driving motor learning
Alberto Antonietti1, Claudia Casellato1, Csaba Erö2, Egidio D’Angelo3, Marc-Oliver
Gewaltig2, Alessandra Pedrocchi1
1Department of Electronics, Information and Bioengineering, Politecnico di Milano,
Milano, Italy; 2Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL),
Biotech Campus, Geneva, Switzerland; 3Department of Brain and Behavioral Sciences,
University of Pavia, Pavia, Italy
Correspondence: Alberto Antonietti (alberto.antonietti@polimi.it)
BMC Neuroscience 2017, 18 (Suppl 1):P199
The brain organization is optimized to drive adaptive behavior. A key role in the
control loop is played by the cerebellum, which implements prediction, timing and
learning of motor commands, through complex plasticity mechanisms [1]. However, how
plasticity is engaged during the behavior is still unclear. Cerebellar properties
emerge in sensorimotor paradigms, such as the Eye Blink Classical Conditioning (EBCC).
In silico simulations based on computational models are fundamental to investigate
the physiological mechanisms. We developed a cerebellar network running on NEST. NEST
is a simulator for spiking neural network models [2], focused on the dynamics, size
and structure of neural systems by the generation of networks of single-point neurons.
We built a network tailored on the mouse cerebellum. The network is made of 71,440
neurons: 250 Mossy Fibers (MF), 5’000 Glomeruli (Glom), 65’600 Granular Cells (GR),
100 Golgi Cells (GO), 400 Purkinje Cells (PC), 40 Inferior Olive cells (IO), 50 Deep
Cerebellar Nuclei (DCN). The connectivity ratios used for the 11 types of synaptic
connections are reported in Table 1.
Three of these synaptic types could undergo specific plastic modifications, in particular
Long Term Potentiation and Depression on different time scales. The numbers of the
cells and the connectivity were taken from the neurophysiological literature. The
model was tested with a simple closed-loop simulation of the EBCC, to check the functionalities
of the network in a learning task [3]. In the EBCC, a Conditioned Stimulus (CS) precedes
an Unconditioned Stimulus (US) by a fixed time interval. The cerebellum is able, after
repeated presentations of CS and US paired during the acquisition phase, to anticipate
the US onset, this action is called Conditioned Response (CR). During the extinction
phase, only the CS is provided. The network, thanks to the distributed plasticity,
was able to learn the CS-US temporal association during the acquisition trials, with
a fast acquisition towards 80% values, and to rapidly unlearn the association during
the extinction trials (Figure 1). We will extend this model to a large-scale reproduction
of the mouse cerebellum, testing more complex paradigms.
Table 1. Connectivity between the neural groups (Convergence and Divergence). In italics
the plastic sites
Presyn
Postsyn
Type
Conv
Div
# Synapses
MF
Glom
Excitatory
1
20
5000
Glom
GR
Excitatory
4
53
262,400
Glom
GO
Excitatory
40
0.77
4000
GO
GR
Inhibitory
3.23
2120
212,000
GR
GO
Excitatory
2000
3
200,000
GR
PC
Excitatory
65,600
400
26,240,000
IO
PC
Teaching
1
10
400
PC
DCN
Inhibitory
40
5
2,000
DCN (30%)
IO
Inhibitory
0.34
1
14
IO
DCN
Excitatory
1
1,41
50
MF
DCN
Excitatory
12
2.4
600
Total number of synapses
26,926,464
Figure 1. Behavioral outcome during the EBCC protocol, with 80 trials of Acquisition
and 20 trials of Extinction. 10 simulations were performed. Solid line: the median
outcome; grey area: the interquartile intervals
Acknowledgements
This work was supported by EU grants: Human Brain Project (HBP 604102) and HBP-Regione
Lombardia.
References
1. D’Angelo E et al.: Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing
Issue. Front. Cell. Neurosci. 2016; 10:176.
2. Gewaltig MO and Diesmann M: NEST (neural simulation tool). Scholarpedia 2007, 2(4):14303.
3. Antonietti et al.: Spiking Neural Network With Distributed Plasticity Reproduces
Cerebellar Learning in Eye Blink Conditioning Paradigms. IEEE Trans. Biomed. Eng.
2016. 63:1.210–219.
P200 Spike-based probabilistic inference with correlated noise
Ilja Bytschok1, Dominik Dold1, Johannes Schemmel1, Karlheinz Meier1, Mihai A. Petrovici1,2
1Kirchhoff-Institute for Physics, Heidelberg University, Im Neuenheimer Feld 227,
69120 Heidelberg, Germany; 2Department of Physiology, University of Bern, Bühlplatz
5, 3012 Bern, Switzerland
Correspondence: Ilja Bytschok (ilja.bytschok@kip.uni-heidelberg.de)
BMC Neuroscience 2017, 18 (Suppl 1):P200
It has long been hypothesized that the trial-to-trial variability in neural activity
patterns plays an important role in neural information processing. A steadily increasing
body of evidence suggests that the brain performs probabilistic inference to interpret
and respond to sensory input [1, 2, 3]. The neural sampling hypothesis [4] interprets
stochastic neural activity as sampling from an underlying probability distribution
and has been shown to be compatible with biologically observed dynamical regimes of
spiking neurons [5]. In these studies, high-frequency Poisson spike trains were used
as a source of stochasticity, which is a common way of representing diffuse synaptic
input. However, this discounts the fact that cortical neurons may share a significant
portion of their presynaptic partners, which can have a profound impact on the computation
these neurons are required to perform. This is not only relevant in biology, but also
for artificial implementations of neural networks [6], where bandwidth constraints
limit the amount of available independent noise channels.
In neural sampling, the firing activity of a network of N Leaky Integrate-and-Fire
(LIF) neurons is represented by a vector of binary random variables (RVs) z ∊ {0, 1}
N
. In such a network, synaptic weights can be adjusted such that the network samples
from a Boltzmann distribution p(z) [5]. In particular, the weights W
ij
control the pairwise correlations r
ij
between RVs. When receiving correlated noise, the correlations r
ij
are changed in a way that cannot be directly countered by changes in W
ij
. We show, however, that this is contingent on the chosen coding: when changing the
state space from {0, 1}
N
to {−1, 1}
N
, correlated noise has the exact same effect as changes in W. Unfortunately, the {−1, 1}-coding
is incompatible with neuronal dynamics, because it would require neurons to influence
each other while they are silent.
However, the translation of the problem to the {−1, 1}
N
space allows the formulation of a two-step compensation procedure. We show how, by
chaining a bijective map from noise correlations to interaction strengths W
ij
’ in {−1, 1}
N
with a second bijective map from (W
ij
’, b
ij
’) in {−1, 1}
N
to (W
ij
, b
ij
) in {0, 1}
N
it is possible to find a synaptic weight configuration that compensates for correlations
induced by shared noise sources. For an artificial embedding of sampling networks,
this allows a straightforward transfer between platforms with different architecture
and bandwidth constraints.
Furthermore, the existence of the above mapping provides an important insight for
learning. Since in the {−1, 1}-coding the correlated noise can be compensated by parameter
changes and because the {−1, 1}-coding can be transformed into a {0, 1}-coding while
keeping the state probabilities invariant, a learning rule for Boltzmann machines
will also find that distribution in the {0, 1}-coding, which we demonstrate in software
simulations. In other words, spiking networks performing neural sampling are impervious
to noise correlations when appropriately trained. This means that, if such computation
happens in cortex, network plasticity does not need to take particular account of
shared noise inputs.
Acknowledgements
Authors Bytschok, Dold and Petrovici contributed equally to this work. This research
was supported by EU grants #269921 (BrainScaleS), #604102 (Human Brain Project) and
the Manfred Stärk Foundation.
References
1. Körding K, Wolpert D: Bayesian integration in sensorimotor learning. Nature 2004
2. Fizser J, Berkes P, Orbán G, Lengyel M: Statistically optimal perception and learning:
from behavior to neural representations. Trends in Cognitive Sciences 2010
3. Rich EL, Wallis JD: Decoding subjective decisions from orbitofrontal cortex. Nature
Neuroscience 2016
4. Buesing L, Bill J, Nessler B, Maass W: Neural dynamics as sampling: a model for
stochastic computation in recurrent networks of spiking neurons. PLoS Comput Biol
2011
5. Petrovici MA, Bill J, Bytschok I, Schemmel J, Meier K: Stochastic inference with
spiking neurons in the high-conductance state. Physical Review E 2016
6. Furber S: Large-scale neuromorphic computing systems. Journal of Neural Engineering
2016
P201 Optimal refractoriness from a rate-distortion perspective
Hui-An Shen, Simone Carlo Surace, Jean-Pascal Pfister
Institute of Neuroinformatics, UZH and ETHZ, Zurich, CH-8057, Switzerland
Correspondence: Jean-Pascal Pfister (jpfister@ini.uzh.ch)
BMC Neuroscience 2017, 18 (Suppl 1):P201
The information transfer from neuron to neuron through chemical synapses undergoes
two stages. In the presynaptic neuron, the (analog) membrane potential is encoded
into a (digital) spike while in the postsynaptic neuron, this digital information
is turned back into an (analog) depolarisation. It has been argued that for a given
inhomogeneous Poisson encoder, the optimal decoder has dynamics that is consistent
with short-term plasticity [1]. However, the optimal encoder is not known. Here, by
studying the rate-distortion performance, we explore how presynaptic refractoriness
influences the performance of the optimal postsynaptic decoder. First, we generalize
the results of [2] and [3] by expressing the mutual information as a function of the
mean natural estimation loss, in the presence of refractoriness. This expression provides
a numerically stable and fast method of computing mutual information between two high-dimensional
random variables. Next, we show with numerical simulations that for a fixed firing
rate ranging from 20-120 Hz, there is an optimal level of refractoriness that minimizes
the distortion, i.e. the mean squared error of the optimal postsynaptic decoder. To
test our theory, we compare this optimal level of refractoriness with an HVC neuron
in Zebra Finch to which the model has been fitted [4].
References
1. Pfister JP, Dayan P, Lengyel M: Synapses with short-term plasticity are optimal
estimators of presynaptic membrane potentials. Nat Neurosci. 2010, 13(10):1271–1275.
2. Atar R, Weissman T: Mutual information, relative entropy, and estimation in the
Poisson channel. IEEE Transactions on Information theory 2012, 58(3):1302–1318.
3. Liptser RS, Shiryaev AN: Statistics of Random Processes II, 2nd Edition. New York:
Springer-Verlag; 2001.
4. Surace SC, Pfister JP: A statistical model for in vivo neuronal dynamics. PloS
one 2015, 10(11):e0142435.
P202 Towards online accurate spike sorting for hundreds of channels
Baptiste Lefebvre,, Marcel Stimberg, Olivier Marre, Pierre Yger
Institut de la Vision, INSERM UMRS 968, CNRS UMR 7210, Paris, France
Correspondence: Pierre Yger (pierre.yger@inserm.com)
BMC Neuroscience 2017, 18 (Suppl 1):P202
Understanding how assemblies of neurons encode information requires recording of large
populations of cells in the brain. In recent years, multi-electrode arrays and large
silicon probes have been developed to record simultaneously from thousands of electrodes
packed with a high density. To tackle the fact that these new devices challenge the
classical way to perform spike sorting, we recently developed a fast and accurate
spike sorting algorithm (available as an open source software, called SpyKING CIRCUS),
validated both with in vivo and in vitro ground truth experiments [1]. The software,
performing a smart clustering of the spike waveforms followed by a greedy template-matching
reconstruction of the signal, is able to scale to up to 4225 channels in parallel,
solving the problem of temporally overlapping spikes. It thus appears as a general
solution to sort, offline, spikes from large-scale extracellular recordings.
In this work, we aim at implementing this algorithm in an “online” mode, sorting spikes
in real time while the data are acquired, to allow closed-loop experiments for high
density electrophysiology. To achieve such a goal, we built a robust architecture
for distributed asynchronous computations and we propose a modified algorithm that
is composed of two concurrent processes running continuously: 1) “a template-finding”
process to extract the cell templates (i.e. the pattern of activity evoked over many
electrodes when one neuron fires an action potential) over the recent time course;
2) a “template-matching” process where the templates are matched onto the raw data
to identify the spikes. The main challenge is to have a continuous update of the set
of templates, with hundreds of electrodes and possible drifts over the time course
of the experiment. A key advantage of our implementation is to be parallelized over
a computing cluster to use optimally the computing resources: all the different processing
steps of the algorithms (whitening, filtering, spike detection, template identification
and fit) can be distributed according to the computational needs. During the clustering,
the most computationally demanding step, templates are detected and tracked over time
using a modified version of the density based clustering algorithm [2] able to handle
data streams. Our software is therefore a promising solution for future closed-loop
experiments involving recordings with hundreds of electrodes.
References
1. P. Yger et al., Fast and accurate spike sorting in vitro and in vivo for up to
thousands of electrodes, BioRxiv 2016.
2. A. Rodriguez et al., Clustering by fast search and find of density peaks, Science
2014.
P203 Modeling orientation preference in the apical and basal trees of L2/3 V1 neurons
Athanasia Papoutsi1, Jiyoung Park2, Ryan Ash2, Stelios Smirnakis2, Panayiota Poirazi1
1IMBB, FORTH, Heraklion, Crete, 70013, Greece; 2Neurology, Baylor College of Medicine,
Houston, Texas, 77030, USA
Correspondence: Athanasia Papoutsi (athpapoutsi@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P203
Pyramidal neurons receive inputs in two anatomically and functional distinct domains
[1], the apical and the basal tree. Inputs to the basal tree, due to their proximity
to the soma, greatly influence neuronal output, whereas the more remote apical tree
has less potential to influence somatic activity. How these inputs co-operate to form
the functional output of the neurons is currently unknown. In this work, we focused
on how inputs to the apical and basal trees shape orientation tuning in L2/3 V1 neurons.
In particular, we investigated how dendritic integration of orientation tuned inputs
to the apical versus basal trees allows for the emergence of stable neuronal orientation
preference. Towards this goal, a model L2/3 V1 pyramidal neuron was implemented in
the NEURON simulation environment. The passive and active properties of the model
neuron were extensively validated against experimental data. Synaptic properties,
number and distribution were also constrained according to available data (Figure 1A).
Using this model neuron, we investigated a) the differences in the mean orientation
preferences of the two trees and b) the distribution of orientation preferences to
individual synapses that allow for the emergence of orientation tuning (Figure 1B).
Given the parameter combinations that allow for the emergence of orientation tuning
(Figure 1C), we found that neuronal orientation tuning follows in large part the orientation
tuning of the basal tree. In addition, we have further identified how apical versus
basal dendritic tree ablation would affect neuronal tuning in the different conditions
implemented. Model results provide insights regarding the ‘tolerance’ to different
input properties at the apical and basal tree in order to achieve stable orientation
preference.
Figure 1. A. Top: From the pool of synapses, 25% were stimulus driven (black dots).
Bottom: Indicative trace showing fluctuations of the membrane potential in the presence
of background synaptic activity. Spikes are truncated for visualization purposes.
B. Each tree was characterized by a μ ± σ orientation preference that was determined
by the preferences of the individual synapses. Here it is shown the portion of synapses
with same/different orientation preferences from the μtree, for σtree = 3, 15, 30,
45 and 60°. Grouping to the reported value (x-axis) includes ± 10° differences. C.
Orientation tuning curve of the model neuron (mean ff ± sem). Right: Indicative voltage
traces of the neuronal responses for different bar orientations (0°, 30°, 60° and
90°)
Reference
1. Larkum ME: A cellular mechanism for cortical associations: an organizing principle
for the cerebral cortex. Trends Neurosci 2012:1–11.
P204 Dual recordings in the mouse auditory brainstem and midbrain reveal differences
in the processing of vocalizations
Richard A. Felix1, Alexander G. Dimitrov1,2, Christine Portfors1
1Department of Integrative Biology and Neuroscience, Washington State University Vancouver,
Vancouver WA 98686, USA; 2Department of Mathematics and Statistics, Washington State
University Vancouver, Vancouver WA 98686, USA
Correspondence: Alexander G. Dimitrov (alex.dimitrov@wsu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P204
Background: A normal functioning auditory system must rely on fast and precise neuronal
responses in order to accurately represent temporal information in complex sounds.
Impairments in temporal processing contribute to a variety of listening disorders,
yet our understanding of mechanisms that govern these processes remains limited. We
examined how enhanced spike timing at the level of the inferior colliculus (IC) in
the midbrain might underlie efficient encoding of vocalizations compared to the cochlear
nucleus (CN), an earlier site in the ascending auditory pathway.
Methods: We recorded neuronal responses to conspecific vocalizations in the IC and
CN of awake, normal-hearing mice that expressed Channelrhodopsin in VGlut2-positive
neurons. We used an optrode that combined the recording of single unit activity with
light delivery to the CN. Once a recording was established in the CN, a second electrode
was placed in the IC and dual recordings were established at locations with matching
frequency tuning. The CN was stimulated with light in the absence of sound to measure
effects in the IC and then responses to sound stimuli were simultaneously recorded
at each site. We assessed the extent of functional connectivity between CN and IC
recording sites, the temporal precision of evoked spiking, and the neuronal selectivity
to vocalization stimuli, using statistical and information-theoretic tools.
Results: We found that stimulating the CN with light caused evoked activity in the
IC when the two recording sites had matched frequency tuning, suggesting that tonotopic
organization reliably predicts functional connectivity between the sites. Despite
matching frequency tuning, IC neurons exhibited greater selectivity to a common set
of vocalization stimuli compared to the dorsal CN (DCN). Overall, CN responses had
higher rates of evoked spiking, while IC responses were more transient and had enhanced
spike timing, suggesting a shift toward the extraction of temporal information contained
in vocalizations at the level of the midbrain (Figure 1).
Figure 1. Relationship between information content and response consistency in mouse
DCN and IC
Conclusion: Neurons in the CN often contributed to activity recorded in the IC. Dual
recordings conducted under the same experimental conditions that have a degree of
functional connectivity provide a strong paradigm for comparing processing at different
stages of the auditory pathway. Enhanced selectivity to vocalizations and temporal
precision of responses in the IC suggests that this region may be important for encoding
biologically important sounds. When auditory processing is impaired, the IC may be
a subcortical site for the generation of auditory disorders typically thought to arise
in the cortex.
P205 Modelling of leg decoupling in the stick insect and its possible significance
for understanding the workings of the locomotor system
Silvia Daun1,2, Tibor I. Toth1
1Department of Animal Physiology, Institute of Zoology, University of Cologne, Cologne,
50674, Germany; 2Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3),
Research Center Juelich, Juelich, 52425, Germany
Correspondence: Silvia Daun (Silvia.Daun@uni-koeln.de)
BMC Neuroscience 2017, 18 (Suppl 1):P205
Amputation and temporary restraint of legs are widely used and accepted methods of
the study of the locomotor systems of insects. The animal is studied during free walking,
and its walking behaviour is compared before and after the amputation. Using the results,
conclusions are drawn with regard to the organization of the locomotor system of the
animal in question. In the stick insect, such investigations were carried out by [1]
and more recently by [2]. In the latter study, it was even observed that the front
legs could reversibly be decoupled by the animal itself and used to carry out search
movements. Nevertheless, the hind and middle legs continued their coordinated walking.
From these and other experimental observations detailed in [1] and [2], the question
naturally arising is: what mechanisms underlie the changes found in the experiments.
The underlying mechanisms obviously belong to the part of the nervous system that
controls and coordinates locomotion. One promising way to study them is by using appropriate
mathematical models. We used an existing model of coordinated stepping of the three
ipsilateral legs of the stick insect [3] to mimic the various decoupling situations
in the stick insect described in [1] and [2]. In the model, the levator-depressor
neuro-muscular control networks (LD systems) of the individual legs play a pivotal
role in producing coordinated stepping of the legs. We identified three main possibilities
of decoupling a single leg: i) disrupting the inter-leg coordination between the legs’
LD systems; ii) blocking the normal function of the central pattern generator of the
LD system of the leg to be decoupled; and iii) changing the activity of the levator
and depressor motoneurones via their associated pre-motor inhibitory interneurones.
Decoupling of the front leg in the model worked with any of the methods i)-iii). It
was easily reversible, in accordance with the observations that such reversible decoupling
happens in natural conditions when the animal uses its front legs for searching. The
hind and middle leg continued their coordinated stepping, like in the experiments
[1, 2]. Decoupling of the hind leg was most effective when method iii) was used. In
this case, the middle and the front leg continued performing coordinated stepping
irrespective of the decoupling method, in agreement with the experimental findings.
In the model, the middle leg took over automatically the role of the hind leg as the
origin of the coordinated stepping. Decoupling the middle leg yielded mixed results:
in some cases, depending on the phase within a stepping period, the coordinated stepping
of the front and hind leg was abolished, in others, it was not but its quantitative
properties were changed. Both types of results were also found in the experiments
[1, 2].
In conclusion, we suggest that, depending on the leg, various mechanisms are possible
to decouple it from the system of inter-leg coordination. In all cases, method iii)
worked most reliably and efficiently. However, the other mechanisms (methods) may
represent redundance and can be activated, if necessary, to bring about decoupling
of the leg.
Acknowledgements
This work was supported by the DFG grants to S. Daun (GR3690/2-1 and GR3690/4-1).
References
1. Graham D: The effect of amputation and leg restraint on the free walking coordination
of the stick insect Carausius Morosus. J Comp Physiol 1977, 116:91–116.
2. Grabowska M, Godlewska E, Schmidt J, Daun-Gruhn S: Quadrupedal gaits in hexapod
animals - inter-leg coordination in free walking adult stick insects. J Exp Biol 2012,
215:4255–4266.
3. Toth TI, Daun-Gruhn S: A three-leg model producing tetrapod and tripod coordination
patterns of ipsilateral legs in the stick insect. J Neurophysiol 2016, 115:887–906.
P206 Spatio-temporal dynamics of key signaling molecules in growth cones
Joanna Jędrzejewska-Szmek1, Nadine Kabbani1,2, Kim T. Blackwel1,3
1Krasnow Institute, George Mason University, Fairfax, VA 22030, USA; 2School of Systems
Biology, George Mason University, Fairfax, VA 22030, USA; 3Bioengineering Department,
George Mason University, Fairfax, VA 22030, USA
Correspondence: Joanna Jędrzejewska-Szmek (jjedrzej@gmu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P206
Growth cones, guided by environmental cues, are necessary for proper neural functioning.
The cues are detected by membrane-bound receptors, which in turn activate a plethora
of signaling pathways. A majority of these pathways is governed by calcium, flowing
into the growth cone through the plasmalemma or from the calcium stores. Both the
magnitude of calcium increase and identity of calcium source seem to determine neural
growth and retraction [1]. Calcium exerts its control through a variety of signaling
molecules that interact non-linearly. This picture is further complicated by recent
findings showing that the ionotropic alpha7 nicotinic receptor (a7nAChR) also has
a metabotropic function and couples to heteromeric Gq proteins. A7nAChR action via
the Gq pathway results in calcium release from the endoplasmic reticulum (ER) modulating
cytoskeletal motility and structural growth [2–4].
Experimental evidence shows that both low and high cytosolic calcium results in growth
cone repulsion, and medium cytosolic calcium results in attraction. It also shows
that calcium influx through the plasmalemma results in repulsion and calcium influx
from the internal stores results in growth. To investigate and unify these seemingly
contradictory observations experimental observations, we developed a stochastic reaction-diffusion
model of calcium, cAMP and Gq activated pathways. The model allows for evaluating
the role of the transient calcium influx through the channel pore (the ionotropic
contribution) compared to the role of calcium release caused by activation of the
Gq subtype of GTP binding protein. Using the model, we investigated whether combined
metabotropic and ionotropic action of a7nAChR, resulting in prolonged increase of
cytosolic calcium, is responsible for experimentally observed growth attenuation.
To test whether we can predict neurite outgrowth and retraction in response to various
environmental stimuli and to elucidate contribution of molecular gradients we looked
at combined action of key signaling molecules. We show that combined activation of
calcium and cAMP activated targets such as PP2B and PP1, CaMKII, PKA and calpain can
explain the non-monotonic dependence of structural growth on calcium levels. Elucidating
the mechanisms underlying synaptic growth will allow for better understanding of mechanisms
of neural development and regeneration
Acknowledgements
The joint NIH-NSF CRCNS program through NSF grant 1515686
References
1. Henley J, Poo M-m: Guiding neuronal growth cones using Ca2+ signals. Trends in
Cell Biol 2004, 14:320–330. doi: 10.1016/j.tcb.2004.04.006
2. Nordman JC, Kabbani N: Microtubule dynamics at the growth cone are mediated by
α7 nicotinic receptor activation of a Gαq and IP3 receptor pathway. FASEB J 2014,
28:2995–3006. doi: 10.1096/fj.14-251439.
3. King JR, Nordman JC, Bridges SP, Lin MK, Kabbani N. Identification and characterization
of a G protein-binding cluster in α7 nicotinic acetylcholine receptors. J Biol Chem
2015, 290:20060–70. doi:10.1074/jbc.M115.647040
4. King JR, Kabbani N: Alpha 7 nicotinic receptor coupling to heterotrimeric G proteins
modulates RhoA activation, cytoskeletal motility, and structural growth. J Neurochem
2016, 138:532–45. doi:10.1111/jnc.13660.
P207 A simulation of EMG signal generation following TMS
Bahar Moezzi1,2, Natalie Schaworonkow3, Lukas Plogmacher3, Mitchell R. Goldsworthy2,4,
Brenton Hordacre2, Mark D. McDonnell1, Nicolangelo Iannella1,5, Michael C. Ridding2,
Jochen Triesch3
1Computational and Theoretical Neuroscience Laboratory, School of Information Technology
and Mathematical Sciences, University of South Australia, Adelaide, Australia; 2Robinson
Research Institute, School of Medicine, University of Adelaide, Adelaide, Australia;
3Frankfurt Institute for Advanced Studies, Frankfurt, Germany; 4Discipline of Psychiatry,
School of Medicine, University of Adelaide, Adelaide, Australia; 5School of Mathematical
Sciences, University of Nottingham, Nottingham, UK
Correspondence: Bahar Moezzi (bahar.moezzi@mymail.unisa.edu.au)
BMC Neuroscience 2017, 18 (Suppl 1):P207
Transcranial magnetic stimulation (TMS) is a technique that allows noninvasive manipulation
of neural activity and is used extensively in both clinical and basic research settings
[1]. The effect of TMS on motor cortex is often measured by electromyography (EMG)
recordings from a small hand muscle, such as the first dorsal interosseous (FDI).
However, the details of how TMS generates responses measured with EMG are not completely
understood. Here, we aim to develop a biophysically detailed computational model to
study the potential mechanisms underlying the generation of EMG signals in response
to TMS.
Our model comprises a feed-forward network of cortical layer 2/3 cells, which drive
morphologically detailed layer 5 corticomotoneuronal cells based on [2]. The cortical
layer 5 cells in turn project to a pool of motoneurons and eventually the muscle.
The EMG signal is the sum of motor unit action potentials. Model parameters are tuned
to match results from EMG recordings from the FDI muscle performed in four human subjects.
The model successfully reproduces several properties of the experimental data. The
simulated EMG signals match experimental EMG recordings in shape and size, and vary
with stimulus and contraction intensities as in experimental data. They exhibit cortical
silent periods that are close to the biological values, and reveal an interesting
dependence on inhibitory synaptic transmission characteristics. Our model predicts
neural firing patterns along the entire pathway from cortical layer 2/3 cells down
to spinal motoneurons. In conclusion, our model successfully reproduces major features
of EMG recordings and should be considered as a viable tool for analyzing and explaining
EMG signals following TMS.
References
1. Hallett M: Transcranial magnetic stimulation and the human brain. Nature 2000,
406:147–150.
2. Rusu CV, Murakami M, Ziemann U, Triesch J. A model of TMS-induced I-waves in motor
cortex. Brain Stimul 2014, 7:401–414.
P208 The effect of LTP, LTD and non-specific LTD on the Recognition of Sparse Noisy
Patterns in Simplified and Detailed Purkinje Cell Models
Reinoud Maex1, Karen Safaryan2, Volker Steuber3
1Department of Cognitive Sciences, Ecole Normale Supérieure, rue d’Ulm 25, 75005 Paris,
France; 2Department of Physics and Astronomy, Knudsen Hall, University of California,
Los Angeles, CA, 90095-0001, USA; 3Centre for Computer Science and Informatics Research,
University of Hertfordshire, College Lane, Hatfield, AL10 9AB, United Kingdom
Correspondence: Reinoud Maex (reinoud.maex@ens.fr)
BMC Neuroscience 2017, 18 (Suppl 1):P208
Classic theories of cerebellar learning suggest that parallel fibre (PF) activity
patterns in cerebellar cortex can be stored and recalled based on long-term depression
(LTD) of PF - Purkinje cell synapses [1, 2]. As in other theories of learning in neural
systems, it is commonly assumed that the weight changes are limited to activated synapses.
However, it has been shown that a non-specific form of PF LTD can spread to neighbouring
synapses that are inactive during learning [3]. Moreover, long-term potentiation (LTP)
of PF synapses has also been found to contribute to cerebellar learning [4].
We have previously studied the effect of non-specific LTD (nsLTD) on pattern recognition
and have shown that nsLTD can provide robustness against local spatial noise in the
input patterns [5]. Here we extend our previous work by studying the functional role
of LTP, and we investigate other determinants of the pattern recognition performance
such as the sparsity and number of patterns and different types of pattern noise.
We compare results from numerical simulations of a morphologically realistic conductance
based Purkinje cell model (as in [2]) with those of a simple linear artificial neural
network (ANN) unit. Further, to better understand the results of the numerical simulations,
we perform a mathematical analysis of the pattern recognition performance of the ANN
unit. As in previous work, we quantify the pattern recognition performance by calculating
a signal-to-noise (s/n) ratio [2, 5].
The simulations and analysis of the ANN unit predict that adding LTP to the learning
rule does not affect the pattern recognition performance, given that the mean and
variance of responses, which appear in the enumerator and denominator of the s/n ratio,
respectively, are equally affected by LTP. In contrast, however, the pattern recognition
performance of the Purkinje cell model was sensitive to the average synaptic weight,
which determined both the spontaneous spike rate and the response to pattern presentation.
Adding LTP in the Purkinje cell model made nsLTD equivalent or superior to LTD at
all noise levels. Moreover, the LTP based normalisation of weights prevented the Purkinje
cell responses from becoming too weak and increased the number of patterns that could
be stored for a given s/n ratio by a factor of 4. Finally, we show that our previous
conclusions hold over a large range of pattern loadings and sparsities, and that local
additive pattern noise can further increase the beneficial effect of nsLTD.
References
1. Marr D: A theory of cerebellar cortex. J Physiol 1969, 202:437–470.
2. Steuber V, Mittmann W, Hoebeek FE, Silver RA, De Zeeuw CI, Hausser M, De Schutter
E: Cerebellar LTD and pattern recognition by Purkinje cells. Neuron 2007, 54:121–136.
3. Wang SS, Khiroug L, Augustine GJ: Quantification of spread of cerebellar long-term
depression with chemical two-photon uncaging of glutamate. Proc Natl Acad Sci USA
2000, 97:8635–8640.
4. Schonewille M, Belmeguenai A, Koekkoek SK, Houtman SH, Boele HJ, van Beugen BJ,
Gao Z, Badura A, Ohtsuki G, Amerika WE, Hosy E, Hoebeek FE, Elgersma Y, Hansel C,
De Zeeuw CI: Purkinje cell-specific knockout of the protein phosphatase PP2B impairs
potentiation and cerebellar motor learning. Neuron 2010, 67:618–628.
5. Safaryan K, Maex R, Adams RG, Davey N, Steuber V: Non-specific LTD at parallel
fibre - Purkinje cell synapses in cerebellar cortex provides robustness against local
spatial noise during pattern recognition. BMC Neuroscience 2011, 12:P314.
P209 Modeling causality of the smoking brain
Rongxiang Tang1, Yi-Yuan Tang2
1Department of Psychology, Washington University in St. Louis, St. Louis, MO 63130,
USA; 2Department of Psychological Sciences, Texas Tech University, TX 79409, USA
Correspondence: Yi-Yuan Tang (yiyuan.tang@ttu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P209
Previous studies indicated that brain areas including prefrontal cortex (e.g., medial
prefrontal cortex, mPFC), posterior cingulate cortex (PCC) and insula involved in
smoking addiction [1]. However, functional connectivity among these regions only shows
the correlative relationship but does not reveal the causal relationship such as the
changes in information flow in these distributed brain areas involved in smoking.
In prior studies [2-3], we applied a newly developed spectral dynamic causal modeling
(spDCM) to resting state fMRI to demonstrate the causal relationships among the core
regions in smoking addiction. Our results suggested that compared to nonsmokers, smokers
had reduced effective connectivity from PCC to mPFC and from right inferior parietal
lobule (R-IPL) to mPFC, a higher self-inhibition within PCC and a reduction in the
amplitude of endogenous neuronal fluctuations driving the mPFC [2]. Given that Granger
causality (GC) and DCM are two main causality methods and have distinct but complementary
ambitions that are usefully considered in relation to the detection of functional
connectivity and the identification of models of effective connectivity [4-5], therefore
it’s important to use a same dataset to compare two models.
We used the dataset of college students previously reported in our study [2]. All
fMRI data were collected using a 3-Telsa Siemens Skyra scanner and processed using
the Data Processing Assistant for Resting-State fMRI, which is based on SPM and Resting-State
fMRI Data Analysis Toolkit [2-3]. For fMRI analyses, we conducted the standard procedures
included slice timing, motion correction, regression of WM/CSF signals and spatial
normalization [3]. A standard GC analysis was also applied to test the causality among
key regions involved in smoking [5-6]. Based on previous literature, in this study
we specified four regions of interest within default mode network (DMN) - medial prefrontal
cortex (mPFC), posterior cingulate cortex (PCC), and bilateral inferior parietal lobule
(Left IPL and Right IPL), same coordinates as in previous spDCM studies [2]. Our results
showed the similar causal relationship among these brain areas.
Conclusions: GC and DCM are complementary: both are concerned with directed causal
interactions. GC models dependency among observed responses, while DCM models coupling
among the hidden states generating observations. Despite this fundamental difference,
the two approaches may be converging.
Acknowledgements
This work was supported by the Office of Naval Research.
References
1. Goldstein RZ, Volkow ND: Dysfunction of the prefrontal cortex in addiction: Neuroimaging
findings and clinical implications. Nat Rev Neurosci 2011, 12:652–669.
2. Tang R, Razi A, Friston KJ, Tang YY: Mapping smoking addiction using effective
connectivity analysis. Frontiers in Human Neuroscience. 2016, 10:195.
3. Razi A, Kahan J, Rees G, Friston KJ: Construct validation of a DCM for resting
state fMRI. Neuroimage 2015, 106:1–14.
4. Friston K, Moran R, Seth AK: Analysing connectivity with Granger causality and
dynamic causal modelling. Curr Opin Neurobiol. 2013, 23:172–8.
5. Seth AK: A MATLAB toolbox for Granger causal connectivity analysis. J Neurosci
Meth 2010, 186:262–273.
6. Zhao Z, Wang X, Fan M, Yin D, Sun L, Jia J, Tang C, Zheng X, Jiang Y, Wu J, Gong
J: Altered effective connectivity of the primary motor cortex in stroke: a resting-state
fmri study with Granger causality analysis. PLoS One. 2016, 11:e0166210.
P210 Modelling of calcium waves in astrocytic networks induced by neural activity
Darya V. Verveyko1, Alexey R. Brazhe2, Andrey Yu Verisokin1, Dmitry E. Postnov3
1Department of Theoretical Physics, Kursk State University, Kursk, 305000, Russian
Federation; 2Department of Biophysics, Lomonosov Moscow State University, Moscow,
119991, Russian Federation; 3Department of Physics, Saratov State National Research
University, Saratov, 410012, Russian Federation
Correspondence: Darya V. Verveyko (allegroform@mail.ru)
BMC Neuroscience 2017, 18 (Suppl 1):P210
We propose two-compartment model of calcium dynamics in astrocyte network, basing
on Ullah model [1]. In order to count the specific features of different parts of
astrocyte network we mark out three types of modelling space: astrocyte soma with
thick branches, thin branches, and extracellular space. We have developed two variants
of equation set which are different in relative contribution of specific ionic currents.
We suppose that activation of astrocyte calcium dynamics is mediated by the extracellular
space, specifically, via diffusion of synaptic glutamate released due to the neuronal
activity, which we describe as some random signal incorporating noise effects.
We have performed a number of simulation runs at different parameter sets for individual
astrocyte and multi-cell network. One of simulation examples within the computational
multi-cell template is given in Figure 1. The global wave emerging in one of the points
passes through the wide region of astrocyte network. The formation of the wave has
a high degree of regularity and periodicity. There are also local regimes where excitation
waves damp passing through a small number of cells.
Figure 1. Calcium global wave in multi-cell ensemble. A. The representative snapshots
of spatial patterns. Numbers from 1 to 8 indicate the cells according to its involvement
in firing pattern. B, C. The time courses of cytosolic Ca2+ and IP3 concentrations,
respectively
Conclusions: We have suggested the advanced model of astrocyte network dynamics, which
fits well the recent experimental findings [2]. Specifically, we have suggested the
development of model equations for intra-astrocyte calcium dynamics, which takes into
account its specific topological features. We have tested the suggested approach for
both individual cell image and multi-cellular structure. The obtained results confirm
that our model is able to reproduce the evolution of spatio-temporal dynamics under
neuronal activity represented by spatially uncorrelated and randomized in time process
of glutamate injection. In multicellular system, a persistent self-organized rhythmicity
of calcium activity in groups was found which can be explained by some interplay between
the refractory time of calcium excitability and noise-triggered processes.
Acknowledgements
This work is partially supported by the Ministry of Education and Science of the Russian
Federation within the research project №3.9499.2017 included into the basic part of
research funding assigned to Kursk State University.
References
1. G. Ullah, P. Jung, A.H. Cornell-Bell: Anti-phase calcium oscillations in astrocytes
via inositol (1, 4, 5)-trisphosphate regeneration. Cell Calcium 2006, 39: 197–208.
2. M. Falcke: Reading the patterns in living cells - the Physics of Ca2+ signaling.
Adv. in Phys. 2004, 53(3): 255–44
P211 Simulated voltage clamp: offline biophysical reconstruction of fast ionic currents
in large cells with uncompensated series resistance
Cengiz Günay1,2, Gabriella Panuccio3, Michele Giugliano3, Astrid A. Prinz1
1Dept. Biology, Emory University, Atlanta, Georgia 30322, USA; 2School of Science
and Technology, Georgia Gwinnett College, Lawrenceville, Georgia 30043, USA; 3Theoretical
Neurobiology & Neuroengineering Lab, Dept. Biomedical Sciences, University of Antwerp,
Antwerp, Belgium
Correspondence: Cengiz Günay (cgunay@ggc.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P211
Characterization of ion channel kinetics from voltage-clamp experiments is inherently
biased by the non-linear voltage error introduced by the resistance of the recording
pipette in series with the membrane resistance (series resistance, Rs) [1]. Modern
patch-clamp amplifiers provide built-in circuits for on-line Rs compensation. However,
because of the nature of these circuits, it is theoretically impossible to achieve
100% Rs compensation without losing stability of the recording. Moreover, fast ionic
voltage-dependent currents, like sodium (Na+) currents, require a high band-width
operation of the Rs compensation circuit, which in turn might result in sudden oscillations
of the cell membrane voltage (Vm). Consequently, Rs compensation is currently a trade-off
between a commonly accepted error tolerance and the crucial need for preventing oscillations.
Here, we build a novel “simulation method” as a new component to a previously developed
computational framework [2] to overcome these limitations. In contrast to the amplifier’s
strategy to force a flat voltage waveform, which is required for generating conventional
current-voltage plots of peak ionic currents, we allow arbitrary voltage waveforms
by simulating voltage-clamp in a computational neuron model and then curve fitting
its output to match recordings to directly estimate Hodgkin-Huxley model parameters
of the channel. The kinetics parameters so obtained are used to reconstruct the unbiased
current trace. We demonstrate our method using voltage-clamp recordings of Na+ currents
from ‘giant’ layer V pyramidal cells of the rat primary somatosensory cortex in the
presence of uncompensated, significantly high (10-20 MΩ) Rs along with the low input
resistance (~40 MΩ) typical of these cells, so as to maximize the compound voltage
clamp errors. As shown in Figure 1, the model computes non-linear artifact currents
and predicted actual Vm values. When Rs compensation is a major concern for the reliability
voltage-clamp data, our approach is capable of overcoming the limitations posed by
currently available hardware- and software-based Rs compensation methods, thus allowing
to fully reconstructing the actual current kinetics.
Figure 1. Offline subtraction of estimated amplifier-unaccounted passive currents.
A. Raw recordings of Na+ currents contaminated by uncompensated artifacts (top) recorded
during the corresponding voltage steps (bottom trace). B. Passive artifacts subtracted
from the current traces (top) and actual Vm (bottom) estimated using the model simulation
method. Note how the actual Vm differs significantly from the desired holding voltage-steps
(see panel A)
Acknowledgements
Career Award at the Scientific Interface (CASI) from the Burroughs Wellcome Fund awarded
to AAP.
References
1. Sakmann, B., and Neher, E. Single-Channel Recording. 2nd Edition, (Springer Science
& Business Media, Plenum Press, New York, 1995).
2. Günay C, Edgerton JR, Li S, Sangrey T, Prinz AA, and Jaeger D. Database analysis
of simulated and recorded electrophysiological datasets with PANDORA’s toolbox. Neuroinformatics
2009, 7: 93–111.
P212 Representing and implementing cognitive sequential interactions
Pablo Varona1, Mikhail I. Rabinovich2
1Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática, Escuela Politécnica
Superior, Universidad Autónoma de Madrid, Madrid, Spain; 2BioCircuits Institute, University
of California, San Diego, CA, USA
Correspondence: Pablo Varona (pablo.varona@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P212
Cognition as observed by imaging experiments involves sequential activations of different
brain regions [1]. The sequential nature of most aspects of cognition is also reflected
in the progression of successive components of decision-making and behavior. In this
work, we present a family of models that describe hierarchical relationships among
cognitive processes represented with robust sequential dynamics. These models build
heteroclinic networks based on the winnerless competition principle where asymmetric
inhibition shapes key properties for sequential information processing. The robustness
of the sequential dynamics in these networks relies on stable heteroclinic channels,
sequences of metastable states and their vicinity connected by separatrices that link
them in a chain.
The models described in this work are implemented with generalized Lotka-Volterra
equations whose variables can represent information perception items and also cognitive
resources such as attention, working-memory and emotion [2–5]. Their hierarchical
interactions give rise to binding and chunking processes. We discuss applications
of these models in three different contexts: (i) the characterization of decision-making
in terms of the sequential evolution of incoming information and the hierarchical
organization of cognitive resources in time; (ii) the use of these models to build
joint robot-human interactions which result in an increased joint creativity of such
team; (iii) the use of these models to drive closed-loop stimulation in novel experiments
to reveal healthy and pathological dynamics of cognitive processes in normal subjects
and in subjects with cognitive impairments. The considered dissipative models are
in general structurally stable and suitable for bifurcation analysis, which helps
their interpretation in relationship with experimental data. Their robustness and
computational efficiency also make them adequate for real-time implementations in
the proposed applications.
Overall, we stress the need to interpret brain imaging experiments in the context
of theoretical studies that describe information flows corresponding to sequential
cognitive processes. The coarse-grained information of current imaging techniques
can be matched to the variables represented in the proposed network models. The results
of such analyses can lead to novel insights linking networks graphs to cognitive dynamics,
and the development of novel technology for rehabilitation purposes and artificial
cognition.
Acknowledgements
This work was funded by MINECO/FEDER DPI2015-65833-P (http://www.mineco.gob.es/) and
ONRG grant N62909-14-1-N279 (PV) and by ONR MURI 14-13-1-0205 and MURI N00014-13-1-0678
(MIR)
References
1. Daselaar SM, Rice HJ, Greenberg DL, Cabeza R, LaBar KS, Rubin DC. The spatiotemporal
dynamics of autobiographical memory: Neural correlates of recall, emotional intensity,
and reliving. Cereb. Cortex. 2008; 18:217–29.
2. Rabinovich MI, Afraimovich VS, Bick C, Varona P. Information flow dynamics in the
brain. Phys. Life Rev. 2012; 9:51–73.
3. Rabinovich MI, Tristan I, Varona P. Hierarchical nonlinear dynamics of human attention.
Neurosci. Biobehav. Rev. 2015; 55:18–35.
4. Rabinovich MI, Simmons AN, Varona P. Dynamical bridge between brain and main. Trends
Cogn. Sci. 2015; 19:453–461.
5. Varona P, Rabinovich MI. Hierarchical dynamics of informational patterns and decision
making. Proc. R. Soc. B. 2016; 283:20160475.
P213 An integrated neuro-mechanical model of C. elegans locomotion
Jack Denham, Thomas Ranner, Netta Cohen
School of Computing, University of Leeds, Leeds, LS2 9JT, UK
Correspondence: Jack Denham (scjde@leeds.ac.uk), Thomas Ranner (T. Ranner@leeds.ac.uk),
Netta Cohen (N.Cohen@leeds.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P213
Across the animal kingdom, the generation and modulation of motor behaviour is attributed
to Central Pattern Generators (CPGs) or neural circuits that endogenously produce
oscillations. The ubiquity of CPGs prompts the use of coupled oscillator models to
describe neural activity and the generation of behaviour. However, CPGs have not been
identified in the forward locomotion system of the small roundworm Caenorhabditis
elegans. In this case, a proprioceptive mechanism, in which motor-neurons respond
to local body stretch, is thought to drive sustained body undulations. Since the wavelength
and frequency of oscillations has been shown to depend on the visco-elasticity of
the surrounding medium [1], it is important to include environmental effects in such
locomotion models [1, 2]. This requires the integration of the nervous system and
body mechanics in a continuous feedback loop which is able to adapt in response to
environmental changes. Here, a biologically grounded model describing neural activity
(adapted from [1]) is integrated into a novel continuum soft-body model [2]. We present
a dynamical systems description of the local pattern generation mechanism with fictive
proprioceptive feedback and compare this with the actual feedback in whole body simulations.
The closed loop neuro-mechanical model is demonstrated to produce realistic travelling
waves down the body in silico. The effect of the material properties of the body is
investigated.
References
1. Boyle JH, Berri S, Cohen N: Gait modulation in c. elegans: an integrated neuro-mechanical
model. Frontiers in computational neuroscience 2012,
6
:10.
2. Cohen N, Ranner T: A new computational method for a model of C. elegans biomechanics:
Insights into elasticity and locomotion performance, arXiv:1702.04988, 2017.
P214 A computational approach to understanding functional synaptic diversity: the
role of nanoscale topography of Ca2+ channels and synaptic vesicles
Maria Reva1, Nelson Rebola1, Tekla Kirizs2, Zoltan Nusser2, David DiGregorio1
1Laboratory of Dynamic Neuronal Imaging, Neuroscience Department, Institute Pasteur,
Paris, France, 75015; 2Institute of Experimental Medicine, Hungarian Academy of Sciences,
Budapest, Hungary, 1083
Correspondence: Maria Reva (maria.reva@pasteur.fr)
BMC Neuroscience 2017, 18 (Suppl 1):P214
Understanding the spatial relationship between the synaptic vesicles and the voltage-gated
Ca2+ channels (VGCCs) is critical for deciphering the determinants of synaptic strength,
time course, and plasticity. Furthermore, synaptic strength, within a homogeneous
population of synapses, is highly heterogeneous, but the underlying mechanisms are
poorly understood. We hypothesize that variations in the nanoscale organization of
VGCCs and synaptic vesicles contribute to the diversity of synaptic function observed
throughout the brain [1]. Because VGCCs and synaptic vesicles can be as close as 10-20 nm,
direct experimental observation of the spatio-temporal dynamics driving synaptic vesicle
fusion is still challenging. We have taken a computational approach to simulate the
spatio-temporal dynamics of Ca2+ -triggered vesicle fusion to examine channel-vesicle
topologies that is consistent with experimental findings.
To understand the influence of topography on synaptic diversity, we performed Monte
Carlo (MC) simulations designed to predict the different functional behavior of inhibitory
and excitatory terminals within the cerebellar cortex. Model parameters were constrained
to experimental data (such as single channel open probability, Ca2+ buffers kinetics,
etc.) leaving only topographical arrangements of VGCCs and location of the release
sensor as variables. In addition, we have analyzed replicas in which the VGCC subunit
Cav2.1 was labeled. Using Ripley’s analysis and mean nearest neighbor distances (NND)
calculations we concluded that the distribution of the Cav2.1 subunit was significantly
different from complete spatial randomness in both excitatory and inhibitory axon
terminals. Then using cluster analysis, we determined that inhibitory terminals exhibited
small clusters, while the labeling on excitatory boutons seemed more amorphous. We
therefore considered an arrangement based on a few simple rules: VGCCs and vesicles
were placed randomly within the AZ, but with a minimal separation, we called this
the exclusion zone (EZ) model. The EZ model produced channel NND distributions that
were consistent with the electron microscopy data. We then performed reaction diffusion
MC simulations, considering perimeter coupled model for inhibitory terminals and the
exclusion topology for excitatory terminals. Our simulations predicted well the experimental
data of Ca2+ chelator inhibition of synaptic release (EGTA inhibition) and release
probability.
Our results suggest that inhibitory terminals use small clusters of VGCC to drive
the fusion of vesicles located in their periphery (perimeter release model) as described
previously at the excitatory calyx of Held synapses [2]. In contrast, excitatory synapses
made by cerebellar parallel fibers require a more random placement of up to 3 times
more VGCCs within the AZ, as well as random placement of vesicles with an exclusion
zone of >40 nm. We therefore suggest that nanoscale distribution of VGCCs and synaptic
vesicles differs among synapses and is a key factor underlying functional synaptic
diversity.
References
1. Chabrol FP, Arenz A, Wiechert MT, Margrie TW, DiGregorio DA: Synaptic diversity
enables temporal coding of coincident multisensory inputs in single neurons. Nat Neurosci
2015, 18(5): 718–727.
2. Nakamura Y, Harada H, Kamasawa N, Matsui K, Rothman JS, Shigemoto R, Silver RA,
DiGregorio DA, Takahashi T: Nanoscale distribution of presynaptic Ca(2 +) channels
and its impact on vesicular release during development. Neuron 2015, 85(1): 145–158.
P215 Is object saliency perceived different cross-culturally: a computational modelling
study
Eirini Mavritsaki1,2, Panos Rentzelas1
1Department of Psychology, Birmingham City University, Birmingham, UK; 2School of
Psychology, University of Birmingham, Birmingham, UK
Correspondence: Eirini Mavritsaki (eirini.mavritsaki@bcu.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P215
Research on cross-cultural differences of visual attention has identified that cultural
membership influence performance in object perception [1, 2]. Participants with collectivist
background focus more on the background (distractors) and omit the target relevant
information while participants from the individualists’ background tend to attend
the target and omit the background information. Previous modelling work from our lab
[3] predicted that in Visual Search task cultural memberships influences the performance
of the tasks. The results showed that simulated efficiency of participants from the
individualist group is significantly higher than simulated efficiency from participants
from the collectivists group when the task is to identify a target amongst distractors
in a classical easy visual search. Work in our lab then confirmed these predictions.
Preliminary behavioral data supports the idea that the effect remains even if the
target is more salient than the distractors. This difference is simulated and explored
further by investigating the changes in the effect for different levels of saliency
using the binding Search over Time and Space (bsSoTS) computational model [4, 5] as
predictor of behavior.
bsSoTS is based on integrate-and-fire neurons that are tighter connected when they
encode a specific characteristic of an item presented in one position on the Visual
Field and loosely connected when they present the same characteristics but items presented
in different positions on the visual field. Moreover, the model incorporates a number
of synaptic currents and processes that allowed us to successfully simulate the Visual
Search experiment [4, 5]. In research, cultural membership is usually investigated
between collectivists (Asian cultures) and individualists’ groups (Western Europeans
cultures) [1, 2]. The experiments that bsSoTS simulated so far are based on individualists’
groups [4, 5]. To simulate therefore the difference in behavior between collectivists
and individualists, we need to simulate the difference observed in collectivists cultures.
To do that we tested the coupling between the neurons that encode a specific item
presented in one position on the Visual Field as a saliency parameter. The same parameter
was used in preliminary modelling work in our lab [3].
The results showed that the saliency parameter successfully simulates the behavioral
results. Additionally, further behavioral work is proposed by investigating the relationship
between the different saliency levels and the observed effect.
References
1. Nisbet RE, Masuda T: Culture and point of view. Proceedings of the National Academy
of Sciences of the United States of America 2003,
100: 11163–11170.
2. Nisbet RE, Peng K, Choi I, Norenzayan A: Culture and systems of thought: Holistic
versus analytic cognition. Psychological Review 2001,
108: 291–310.
3. Mavritsaki E, Rentzelas P: Cross-cultural differences in visual attention: A computational
modelling study. BMC Neuroscience,
16: 204.
4. Mavritsaki E, Humphreys GW: Temporal binding and segmentation in Visual Search:
A computational neuroscience analysis. Journal of Cognitive Neuroscience 2015,
28: 1553–1567
5. Mavritsaki E, Heinke D, Allen HA, Deco G, Humphreys GW: Bridging the gap between
physiology and behavior: Evidence from the sSoTS model of human visual attention.
Psychological Review 2011,
118: 3–41.
P216 NeuroNLP: a natural language portal for aggregated fruit fly brain data
Nikul H. Ukani1, Adam Tomkins2, Chung-Heng Yeh1, Wesley Bruning3, Allison L. Fenichel4,
Yiyin Zhou1, Yu-Chi Huang5, Dorian Florescu2, Carlos Luna Ortiz2, Paul Richmond6,
Chung-Chuan Lo5, Daniel Coca2, Ann-Shyn Chiang5, Aurel A. Lazar1
1Department of Electrical Engineering, Columbia University, New York, NY 10027, USA;
2Department of Automatic Control & Systems Engineering, The University of Sheffield,
Sheffield, S1 3JD, UK; 3Department of Computer Science, Columbia University, New York,
NY 10027, USA; 4Data Science Institute, Columbia University, New York, NY 10027, USA;
5Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan; 6Department
of Computer Science, The University of Sheffield, Sheffield, S1 4DP, UK
Correspondence: Aurel A. Lazar (aurel@ee.columbia.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P216
NeuroNLP, a key application on the Fruit Fly Brain Observatory [1] platform (FFBO,
http://fruitflybrain.org), provides a modern web-based portal for navigating fruit
fly brain circuit data. Increases in the availability and scale of fly connectome
data demand new, scalable and accessible methods to facilitate investigation into
the functions of the complex circuits being uncovered. Combining data from multiple
sources into a single database, with a common data model, NeuroNLP facilitates access
to data from various sources simultaneously. It is built on top of the NeuroArch database
[2] which codifies fly connectome data from both the FlyCircuit database [3] and the
Janelia Fly Medulla data [4]. The former hosts meso-scale connectome data on the whole-brain
level and the latter contains detailed, micro-scale synaptic information about the
Medulla neuropil. NeuroNLP allows users to probe biological circuits in the NeuroArch
database with plain English queries, such as “show glutamatergic local neurons in
the left antennal lobe” and “show neurons with dendrites in the left mushroom and
axons in the fan-shaped body”, replacing the cumbersome menus prevalent in today’s
neurobiological databases. This enables in-depth exploration and investigation of
the structure of brain circuits, using intuitive natural language queries that are
capable of revealing latent structure and information. Equipped with powerful 3D visualization,
NeuroNLP standardizes tools and methods for graphical rendering, representation, and
manipulation of brain circuits, while integrating with existing databases such as
the FlyCircuit. It currently supports queries to show, add, filter and remove neurons
based on 1) the parent neuropil, 2) neuron type (local or projection), 3) dendritic/axonal
arborization, 4) neurotransmitter and 5) related postsynaptic or presynaptic neurons.
The graphical user interface complements the natural language queries with additional
controls for exploring neural circuits. Designed with an open-source, modular structure,
it is highly scalable and extensible to additional databases and languages. Accessible
through a laptop or smartphone (Figure 1) at https://neuronlp.fruitflybrain.org, NeuroNLP
significantly increases the accessibility of fruit fly brain data, streamlining the
way we explore and interrogate distal data sources to open new avenues of research,
and enrich neuroscience education.
Figure 1. Smartphone screenshot of NeuroNLP showing 16 lobula plate tangential cells.
Each neuron can be cross-linked to the FlyCircuit Database (left panel)
References
1. Ukani NH, Yeh C-H, Tomkins A, Zhou Y, Florescu D, Ortiz CL, Huang Y-C, Wang C-T,
Richmond P, Lo C-C et al., The Fruit Fly Brain Observatory: from structure to function.
Neurokernel Request for Comments, Neurokernel RFC #7, 2016. DOI: 10.1101/092288.
2. Givon LE, Ukani NH, Lazar AA, NeuroArch: A Graph dB for Querying and Executing
Fruit Fly Brain Circuits, Neurokernel Request for Comments, Neurokernel RFC #4, 2015.
DOI: 10.5281/zenodo.31947.
3. Chiang A-S, Lin C-Y, Chuang C-C, Chang H-M, Hsieh C-H, Yeh C-W, Shih C-T, Wu J-J,
Wang G-T, Chen Y-C et al., Three-dimensional reconstruction of brain-wide wiring networks
in Drosophila at single-cell resolution. Cell 2011, 21(1):1–11.
4. Takemura S, Xu CS, Lu, Z, Rivlin PK, Parag T, Olbris DJ, Plaza S, Zhao T, Katz
WT, Umayam L et al., Synaptic circuits and their variations within different columns
in the visual system of Drosophila. PNAS 2015, 112(44):13711–13716.
P217 Towards prediction of plasticity response to paired cTBS from resting state network
connectivity
Bahar Moezzi1, Brenton Hordacre1, Mitchell R. Goldsworthy1,2, Michael C. Ridding1
1Robinson Research Institute, School of Medicine, University of Adelaide, Adelaide,
Australia; 2Discipline of Psychiatry, School of Medicine, University of Adelaide,
Adelaide, Australia
Correspondence: Bahar Moezzi (bahar.moezzi@mymail.unisa.edu.au)
BMC Neuroscience 2017, 18 (Suppl 1):P217
Paired continuous theta burst stimulation (cTBS) is a non-invasive brain stimulation
technique that can induce neuroplastic change in the primary motor cortex [1]. The
response shows high intersubject variability and having a marker that might predict
response would be useful in many situations. Our hypothesis is that a more strongly
connected cortical network is associated with a greater plasticity response. To test
this hypothesis, we quantify the correlation between graph theoretical measures of
EEG connectivity data and the plasticity response to paired cTBS. We use state of
the art methodologies in order to provide biological markers of response to paired
cTBS to be used in their prediction.
We tested eighteen healthy adults (8 male, 1left handed) with a mean age of 24.2 (SD
6.0). Three minutes of continuous resting state EEG with open eyes was acquired. Baseline
MEPs (n = ?) were recorded and then paired cTBS was applied to the left primary motor
cortex, followed by three blocks of 20 TMS pulses. Surface EMG was used to record
the motor evoked potential from the right first dorsal interosseous (FDI) muscle.
We preprocessed EEG data and removed artefacts.
Graph theory provides a method to characterize the brain as a set of nodes interconnected
by a set of edges [2]. It is suggested that an intracortical electrical source approach
in graph theoretical analysis of EEG data is superior to the analysis at the surface
level. Debiased weighted phase lag index is used as a measure of functional connectivity
in the source space among the regions of interest. The connectivity matrix is thresholded
and a graph is constructed. Several graph theoretical measures including degree, density,
distance, clustering coefficient and characteristic path length are computed. Each
participant’s plasticity response to paired cTBS is correlated with that participant’s
graph theoretical measures (at each region of interest).
Preliminary analysis shows that the distance from the site of stimulation associates
with the response to paired cTBS, while degree, density, clustering coefficient and
characteristic path length do not. These findings suggest that graph theoretical measures
of network connectivity may have some utility in predicting the neuroplasticity response
to paired cTBS.
References
1. Goldsworthy MR, Pitcher JB, Ridding MC: Neuroplastic modulation of inhibitory motor
cortical networks by spaced theta burst stimulation protocols. Brain stimul 2013,
6:340–345.
2. Bullmore ET, Sporns O: Complex brain networks: graph theoretical analysis of structural
and functional systems. Nature Rev Neurosci 2009, 10:186–98.
P218 Mathematical Analysis of Transient “domino effect” like Brain Dynamics
Jennifer L. Creaser1, Congping Lin1, Peter Ashwin1, Jonathan T. Brown2, Thomas Ridler2
1Department of Mathematics, University of Exeter, Exeter, EX4 4QD, UK; 2Institute
of Biomedical and Clinical Sciences, University of Exeter Medical School, Exeter,
EX4 4PS, UK
Correspondence: Jennifer L. Creaser (j.creaser@exeter.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P218
There has been much research into complex neurological diseases such as, for example,
epilepsy and Alzheimer’s disease, however much remains unknown. It has become clear
that such diseases are associated with abnormal brain network function including hyperexcitability.
Brain network models used to study excitability, are often characterized by different
dynamic regimes, such as alternating rest and excited states. The transient dynamics
responsible for transitions between dynamic states are often discounted or overlooked
in favour of the long term asymptotic behaviour. However, analysis of these transitions
is instrumental in understanding, for example, the onset and evolution of epileptic
seizures.
We consider a model of seizure initiation represented by a network of diffusively
coupled bi-stable neurones driven by noise. Nodes in the network can switch between
the quiescent attractor and active attractor due to noise fluctuations. We focus on
the case of sequential escapes of nodes and the associated escape times. Understanding
the factors controlling sequential transitions between stable/unstable attractors
is important as they have been implicated in a diverse range of brain functions associated
with neuronal timing, coding, integration as well as coordination and coherence [1,
2]. Network properties such as the coupling and excitability of nodes in such systems
can promote (or suppress) escape of others on the network. We aim to quantify and
characterise the escape times in terms of the coupling and excitability of nodes.
We apply our theoretical framework to investigate escape times to the propagation
of epileptiform activity in parasagittal brain slices containing mouse medial entorhinal
cortex (mEC). We observe sequential recruitment of electrodes to the ictal-like state
and can determine the escape time, that is the equivalently average burst start time
of each electrode. The sequential recruitment of electrodes to the ictal-like state
could be seen as sequential escapes to an excited state in the underlying functional
brain networks. We explore differences in intrinsic (node) excitability across the
mEC by incorporating an excitability gradient into our prototypical bi-stable model.
Figure 1 shows preliminary findings comparing the average burst start time observed
in experiments (grey) and computed with the bi-stable model (black). In this presentation,
I will address the question how a network’s structure and its properties influence
sequential recruitment/escape of nodes in a network.
Figure 1. The average start time of ictal activity relative to ventral-most channel
recorded from along the dorso-ventral axis of the mEC in vitro using a 16-shank silicon
probe array (grey) with the average start time for each channel computed using 1000
simulations of a unidirectionally coupled 16 node bi-stable system with a linear excitability
gradient (black)
References
1. Rabinovich, MI, Pablo V: Robust transient dynamics and brain functions. Front Comput
Neurosci 2011, 5: 24–33.
2. Rabinovich, MI, Ramon H, Gilles L: Transient dynamics for neural processing. Science
2008, 321(5885): 48–50.
P219 Synchronized neocortical dynamics during NREM sleep
Daniel Levenstein1,2, Brendon O. Watson2,3, György Buzsáki1,2, John Rinzel1,4
1Center for Neural Science, New York University, New York, NY, 10003, USA; 2NYU Neuroscience
Institute, New York University, New York, NY, 10016, USA; 3Dept. of Psychiatry, Weill
Cornell Medical Center, New York, NY, 10065, USA; 4Courant Institute for Mathematical
Sciences, New York University, New York, NY, 10012, USA
Correspondence: Daniel Levenstein (dl2820@nyu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P219
During periods of behavioral quiescence such as NREM sleep, quiet wakefulness, and
under anesthesia, neocortical populations can show ‘synchronized dynamics’ [1]: low-frequency
alternations between low-rate spiking (UP states) and population-wide inactivity (DOWN
states). Previous work has indicated that these dynamics are mediated by the interaction
of recurrent excitation and neuronal adaptation [1–3]. Using a Wilson-Cowan model
(Figure 1A), we show that synchronized regimes are seen during low levels of drive
to a recurrent adapting neural population. Due to the possibility for both noise-induced
and adaptation-induced transitions, this type of oscillation can show a range of spectral
properties and UP/DOWN state dwell time statistics, which fit into 4 broad classes
of synchronized regimes (Figure 1B). Using a nonparametric distribution-matching method,
we find that this idealized model is able to reproduce the dwell time statistics of UP/DOWN
states from multiple behavioral contexts in vivo.
During NREM sleep [4], DOWN states are coincident with large deflections in the LFP/EEG
in a stereotyped pattern termed the ‘slow oscillation’. Unlike synchronized dynamics
in other behavioral states (e.g. [5]), we find that the NREM slow oscillation is best
represented by an ‘ExcitableUP’ regime, in which noise or perturbation of a stable
UP state can induce brief DOWN states (Figure 1C). Our model reveals a mechanistic
basis for multiple features of NREM sleep that are thought to be related to mnemonic
and homeostatic functions [6]: impulse-initiated slow waves and sequential activity
at the DOWN->UP transition accompanied by gamma-band activity.
Figure 1. Synchronized dynamics in an adapting Wilson-Cowan model. A. Model schematic
and equations. B. Synchronized regimes available to the model. (Left) Phase plane.
(Right) Simulated time courses and dwell time distributions. C. State diagram in I-W
reveals parameter domain for each synchronized regime. Color indicates similarity
to NREM sleep. Solid/dashed line: saddle-node/Hopf bifurcations
References
1. Harris KD, Thiele A: Cortical state and attention. Nature Reviews Neuroscience
2011. 12(9):509–523.
2. Parga N, Abbott LF: Network model of spontaneous activity exhibiting synchronous
transitions between up and down States. Frontiers in Neuroscience 2007; 1(1):57–66.
3. Compte A, Sanchez-Vives MV, McCormick DA, Wang XJ: Cellular and network mechanisms
of slow oscillatory activity and wave propagations in a cortical network model. J.
Neurophys 2003; 89(5):2707–2725.
4. Watson BO, Levenstein D, Greene JP, Gelinas JN, Buzsáki G: Network Homeostasis
and State Dynamics of Neocortical Sleep. Neuron 2016; 90(4):839–852.
5. Mochol G, Hermoso-Mendizabal A, Sakata S, Harris KD, de la Rocha, J: Stochastic
transitions into silence cause noise correlations in cortical circuits. PNAS 2015;
112(11):3529–3534.
6. Levenstein D, Watson BO, Rinzel J, Buzsáki G. Sleep regulation of the distribution
of cortical firing rates. Current Opinion in Neurobiology 2017. In press.
P220 Accumulation process and multi-layer mechanisms of perceptual alternation in
auditory streaming
Rodica Curtu1, Anh Nguyen1, John Rinzel2
1Department of Mathematics, The University of Iowa, Iowa City, IA 52242, USA; 2Courant
Institute of Mathematical Sciences, New York University, New York, NY 10003, USA
Correspondence: Rodica Curtu (rodica-curtu@uiowa.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P220
In daily life, the auditory system sorts the mixture of sounds from different sources
into specific acoustic information by grouping acoustic events over time and forming
internal representations of sound streams. A particular set of stimuli that have been
used intensively to study that phenomenon consists of sequences of alternating high
(A) and low (B) pure tones presented as repeated triplets, ABA_ABA_….Depending on
the frequency separation (df) between the two tones, subjects report either of two
percepts: “integration” (a single, coherent stream of high and low tones, like a galloping
rhythm) or “segregation” (two parallel distinct streams). In our lab, the psychophysical
experiment was conducted on 15 human subjects of normal hearing. They were prompted
to listen to repeating sequences of ABA_ triplets at df = 3, 5, 7 semitones difference,
with a total of 675 trials per df condition. Each sequence was comprised of sixty
500 ms-long triplets, resulting in a 30 s-long presentation. Subjects were asked to
press and hold different buttons on a keypad when they perceived integration and segregation,
respectively. Data analysis revealed time course and statistical distribution of perceptual
switching. After the stimulus onset, it takes several seconds for the trial-averaged
probability of stream segregation to build up, and the first percept is typically
integration. Also, subjects report spontaneous alternations between the two percepts,
and the percept durations are gamma-distributed. Furthermore, a previous study reveals
that there are similarities between build-up functions of stream segregation from
psychophysical experiments (psychometric functions) and those from multi-unit recordings
from monkeys’ primary auditory cortex (area A1) (neurometric functions) [1]. In this
presentation, we first demonstrate that a signal-detection model introduced in [1]
to compute neurometric functions, is not sufficient to produce realistic percept durations
as reported experimentally. In particular, mean spike counts extracted from cortical
recordings [1] were used to generate neuronal responses, which were used as inputs
to a signal-detection model. We showed that this model produces percept durations
whose distribution is exponential (not gamma) and whose means are significantly smaller
than those reported experimentally. We propose an extension to this model in the form
of a multi-stage feedforward auditory network with components: i) area “A1” whose
local outputs (mean spike counts) are subject to threshold-based binary classifiers
(binary neurons); ii) An ensemble of binary neurons (BN) receiving local input from
“A1”; and iii) Two competing units (“the accumulators”) whose activities depend on
accumulated evidence from neuronal ensemble BN for each of the two percepts, integration
and segregation. The suppressed neuronal unit accumulates evidence against the current
percept while the dominant unit gradually reduces its activity. Both are drifting
towards their given thresholds.
Conclusion: The proposed evidence accumulation model is able to reproduce qualitatively
and quantitatively switching behavior between integration and segregation in auditory
streaming. At each df the model produced percept durations whose distribution is gamma-like
and whose means are comparable to those obtained in our psychophysical experiment.
Acknowledgements
This material is based upon work supported by the National Science Foundation under
Grant Number CRCNS 1515678
References
1. C. Micheyl, B. Tian, R. Carlyon, R. Rauschecker: Perceptual organization of tone
sequences in the auditory cortex of awake macaques. Neuron 2005, 48:139–148.
2. D. Barniv, I. Nelken: Auditory streaming as an online classification process with
evidence accumulation. PLOS ONE 2015.
3. R. Cao, A. Pastukhov, M. Mattia, J. Braun: Collective Activity of Many Bistable
Assemblies Reproduces Characteristic Dynamics of Multistable Perception. J Neurosci
2016, 36(26):6957–6972.
P221 The Necessity of Sleep and Wake: Synaptic Homeostasis via System-Level Plasticity
and the Ascending Arousal System
Sahand Assadzadeh1,2, Peter A. Robinson1,2
1School of Physics, The University of Sydney, NSW 2006, Sydney, Australia; 2Center
for Integrative Brain Function, The University of Sydney, NSW 2006, Sydney, Australia
Correspondence: Sahand Assadzadeh (sahanda@physics.usyd.edu.au)
BMC Neuroscience 2017, 18 (Suppl 1):P221
One of the important functions of sleep is believed to be the regulation of synaptic
weights in the brain. Mounting experimental evidence has found that on average, synapses
that are upscaled during wakefulness are downscaled during sleep, providing a possible
mechanism through which synaptic stability is maintained in the brain. This is often
referred to as the synaptic homeostasis hypothesis (SHH) [1]. However, the questions
of how and why sleep is necessary to fulfill this function remain unanswered. Neural
field theory (NFT) has shown that synaptic plasticity dynamics depend strongly on
network level effects, such as the overall system frequency response, with especially
enhanced plasticity at resonances [2]. NFT is used to study the system-level effects
of plasticity in the corticothalamic system, where arousal states are represented
parametrically by the connection strengths of the system, among other physiologically
based parameters (Fig. 1). Here it is found that the plasticity dynamics have no fixed
points or closed cycles in the parameter space of the connection strengths; but parameter
subregions exist where flows have opposite signs. Remarkably, these subregions coincide
with previously identified regions corresponding to wake and slow-wave sleep, thus
demonstrating the role of state-dependent activity on the sign of synaptic modification.
We then show that a closed cycle in the parameter space is possible by coupling the
plasticity dynamics to that of the ascending arousal system (AAS), which moves the
brain back and forth between sleep and wake, and thus between the opposite-flow subregions
to form a closed loop. In this picture, both wake and sleep are necessary to stabilize
connection weights in the brain, because each modifies synaptic strengths in an opposite
direction relative to the other.
Figure 1. Evolution of connection strengths around a wake-sleep cycle forming a closed
loop in arousal state space. The blue line represents plastic effects during wakefulness
that result an increase of the corticothalamic and corticocortical loop gains in the
corticothalamic system, with red lines corresponding to the opposite effect observed
during slow-wave sleep. Thin lines indicate the action of the AAS in switching between
wake and sleep states
Acknowledgements
This work was supported by the Australian Research Council under Center of Excellence
for Integrative Brain Function Grant CE140100007 and Laureate Fellowship Grant FL140100025.
References
1. Tononi G, Cirelli C. Sleep and the Price of Plasticity: From Synaptic and Cellular
Homeostasis to Memory Consolidation and Integration. Neuron. 2014; 81(1): 12–34.
2. Robinson PA. Neural field theory of synaptic plasticity. J Theor Biol. 2011; 285(1):
156–163.
P222 Low- and high-mode waking states in the corticothalamic system
Paula Sanz-Leon1,2, Peter A. Robinson1,2
1School of Physics, University of Sydney, Sydney, New South Wales, Australia; 2Center
for Integrative Brain Function, University of Sydney, Sydney, New South Wales, Australia
Correspondence: Paula Sanz-Leon (paula.sanz-leon@sydney.edu.au)
BMC Neuroscience 2017, 18 (Suppl 1):P222
A neural field model of the corticothalamic system has multistable regions of five
steady-state solutions, up to three of which are linearly stable [1]; and, up to two
of which lie within firing rate levels that are considered moderate, yet normal, in
adult human physiology [2]. This confirms the existence of additional arousal states
beyond the traditional steady states which have been identified with either normal
or seizure-like activity [2]. The signature of these additional states, which we call
H-mode states, is an overall increased level of activity up to 35 s−1 [blue dots in
Figs 1(a) and 1(b)] with respect to the canonical waking states, or L-mode states
(black dots). More specifically, compared to the L-states (illustrated as black dots),
the H-states exhibit enhanced thalamic activity. In Fig. 1(c) mean firing rates are
arranged in parallel coordinates where the coordinates correspond to cortical (ϕe),
reticular (ϕr), and relay nuclei (ϕs) firing rates. This type of plot allows for the
identification of trends within a group, and for the comparison with another group.
Here, we observe that the qualitative behavior of the H-states (blue lines) is similar
to the one of the L-states (black lines): ϕe < ϕr and ϕs < ϕr. However, in the H-states,
despite the large dispersion of relay activity, cortical activity remains relatively
constant. In Fig. 1(d), we show the power spectra for both L- and H-states (illustrated
in black and blue lines, respectively). The H-states (i) have higher power density
than the L-states over all the frequency range (0 < f < 45 Hz); and (ii) have a 5-order
of magnitude increase in the power in the high-beta and gamma bands (20-35 Hz) with
respect to the baseline spectra of waking states. This last result is consistent with
focused and hyperarousal states found in the literature [3]. In hyperarousal increased
thalamic activity is linked to high levels of attention and gamma enhancements expected
due to increased activity in the relay nuclei of the thalamus.
Figure 1. Comparison of L-mode states and H-mode states from multistable regions of
the corticothalamic system. Black dots and lines correspond to properties of L-states
(fa < 20 s−1), while blue dots and lines are those of the H-states (fa around 30 s−1).
Panels A and B are the steady states in ϕe-ϕr and ϕe-ϕs space, respectively. Panel
C shows a parallel coordinate plot of the corticothalamic firing rates. Panel D shows
the spectral signature the L-states and H-states
References
1. Sanz-Leon P and Robinson PA: Multistability in the corticothalamic system. J. Theor.
Biol. 2017 (under review)
2. Robinson PA, Rennie CJ, Wright JJ, Bahramali H, Gordon E, Rowe DL: Prediction of
electroencephalographic spectra from neurophysiology. Phys. Rev. E 2001; 63:021903.
3. GrØnli J, Rempe MJ, Clegern WC, Schmidt M and Wisor JP. Beta EEG reflects sensory
processing in active wakefulness and homeostatic sleep in quiet wakefulness. J. Sleep
Res. 2001;
25:257–268.
P223 Closed-loop temporally structured light stimulation in weakly electric fish
Caroline G. Forlim1,2, Lírio O. B. de Almeida 3, Ángel Lareo4, Reynaldo D. Pinto3,
Pablo Varona4, Francisco B. Rodríguez4
1Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center
Hamburg-Eppendorf, Hamburg, 20246, Germany; 2Departamento de Física Geral, Universidade
de Sao Paulo, Sao Paulo, 05508-090, Brazil; 3Instituto de Física de Sao Carlos, Universidade
de Sao Paulo, Sao Carlos, 13560-970, Brazil; 4Escuela Politécnica Superior, Universidad
Autonoma de Madrid, Madrid, 28049, Spain
Correspondence: Caroline G. Forlim (c.garcia-forlim@uke.de), Francisco B. Rodríguez
(f.rodriguez@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P223
Closed-loop stimulation is a promising technique for neuroscience studies, especially
in behavioral experiments [1, 2]. Weakly electric fish discharge short electric pulses
or waves through an electric organ and detect small changes in the electric field
using electroreceptors [1, 3]. These fish live in turbid waters and use electrical
sensing as an additional sense to increase visual details. In addition, their electric
pulses are also used to communicate by changing their inter pulse intervals depending
on the behavioral context [3]. Recently, attention has been paid to the visual system
[4]. However, most experiments assessing vision were conducted with periodic flashlights
lasting just a few seconds and moreover, in restrained animals.
We developed the first closed-loop setup that uses temporally structured light as
a stimulus for long periods in freely swimming fish. In these closed-loop protocols,
the light pulses are triggered based on the real time monitored electrical activity,
resulting in stimulus with similar complex temporal structure as the electrical signaling
of the fish. The setup can be easily adapted to different stimulus modalities such
as mechanical, acoustic and electrical stimulation allowing studies of multisensory
integration.
Our validation protocol consisted of 15 min control session followed by 15 min light
pulse stimulation in Gnathonemus petersii. The light stimuli were either triggered
by the fish’s own electrical activity and therefore with complex temporal structure
or periodic. It is important to emphasize that the main differences between these
two stimuli is the temporal structure, the closed-loop share similar complex temporal
structure as the electrical signaling and the periodic does not, no temporal structure
is encoded in the light stimulus. We show that, for long light stimulation periods,
fish decreased the discharge rate. The decrease in discharge was more accentuated
when light stimuli were triggered by the fish’s electrical activity as opposed to
periodic stimuli, meaning that probably the information encoded in the temporal structure
was somehow meaningful for the fish and that the brain processed it distinctly from
a simple periodic structure.
To the best of our knowledge, this is the first study on how light can influence the
fish electrical system for long periods of time. The results give rise to important
questions on the influence of light in electrocommunication and the processing of
multisensory information, which can be addressed using the proposed methodology.
Acknowledgements
This work was funded by Spanish projects of Ministerio de Economía y Competitividad/FEDER
TIN2014-54580-R, DPI2015-65833-P, ONRG grant N62909-14-1-N279, Spanish-Brazilian Cooperation
PHB2007-0008 and 7ª Convocatoria De PROYECTOS de COOPERACION INTERUNIVERSITARIAUAM-SANTANDER
con America Latina and Brazilian Agency of Conselho Nacional de Desenvolvimento Científico
e Tecnológico and Fundação de Amparo à Pesquisa do Estado de São Paulo.
References
1. Forlim CG, Pinto RD, Varona P, Rodríguez FB. Delay-Dependent Response in Weakly
Electric Fish under Closed-Loop Pulse Stimulation. PLoS ONE 2015;10:e0141007. doi:10.1371/journal.pone.0141007.
2. Lareo A, Forlim CG, Pinto RD, Varona P, Rodriguez F. de B. Temporal Code-Driven
Stimulation: Definition and Application to Electric Fish Signaling. Front Neuroinform
2016;10:41. doi:10.3389/fninf.2016.00041.
3. Bullock TH, Hopkins CD, Popper AN, Fay RR, editors. Electroreception. vol. 21.
Springer New York; 2005.
4. Pusch R, Kassing V, Riemer U, Wagner HJ, von der Emde G, Engelmann J. A grouped
retina provides high temporal resolution in the weakly electric fish Gnathonemus petersii.
J Physiol Paris 2013;107:84–94.
P224 Information-theoretic analysis of temporal code-driven stimulation applied to
electroreception
Ángel Lareo1, Caroline Garcia Forlim2, Reynaldo D. Pinto3, Pablo Varona1, Francisco
B. Rodríguez1
1Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela
Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain; 2Clinic and Policlinic
for Psychiatry and Psychotherapy, University Medical Center, Hamburg-Eppendorf, Hamburg,
Germany; 3Lab. Neurodynamics/Neurobiophysics - Dept. Physics and Interdisciplinary
Sciences - Institute of Physics of São Carlos, Universidade de São Paulo, São Paulo,
Brazil
Correspondence: Ángel Lareo (angel.lareo@uam.es), Francisco B. Rodríguez (f.rodriguez@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P224
Biological systems can encode information in a sequential manner, and temporal encoding
gives rise to complex temporal patterns of activity. Thus, information processing
in those systems can be analyzed studying the temporal structure of event trains.
This is the approach followed by a recently defined real time stimulation methodology,
temporal code-driven stimulation (TCDS) [1]. TCDS is a closed-loop stimulation protocol
that first digitizes and binarizes a biological signal and then delivers the stimulus
when a predefined code is detected. This code represents the sequential activity in
the signal whose meaning is the goal of the system study. The methodology can use
the study of changes in the information processing of a given biological system among
different sessions: code-driven stimulation sessions, control sessions without stimulation
and open-loop stimulation sessions.
In order to test this methodology, an implementation of TCDS using hard real time
has been applied to electroreception using the weakly electric fish Gnathonemus Petersii.
The electromotor neurons of this animal generate electrical signal pulses which can
be measured in a water tank using appropriate hardware [2, 3]. These signals follow
a temporal coding scheme [4] where information is encoded in the inter-pulse interval
(IPI) [5]. Thus, it constitutes a convenient animal model to test closed-loop stimulation
methods in an alive and freely-behaving biological system. The TCDS protocol binary
digitizes the signal of the fish detecting the presence or absence of a pulse event
during the binarization period and uses this codification to stimulate after detecting
a preselected code from the fish’ activity. Analysis of information processing in
weakly electric fish is done in previous studies in terms of IPIs distribution [1].
We complement the analysis of the TCDS protocol with a measure based on information
theory: Transitions between codes. As a proof of concept, we used 4-bit codes and
selected as the trigger a code with mean probability of occurrence during control
sessions. Codes were grouped by the number of pulses in them, defining three sets:
low, medium and high number of pulses. Preliminary results applying TCDS to electroreception
in weakly electric fish indicates that it distinctly conditions the response of the
system when stimulating after a predetermined code. This conclusion is also drawn
by analyzing the probability of transitions between codes, as an increase in low-low
transition probability is detected when the system is stimulated with the code 0101.
Acknowledgements
We acknowledge support from MINECO/FEDER TIN2014-54580-R, DPI2015-65833-P (http://www.mineco.gob.es/)
and ONRG grant N62909-14-1-N279.
References
1. Lareo A, Forlim CG, Pinto RD, Varona P, Rodriguez F: Temporal Code-Driven Stimulation:
Definition and Application to Electric Fish Signaling. Frontiers in Neuroinformatics
2016, 10:41.
2. Forlim CG, Pinto RD: Automatic realistic real time stimulation/recording in weakly
electric fish: Long time behavior characterization in freely swimming fish and stimuli
discrimination. PLoS ONE 2014, 9:e84885 + .
3. Forlim CG, Pinto RD, Varona P, Rodriguez FB: Delay-dependent response in weakly
electric fish under closed-loop pulse stimulation. 2015, 10.
4. Baker CA, Kohashi T, Lyons-Warren AM, Ma X, Carlson BA: Multiplexed temporal coding
of electric communication signals in mormyrid fishes. The Journal of experimental
biology 2013, 216:2365–2379.
5. Carlson BA: Electric signaling behavior and the mechanisms of electric organ discharge
production in mormyrid fish. Journal of Physiology-Paris 2002, 96:405–419.
P225 Gain control mechanism based on lateral inhibition of antennal lobe improves
pattern recognition performance under wide concentration variability
Aaron Montero1, Thiago Mosqueiro2, Ramon Huerta1,2, Francisco B. Rodriguez1
1Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática, Escuela Politécnica
Superior, Universidad Autónoma de Madrid, Madrid, 28049, Spain; 2BioCircuits Institute,
University of California, San Diego, La Jolla, CA 92093-0402, USA
Correspondence: Aaron Montero (aaron.montero.m@gmail.com), Francisco B. Rodriguez
(f.rodriguez@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P225
Many animals depend on odor information for living. Although different levels of concentration
produce variation in the activation patterns observed in olfactory receptor neurons,
most animals can correctly recognize the identity of odors regardless of their concentration.
It is not clear yet what mechanisms olfactory systems employ to recognize the same
stimulus regardless of their concentrations. Experiments suggest that in insects this
concentration invariance appears in the Antennal Lobe, where the activity of Projection
Neurons remains nearly constant, even though the concentration changes [1]. One hypothesis
is that the Local Neurons are responsible to down regulate the levels of activity
(also known as gain control) by laterally inhibiting the Projection Neurons [2]. We
examine the impact of this gain control mechanism on pattern recognition by designing
a biologically plausible model based on the interactions between Local and Projection
Neurons. For this purpose, we used a computational model that represents the olfactory
system of insects by a single hidden layer network [3, 4, 5]. We consider three layers:
Antennal Lobe, Kenyon cells and Mushroom Body Output Neurons. In order to simulate
the activation patterns of Antennal Lobe for different concentration levels, we used
Gaussian functions with a variable height and width, where their centers encode the
identity of the odor. We used datasets of 3000 patterns divided into 10 pattern classes
and 3 concentration levels. To model the intrinsic variations observed in real olfactory
systems, we added a multiplicative white noise to these Gaussians with 3 different
levels (small, medium, large). The performance of a network with this gain control
mechanism presented significantly lower classification error rate than a network without
gain control, with an improvement of ~45%. A network with this gain control achieved
a classification error of ~0% for sets of patterns with small and medium noise and
<5% for large noise. These results suggest that gain control mechanism does not only
suppress outbursts of activity from input layers but also greatly improves learning
in Mushroom Bodies. Finally, because this mechanism does not depend on any synaptic
plasticity, in agreement with the biological literature, it can also be applied to
chemical sensors in electronic devices for controlling changes in environmental conditions
[6, 7].
Acknowledgements
This research was supported by TIN2014-54580-R, BES-2011-049274, NIH grant R01GM113967
and CNPq grant 234817/2014-3.
References
1. Stopfer M, Jayaraman V, and Laurent G: Intensity versus identity coding in an olfactory
system. Neuron 2003, 39:991–1004.
2. Olsen SR, Wilson RI: Lateral presynaptic inhibition mediates gain control in an
olfactory circuit. Nature 2008 452(7190):956–960.
3. Huerta R and Nowotny T: Fast and robust learning by reinforcement signals: Explorations
in the insect brain. Neural Comput. 2009, 21:2123–2151.
4. Montero A, Huerta R, and Rodriguez FB: Regulation of specialists and generalists
by neural variability improves pattern recognition performance. Neurocomputing, 2015,
151:69–77.
5. Montero A, Huerta R, Rodriguez FB: Specialist neurons in feature extraction are
responsible for pattern recognition process in insect olfaction. Artificial Computation
in Biology and Medicine - International Work-Conference on the Interplay Between Natural
and Artificial Computation (IWINAC), Elche, Spain; 2015. part I p. 58–67.
6. Trincavelli M, Vergara A, Rulkov N, Murguia JS, Lilienthal A, Huerta R: Optimizing
the operating temperature for an array of mox sensors on an open sampling system.
AIP Conference Proceedings, 2011, 1362:225.
7. Huerta R, Mosqueiro T, Fonollosa J, Rulkov NF, Rodriguez-Lujan I: Online decorrelation
of humidity and temperature in chemical sensors for continuous monitoring. Chemometr
Intell Lab Syst, 2016, 157:169–176.
P226 Maximum Relative Area as a Feature for Adaptability in ERP-based BCI Systems
Vinicio Changoluisa1,2, Pablo Varona1, Francisco B. Rodriguez1
1Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática. Escuela Politécnica
Superior, Universidad Autónoma de Madrid, Madrid, Spain; 2Universidad Politécnica
Salesiana, Quito, Ecuador
Correspondence: Vinicio Changoluisa (fchangoluisa@ups.edu.ec), Francisco B. Rodriguez
(f.rodriguez@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P226
Adaptive Brain Computer Interfaces (BCI) are an important research topic in the last
years. However, a critical and pending problem is their variable performance even
within subjects. In event-related potentials (ERP)-based BCIs the variability of amplitude
and latency impair the detection of the ERP components. In order to overcome those
problems, target and non-target stimuli are repeated several times (trials). Repetitions
can cause fatigue and a decrease in task performance. Therefore, achieving high accuracy
with a few stimuli is a challenge. We propose a methodology that contributes to the
management of variability in ERP-based BCIs through the characterization of the maximum
relative voltage area (maxRAUC) in the region of the EEG signal where a ERP component
can be located. We call maxRAUC relative since it is a maximum value within each trial,
not the maximum value of all trials. This method calculates maxRAUC incrementally
in time for each stimulus. The one with the highest value is considered a target stimulus.
In this way, the differences between a target and a non-target stimulus are maximized.
Electrodes having the highest maxRAUC in the ERP region of the signal are potentially
likely to have better characteristics for detecting ERP effectively. Our method was
tested with a linear classifier (LDA) based on the Krusienski method (KM) [1] and
the dataset_IIb of the BCI competition (http://www.bbci.de/competition/ii/). This
dataset contains the data of one user, divided into three sessions: two training sessions
(called 10 and 11) and one session to test the classifier. Users were stimulated through
P300 Speller Paradigm described in the competition. The electrodes with the largest
maxRAUC were found in the central and frontal lobes. We checked the influence of these
electrodes on the system’s adaptability and evaluated the classifier with two configurations:
the first, with 8 electrodes used in KM; and the second, by replacing Fz and Cz by
the electrodes among those with the higher maxRAUC of each session. With this electrode
selection, the accuracy of the classifier improved and reached 100% success with a
low number of trials, see Table 1. We also validated the robustness of our method
by combining data from training sessions 10 and 11.
Table 1. Trials needed to achieve 100% success in each session. Common Electrodes
(CE): Pz, P3, P4, PO7, PO8, Oz. We emphasize the best results with italic font
Session 10
Session 11
Session 10 + 11
Electrode configuration
Elect.
Trials
Elect.
Trials
Elect.
Trials
KM electrodes
Cz + Fz + CE
4
Cz + Fz + CE
12
Cz + Fz + CE
9
CE + 2 maxRAUC electrode
C1 + FPz + CE
3
C1 + FC1 + CE
6
C3 + F1 + CE
4
CE + 1 maxRAUC electrode
C3 + CE
3
F1 + CE
9
F1 + CE
5
In summary, here we propose a new methodology to extract additional information from
EEG electrodes that contributes to manage the adaptability of ERP-based BCIs. This
method adapts to the variability of each session and helps to decrease the number
of electrodes and trials necessary to achieve a 100% success. The maxRAUC contributes
to early detection of ERP and further adaptation. This method can also be applied
to other ERP components (N200, N100, etc.) which are considered for future work.
Acknowledgements
This work was funded by Spanish projects of Ministerio de Economía y Competitividad/FEDER
TIN2014-54580-R, DPI2015-65833-P and Predoctoral Research Grants 2015-AR2Q9086 of
the Government of Ecuador (SENESCYT).
Reference
1. Dean J Krusienski, Eric W Sellers, François Cabestaing, Sabri Bayoudh, Dennis J
McFarland, Theresa M Vaughan, and Jonathan R Wolpaw: A comparison of classification
techniques for the P300 Speller. Journal of neural engineering 2006, 3(4):299–305.
P227 Intrinsically stochastic neuron models for use in network simulations
Vinícius L. Cordeiro, César C. Ceballos, Nilton L. Kamiji, Antonio C. Roque
Departamento de Física-FFCLRP, Universidade de São Paulo, Ribeirão Preto, SP 14040-901,
São Paulo, Brazil
Correspondence: Vinícius L. Cordeiro (vinicius.lima.cordeiro@usp.br)
BMC Neuroscience 2017, 18 (Suppl 1):P227
Experimental evidence suggest that neurons are inherently stochastic systems displaying
trial-to-trial response variability [1]. This stochasticity may have functional consequences
on network behavior, so it is important to construct stochastic single-neuron models
to be used in network simulations. There are basically two ways of constructing a
stochastic neuron model [2, 3]. One is to consider a deterministic model, e.g. the
leaky integrate-and-fire (LIF), Izhikevich or AdEx model [2], and add stochastic terms
to the inputs received by the neuron. The other is to model a spike as an intrinsically
stochastic event. The second way can be implemented in two different but equivalent
manners: by a randomly varying spike threshold as in the escape noise model [4], or
by a spike probability function Φ(V), which depends on the membrane potential V as
in the simplified version of the Galves-Löcherbach (GL) discrete-time model [5] recently
proposed by Brochini et al. [3].
Here we have considered the Brochini et al. [3] version of the GL model (from here
onwards simply called GL model) and empirically determined the probability function
Φ(V) so that the model can describe stochastic firing behaviors of the two most import
cortical cell types, namely regular (RS) and fast (FS) spiking neurons [6]. To determine
Φ(V) for these two cell types, biophysically detailed models of RS and FS neurons
were chosen from the neuron database ModelDB (http://senselab.med.yale.edu/modeldb/)
and submitted to realistic patterns of synaptic input. The detailed neuron model simulations
were done in NEURON [7]. These simulations generated time series of membrane potential
values V
t for the detailed RS and FS neuron models. From these time series, we determined
action potential onset values V
th from the dV/dt versus V phase space using so-called Method II of [8]. For each
action potential, the voltage values above threshold were discarded and with the remaining
ones we constructed two distribution histograms, one for all voltage values (including
V
th) and the other for threshold values only. The histograms were superposed as in
Figure 12 of [9] to allow an estimate of the probability of firing for each discretization
bin.
The resulting probability functions display nonlinear exponential behavior. Based
on them we constructed stochastic GL models for RS and FS neurons and submitted them
to simulated input currents to obtain frequency-current (FI) curves. These stochastic
neuron models can be used in large-scale simulations of cortical network models.
Acknowledgements
This work was produced as part of the activities of FAPESP Research, Disseminations
and Innovation Center for Neuromathematics (grant 2013/07699-0, S. Paulo Research
Foundation). NLK is supported by a FAPESP postdoctoral fellowship (grant 2016/03855-5).
ACR is partially supported by a CNPq fellowship (grant 306251/2014-0).
References
1. Longtin A: Neuronal noise. Scholarpedia 2013, 8(9):1618.
2. Gerstner W, Kistler WM, Naud R, Paninski L: Neural Dynamics: From Single Neurons
to Networks and Models of Cognition. Cambridge University Press 2014.
3. Brochini L, Costa AA, Abadi M, Roque AC, Stolfi J, Kinouchi O: Phase transitions
and self-organized criticality in networks of stochastic spiking neurons. Sci Rep
2016, 6:35831.
4. Gerstner W, van Hemmen L: Associative memory in a network of ‘spiking’ neurons.
Network 1992, 3:139–164.
5. Galves A, Löcherbach E: Infinite systems of interacting chains with memory of variable
length: a stochastic model for biological neural nets. J Stat Phys 2013, 151:896–921.
6. McCormick DA, Connors BW, Lighthall JW, Prince DA: Comparative electrophysiology
of pyramidal and sparsely spiny stellate neurons of the neocortex. J Neurophysiol
1985, 54:782–806.
7. Carnevale NT, Hines ML: The NEURON Book. Cambridge University Press; 2006.
8. Sekerli M, Del Negro CA, Lee RH, Butera RJ: Estimating action potential thresholds
from neuronal time-series: new metrics and evaluation of methodologies. IEEE Trans
Biomed Eng 2004, 51:1665–1672.
9. Azouz R, Gray CM: Cellular mechanisms contributing to response variability of cortical
neurons in vivo. J Neurosci 1999, 19:2209–2223.
P228 Modeling action potential and network effects after site-directed RNA editing
of sodium channels
William W. Lytton1,2, Andrew Knox3, Joshua J. C. Rosenthal4
1Depts. of Physiology & Pharmacology and Neurology, SUNY Downstate, Brooklyn, NY 11203
USA; 2Dept. of Neurology, Kings County Hospital, Brooklyn, NY 11203 USA; 3Dept. of
Neurology, University of Wisconsin, Madison, WI 53705 USA; 4Dept. of Neurobiology,
Marine Biological Laboratory, Woods Hole, MA 02543 USA
Correspondence: William W. Lytton (bill.lytton@downstate.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P228
New techniques now make it possible to modify messenger RNA and thereby modify specific
proteins in vivo. Experimentally, we have edited RNA using adenosine deamination to
modify the mammalian fast sodium (Naf) channel (NaV1.4) by converting a key lysine
residue to arginine in the selectivity region that is part of the aspartate-glutamate-lysine-alanine
motif (DEKA to DERA). This change allows the channel to be permeable to both Na and
K, effectively changing the reversal potential associated with this conductance to
a value intermediate between the Nernst potentials of those two ions. The degree of
alteration in the Naf channel can be manipulated, producing a mixed population of
native and mutated channels. We modeled the effects of this manipulation on the classical
Hodgkin-Huxley model of action potential propagation in the squid axon, as well as
in other axonal models closer to mammalian morphology and temperature. As expected,
action potential amplitude was reduced at higher percentages of the modified Naf channel,
reaching a point where an action potential could no longer be maintained at the maximal
conductance provided. Action potential conduction velocity was fast (approximately
10 mm/ms) when using a high-impedance axon termination, and showed little fall off
with increased percent of modified channel. Conduction velocity was much slower (approximately
2 mm/ms) when using a low impedance termination, and showed a 20% falloff with increase
in percent of the modified channel. These results were seen both at squid axon temperature
and Ra (6.3o C and 34.5 O-cm) and at mammalian values (37o C and 250 O-cm). Action
potentials were formed at lower sodium channel density and conducted at greater velocity
at the low temperature, where the more prolonged activation due to the slower kinetics
provided increased effect at neighboring locations.
RNA editing is being used experimentally to erase the mutations that introduce the
premature termination codons that lead to cystic fibrosis. This manipulation has potential
for clinical use in patients with this deadly genetic disease. Similarly, clinical
manipulation of the RNA for the sodium channel has potential for use in intractable
epilepsies such as Lennox-Gastaux syndrome, where neither surgical nor pharmacological
intervention is generally effective.
Acknowledgements
The authors would like to acknowledge NIH support from EB02290301 (WL), EB017695 (WL),
MH086638 (WL), NS087726 (JR).
P229 Movement-related delta-theta synchronization in young and elderly healthy subjects
Silvia Daun1,2, Svitlana Popovych1,2, Liqing Liu1,2, Bin A. Wang1, Tibor I. Tóth2,
Christian Grefkes1,3, Gereon R. Fink1,3, Nils Rosjat1,2
1Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research
Center Juelich, Juelich, 52428 Germany; 2Heisenberg Research Group of Computational
Neuroscience - Modeling Neural Network Function, Department of Animal Physiology,
Institute of Zoology, University of Cologne, Cologne, 50674, Germany; 3Department
of Neurology, University Hospital Cologne, Cologne, 50937, Germany
Correspondence: Silvia Daun (silvia.daun@uni-koeln.de)
BMC Neuroscience 2017, 18 (Suppl 1):P229
The wealth of data showing that human motor performance is affected by normal ageing
is contrasted by the dearth of data on ageing effects on the neural processes underlying
action. For example, it remains to be elucidated how the different phases of an action
(i.e., preparation, initiation and execution) are expressed in neural oscillations
and how these are affected by normal ageing. The interest in ageing-related changes
of motor performance and the neural basis thereof are governed by the quest for more
detailed insights into the possible reorganization of the key phases of an action.
For this reason, it is apt and timely to study ageing-dependent effects on the neural
organization of motor performance in more detail. The crucial point of such investigations
is the study of synchronization, a key mechanism underlying the coordination of distinct
neural populations in shaping complex motor tasks.
In an earlier EEG-study [1] on young adults, we found that when generating unilateral
index-finger movements, local oscillations in the δ-θ frequency band over the centroparietal,
central and frontocentral regions (corresponding to the primary motor area (M1), the
supplementary motor area (SMA) and the pre-motor area (PM), respectively) exhibited
robust phase locking both prior to and during the movement. The local oscillations
were most pronounced in the hemisphere contralateral to the moving hand in both externally
and internally triggered actions. A subsequent study [2] using an identical experimental
paradigm with a population of older adults found that the local phase locking in the
δ-θ frequency band was also present during the motor acts of the older participants.
To investigate the neural processes underlying ageing-related dependence of the motor
performance in more detail, we employed inter-regional phase-locking analysis by calculating
the phase-locking values (PLVs) from the EEG records of the two data sets mentioned
above. PLV measures the extent of instantaneous synchronization between two distinct
brain regions.
Our analysis revealed significant PLV in both age groups in the δ-θ frequencies around
movement onset. Invariant sub-networks were established by strong PLV between brain
areas involved in the motor act, which were different in older and younger subjects.
More intra- and inter-hemispheric PLVs occurred in older than in younger subjects.
Furthermore, data suggest that older subjects compensate for the diminished connectivity
observed between contralateral M1 and SMA, and ipsilateral PM and SMA during movement
preparation and execution by establishing additional intra- and inter- hemispheric
connections.
Based on the above findings on local and inter-regional phase locking, we built a
mathematical model consisting of phase oscillators representing two main regions of
the motor network, i.e. SMA and M1. This simple model is capable of reproducing the
effects of increased PLI and, independently of this, the effect of increased PLV between
both regions. After extending the network model to all core motor regions and fitting
the model parameters to the experimental data it will serve as a tool to make predictions
on disturbed networks dynamics, e.g. decoupling of nodes.
References
1. Popovych S, Rosjat N, Tóth TI, Wang BA, Liu L, Abdollahi RO, Viswanathan S, Grefkes
C, Fink GR, Daun S: Movement-related phase locking in the delta-theta frequency band.
NeuroImage 2016, 139: 439–449.
2. Liu L, Rosjat N, Popovych S, Yeldesbay A, Wang BA, Tóth TI, Grefkes C, Fink GR,
Daun S: Movement related intra-regional phase locking in the delta-theta frequency
band in young and elderly subjects. Program No. 624.08. 2016. Neuroscience Meeting
Planner. San Diego, CA: Society for Neuroscience, 2016. Online.
P230 ePyNN: a low cost embedded system for simulating Spiking Neural Networks
Abraham Perez-Trujillo1, Andres Espinal2, Marco A. Sotelo-Figueroa2, Ivan Cruz-Aceves3,
Horacio Rostro-Gonzalez1
1Department of Electronics, University of Guanajuato, 36885 Salamanca, Guanajuato,
Mexico; 2Department of Organizational Studies, University of Guanajuato, 3625 Guanajuato,
Mexico; 3CONACYT, Mathematics Research Center (CIMAT), 36000 Guanajuato, Mexico
E-mail: Horacio Rostro-Gonzalez (hrostrog@ugto.mx)
BMC Neuroscience 2017, 18 (Suppl 1):P230
In this work, we present a low cost embedded system to simulate Spiking Neural Networks
through PyNN [1]. PyNN is a Python library widely used in the neuroscience community
to simulate at software and hardware level several existent simulators (NEURON, NEST,
PCSIM and BRIAN) by acting as an interface to unify the different instructions and
neuron model definitions. At hardware level, serves as a high-level interface to directly
map spiking neuron models on the SpiNNaker neuromorphic system [2]. Albeit, SpiNNaker
and other systems such as TrueNorth have demonstrated tremendous capabilities to process
information such as the brain does, these systems are still unreachable for the large
community who wants to implement or validate simplest models on a hardware platform.
In this regard, we developed ePyNN, which is the PyNN simulator embedded on a Raspberry
Pi 3 board, which has a 1.2 GHz 64-bit quad-core ARMv8 CPU. Here, we have been able
to implement a neural network with the ≪ if_curr_exp ≫ model, which is a leaky integrate-and-fire
model with fixed threshold and exponentially-decaying post-synaptic conductance to
generate real time locomotion patterns expressed as spike trains for a hexapod robot
[3, 4]. Specifically, we designed a network of 12 neurons, where each of them controls
one of the degrees of freedom (servomotors) of the robot with a specific topology,
which was offline performed by an evolutionary approach. Finally, the ePyNN has been
successfully validated on a real hexapod robot (Figure 1C) for three different locomotion
gaits (walk, jog and run) running in real time (Figure 1 A, B).
Figure 1 A. Biological Patterns B. Generated patterns C. Robot + ePyNN platform
Acknowledgements
This research has been supported by the CONACYT project “Aplicación de la Neurociencia
Computacional en el Desarrollo de Sistemas Robóticos Biológicamente Inspirados” (No
269798).
References
1. Davison AP, Bruderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L,
Yger P: PyNN: A Common Interface for Neuronal Network Simulators. Front Neuroinform
2008, 2:11.
2. Furber SB, Galluppi F, Temple S, Plana LA: The SpiNNaker Project. Proceedings of
the IEEE 2014, 102(5):652–665.
3. Rostro-Gonzalez H, Cerna-Garcia PA, Trejo-Caballero G, Garcia-Capulin CH, Ibarra-Manzano
MA, Avina-Cervantes JG, Torres-Huitzil C: A CPG system based on spiking neurons for
hexapod robot locomotion. Neurocomputing 2015, 170:47–54.
4. Espinal A, Rostro-Gonzalez H, Carpio M, Guerra-Hernandez EI, Ornelas-Rodriguez
M, Sotelo-Figueroa M: Design of Spiking Central Pattern Generators for Multiple Locomotion
Gaits in Hexapod Robots by Christiansen Grammar Evolution. Frontiers in Neurorobotics
2016, 10:6.
P231 Temporal structure of bilateral coherence in essential and physiological hand
tremor
Martin Zapotocky1,2, Soma Chakraborty1,2, Martina Hoskovcová2, Jana Kopecká2, Olga
Ulmanová2, Evžen Růžička2
1Institute of Physiology, Czech Academy of Sciences, Prague, 14220, Czech Republic;
2Department of Neurology, First Faculty of Medicine, Charles University in Prague,
120 00, Czech Republic
Correspondence: Martin Zapotocky (zapotocky@biomed.cas.cz)
BMC Neuroscience 2017, 18 (Suppl 1):P231
Pathological hand tremor is associated with a number of neurological diseases and
may significantly impede motor functions in the patient. The most common pathological
type is essential tremor (ET), found in 4.6% of the population aged over 65 years
[1]. The neurophysiological basis of ET is still under debate, and recent literature
suggests that patients with the ET diagnosis may in fact fall into several categories
with distinct disease origins [2]. Detailed quantitative analysis of the features
of the tremor may help in further classification and in clarifying the underlying
neurophysiological mechanisms.
Depending on the underlying mechanism, the tremors in the left hand and right hand
may be coupled or independent. In the previous literature on tremors, this bilateral
coupling was assessed using stationary spectral coherence analysis, both on the level
of hand kinematics and of muscle activity. Highly prevalent bilateral coherence was
found for orthostatic [3] and psychogenic [4] tremors, while for other tremor types
including ET, such coupling was only rarely reported. In our recent study [5], we
used nonstationary, wavelet-based coherence analysis of kinematic recordings to show
that the oscillations of the two hands are intermittently coupled in ET. We found
that intervals of strong bilateral coherence, lasting for up to a dozen seconds, alternate
with time intervals of insignificant coherence. We also observed intermittent bilateral
coherence for physiological tremor (a normal hand oscillation of low amplitude) recorded
in healthy subjects.
Here we further extend the analysis of Ref. [5], based on the same dataset of accelerometric
recordings obtained from 34 ET patients and 42 healthy subjects. We analyze the distribution
of durations of the bilaterally coherent time intervals extracted from wavelet analysis,
and examine its dependence on the tremor type (physiological vs. essential) and on
the hand position. The statistical significance of the coherence intervals is evaluated
with surrogate analysis, using “natural” surrogates (the hand acceleration recorded
from other subjects), as well as artificially constructed surrogates that have randomized
Fourier phases but match the power spectrum and value distribution of the recorded
time series [6]. We analyze separately the bilateral coupling of tremor amplitude,
and evaluate its contribution to the bilateral coherence of tremor as assessed by
spectral/wavelet coherence.
Acknowledgements
Supported by Czech Science Foundation (P304/12/G069), Charles University in Prague
(Progres Q27, SVV NeST III), and Czech Health Research Council (AZV 16-28119A).
References
1. Louis ED, Ferreira JJ: How common is the most common adult movement disorder? Update
on the worldwide prevalence of essential tremor. Mov Disord 2010, 25(5):534–41.
2. Louis ED: Essential tremors: a family of neurodegenerative disorders? Arch Neurol
2009, 66(10):1202–1208.
3. Lauk M, Köster B, Timmer J, Guschlbauer B, Deuschl G, Lücking CH. Side-to-side
correlation of muscle activity in physiological and pathological human tremors. Clin
Neurophysiol 1999, 110:1774–1783.
4. Raethjen J, Kopper F, Govindan RB, Volkmann J, Deuschl G: Two different pathogenetic
mechanisms in psychogenic tremor. Neurology 2004, 63:812–815.
5. Chakraborty S, Kopecká J, Šprdlík O, Hoskovcová M, Ulmanová O, Růžička E, Zapotocky
M: Intermittent bilateral coherence in physiological and essential hand tremor. Clin
Neurophysiol 2017, 128(4):622–634.
6. Schreiber T, Schmitz A: Improved surrogate data for nonlinearity tests. Phys Rev
Lett 1996 77(4):635–638.
P232 Detecting joint pausiness in parallel spike trains
Matthias Gärtner1, Sevil Duvarci2, Jochen Roeper2, Gaby Schneider1
1Institute of Mathematics, Goethe-University, Frankfurt, Germany; 2Neuroscience Center,
Institute of Neurophysiology, Goethe-University, Frankfurt, Germany
Correspondence: Matthias Gärtner (gaertner@math.uni-frankfurt.de)
BMC Neuroscience 2017, 18 (Suppl 1):P232
Transient periods with reduced neuronal discharge - called ‘pauses’ - have recently
gained increasing attention. In dopamine neurons, pauses are considered important
teaching signals, encoding negative reward prediction errors. Particularly simultaneous
pauses are likely to have increased impact on information processing. Available methods
for detecting joint pausing analyze temporal overlap of pauses across spike trains.
Such techniques are threshold dependent and can fail to identify joint pauses that
are easily detectable by eye, particularly in spike trains with different firing rates.
We introduce a new statistic called ‘pausiness’ that measures the degree of synchronous
pausing in spike train pairs and avoids threshold-dependent identification of specific
pauses. A new graphic termed the ‘cross-pauseogram’ compares the joint pausiness of
two spike trains with its time shifted analogue, such that a (pausiness) peak indicates
joint pausing. When assessing significance of pausiness peaks, we use a stochastic
model with synchronous spikes to disentangle joint pausiness arising from synchronous
spikes from additional ‘Joint Excess Pausiness’ (JEP). Parameter estimates are obtained
from auto- and cross-correlograms, and statistical significance is assessed by comparison
to simulated cross-pauseograms.
Our new method was applied to dopamine neuron pairs recorded in the ventral tegmental
area of awake behaving mice. Significant JEP was detected in about 20% of the pairs.
Given the neurophysiological importance of pauses and the fact that neurons integrate
multiple inputs, our findings suggest that the analysis of JEP can reveal interesting
aspects in the activity of simultaneously recorded neurons.
Acknowledgements
This work was supported by the Priority Program 1665 of the DFG (DU 1433/1-1 to SD
and JR, and SCHN 1370/2-1 to MG and GS), by an EMBO long-term fellowship (ALTF_210-2012
to SD), and by the German Federal
Ministry of Education and Research (BMBF, 01ZX1404B to GS).
P233 A stochastic model relates responses to bistable stimuli to underlying neuronal
processes
Stefan Albert1, Katharina Schmack2, Gaby Schneider1
1Institute of Mathematics, Goethe-University, Frankfurt a.M., Germany; 2Department
of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
Correspondence: Stefan Albert (albert@math.uni-frankfurt.de)
BMC Neuroscience 2017, 18 (Suppl 1):P233
Viewing of ambiguous stimuli can lead to bistable perception alternating between the
possible percepts. The respective response patterns show differences between schizophrenic
patients and healthy controls [1, 2]. At the same time, these patterns show similarities with
spiking patterns of dopaminergic cells [3] that may be related to schizophrenia spectrum
disorders. Specifically, oscillatory behavior [4] with single percept changes occurs
during continuous viewing of ambiguous stimuli, and stable more or less regular periods
followed by bursts of percept changes are observed during intermittent viewing of
ambiguous stimuli.
Therefore, we propose a stochastic model that provides a link between the observed
response patterns and potential underlying neuronal processes. To that end, we first
develop a Hidden Markov Model that captures the observed group differences by describing switches
between stable and unstable states in the intermittent presentation and using only
one state in continuous presentation. Second, the model is embedded into a hierarchical
model that describes potential underlying neuronal activity as difference between two
competing neuronal populations similar to [5]. This differential activity is assumed
here to generate switching between (i) the two conflicting percepts and between (ii)
stable and unstable states with comparable mechanisms on different neuronal levels.
Using only a small number of parameters, the model can be fitted to a large data set
of perceptual responses of schizophrenic patients and healthy controls under continuous
and intermittent stimulation. The model can closely reproduce a wide variety of response
patterns and is able to capture and to provide potential neuronal mechanisms for group
differences between healthy controls and schizophrenic patients such as the weaker
tendency to stabilized perception in the patient group under intermittent stimulation
[2].
Acknowledgements
This work was supported by the German Federal Ministry of Education and Research (BMBF,
Funding number: 01ZX1404B; SA, KS, GS).
References
1. Schmack K, Gòmez-Carrillo de Castro A, Rothkirch M, Sekutowicz M, Rössler H, Haynes
J, Heinz A, Petrovic P, Sterzer S: Delusions and the Role of Beliefs in Perceptual
Inferences. J Neurosci E 2013, 33(34):13701–13712.
2. Schmack K, Schnack A, Priller J, Sterzer P: Perceptual instability in schizophrenia:
Probing predicitive coding accounts of delusions with ambiguous stimuli. Schizophr
Res Cog 2015, 2(2):72–77.
3. Bingmer M, Schiemann J, Roeper J, Schneider G: Measuring burstiness and regularity
in oscillatory spike trains. J Neurosci Methods 2011, 201: 426–437.
4. Brascamp JW, Pearson J, Blake R, van den Berg AV: Intermittent ambiguous stimuli:
Implicit memory causes periodic perceptual alternations. J Vis 2009, 9(3): 1-23.
5. Gigante G, Mattia M, Braun J, Del Guidice P: Bistable perception Modeled as Competing
Stochastic Integration at Two Levels. PLoS Comput Bio 2009, 5(7): e1000430.
P234 Function and energy consumption constrain biophysical properties of neurons -
an example from the auditory brainstem
Michiel Remme1,2, John Rinzel3,4, Susanne Schreiber1,2
1Institute for Theoretical Biology, Humboldt University, 10115 Berlin, Germany; 2Bernstein
Center for Computational Neuroscience Berlin, Germany; 3Center for Neural Science,
New York University, New York, NY 10003, United States; 4Courant Institute of Mathematical
Sciences, New York University, New York, NY 10012, United States
Correspondence: Michiel Remme (michiel.remme@hu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P234
Neural morphology and membrane properties vary greatly between cell types in the nervous system.
While the function of neurons is thought to be the key constraint for their biophysical properties,
additional constraints may further shape neuronal design and explain observed properties.
Here, we focus on principal neurons in the MSO nucleus of the auditory brainstem and
show that a tradeoff between a functionally relevant computation and energy consumption predicts
optimal ranges of biophysical parameters.
Biophysical properties of MSO cells as well as their function are well characterized:
MSO cells encode the direction of sound in the horizontal plane. Inputs to MSO cells
are phase-locked to sound wave stimuli to each ear and the interaural time difference
(ITD) of sound waves is used to compute source location. To achieve sensitivity to
ITDs in the range of tens of μs, MSO cells have specialized membrane properties, including
a very fast membrane time constant (~1 ms) and a low-threshold potassium current (IKLT),
both contributing to a very short input integration window [1]. Furthermore, MSO cell
function is supported by their bipolar morphology, with inputs from the two ears segregated
to the two main dendrites [2].
Next to function, energy use can be assumed to significantly constrain MSO cell properties. Overall,
the brain accounts for a disproportionately large part (~20%) of the energy budget, with
metabolic energy being mostly spent on synaptic input, action potentials, and resting potentials
[3]. MSO cells, in particular, receive inputs at very high rates (hundreds of Hz), generate
action potentials at similarly high rates, and display a very leaky membrane.
Here, we quantify and contrast sensitivity of MSO cells to ITDs as well as the associated metabolic
cost. We developed a simplified dendritic model of an MSO cell that includes the KLT-current.
We first fit the model to experimental data from [1] and then explored how varying
the morphological and membrane parameters affects performance and energy consumption.
We found that most experimentally constrained parameters were close to a functional
optimum; if a wider range of functionally good values was available, the fitted parameters
tended towards lower energy usage. Interestingly, we found that the KLT-current increases
energy costs, but strongly improves coincidence detection, beyond passive capabilities.
We next explored the full parameter space by considering 100,000 models with random
combinations of parameters. The experimentally constrained model was among the top
13% regarding performance and top 12% regarding energy efficiency (i.e., sensitivity
per energy). Exploration of the full parameter space highlighted that two model features
explain most of their performance and energy consumption: 1) the level of saturation
of the driving force of the synaptic conductance inputs and 2) the width of the somatic
compound EPSPs. We conclude that the neural design of MSO cells is indeed compatible
with both functional and energetic constraints, with a preference of function over
cost.
Acknowledgements
This work was supported by the Einstein Foundation Berlin and the German Federal Ministry
of Education and Research (01GQ0901, 01GQ1403).
References
1. Mathews PJ, Jercog PE, Rinzel J, Scott LL, Golding NL. Control of submillisecond
synaptic timing in binaural coincidence detectors by Kv1 channels. Nat Neurosci 2010.
13:601–609.
2. Agmon-Snir H, Carr CE, Rinzel J: The role of dendrites in auditory coincidence
detection. Nature 1998, 393:268–272.
3. Attwell D, Laughlin SB: An energy budget for signaling in the grey matter of the
brain. J Cereb Blood Flow Metab 2001, 21:1133–1145.
P235 The Brain Simulation Platform of the Human Brain Project: collaborative web applications
and tools for data-driven brain models
Michele Migliore1, Carmen A. Lupascu1, Luca L. Bologna1, Rosanna Migliore1, Stefano
M. Antonel2, Jean-Denis Courcol2, Felix Schürmann2
1Institute of Biophysics, National Research Council (CNR), Palermo, Italy; 2Blue Brain
Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
Correspondence: Michele Migliore (michele.migliore@cnr.it)
BMC Neuroscience 2017, 18 (Suppl 1):P235
The Brain Simulation Platform (BSP) of the Human Brain Project (HBP) provides a large
set of tools to build, reconstruct, simulate and analyze data-driven brain models
in a collaborative manner (Figure 1). The available tools are organized by use cases,
consisting of selected procedures illustrating specific practical examples on how
to exploit the Platform capabilities to pursue scientific goals.
The platform is designed to target users with different background and expertize such
as: a) “end-users”, interested in using the platform in a user-friendly manner, b)
“power-users”, able to take advantage of the platform services while integrating their
own High Performance Computing resources, c) “expert-users”, who can contribute to
the development of the tools, and d) “co-design developers” who are early adopters
of initial versions of the platform facilities.
In this poster, we will give an overview of the current BSP release, the services
it provides and the collaborative approach underlying its design. To illustrate the
potential of the platform, and how users with different background can take full advantage
of its tools, we will demo a few use cases in which “end-users” and-or “expert-users”
are guided through step-by-step python-based jupyter notebook and web applications
graphical interfaces (Figure 1).
Figure 1. The HBP Brain Simulation Platform web interface. A. BSP Overview web page.
B and C. synaptic events Fitting and Electrophysiological Feature Extraction GUIs,
developed as a jupyter notebook and a web app respectively
Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 720270.
P236 A Single Pyramidal-Cell and Network Computational Model of the Hippocampal CA3
Region
Sami Utku Çelikok1, Eva M. Navarro-López2, Neslihan Serap Şengör3
1Biomedical Engineering Department, Boğaziçi University, Istanbul, 34342, Turkey;
2School of Computer Science, The University of Manchester, Manchester, M13 9PL, UK;
3Electronics and Communication Department, Istanbul Technical University, Istanbul,
34469, Turkey
Correspondence: Sami Utku Çelikok (utku.celikok@boun.edu.tr)
BMC Neuroscience 2017, 18 (Suppl 1):P236
Hippocampal subarea CA3 has long drawn attention for its major role in encoding spatial
representations and episodic memories [1]. Due to the presence of rich recurrent feedback
connections, CA3 has been considered to play a key role in long-term memory formation.
Moreover, CA3 has long been proposed as an auto-associative network capable of pattern
completion and path integration for the retrieval and storage of episodic/declarative
memory traces [2]. A broad range of experimental studies have supported the idea that
hippocampal oscillations must be taken into consideration while investigating the
region as a memory network. Empirically-validated studies on freely moving rats have
identified two major oscillatory patterns of hippocampal activity in a behaviour-dependent
context: theta- (4–8 Hz) and gamma-band (30–100 Hz) frequency rhythms [3, 4]. In rodents
and humans, gamma rhythms embedded into theta oscillations become prominent during
memory functions, object exploration, and spatial navigation [1]. The consideration
of the spiking patterns of the neurons during oscillatory regimes is key to uncover
the significance of hippocampal network oscillations in different processes. When
the broad electrophysiological repertoire of CA3 pyramidal cells is considered, the
computational description of the network requires a neural model. This model has to
be simple enough to support a large hippocampal network, but still rich enough to
capture complex pyramidal-cell dynamics. This is precisely what we propose here: a
single-cell computational model for a CA3 pyramidal neuron that is used as the basic
element to form a CA3 network model which will be able to reproduce key hippocampal
oscillatory patterns. The spiking patterns of the offered single-cell model capture
some essential features of well-known hippocampal spiking behaviour, such as: spike
broadening at the end of a burst, rebound bursting, low-frequency bursts, and high-frequency
tonic spiking (Figure 1). Moreover, the model for the CA3 population is also able
to generate theta and gamma-band oscillations, known to be present in the CA3 region.
Figure 1. A. Single-cell model results. Upper-left: Initial spike generation, upper-right:
rebound bursting in response to hyperpolarisation, bottom: burst-to-tonic spike transition
with increased input current. B. Population model spectrograms. Upper: gamma-band
oscillations in the network, bottom: theta-band oscillations in the network
References
1. O’Keefe J, Nadel, L: The Hippocampus as a Cognitive Map. Oxford, UK: Oxford University
Press; 1978.
2. Samsonovich A, McNaughton BL: Path integration and cognitive mapping in a continuous
attractor neural network model. J Neurosci 1997, 17(15):5900–5920.
3. Gloveli T, Kopell N, Dugladze T: Neuronal activity patterns during hippocampal
network oscillations in vitro. In: Hippocampal Microcircuit 2010, Springer 247–276.
4. Leung LS, Lopes da Silva F, Wadman WJ: Spectral characteristics of the hippocampal
EEG in the freely moving rat. Clin Neurophysiol 1982, 54:203–219.
P237 Functional connectivity between prefrontal cortex and striatum showed by computational
model
Rahmi Elibol, Neslihan Serap Sengor
Electronics and Communication Engineering, Istanbul Technical University, Istanbul,
Turkey
Correspondence: Rahmi Elibol (rahmielibol@itu.edu.tr)
BMC Neuroscience 2017, 18 (Suppl 1):P237
It is well-known that there is a strong correlation between cortex and striatal activity
especially during progression of action selection and goal directed behavior. This
interaction between cortex and striatum project back to the cortex through direct
and indirect pathways and over thalamus forming a closed loop [1]. Such structural
associations of the brain are called structural connectivity or connectome. Due to
the development of measurement technologies as fMRI, more work has been carried to
build up the association between the different areas of the brain and the cognitive
processes, and such associations are called functional connectivity or functional
connectome. Besides these, the processes carried out at neuronal level and/or the
changes at synaptic connections which give rise to relations that are observed at
frequency and/or phase levels is called dynome [2]. The structural connection between
cortex and striatum is already known and their functional connectivity has been shown
with experimental studies. In this work, based on the experimental results given in
[3], a computational model is proposed based on the dynamical connection of neurons
and synapses showing the dynome relation between cortex and striatum.
During the experimental studies that have been explained in [3], LFP in prefrontal
cortex and striatum are measured. Beta and gamma frequency bands have been observed
and with PLV, the correlation between cortical and striatal activity has been shown
[3, 4]. These experimental results have been recreated with the computational model
proposed and it is shown that the results given in Figure 1 are similar to the experimental
results. The simulations are carried out by considering the similar conditions considered
in experiments. The stimuli are applied as in the experimental work and the role of
different reward quantities is investigated by changing the dopamine levels.
Figure 1. The correlation between the PFC and striatal activity: A. The activity in
PFC B. The activity in striatum, C. The correlation between PFC and striatum. The
activities in PFC and striatum are given with normalized firing rate values. The results
show the there is a correlation between cortex and striatum
References
1. GE Alexander, MD Crutcher, MR DeLong, Basal ganglia-thalamocortical circuits: Parallel
substrates for motor, oculomotor, “prefrontal” and “limbic” functions. Progress in
Brain Research, 85, 119–146, http://dx.doi.org/10.1016/S0079-6123(08)62678-3.
2. NJ Kopell, HJ Gritton, MA Whittington, MA Kramer: Beyond the connectome: the dynome.
Neuron 2014, 83(6):1319–1328. doi: 10.1016/j.neuron.2014.08.016.
3. Y Zhang, X Pan, R Wang, M Sakagami: Functional connectivity between prefrontal
cortex and striatum estimated by phase locking value. Cogn Neurodyn. 2016, 10(3):245–254.
doi: 10.1007/s11571-016-9376-2.
4. EG Antzoulatos, EK Miller: Increases in functional connectivity between prefrontal
cortex and striatum during category learning. Neuron 2014, 83(1):216–225. doi: 10.1016/j.neuron.2014.05.005.
P238 A spiking neural network model of basal ganglia-thalamocortical circuit with
Brian2
Mustafa Yasir Özdemir, Neslihan Serap Şengör
Electronic-Communication Department, İstanbul Technical University, İstanbul, Turkey
Correspondence: Mustafa Yasir Özdemir (musyasoz@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P238
Basal ganglia circuit which is located in the midbrain has an essential role in action
selection, decision making and reward based learning processes. In this work, especially
basal ganglia-thalamocortical circuit responsible for motor control giving rise to
voluntary movement is considered.
The characteristics of neuronal activity and their functional abilities, properties
of synaptic connections, effect of neurotransmitters as dopamine and the relation
between different nuclei defined by pathways, all these are effective in realizing
voluntary movement. It is long known that abnormalities in dopamine level influence
basal ganglia operations negatively giving rise to neurological disorders like Parkinson’s
Disease, Hungtinton’s chorea, hemiballismus, dystonia [1].
The equations written for neuronal activity are complicated and simulations of computational
models are especially versatile to predict the neuronal activity. Computational models
reflect the consequences of various assumptions made in forming the models [2]. Most
computational models of basal ganglia circuits consider a specific process and only
partly reflect their nature and function. In this work, an attempt is made to obtain
a holistic model of basal ganglia-thalamocortical circuit in Brian 2 environment to
ease the further improvement and testing of the model by the neuroscientist.
Here a spiking neural network model is realized to configure the entire properties
of basal ganglia circuit. The characteristic neuronal activities of each substructure
are obtained by modification of Izhikevich neuron model [3]. The proposed model of
basal ganglia-thalamocortical circuit is also capable of showing the dopamine effect
on the processes due to the modified striatum neurons. Medium spiny neurons which
have different dopamine receptors are considered in the model separately. Also, direct,
indirect and hyper-direct pathways exist in the model and effect of dopamine on these
pathways can be observed in the simulations. Synaptic connections configured to realize
learning and probability of connections are set according to the research presented
in the literature. The model is formed with inspiration from another study [4] and
realized on Brian2 simulator.
The simulation results of the model are given by raster plots, firing rates and time-frequency
analysis. The stimulus activity in the cortex is projected to the thalamus in the
simulations and the model reveals the role of direct, indirect and hyper-direct pathways
on the formation of this projection separately.
References
1. Wichmann T, DeLong MR: Deep Brain Stimulation for Neurologic and Neuropsychiatric
Disorders. Neuron 2006, 52(1): 197–204.
2. Schroll H, Hamker FH: Computational models of basal-ganglia pathway functions:
focus on functional neuroanatomy. Frontiers in Sys Neu 2013, doi:10.3389/fnsys.2013.00122.
3. Izhikevich EM: Which Model to Use for Cortical Spiking Neurons? IEEE Trans Neural
Networks 15:1063–1070.
4. Çelikok U, Navarro-Lopez EM, Şengör NS: A computational model describing the interplay
of basal ganglia and subcortical background oscillations during working memory processes.
arXiv:1601.07740
P239 Coordinate-transformation spiking neural network for spatial navigation
Tianyi Li, Angelo Arleo, Denis Sheynikhovich
Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut de la Vision, 17
rue Moreau, 75012 Paris, France
Correspondence: Denis Sheynikhovich (denis.sheynikhovich@upmc.fr)
BMC Neuroscience 2017, 18 (Suppl 1):P239
Spatial navigation in primates is thought to be mediated by neural networks linking
the dorsal visual pathway (including parietal and retrosplenial cortices) and the
medial temporal lobe [1]. Neurons along this pathway are sensitive to visual cues
of varying complexity (from simple visual features to views of spatial scenes [2,
3]) and have been characterized to code environmental features in different reference
frames (from egocentric eye- or head-centered representations early in the pathway
to allocentric world-centered ones later in the pathway [3, 4]). However, neural mechanisms
underlying the transformation between egocentric-visual and allocentric-spatial representations
remain poorly understood.
In this work, we present a spiking-neural-network model of visuo-spatial coordinate
transformation that receives input in the form of realistic head-centered visual input
with limited view field. After processing this input with V1-like orientation-sensitive
neuronal filters, it is transformed to an allocentric directional frame using two
mechanisms, experimentally observed along the dorsal pathway. First, head direction
signal, thought to be provided by the retrosplenial cortex, is used by the network
to align egocentric input views with a world-centered directional frame [4]; Second,
short-term visual working memory in the parietal network serves to link subsequent
views during head rotation into scene-like representation of visual features. The
output of the coordinate-transformation network serves as input to the hippocampus,
where location-sensitive neuronal responses are learned using spike-timing-dependent
plasticity.
Neuronal activities in the model are shown to reproduce basic features of dorsal-pathway
neurons. In particular, in an experimental setup mimicking an animal sitting in front
of a screen, visual receptive fields of model parietal/retrosplenial neurons code
features in head- or world-centered reference frames, and firing activities in the
transformation network exhibit gain fields with respect to head direction, as observed
in classical experiments with monkeys. In a setup where the simulated animal explores
an experimental environment, modeled hippocampal cells exhibit location-sensitive
firing fields after learning. These purely visual place fields are influenced by changes
in the visuo-spatial environmental layout (e.g. its spatial geometry [5]), and are
modulated by currently observed view [2]. Moreover, spike synchrony patterns in this
model reflect environment topology [6]. This model links the processing of low-level
visual features in the brain with high-level cognitive processes implicated in spatial
navigation.
Acknowledgements
This research was supported by ANR - Essilor SilverSight Chair ANR-14-CHIN-0001
References
1. Kravitz DJ, Saleem KS, Baker CI, Mishkin M: A new neural framework for visuospatial
processing. Nat Rev Neurosci. 2011, 12:217–230.
2. Ekstrom AD: Why Vision is Important to How We Navigate. Hippocampus 2015, 25:731–735.
3. Snyder LH, Grieve KL, Brotchie P, Andersen R: Separate body- and world-referenced
representations of visual space in parietal cortex. Nature 1998, 394:887–891.
4. Byrne P, Becker S, Burgess N: Remembering the past and imagining the future: A
neural model of spatial memory and imagery. Psychol Rev. 2007, 114:340–375.
5. Sheynikhovich D, Chavarriaga R, Strösslin T, Arleo A, Gerstner W, Strosslin T,
Arleo A, Gerstner W: Is there a geometric module for spatial orientation? Insights
from a rodent navigation model. Psychol Rev. 2009, 116:540–566.
6. Curto C, Itskov V: Cell Groups Reveal Structure of Stimulus Space. PLoS Comput
Biol. 2008, 4:e1000205.
P240 Micro-connectomics with cognitive task selectivity
Akihiro Nakamura1, Masanori Shimono1,2
1Osaka University, Toyonaka, Osaka, Japan; 2Riken Brain Science Institute, Saitama,
Japan
Correspondence: Masanori Shimono (smn@bpe.es.osaka-u.ac.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P240
Various cognitive functions of our brain are realized by interactions among a large
number of neurons. Traditionally, the selectivity of neuronal activity to individual
cognitive tasks has been studied [1]. In order to understand the function of the brain
more deeply, we need to investigate the micro-connectome, which is a comprehensive
map of connectivity or interactions of neurons or synapses, beyond the basic statistical
observations of its individual elements [2]. This study reports the interactions among
neurons measured from the anterior lateral motor cortex (ALM) of mice using calcium
fluorescence imaging and focuses on selectivity for cognitive planning of directed
licking behaviors [3]. We reconstructed the functional networks from the spiking activities
of the neuron ensembles at resting periods and compared them with the motion-selectivity
of individual neurons (Figure 1). The network structure was characterized using graph
theory [4]. Past studies [3] have declared that significant activities can be observed
in layer 5 of the ALM. However, the contributions of different layers were not reported.
Our connectome analyses also consistently showed that, in layer 5 of the ALM, a simple
connection strength measure in motion-selective neurons was significantly stronger
than in motion-nonselective cells. Surprisingly, in layer 2, a Centrality measure
was significantly higher in selective cells, especially contralateral selective cells,
than in non-selective cells. Centrality represents that the cell is in an important
position within the network. It has been repeatedly reported that the effective connectivity,
the estimated neuronal activities recorded using Ca Imaging technique in the resting
period, reflects the underlining structural synaptic connectivity fairly well [5].
Therefore, our results suggest that the neurons involved in motor-planning were located
at highly central positions in the micro-connectome from the structural design. Because
of the position, they will be able to influence a large number of neuropiles within,
and probably beyond, the ALM. If we observe the brain more widely, layer 5 exists
on the bottom-up information flow that originally came from the thalamus, and layer
2 exists on the top-down information flow relatively close to the output to the thalamus.
Therefore, layer 2 in the micro-connectome may represent a different functional role
of the motor-planning than the neuron group existing in layer 5. Our findings and
methodological schemes will contribute to a more accurate understanding of cognitive
functions, the effects of aging, and various neurodegenerative diseases.
Figure 1. The general concept of this study. A. Neuronal activities when rodents are
taking rest (or just waiting a task) or when performing licking tasks were recorded
using Ca Imaging technique. B. is an example of effective/functional networks of neurons
reconstructed from the neuronal dynamics. The differences of markers show differences
of responses of neurons. (Neurons responding selectively to contralateral lickings
(△), to ipsilateral lickings (□), and neurons showing no responses to these licking
behaviors (○))
References
1. Hubel DH, Wiesel TN: Receptive fields and functional architecture of monkey striate
cortex. The Journal of physiology 1968, 195(1): 215–243.
2. Shimono M, Beggs, JM: Functional clusters, hubs, and communities in the cortical
microconnectome. Cerebral Cortex 2015,
25
(10): 3743–3757.
3. Li, N, Chen, TW, Guo ZV, Gerfen CR, Svoboda, K.: A motor cortex circuit for motor
planning and movement. Nature 2015, 519(7541): 51–56.
4. Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of
structural and functional systems. Nature Reviews Neuroscience 2009, 10(3): 186–198.
5. Stetter O, Battaglia D, Soriano J, Geisel T: Model-free reconstruction of excitatory
neuronal connectivity from calcium imaging signals. PLoS Comput Biol 2012, 8(8): e1002
P241 Does reinforcement learning explain zone-allocation behavior between two competing
mice?
Youngjo Song1, Sol Park1,2, Ilhwan Choi2, Jaeseung Jeong1,3, Hee-sup Shin2
1Department of Bio and Brain Engineering, KAIST, Daejeon, 34141, Republic of Korea;
2Center for Cognition and Sociality, IBS, Daejeon, 34047, Republic of Korea; 3Program
of Brain and Cognitive Engineering, KAIST, Daejeon, 34141, Republic of Korea
Correspondence: Youngjo Song (jsjeong@kaist.ac.kr)
BMC Neuroscience 2017, 18 (Suppl 1):P241
In the previous study (Choi et al., in revision), we observed two mice showing cooperative-like
behavior in the competitive situation over rewards. We have also shown that this cooperative-like
behavior enhanced mutual rewords and produced payoff equity between two competing
mice. However, the origin of this behavior is not clear. Thus, the aim of this study
is to address whether the cooperative-like behavior could be explained by reinforcement
learning or not. In the behavior chamber for mice, two light cues which indicate two
reward zones, respectively. If a mouse goes the left reward zone when the left light
cue turns on, the mouse gets reward, and a mouse can get rewards if the mouse get
in the right reward zone when the right light cue turns on. The reward is given by
wireless brain stimulation from the electrode implanted in the Medial forebrain bundle
(MFB), the pleasure center in the mouse brain. When the mice learned the meaning of
light cues, we performed the pair test in which the two mice released in one training
chamber. In this experiment, 15 out of 19 pairs showed the tendency to separate and
allocate their own reward zone by themselves. In other words, those mice had their
own preferred sides and did not interfere opponent’s preferred side (we called this
behavior as ‘zone-allocation behavior’). We followed the ethical guidelines of the
Institutional Animal Care and Use Committee in the KAIST. This behavior could be considered
as a heuristic rule of reciprocity and cooperation. To investigate if the reinforcement
learning can explain this behavior in two competing mice, we developed computational
model based on the Temporal difference (TD) learning model. In this computational
simulation, the environment is set up identically with the real training room. The
model mouse makes decisions only based on a state-action value function which is updated
by the TD rule. We found that the computational model successfully mimicked the zone
allocating behavior between two model mice. Two types of pairs in our model were observed.
The first type is a pair dividing their own reward zone each other, which indicates
each mouse obtained its own preferred side (Figure 1A). This can be thought as a case
of zone allocating pair in actual experiment. The second type is that one mouse dominates
both side of reward zone (Figure 1B). From repetitive iterations, we obtained 75%
of model mouse pairs showing the zone allocating behavior, which is quite consistent
with the experimental results of the real zone-allocating pair ratio (69%). Moreover,
we examined whether a mouse achieve this behavior when it uses model-based learning.
We used Dyna-Q algorithm to implement this model mouse. Zone allocating behavior,
however, could not be achieved. If it uses model-based learning, it updates its state-action
value too often. Therefore, the mouse’s behavior did not converge.
Figure 1. A. State-action values (Q-value) of a pair of mice showing zone-allocation
behavior. In mouse1, Q-value for R(right) reward zone is larger than Q-value for L(left)
reward zone. It means that mouse1 prefer R reward zone. In the same way, mouse2 prefer
L reward zone. Moreover, Q-value of mouse1 for L reward zone and Q-value of mouse2
for R reward zone becomes less than 0.2. It means that each mouse didn’t interfere
opponent’s preferred side. B. State-action values (Q-value) of a pair of mice not
showing zone-allocation behavior. Q-value of mouse2 for reward zone is converging
to zero. It means that mouse2 prefer not to move, so mouse1 got all the reward
Conclusion: This computational result supports the hypothesis that the zone allocating
rodent behavior can be explained from positive reinforcement learning (particularly
model-free learning). Zone-allocation might be a strategy to maximize reward and to
minimize cost in aspect of reinforcement learning in competitive situation. We suggest
that, to investigate the social heuristic behavior, it might be crucial to remove
convergent egoistic characteristic of animal behavior.
References
1. Richard S. Sutton, Andrew G. Barto:Reinforcement Learning: An Introduction. The
MIT press; 1998.
2. Paul W. Glimcher, Ernst Fehr: Neuroeconomics, 2
nd
Edition. Academic press; 2014
P242 Optimal synaptic scaling emerges from Hebbian learning rules in balanced networks
Sadra Sadeh1, Padraig Gleeson1, R. Angus Silver1
1Department of Neuroscience, Physiology and Pharmacology, University College London,
London WC1E 6BT, UK
Correspondence: Sadra Sadeh (s.sadeh@ucl.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P242
Synaptic connectivity varies widely across cell types and brain regions and connections
are formed and lost during development and learning. However, normal function cannot
be maintained by simply adding or subtracting excitatory synaptic inputs onto a neuron,
since this will cause neurons to become hyper- or hypo-excitable, resulting in network
instability and loss of function. How then do neurons scale their synaptic input to
maintain function? Theoretical work suggests that the optimal way of scaling of synaptic
weights (J) as the number of synaptic connections per neuron (degree, K) is J ~ 1/√K
[1], a result that has recently been confirmed experimentally [2]. However, the mechanisms
by which such optimal scaling arises are unknown. To address this question, we implemented
Hebbian-like plasticity rules at excitatory (E) and inhibitory (I) synapses in large-scale
balanced spiking networks of primary visual cortex [3]. As K was increased in the
networks we found that synaptic weight decreased with a dependence of J = 1/K0.6,
close to the theoretically optimal scaling [1] and closely matching that found experimentally
[2]. Interestingly, optimal synaptic scaling emerged when Hebbian plasticity was present
at both E and I synapses. In contrast, spiking networks relying solely on plasticity
of I → E synapses to balance excitation and inhibition [4] did not exhibit optimal
scaling. A simplified mean-field analysis of network dynamics explained the dependence
of J on K in networks with Hebbian-like plasticity of E and I synapses, while revealing
why the optimal scaling does not always hold in networks with plasticity of only I → E
synapses.
Irrespective of the initial weights and number of synaptic connections, spiking networks
with Hebbian-like plasticity of E and I synapses robustly self-regulated themselves
through recurrent inhibition and learning into a low activity regime where the activity
of the E neuronal population exhibited a long tail of activity. Notably, this was
accompanied by higher activity and lower selectivity of I neurons, consistent with
experimental observations. Examination of the input-output relationship of individual
current-based or conductance-based neurons revealed that optimal synaptic scaling
robustly preserved neuronal gain as the number of synaptic inputs was altered. Moreover,
contrast-invariant input tuning curves translated to contrast-invariant output tuning
curves only when the optimal (1/√K) scaling of weights was preserved. Our results
thus suggest that Hebbian learning in both E and I connections is necessary for preserving
cortical computation and function during changes in synaptic connectivity. These findings
have important implications for cortical function during development, and cortical
dysfunction during brain diseases.
Acknowledgements
yFunded by the Wellcome Trust and the ERC.
References
1. van Vreeswijk C, Sompolinsky H: Chaos in neuronal networks with balanced excitatory
and inhibitory activity. Science 1996, 274(5293):1724–1726.
2. Barral J, Reyes AD: Synaptic scaling rule preserves excitatory-inhibitory balance
and salient neuronal network dynamics. Nat Neurosci 2016, 19(12):1690–1696.
3. Sadeh S, Clopath C, Rotter S: Emergence of Functional Specificity in Balanced Networks
with Synaptic Plasticity. PLoS Comput Biol 2015, 11(6): e1004307.
4. Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W: Inhibitory Plasticity Balances
Excitation and Inhibition in Sensory Pathways and Memory Networks. Science 2011, 334(6062):1569–1573.
P243 Deciphering the contributions of oriens-lacunosum/moleculare (OLM) cells during
local field potential (LFP) theta rhythms in CA1 hippocampus
Alexandra Pierri Chatzikalymniou1,2 Frances K. Skinner1,3,2
1Krembil Research Institute, University Health Network, Toronto, ON, Canada; 2Department
of Physiology, University of Toronto, Toronto, ON, Canada; 3Department of Medicine
(Neurology), University of Toronto, Toronto, ON, Canada
Correspondence: Alexandra Pierri Chatzikalymniou (alexandra.chatzikalymniou@mail.utoronto.ca)
BMC Neuroscience 2017, 18 (Suppl 1):P243
In the hippocampus, one of the most prevalent LFP rhythms is the 3–12 Hz “theta” oscillation
[1]. This LFP theta rhythm is tightly correlated with spatial navigation, episodic
memory and rapid eye movement (REM) sleep [1]. Recent work by Goutagny and colleagues
[4] showed that theta rhythms emerge in the CA1 region of an intact in vitro hippocampus
preparation due to local interactions between hippocampal interneurons and pyramidal
(PYR) cells. Oriens-lacunosum/moleculare (OLM) cells are a major class of GABAergic
interneurons in the hippocampus [5]. In addition to inhibiting distal dendrites of
PYR cells in stratum LM, OLM cells disinhibit PYR cells in stratum radiatum, an inner
to middle layer, by inhibiting interneurons that target PYR cells in that region [5].
Our goal is to examine the contributions of OLM cells to ongoing LFP theta rhythms
in the context of the intact in vitro preparation using computational modeling. We
use network models of OLM cells, bistratified cells (BiCs), and basket/axo-axonic
cells (BC/AACs) that target PYR cells in specific layers [3], and assess the role
of OLM cells as their interactions with BiCs and the PYR cell vary. We find that the
LFP power is mostly affected by changes in the synaptic conductance from OLM cells
to BiCs rather than by synaptic conductance changes from BiCs to OLM cells, indicating
a more important role for the former. This observation suggests that progressive inhibition
of OLM cells and thus progressive decrease of their synaptic inputs onto the PYR cell
does not strongly alter LFP characteristics whereas progressive inhibition of BiCs
does. Decomposition of the LFP signal reveals that fluctuations in power occur due
to BiC and BC/AAC synaptic inputs onto the PYR cell rather than to OLM cell synaptic
inputs onto the PYR cell. Selective removal of either OLM cells or BiCs/BCs/AACs reveal
minimal contribution of the OLM cells to the total LFP power across the dendritic
tree. Conversely, the BiCs/BCs/AACs generated LFP component comprises approximately
90% of the total signal. Furthermore, changes in synaptic weights from OLM cells to
the PYR cell do not produce substantial changes in the LFP.
Brain rhythms can be considered as representations of brain function [1, 2]. Given
that particular inhibitory cell populations and abnormalities in theta rhythms are
associated with disease states [2], it is important to understand the cellular contributions
to LFP theta rhythm modulations. Our results show that OLM cells prominently contribute
to local LFP theta through their interactions with other local inhibitory cell types.
Decomposition of the LFP reveals little contribution of synaptic inputs from OLM cells
onto the PYR cell. In CA1 PYR cells, distal and middle apical dendrites comprise two
distinct dendritic domains with separate branching [6]. Since we find that maximum
LFP power is recorded around the soma and the proximal dendrites, OLM cell contributions
to LFP theta can be understood in the context of the cytoarchitectonic separation
of the of distal and proximal dendrites in PYR cells which prohibits distal inhibitory
inputs from effectively propagating to the soma.
Acknowledgements
Supported by NSERC Canada, Margaret J. Santalo Fellowship (Physiology, Univ Toronto)
and SciNet HPC.
References
1. Buzsáki G: Theta oscillations in the hippocampus. Neuron 2002, 33:325–340.
2. Colgin L: Rhythms of the hippocampal network. Nat Neurosci Rev 2016, 17:239–249.
3. Ferguson KA, Huh CYL, Amilhon B, Williams S, Skinner FK: Network models provide
insight into how oriens-lacunosum-moleculare (OLM) and bistratified cell (BSC) interactions
influence local CA1 theta rhythms. Front Syst Neurosci 2015, 9:110.
4. Goutagny R, Jackson J, Williams S: Self-generated theta oscillations in the hippocampus.
Nat Neurosci 2009, 12:1491–1493.
5. Maccaferri G: Stratum oriens horizontal interneurone diversity and hippocampal
network dynamics. J Physiol 2005, 562.1:73–80.
6. Spruston N: Pyramidal neurons: dendritic structure and synaptic integration. Nature
Neurosci Rev 2008, 9:206.
P244 Nonlinear optimal control of brain networks
Lazaro M. Sanchez-Rodriguez, Roberto C. Sotero
Hotchkiss Brain Institute and Department of Radiology, University of Calgary, Calgary,
Alberta, Canada, T2 N 1N4
Correspondence: Lazaro M. Sanchez-Rodriguez (lazaro.sanchezrodrgu@ucalgary.ca)
BMC Neuroscience 2017, 18 (Suppl 1):P244
The problem of controlling brain networks has been the focus of several recent studies
given its relationship to brain stimulation. In this work, we introduce the State-Dependent
Ricatti Equation formalism (SDRE) [1] for the computation of optimal control signals
in nonlinear brain networks. Firstly, the optimal input for the abatement of epileptic-like
activity in the model proposed in [2] was calculated (see Figure 1B). Additionally,
we looked at higher dimensional systems consisting of coupled autonomous Duffing oscillators
(see Figure 1, panels C-E). In the linear case our results are in agreement with those
obtained in [3]. However, as the strength of the non-linearity increases, the fraction
of the networks that can be controlled is generally lower whereas the cost of controlling
the systems grows. Thus, we find evidence for supporting the use of realistic nonlinear
modeling of electrical neural activity in the design of optimal controllers for brain
networks.
Figure 1. SDRE-optimal control of the networks. A. General scheme. B. Controlling
the model in [2]. As soon as the control signal (top right corner) is sent, the diseased
solution –in red– is derived to normal background activity. C. Typical trajectory
for a controlled network of autonomous Duffing oscillators coupled through a scale-free
connectivity matrix. Stimuli are inputted over the nodes with lower degree –third
part of the total number of nodes in the network. D. Expected cost for the control
over 25 scale-free networks (N = 100, mean degree ≈ 6). The numbers over each of the
error bars indicate the fraction of the realizations of the network in which control
is achieved as the non-linearity (coefficient of the cubic term) is changed. For strengths
past 125, none of the networks can be controlled. In this case, the costs are infinitely
high in theory. They are represented as red asterisks at the top of the panel. E.
Analogue to D for randomizations of the previously computed scale-free networks
References
1. Jayaram A, Tadi M: Synchronization of chaotic systems based on SDRE method. Chaos
Solitons Fractals 2006, 28:707–715.
2. Taylor PN, Thomas J, Sinha N, Dauwels J, Kaiser M, Thesen T, Ruths J: Optimal control
based seizure abatement using patient derived connectivity. Front. Neurosci 2015,
9:1–10.
3. Liu YY, Slotine JJ, Barabási AL: Controllability of complex networks. Nature 2011,
473:167–173.
P245 An inhibitory microcircuit that amplifies the redistribution of somatic and dendritic
inhibition
Loreen Hertäg1, Owen Mackwood1, Henning Sprekeler1
1Modelling of Cognitive Processes, Berlin Institute of Technology and Bernstein Center
for Computational Neuroscience, Berlin, 10587, Germany
Correspondence: Loreen Hertäg (loreen.hertaeg@tu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P245
GABAergic interneurons constitute only a small fraction of neurons in the brain, but
their importance for brain function is undeniable [1]. Moreover, they display a large
diversity in their biophysical, physiological and anatomical properties [2], suggesting
a functional ‘division of labor’. However, the computational roles of the various
interneuron types and how they are supported by their individual properties is largely
unknown.
A striking difference between inhibitory cell types is that they form synapses onto
different compartments of their postsynaptic targets. Parvalbumin- (PV) and somatostatin
(SOM)-expressing interneurons, in particular, seem to predominantly target the perisomatic
regions and the dendrites, respectively. As SOM and PV cells are also connected, it
has been suggested that inhibition can be dynamically redistributed between the dendrites
and somata of pyramidal cells (PCs) [3, 4]. Here, we argue that a different cortical
sub-circuit consisting of SOM- and vasoactive intestinal peptide (VIP)-expressing
interneurons is optimized to control this redistribution by amplifying small top-down
control signals.
To support this hypothesis, we performed a mathematical analysis and simulations of
a network model comprising excitatory PCs and inhibitory PV, SOM and VIP neurons.
The connectivity in the circuit was chosen according to experimental findings [4].
We show that the SOM-VIP circuit can serve as an amplifier that translates small top-down
signals onto VIP cells [5, 6] into large changes in the somato-dendritic distribution
of inhibition onto PCs. Taken to the extreme, the circuit can generate winner-take-all
(WTA) dynamics that implement a binary switch for somato-dendritic inhibition.
Furthermore, we interpret key properties of the SOM-VIP sub-circuit in the light of
this hypothesis. We show that the striking lack of recurrent inhibition as well as
the presence of short-term synaptic facilitation (STF) observed among VIP and SOM
cells strengthens the amplification properties of the network. Artificially including
recurrent inhibitory connections within the VIP or SOM populations not only weakens
the amplification, but can also lead to pathological conditions in which almost all
cells within each population are silenced. These pathological states are not observed
when firing rate adaptation is included that is, indeed, a common feature of SOM and
VIP neurons.
In summary, our analysis shows that the SOM-VIP sub-circuit is well suited to redistribute
inhibition onto soma and dendrites of excitatory PC neurons by amplifying small changes
in the input signal to VIP cells. The synaptic and neural properties, including lack
of recurrence, presence of STF and firing rate adaptation, underpin this computation
by strengthening the amplification properties and/or avoiding pathological states.
Acknowledgements
yThe project is funded by the German Federal Ministry for Education and Research,
FKZ 01GQ1201.
References
1. Isaacson, JF, Scanziani M: How inhibition shapes cortical activity. Neuron 2011,
72(2): 231–243.
2. Tremblay, R, Lee, S, and Rudy, B: GABAergic interneurons in the neocortex: from
cellular properties to circuits. Neuron 2016, 91(2): 260–292.
3. Pouille, F, Scanziani, M: Routing of spike series by dynamic circuits in the hippocampus.
Nature 2004, 429(6993): 717–723.
4. Pfeffer, CK, Xue, M, He, M, Huang, ZJ, Scanziani, M: Inhibition of inhibition in
visual cortex: the logic of connections between molecularly distinct interneurons.
Nature neuroscience 2013, 16(8): 1068–1076.
5. Lee, S, Kruglikov, I, Huang, ZJ, Fishell, G, Rudy, B: A disinhibitory circuit mediates
motor integration in the somatosensory cortex. Nature neuroscience 2013, 16(11): 1662–1670.
6. Pi, HJ, Hangya, B, Kvitsiani, D, Sanders, JI, Huang, ZJ, Kepecs, A: Cortical interneurons
that specialize in disinhibitory control. Nature 2013, 503(7477), 521–524.
P246 Learning grid cells in recurrent neural networks
Steffen Puhlmann1, Simon N. Weber1,2, Henning Sprekeler1,2
1MKP, Modelling of cognitive processes, Berlin Institute of Technology, 10587 Berlin,
Germany; 2Bernstein Center for Computational Neuroscience, 10115, Berlin, Germany
Correspondence: Steffen Puhlmann (s.puhlmann@campus.tu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P246
Grid cells are spatially tuned neurons in the entorhinal cortex, whose spatial firing
fields tessellate the environment with a hexagonal lattice. The mechanisms that underlie
this highly symmetric firing pattern are currently subject to intense debate [1].
As an alternative to attractor and oscillatory interference models that perform path
integration and assume a specific connectivity [1], we recently suggested that grid
cells could be learned in a feedforward network by interacting excitatory and inhibitory
plasticity on spatially modulated inputs [2]. A central prerequisite for the suggested
mechanism is that inhibitory inputs have a broader spatial tuning than their excitatory
counterparts. Given that recurrent inhibition is abundant in entorhinal cortex [3]
and spatially tuned [4], we reasoned that this broadened inhibition could be the result
of recurrent processing.
To corroborate this hypothesis, we analyzed a recurrent network model consisting of
excitatory and inhibitory rate neurons. For the sake of the argument, only the excitatory
neurons in the network receive external, spatially modulated excitatory input. All
synapses in the network are plastic, with Hebbian plasticity on the excitatory synapses
and homeostatic plasticity on the inhibitory synapses [5]. When exposing the network
to inputs that mimic the movement of an animal on a linear track, a large fraction
of cells in the recurrent network rapidly develops a grid-like firing pattern. We
find that the underlying mechanism is robust to details of the spatial input tuning
and that the spatial scale of the resulting grids is primarily determined by the spatial
autocorrelation length of inputs. Based on insights from earlier work on the interaction
of excitatory and inhibitory synaptic plasticity [6, 2], we identify key mechanisms
in the circuit that are required for the formation of grid cells: 1) a smooth, saturating
nonlinearity in the interneurons, which ensures that their spatial tuning is broader
than the tuning of their excitatory drive, and 2) sufficiently many and diverse excitatory
inputs to the inhibitory neurons.
Based on these findings, we suggest that grid cells could be bootstrapped from a large
variety of spatially modulated excitatory inputs to a recurrent network of excitatory
and inhibitory neurons with synaptic plasticity on all synapses.
Acknowledgements
The project is funded by the German Federal Ministry for Education and Research, FKZ
01GQ1201.
References
1. Giocomo LM, Moser MB, Moser EI: Computational models of grid cells. Neuron 2011,
71(4):589–603.
2. Weber SN, Sprekeler H: Learning place cells, grid cells and invariances: A unifying
model. bioRxiv 2017, 102525.
3. Couey JJ, Witoelar A, Zhang SJ, Zheng K, Ye J, Dunn B, Czajkowski R, Moser MB,
Moser EI, Roudi Y, et al.: Recurrent inhibitory circuitry as a mechanism for grid
formation. Nat Neurosci 2013, 16(3):318–324.
4. Buetfering C, Allen K, Monyer H: Parvalbumin interneurons provide grid cell-driven
recurrent inhibition in the medial entorhinal cortex. Nat Neurosci 2014, 15(5):710–718.
5. Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W: Inhibitory plasticity balances
excitation and inhibition in sensory pathways and memory networks. Science 2011,
334
(6062):1569–1573.
6. Clopath C, Vogels TP, Froemke RC, Sprekeler H: Receptive field formation by interacting
excitatory and inhibitory synaptic plasticity. bioRxiv 2016, 066589.
P247 A model of perceptual learning, biases, and roving
David Higgins1,2, Henning Sprekeler1,2
1Modelling of Cognitive Processes, TU Berlin, 10587, Germany; 2Bernstein Center for
Computational Neuroscience, Berlin, 10115, Germany
Correspondence: David Higgins (dave@uiginn.com)
BMC Neuroscience 2017, 18 (Suppl 1):P247
Roving is a random task-sequencing paradigm, in perceptual learning, whereby multiple
tasks are learned in a randomly interleaved sequence. For certain experiments, such
as bisection tasks, human subjects appear to be unable to learn the individual tasks
under roving conditions [1]. In general, theoretical descriptions of perceptual learning
experiments have resorted to approaches involving tuning of inputs, using either recurrence
or suppression [2, 3]. However, these approaches have exhibited only partial success
in tackling roving. In 2012, Herzog et al. [4] proposed a theoretically inspired explanation
involving a constant drift in synaptic efficacies in the system (unsupervised bias),
due to an inability to maintain accurate task specific estimates of performance. This
leads to a failure to learn using feedback. We update this approach, adding additional
features, which though adding realism tend to counteract the action of the unsupervised
bias. We then use this model to examine whether the unsupervised bias is sufficient
to explain roving or not.
The proof-of-concept model proposed in Herzog et al. [4] does indeed lead to a failure
to correctly learn during roving but, while it fails due to the mooted unsupervised
bias in the learning rule, the implementation relies on unbounded weight growth, an
unrealistic phenomenon. We introduce a simple weight normalisation term, to counteract
the unbounded weight growth, and implement a cognitive bias, often observed in human
subjects, towards 50:50 presentation ratios. We thus discover a more appropriate model
of human perceptual learning performance. Our model (i) learns correctly on a single
bisection or vernier task, (ii) fails to learn during roving of multiple tasks, (iii)
exhibits the human tendency towards 50:50 ratios of choice, thus failing when a 75:25
ratio is used, and (iv) correctly learns when informed of the altered presentation
ratio, similarly to human subjects (unpublished data). A further extension to the
original model, operating on a much slower timescale, allows the task critic system
to learn over time to separately identify the tasks. This ultimately leads to learning
of the initially unlearnable tasks, as seen in [5].
Our model can be seen as the distillation of the mechanism of failure to learn due
to the unsupervised bias. Consistent with intuitions within the perceptual learning
community, our model indicates that the degree of overlap in task representations,
combined with the unsupervised bias, leads to the difference in outcomes between successful
transfer learning versus failure. Interestingly, a cognitive bias in the task presentation
ratio appears to be quite helpful in a range of presentation paradigms, often counteracting
the unsupervised bias and rescuing potential failures to learn correctly. Our work
would combine quite well with the more detailed work of Liu et al. [6] to provide
a full model of perceptual learning in the visual system.
References
1. Otto, TU, Herzog MH, Fahle M, Zhaoping L: Perceptual Learning with Spatial Uncertainties.
Vision Research 2006, 46(19): 3223–3233.
2. Zhaoping, L, Herzog MH, Dayan P: Nonlinear Ideal Observation and Recurrent Preprocessing
in Perceptual Learning. Network 2003, 14(2): 233–247.
3. Schäfer, R, Vasilaki E, Senn W: Adaptive Gain Modulation in V1 Explains Contextual
Modifications during Bisection Learning. PLOS Comput Biol 2009, 5(12): e1000617.
4. Herzog, MH, Aberg KC, Frémaux N, Gerstner W, Sprekeler H: Perceptual Learning,
Roving and the Unsupervised Bias. Vision Research 2012, 61: 95–99.
5. Parkosadze K, Otto TU, Malania M, Kezeli A, Herzog M: Perceptual Learning of Bisection
Stimuli under Roving: Slow and Largely Specific. Journal of Vision 2008, 8(1): 5.
6. Liu J, Dosher BA, Lu ZL: Augmented Hebbian Reweighting Accounts for Accuracy and
Induced Bias in Perceptual Learning with Reverse Feedback. Journal of Vision 2015,
15(10): 10–10.
P248 Presynaptic inhibition provides a rapid stabilization of recurrent excitation
in the face of plasticity
Laura B. Naumann1,2, Henning Sprekeler1,2
1Modelling of Cognitive Processes, Berlin Institute of Technology, Berlin, Germany;
2Bernstein Center for Computational Neuroscience, Berlin, Germany
Correspondence: Laura B. Naumann (laura-bella.naumann@bccn-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P248
Synaptic plasticity in recurrent neural networks is believed to underlie learning
and memory in the brain. One practical problem of this hypothesis is that recurrent
excitation forms a positive feedback loop that can easily be destabilized by synaptic
plasticity. Numerous homeostatic mechanisms have been suggested to stabilize plastic
recurrent networks [1], but recent computational work indicates that all these mechanisms
share a major caveat: An effective rate stabilization requires a homeostatic process
that operates on the order of seconds, while experimentally observed mechanisms such
as synaptic scaling occur over much longer timescales [2].
Here, we suggest presynaptic inhibition as an alternative homeostatic process, which
does not suffer from this discrepancy in timescales. Experimental studies have revealed
that excess network activity can trigger an inhibition of transmitter release at excitatory
synapses through the activation of presynaptic GABAB receptors, which effectively
weakens synaptic strength [3]. This attenuation of recurrent interactions has been
observed to be fully reversible and acts on timescales of 100 s of milliseconds, thus
constituting a candidate mechanism for the rapid compensation of synaptic changes.
To highlight the beneficial properties of presynaptic inhibition in excitatory recurrent
circuits, we analyzed a simple rate-based recurrent network model. Presynaptic inhibition
is mimicked by multiplicatively scaling down recurrent excitatory weights in response
to excess population activity. Using analytical and numerical methods, we show that
presynaptic inhibition ensures a gradual increase of firing rates with growing recurrent
excitation, even for very strong recurrence (Fig. 1A). An in-depth mathematical analysis
of the underlying dynamical system further reveals that the stability of non-zero
fixed points (Fig 1A, filled markers) is largely independent of model parameters.
In contrast, classical subtractive postsynaptic inhibition is unable to control recurrent
excitation once it has surpassed a critical value (Fig. 1B). Moreover, we investigate
the conditions under which presynaptic inhibition can stabilize recurrent networks
if Hebbian assemblies are imprinted.
In summary, the multiplicative character of presynaptic inhibition provides a powerful
homeostatic mechanism to rapidly reduce effective recurrent interactions while retaining
synaptic weights and hence conserving the underlying connectivity. It might therefore
set the stage for stable learning without interfering with plasticity at the level
of single synapses.
Figure 1. Steady state firing rates as a function of recurrent strength for different
input intensities I
ext. A. presynaptic inhibition B. postsynaptic inhibition
References
1. Abbott LF, Nelson SB: Synaptic plasticity: taming the beast. Nat Neurosci 2000,
3:1178–1490.
2. Zenke F, Gerstner W: Hebbian plasticity requires compensatory processes on multiple
timescales. Phil Trans R Soc B 2017, 372(1715):20160259.
3. Urban-Ciecko J, Fanselow EE, Barth AL: Neocortical Somatostatin Neurons Reversibly
Silence Excitatory Transmission via GABAb Receptors. Curr Biol 2016, 25(6):722–731.
P249 A grid score for individual spikes of grid cells
Simon N. Weber1,2, Henning Sprekeler1,2
1Berlin Institute of Technology, 10587 Berlin, Germany; 2Bernstein Center for Computational
Neuroscience, 10115 Berlin, Germany
Correspondence: Simon N. Weber (weber@tu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P249
The location-specific firing of cells in the entorhinal cortex is subject to extensive
experimental and theoretical research. When classifying the tuning properties of entorhinal
cells, researchers distinguish between grid cells, i.e., cells whose firing locations
form a hexagonal grid, and cells that fire periodically but without hexagonal symmetry
[1–3]. This classification requires a measure for the symmetry of spatially modulated
firing patterns — a grid score. The most established grid score is computed in multiple
stages [e.g., 4]. Spike locations are transformed into a rate map. Subsequently, an
autocorrelogram of the rate map is cropped, rotated and correlated with its unrotated
copy. The final grid score is obtained from the resulting correlation-vs-angle function
at selected angles. This procedure results in a global grid score for the firing pattern,
whose exact value depends on the parameter choices required at each stage.
Here we suggest a new approach that computes a local grid score — and the local grid
orientation — for each individual spike, directly from spike locations. We compare
it to established grid scores and show that it is at least as reliable in quantifying
the global grid score of the spike pattern and robust to noise on the spike locations.
The score enables the plotting of spike locations, color coded with the local grid
score or the local orientation of the grid and could thus simplify the visualization
of experimental data. More specifically, it could be used to quantify and highlight
recent experimental findings, like boundary effects on the structure of grids in asymmetric
enclosures [5], drifts in grid orientation along the arena [6] or the preferred alignment
of grids to one of the boundaries [6]. The grid score is applicable to any n-fold
symmetry.
We provide a public Python package (using SciPy and NumPy) that efficiently determines
the grid score directly from spike locations.
Acknowledgements
Funded by the German Federal Ministry for Education and Research, FKZ 01GQ1201.
References
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spatially periodic bands. Science 2012, 337(6096):853–857.
2. Buetfering C, Allen K, Monyer H: Parvalbumin interneurons provide grid cell-driven
recurrent inhibition in the medial entorhinal cortex. Nature Neurosci 2014, 17(5):710–718.
3. Kropff E, Carmichael JE, Moser MB, Moser EI. Speed cells in the medial entorhinal
cortex. Nature 2015, 523(7561), 419–424.
4. Sargolini F, Fyhn M, Hafting T, McNaughton BL, Witter MP, Moser MB, Moser EI: Conjunctive
representation of position, direction, and velocity in entorhinal cortex. Science
2016, 312(5774), 758–762.
5. Krupic J, Bauza M, Burton S, Barry C, O’Keefe J: Grid cell symmetry is shaped by
environmental geometry. Nature 2015, 518(7538), 232–235.
6. Stensola T, Stensola H, Moser MB, Moser EI: Shearing-induced asymmetry in entorhinal
grid cells. Nature 2015, 518(7538), 207–212.
P250 Cortical circuits implement optimal integration of context
Ramakrisnan Iyer, Stefan Mihalas
Allen Institute for Brain Science, Seattle, WA, 98109, USA
Correspondence: Stefan Mihalas (stefanm@alleninstitute.org)
BMC Neuroscience 2017, 18 (Suppl 1):P250
Neurons in the primary visual cortex (V1) predominantly respond to a patch of the
visual input, their classical receptive field. These responses are modulated by the
visual input in the surround [1]. This reflects the fact that features in natural
scenes do not occur in isolation: lines, surfaces are generally continuous, and the
surround provides context for the information in the classical receptive field. It
is generally assumed that the information in the near surround is transmitted via
lateral connections, between neurons in the same area [1]. A series of large scale
efforts have recently described the relation between the lateral connectivity and
visual evoked responses and found like-to-like connectivity between excitatory neurons
[2, 3]. Additionally, specific cell type connectivity for inhibitory neuron types
has been described [4]. However current normative models of cortical function rely
on sparsity [5], saliency [6] predict functional inhibition between similarly tuned
neurons. What computations are consistent with the observed structure of the lateral
connections between the excitatory and diverse types of inhibitory neurons? We combined
natural scene statistics [7] and mouse V1 neuron responses [8] to compute the lateral
connections and computations of individual neurons which would optimally integrate
information from the classical receptive field with that from the surround. The direct
implementation requires single neurons to make complex computations on their inputs.
While it is possible for such computations to be implemented by the dendritic trees,
we show that an approximation can be achieved with relatively simple neurons. We show
that this network has “like-to-like” lateral connections between excitatory neurons
similar to the observed one [2, 3], distance dependence of connections similar to
the observed ones [9], and requires three classes of inhibitory neurons: one performing
local normalization, one surround inhibition, and one gating the inhibition from the
surround, similar to anatomical [4] and physiological studies. This method generates
an entire connectivity matrix for lateral connections in a layer in a purely unsupervised
fashion, such that it generates testable hypotheses for connectome studies. Additionally,
when these lateral connections are implemented in a neuronal network the reconstruction
of natural scenes is significantly improved. For images with different statistics,
such as independent and identically distributed random patches, using a natural scene
prior hurts reconstruction. However, an additional gating mechanism allows optimal
reconstruction for this type of features as well. We hypothesize that this computation:
optimal integration of contextual cues is a general property of cortical circuits,
and the rules constructed for mouse V1 generalize to other areas and species.
References
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9. Levy R. B. and Reyes A. D. Spatial Profile of Excitatory and Inhibitory Synaptic
Connectivity in Mouse Primary Auditory Cortex. Journal of Neuroscience, 32(16), 2012.
P251 Neural cross-frequency coupling functions in the resting state with eyes open
and eyes closed
Valentina Ticcinelli1, Tomislav Stankovski1,2, Peter V. E. McClintock1 and Aneta Stefanovska1
1Department of Physics, Lancaster University, Lancaster LA1 4YB, United Kingdom; 2Faculty
of Medicine, Ss Cyril and Methodius University, Skopje 1000, Macedonia
Correspondence: Valentina Ticcinelli (v.ticcinelli@lancaster.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P251
The electrophysiological activity of the brain emerges from interactions between large-scale
neuronal ensembles [1], and is regulated by different types of cross-frequency coupling
[2–5]. The latter are usually characterised by their coupling strength and directionality
[2–4]. However, it is also possible to investigate the functional mechanisms of the
interaction [5]. We introduce dynamical Bayesian inference for estimation of the coupling
functions of neural oscillations in the presence of noise [6–8]. All of the possible
phase-to-phase interactions between the oscillators of the network are inferred. Thus,
the coupling can be decomposed into its partial functional contributions [8]. This
allows one e.g. to isolate the estimated direct coupling between two nodes from the
possible common coupling and self-coupling also involved in the interaction. As an
illustrative example, the method is applied to characterization of the phase-to-phase
neural coupling functions in electroencephalographic (EEG) data from the Neurophysiological
Biomarker Toolbox (NBT) dataset [9]. Comparisons are made between the resting states
with the eyes open (EO) and eyes closed (EC). We constructed the network by investigating
the couplings between delta and alpha waves extracted from any pair of probes within
the 10–20 measuring system; and we used phase-shuffled surrogates to test the significance
of the inferred direct coupling strength. In doing so, we confirmed the earlier observation
that the direct coupling is stronger in the EC state [10]. By investigating the form
of the coupling functions, we were able to evaluate both inter-subject and intra-subject
variability. We also evaluated the time variability of the form of the coupling. We
showed that the coupling function is significantly less variable for the EC state.
In a wider context, the method could in principle be applied to any pair of coupled
oscillations in the same way as in the example shown here.
Acknowledgements
This work was supported by the Engineering and Physical Sciences Research Council
(UK) [Grant No.EP/100999X1], by the EU projects BRACCIA [517133] and COSMOS [642563],
and by the Action Medical Research (UK) project MASDA [GN1963]. VT is supported by
a PhD grant from the Department of Physics, Lancaster University. Our grateful thanks
are due to Klaus Lehnertz and Andreas Daffertshofer for valuable discussions. We also
thank Lall Hussain for pointing out the toolbox and for his initial contribution to
the work, and Lars Michels and the NBT research team for sharing their data and for
most useful comments.
References
1. Breakspear, M., Heitmann, S., and Daffertshofer, A.: Generative models of cortical
oscillations: neurobiological implications of the Kuramoto model, Front. Human Neurosci.
2010, 4:190.
2. Friston, K. J., Harrison, L., and Penny, W.: Dynamic causal modelling, Neuroimage
2003, 19: 1273–1302
3. Jensen, O. and Colgin, L. L.: Cross-frequency coupling between neuronal oscillations,
Trends Cognit. Sci. 2007, 11: 267–269.
4. Varela, F., Lachaux, J.-P., Rodriguez, E., and Martinerie, J. (). The brainweb:
phase synchronization and large-scale integration, Nat. Rev. Neurosci. 2001, 2: 229–239.
5. Kralemann, B., Cimponeriu, L., Rosenblum, M., Pikovsky, A., and Mrowka, R.: Phase
dynamics of coupled oscillators reconstructed from data, Phys. Rev. E 2008, 77: 066205.
6. Stankovski, T., Duggento, A., McClintock, P. V. E., and Stefanovska, A.: Inference
of time-evolving coupled dynamical systems in the presence of noise, Phys. Rev. Lett.
2012, 109: 024101.
7. Stankovski, T., Duggento, A., McClintock, P. V., and Stefanovska, A.: A tutorial
on time-evolving dynamical Bayesian inference, The European Physical Journal Special
Topics 2014, 223.13: 2685–2703.
8. Stankovski, T., Ticcinelli, V., McClintock, P. V. E., and Stefanovska, A.: Coupling
functions in networks of oscillators, New J. Phys. 2015, 17: 035002.
9. Neurophysiological Biomarker Toolbox [https://www.nbtwiki.net/]
10. Deco, G., Jirsa, V. K., and McIntosh, A. R.: Emerging concepts for the dynamical
organization of resting-state activity in the brain, Nat. Rev. Neurosci. 2010, 12:
43–56.
P252 Dissecting the total astrocytic potassium current in a computational model
Predrag Janjić1, Dimitar Solev3, Gerald Seifert2, Ljupčo Kocarev1, Christian Steinhäuser2
1Laboratory for Complex Systems and Networks, Macedonian Academy of Sciences and Arts,
Skopje, Macedonia; 2Institute of Cellular Neurosciences, University of Bonn Medical
School, Bonn, Germany; 3Unaffiliated, dimitar.solev@gmail.com
Correspondence: Predrag Janjić (predrag.a.janjic@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P252
Despite the growing experimental evidence about composition of the K+ conductance
in mammalian astroglia [1], the origin of its typical linear whole cell I/V relation
has not been addressed in detail with a computational model. We have used data of
pharmacologically isolated Kir4.1, K2P and Kv4 K+ currents in freshly isolated astrocytes
from mouse hippocampus [2] to describe mathematically equilibrium I/V characteristics
and activation kinetics where applicable, for each current component separately. To
account in more detail for the notable outward current in weakly rectifying Kir4.1
channels, we propose an extension of the Hagiwara model of dominant inwardly-rectifying
Kir current, by adding a residual outward component. Allowing for a separate outward
Kir current component, additive to the standard Boltzman equation we achieved a much
better fit of the Ba2+sensitive current (Fig. 1). Assuming a short-pore structure
of Kir4.1 channels we describe the outward voltage- and concentration-dependence of
Kir4.1 permeability using the transition-state theory of the Eyring reaction-rate
formalism, considering that the Mg2+block shapes the permeability of weak rectifier
channels. Our extended model exposes several parameters whose ranges could be more
precisely estimated by molecular dynamics simulation studies, or with single channel
measurements in targeted point-mutation studies. In addition to describing the steady-state
voltage dependence of K2P currents (represented in our case mostly by currents through
TREK-1 and TREK-2 channels) its activation kinetics has been estimated, which smoothed
out the contribution of this current component in differential models of K+ homeostasis
or other dynamic phenomena. Inactivating 4-AP sensitive currents through Kv4 channels
which are expressed at low density by astrocytes, have been modeled using standard
Hodgkin-Huxley model. Added up, all three modeled current components successfully
described the voltage-dependence of total experimental whole-cell K+ currents. We
exemplified the usefulness of our model by simulating astrocytic currents in elevated
K+ concentration in a single ECS pocket apposing the glial membrane. We believe that
such a detailed model, which separately describes the individual current components,
could be useful in describing the impact of channelopathies underlying altered astrocytic
electrophysiology.
Figure 1. Total Ba2+sensitive current through Kir4.1 channels in an isolated astrocyte
(circles) has been initially fitted by the Hagiwara model to describe the inwardly-rectifying
part (blue line, extending to -120 mV). Residual outward Kir current (red triangles)
obtained after subtracting inward current from the control, has been fitted with a
1-site, 2-barrier reaction rate model (red line). Added together (black line) they
represent the total fit of Kir4.1 current. Both model components describe voltage-
and concentration-dependence. External K+ concentration has been elevated to [K]o = 5 mM
in all measurements, with [K]i = 130 mM. Numerical fit has been limited to -120 mV
due to distortions from external Na+ block for very negative voltages, beyond the
physiological voltage range.
References
1. Seifert G, Henneberger C, Steinhäuser C: Diversity of astrocyte potassium channels
- An update, Brain Res Rev 2017, in-press, available online at: http://dx.doi.org/10.1016/j.brainresbull.2016.12.002
2. Seifert G, Hüttmann K, Binder DK, Hartmann C, Wyczynski A, Neusch C, Steinhäuser
C: Analysis of Astroglial K+ Channel Expression in the Developing Hippocampus Reveals
a Predominant Role of the Kir4.1 Subunit, J. Neurosci 2009; 29 (23): 7474–7488.
P253 Information rate of multiple synaptic release sites with separately released
vesicles during short-term depression
Mehrdad Salmasi1,2,3, Stefan Glasauer1,2,3,4, Martin Stemmler2,5
1Graduate School of Systemic Neurosciences, Ludwig-Maximilian University, Munich,
Germany; 2Bernstein Center for Computational Neuroscience, Munich, Germany; 3German
Center for Vertigo and Balance Disorders, Ludwig-Maximilian University, Munich, Germany;
4Department of Neurology, Ludwig-Maximilian University, Munich, Germany; 5Department
of Biology II, Ludwig-Maximilian University, Munich, Germany
Correspondence: Mehrdad Salmasi (mehrdad.salmasi@lrz.uni-muenchen.de)
BMC Neuroscience 2017, 18 (Suppl 1):P253
Chemical synapses are conduits for much of the brain’s information, but they are inherently
unreliable, with unprovoked, spontaneous release of neurotransmitter alternating with
intermittent unresponsiveness of the synapse to action potentials. A release of vesicle
at a synapse depresses the probability of further releases in the short term. Given
the stochastic nature of synapses, synaptic information efficacy has been used to
quantify information transmission through synapses [1, 2]. Many theoretical approaches
treat the synapse as a static, monolithic communication device. Yet many synapses
have multiple release sites, each subject to separate short-term dynamics. These multiple
sites compensate for the unreliability of individual release sites. Here we seek to
quantify how the number of release sites affects the information efficacy of a synapse
with short-term depression. In addition, we study the trade-off between the reliability
of information transmission and energy consumption at the synapse.
To analyze the amount of information that a neuron can transfer through its release
sites during short-term depression (Fig. 1A), we model each release site as a binary
asymmetric channel whose state (release probability) is determined by its release
history. A Markov chain of state transitions implements short-term depression followed
by exponential recovery of the release site. It is assumed that the release sites
are independent and the released vesicles are separable. We prove that the mutual
information rate between the input spike process, X, and the release outcomes of the
release sites, (Y
1, Y
2,…, Y
K
), is equal to the statistical average over the information rates of an equivalent
communication channel for every possible state combination of the release sites. Using
the derived expression, we show the compensatory effect of having multiple release
sites in Fig. 1B. The dashed black line connects the capacity values of the neuron
for different number of release sites. For a neuron with larger number of release
sites, capacity is achieved at higher input spike rates. We then normalize the information
rate by the energy consumed for the release and assess the compromise between the
energy and the information rate of the neuron.
Figure 1. A. Information transmission through multiple release sites of a neuron.
B. Mutual information rate of the neuron’s release site(s) as a function of normalized
input spike rate, for different numbers of release sites. The normalized spike rate
is the probability of having a spike in a time bin, and is equal to one when there
is one spike per time bin
Acknowledgements
This work was supported by the BMBF grant 01EO1401 (German Center for Vertigo and
Balance Disorders).
References
1. London M, Schreibman A, Häusser M, Larkum ME, Segev I: The information efficacy
of a synapse. Nature Neuroscience 2002, 5(4), 332-340.
2. Fuhrmann G, Segev I, Markram H, Tsodyks M: Coding of temporal information by activity-dependent
synapses. Journal of Neurophysiology 2002, 87(1), 140–148.
P254 Properties of recurrent networks at maximum capacity for storing sequences of
network states
Danke Zhang, Chi Zhang, Armen Stepanyants
Department of Physics and Center for Interdisciplinary Research on Complex Systems,
Northeastern University, Boston, MA 02115, USA
Correspondence: Armen Stepanyants (a.stepanyants@neu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P254
The ability of neural networks to associate successive states of network activity
lies at the basis of various cognitive functions. In this study, we hypothesized that
ubiquitous features of cortical network structure and dynamics develop as a result
of continual memory storage. To test this hypothesis, we consider recurrent McCulloch
and Pitts networks in which neurons may belong to different classes defined by their
excitatory or inhibitory nature, firing probability, and robustness to noise. Learning
in the network is mediated by changes in connection weights in the presence of constraints
on the l
1-norms of presynaptic weights of individual neurons.
To determine the memory storage capacity of the network we train the network on a
set of random sequences of different lengths and subsequently test the retrieval of
learned memories. The maximum (critical) capacity of the network is defined as the
sequence length for which the success rate in memory retrieval equals 0.5. To retrieve
a learned sequence, we initialize the network state at the beginning of the sequence
and monitor memory playout. The sequence is considered to be retrieved successfully
if the network states during the retrieval do not deviate substantially from the learned
sequence. In practice, there is no need to precisely define the threshold amount of
deviation. This is because for large networks, e.g. N > 100 neurons, the Hamming distance
between the final states of the learned and retrieved sequences either remains within
~N
0.5, or diverges to ~N. Memory retrieval in the former case is said to be successful,
while in the latter case the memory could not be retrieved.
We performed numerical simulations for networks of N = 1000 neurons and also solved
the problem theoretically in the thermodynamic limit by using the replica theory [1–3].
The results show that critical networks have unique structural and dynamic properties
which resemble those observed in many cortical systems from cerebellum to neocortex
to hippocampus. First, we find that, consistent with the experimental data, probability
of inhibitory connections in critical networks is greater than 0.5, whereas excitatory
connectivity is sparse with connection probabilities less than 0.5. Second, we compare
the distributions of connection weights in critical networks with the distributions
of amplitudes of excitatory and inhibitory postsynaptic potentials. Due to the presence
of very strong connections, the latter distributions typically have long, super-exponential
tails. We show that in critical networks this feature can result from the heterogeneity
of properties of individual neurons. Third, we find that with increasing robustness,
critical networks exhibit a phase transition from networks with ordered dynamics quickly
terminating in a frozen state, to networks with chaotic dynamics during which neurons
exhibit irregular and correlated firing activity with average correlation coefficients
in the 0.1–0.2 range. Finally, we show that the observed transition is accompanied
with the emergence of neuron clusters, existence of which is suggested by recent experimental
studies [4]. These results are consistent with the idea that cortical networks are
operating in a critical state configured at the edge of order-to-chaos phase transition.
Acknowledgements
This work is supported by Air Force grant FA9550-15-1-0398 and NSF grant IIS-1526642
References
1. Brunel N, Hakim V, Isope P, Nadal JP, Barbour B: Optimal information storage and
the distribution of synaptic weights: perceptron versus Purkinje cell. Neuron 2004,
43(5):745-757.
2. Chapeton J, Gala R, Stepanyants A: Effects of homeostatic constraints on associative
memory storage and synaptic connectivity of cortical circuits. Frontiers in computational
neuroscience 2015, 9:74.
3. Chapeton J, Fares T, LaSota D, Stepanyants A: Efficient associative memory storage
in cortical circuits of inhibitory and excitatory neurons. Proc Natl Acad Sci U S
A 2012, 109(51):E3614–3622.
4. Perin R, Berger TK, Markram H: A synaptic organizing principle for cortical neuronal
groups. Proc Natl Acad Sci U S A 2011, 108(13):5419–5424.
P255 Modulation of epileptic activity in thalamo-cortical networks by input from the
cerebellar nuclei
Julia Goncharenko1, Lieke Kros2, Neil Davey1, Christoph Metzner1, Chris de Zeeuw2,
Freek Hoebeek2, Volker Steuber1
1Centre for Computer Science and Informatics Research, University of Hertfordshire,
Hatfield, AL10 9AB, UK; 2Department of Neuroscience, Erasmus MC, Wytemaweg 80, 3015
CN, Rotterdam, the Netherlands
Correspondence: Julia Goncharenko (i.goncharenko@herts.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P255
Epilepsy is one of the most prevalent neurological diseases in humans, affecting people
of all ages. One of the most common forms of epilepsy in children is absence epilepsy
[1]. Characteristic symptoms of absence epilepsy are sudden seizures that are accompanied
by periods of behavioral arrest and impaired consciousness [1]. As in other forms
of epilepsy, these seizures are electrophysiologically described by neuronal oscillations
in thalamo-cortical networks and appear as generalized spike-and wave discharges (GSWDs)
in the electroencephalogram (EEG) [2]. Oscillatory activity in cerebral cortex and
thalamus can be caused by excessive inhibition in thalamus or by excessive cortical
activity [2]. It has been suggested that the initiation of absence seizures can be
triggered by events that switch neuronal activity in thalamo-cortical networks from
normal asynchronous activity to synchronised oscillations [2].
Previous experimental studies have shown that oscillatory activity in thalamo-cortical
networks and the accompanying GSWDs can be disrupted by stimulation of the thalamus
[3]. Recently, it has been found that optogenetic activation of neurons in the cerebellar
nuclei (CN) is a powerful tool to stop epileptic absence seizures using a closed-loop
system in two unrelated mouse models [4]. Due to their anatomical bottleneck location,
CN neurons can control the balance of excitation and inhibition in thalamus, resetting
the oscillatory activity in thalamo-cortical loops. However, the mechanism underlying
the disruption of thalamo-cortical oscillations and absence seizures by stimulation
of the CN remains unknown.
Here we use computer simulations to investigate the mechanisms underlying the termination
of absence seizures by optogenetic stimulation of CN neurons. We simulate a thalamo-cortical
network model of adaptive exponential integrate-and-fire neurons, displaying complex
intrinsic properties such as low-threshold spiking, regular spiking, fast spiking
and adaptation [5]. The network activity can exhibit oscillatory or asynchronous irregular
(AI) dynamics, depending on the level of adaptation in cortical cells [5]. We use
electrophysiologically recorded spike trains that result from optogenetic activation
of CN neurons in mouse models of absence epilepsy as input to the network model to
analyse the mechanism of reverting abnormal oscillatory activity to the normal AI
state. Our results illustrate how input from the CN can control oscillatory activity
in thalamo-cortical networks and therefore provide a mechanism to terminate epileptic
absence seizures.
References
1. Berg AT, Berkovic SF, Brodie MJ, et al.: Revised terminology and concepts for organization
of seizures and epilepsies: report of the ILAE Commission on Classification and Terminology,
2005–2009. Epilepsia 2010, 51:676–685.
2. Snead OC III: Basic mechanisms of generalized absence seizures. Ann Neurol 1995,
37(2):146–157.
3. Paz JT, Davidson TJ, Frechette ES, et al.: Closed-loop optogenetic control of thalamus
as a tool for interrupting seizures after cortical injury. Nat Neurosci 2013, 16:64–70.
4. Kros L, Eelkman Roda OHJ, Spanke JK, et al.: Cerebellar Output Controls Generalised
Spike-and-Wave Discharge Occurrence. Ann Neurol 2015, 77(6):1027–1049.
5. Destexhe A.: Self-sustained asynchronous irregular states and up-down states in
thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons.
J Comput Neurosci 2009, 27:493–506.
P256 The effect of homeostatic structural plasticity on associative memory in a network
with spike-time dependent inhibitory synaptic plasticity
Ankur Sinha, Christoph Metzner, Roderick Adams, Michael Schmuker, Neil Davey, Volker
Steuber
UH Biocomputation Group, University of Hertfordshire, Hatfield, AL10 9AB, UK
Correspondence: Ankur Sinha (a.sinha2@herts.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P256
The stability of neuronal networks that are continuously modified by various activity
dependent processes and their robustness to lesions or deafferentation necessitate
the co-existence of complementary homeostatic mechanisms [1]. Recent research has
studied these homeostatic plasticity mechanisms using both experiments and computational
modelling.
In a computational study of homeostatic synaptic plasticity, Vogels and collaborators
showed that inhibitory synaptic plasticity governed by a symmetric spike timing dependent
plasticity rule successfully stabilises a spiking neuronal network to an asynchronous
irregular (AI) state, as is observed in the cortex [2]. The proposed model also permitted
the storage and recall of non-attractor Hebbian associative memories in the network.
Butz and van Ooyen recently presented a spiking neural network model of homeostatic
structural plasticity [3]. In their study, neurons in the network attempt to maintain
a fixed level of electrical activity by forming or breaking synaptic connections as
required. The structural reorganisation of the network is also shown to replicate
experimentally observed aspects of the restructuring of the visual cortex following
deafferentation by focal retinal lesions [4, 5].
In the present study, we investigate the capacity of a cortical network model balanced
by homeostatic inhibitory plasticity to store and recall non-attractor Hebbian associative
memories. Extending our previous work [6], we investigate the functional effect of
homeostatic structural plasticity on associative memory performance during network
deafferentation and repair. We explore the interaction between the two homeostatic
mechanisms, inhibitory spike-time dependent synaptic plasticity and structural plasticity,
and investigate how the experimentally observed AI state is affected by the coexistence
of these two homeostatic mechanisms that operate on different time scales. Furthermore,
we discuss enhancements to the model of structural plasticity aimed at increasing
biological plausibility and study their effect on memory capacity. Finally, we report
on the variation in associative memory performance during network deafferentation
and repair, and discuss the parameters that affect it.
References
1. Turrigiano GG: Homeostatic plasticity in neuronal networks: the more things change,
the more they stay the same. Trends in neurosciences 1999, 22(5): 221–227.
2. Vogels T, Sprekeler H, Zenke F, Clopath C, Gerstner W: Inhibitory plasticity balances
excitation and inhibition in sensory pathways and memory networks. Science 2011, 334:1569–1573.
3. Butz M, van Ooyen A: A simple rule for dendritic Spine and axonal bouton formation
can account for cortical reorganization after focal retinal lesions. PLoS Comput Biol.
2013, 9(10): e1003259
4. Keck T, Mrsic-Flogel TD, Afonso MV, Eysel UT, Bonhoeffer T, Hübener M: Massive
restructuring of neuronal circuits during functional reorganization of adult visual
cortex. Nature Neuroscience 2008, 11(10): 1162–1167.
5. Yamahachi H, Marik SA, McManus JN, Denk W, Gilbert CD: Rapid axonal sprouting and
pruning accompany functional reorganization in primary visual cortex. Neuron 2009,
64(5): 719–729.
6. Sinha A, Davey N, Adams R, Steuber V: Structural plasticity and associative memory
in balanced neural networks with spike-time dependent inhibitory plasticity. BMC Neuroscience
2015, 16(1): P235.
P257 The dependence of arithmetic operations on input location in cerebellar nucleus
and cortical pyramidal neurons
Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Michael Schmuker,
Volker Steuber
Centre for Computer Science and Informatics Research, University of Hertfordshire,
Hatfield, AL10 9AB, UK
Correspondence: Maria Psarrou (m.psarrou@herts.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P257
Neurons are constantly bombarded with numerous synaptic signals, which are integrated
in order to generate output spikes. A simple way to depict neuronal computations is
to plot the relationship between the neuronal input rate and the corresponding output
spike rate, that is, the Input - Output relationship (I-O, or transfer function) [1].
A change in the slope or gain of the I-O curve in the presence of different cellular
and synaptic mechanisms, such as synaptic noise, shunting inhibition or synaptic plasticity
is an indicator of ongoing multiplicative operations [1–4]. Gain modulation is a brain-wide
principle of neuronal computation, enabling nonlinear combinations of sensory and
cognitive information. An essential component of gain modulation is that a modulatory
input alters the sensitivity of the neuron to the original (driving) input, without
changing its selectivity [5]. Different nonlinearities in the relationships between
input firing rate, excitatory synaptic conductance and output firing rate have been
shown to underlie gain modulation [2, 4]. In the present study, we investigate in
two different types of neurons whether the dendritic location of excitatory input
affects the arithmetic operation performed by different modulatory inhibitory inputs.
We used two well described morphologically realistic conductance based models, a cerebellar
nucleus (CN) neuron model [6] and a layer V pyramidal neuron model [7], and we explore
various driving and modulatory input conditions. Modulatory input was provided either
by distributed synaptic inhibitory input or a tonic somatic inhibitory conductance.
When the driving and modulatory input were both of synaptic nature, we observed a
correlation between the distance of the excitatory driving input from the soma and
the extent of the multiplicative gain change in both the CN and the layer V pyramidal
neurons. In the CN neuron, we found that excitatory inputs underwent additive operations
when delivered in somatic and perisomatic areas, and multiplications when delivered
to distal dendritic areas. In contrast, in the layer V pyramidal neuron excitatory
driving input was always multiplied, independent of the synapse location. In all cases
where inputs underwent multiplicative operations, the mapping between synaptic excitatory
conductance and output firing rate revealed a nonlinearity, with more pronounced nonlinearities
due to dendritic saturation in distal synaptic locations corresponding to larger multiplicative
gain changes. To show that these non-linear mappings between input conductance and
output rate were the basis of the multiplicative gain changes, we drove the two neuronal
types with excitatory current injections, at the soma or different dendritic locations,
in the presence of modulatory tonic somatic inhibition. In this case, the arithmetic
operations performed in all distinct neuronal locations were additive shifts. Moreover,
synaptic inhibition had a greater effect on neuronal output than somatic tonic inhibition.
Our results indicate that the location and the nature of excitatory inputs affect
in a systematic way whether the input undergoes a multiplicative or additive operation.
The extent of these operations is also related to the nature of the inhibitory input.
Furthermore, different neuronal types might perform different operations when the
inputs are received in their perisomatic areas.
References
1. Silver RA. Neuronal arithmetic. Nat Rev Neurosci. 2010; 11:474–89.
2. Prescott S a, De Koninck Y. Gain control of firing rate by shunting inhibition:
roles of synaptic noise and dendritic saturation. Proc. Natl. Acad. Sci. U. S. A.
2003; 100:2076–81.
3. Chance FS, Abbott LF, Reyes AD. Gain modulation from background synaptic input.
Neuron 2002; 35:773–82.
4. Rothman JS, Cathala L, Steuber V, Silver RA. Synaptic depression enables neuronal
gain control. Nature 2009; 457:1015–8.
5. Salinas E, Sejnowski TJ. Gain modulation in the central nervous system: where behavior,
neurophysiology, and computation meet. Neuroscientist 2001; 7:430–40.
6. Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D. Determinants of
synaptic integration and heterogeneity in rebound firing explored with data-driven
models of deep cerebellar nucleus cells. J. Comput. Neurosci. 2011; 30:633–58.
7. Hay E, Hill S, Schürmann F, Markram H, Segev I. Models of neocortical layer 5b
pyramidal cells capturing a wide range of dendritic and perisomatic active properties.
PLoS Comput. Biol. 2011; 7
P258 A Framework for Automated Validation and Comparison of Models of Neurophysiological
and Neurocognitive Biomarkers of Psychiatric Disorders
Christoph Metzner1, Achim Schweikard2, Tuomo Mäki-Marttunen3, Bartosz Zurowski4 and
Volker Steuber1
1Centre for Computer Science and Informatics Research, University of Hertfordshire,
College Lane, Hatfield, AL10 9AB, United Kingdom; 2Institute for Robotics and Cognitive
Systems, University of Luebeck, Luebeck, 23562, Germany; 3NORMENT, Institute of Clinical
Medicine, University of Oslo, Oslo, Norway; 4Department of Psychiatry, University
of Luebeck, Schleswig-Holstein, Luebeck, 23562, Germany
Correspondence: Christoph Metzner (c.metzner@herts.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P258
Research on psychiatric disorders has gradually shifted its focus from complex clinical
phenotypes towards the identification of biomarkers and endophenotypic measures. Computational
approaches have gained significantly more attention over the last years, and this
has led to the emergence of ‘Computational Psychiatry’ as an independent discipline.
Computational modelling of biomarkers promises to more readily shed light on the mechanisms
underlying disorders and to facilitate the discovery of novel medications [1]. However,
in order to develop a computational model, scientists need to have an in-depth understanding
of the current, relevant experimental data, the current state of computational modeling
and the state-of-the-art of statistical testing. Based on this knowledge, they have
to choose the appropriate criteria with which the model predictions and experimental
observations will be compared [2]. In a field where both the number of experimental
and computational studies grows rapidly, as is the case for psychiatry, this becomes
more and more impracticable. Omar et al. therefore proposed a framework for automated
validation of scientific models, SciUnit [3]. Here, we propose to adopt this framework
for the computational psychiatry community and to collaboratively build common repositories
of experimental observations, computational models, test suites and tools. As a case
in point, we have implemented test suites for auditory steady-state response deficits
in schizophrenic patients, which are based on observations from several experimental
studies [4–6], and we demonstrate how existing computational models [6, 7] can be
validated against these observations and compared against each other. We have included
sets of observations from three experimental studies, which concur on most findings
but also disagree on some. This allows us to demonstrate the usefulness of our approach
in highlighting and clarifying existing, potentially conflicting, experimental data.
We have included computational models that not only comprise biophysically detailed
as well as abstract models, but that also differ in implementation (native Python
vs. Genesis vs NeuroML2), in order to demonstrate the flexibility of the approach.
Furthermore, this additionally allows us to showcase the ability of the framework
to compare models against each other based on a set of experimental observations.
Furthermore, our approach enables us to assess the variability of the produced model
output, and therefore the robustness of the findings, by generating a distribution
of model instances where certain parameters, such as the precise timing of noise (however,
not strength and type of noise) or the precise connectivity (however, not the distribution
of connections) vary, which then are used to produce a distribution of model outputs.
This can inform on the robustness of the findings and be compared against the variability
of experimental observations.
References
1. Siekmeier, P.: Computational modeling of psychiatric illnesses via well-defined
neurophysiological and neurocognitive biomarkers. Neurosci Biobehav Rev 2015, 57:
365–380
2. Gerkin, R.C. and Omar, C.: NeuroUnit: Validation Tests for Neuroscience Models.
Front. Neuroinform. Conference Abstract: Neuroinformatics 2013
3. Omar, C., Aldrich, J., and Gerkin, R.C.: Collaborative infrastructure for test-driven
scientific model validation. In CompanionProceedings of the 36th International Conference
on Software Engineering, ACM, 2014.
4. Kwon J.S., O’Donnell B.F., Wallenstein G.V., Greene R.W., Hirayasu Y., Nestor P.G.,
Hasselmo M.E., Potts G.F., Shenton M.E., and McCarley R.W..: Gamma frequency–range
abnormalities to auditory stimulation in schizophrenia. JAMA Psychiatry 1999, 56(11):1001–1005
5. Krishnan, G.P., Hetrick, W.P., Brenner, C.A., Shekhar, A., Steffen, A.N., and O’Donnell,
B.F.: Steady state and induced auditory gamma deficits in schizophrenia. Neuroimage
2009 47(4):1711–1719
6. Vierling-Claassen, D., Siekmeier, P., Stufflebeam, S., and Kopell, N.: Modeling
GABA alterations in schizophrenia: a link between impaired inhibition and altered
gamma and beta range auditory entrainment.
J Neurophysiol 2008, 99(5):2656–2671
7. Metzner, C., Schweikard, A. and Zurowski, B.: Multi-factorial modeling of impairment
of evoked gamma range oscillations in schizophrenia. Front Comp Neurosci 2016, 10
P259 Synergetic and redundant information flow in dynamical systems: an operative
definition based on prediction
Daniele Marinazzo1, Luca Faes2, Sebastiano Stramaglia3
1Department of Data Analysis, Ghent University, Ghent, B9000, Belgium; 2BIOtech, Dept.
of Industrial Engineering, University of Trento, and IRCS-PAT FBK, 38010 Trento, Italy;
3Dipartimento di Fisica, Università degli Studi Aldo Moro, Bari, and INFN, Sezione
di Bari, 70123 Bari, Italy
Correspondence: Daniele Marinazzo (daniele.marinazzo@ugent.be)
BMC Neuroscience 2017, 18 (Suppl 1):P259
Information theoretic treatment of groups of correlated degrees of freedom can reveal
their functional roles as memory structures or information processing units. Furthermore,
by looking at the common amount of information shared in a group of variables we can
tell whether they are mutually redundant or synergetic. The application of these insights
to identify functional connectivity structure is a promising line of research. Another
topic of general interest is the understanding of couplings between dynamical systems
and their parts. Transfer entropy and Granger causality are popular approaches used
to distinguish effectively driving and responding elements and to detect asymmetry
in the interaction of subsystems. These two methods can be unified under some conditions,
opening new computational and methodological perspectives. Several techniques can
evidence sets of variables which provide information for the future state of the target.
This information can be synergetic or redundant, with important implication on our
understanding of the functioning of the dynamical system under analysis.
Importantly, not taking into account the joint dynamical influence of two or more
variables can lead to bias and wrong estimations of links (false positive and false
negatives).
In the field of information theory these concepts are often defined and studied by
means of axioms. Here we will instead use an operative definition based on reduction
in variance, using the unnormalized version of Granger causality. We will present
an application to simulated datasets and neuroimaging data, such as the one depicted
in Figure 1, where average redundant and synergetic contributions, computed on 116
brain regions from 90 subjects from the Human Connectome Project dataset are depicted.
Figure 1. Synergetic and redundant influences between 116 brain regions from the AAL
template, averaged over 90 subjects from the HCP dataset. A: matrix of synergetic/redundant
contributions (top) and dendrograms (bottom). B: Redundant and synergetic contributions
for two representative regions, a cortical one (top) and a cerebellar one (bottom)
Reference
1. Stramaglia S, Angelini L, Wu G, Cortes J, Faes L, Marinazzo D: Synergetic and redundant
information flow detected by unnormalized Granger causality: application to resting
state fMRI. IEEE Trans. Biomed. Eng. 2016, 63 (12):2518–2524.
P260 Forming and Using Hierarchical Cognitive Maps: a Neural Network Model
Henry O. C. Jordan, Simon M. Stringer
OFTNAI, Dept. Experimental Psychology, University of Oxford, South Parks Road, OX1
3UD, Oxford, UK
Correspondence: Henry O. C. Jordan (hocjordan@live.co.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P260
Clear evidence exists that model-based planning using cognitive maps occurs in mice
and humans [1] and such planning has been modelled by researchers such as Gaussier
et al.[2] and Ponulak & Hopfield [3] using a gradient or propagating-wave approach.
It is also well-known that planning can be hierarchical [4]; such hierarchical planning
has been explored by computer scientists [5, 6] but almost exclusively in model-free
reinforcement learning tasks. We combine these strands of research to create an unsupervised
neural network model of hierarchical planning using cognitive maps [1, 3] and policy-based
option discovery [6]. Our model:
Receives sensory information from a simulated agent as it explores a grid environment.
Stores the structure of that environment within recurrent neural connections as a
cognitive map.
Uses this internal map to reach goal states via a propagating wave front mechanism.
Solves a set of planning tasks and identifies common elements (‘options’) within those
solutions.
Uses these common elements (‘options’) to speed up the planning process for further
tasks.
Our model plans by causing a wave of neural activation to propagate from the goal
state to the agent’s current state. Learned options act as shortcuts for this wave,
allowing the agent to speed up navigation through well-travelled areas (Fig. 1). We
also demonstrate that options provide more benefit in larger, more structured environments.
Conclusion: Previous work [5, 6] has shown that including options increases an agent’s
ability to accumulate reward. In contrast, we show that learning and using options
can instead increase the processing-per-step efficiency of action selection when making
decisions in a learned environment. Our model also demonstrates that such hierarchical
learning and planning can be performed by an unsupervised neural network and therefore
hints at a biological implementation.
Figure 1. Planning per Action, with (+) and without (-) learned options. This box
plot shows how many time steps an agent requires to select its next action, averaged
over 100 trials in each of four different environments: an open environment without
obstacles, a structured maze with four rooms, a big open environment with 4x as many
states, and a big maze
Acknowledgements
We thank the Oxford Foundation for Theoretical Neuroscience and AI (OFTNAI) for supporting
this work.
References
1. Dolan RJ, Dayan P. Goals and habits in the brain. Neuron. 2013. p. 312–25.
2. Gaussier P, Revel A, Banquet JP, Babeau V. From view cells and place cells to cognitive
map learning: Processing stages of the hippocampal system. Biol. Cybern. 2002;86:15–28.
3. Ponulak F, Hopfield JJ. Rapid, parallel path planning by propagating wavefronts
of spiking neural activity. Front. Comput. Neurosci. 2013;7:98.
4. Botvinick MM, Niv Y, Barto AC. Hierarchically organized behavior and its neural
foundations: a reinforcement learning perspective. Cognition. 2009;113:262–80.
5. Sutton RS, Precup D, Singh S. Between MDPs and semi-MDPs: A framework for temporal
abstraction in reinforcement learning. Artif. Intell. 1999;112:181–211.
6. Girgin S, Polat F, Alhajj R. Improving reinforcement learning by using sequence
trees. Mach. Learn. 2010;81:283–331.
P261 Harmonic SSEP Spectra are Determined by Modulation of Population Firing Rate
- a Modeling Study
Elżbieta Gajewska-Dendek, Piotr Suffczyński
Department of Biomedical Physics, Institute of Experimental Physics, University of
Warsaw, Warsaw, 02-093 Poland
Correspondence: Elżbieta Gajewska-Dendek (egd@fuw.edu.pl)
BMC Neuroscience 2017, 18 (Suppl 1):P261
Steady State Evoked Potentials (SSEP) are EEG signal responses to periodically changing
stimulus. SSEPs consist of a strong fundamental response and sometimes also its harmonic
and subharmonic frequencies. The SSEP can be observed in visual, auditory and somatosensory
modalities. Despite multiple applications of SSEP in cognitive neuroscience, clinical
neuroscience and brain computer interfaces (BCI), some basic questions concerning
this phenomenon still remain open: what is the physiological mechanism of generation
of harmonic spectra and what determines relative spectral power of SSEP at fundamental
frequency and at the harmonics.
The aim of this study was to investigate the SSEP generation mechanisms and its characteristic
with a realistic computational model. The presented results are an extension from
previously published version [1]. The model consists of single compartment excitatory
and inhibitory cells of the Hodgkin-Huxley type, arranged in multiple cortical columns.
The network contains 8000 neurons and more than 106 synapses, based on connectivity
data from cat primary visual cortex. The sensory stimulus is modeled as 7 to 50 Hz
square or sine modulated rate of Poisson process. The simulated EEG signal is as a
sum of synaptic currents of all pyramidal neurons. Additionally, for the square stimulus,
we varied duty cycle: 50% (default), 33% and 66% in order to investigate whether SSEP
spectral power depends on magnitude of ON and OFF responses or on the overall energy
of the stimulus. The magnitude of transient responses was determined by firing adaptation
strength of excitatory cells by modulating the conductivity of Ca-dependent potassium
current. We compare the simulation data with experimental EEG recordings obtained
in somatosensory cortex during vibrotactile stimulation as well as from visual cortex
in response to flickering stimuli. The spectra of modeled SSEP exhibit fundamental
and higher harmonic frequencies, similarly to experimental observations. The neurons
firing rates are approximately constant and much lower than stimulus frequencies.
The network oscillation emerges from irregular and sparse firing of individual neurons
but in phase with the population fundamental rhythm. The harmonic frequencies cannot
be directly related to firing of individual neurons but rather to EEG waveform resulting
from overall network activity. Additionally, our modeling study shows that the SSEP
power is dependent on both the stimulus duty cycle and degree of adaptation: in general,
the largest spectral power of dominant frequency was observed for duty cycle 50%,
and medium adaptation strength. The signal energy increased for lower duty cycle (<66%)
and low adaptation, or for higher duty cycle (>33%) and stronger neuronal adaptation.
Reference
1. Gajewska-Dendek E, Suffczyński P: Investigation of SSEP by means of a realistic
computational model of the sensory cortex. In: Villa AEP, Masulli P, Pons RAJ. Artificial
Neural Networks and Machine Learning - ICANN 2016: 25th International Conference on
Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part
I. Springer International Publishing; 2016. p. 532.
P262 Computational measure to account for erroneous neural deactivation when oxygen
supply cannot meet metabolic demand in neuroimaging studies
Nicoladie Tam1, George Zouridakis2, Luca Pollonini2
1Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA;
2Departments of Engineering Technology, Computer Science, and Electrical and Computer
Engineering, University of Houston, Houston, TX, 77204, USA
Correspondence: Nicoladie Tam (nicoladie.tam@unt.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P262
Introduction: Functional near-infrared spectroscopy (fNIRS) is an emerging optical
imaging technique that can detect the neural activation/deactivation based on the
optical absorption characteristics of both oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin
(deoxy-Hb). The supply of oxygen to neural tissues can be affected not only by vasoconstriction
or vasodilation, but also by the limited capacity of the system to supply more oxygen
when demand exceeds the maximum oxygen availability. When this rate-limiting condition
occurs, a decrease in oxygen availability is detected that can be erroneously interpreted
as deactivation. Thus, absolute changes in oxy-/deoxy-Hb concentrations do not always
represent neural activation/deactivation. It is therefore necessary to develop alternative
measures to account for such a paradox, so that a decrease in Hb concentration is
not misinterpreted as deactivation.
Methods: Rather than using absolute oxy-Hb and deoxy-Hb concentration as the metric,
we propose to normalize these measures by the total blood volume (oxy-Hb + deoxy-Hb),
so that they become oxy-Hb/(oxy-Hb + deoxy-Hb) and deoxy-Hb/(oxy-Hb + deoxy-Hb), respectively.
We observed in previous studies [1–4] that the oxygen demand could exceed the oxygen
supply in the cortex for such a motor task. To test the proposed measures of hemodynamic
responses, 75 human subjects were recruited to perform arm movements in two orthogonal
directions (front-back and left-right) while we recorded the hemodynamic responses
from the motor and prefrontal cortices.
Results: Using the proposed measures, we were able to detect the relative changes
in oxy- and deoxy-Hb concentrations in relation to the blood supply (i.e., the total
blood volume). In most circumstances, when deoxy-Hb (oxygen extraction) concentration
increased, oxy-Hb (oxygen delivery) concentration decreased simultaneously. In certain
phases of movement execution, oxygen extraction (deoxy-Hb) appeared to remain constant
after it had increased, while oxygen delivery (oxy-Hb) continued to decrease. When
oxygen availability was restricted, the ability to extract more oxygen was also limited,
resulting in an apparent maxed-out response. The analysis showed that the paradoxical
hemodynamic changes in deoxy-Hb could be compensated by the normalized measures. This
metric could indicate an increase in normalized deoxy-Hb response (oxygen demand),
in spite of a detected decrease in both deoxy-Hb (oxygen extraction) and oxy-Hb (oxygen
delivery) concentrations.
Conclusions: The proposed normalized oxy- and deoxy-Hb measures can correctly detect
relative changes in oxygen demand with respect to the available oxygen (oxy-Hb + deoxy-Hb),
even when oxygen supply cannot meet demand. We have established an alternative measure
to account for the erroneous interpretation of neural deactivation.
References
1. Tam ND, Pollonini L, Zouridakis G: Decoding movement direction using phase-space
analysis of hemodynamic responses to arm movements based on functional near-infrared
spectroscopy. In: 38th Annual International Conference of the IEEE Engineering in
Medicine & Biology Society: August 16-20, 2016 2016; Orlando, FL: IEEE; 2016: 1580–1583.
2. Tam ND, Pollonini L, Zouridakis G: Phase space analysis of hemodynamic responses
to intentional movement directions using functional near-infrared spectroscopy (fNIRS)
optical imaging technique. In: 25th Annual Computational Neuroscience Meeting: CNS-2016:
July 2–7, 2016 2016; Jeju, South Korea; 2016: 54.
3. Tam ND, Zouridakis G: Temporal decoupling of oxy- and deoxy-hemoglobin hemodynamic
responses detected by functional near-infrared spectroscopy (fNIRS). Journal of Biomedical
Engineering and Medical Imaging 2014, 1(2):18–28.
4. Tam ND, Zouridakis G: Decoding movement direction from motor cortex recordings
using near-infrared spectroscopy. In: Infrared Spectroscopy: Theory, Developments
and Applications. edn. Hauppauge, NY: Nova Science Publishers, Inc.; 2014.
P263 Detecting brain hubs following brief mindfulness training
Yi-Yuan Tang1, Rongxiang Tang2
1Department of Psychological Sciences, Texas Tech University, Lubbock, TX 79409, USA;
2Department of Psychological & Brain Sciences, Washington University in St. Louis,
St. Louis, MO 63130, USA
Correspondence: Yi-Yuan Tang (yiyuan.tang@ttu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P263
Many studies have shown that mindfulness training improves attention control, emotion
regulation and cognitive performance through changing brain activity and the efficiency
of brain networks [1–4]. Graph theory analysis could reveal the role of specific functional
areas that are particularly important for integrating information across the whole-brain
networks, called hubs [5, 6]. However, what hubs involve in mindfulness training and
support positive changes are not well understood. Here, we applied a novel graph theory
analysis to resting-state fMRI data to identify brain hubs induced by a brief mindfulness
training - integrative body–mind training (IBMT), which was previously reported in
our series of randomized studies [1–4].
Forty-two (21 ± 1.6 years old) healthy college students were recruited and randomly
assigned to an IBMT group or a relaxation group (RT). The participants had no previous
training experience and received 4 weeks of IBMT or RT training with 30 min per session
for 20 sessions (~10 h training in total). All subjects gave written informed consent
in accordance with the Declaration of Helsinki. The protocol was approved by the local
Institutional Review committee. Neuroimaging data was collected using a 3-Telsa Siemens
Allegra scanner and pre-processed following the standard procedures included slice
timing, motion correction, regression of WM/CSF signals and spatial normalization
[4]. For network parcellation and construction, we used a well-validated parcellation
scheme consisting of 333 cortical parcels that are distributed across the brain and
assigned to 13 different functional networks [5]. We applied network-based approach
towards neuroimaging data and two network measures - global efficiency and participation
coefficient were computed based on literature [6]. Compared to RT, after 10 h training,
IBMT induced significant reduction of global efficiency in the midline default mode
network (DMN) and increased participation coefficient at ventral anterior cingulate
cortex (vACC).
Conclusions: This study utilized a novel graph theory analysis of functional networks
to assess the brain efficiency and participation of hubs following brief mental training.
Consistent with our and other research, our results suggest that brief mindfulness
training IBMT significantly reorganizes DMN activity and network efficiency that may
reallocate more resources for better self-control through the key hub in the vACC.
Acknowledgements
This work was supported by the Office of Naval Research.
References
1. Tang YY, Holzel BK, Posner MI: The neuroscience of mindfulness meditation. Nat
Rev Neurosci. 2015, 16: 213–225.
2. Tang YY, et al.: Short-term meditation training improves attention and self-regulation.
Proceedings of the National Academy of Sciences, USA. 2007, 104:17152–17156.
3. Tang YY, et al.: Central and autonomic nervous system interaction is altered by
short term meditation. Proceedings of the National Academy of Sciences, USA. 2009,
106: 8865–70
4. Tang YY, Tang R, Posner MI: Brief meditation training induces smoking reduction.
Proceedings of the National Academy of Sciences, USA. 2013, 110: 13971–13975.
5. Gordon EM, et al.: Generation and evaluation of a cortical area parcellation from
resting-state correlations. Cerebral Cortex 2016, 26: 288–303.
6. Rubinov M, Sporns O: Complex network measures of brain connectivity: uses and interpretations.
Neuroimage 2010, 52: 1059–1069.
P264 Interplay between propagation delay and frequency of oscillation determines emergent
structures of neuronal networks driven by triplet-based STDP
Mojtaba Madadi Asl1, Alireza Valizadeh1,2, Peter A. Tass3
1Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS),
Zanjan, 45195-1159, Iran; 2School of Cognitive Sciences, Institute for Research in
Fundamental Sciences (IPM), Tehran, 19395-5746, Iran; 3Department of Neurosurgery,
School of Medicine, Stanford University, Stanford, CA, 94305, USA
Correspondence: Mojtaba Madadi Asl (m.madadi@iasbs.ac.ir)
BMC Neuroscience 2017, 18 (Suppl 1):P264
Spike-timing-dependent plasticity (STDP) adjusts synaptic strengths according to the
relative timing of pre- and postsynaptic spikes [1]. The classic STDP rule eliminates
bidirectional connections between two coupled neurons and turns them into unidirectional
connections [2, 3]. As shown recently, by taking into account dendritic and axonal
propagation delays, the conventional pair-based additive STDP may lead to both unidirectional
and bidirectional connections, or decouple neurons by weakening reciprocal connections
in both directions [4]. The triplet-based STDP, however, employs triplets of spikes
that modify synaptic strengths [5]. Hence, the latter captures the effect of frequency
of oscillations on the pre-post pairing. Here, we provide a general theoretical framework
by assuming that the neurons are phase-locked with a phase lag which is determined
by the temporary values of the synaptic strengths, propagation delays, frequency of
oscillation, and the phase sensitivity of the neurons, and explore how the final configuration
of the system can be predicted. In the absence of propagation delays, low-frequency
oscillation leads to unidirectional connection for both pair- and triplet-based STDP.
However, for higher frequencies, the triplet-based model has a tendency to achieve
bidirectional connections, but results for the pair-based are the same as in the low-frequency
regime. We show that employing triplet-based STDP leads to diverse connectivity patterns
of oscillatory neurons in the presence of propagation delays, which qualitatively
differ from the results obtained by pair-based STDP. In particular, large axonal propagation
delay in the high-frequency regime is associated with a stable decoupling of both
reciprocal synapses when the neurons are in a phase-locked state (see Figure 1F).
Figure 1. Theoretical prediction of triplet-based synaptic plasticity modification.
A-C. Low-frequency regime. The colors show the phase lag of spiking of the neurons
derived from the joint phase model and the vector field shows the direction of change
in the joint synaptic strengths. The yellow curves denote the simulated synaptic strengths
for a random initial value. D-F. High-frequency regime. Total time of simulations
is 10 s. The dendritic delay is fixed at 0.2
References
1. Bi GQ, Poo MM: Synaptic modifications in cultured hippocampal neurons: dependence
on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 1998, 18(24):
10464–10472.
2. Song S, Miller KD, Abbott LF: Competitive Hebbian learning through spike-timing-dependent
synaptic plasticity. Nat Neurosci 2000, 3(9): 919–926.
3. Bayati M, Valizadeh A: Effect of synaptic plasticity on the structure and dynamics
of disordered networks of coupled neurons. Phys Rev E 2012,
86(1): 011925.
4. Madadi Asl M, Valizadeh A, Tass PA: Dendritic and Axonal Propagation Delays Determine
Emergent Structures of Neuronal Networks with Plastic Synapses. Sci Rep 2017, 7(39682).
doi: 10.1038/srep39682.
5. Pfister JP, Gerstner W: Triplets of spikes in a model of spike timing-dependent
plasticity. J Neurosci 2006, 26(38): 9673–9682.
P265 Plasticity and network implications of a synaptic LPA-signalling pathway
Andreas Nold1†, Wei Fan2, Sara Konrad1, Heiko Endle2, Johannes Vogt2†, Tatjana Tchumatchenko1†
1Theory of Neural Dynamics, Max Planck Institute for Brain Research, 60438 Frankfurt,
Germany; 2Institute for Microscopic Anatomy and Neurobiology, University Medical Center,
Johannes Gutenberg University, 55131 Mainz, Germany
Correspondence: Andreas Nold (andreas.nold@brain.mpg.de)
†equal contribution
BMC Neuroscience 2017, 18 (Suppl 1):P265
We explore the implications of a synaptic signalling pathway involving the plasticity-related
gene 1 (PRG-1), a molecule which is located at the postsynaptic membrane and modulates
glutamatergic transmission, for the stability of the steady states of cortical circuit
models. An accurate synaptic function is of crucial importance for learning and memory
formation. Recently, the importance of this postsynaptic control element has been
shown for synaptic signalling: Deletion of PRG-1 in mice leads to neuronal hyper excitability
[1], and a single nucleotide polymorphism (SNP) in the PRG-1 gene affecting approx.
5 million European and US citizens is linked to psychiatric disorders such as schizophrenia
[2].
PRG-1 modulates glutamatergic transmission via its ability to take up lysophosphatidic
acid (LPA), which is synthetized by autotaxin, from the synaptic cleft [1, 2]. Inhibition
of LPA-uptake, as present in PRG-1 deficient mice, leads to elevated synaptic LPA
levels which via LPA2-receptors lead to higher levels of presynaptic intracellular
Ca2+, higher vesicle release probabilities, and ultimately to neuronal hyperexcitability.
This pathway was recently suggested to modulate synaptic short-term plasticity properties
in the hippocampus [3]. Presynaptically, the pathway is modulated by glutamate which
stimulates autotaxin (ATX) activity and thereby LPA-synthesis.
Here, we present a synaptic model implementing this LPA-based signalling pathway as
an extension of the classical Tsodyks-Markram model for short-term synaptic plasticity
[4], and explore the implications at a single synapse level, depending on increasing
pre- and postsynaptic firing rates. The former leads to higher levels of LPA production
via ATX, therefore increasing the LPA-concentration in the synaptic cleft, and elevating
the presynaptic Calcium concentration, which leads to higher vesicle release probabilities.
The latter modulates the activity of LPA-uptake via PRG-1. Implications are explored
for short-term facilitation and depression of synapses, both for wild-type, as well
as PRG-1 deficient cases.
We propose an efficient network implementation and analyze how loss of PRG-1 function
affects the steady states of cortical circuits models, characterized by large, sparsely
connected spiking networks in an asynchronous balanced excitation-inhibition regime.
Results for bistable states of the network [5] are presented.
Acknowledgements
We acknowledge financial support from DFG through the CRC1080 and from Max Planck
Society.
References
1. Trimbuch T, Beed P, Vogt J, Schuchmann S, Maier N, Kintscher M, Breustedt J, Schuelke
M, Streu N, Kieselmann O, Brunk I. Synaptic PRG-1 modulates excitatory transmission
via lipid phosphate-mediated signaling. Cell 2009, 138(6):1222–35.
2. Vogt J, Yang JW, Mobascher A, Cheng J, Li Y, Liu X, Baumgart J, Thalman C, Kirischuk
S, Unichenko P, Horta G. Molecular cause and functional impact of altered synaptic
lipid signaling due to a PRG‐1 gene SNP. EMBO Mol Med 2016, 8(1):25–38.
3. Vogt J, Kirischuk S, Unichenko P, Schlüter L, Pelosi A, Endle H, Yang JW, Schmarowski
N, Cheng J, Thalman C, Strauss U. Synaptic Phospholipid Signaling Modulates Axon Outgrowth
via Glutamate-dependent Ca2 + -mediated Molecular Pathways. Cerebral Cortex 2017,
27:131–145.
4. Tsodyks M, Pawelzik K, Markram H. Neural networks with dynamic synapses Neural
Comput 1998, 10(4):821–35.
5. Mongillo G, Hansel D, van Vreeswijk C. Bistability and spatiotemporal irregularity
in neuronal networks with nonlinear synaptic transmission. Phys Rev Lett 2012, 108(15):158101.
P266 Postsynaptic Activity-Dependent Synaptic Scaling Enables the Functional Organization
of Memories
Juliane Herpich1,2, Christian Tetzlaff1,2
1Third Institute of Physics - Biophysics, Department of Computational Neuroscience,
University of Goettingen, Goettingen, Germany; 2Bernstein Center for Computational
Neuroscience, University of Goettingen, Goettingen, Germany
Correspondence: Juliane Herpich (juliane.herpich@phys.uni-goettingen.de)
BMC Neuroscience 2017, 18 (Suppl 1):P266
As known from everyday life, humans are permanently exposed to a variety of sensory
inputs from their environment. Thereby, the ongoing challenge, humans have to deal
with, is to continuously and adaptively respond to these sensory stimulations. On
the neuronal level, modification of synapses (interface between two neurons) is a
weighty mechanism for adapting the response properties of neurons according to their
external stimulation. Hereby, synaptic plasticity is the main mechanism underlying
learning [1–4] and, in combination with a homeostatic mechanism, yields the formation
of strongly interconnected subgroups of neurons [5–7], so-called Hebbian cell assemblies
(CAs) [1]. Such a CA represents the learned memory trace of the corresponding environmental
stimulus [1]. Moreover, dependent on the details of the stimuli, humans exhibit the
remarkable ability to organize memories (i.e. CAs), thus, to connect, generalize,
and discriminate them, which supports the integration of novel stimuli and enables
complex behavior [8, 9]. How these memory organizations are realized on a neuronal
level based on the idea of cell assemblies is still unknown.
Here, we analyze in a theoretical neuronal network model whether the interaction of
synaptic plasticity with different formulations of synaptic scaling [10] fulfills
basic requirements of single synapse dynamics, such as sensitivity to stimulations
and stability in their weight dynamics. Our analyses show that synaptic plasticity
in combination with synaptic scaling dependent on the local (synapse specific) synaptic
weight and the global (dendritic-branch specific) postsynaptic activity suffices the
aforementioned characteristics of synapse dynamics. For simplicity, we abstract the
neuronal dynamics of the network to its respective dynamics in a mean-field model
of two interconnected, homogeneous populations of neurons. These populations serve
as memory representations on the neuronal level (i.e. CAs; strong synaptic weights).
Given different stimulation protocols, these two CAs can be dynamically connected
with each other. Our analyses show that, dependent on the stimulation protocol, the
CAs can be associated, discriminated, or can form a sequence. Remarkably, this rich
repertoire of memory interactions is only present if the CAs themselves are in a matured
state, in other words, if the timescale of the synaptic dynamics within the cell assemblies
are slower than between them. This indicates that the interaction between memory items
strongly depends on the internal state of the cell assemblies involved.
In summary, this work reveals a neuronal network model that is capable to exhibit
with a local (Hebbian synaptic plasticity and synaptic weight-dependent scaling) and
global (postsynaptic activity-dependent scaling) acting learning rule different functional
organizations of memories observed in human behavior.
References
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2. Anderson R.C. and Kulhavy R.W.: Learning Concepts from Definitions 1. American
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9.3:385–390, 1972.
3. Martin S.J., Grimwood P.D., and Morris R.G.M.: Synaptic plasticity and memory:
An evaluation of the hypothesis. Annual Review Neuroscience, 23:649–711, 2000
4. Eichenbaum H.: The cognitive neuroscience of memory: An introduction. Oxford University
Press, 2011.
5. Abbott L.F. and Nelson S.B.: Synaptic plasticity: taming the beast. Nature neuroscience,
3:1178–1183, 2000.
6. Tetzlaff C., Kolodziejski C., Timme M., and Wörgötter F.: Synaptic scaling in combination
with many generic plasticity mechanisms stabilizes circuit connectivity. Frontiers
in Computational Neuroscience, 5:47, 2011.
7. Tetzlaff C., Kolodziejski C., Timme M., Tsodyks M., and Wörgötter F.: Synaptic
scaling enables dynamically distinct short- and long-term memory formation. PLoS Computational
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10. Turrigiano G.G., Leslie K.R., Desai N.S., Rutherford, L.C., and Nelson, S.B.:
Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature,
391
(6670):892–896, 1998.
P267 Input-dependent Synaptic Consolidation of Memory Representations
Jannik Luboeinski1, Christian Tetzlaff2
1Third Institute of Physics - Biophysics, Department of Computational Neuroscience,
University of Göttingen, Göttingen, Germany; 2Bernstein Center for Computational Neuroscience,
University of Göttingen, Göttingen, Germany
Correspondence: Jannik Luboeinski (jannik.luboeinski@uni-goettingen.de)
BMC Neuroscience 2017, 18 (Suppl 1):P267
Long-term synaptic plasticity serves as the main process underlying learning and forming
sustained memory representations in neuronal networks. In conjunction with homeostatic
plasticity, long-term synaptic plasticity leads to the formation of strongly interconnected
subgroups of neurons [1, 2]. These subgroups are referred to as memory representations
or Hebbian cell assemblies [3]. Experiments have identified two different types of
long-term synaptic plasticity [4, 5, 6, 7]: On the one hand, early-phase plasticity
acts on a time scale of a few hours, while, on the other hand, late-phase plasticity
operates on a time scale of more than eight hours up to several days. The transition
of a synapse from the early-phase to the late-phase state is called synaptic consolidation
and requires protein synthesis in the soma of the postsynaptic neuron. These proteins
are transported along the dendrite and lead to a state-switch of those synapses that
have formed a tag beforehand (synaptic tagging and capture hypothesis [2]). Protein
synthesis as well as the formation of tags depend on the ongoing neuronal activity
and external stimuli. However, the impact of synaptic consolidation on the formation
and maintenance of cell assemblies, thus on their stability, is still widely unknown.
Here, we investigate in a theoretical network model under which input conditions synaptic
consolidation yields the stabilization of cell assemblies, transferring the corresponding
synapses from the early-phase to the late-phase state. For this examination, we developed
and analyzed a spiking network model based on a well-known single-synapse model of
the processes of synaptic consolidation [8, 9, 10]. Interestingly, our results show
that the dynamics of synaptic consolidation are not homogeneous within a cell assembly:
namely, the system shows a discrimination between the induction of late-phase plasticity
and early-phase plasticity. In the ‘core’ of the cell assembly, synapses are in the
long-living late-phase state, whereas in the surroundings of the core (‘halo’), synaptic
changes do not overcome the early phase. Further analysis indicates that this discrimination
could imply functionally important principles. In summary, our work provides a further
step in understanding the step-by-step consolidation of memory representations in
biologically realistic neuronal networks.
References
1. Tetzlaff C, Kolodziejski C, Timme M, Wörgötter F: Synaptic scaling in combination
with many generic plasticity mechanisms stabilizes circuit connectivity. Frontiers
in Computational Neuroscience 2011, 5:47.
2. Tetzlaff C, Kolodziejski C, Timme M, Tsodyks M, Wörgötter F: Synaptic scaling enables
dynamically distinct short- and long-term memory formation. PLoS Computational Biology
2013, 9:e1003307.
3. Hebb DO: The Organization of Behaviour, 1st Edition. New York: Wiley; 1949.
4. Abraham WC: How long will long term potentiation last? Phil. Trans. R. Soc. B 2003,
258:735–744.
5. Frey U, Morris R: Synaptic tagging and long-term potentiation. Nature 1997, 385:533–536.
6. Redondo R, Morris RGM: Making memories last: the synaptic tagging and capture hypothesis.
Nat. Rev. Neurosci. 2011, 12:17–30.
7. Sajikumar S, Navakkode S, Frey JU: Identification of compartment- and process-specific
molecules required for “synaptic tagging” during long-term potentiation and long-term
depression in hippocampal CA1. J. Neurosci. 2007, 27:5068–5080.
8. Barrett AB, Billings G, Morris RGM, van Rossum MC: State based model of long-term
potentiation and synaptic tagging and capture. PLoS Comput. Biol. 2009, 5:e1000259.
9. Clopath C, Ziegler L, Vasilaki E, Büsing L, Gerstner W: Tag-trigger-consolidation:
a model of early and late long-term potentiation and depression. PLoS Comput. Biol.
2008, 4:e10000248.
10. Li Y, Kulvicius T, Tetzlaff C: Induction and consolidation of calcium-based homo-
and heterosynaptic potentiation and depression. PLoS One 2016, 11:e0161679.
P268 Why working memory is not a reservoir: the role of transient dynamics and attractors
when processing unreliably timed inputs
Timo Nachstedt1,2, Christian Tetzlaff1,2
1Third Institute of Physics, Universität Göttingen, Göttingen, 37077, Germany; 2Bernstein
Center for Computational Neuroscience, Göttingen, 37077, Germany
Correspondence: Timo Nachstedt (timo.nachstedt@phys.uni-goettingen.de)
BMC Neuroscience 2017, 18 (Suppl 1):P268
Working memory (WM) refers to the ability of humans and animals to store as well as
to process the continuously incoming stream of stimuli and information on short time
scales [1]. The neuronal dynamics implementing these two core functions of WM, to
store and to process information, are still a matter of debate. In particular, it
is unclear whether working memory relies on attractor dynamics [2] or whether it is
realized by transient dynamics [3]. Several pieces of experimental evidence as well
theoretical considerations provide support for both of these seemingly contradictory
hypotheses.
Here, we approach this debate by considering the unreliability of the timing of the
stimuli received by the WM. Quite obviously, when interacting with the environment,
subjects cannot rely on precisely timed input stimuli. The consequence of unreliability
of input stimuli on the operation of WM has been psychologically studied on subjects
performing the N-back task. It has been found that, in this task, introducing unpredictability
of the occurrence timing of the stimuli does not significantly influence the subject’s
performance [4]. Based on this finding, we investigate which kind of neuronal dynamics
enables a network to perform the N-back task with a comparable level of robustness
with respect to variances in the stimuli timing.
The most widely used network model of transient neuronal dynamics is the framework
of reservoir networks [5, 6]. We test the performance of reservoir networks trained
with different learning algorithms and with different feedback topologies on the N-back
task. Interestingly, we find that introducing already small variations in the timing
of the input stimuli reduces the performance of reservoir networks in the N-back task
significantly. We show that the performance can be restored by explicitly training
the network to represent past input stimuli via the activity states of feedback loops.
As this, in turn, effectively introduces attractor states into the network, we conclude
that only by exploiting the properties of both, attractor states as well as of transient
dynamics, a neuronal network is able to achieve a performance comparable to the one
found in working memory experiments. Task-relevant information is stored in attractor
states and processing of information is accomplished by transient dynamics. As a consequence,
we predict that in the N-back task, an explicit recall stimulus should avoid a drop
in performance resulting from introducing delays between the current stimulus and
the execution of the respective action. Thus, we provide an experimentally verifiable
hypothesis about the underlying dynamics of WM ruling out pure transient reservoir
networks as a plausible model.
References
1. Baddeley AD: Working memory: theories, models, and controversies. Annu Rev Psychol
2012, 63:1–29.
2. Riley MR, Constantinidis C: Role of prefrontal persistent activity in working memory.
Front Syst Neurosci 2016, 9:181.
3. Barak O, Sussillo D, Romo R, Tsodyks M, Abbot LF: From fixed points to chaos: three
models of delayed discrimination. Prog Neurobiol 2013, 103:241–222.
4. Koppe G, Gruppe H, Sammer G, Gallhofer B, Kirsch P, Lis S: Temporal unpredictability
of a stimulus sequence affects brain activation differently depending on cognitive
task demands. Neuroimage 2014, 101:236–244.
5. Jaeger H: The “echo state” approach to analyzing and training recurrent neural
networks. Tech. Rep., GMD - German National Research Institute for Computer Science;
2001.
6. Maass W, Natschläger T, Markram H: Real-time computing without stable states: a
new framework for neural computation based on perturbations. Neural Comput 2002, 14:2531–2560.
P269 Application of spike train synchrony measure Spike-contrast to quantify the effect
of bicuculline on cortical networks grown on microelectrode arrays
Manuel Ciba1, Andreas Bahmer2 and Christiane Thielemann1
1Biomems lab, Faculty of Engineering, UAS Aschaffenburg, 63743 Aschaffenburg, Germany;
2Comprehensive Hearing Center, University ENT-Clinic Würzburg, 97080 Würzburg, Germany
Correspondence: Manuel Ciba (manuel.ciba@h-ab.de)
BMC Neuroscience 2017, 18 (Suppl 1):P269
In neurosciences, it is assumed that neuronal information is mainly coded in the timing
of spikes which is why spike trains are typically the data base for further analyzing
procedures [1]. Synchrony is an important parameter since it is related to basic brain
functions [2, 3] as well as to pathological states [4]. In order to quantify synchrony
between two or more spike trains, several methods have been established, being either
time scale dependent or time scale independent.
The use of a new time scale independent and multivariate synchrony measure is proposed,
called Spike-contrast, with the aim to apply it on spike train data sets recorded
from cortical networks with a biochemically induced synchrony increase. The histogram
based approach, calculates a visual contrast of a raster plot across different time
scales to quantify synchrony and is computational efficient when dealing with large
number of parallel spike trains, which makes it suitable for the analysis of big data
volumes recorded from high-density microelectrode arrays (MEA) Although its basic
principal is different to existing time scale independent measures like SPIKE-distance
[5], synchrony values of Spike-contrast and SPIKE-distance show a high correlation
for test data from Poisson spike models and Izhikevich networks. However, their results
diverge, when applied to spike train data containing synchronized bursts made of non-synchronized
spikes: Whereas SPIKE-distance considers all time scales equally important [6] (and
therefore is also sensitive to non-synchronized spikes in bursts), Spike-Contrast
considers such spike trains perfectly synchronized.
The reflection of larger time scales in the range of burst duration has been suggested
before by [7, 8] to analyze bicuculline induced synchrony modification. Here, the
new algorithm Spike-contrast, prioritizing large time scales, shall be applied on
experimental data recorded from cortical rat neurons grown on microelectrode arrays
(MEA). Signals were recorded in a control situation and with bicuculline (10 µM),
a well-known drug that blocks the inhibitory action of GABAA receptors.
We find that Spike-contrast is able to significantly quantify the increase in synchrony
induced by bicuculline. Statistical significance is higher than from other synchrony
measures like SPIKE-distance, suggesting that the bicuculline mediated synchrony increase
is more distinct in larger time scales and that Spike-contrast is an appropriate synchrony
measure for spike trains taken from experimental data, e.g. in biosensor applications
[9].
References
1. Rieke F: Spikes: exploring the neural code. MIT press, 1999.
2. Engel AK, Fries P, Singer W: Dynamic predictions: oscillations and synchrony in
top–down processing. Nature Reviews Neuroscience, 2(10):704–716, 2001.
3. Ward LM: Synchronous neural oscillations and cognitive processes. Trends in cognitive
sciences, 7(12):553–559, 2003.
4. Uhlhaas PJ, Singer W: Neural synchrony in brain disorders: relevance for cognitive
dysfunctions and pathophysiology. Neuron, 52(1):155–168, 2006.
5. Kreuz T, Chicharro D, Houghton C, Andrzejak RG, Mormann F: Monitoring spike train
synchrony. Journal of neurophysiology, 109(5):1457–1472, 2013.
6. Satuvuori E, Mulansky M, Bozanic N, Lenk K, Kreuz T: Measures of spike train synchrony
for data with multiple time-scales. arXiv preprint arXiv:1702.05394, 2017.
7. Selinger JV, Pancrazio JJ, Gross GW: Measuring synchronization in neuronal networks
for biosensor applications. Biosensors and Bioelectronics, 19(7):675–683, 2004.
8. Chiappalone M, Vato A, Berdondini L, Koudelka-Hep M, Martinoia S: Network dynamics
and synchronous activity in cultured cortical neurons. International journal of neural
systems, 17(02):87–103, 2007.
9. Flachs D, Ciba M: Cell-based sensor chip for neurotoxicity measurements in drinking
water. Lékař a Technika - Clinician and Technology, 46(2):46–50, 2016.
P270 Cortical states affect the optimal linear readout of network dynamics
Eric S. Kuebler1, Joseph S. Tauskela2, & Jean-Philippe Thivierge1
1School of Psychology, Faculty of Social Science, University of Ottawa, Ottawa, Ontario,
Canada; 2Human Health Therapeutics, National Research Council of Canada, Ottawa, Ontario,
Canada
Correspondence: Eric S. Kuebler (ekueb021@uottawa.ca)
BMC Neuroscience 2017, 18 (Suppl 1):P270
Spontaneous neuronal activity in vitro is often characterized by network bursts, whereby
a large proportion of neurons are active in close temporal contiguity. How this network
state affects the optimal linear readout of neuronal dynamics remains an unresolved
question in neuroscience. Here, we recorded from dissociated cortical neurons using
multi-electrode arrays (MEAs) and computed a ‘spike-triggered average’ of population
activity for each electrode (N = 59), by measuring the probability of co-occurrences
between the spiking activity on the electrode of interest and that recorded on each
of the other electrodes, termed the ‘preferred network state’. Results show that despite
fluctuations in spontaneous activity over time, population activity over all electrodes
can be described by a low-dimensional attractor with N-1 parameters, substantially
fewer than the number of parameters required for pairwise correlations (N
2). To test whether activity across different networks could be accurately discriminated,
preferred network states from the first half of recordings (10 min) were fed into
a linear model trained with a Fisher criterion. Then, the model was tested by presenting
it spiking activity from six networks sequentially. We found that this model was useful
in successfully discriminating amongst different networks with less than 3% error
rate (Figure 1A). Further, the linear model was robust to adjustments in the number
of electrodes included for input to the LDA (Figure 1B), suggesting that fewer than
N-1 parameters can be useful to accurately discriminate between networks. Using simulations
of neural activity in a branching model, we show that network activity near a critical
regime (but not necessarily at the exact critical point) is optimally discriminated
by a linear readout.
Conclusion: Overall, our results point to a dynamical signature for representations
of cortical activity whereby states near the critical point are most amenable to decoding
by linear downstream structures. Computation in the brain may occur by distributed
processing whereby several subnetworks are each responsible for contributing to dynamical
neuronal representations.
Figure 1. A. Preferred network states of several networks, denoted by colour. Top
panel: ground truth data. Bottom panel: predictions based on the linear model. B.
Correct rate of classification as a function of the number of electrodes used for
input
P271 Workflow, data format and tools to register neuron morphologies to a reference
brain atlas
Rembrandt Bakker1,2, María García-Amado3, Marian Evangelio3, Francisco Clascá3, Paul
Tiesinga1
1Neuroinformatics department, Donders Institute for Brain, Cognition and Behaviour,
Radboud University Nijmegen, Nijmegen, The Netherlands; 2Institute of Neuroscience
and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN
Institute I, Jülich Research Centre, Jülich, Germany; 3Departamento de Anatomía, Histología
y Neurociencia, Facultad de Medicina, Universidad Autónoma de Madrid, Madrid, Spain
Correspondence: Rembrandt Bakker (r.bakker@donders.ru.nl)
BMC Neuroscience 2017, 18 (Suppl 1):P271
Neuronal reconstructions are essential building blocks for neuronal tissue simulation
at subcellular resolution. Several databases with thousands of reconstructed morphologies
exist, the largest being NeuroMorpho.Org [1]. In this database, the location of each
neuron is described in terms of species and brain region. For a neuronal tissue builder
like the one created in the Human Brain Project (HBP), this information is too coarse.
Only neurons that are registered into a spatial reference system can be used. Here
we present a workflow and accompanying tools to do this, based on the manual annotation
of a few points on the neuron.
Our use-case is the atlas registration of long-range projection neurons in mouse,
which is particularly challenging since the neurons run across many brain regions.
The steps of the workflow are:
The experimental lab uses the widely used Neurolucida (MBF BioScience) software to
reconstruct the neuron. For a small set of points, the location is eye-balled in the
Franklin-Paxinos mouse brain atlas [2], and added to the reconstruction as a marker
point.
The Neurolucida file (choice of binary, asci, xml) is read by a newly written open-source
parser (https://www.npmjs.com/package/morphology_io), and converted to a newly developed
SWC + format (https://github.com/HumanBrainProject/swcPlus), an extension of the widely
used SWC format [3].
The markers with atlas coordinates are extracted from the SWC + file by a python script
and used to estimate an affine transformation that maps the local coordinate system
to the reference space. The transformation parameters are saved to the SWC + file.
The Morphology Viewer [4], a new web-based morphology suite, recognizes the transformation
parameters in the file, and displays the neuron along with sections from the atlas,
see screenshot in Fig. 1.
In the use-case, 50 points were manually mapped to the atlas. To accelerate the procedure,
this can be reduced to about 10 points. An alternative approach to this workflow is
investigated in sub-project 5 of the HBP. It uses a set of tissue-sections from which
the neuron was reconstructed to semi-automatically find a similar location in the
reference atlas using the AligNII tool (http://www.nesys.uio.no/AligNII/).
Figure 1. Registered neuron with soma and dendrites (red) in region LPLR and the main
axonal arbor (blue) in area V1
Acknowledgements
Supported by the European Union Seventh Framework Programme (FP7/2007-2013) under
grant agreement numbers 604102 (HBP RUP) and 720270 (HBP SGA1).
References
1. Halavi M, Polavaram S, Donohue DE, et al.: NeuroMorpho.org implementation of digital
neuroscience: dense coverage and integration with the NIF. Neuroinformatics 2008,
6(3):241–252. doi:10.1007/s12021-008-9030-1.2.
2. Paxinos G, Franklin K: the Mouse Brain in Stereotaxic Coordinates, 4
th
Edition. Academic Press; 2013.
3. Cannon RC, Turner DA, Pyapali GK, Wheal HV: An on-line archive of reconstructed
hippocampal neurons.
J Neurosci Methods 1998, 84(1–2):49–54. doi: 10.1016/S0165-0270(98)00091-0.
4. HBP Neuron Morphology Viewer [http://neuroinformatics.nl/HBP/morphology-viewer/]
P272 Simple models of closed-loop cortical-environment interactions
Christopher L. Buckley1, Taro Toyoizumi2
1Informatics, University of Sussex, Brighton, BN1 9RH, UK; 2Brain Science Institute,
RIKEN, Wako, Saitama. 351-0106, Japan
Correspondence: Christopher L. Buckley (c.l.buckley@sussex.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P272
Many investigations of the neural circuits underlying behaviour have commonly started
from the assumption that the brain is an input/output device. On this view, the brain
operates in an open-loop, mapping sensory input caused by the environment (exafferent
input) to appropriate motor output. However, during active behaviour (e.g., running,
whisking, swimming) sensory input is directly shaped by motor actions and sensory
perceptions inform future motor commands forming a closed-loop between the brain and
environment, see Fig. 1.
Figure 1. A schematic of brain/body/environment interaction during active behaviour.
Motor actions on the body/environment impact on sensory areas as reafferent input
(black arrow) which is combined with exafferent input (brown arrows) to form the sensory
stream. Motor areas also send efferent signals, or corollary discharges, to sensory
systems (magenta arrows). Further during active behavior sensory input also informs
motor output (red arrow). Thus, neuronal dynamics in sensory areas are affected by
three forms of feedback: environmental feedback (yellow dashed), internal sensor/motor
feedback (blue dashed) and endogenous recurrent feedback (cyan dashed)
The onset of active behaviours coincides with marked changes in cortical dynamics.
Typically, synchronous fluctuations of neural activity are strongly modulated by [2–4].
Further active behaviours shape neuronal responses. For example, the onset of running
sharpens response is visual cortex [4] but suppresses responses in auditory cortex
[3]. In the barrel cortex responses to brief whisker perturbations are suppressed
by whisking but responses to active touch events (when the whisker is actively driven
into an object) are enhanced [5]. By analysing simple dynamical models, we examine
to what extent these phenomena can be accounted for by the closed-loop feedback circuits
necessary for active behaviour, see Fig. 1. In particular, we argue that the onset
of these feedback loops suggests a parsimonious account of the changes to synchronous
neuronal fluctuations and sensory responses caused by the presence of active behaviour
and can account for mismatch responses caused by the interruption of environmental
feedback. Lastly we discuss the development of an experimental setup to test these
ideas that utilizes closed-loop interactions in larval zebrafish behaving in a closed-loop
virtual reality.
References
1. von Holst E. Relations between the central Nervous System and the peripheral organs.
The British Journal of Animal Behaviour 1954, 2:89–94.
2. Crochet S, Petersen CCH. Correlating whisker behavior with membrane potential in
barrel cortex of awake mice. Nat Neurosci. 2006, 9: 608–610.
3. Schneider DM, Nelson A, Mooney R. A synaptic and circuit basis for corollary discharge
in the auditory cortex. Nature 2014, 513: 189–194.
4. Niell CM, Stryker MP. Modulation of visual responses by behavioral state in mouse
visual cortex. Neuron. 2010, 65: 472–479
5. Crochet S, Poulet JFA, Kremer Y, Petersen CCH. Synaptic mechanisms underlying sparse
coding of active touch. Neuron 2011, 69:1160–1175.
P273 Encoding multiple spaces in grid-cells networks
Alexis M. Dubreuil1, Rémi Monasson1, Alessandro Treves2
1Laboratoire de Physique Théorique, Ecole Normale Supérieure, Paris, France; 2Cognitive
Neuroscience, SISSA, Trieste, Italy
Correspondence: Alexis M. Dubreuil (alexis.dubreuil@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P273
Grid-cells found in entorhinal cortex form a representation of space through their
receptive fields that pave space with triangular lattices. It has been proposed that
the firing of grid-cells with similar grid spacing is supported by recurrent connectivity,
such that network dynamic lies on a 2-D manifold of stable states [1]. Recent empirical
evidence suggests that the grid-cell code is not limited to physical space encoding
in entorhinal cortex, but could also subserve encoding of more abstract spaces in
other cortical areas [2]. In order to assess the efficiency of neural networks in
using a grid-cell code, the present study is focused on the problem of counting the
number of grid-like manifolds that can be reliably embedded in a neural network, this
number being referred to as the storage capacity of the network. We consider a network
of binary neurons in which each manifold is imprinted in the connectivity matrix via
a Hebbian component (neurons with similar receptive fields in a manifold are connected).
We compute the number of stable manifolds by extending replica calculations developed
in [3] for a model of place cells. Such analytical calculations allow us to explore
the performance of various grid-cell codes. We focus on two characteristics of such
codes. First, we explore how the typical paving of space in a manifold impact network’s
storage capacity (see Figure 1 A and B). Second we explore how the storage capacity
depends on the parameters of the Hebbian rule shaping the connectivity matrix. Overall,
our study suggests that a grid-cell code can make an efficient use of neural resources
since up to 200 stable manifolds can be imprinted in the connectivity matrix of a
network of 10,000 neurons (see Figure B), roughly the size of a cortical column.
Figure 1. A. Example of the receptive field of a neuron that paves the 2-D flat space.
We implement different kinds of paving, for instance a square lattice (left), or a
hexagonal lattice (right) as those found in entorhinal cortex. B. Storage capacity
of the network as a function of the amount of thermal noise T in the network’s dynamics.
For a given T, the lines give the maximal number of stable manifolds the network can
support, given the number of neurons it is composed of
References
1. Burak Y, Fiete IR: Accurate path integration in continuous attractor network models
of grid cells. PLoS Computational Biology 2009, 5(2): e1000291. doi:10.1371/journal.pcbi.1000291
2. Constantinescu AO, O’Reilly JX, Behrens TEJ: Organizing conceptual knowledge in
humans with a gridlike code. Science 2016, 352(6292):1464–1468.
3. Monasson R, Rosay S: Crosstalk and transitions between multiple spatial maps in
an attractor neural network model of the hippocampus: Phase diagram. Phys Rev E 2013,
87:062813.
P274 Storage capacity of threshold-linear networks for gridlike continuous attractors
Davide Spalla, Sophie Rosay, Alessandro Treves
Cognitive Neuroscience Sector, SISSA, Trieste, 34136, Italy
Correspondence: Davide Spalla (dspalla@sissa.it)
BMC Neuroscience 2017, 18 (Suppl 1):P274
Attractor neural networks play an important role in modeling the mechanisms of spatial
memory, allowing for analytical and computational analyses of the recurrent circuitry
of hippocampal and cortical networks known to be involved in the cognitive representation
of space.
Here we study a recurrent network of threshold-linear neurons, and its capacity for
storing multiple spatial maps as continuous attractors.
In this model, storage capacity can be analytically evaluated in the mean field approximation,
as shown in [1].
The existence of a retrieval phase reduces to the existence of the solutions of a
single equation, which disappear at a critical value of the storage load. This result
holds both for a sparse activity model suitable for a plausible description of CA3
place cells, and for a toy model version with periodic boundary condition on a two-dimensional
torus.
Imposing now periodic hexagonal boundary condition on the connectivity allows us to
model grid cell like behavior, and to calculate the regime in which the system can
retrieve and/or maintain a representation of position in one of the stored environments.
Surprisingly, the storage capacity appears to be very much higher than in the “square”
toy model, by several orders of magnitude.
In graded-response networks, however, mixed states of multiple attractors (corresponding
to recall of multiple environments) are known to also exhibit stability, even though
in a limited region of parameter space [2].
Also in our “hexagonal continuous attractor” network we find, in addition to the “classical”
retrieval behavior, a regime in which external cues trigger retrieval in more than
one map simultaneously, in a similar way as described in [3] for two 1D periodic manifolds.
Our study generalizes these results to 2-dimensional uncorrelated maps, and extends
them to the case with a large number of stored maps.
References
1. Battaglia F, Treves A: Attractor neural networks storing multiple space representations:
A model for hippocampal place fields. Physical Review E. 1998, 58(6):7738.
2. Roudi Y, Treves A: Disappearance of spurious states in analog associative memories.
Phys Rev E Stat Nonlin Soft Matter Phys. 2003, 67(4 Pt 1):041906.
3. Romani S, Tsodyks M: Continuous attractors with morphed/correlated maps. PLoS Comput
Biol. 2010; 6(8)
P275 A network model of the turnover dynamics of excitatory and inhibitory synapses
in cortex
Florence I. Kleberg, Jochen Triesch
Neuroscience Lab, Frankfurt Institute for Advanced Studies, Frankfurt am Main, Hessen,
60438, Germany
Correspondence: Florence I. Kleberg (kleberg@fias.uni-frankfurt.de)
BMC Neuroscience 2017, 18 (Suppl 1):P275
It has long been known that there is substantial turnover of excitatory synapses in
cortex during both development and adult life [1]. Recent experiments using markers
for GABAergic synapses have shown that inhibitory synapses are also highly dynamic
[2–3]. Specifically, inhibitory synapses exhibit approximately exponentially decreasing
survival fractions and show reduced lifetimes when sensory input is decreased [3].
Here we show that such dynamics of excitatory and inhibitory synapses result from
a combination of structural plasticity, Spike-Timing Dependent Plasticity (STDP),
and multiplicative normalisation in a Self-Organizing Recurrent Neural Network (SORN;
[4]) of Leaky Integrate-and-Fire neurons with membrane noise and external Poisson
inputs. Both synapse types are grown from an initially unconnected network state by
random synapse creation. Synapses whose efficacies fall below a threshold are pruned.
We find that excitatory and inhibitory synaptic weights develop lognormal-like distributions
as observed experimentally [2] and that the lifetimes of synapses follow a power law-like
distribution. Furthermore, we find that the fraction of surviving inhibitory synapses
decays approximately exponentially as observed experimentally ([3]; Figure 1A) and
is modulated by the strength of potentiation (LTP) and depression (LTD) in the inhibitory
STDP rule. Finally, depriving the network of external input decreases the survival
fraction of inhibitory synapses as observed in vivo ([3]; Figure 1A). To gain deeper
insight into the underlying mechanisms we formulate a statistical model of the time
evolution of synaptic efficacies and find that it well describes the power law-like
lifetimes and exponential-like decreasing survival fractions (Figure 1B). We conclude
that the experimentally observed turnover dynamics of inhibitory synapses can be explained
by local, biologically plausible plasticity mechanisms and are well described by a
simple stochastic model.
Figure 1. A. Survival fractions of inhibitory synapses in the SORN are shown by the
solid grey lines. Light grey, with external input to the network. Dark grey, without
external input. The exponential fit is shown by the dotted line. B. Survival fractions
of weights in a simple stochastic model are shown by the solid grey line. The exponential
fit is shown by the dotted line. The time scale of plasticity in the simulations in
A and B is sped up compared to experimental findings in order to save computation
time
References
1. Berry KP, Nedivi E: Experience-dependent structural plasticity in the visual system.
Annu. Rev. Vis. Sci. 2016, 2:17–35.
2. Rubinski A, Ziv NE: Remodeling and Tenacity of Inhibitory Synapses: Relationships
with Network Activity and Neighboring Excitatory Synapses. PLoS Comput Biol 2015,
11(11): e1004632.
3. Villa KL, Berry KP, Subramanian J, Cha JW, Oh WC, Kwon H, Kubota Y, So PTC, Nedivi
E: Inhibitory Synapses are repeatedly assembled and removed at persistent sites in
vivo. Neuron 2016, 89(2):756–789.
4. Lazar A, Pipa G, Triesch J: SORN: a self-organizing recurrent neural network. Front
Comp Neurosci 2009, 3:23
P276 Retinal prostheses from stimulation to seeing
Willy Wong1,2, Bruno de Oliveira Floriano1, Toshihiko Matsuo3, Tetsuya Uchida4
1Dept. of Electrical and Computer Engineering, University of Toronto, Toronto, ON,
M5S3G4, Canada; 2Institute of Biomaterials and Biomedical Engineering, University
of Toronto, Toronto, ON, M5S3G9, Canada; 3Ophthalmology, Okayama University Medical
School, Okayama, 700-8558, Japan; 4Polymer Materials Science, Faculty of Engineering,
Okayama University, Okayama, 700-8530, Japan
Correspondence: Willy Wong (willy.wong@utoronto.ca)
BMC Neuroscience 2017, 18 (Suppl 1):P276
A major concern with the design of any prosthetic device is the need for objective
evaluation. Is there a way to better evaluate what the person senses without resorting
to subjective testing after implantation? This study addresses a new approach to retinal
prosthetic design, together with detailed modelling of retinal function to tackle
the question of what is a person actually “sees”. Retinitis pigmentosa and age-related
macular degeneration can lead to blindness due to loss of the photoreceptor layer.
The Okayama University-type Retinal Prosthesis (OUReP) is a new approach to retinal
implant design which does not involve stimulation via micro-electrode arrays [1].
Instead a thin film consisting of photosensitive dye molecules attached to a polyethylene
layer is placed at back of the eye. The dye is designed to respond only in the visible
wavelengths and generates an electric potential in response to incident light likely
due to the formation of dipoles. Currently OUReP has been successfully tested in rats
with a next step in rabbit implantation. A current problem is understanding how the
dye works to facilitate vision.
We have been studying the effect of the potential on the visual pathway by first modelling
the physiological system as an electromagnetic boundary value problem. The potential
was solved computationally in COMSOL to obtain the extracellular potential outside
the retinal cells. This is what generates activity in the retinal cells and ultimately
drives action potentials in the optic nerve. The neural activity was then solved using
Hodgkin-Huxley equations and the cable model with the ‘activating function’ [2] derived
from the extracellular potential solved from Laplace’s equation. From this, we found
that ganglion cells are excited by stimulating the OUReP photosensitive dye layer
at ambient light levels.
The next step involves exploring the neural code behind ‘seeing’. A theory of sensory
information processing has been in development for nearly 50 years [3] and provides,
perhaps for the first time, an accurate model of the firing rate response in primary
afferent neurons [4]. This approach is based on the entropy of the sensory signal
and calculates the response due to uncertainty associated with the input. The theory
has been shown to work well across different modalities as well as for different animal
species. When applied to the retina, a single equation of 5 parameters together with
a coupling equation provides a good estimate of the potential of retinal bipolar cells,
and also the average spike rate response of retinal ganglion cells for both on-centre
and off-centre cells. The population of retinal cells is diverse, and it is not likely
that a simple model can encompass the entirety of the physiological response. However,
this is a first step in understanding the quantitative functioning of the retina.
By combining the physiological response solved through the activating function, and
comparing this with predictions from entropy theory, we can better estimate what a
person will see, and thus provide a more objective assessment of implant function
as well as guide future development.
Acknowledgements
This work was supported by a grant from Natural Science and Engineering Research Council
of Canada to WW. BF acknowledges the support of Ciências sem Fronteiras.
References
1. Alamusi, Matsuo T, Hosoya O, et al. Vision maintenance and retinal apoptosis reduction
in RCS rats with Okayama University-type retinal prosthesis (OUReP™) implantation.
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2. Werginz P, Benav H, Zrenner E, Rattay F. Modeling the response of ON and OFF retinal
bipolar cells during electric stimulation. Vision Res 2015, 111(Pt B):170–181.
3. Norwich, K.: Information, Sensation and Perception. New York: Academic Press; 1993.
4. Wong W. On the Asymptotic, Near-Equilibrium Sensory Response. arXiv. 2013 :1307.6445
P277 Metabolic cost of neuronal oscillations
Domenica Dibenedetto1, Kâmil Uludağ1,2
1Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht,
The Netherlands; 2Maastricht Brain Imaging Centre (MBIC), Faculty of Psychology &
Neuroscience, Maastricht University, Maastricht, The Netherlands
Correspondence: Domenica Dibenedetto (domenica.dibenedetto@maastrichuniversity.nl)
BMC Neuroscience 2017, 18 (Suppl 1):P277
The association between cognitive functions in humans, the increase in glucose and
oxygen utilization and the expression of energy metabolism genes is an active area
of research [1]. The human brain accounts for at least 20% of the body’s energy consumption
[2]. Much of the brain’s energy use goes on to re-establish electrochemical gradients
following action potentials and synaptic currents [3, 4]. The high energy demand at
the synapse implies that local mechanisms must exist to sense synaptic activity and
provide the energy substrates necessary to sustain pre- and postsynaptic processes.
Here, we investigate the relationship between ionic currents associated with neuronal
activity at the synaptic site and the brain energy consumption using both experimental
data and mathematical models. Using a bottom-up approach, we model a recurrent network
of excitatory-inhibitory neurons, stimulated with realistic dynamic inputs, in order
to determine the metabolic costs of neuronal oscillations [5]. An important early
finding from studies in rat was that energy use by neurons (oxidative glucose consumption)
is linearly correlated to excitatory neuronal activity (glutamate release) [6]. As
first step, we are investigating how the various ionic currents measured at the excitatory
synaptic site respond at different frequency ranges of a square-pulsed input signal
through a parameters space exploration study. A morphologically and functionally realistic
pyramidal neuron is considered as postsynaptic compartment [7]. This new physiologically
inspired conductance-based [4] neuron-model can be the basis of a more complex network
in order to monitor metabolism at micro-circuit level. The result of the modelling
will be linked to non-invasive neuroimaging modalities, such as fMRI and EEG [8, 9],
which are related to either the local metabolic costs of neuronal activity or local
synchronicity of the microcircuit. Studying the mechanisms of brain metabolism is
of great interest in order to understand not only the fundamental physiological phenomena
of brain functions, but also the significance of alterations in functional brain imaging
signals detected in several neurodegenerative disorders affecting cognitive processes.
References
1. Magistretti, Pierre J. and I. Allaman, A Cellular Perspective on Brain Energy Metabolism
and Functional Imaging. Neuron. 86(4): p. 883–901.
2. Attwell, D. and S.B. Laughlin, An energy budget for signaling in the grey matter
of the brain. J Cereb Blood Flow Metab, 2001. 21(10): p. 1133–45.
3. Harris, Julia J., R. Jolivet, and D. Attwell, Synaptic Energy Use and Supply. Neuron.
75(5): p. 762–777.
4. Hines, M.L. and N.T. Carnevale, The NEURON simulation environment. Neural Comput,
1997. 9(6): p. 1179–209.
5. Mazzoni, A., et al., Understanding the relationships between spike rate and delta/gamma
frequency bands of LFPs and EEGs using a local cortical network model. Neuroimage,
2010. 52(3): p. 956–72.
6. Sibson, N.R., et al., Stoichiometric coupling of brain glucose metabolism and glutamatergic
neuronal activity. Proc. Natl. Acad. Sci. U.S.A. 95, 316–321.
7. Hay, E., et al., Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide
Range of Dendritic and Perisomatic Active Properties. PLoS Comput. Biol., 2011. 7,
e1002107.
8. Raichle, M.E. and M.A. Mintun, Brain work and brain imaging. Annu Rev Neurosci,
2006. 29: p. 449–76.
9. Logothetis, N.K., et al., Neurophysiological investigation of the basis of the
fMRI signal. Nature, 2001. 412(6843): p. 150–7.
P278 Multi-cluster structure and dynamics in networks of coupled phase oscillators
through different classes of STDP profiles
Abdorreza Goodarzinick1, Mojtaba Madadi Asl1, Alireza Valizadeh1,2
1Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS),
Zanjan 45137-66731, Iran; 2School of Cognitive Sciences, Institute for Research in
Fundamental Sciences (IPM), Tehran 19395-5746, Iran
Correspondence: Abdorreza Goodarzinick (a.goodarzinick@iasbs.ac.ir)
BMC Neuroscience 2017, 18 (Suppl 1):P278
In the brain networks, strength of synaptic connections is adjusted according to relative
spike timing of pre- and post- synaptic neurons, known as spike-timing-dependent plasticity
(STDP) [1]. The theoretical and simulational studies have shown different emergent
collective activity and patterns of connectivity when using different STDP profiles
with different set of parameters. For example, depending on the parameter set, STDP
can either promote or oppose synchrony [2]. It also might lead to potentiation of
bidirectional connections or eliminate two-neuron loops by depressing one of the connections
for every pair of reciprocally connected neurons [3].
Here, we have inspected how different biologically observed STDP profiles can result
in different emergent dynamics. Based on Kuramoto model for description of the phase
dynamics in neuronal ensembles [4, 5], we have designed classes of plasticity functions
with symmetric and anti-symmetric profiles, mimicking typical forms of STDP for excitatory
and inhibitory connections (see Figure 1). Related sets of differential equations
for evolution of the phases and the synapses are analytically solved to justify results
of numerical simulations. The main observation is that while anti-symmetric profile
promotes one structural cluster with almost synchronized dynamics, symmetric profiles
lead to multi-cluster structure with various phase relations within and between the
clusters.
Figure 1. Steady-state distribution of phase differences and connection strengths
which are emerged through the function of 3 different sets of plasticity profiles.
In the upper panels the plasticity profile, distribution of phase lags and effective
plasticity function which is obtained by convolution of the phase lags and the plasticity
profile are shown. The final pattern of the phase lags and the synaptic weights between
each pair of oscillators are shown in the bottom panels
References
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on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 1998, 18(24):
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2. Lubenov E V, Siapas AG: Decoupling through synchrony in neuronal circuits with
propagation delays. Neuron 2008, 58:118–131.
3. Madadi Asl M, Valizadeh A, Tass PA: Dendritic and Axonal Propagation Delays Determine
Emergent Structures of Neuronal Networks with Plastic Synapses. Sci Rep 2017, 7:39682.
4. Kuramoto, Y: Chemical oscillations, waves, and turbulence. Berlin: Springer; 1984.
5. Maistrenko YL, Lysyansky B, Hauptmann C, Burylko O, Tass PA: Multistability in
the Kuramoto model with synaptic plasticity. Phys Rev E 2007, 75:066207.
P279 Multi-scale network structure of macaque visual cortex: connectivity map, cortical
architecture, and layer-specific pathways
Maximilian Schmidt1,2, Rembrandt Bakker1,3, Claus C. Hilgetag4,5, Markus Diesmann1,6,7,
Sacha J van Albada1
1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation
(IAS-6), Jülich Research Centre and JARA, Jülich, Germany; 2Laboratory for Neural
Circuit Theory, RIKEN Brain Science Institute, Wako, Japan; 3Donders Institute for
Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands;
4Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg,
Germany 5Department of Health Sciences, Boston University, Boston, MA, USA; 6Department
of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University,
Aachen, Germany; 7Department of Physics, Faculty 1, RWTH Aachen University, Aachen,
Germany
Correspondence: Sacha J van Albada (s.van.albada@fz-juelich.de)
BMC Neuroscience 2017, 18 (Suppl 1):P279
Extensive available axonal tracing data along with predictive connectomics allows
a novel quantitative description of the network structure of macaque cortex. Since
the effects of connectivity on network dynamics are influenced by the size of cortical
populations, and since neuron density is predictive of connectivity [1, 2], it is
relevant to also characterize numbers of neurons when deriving a connectivity map.
In this study, we integrate data on cortical architecture and axonal tracing data
into a multi-scale account of the network structure of macaque vision-related cortex.
The resulting connectivity map predicts the connection probability between any two
neurons based on their types, areas, and layers. Combining cell densities with published
micrographs provides a quantification of the reduction of relative layer 4 thickness
with cell density from structurally differentiated to less differentiated areas. Similarly,
total cortical thickness decays with cell density. Under the assumption of a relatively
constant density of synapses, this yields denser connectivity in structurally less
differentiated areas. Combined anterograde and retrograde tracing data reveal that
synaptic target patterns of corticocortical connections depend on the laminar origin
of the projection in a manner that complements earlier accounts of the association
between source and target patterns [3, 4]. Statistically assigning synapses to target
neurons based on dendritic length in anatomical reconstructions [5] suggests that
layer 4 neurons receive non-negligible feedback. Our layer-specific connectivity map
enables a novel characterization of direct and polysynaptic pathways through the network.
It can be tested in simulations and experiments whether these directionally specific
paths open up channels for targeted corticocortical communication, akin to recently
highlighted hierarchically differential oscillatory interactions [6, 7].
Acknowledgements
Helmholtz Portfolio Supercomputing and Modeling for the Human Brain (SMHB), European
Union (BrainScaleS, grant 269921 and Human Brain Project, grant 604102), the Jülich
Aachen Research Alliance (JARA), and the German Research Council (DFG grants SFB936/A1,Z1
and TRR169/A2).
References
1. Hilgetag CC, Medalla M, Beul SF, Barbas H: The primate connectome in context: principles
of connections of the cortical visual system. NeuroImage 2016, 134:685–702.
2. Beul SF, Barbas H, Hilgetag CC: A predictive structural model of the primate connectome.
arXiv preprint 2015, arXiv:1511.07222.
3. Felleman DJ, Van Essen DC: Distributed hierarchical processing in the primate cerebral
cortex. Cereb Cortex 1991, 1:1–47.
4. Markov NT, Ercsey-Ravasz M, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H:
Cortical high-density counterstream architectures. Science 2013, 342:1238406.
5. Binzegger T, Douglas RJ, Martin KA: A quantitative map of the circuit of cat primary
visual cortex. J Neurosci 2004, 24:8441–8453.
6. Van Kerkoerle T, Self MW, Dagnino B, Gariel-Mathis MA, Poort J, Van Der Togt C,
Roelfsema PR: Alpha and gamma oscillations characterize feedback and feedforward processing
in monkey visual cortex. PNAS 2014, 111(40), 14332–14341.
7. Bastos AM, Vezoli J, Bosman CA, Schoffelen JM, Oostenveld R, Dowdall JR, De Weerd
P, Kennedy H, Fries P: Visual areas exert feedforward and feedback influences through
distinct frequency channels. Neuron 2015, 85: 390–401.
P280 The interaction of synaptic and structural plasticity in recurrent networks
Michael Fauth, Mark van Rossum
School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
Correspondence: Michael Fauth (mfauth@gwdg.de)
BMC Neuroscience 2017, 18 (Suppl 1):P280
The connectivity of cortical networks determines the way they process information.
Changes in the connectivity and, hence, in the information processing in response
to certain stimuli are associated with learning and memory formation [1, 2]. There
are two classes of activity-dependent processes that change the connectivity of cortical
networks: synaptic plasticity, which changes the transmission efficacies or weights
of the synapses, and structural plasticity, which creates and removes synapses. These
processes are strongly interacting, for example, the lifetime of synapses depends
on their synaptic weight and correlated quantities as the volume of the corresponding
dendritic spine head [3, 4]. Hence, to understand how memories are stored in the connectivity,
we analyze the interaction of synaptic and structural plasticity in recurrent networks.
Moreover, we address the question how memories stored by the connectivity can be maintained
on timescales of months and years, although the underlying synapses are removed or
exchanged on the timescale of days [4, 5].
Using mean-field analysis and simulations, we show that the synapses in a population
of recurrently connected neurons exhibit a collective dynamics which gives rise to
two stable states: the population can be either weakly interconnected or strongly
interconnected with synapses stabilizing each other. The population remains in its
current state despite the creation or removal of individual synapses, such that information
about the population state can be retained much longer than the lifetime of individual
synapses. Moreover, the population can be brought to either state by changing the
input stimulation. These results also extend to sub-populations of the network. For
example, when providing a small subset of neurons in a network with a higher input
current, this subset becomes highly interconnected, effectively forming a Hebbian
cell assembly [6–8].
Interestingly, this collective dynamics can be implemented independent of the bistability
of neuronal activities controlled by synaptic weights in recurrently connected populations,
which has been proposed as a model of observed persistent activity [9]. As a consequence,
even at low activities, a (sub-)population can remain connected with many synapses
with relatively small weights. This, in turn, allows for a rapid increase of these
weights upon retrieval or relearning, which might be related to Ebbinghaus’ savings
phenomenon.
References
1. Yang G, Pan F, Gan WB: Stably maintained dendritic spines are associated with lifelong
memories. Nature 2009, 462: 920–924.
2. Xu T, Yu X, Perlik AJ, Tobin WF, Zweig JA, et al.: Rapid formation and selective
stabilization of synapses for enduring motor memories. Nature 2009, 462:915–919
3. Yasumatsu, N, Matsuzaki, M, Miyazaki, T, Noguchi, J & Kasai, H: Principles of Long-Term
Dynamics of Dendritic Spines. J Neurosci 2008, 28:13592–13608
4. Loewenstein, Y; Yanover, U, Rumpel, S: Predicting the dynamics of network connectivity
in the neocortex. J Neurosci 2015, 35:12535–12544
5. Fauth, M., Wörgötter, F., Tetzlaff, C.: Formation and Maintenance of Robust Long-Term
Information Storage in the Presence of Synaptic Turnover. PLoS Comput Biol 2015, 11:e1004684
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enables dynamically distinct short- and long-term memory formation. PLoS Comput Biol
2013, 9, e1003307
7. Litwin-Kumar, A, Doiron, B: Formation and maintenance of neuronal assemblies through
synaptic plasticity. Nat Commun, 2014, 5:5319
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to form and retrieve memories in spiking neural networks Nat. Commun 2015, 6:6922
9. Brunel, N: Persistent activity and the single-cell frequency-current curve in a
cortical network model. Network 2000, 11:261–280
P281 Automatic calibration for hybrid circuits of living and artificial neurons
Manuel Reyes-Sánchez, Irene Elices, Rodrigo Amaducci, Carlos Muñiz, Francisco B. Rodríguez,
Pablo Varona
Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática, Escuela Politécnica
Superior, Universidad Autónoma de Madrid, Madrid, Spain
Correspondence: Pablo Varona (pablo.varona@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P281
Hybrid circuits are networks built with living neurons and artificial model (electronic
or software) neurons and connections [1]. These circuits allow characterizing neural
network dynamics, probing circuit function and have been used to assess the role of
individual cells and synapses in specific networks (e.g. see [2–6]).
The temporal scale and amplitude ranges of membrane voltage in living neurons are
in general different from the characteristic time scales and amplitudes of the corresponding
voltage variables in the models, and thus in most cases signals from/to the living
neuron and the model have to be scaled, and/or offset. Model integration time and
real time deadlines to implement the hybrid circuit closed-loop are also part of the
problem. The process to calibrate both amplitude and time domains is a main impairment
in the construction of hybrid circuits. Manual calibration is often difficult and
entails a long time and damage risk, something critical due to the limited survival
time of the biological preparations and their low resistance to current injection
beyond unknown physiological limits. Recording drift is also an issue that has to
be addressed in hybrid configurations.
In this work, we present a protocol to perform automatic calibration in hybrid circuits.
The protocol is based on achieving a target synchronization level through an artificial
electrical connection between the living and the artificial neuron in a regime that
guarantees active generation of action potentials. Based on the synchronization criteria,
parameters of both temporal scale (model integration time and acquisition/stimulation
time constraints) and amplitude scale (voltage and current from/to the living neuron)
are set automatically in just a few seconds.
We illustrate our protocol by building a hybrid circuit in the pyloric central pattern
generator of Carcinus Maenas. The automatic calibration algorithm allows the construction
of hybrid circuits in minutes. By reducing calibration time and the risk of damaging
the preparation, it is possible to extend the experimental time for the goal given
to the hybrid circuit, for instance the exploration of specific dynamical regimes.
The automatic search of model parameters in hybrid circuits also allows tuning the
best model configuration in the experiment. The proposed algorithm can be easily generalized
for any electrophysiological preparation.
Acknowledgements
We acknowledge support from MINECO/FEDER DPI2015-65833-P, TIN2014-54580-R (http://www.mineco.gob.es/)
and ONRG grant N62909-14-1-N279.
References
1. Szucs A, Varona P, Volkovskii AR, Abarbanel HDI, Rabinovich MI, Selverston AI.
Interacting Biological and Electronic Neurons Generate Realistic Oscillatory Rhythms.
Neuroreport. 2000; 11:563–9.
2. Pinto RD, Varona P, Volkovskii AR, Szücs A, Abarbanel HDI, Rabinovich MI. Synchronous
behavior of two coupled electronic neurons. Phys. Rev. E. 2000; 62:2644–56.
3. LeMasson G, Masson SR-L, Debay D, Bal T. Feedback inhibition controls spike transfer
in hybrid thalamic circuits. Nature. 2002; 417:854.
4. Nowotny T, Varona P. Dynamic Clamp Technique. In: Jaeger D, Jung R, editors. Encycl.
Comput. Neurosci. Springer New York 2015. p. 1048–51.
5. Hooper RM, Tikidji-Hamburyan RA, Canavier CC, Prinz AA. Feedback control of variability
in the cycle period of a central pattern generator. J. Neurophysiol. 2015; 114:2741–52.
6. Linaro D, Couto J, Giugliano M. Real-time Electrophysiology: Using Closed-loop
Protocols to Probe Neuronal Dynamics and Beyond. J. Vis. Exp. 2015; 100:e52320.
P282 Role of asymmetry in shaping spiking-bursting activity of Central Pattern Generators
Irene Elices1, David Arroyo1, Rafael Levi1,2, Francisco B. Rodriguez1, Pablo Varona1
1Grupo de Neurocomputación Biológica, Dpto de Ingeniería Informática, Escuela Politécnica
Superior, Universidad Autónoma de Madrid, Madrid, Spain; 2Department of Biological
Sciences, University of Southern California, Los Angeles, CA, USA
Correspondence: Irene Elices (irene.elices@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P282
Motor rhythmic patterns in many biological systems are produced by Central Pattern
Generators. These networks are typically based on reciprocal inhibitory subcircuits
responsible for the production of alternating spiking-bursting activity and the intrinsic
dynamics of their constituent cells [1, 2]. In many modelling studies, neurons are
considered identical and the reciprocal inhibition, a hallmark of CPG connectivity,
is frequently modeled as a symmetric interaction. In this study, we emphasize the
importance of asymmetry in the generation and coordination of CPG rhythms from a computational
and experimental point of view.
The conductance-based models used in our computational study are inspired by the crustacean
pyloric CPG [3, 4]. In particular, the network considered in this work is built up
with four Hodgkin-Huxley type neurons and simplified versions of the known connection
topologies of the pyloric CPG. The chosen neuron model displays a wide dynamical regime
that includes irregular spiking-bursting modes similar to the observed behavior of
CPG neurons in isolation. Using these models, we studied the role of asymmetric maximal
synaptic conductances, time constants and gap-junction connectivity in the production
of regular and irregular bursting activity. Our results show that large regions of
both regular and irregular but coordinated rhythms exists as a function of the asymmetry
in the circuit. Both asymmetric maximal conductances and inhibitory synaptic time
scales contribute to the shaping of wide regimes of regular and irregular triphasic
spiking-bursting activity.
Our experimental results of irregular spiking-bursting activity in Carcinus maenas
indicate the relevant role of asymmetry in producing a triphasic rhythm while maintaining
an observed dynamical invariant. Irregularity induced by ethanol [5] revealed the
heterogeneity of neuron activity within the CPG circuit, and the resultant irregular
pattern could be explained by asymmetry of the synaptic connections. Our recordings
of CPG activity at irregular regimes illustrate that the dynamics of neurons and their
connections actively bound flexibility to produce a coordinated robust rhythm.
The distinct sources of asymmetry in the model, in particular maximal conductances
and two different synaptic time scales, play a key role in producing triphasic rhythms
similar to the pyloric’s CPG. Overall, our experimental and modeling results show
that the study of asymmetric circuit components and their dynamical interaction help
to understand how flexibility and robustness are balanced in central pattern generator
circuits.
Acknowledgements
We acknowledge support from MINECO/FEDER DPI2015-65833-P and TIN2014-54580-R (http://www.mineco.gob.es/)
and ONRG grant N62909-14-1-N279.
References
1. Marder E, Calabrese RL: Principles of rhythmic motor pattern generation. Physiol
Rev 1996, 76:687–717.
2. Selverston AI, Rabinovich MI, Abarbanel HDI, Elson R, Szücs A, Pinto RD, Huerta
R, Varona P: Reliable circuits from irregular neurons: a dynamical approach to understanding
central pattern generators. J Physiol 2000, 94:357–374.
3. Elices I, Varona P: Closed-loop control of a minimal central pattern generator
network. Neurocomputing 2015, 170:55–62.
4 Elices I, Varona P: Asymmetry Factors Shaping Regular and Irregular Bursting Rhythms
in Central Pattern Generators. Front Comput Neurosci 2017, 11:9.
5. Elices I, Arroyo D, Levi R, Rodriguez FB, Varona P: Assessing irregularity and
coordination of spiking-bursting rhythms in central pattern generators. BMC Neuroscience
2016, 17(Suppl 1):O1
P283 Rivalry with irregular spiking: resolving mutual inhibition and the balanced
state
Ben Cohen, Carson Chow, Shashaank Vattikuti
Lab of Biological Modeling, NIDDK/NIH, Bethesda, MD, 20814, USA
Correspondence: Ben Cohen (benjapaulcohen@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P283
Perceptual rivalry is the subjective experience of alternations between competing
percepts when an individual is presented with an ambiguous stimulus. Rivalry has been
modeled extensively, and was recently proposed as a canonical neural computation [1].
Mutual inhibition, a network architecture where pools of neurons inhibit one another,
has been the cornerstone of models of rivalry [2]. In such a network, inhibition dominates
one pool, leading to sparse firing, while the opposing pool fires rigorously under
net excitation. This difference in inputs is what drives rivalry, and yet it appears
to conflict with balanced state theory, in which net excitation and inhibition approximately
balance. Balanced state theory has been used to explain how neurons fire irregularly
in response to stimuli, exhibiting Poisson-like inter-spike-interval histograms [3].
Therefore, we investigated rivalry with asynchronous irregular spiking in a ring of
leaky integrate-and-fire neurons. We find that rivalry can exist in synchronous or
asynchronous, as well as regular or irregular states, and we delineate parameter regimes
for each.
References
1. Shashaank Vattikuti, Phyllis Thangaraj, Hua W. Xie, Stephen J. Gotts, Alex Martin,
Carson C. Chow: Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry,
and Rivalry Memory. PLOS Computational Biology, 2016, 12(5):e1004903
2. Jeffery Seely, Carson C. Chow: Role of mutual inhibition in binocular rivalry.
J Neurophysiol, 2011, 106:2136 –2150
3. van Vreeswijk C, Sompolinsky H: Chaotic Balanced State in a Model of Cortical Circuits.
Neural Computation 1998, 10:1321–1371
P284 Interplay between inhibition and connectivity structure in driving synchronization
and functional properties of neural networks
Elena Bertolotti1,2, Raffaella Burioni1,2, Matteo di Volo3,4,5, Alessandro Vezzani1,6
1Department of Mathematical, Physical and Computer Sciences, University of Parma,
Parma, Italy, 43124; 2INFN, Gruppo Collegato di Parma, Parma, Italy, 43124; 3Group
for Neural Theory, Departément des Etudes Cognitives, Ecole Normale Supérieure, Paris,
France; 4Centro Interdipartimentale per lo Studio delle Dinamiche Complesse, Sesto
Fiorentino, Italy, 1-50019; 5Indiana University–Purdue University, Indianapolis, Indiana
46202, USA; 6IMEM-CNR, Parma, Italy, 43124
Correspondence: Elena Bertolotti (elena.bertolotti1@fis.unipr.it)
BMC Neuroscience 2017, 18 (Suppl 1):P284
We theoretically and numerically investigate the interplay between the presence of
a fraction of inhibitory neurons in a neural network and their hub character (their
relative connectivity with respect of the rest of the network units) in synchronization
and input processing of a neural network. The starting point comes from a recent paper
by Bonifazi et al. [1], which has put into evidence that hub neurons are typically
inhibitory, suggesting a unifying view of cooperation between inhibition and connectivity
structure as a driving of synchronization properties in neural networks. In our model,
we consider a leaky-integrate-and-fire neural network composed by inhibitory and excitatory
neurons with a short term synaptic plasticity mechanism. In order to emphasize the
control role of highly connected neurons, both in input and in output direction, we
build networks where input and output connectivities are the same for each neuron.
Moreover, we apply a heterogeneous mean-field approach to the finite size network
dynamics, that lets us speed up numerical computations and highlight the role of neuronal
connections distributions. Then we can tune the fraction of inhibitory neurons fI
and their connectivity level to study the cooperation between hub character and inhibition.
We show how the interplay of these two ingredients gives rise to a wide range of dynamical
regimes and different ability to process external inputs. Depending on f
I
, highly connected inhibitory nodes strongly drive the synchronization properties
of the overall network through dynamical transitions from partially synchronous to
asynchronous regimes. Furthermore, a metastable regime with long-time memory of external
inputs emerges for a specific fraction of hub inhibitory neurons, underlining the
role of inhibition and connectivity also for input processing in neural networks.
Reference
1. Bonifazi P, Goldin M, Picardo MA, Jorquera I, Cattani A, Bianconi G, Represa A,
Ben-Ari Y, Cossart R: GABAergic hub neurons orchestrate synchrony in developing hippocampal
networks. Science 2009, 326:1419–1424
P285 A mechanistic model of dopamine modulated learning in the olfactory system of
Drosophila
Bayar Menzat, Tim P. Vogels
Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
Correspondence: Bayar Menzat (bayar.menzat@cncb.ox.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P285
The fruit fly memorises previous experiences of different odours along with information
on whether the odour was associated with reward or punishment in an area called the
mushroom body. The pairing of an odour with reward or shock process leads to odour
specific modification in the responses of motor biasing output neurons (MBONs). MBONs
receive excitatory input from odour identity coding Kenyon Cells (KCs). The connection
between KCs and MBONs is targeted by dopaminergic neurons (DAs) [1]. The KC-MBON synapses
change their efficacies when learning occurs, with experimental evidence proposing
that the Spike-Timing-Dependent Plasticity synaptic change rule is present [2]. STDP
changes the strength of a connection between two neurons depending on the precise
timing of their action potentials [3]. When an odour is presented alone the firing
rate of MBONs has been shown to increase, while paired odour and reinforcement presentation
has shown to decrease the activity of the same MBONs [4, 5]. Here, we introduce a
reward modulated STDP learning rule, where the learning rate of STDP is controlled
by the firing rate of dopaminergic neurons. To test this learning rule, we simulate
simultaneous presentation of odour and reward in a spiking model of the olfactory
circuit. We find that our learning rule can create a stable memory of the value (positive,
negative or neutral) of an odour in the excitatory weights of the KC-MBON synapses. Our
model can reproduce experimentally observed bi-directional changes in the firing rates
of MBONs after learning. Finally, we show that when we combined our learning rule
with excitatory feedback from MBONs to DA neurons, dopamine modulated learning plasticity
can provide a mechanism for learning the uncertainty of a reward. Building on this
result, we make experimental predictions of which odour will be selected between two
odours associated with uncertain reward. By proposing a simple mechanistic model of
dopamine mediated learning our work has improved the understanding of the role of
dopamine in the fruit fly olfactory learning.
References
1. Scott Waddell, Neural Plasticity: Dopamine Tunes the Mushroom Body Output Network,
Current Biology 2016, 26, no. 3, R109–12, doi:10.1016/j.cub.2015.12.023.
2. Stijn Cassenaer and Gilles Laurent: Conditional Modulation of Spike-Timing-Dependent
Plasticity for Olfactory Learning. Nature 2012, 482, no. 7383: 47–52, doi:10.1038/nature10776.
3. Guo-qiang Bi and Mu-ming Poo: Synaptic Modifications in Cultured Hippocampal Neurons:
Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type. Journal
of Neuroscience 1998, 18, no. 24: 10464–72, doi:10.1038/376074a0.
4. David Owald et al.: Activity of Defined Mushroom Body Output Neurons Underlies
Learned Olfactory Behavior in Drosophila, Neuron 2015, 86, no. 2: 417–27, doi:10.1016/j.neuron.2015.03.025.
5. Toshihide Hige et al.: Heterosynaptic Plasticity Underlies Aversive Olfactory Learning
in Drosophila. Neuron 2015, 88, no. 5: 985–98, doi:10.1016/j.neuron.2015.11.003.
P286 Saliency-based Gaze Prediction based on the Neural Population for Integrating
the Direction of Figure
Nobuhiko Wagatsuma
School of Science and Engineering, Tokyo Denki University, Hiki, Saitama 350-0394,
Japan
Correspondence: Nobuhiko Wagatsuma (nwagatsuma@rd.dendai.ac.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P286
Selective attention is a function of the brain that allocates its computational resource
to the momentarily most important subsets of a visual scene. Saliency models were
used to predict the locations of selective attention and gaze [1]. I propose the biologically
plausible saliency model based on the neural population for integrating activities
in intermediate-level visual areas with neurons selective to the direction of figure
(DOF). Russell et al. demonstrated that the DOF integration played an important role
for computing saliency [2]. In addition, computational study hypothesized that a vast
variety of surrounding organizations by connections from early- to intermediate-level
visual areas were a basis for the neural selectivity of the DOF [3]. I extended the
previous saliency model [2] by introducing a variety of spatial patterns of synaptic
connectivity for integrating the neural responses to the DOF. In this work, a population
of model neurons underlay the determination of saliency magnitude. I tested hundreds
of DOF organizations, and found that my proposed saliency model not only reproduced
the characteristics of perceptual organization but also captured object locations
in natural images (Figure 1A). Furthermore, the gaze prediction accuracy shown by
my saliency mechanism was significantly higher than that by previous models [1, 2]
(Figure 1B). These results suggested a crucial role of various synaptic patterns in
DOF integration and a neural population coding of saliency to determine selective
attention and predict the locations of gaze.
Figure 1. Simulation results. A. Examples of images and saliency maps calculated using
previous models [1, 2] and my proposed model. B. Results of gaze estimation. I drew
receive operating characteristics (ROC) curves with MIT data set (1003 images and
fixation data) [4] for quantifying the responses of saliency models
Acknowledgements
This work was partly supported by KAKENHI (no.26880019) and Technology of Tokyo Denki
University Grant Number Q16 J-04.
References
1. Itti L, Koch C, Niebur E: A model of saliency-based visual attention for rapid
scene analysis. IEEE Trans Pattern Analy Mach Intell 1998, 20:1254–1259.
2. Russell AF, Mihalas S, von der Heydt R, Niebur E, Etienne-Cummings R: A model of
proto-object based saliency. Vision Res 2014, 94:1–15.
3. Sakai K, Nishimura H: Surrounding suppression and facilitation in the dtermination
of border ownership. J Cogn Neurosci 2006, 18: 562–579.
4. Judd T, Ehinger K, Durand F, Torralba A: Learning to predict where human look.
IEEE International Conference on Computer Vision 2009, 12: 2106–2113.
P287 Investigating the differences in ion channel properties between ON and OFF ganglion
cells: a combination of modelling and optimization approach
Susmita Saha1, Reena Kapoor1, Robert Kerr2, John Wagner1
1IBM Research Australia, VIC 3006, Melbourne, Australia; 2NeuroEngineering Laboratory,
Electrical & Electronic Engineering, The University of Melbourne, Melbourne, Australia
Correspondence: Susmita Saha (susmitas@au1.ibm.com)
BMC Neuroscience 2017, 18 (Suppl 1):P287
Experimental studies have demonstrated differences in the intrinsic physiological
responses between ON and OFF retinal ganglion cells (RGCs). OFF cells exhibit intrinsic
spontaneous activity, subthreshold membrane potential oscillations, rebound excitation
and burst firing. ON cells display none of the aforementioned intrinsic phenomena.
Previous modeling studies [1, 2] showed how special properties of low-voltage-activated
(T-type) calcium currents can explain the physiological differences between ON and
OFF RGCs, while assuming that most of the other ion channel properties are similar.
In our study, using a combination of computer simulations of single compartment, Hodgkin-Huxley
type neurons and Bayesian optimisation, we optimised the leak reversal potential and
all literature-reported ion channel conductance densities against experimental findings
from mouse ON and OFF RGCs to estimate the potential contributions of other ion channels.
Optimising the larger set of conductances suggested two distinct sets of parameters
for ON and OFF cells (Table 1). In agreement with previous findings [1, 2], the low-voltage-activated
calcium conductance (glva) is indeed higher in OFF than ON cells, but our results
(Figure 1) suggested further that glva is the main contributor to the differences
between ON and OFF cells. In addition, we found that the voltage-gated sodium channel
conductance may be very different in ON and OFF cells, as also suggested in [2]. In
addition, our analysis (not shown here) also suggests that mainly the fast inactivating
sodium current, not the persistent sodium current, play a significant role in generating
distinct properties in ON and OFF GCs. Overall, our single neuron model with optimised
ion channel parameters was able to demonstrate a major dependence of ON and OFF cell
specific intrinsic activity on the sodium and calcium currents.
Table 1. Optimized intrinsic parameters in ON and OFF GCs (% gap = 100*(((OFF value-ON
value)/ON value))
Parameter
ON
OFF
% gap
Leak reversal potential (mV), VL
−60.4
−58.1
4
Leak (S/cm2),
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g
¯
L
4E−05
3E−05
20
A-type potassium (S/cm2),
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K
,
A
0.035
0.036
5
Low-voltage-activated calcium (S/cm2),
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g
¯
l
v
a
1E−04
8E−04
523
Persistent sodium (S/cm2),
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N
a
p
1E−07
3E−08
73
Calcium (S/cm2),
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C
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0.008
0.009
11
Hyperpolarization-activated (S/cm2),
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g
¯
h
4E−06
3E−06
23
Potassium (S/cm2),
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k
0.049
0.059
20
Ca-activated potassium (S/cm2),
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g
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k
(
C
a
)
7E−05
9E−05
39
Fast inactivating sodium (S/cm2),
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\begin{document}$$ \bar{g}_{Na} $$\end{document}
g
¯
N
a
0.025
0.077
207
Figure 1. Relative % gap (round((%gap/total_gap) *100) between two optimised parameter
sets in ON and OFF RGC
Conclusion: The optimised cell-specific ion channel parameters imply that
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g
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l
v
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and
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exhibited the most important role in explaining intrinsic behavioural differences
in ON and OFF retinal ganglion cells.
References
1. Kameneva T, Meffin H, Burkitt AN: Modelling intrinsic electrophysiological properties
of ON and OFF retinal ganglion cells. J. Comput. Neurosci 2011; 31:547–61.
2. Guo T, Tsai D, Morley JW, Suaning GJ, Kameneva T, Lovell NH, et al.: Electrical
activity of ON and OFF retinal ganglion cells: a modelling study. J. Neural Eng 2016;
13:25005.
P288 Response reversal during top-down modulation in cortical circuits with multiple
interneuron types
Luis C. Garcia del Molino1, Guangyu Robert Yang1, Jorge F. Mejias1, and Xiao-Jing
Wang1,2
1Center for Neural Science, New York University, New York, NY 10003, USA; 2NYU-ECNU
Institute of Brain and Cognitive Science, NYU Shanghai, Shanghai, 200122, China
Correspondence: Luis C. Garcia del Molino (garciadelmolino@nyu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P288
Three major non-overlapping classes of interneurons expressing parvalbumin, somatostatin,
or vasoactive intestinal peptide (henceforth denoted PV, SST and VIP respectively)
show cell type specific connectivity within themselves and with excitatory neurons
leading to a canonical microcircuit across cortex [1, 2]. Dissecting the dynamics
of this microcircuit is essential to our understanding of the mammalian cortex. However,
experiments recording from this circuit often report counterintuitive and seemingly
contradictory findings.
One particular example of complex behavior is the modulation of responses to visual
stimuli during locomotion, when V1 activity significantly increases with respect to
immobility [3]. VIP interneurons are known to be involved in such modulation because
artificially activating (damaging) them mimics (blocks) the effect of running on visual
response [4]. Since VIP cells inhibit SST cells which in turn inhibit excitatory,
PV and VIP cells, a natural explanation for this phenomenon is disinhibition [5]:
upon activation of VIP cells the SST population is inhibited and therefore neurons
targeted by the SST population are disinhibited, raising the overall rate of the excitatory
neurons. However recent experiments show that the network behavior might be more complex.
In particular, in the absence of visual stimulation, the activation of VIP cells results
in an average decrease of SST population activity [4, 6] whereas in the presence of
visual stimulation the response of SST cells is reversed and its rate increases during
locomotion [6, 7] which appears to challenge the disinhibition hypothesis. This observation
suggests that the nature of the interaction between VIP and SST could be stimulus
dependent.
We developed a general theoretical framework to explain such response reversal, and
we showed how these complex dynamics can emerge in circuits that possess two key features:
the presence of multiple interneuron populations and a non-linear dependence between
the input and output of the populations. Furthermore, we built a cortical circuit
model and the comparison of our simulations with real data shows that our model reproduces
the complex dynamics observed experimentally in mouse V1. Our explicit calculations
allowed us to pinpoint the connections critical to response reversal, and to predict
the existence of more types of complex dynamics that could be experimentally tested.
References
1. Jiang X, Shen S, Cadwell CR, Berens P, Sinz F, Ecker AS, Patel S, Tolias AS: Principles
of connectivity among morphologically defined cell types in adult neocortex, Science
2015, 350(6264):p.aac9462.
2. Pfeffer CK, Xue M, He M, Huang ZJ, Scanziani M: Inhibition of inhibition in visual
cortex: the logic of connections between molecularly distinct interneurons. Nature
Neuroscience 2013, 16(8):1068–1076.
3. Niell CM, Stryker MP: Modulation of Visual Responses by Behavioral State in Mouse
Visual Cortex. Neuron 2010, 65(4):472–479.
4. Fu Y, Tucciarone JM, Espinosa JS, Sheng N, Darcy DP, Nicoll RA, Huang ZJ, Stryker
MP: A Cortical Circuit for Gain Control by Behavioral State. Cell 2014, 156(6):1139–1152.
5. Wang XJ, Tegnér J, Constantinidis C, Goldman-Rakic PS: Division of labor among
distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory.
PNAS 2004, 101(5):1368–1373.
6. Dipoppa M, Ranson A, Krumin M, Pachitariu M, Carandini M, Harris KD: Vision and
locomotion shape the interactions between neuron types in mouse visual cortex. bioRxiv
2016, p.058396
7. Pakan JM, Lowe SC, Dylda E, Keemink SW, Currie SP, Coutts CA, Rochefort NL: Behavioral-state
modulation of inhibition is context-dependent and cell type specific in mouse visual
cortex. eLife. 2016, 23; 5:e14985.
P289 Cellular and Network Modeling of the Striatal Microcircuit in a Mouse Model of
Huntington’s Disease
Hanbing Song1, Joseph Goodliffe2, Jennifer Luebke2, Christina M. Weaver1
1Department of Mathematics, Franklin and Marshall College, Lancaster, PA, 17604, USA;
2Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston,
MA, 02118, USA
Correspondence: Hanbing Song (hsong1@fandm.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P289
Huntington’s disease (HD) is a neurodegenerative disorder of the central nervous system
characterized by movement, cognitive, and psychiatric disturbance. In HD, massive
structural and functional neuropathology occurs in the striatum, particularly to spiny
projection neurons (SPNs). SPNs are part of either direct or indirect pathways of
the basal ganglia, and labeled either dSPNs or iSPNs respectively. The mechanisms
underlying the vulnerability of SPNs in HD are unclear, and dSPNs and iSPNs are functionally
distinct and differentially affected in HD [1]. A commonly employed transgenic mouse
model of HD, Q175, exhibits changes of molecular phenotypes, specific neuronal dysfunction,
and subtle but significant movement disorders [2]. Recent in vitro experiments on
12-month old animals showed that, compared to wildtype (WT), dSPNs in Q175 animals
showed increased input resistance, reduced rheobase and reduced amplitude in action
potentials. In addition, both dSPNs and iSPNs exhibited greater dendritic complexity
and lower spine density, along with altered frequencies of spontaneous post-synaptic
currents (reduced excitatory and increased inhibitory). This project models the SPNs
of the striatum to further our understanding of these observed changes in Q175 vs.
WT mice. First, we used morphoelectrotonic transforms of reconstructed SPNs to predict
that dendritic signal attenuation is greater in SPNs from Q175 animals. Then, we used
our parameter optimization method [3], implemented in NEURON (https://www.neuron.yale.edu/neuron/)
on a published 189-compartment conductance-based model SPN [4], to acquire a set of
parameters depicting the passive membrane properties and active channel gating (conductance
and kinetics) of SPNs so that the difference of model output and empirical recording
experimental data could be minimized. We found proper fits to the Q175 data only after
increasing the branching complexity in the published morphology. Differences in reversal
potential of the leak channel and inward-rectifying potassium channel (KIR) contributed
to the increased excitability in Q175 dSPNs, consistent with empirical observations
in mouse models of HD. Finally, we constructed a microcircuit network model of both
dSPNs and iSPNs and fast spiking interneurons (FSIs) [5] that reflects the empirical
findings and incorporates the optimized parameters in our single neuron study. In
the WT network, dSPNs and iSPNs fired in nearly equal proportion in response to input
from FSIs and the cortex and thalamus, predicting a balanced condition for motor movements.
Perturbing our model network consistent with the Q175 experiments (e.g., altering
the frequency of synaptic inputs received by SPNs) resulted in an imbalanced firing
pattern among dSPNs and iSPNs, consistent with what is thought to occur in HD pathology.
These models provide a novel way to explore how individual neuron and network properties
contribute to functional pathology of the striatal microcircuit in Q175 mice, in order
to better understand the deleterious effects of mutant Huntingtin in the human brain.
Acknowledgements
This research was supported by the CHDI Foundation, and used the Neuroscience Gateway
and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported
by National Science Foundation grant number ACI-1053575.
References
1. Galvan L, André VM, Wang EA, Cepeda C, Levine MS: Functional differences between
direct and indirect striatal output pathways in Huntington’s disease. J Huntingtons
Dis 2012, 1:17
2. Rangel-Barajas C and Rebec GV: Dysregulation of corticostriatal connectivity in
Huntington’s disease: a role for dopamine modulation. J Huntingtons Dis 2016, 5:303.
3. Rumbell TH, Draguljić D, Yadav A, Hof PR, Luebke JI, Weaver CM: Automated evolutionary
optimization of ion channel conductances and kinetics in models of young and aged
rhesus monkey pyramidal neurons. J Comput Neurosci 2016, 41:65
4. Evans RC, Morera-Herreras T, Cui Y, Du K, Sheehan T, Kotaleski JH, Venance L, Blackwell
KT: The effects of NMDA subunit composition on calcium influx and spike timing-dependent
plasticity in striatal medium spiny neurons. PLoS Comput Biol 2012, 8:e1002493.
5. Damodaran S, Evans RC, Blackwell KT: Synchronicity of fast-spiking interneurons
balances medium-spiny neurons. J Neurophysiol 2014, 111:836.
P290 Convolutional Neural Network-based Interictal Epileptiform Discharge Detection
John Thomas1, Nishant Sinha2,3, Nikhita Shaju1,4, Tomasz Maszczyk1, Jing Jin1, Sydney
S. Cash5, Justin Dauwels1†, M. Brandon Westover5†
1School of Electrical and Electronic Engineering, Nanyang Technological University,
Singapore 639798, Singapore; 2Institute of Neuroscience, Faculty of Medical Sciences,
Newcastle University, Newcastle upon Tyne, UK; 3School of Computing Science, Newcastle
University, Newcastle upon Tyne, UK; 4Department of Electrical and Electronics Engineering,
Vellore Institute of Technology, Vellore, Tamil Nadu, India; 5Neurology Department,
Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
Correspondence: Justin Dauwels (jdauwels@ntu.edu.sg)
†Contributed equally as co-senior authors
BMC Neuroscience 2017, 18 (Suppl 1):P290
Interictal epileptiform discharges (IEDs) are identified as one of the distinctive
biomarkers of epilepsy. The clinical gold standard for IED detection is visual inspection
by trained clinical neurophysiologists (CNs). Therefore, the diagnosis becomes a heavily
expert-centered process. Automated or semi-automated IED detection systems could overcome
this current problem. Convolutional neural networks (CNNs) are multilayer feed-forward
deep neural networks that are widely applied for classification and prediction. In
this study, we develop an efficient CNN-based IED detector and compare the performance
with the traditional support vector machine (SVM)-based IED detector. A similar study
has been performed in [1], but here we study the problem in greater depth and on a
much larger dataset. We analyze 30-minute EEG recordings of 93 patients with epilepsy.
The data was recorded according to the standard 10-20 electrode placement system at
Massachusetts General Hospital (MGH), Boston. IEDs were independently annotated by
two CNs. Each IED was extracted as a 500-millisecond waveform. A total of 18,164 IEDs
were extracted. The CNN was developed using Tensorflow-r0.12 [2] with a Tesla K40
GPU. We developed the CNN model with five layers: an input layer, convolutional layer,
pooling layer, fully connected layer, and output layer. Four convolutional filters,
each of size (1 × 4), were applied in the convolutional layer. The Rectified linear
unit (ReLU) activation function was applied with 100 neurons in the hidden layer.
Training was performed until 99.99% training accuracy was obtained. To prevent over-fitting,
weights to the output layer were dropped with a probability of 50% in each training
epoch. 5-fold cross-validation results are presented in Table 1. For diagnostic purposes,
CNs are most concerned with identifying the presence vs absence of IEDs in any given
EEG recording, as opposed to detecting all instances of IEDs. False positives pose
a major challenge, as there are typically many more background waveforms than IEDs
(1000:1 imbalance). The CNN provides high sensitivity at very low false positive rates
(see Figure 1), and thus is substantially less prone to false positives compared to
the SVM.
Table 1. Cross-validation results for CNN and SVM-based IED detector systems
Performance indices
CNN
SVM
Sensitivity
99.09%
32.18%
Specificity
93.22%
98.95%
BAC
96.15%
65.57%
AUC
0.966
0.829
Figure 1. The ROC plots of CNN and SVM-based IED detector systems
Conclusions: The CNN-based IED detector outshines the traditional SVM-based system
and methods proposed in the literature in terms of sensitivity and specificity. Moreover,
this study considers a much larger data set than similar studies in the literature.
References
1. A. R. Johansen, J. Jin, T. Maszczyk, J. Dauwels, S. S. Cash, and M. B. Westover:
Epileptiform Spike Detection via Convolutional Neural Networks, Proc. IEEE ICASSP
2016, pp. 754–758.
2. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A.
Davis, J. Dean, M. Devin et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous
Distributed Systems, 2015. Software available from tensorflow.org.
P291 Effects of Hebbian learning on networks of Kuramoto phase oscillators with time
delay
Maryam Karimian1, Domenica Dibenedetto1, Michelle Moerel1,2, Peter De Weerd1,2, Thomas
Burwick4, Ronald L. Westra1,3
1Maastricht Centre for Systems Biology, Maastricht University, 6229ER, Maastricht,
The Netherlands; 2Department of Cognitive Neuroscience, Maastricht University, 6229ER,
Maastricht, The Netherlands; 3Department of Data Science and Knowledge Engineering,
Maastricht University, 6211LH, Maastricht, The Netherlands; 4Frankfurt Institute for
Advanced Studies, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
Correspondence: Maryam Karimian (maryam.karimian@maastrichtuniversity.nl)
BMC Neuroscience 2017, 18 (Suppl 1):P291
Neuronal oscillations are crucial for various cognitive functions, including learning.
Among neuronal populations, patterns of synchronization can drive connectivity changes,
in turn modifying oscillations and synchronization. To study changes in oscillation
patterns with learning, we modeled brain processing using a directed random network
of phase-coupled oscillators interacting according to the Kuramoto model [1]. We incorporated
two extensions into the Kuramoto model: spatial embedding through coupling delays,
and synaptic plasticity according to a Hebbian learning formulation containing learning
associated parameters [2], i.e. learning rate (LR), which determines the speed of
learning, and learning enhancement (LE), which limits the range of coupling weights.
We investigated the structural and functional changes in the network with learning
using graph theory and synchronization evaluating tools, respectively. To study the
structural changes, we calculated the small-worldliness (SW) [3] of the network throughout
the simulated time. Our preliminary results show that with learning the network is
reweighted into a new structure with relatively high levels of SW (Fig. 1A), but a
fully connected pattern. To study the functional changes, we measured the degree of
synchronization for each combination of learning parameters. We observed that specific
combinations of learning parameters led the network to show features of either single
cluster synchronization, or split oscillators into two anti-phase synchronized clusters.
These two synchronization features are evaluated by measuring global order parameters
R1 and R2 [2], respectively. The introduction of time delay affected the network dynamics
and structure changes especially in early stages of learning (Fig. 1A, C), but the
global behavior observed (Fig. 1B, C) doesn’t change. In summary, this enhanced Kuramoto
model seems promising as this induces a SW network topology. However, as the network
obtained after learning is almost fully connected (unlike the efficiency typical for
brain networks), crucial next steps include the exploration of biologically plausible
ways to prune the network in order to increase the wiring cost efficiency, prior to
the application of the model to neuroscientific data.
Figure 1. A. Small-worldliness changes over time for three combinations of (LE, LR)
in different states of synchronization. Differences (R1 - R2) averaged over the last
100 time steps (0.1 s) of simulation time (500 s) for cases of having (B) and not
having (C) time delay
References
1. Juan A. Acebrón, L. L. Bonilla, Conrad J. Pérez Vicente, Félix Ritort, and Renato
Spigler: The Kuramoto model: A simple paradigm for synchronization phenomena. Rev.
Mod. Phys 2005. 77.
2. Niyogi, R. K. & English, L. Q.: Learning-rate-dependent clustering and self-development
in a network of coupled phase oscillators. Phys. Rev. E 2009, 80.
3. Bassett, D. S. & Bullmore, E. D.: Small-World Brain Networks. Neuroscientist 2006,
12: 512–523.
P292 Biophysical modelling of resting brain states and inhibitory synaptic plasticity
Romesh Abeysuriya1, Jonathan Hadida1,2, Stamatios Sotiropoulos2, Saad Jbabdi2, Mark
Woolrich1,2
1Oxford Centre for Human Brain Activity, Oxford, United Kingdom; 2Oxford Centre for
Functional MRI of the Brain, Oxford, United Kingdom
Correspondence: Romesh Abeysuriya (romesh.abeysuriya@psych.ox.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P292
Achieving realistic neural dynamics in biophysical models typically requires extremely
fine tuning of parameters, whereas in the real brain dynamics are robust even to significant
changes including sleep and wake. One possible explanation is that robust dynamics
are facilitated by homeostatic mechanisms that are able to dynamically rebalance brain
networks. In this study, we use one such mechanism, inhibitory synaptic plasticity
(ISP), to achieve a local balance between excitation and inhibition, and investigate
the effect this has on resting brain states. We simulated neural activity in 68 cortical
brain regions using the relatively simple Wilson-Cowan neural mass model. Each brain
region consists of an excitatory and an inhibitory population of neurons. Long-range
white matter connections link excitatory populations with distance-dependent propagation
delays, while inhibitory connections are purely local. Anatomical connectivity weights
were estimated using pre-processed diffusion MRI data from the Human Connectome Project
[1] using probabilistic tractography (40 subjects). ISP was incorporated in each brain
region as a dynamic change in the local inhibitory connection depending on the difference
between excitatory activity and a preselected target level of activity [2, 3]. For
comparison to experimental data, we used resting state MEG recordings from 55 healthy
controls. Source-space parcel timecourses were computed for the same brain regions
as the model. Compared to previous work using coupled Kuramoto phase oscillators [4],
we find the Wilson-Cowan model is even more sensitive to the network coupling strength
because the amplitude of oscillations in neural activity can vary. ISP successfully
adjusts local inhibition to balance excitatory activity across the network (Fig. 1A),
reducing this sensitivity. As a result, at intermediate delays the network exhibits
metastable dynamics and amplitude envelope functional connectivity that is well correlated
with experimental data over a wide range of global coupling strengths (Fig. 1B-1D).
Simple neural mass models are largely unable to predict frequency-specific connectivity,
and we have focused primarily on alpha connectivity here. Future work will investigate
simultaneous prediction of the different patterns of connectivity seen in experimental
data across frequency bands.
Figure 1. A. Local inhibition balances long-range excitation. B. Correlation between
data and model alpha band functional connectivity (amplitude envelope correlation).
Alpha band functional connectivity profiles C. in MEG data D. in the model
References
1. Sotiropoulos SN, Jbabdi S, Xu J, Andersson JL, Moeller S, Auerbach EJ, et al. Advances
in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage
2013, 80:125–143.
2. Vogels TP, Froemke RC, Doyon N, Gilson M, Haas JS, Liu R, et al. Inhibitory synaptic
plasticity: spike timing-dependence and putative network function. Front. Neural Circuits
2013, 7:119.
3. Hellyer PJ, Jachs B, Clopath C, Leech R. Local inhibitory plasticity tunes macroscopic
brain dynamics and allows the emergence of functional brain networks. Neuroimage 2016,
124:85–95.
4. Cabral J, Luckhoo H, Woolrich M, Joensson M, Mohseni H, Baker A, et al. Exploring
mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions
lead to structured amplitude envelopes of band-pass filtered oscillations. Neuroimage
2014, 90:423–435.
P293 Temporal pattern recognition and control of animat foraging by evolving small
networks of adaptive exponential integrate-and-fire neurons
Chama Bensmail1,2, Volker Steuber1, Borys Wrobel2,3
1Centre for Computer Science and Informatics Research, University of Hertfordshire,
Hatfield, AL10 9AB, UK; 2Biology, Adam Mickiewicz University, 61-712 Poznan, Poland;
3Systems Modelling IOPAN, 81-701 Sopot, Poland
Correspondence: Chama Bensmail (chamabens@evosys.org)
BMC Neuroscience 2017, 18 (Suppl 1):P293
Temporal pattern recognition is a common computational task that can be performed
by neural networks. The networks investigated in this work are evolved with a biologically
inspired artificial life platform [1], and consist of up to five adaptive exponential
integrate-and-fire neurons with parameters producing tonic spiking with constant input
current [2]. The animat forages in a 2D open world where it receives signals—we will
describe them as 3-letter words, but the letters can be also seen as flashes of light
(with 3 different colors) or musical notes (with 3 different frequencies). These words
are emitted from two sources: a target, which emits the word ABC, and a distractor,
which emits all the other 26 words consisting of a, b and c. The words and letters
never overlap (see Figure 1). Each word lasts for 25-ms, with 2-ms intervals of silence
between letters, and 20-ms intervals between words. When the animat touches an object
(initially placed randomly), the object disappears, and another object of the same
type reappears in another random position. The animat is equipped with 6 sensors;
2 per letter, which provide the input to the network (one input for the difference
of signal intensity on two sides of the animat, and the other input for the average
of signal intensity). The animat has 2 actuators, whose activity is driven by the
number of spikes produced by the output neurons during the previous 120-ms. When the
activity of one actuator (say, left) is higher, the animat turns (here, right); when
both activities are equal, the animat moves straight, when both activities equal 0,
the animat stops.
We used a genetic algorithm to obtain several small networks which discriminate efficiently
the target pattern from all the other 3-letter words. Our results show that evolving
in the presence of small amounts of noise on the duration of the letters results in
a more efficient discriminator than evolution without such noise. The noisy pattern
had letters with 5-, 7-, and 9-ms duration, with the duration ordered randomly, the
pattern without noise had all letters with 7-ms duration. Only the networks evolved
with a noisy pattern were robust to even noisier patterns (for example, they recognized
patterns in which letters had 1-, 7-, and 13-ms duration, with random order of duration).
Both the networks evolved with and without noise on the length of the letters were
robust also to the change in the actuator forces and on silence intervals between
words (up to 200-ms); number of objects in the world (up to 2 objects of each type).
Figure 1. The champion environment and its highly noisy input vector (from left to
right: ABC, aac, ABC, ABC, aba, cab). When a target is hit; it becomes a black circle.
The green boxes are the actuator activities and the height of the bars corresponds
to the sensory activity
Acknowledgements
This work was supported by the Polish National Science Center (project EvoSN, UMO-2013/08/M/ST6/00922).
References
1. Wróbel, B. Evolution of spiking neural networks robust to noise and damage for
control of simple animats. In: Proceedings of the 14th International Conference on
Parallel Problem Solving from Nature 2016.
2. Naud, Richard, et al. Firing patterns in the adaptive exponential integrate-and-fire
model. Biological cybernetics 2008; 19.4-5:335.
P294 Learning Continuous Attractor Neural Networks from Continuously Morphed Patterns
Xiaolong Zhou1,2,†, Zilong Ji2,†, Xiao Liu2, Yan Xia2, and Si Wu2
1School of Systems Science, Beijing Normal University, Beijing 100875, China; 2State
Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute for Brain
Research, Beijing Normal University, Beijing 100875, China
Correspondence: Si Wu (wusi@bnu.edu.cn)
†Equal contribution
BMC Neuroscience 2017, 18 (Suppl 1):P294
Continuous attractor neural networks (CANNs) have been widely used as a canonical
model for neural information representation [1]. They have been successfully applied
to describe the encoding of a number of continuous features in neural systems, such
as orientation, moving direction, head direction, and spatial location of objects
[1]. It remains unclear, however, how a neural system acquires such a network structure
in practice. Compared to the Hopfield network, the key property of a CANN is that
it holds a continuous family of stationary states, which form an (approximately) flat
subspace in the network states, rather than being isolated with each other with high-energy
barriers. Hopfield network is learned by Hebbian learning from statistically independent
memory patterns. It has been suggested that a CANN may be learned by Hebbian learning
from correlated patterns, and in the ideal situation, from continuously morphed patterns.
The challenge for memorizing correlated patterns is that the classical Hebb rule merges
correlated patterns into a single attractor, corresponding to the pattern having the
maximum overlap with others. To overcome this difficulty, two methods which modifies
the Hebb rule were proposed. One considers the “popularity” of a neuron, i.e., the
involvement of a neuron in all memory patterns [2]. If a neuron is very popular, then
its contribution in Hebbian learning is decreased accordingly. By this, the network
can store some correlated patterns, but requires that within a pattern, neuronal activities
are statistically independent, a condition hardly satisfied in reality. The other
approach considers the “novelty” of a newly presented memory pattern, measured by
the Hamming distance between the new pattern and those already stored in the network
[3]. If a new pattern is novel, then the pattern is learned by the Hebb rule; otherwise,
the learning effect is diminished accordingly. This method works well in certain cases,
but still has the shortcoming of that the learned result is rather sensitive to the
presenting order of patterns. In this study, we propose a new method to learn a CANN
from correlated patterns. The method applies the Hebb rule only after correlated patterns
are orthogonalized by the Gram-Schmidt rule [4]. In effect, this method contains two
operations, pattern separation and novelty detection, and these two operations appear
to be biologically plausible and may happen in Dentate Gyrus and CA1, respectively.
We apply this method to memorize continuous morphed patterns and learn a CANN successfully.
The result is shown in Figure 1.
Figure 1. Learning a CANN from continuously morphed patterns. A. The morphed patterns.
B. The learned network has an (approximately) flat subspace of low energy storing
the morphed patterns, a key property of a CANN. C. The learned neuronal connection
weights are translational invariant in space, a characteristic of a CANN
References
1. Wu S, Wong KM, Fung CA, Mi Y, Zhang W: Continuous attractor neural networks: candidate
of a canonical model for neural information representation. F1000Research 2016, 5.
2. Kropff E, TrevesA: Uninformative memories will prevail: the storage of correlated
representations and its consequences. HFSP journal 2007, 1(4), 249–262.
3. Blumenfeld B, Preminger S, Sagi D, Tsodyks M: Dynamics of memory representations
in networks with novelty-facilitated synaptic plasticity. Neuron 2006, 52(2), 383–394.
4. Srivastava V, Sampath S, Parker DJ: Overcoming Catastrophic Interference in Connectionist
Networks Using Gram-Schmidt Orthogonalization. PloS one 2014, 9(9), e105619.
P295 Learning Peri-saccadic Receptive Field Remapping in Lateral Intraparietal Area
from Visual Experience
Xiao Wang, Mingsha Zhang, and Si Wu
State Key Laboratory of Cognitive Neuroscience & Learning, IDG/McGovern Institute
for Brain Research, Beijing Normal University, Beijing 100875, China
Correspondence: Xiao Wang (wxbnu@mail.bnu.edu.cn), Mingsha Zhang (mingsha.zhang@bnu.edu.cn),
Si Wu (wusi@bnu.edu.cn)
BMC Neuroscience 2017, 18 (Suppl 1):P295
Our eyes move constantly at a frequency of 3–5 times per second, called saccade. A
saccade induces sweeping of visual images on the retina, yet we perceive the world
to be stable. It has been suggested that the brain achieves this visual stability
via predictive remapping of neuronal receptive field (RF), i.e., neurons respond to
stimuli appearing in their future receptive fields (FRFs) before the eye actually
moves [1]. Recently, Wang et al. unveiled the detailed time course of neuronal peri-saccadic
remapping in the lateral intraparietal area (LIP) [2]. They found that around a saccade,
the neuronal RF expands along the saccadic trajectory temporally, covering the current
RF (CRF), the FRF, and the region the eye will sweep through during the saccade. A
cortical wave (CW) model was also proposed, which attributes RF remapping as the consequence
of neural activities propagated in LIP triggered by the joint effect of visual stimuli
and the corollary discharge (CD) signal responsible for the saccade [2]. This CW model
successfully reproduced the experimental data, however, its biological plausibility
remains unresolved. In this study, we address this issue by building up a computational
model to demonstrate that the CW model can be naturally learned from visual experiences
at the developmental stage of the brain via the biologically plausible spiking-time-dependent-plasticity
(STDP).
We build a two-layer network, with one layer consisting of LIP neurons and the other
Superior Colliculus (SC) neurons. Initially, neuronal connections in LIP are bidirectional
and weak, and the connections from SC to LIP are non-selective. An eye movement due
to saccade causes a static visual image to “sweep” through the retina passively as
if the visual stimulus is moving in the opposite direction of the saccade. This “moving”
stimulus activates LIP neurons sequentially in the retinotopic map; meanwhile, SC
neurons which convey the saccadic information also respond due to the CD signal. Suppose
this process repeats many times, according to STDP, a connection path in the opposite
direction of the saccade between LIP neurons will be learned, and a connection from
SC to LIP matching the saccadic and remapping directions will be formed. Over many
such visual experiences at different spatial locations and in different directions,
a remapping network in LIP is completed. Consequently, a visual stimulus at FRF, combined
with the CD signal from SC, can elicit a cortical wave in LIP which propagates from
FRF to CRF of the neuron along the opposite direction of the saccade, exhibiting the
peri-saccadic RF remapping phenomenon as observed in the experiment.
References
1. Duhamel JR, Colby CL, Goldberg ME: The updating of the representation of visual
space in parietal cortex by intended eye movements [J]. Science 1992, 255(5040): 90.
2. Wang X, Fung CA, Guan S, Wu S, Goldberg ME, Zhang M: Perisaccadic receptive field
expansion in the lateral intraparietal area. Neuron 2016, 90(2):400–409.
P296 Axonal tree morphology dictates information coding
Netanel Ofer1,2, Orit Shefi1,2, Gur Yaari1
1Faculty of Engineering, Bar Ilan University, Ramat Gan, 52900, Israel; 2Institute
for Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002,
Israel
Correspondence: Netanel Ofer (netanel.ofer@biu.ac.il)
BMC Neuroscience 2017, 18 (Suppl 1):P296
Neurons display various branching tree structures, and generate diverse activity patterns
[1–3]. Studying the influence of morphology on electrical activity is of crucial importance
for understanding brain functionality [4]. We have investigated the connection between
tree structure and electrical activity by studying signal propagation and information
coding along two basic morphological building blocks: unbranched axonal segments,
and axonal branching points. We did it by solving numerically the Hodgkin Huxley model,
as well as an adapted model for cortical neurons [5], that enable usage of parameter
values that reflect better the conditions for the mammalian nervous system. In an
unbranched axon, spike failures occur in high frequency trains. The effect on the
propagated signal depends on the frequency of the spike train, axon diameter and axon
length. In axonal branching points, signals are modified in a way that depends on
the frequency of the spike train, and geometrical parameters of the branching point.
Combined effects of these two elements can lead to asymmetric responses even between
two sibling branches with identical diameters. These asymmetric conductions could
be produced from geometrical properties alone. We have systematically characterized
the firing patterns as a function of train frequency and morphometric parameters,
revealing distinct patterns of activities such as trains, blockage, intermitted trains,
single or several spikes, and stuttering. Adding up responses from many of these simple
elements yields a rich repertoire of non-trivial activities that can be used as encoding
mechanism for computational tasks. In light of these new theoretical results, we have
extended our study to analyze real whole neuron structures, using reconstructed data
obtained from publically available large data repositories from NeuroMorpho.Org [6]
and the Blue Brain Project [7, 8]. We have examined the morphological parameters in
all neuron types, and interneurons in particular. We clustered interneurons by their
geometrical parameters and divided them into groups according to the signal modulations
that their geometry dictates. Our results may advance interneurons classification
by axonal tree morphology, suggesting that different cells generate different activity
patterns. This detailed morphometric description of cells, together with understanding
how geometry determines information flow, opens the door for deducing functionality
from anatomical data.
References
1. DeFelipe J, López-Cruz PL, Benavides-Piccione R, Bielza C, Larrañaga P, Anderson
S, et al. New insights into the classification and nomenclature of cortical GABAergic
interneurons. Nature Reviews Neuroscience. 2013; 14:202–216.
2. Kepecs A, Fishell G. Interneuron cell types are fit to function. Nature. 2014;
505:318–326.
3. Polavaram S, Gillette TA, Parekh R, Ascoli GA. Statistical analysis and data mining
of digital reconstructions of dendritic morphologies. Frontiers in neuroanatomy. 2014;
8:138.
4. Ofer N, Shefi O. Axonal geometry as a tool for modulating firing patterns. Applied
Mathematical Modelling. 2016; 40:3175–84.
5. Mainen ZF, Sejnowski TJ. Influence of dendritic structure on firing pattern in
model neocortical neurons. Nature. 1996;382:363.
6. Ascoli GA, Donohue DE, Halavi M. NeuroMorpho.Org: A Central Resource for Neuronal
Morphologies. J. Neurosci. 2007; 27:9247–51.
7. Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez CA, et al. Reconstruction
and simulation of neocortical microcircuitry. Cell. 2015; 163:456–492.
8. Ramaswamy S, Courcol J-D, Abdellah M, Adaszewski SR, Antille N, Arsever S, et al.
The neocortical microcircuit collaboration portal: a resource for rat somatosensory
cortex. Frontiers in neural circuits [Internet]. 2015 [cited 2015 Dec 17];9. Available
from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4597797/
P297 The Neuroscience Gateway Portal–High Performance Computing for Neuroscientists
Ted Carnevale1, Amit Majumdar2, Subhashini Sivagnanam2, Kenneth Yoshimoto2
1Neuroscience Department, Yale University, New Haven, CT 06510, USA; 2San Diego Supercomputer
Center, University of California San Diego, San Diego, CA 92093-0505, USA
Correspondence: Ted Carnevale (ted.carnevale@yale.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P297
The Neuroscience Gateway Portal [1] (NSG http://www.nsgportal.org) catalyzes computational
neuroscience research that involves large scale simulations and/or data analysis by
lowering or eliminating administrative and technical barriers to using High Performance
Computing (HPC) resources. It does this by
(1) providing free and open access to supercomputers using time that is acquired via
the peer reviewed allocation process managed by the Extreme Science and Engineering
Discovery Environment (XSEDE), the virtual organization that coordinates US academic
supercomputer centers
(2) offering a simple web-based user interface for accessing HPC resources and
(3) more recently, adding a RESTful interface that enables programmatic access to
HPC resources.
NSG is enabling participation by the wider neuroscience community in research that
would otherwise involve too great a computational burden, such as large scale and
detailed models of cells and networks, parameter optimization, brain image processing,
connectome pipelines etc. Since its inception in early 2013, it has provided about
10,000,000 core hours of supercomputer time to neuroscientists, and has enabled more
than 50 publications and posters. In addition, many developers of new network modeling
tools, data driven parameter optimization pipelines (e.g. BluePyOpt from the Human
Brain Project), and data analysis tools are using the NSG to disseminate their results
to the neuroscience community.
NSG currently has about 450 registered users. Total core hour usage, per-user core
hour consumption rate, and the number of users have all been growing at a rapid rate;
annual usage is projected to exceed 10,000,000 core hours in 2017.
Developing and operating the NSG has given us a unique opportunity to understand and
analyze how a very diverse range of neuroscientists are using an environment like
the NSG, and examine their growing need for supercomputer power, as well as associated
issues and needs for collaboration, data sharing/management and various forms of computing.
Acknowledgements
Supported by NSF 1458840 (A.M., S.S., K.Y); NSF 1458495, NIH/NIDCD DC 009977, NIH/NINDS
NS 011613 (T.C).
Reference
1. Sivagnanam S, Majumdar A, Yoshimoto K, Astakhov V, Bandrowski A, Martone ME, Carnevale
NT: Introducing the Neuroscience Gateway, Proceedings of the 5th International Workshop
on Science Gateways, volume 993, CEUR-WS.org, 2013. [http://ceur-ws.org/Vol-993/paper10.pdf]
P298 Changes of electrophysiological neuronal properties in lithium-pilocarpine model
of epilepsy
Elena Y. Smirnova1,2, Dmitry V. Amakhin2, Sergey L. Malkin2, Anton V. Chizhov1,2,
Aleksey V. Zaitsev2
1Ioffe Institute, St.-Petersburg, 194021, Russia; 2Sechenov Institute of Evolutionary
Physiology and Biochemistry of RAS, St.-Petersburg, 194223, Russia
Correspondence: Elena Y. Smirnova (elena.smirnova@mail.ioffe.ru)
BMC Neuroscience 2017, 18 (Suppl 1):P298
The lithium-pilocarpine (LP) model of epilepsy belongs to a group of animal models
that replicate the general progression of events as observed in humans [1]. We aimed
to determine, whether LP-induced status epilepticus (SE) affects electrophysiological
properties of neurons and then, if does, what is the nature of the effect. Using whole-cell
patch-clamp we compared passive properties, single-spike and spike-pattern attributes
of pyramidal neurons in entorhinal cortex (ERC) slices of control (53 cells) and LP-treated
rats (47 LP-cells, recorded in a day after SE), as well as in, presumably, less damaged
prefrontal cortex (37 control vs 35 LP-neurons). LP-cells had reduced input resistance
(R
in
), time constant (τ
m
), first instantaneous frequency (IF
1
) and amplitude of spikes (RA), and increased rheobase (Rb) and current inducing maximal
firing rate (I
max
). Apart from the decrease of spike amplitude, all the effects were stronger in ERC.
Because the decrease of R
in
can explain all the other effects, we then aimed to clarify whether R
in
change is caused by the disturbances of synaptic, passive or active channels.
First, we compared electrophysiological properties before and after the blockade of
synaptic currents by APV, DNQX and bicuculline. Analysis showed that all of the parameters
changed by SE sustain after the blockade of synaptic currents. Thus, the main source
of SE-induced changes is not synaptic.
Next, we used the dynamic-clamp which allowed us to simulate additional potassium
and nonspecific currents [2]. We attempted to clarify which of the neuronal properties
are affected by the leak current (I
L
) and which ones are by the potassium current which induces spike adaptation (I
ad
). The effect of I
L
in control rats was similar to the effect of SE. Addition of the leak current led
to statistically significant decreases of R
in
, τ
m
, I
max
, IF
1
, RA, magnitude of sag, and increases Rb, stationary IF, and gain (only in ERC). We
then mimicked I
ad
by using the approximation from [3]. Addition of I
ad
did not change R
in
, increases IF
1
, and decreases firing rate, as well as time to the first spike.
Thus, the decrease of R
in
after SE is more likely to be induced by I
L
as well as the decrease of τ
m
, time to the first spike, RA, IF
1
and increase of Rb. Overall, our results suggest that LP-induced SE mainly increases
the leak conductance and keeps other factors intact.
Acknowledgements
This work was supported by the Russian Science Foundation (project 16-15-10202).
References
1. Furman M: Seizure Initiation and Propagation in the Pilocarpine Rat Model of Temporal
Lobe Epilepsy. J Neurosci 2013, 33(42):16409 –16411.
2. Smirnova EY, Zaitsev AV, Kim KK, Chizhov AV: The domain of neuronal firing on a
plane of input current and conductance. J Comput Neurosci 2015, 39(2): 217–233.
3. Kopell N, Ermentrout GB, Whittington MA, Traub RD: Gamma rhythms and beta rhythms
have different synchronization properties. Proc Nat Acad Sci USA 15 2000, 97(4):1867–1872.
P299 Depolarizing GABA leads to interneuron-based interictal discharges: experimental
and mathematical models
Anton V. Chizhov1,2, Dmitry V. Amakhin2, Aleksey V. Zaitsev2
1Ioffe Institute, St.-Petersburg, 194021, Russia; 2Sechenov Institute of Evolutionary
Physiology and Biochemistry of RAS, St.-Petersburg, 194223, Russia
Correspondence: Anton V. Chizhov (anton.chizhov@mail.ioffe.ru)
BMC Neuroscience 2017, 18 (Suppl 1):P299
In in vitro experimental model of temporal lobe epilepsy, we observe the repeating
sequences of interictal discharge (IID) regimes and seizure-like events, where IID
are initiated by interneurons. We used an extracellular medium with high potassium/low
magnesium concentration with the addition of 4-AP in order to provoke epileptiform
activity in combined hippocampus/entorhinal cortex slices of the rat brain [1]. Two
types of IID were observed. For each type, AMPA, NMDA, and GABA-A synaptic components
have been estimated by means of multiple recordings on different voltage levels in
the voltage-clamp whole cell configuration. As found, IIDs of the first type (IID1)
reflect synchronization in the network of interneurons, thus they are characterized
by a pure GABAergic current recorded in an excitatory neuron. IIDs of the second type
(IID2) are composed of early GABAergic and later glutamatergic components.
We have reproduced the IIDs in our mathematical model, using the conductance-based
refractory density approach [2] which provides both a biophysically detailed description
of neuronal populations in terms of ionic channel conductances for one- or two-compartment
neurons and good precision for statistically equilibrium and non-equilibrium regimes
of ensemble activity. Coupled excitatory and inhibitory neurons interact via glutamatergic
and GABAergic plastic synapses. IID1 s and IID2 s were well reproduced in the model.
In simulations, the only parameter that controlled the regimes was the reversal potential
of GABA-A current, V
GABA. Switching from the control silent state to IID1 s and then to intermittent IID2 s
and IID1 s, and finally, only IID2 s occurs with depolarization of V
GABA. We hypothesize that in the experiments V
GABA was depolarized because of depressed action of potassium-chloride cotransporters
in the conditions with high extracellular potassium concentration. We have also found
the synaptic depression to be a crucial factor, which provides ceasing of each of
the discharges and determines their duration. Overall, our study reveals the mechanisms
of pathological synchronization with the primary role of excitatory GABA receptors
in the interneuronal network.
Acknowledgements
This work was supported by the Russian Science Foundation (project 16-15-10201).
References
1. Amakhin DV, Ergina JL, Chizhov AV, Zaitsev AV: Synaptic Conductances during Interictal
Discharges in Pyramidal Neurons of Rat Entorhinal Cortex. Front. in Cell. Neuroscience
2016, 10: 233.
2. Chizhov AV, Graham LJ: Population model of hippocampal pyramidal neurons, linking
a refractory density approach to conductance-based neurons. Phys Rev E 2007, 75: 011924.
P300 Neural activity in distinct navigation modes of flying pigeons
Margarita Zaleshina, Alexander Zaleshin
Moscow Institute of Physics and Technology, Moscow, 117303, Russia
Correspondence: Margarita Zaleshina (zaleshina@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P300
In spatial navigation, there are certain tasks of choosing a route or correcting a
route depending on external conditions. It is necessary to respond to changes in the
visual environment, to compare the expected and observed landscape. Quick reaction
is required in case of unexpected obstacles on the pathway. Perception is detailed
as the target is approached. Orientation awareness is accompanied by various types
of neuronal activities. It can be observed in brain cells associated with navigation
(including place cells, grid cells, head-direction cells) [1], and in combinations
of rhythms of neural ensembles [2]. Contributions of different band oscillations during
route selection can be independent [3]. Observed brain activities are different in
tasks with the specified position of the visual cue or with underspecified movement
goal [4].
This work proposes a model to identify the characteristics of brain activity of flying
pigeons with different modes of space perception. Pigeons fly home based on familiar
landmarks and landscape features [5], solar, stellar and magnetic cues, polarized
light patterns [6], and other references to geographical location. Pigeons have color
and ultraviolet vision, their eyes distinguish the 75 frames per second, field of
view is 340 degrees. Comparison of EEG responses to visual landmarks in flying pigeons
was described [7].
The work considers pigeon flight on known route in three modes: 1. Stationary flight
at an altitude of 100-300 meters, speed of 60 km/h. For flight in a given direction
it is necessary to take into account the influence of wind (drift angle). 2. Response
to danger or sudden changes. Pigeons are more sensitive to radial motion when there
is an acceleration as opposed to a constant velocity [8]. 3. Descent and landing.
Birds begin to fly in circles at an altitude of 30-50 meters.
In the computational model, it is assumed that each mode is accompanied by a characteristic
set of rhythms of neural ensembles (for quiet flight, for alarm and for approaching
to visible goal). Representation of brain activity as sets of rhythms depends on the
type of mode. In model, recognition of textures and borders in the mode “stationary
flight” is additionally encoded by the phase of rhythms with lower frequency. Interactions
between cortical rhythms may generate a third frequency [9]. Route reference points
are additionally encoded by the amplitude of rhythms in all modes. QGIS (http://www.qgis.org)
allows to integrate data received from various sources simultaneously. In the work,
GPS track of flights and landscape maps are performed in QGIS (similarly, QGIS was
applied in [6]). In addition, the program allows to combine results of EEG data processing
with the spatial characteristics of pigeon flight. In the spatial representation of
the model takes into account the distances between the reference points on the ground.
References
1. Spiers HJ, Barry C: Neural systems supporting navigation. Curr Opin Behav Sci Elsevier
Ltd 2015, 1:47–55.
2. Watrous AJ, Fell J, Ekstrom AD, Axmacher N: More than spikes: Common oscillatory
mechanisms for content specific neural representations during perception and memory.
Curr Opin Neurobiol Elsevier Ltd 2015, 31:33–39.
3. Brinkman L, Stolk A, Marshall TR, Esterer S, Sharp P, Dijkerman HC, et al.: Independent
Causal Contributions of Alpha- and Beta-Band Oscillations during Movement Selection.
J Neurosci 2016, 36:8726–8733.
4. Gertz H, Lingnau A, Fiehler K: Decoding Movement Goals from the Fronto-Parietal
Reach Network. Front Hum Neurosci 2017, 11:84.
5. Gagliardo A, Ioale P, Savini M, Dell’Omo G, Bingman VP: Hippocampal-dependent familiar
area map supports corrective re-orientation following navigational error during pigeon
homing: A GPS-tracking study. Eur J Neurosci 2009, 29:2389–2400.
6. Blaser N, Guskov SI, Meskenaite V, Kanevskyi VA, Lipp HP: Altered Orientation and
Flight Paths of Pigeons Reared on Gravity Anomalies: A GPS Tracking Study. PLoS One
2013, 8(10):e77102.
7. Vyssotski AL, Dell’Omo G, Dell’Ariccia G, Abramchuk AN, Serkov AN, Latanov AV.,
et al.: EEG Responses to Visual Landmarks in Flying Pigeons. Curr Biol Elsevier Ltd
2009, 19:1159–1166.
8. Nankoo JF, Madan CR, Spetch ML: Wylie DR: Perception of complex motion in humans
and pigeons (Columba livia). Exp Brain Res 2014, 232:1843–1853.
9. Roopun AK: Temporal interactions between cortical rhythms. Front Neurosci 2008,
2:145–154.
P301 The Role of the Receptive Field Structure in Neuronal Compressive Sensing Signal
Processing
Victor J. Barranca, George Zhu
Department of Mathematics and Statistics, Swarthmore College, Swarthmore, PA 19081,
USA
Correspondence: Victor J. Barranca (vbarran1@swarthmore.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P301
The receptive field structure ubiquitous in the visual system is believed to play
a crucial role in encoding stimulus characteristics, such as contrast and spectral
composition. However, receptive field architecture may also result in unforeseen difficulties
in processing particular classes of images. We explore the potential functional benefits
and shortcomings of localization and center-surround paradigms in the context of an
integrate-and-fire neuronal network model. Utilizing the sparsity of natural scenes,
we derive a compressive-sensing based theoretical framework for network input reconstructions
based on neuronal firing rate dynamics [1, 2]. This formalism underlines a potential
mechanism for efficiently transmitting sparse stimulus information, and further suggests
sensory pathways may have evolved to take advantage of the sparsity of visual stimuli
[3, 4]. Using this methodology, we investigate how the accuracy of image encoding
depends on the network architecture.
We demonstrate that the receptive field structure does indeed facilitate marked improvements
in natural stimulus encoding at the price of yielding erroneous information about
specific classes of stimuli. Relative to uniformly random sampling, we show that localized
random sampling yields robust improvements in image reconstructions, which are most
pronounced for natural stimuli containing a relatively large spread of dominant low
frequency components. This suggests a novel direction for compressive sensing theory
and sampling methodology in engineered devices. However, for images with specific
gray-scale patterning, such as the Hermann grid depicted in Fig. 1, we show that localization
in sampling produces systematic errors in image encoding that may underlie several
optical illusions. We expect that these connections between input characteristics,
network topology, and neuronal dynamics will give new insights into the structure-function
relationship of the visual system.
Figure 1. A. The network-averaged firing rate dependence on the external-drive strength
scaling, computed using model simulation and theoretical linear input-output mapping.
B. Original image (left) and CS reconstruction (right) using localized random sampling
of the network dynamics. C. Hermann grid illusion. D. Reconstructions of (C) for various
choices of receptive field size scaling and excitatory center region radius
References
1. Field DJ: What is the Goal of Sensory Coding? Neural Comput. 1994, 6: 559–601.
2. Candes EJ, Romberg JK, Tao T: Stable Signal Recovery from Incomplete and Inaccurate
Measurements. Commun Pur Appl Math. 2006, 59(8): 1207–1223.
3. Barranca VJ, Kovacic G, Zhou D, Cai D: Sparsity and Compressed Coding in Sensory
Systems. PLoS Comp Biol. 2014, 10(8):e1003793.
4. Barranca VJ, Kovacic G, Zhou D, Cai D: Improved Compressive Sensing of Natural
Scenes using Localized Random Sampling. Sci Rep. 2016, 6:31976.
P302 FuNS with E/I balance: critical dynamics maximize stability of neural networks
Quinton M. Skilling1, Daniel Maruyama2, Nicolette Ognjanovski3, Sara J. Aton3, and
Michal Zochowski1,2
1Biophysics Program, University of Michigan, Ann Arbor, MI 48109, United States; 2Department
of Physics, University of Michigan, Ann Arbor, MI 48109, United States; 3Department
of Cellular, Molecular, and Developmental Biology, Ann Arbor, MI 48109, United States
Correspondence: Quinton M. Skilling (qmskill@umich.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P302
The mammalian brain naturally balances excitation and inhibition. This results in
complex dynamics vital for important cognitive functions such as the formation of
new memories. Excitatory/inhibitory (E/I) balance has been shown to result in scale-free
distributions of population behavior known as neuronal avalanches, a hallmark of self-organized
criticality in the brain. Recently, we have shown using models well-rooted in physics
that new memories are stored only when the system dynamics reside near a critical
point and are characterized by enhanced stability of spiking activity which we refer
to as functional network stability (FuNS) [1]. Here, we expand on this work through
direct modeling of neuronal networks where E/I balance is tightly controlled. Proximity
to criticality at E/I balance is verified via calculation of neuronal avalanches as
well as through calculating functional connectivity correlation between neurons for
increasing separation distance between them. Introducing a region of increased coupling,
such as the synaptic potentiation involved in learning, increases FuNS in networks
exhibiting E/I balance significantly over networks whose dynamics arise primarily
through excitatory or inhibitory inputs. Our results indicate that networks with balanced
excitation and inhibition have an increased ability to store memories through increased
functional network stability, a phenomenon due in part to critical dynamics in neural
systems.
Reference
1. Skilling QM et al. 24 Feb 2017. “Criticality, stability, competition, and consolidation
of new representations in brain networks.” arXiv: 1702.07649.
P303 Neural oscillations modulate the network dynamics around E-I balance in memory
consolidation
Jiaxing Wu1, Nicolette Ognjanovski2, Sara Aton2, Michal Zochowski3
1Applied Physics Program, University of Michigan, Ann Arbor, MI, 48109, USA; 2Department
of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor,
MI, 48109, USA; 3Biophysics Program and Department of Physics, University of Michigan,
Ann Arbor, MI, 48109, USA
Correspondence: Jiaxing Wu (jxwu@umich.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P303
Rhythmic activities of different frequency bands have been observed universally in
the brain and are thought to play important roles in various cognitive processes [1].
However, the fundamental mechanism of how these neural oscillations contribute to
brain activities is still an open question. Recently, via both computational simulations
and in vivo experiments, we found that oscillations are essential for memory consolidation
as they mediate network functional stability. We have shown computationally, that
various network properties such as firing rate, synchrony, mean phase coherence are
enhanced in the presence of external oscillations around Excitatory-Inhibitory (E-I)
balance, where E-I ratio is calculated based on the excitatory and inhibitory synaptic
strength and neuronal firing frequency. We have investigated this effect for both
type 1 (integrator) neurons as well as type 2 (resonator) cells. The networks composed
of resonator neurons are more sensitive to the oscillatory drive than the networks
composed of integrator neurons, however both show significant changes in firing patterns.
We show that global oscillations causally organize firing patterns between heterogeneous
networks composed of dense neuronal clusters that are loosely connected with each
other, facilitating communication and information transfer between spatially distributed
brain regions. Most importantly, near Excitatory-Inhibitory (E-I) balance, oscillations
increase both functional connectivity between neurons and coherence between spikes
and local field potential (LFP), as well as enhance network functional stability,
thus leading to faster changes in network structural connectivity patterns thought
to underlie learning and memory consolidation. These in silico observations are supported
by our experimental data [2]. In summary, our results show that neural oscillations
together with network state near E/I balance coordinate the network dynamics and contribute
to memory consolidation.
References
1. Buzsaki G: Rhythms of the Brain, Oxford University Press 2006.
2. Ognjanovski N, Schaeer S, Wu J, Mofakham S, Maruyama D, Zochowski M, Aton SJ: Parvalbumin-expressing
interneurons coordinate hippocampal network dynamics required for memory consolidation.
Nature Communications (In Press).
P304 Cellular and network properties of interneuron networks dictate variable clustering
patterns in both strictly inhibitory and E-I neural networks
Scott Rich1, Victoria Booth2, Michal Zochowski3
1Applied and Interdisciplinary Mathematics Program, University of Michigan, Ann Arbor,
MI 48104, USA; 2Departments of Anesthesiology and Mathematics, University of Michigan,
Ann Arbor, MI 48104, USA; 3Departments of Physics and Biophysics, University of Michigan,
Ann Arbor, MI 48104, USA
Correspondence: Scott Rich (sbrich@umich.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P304
The diverse population of interneurons in the hippocampus is pivotal to the formation
of oscillatory electrical activity that contributes to memory processing [1], while
in the cortex such interneurons and rhythms are implicated in potential mechanisms
underlying selective attention [2]. Computational research has shown that these rhythms
can be generated in purely inhibitory networks or networks with both excitatory and
inhibitory neurons (E-I networks). However, the dynamics and mechanisms generating
them depend on properties of the inhibitory network.
Simulations of strictly inhibitory networks in our recently published work [3] demonstrate
that the intrinsic cellular properties and connectivity density alter the bursting
properties exhibited in randomly connected, heterogeneous inhibitory networks. We
analyze networks with three types of model neurons that are classified into the classical
Type I or Type II categories using their current-frequency relation (IF curve) and
Phase Response Curve (PRC), while additionally investigating the role of an M-type
adaptation current adaptation current. Across simulations we vary the degree of cellular
heterogeneity, the intrinsic firing frequency of neurons, and the time scale of decay
of synaptic inhibition. The observed dynamics often differ from those predicted by
the Interneuron Network Gamma (ING) mechanism [4], as well as from results in all-to-all
connected networks. While the networks studied here synchronize into a single cluster
of active neurons when said neurons are Type I, analogous networks of Type II neurons
without adaptation segregate into two mutually exclusive clusters organized by the
cells’ intrinsic firing frequencies. When the neurons are modeled as Type II with
adaptation, we observe dynamics similar to those seen in networks of either Type I
or Type II neurons depending upon network parameters, although the adaptation current
does imbue these networks with additional unique behaviors. The mechanisms underlying
this variety of dynamics is explained by changes in profiles of the PRCs across the
different neuron types.
One additional property of Type I inhibitory networks is the different synchrony patterns
exhibited when the inhibitory synapses are strong or weak. By expanding our research
to E-I networks, we have shown that this property plays an important role in the dynamics
of excitatory neurons in these larger networks. When inhibitory to inhibitory synapses
(I-I) in an E-I network are sufficiently strong, the dynamics match those predicted
by the PING mechanism [5]. When these synapses are weakened, networks exhibit rhythmic
bursting for weaker excitatory to inhibitory connectivity and can exhibit rhythms
slower than the typical gamma frequency, two features that expand upon the types of
dynamics typically described by PING theory. However, with weak I-I synapses, the
dynamics of the excitatory cell bursts tend to become disorganized and aperiodic for
stronger excitatory to inhibitory connectivity, due to more variable activity patterns
in the inhibitory network. These results indicate that the strength of I-I connectivity
plays a crucial role in dictating the type, strength and robustness of excitatory
bursting patterns in an E-I network, analogously to how cell type dictates the type
of dynamics seen in strictly inhibitory networks.
References
1. Bartos M, Vida I, Jonas P: Synaptic mechanisms of synchronized gamma oscillations
in inhibitory interneuron networks. Nat. Rev. Neurosci. 2007, 8: 45–56.
2. Fries P: A mechanism for cognitive dynamics: neuronal communication through neuronal
coherence. Trends Cogn. Sci. 2005, 9: 474–480.
3. Rich S, Booth V, Zochowski M: Intrinsic cellular properties and connectivity density
determine variable clustering patterns in randomly connected inhibitory neural networks.
Front. Neural Circuits 2016, 10.
4. Whittington M, Traub RD, Kopell N, Ermentrout B, Buhl E. Inhibition-based rhythms:
experimental and mathematical observations on network dynamics. Int. J. Psychophysiol. 2000:
38, 315–336.
5. Kopell N, Borgers C, Pervouchine D, Malerba P, Tort A. Gamma and theta rhythms
in biophysical models of hippocampal circuits in Microcircuits, A Computational Modeler’s
Resource Book, Springer Series in Computational Neuroscience. 2010: 423–457.
P305 Synaptic failure in functional network activity
Maral Budak1, Michal Zochowski1,2
1Department of Biophysics, University of Michigan, Ann Arbor, Michigan, 48109, USA;
2Department of Physics, University of Michigan, Ann Arbor, Michigan, 48109, USA
Correspondence: Maral Budak (mbudak@umich.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P305
Human brain is a complex network including 1011 neurons connected by 1014 synapses.
Information processing occurs in separate, functionally specialized regions, which
also coordinate and integrate efficiently for the brain to function in a coherent
way. To exhibit these features, brain networks are considered to have small-world
and scale-free network structure properties [1].
Neurons in the brain communicate with each other via connections called synapses in
order to be able to synchronize and perform certain tasks. Thus, failures at these
synaptic connections have detrimental effects such as loss of consciousness or neurodegenerative
diseases. Our objective is understanding the effect of decreased synaptic transmission
on brain networks as a whole as well as on the formation of globally coherent states.
The results may be applied to understand how anesthetics brings the loss of consciousness
by changing brain dynamics and early diagnosis of some neurodegenerative diseases
such as Alzheimer’s and ALS, in which cases synaptic failure is the earliest symptom
[2, 3]. For this purpose, both small-world and scale-free networks, which are prevalent
in brain, are modeled with integrate- and-fire excitatory neurons. Synaptic failure
is introduced to the model by a parameter which randomly determines whether neurons
get signals from the others they’re connected to or not. This parameter also depends
on the spiking history of the neurons, i.e. synapses are more likely to fail if the
presynaptic neuron is more recently fired.
After various simulations, quantitative measures are done with neurons’ spike times
in order to determine how synchronous and coherent the networks behave. As a result,
we demonstrate that more local connections favor more coherent and synchronous behavior
with increasing synaptic transmission. Moreover, we show that scale-free networks
with different directionalities respond to synaptic failure in different ways. However,
neurons with moderate degrees are more coherent than other neurons in all scale-free
network structures. Also, when hubs in scale-free networks are disconnected, the effect
is bigger than the disconnection of lower-degree neurons on the network. We also observed
that the dependence on spiking history affects synchronization and coherent state
formation in different ways for different network structures.
References
1. Bocaletti S, Latora V, Moreno Y, Chavez M, Hwang D: Complex networks: Structure
and dynamics. Physics Reports. 2006;424(4-5):175–308.
2. Wishart T, Parson S, Gillingwater T: Synaptic Vulnerability in Neurodegenerative
Disease. Journal of Neuropathology & Experimental Neurology. 2006;65(8):733–739.
3. Alkire M, Hudetz A, Tononi G: Consciousness and Anesthesia. Science. 2008;322(5903):876–880.
P306 Data-driven multiscale model of mouse M1 microcircuits
Salvador Dura-Bernal1, Samuel A. Neymotin1,3, Benjamin A. Suter4, Gordon M. G. Shepherd2,
William W. Lytton1,5
1Department Physiology & Pharmacology, SUNY Downstate, Brooklyn, NY 11203, USA; 2Department
Physiology, Northwestern University, Chicago, IL 60611, USA; 3Department Neuroscience,
Brown University, Providence, RI 02912, USA; 4Institute of Science and Technology
(IST) Austria, Klosterneburg, 3400, Austria; 5Kings County Hospital Center, Brooklyn,
NY 11203, USA
Correspondence: Salvador Dura-Bernal (salvadordura@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P306
We developed a detailed multiscale computational model of mouse primary motor cortex
(M1) microcircuits, based on novel data provided by experimentalist collaborators.
The model simulates at scale a cylindrical volume with a diameter of 300 μm and cortical
depth 1350 μm of M1. It includes over 10,000 cells distributed across cortical layers
based on measured cell densities, with more than 40 million synaptic connections.
Neuron models were optimized to reproduce the current-clamp electrophysiological properties
of major classes of M1 neurons, with a special emphasis on layer 5 corticospinal (SPI)
and corticostriatal (STR) neurons. Cell ionic channel distributions were constrained
by literature and optimized to reproduce with high precision in vitro recordings for
these two cell types, which used detailed cell morphologies with 700 + compartments
from morphological reconstructions. The network was driven by the main long-range
inputs to M1: thalamus, primary and secondary somatosensory cortex (S1, S2), contralateral
M1, secondary motor cortex (M2), and orbital cortex (OC). Local and long-range network
connectivity was based on optogenetic circuit mapping studies which have demonstrated
that connection strength cannot be fully defined by layer identification but depends
strongly on cortical depth and on the subtype of pyramidal cell. Therefore, highly
specific synaptic input positional distribution along dendritic trees of these different
types were incorporated. We hypothesize that these distinct patterns of dendritic
innervation will have different effects that reflect roles in multiple co-existing
neural coding patterns.
The model was developed using NetPyNE, a novel Python package that facilitates the
development of biological neuronal networks in the NEURON simulator, and emphasizes
the incorporation of multiscale anatomical and physiological data at varying levels
of detail. Parallel simulations were executed on the XSEDE San Diego Supercomputer
Center. Our M1 model incorporates quantitative experimental data from 14 publications,
therefore accumulating previously isolated knowledge into a unified framework.
We studied the output of the 15 local M1 populations in response to increased input
from each of the seven long-range inputs modeled. Sensory information from S1, S2
and sensory thalamus primarily modulated M1 superficial layers, which projected unidirectionally
to layer 5B corticospinal neurons. Secondary motor cortical areas, as well as basal
ganglia-relaying thalamic direct inputs, also modulated the same layer 5B circuits.
Firing rates, oscillations, and information transfer (measured using Granger causality
and normalized transfer entropy) demonstrated differences in dynamics and information
flow along the two parallel pathways is encoded, transformed and integrated. At the
dendritic scale, the distinct innervation patterns in corticospinal neurons facilitated
the integration of information from distinct regions, and the modulatory role of HCN
(H current) ion channels, which has been hypothesized to provide a mechanism to translate
action planning into action execution.
Acknowledgements
Research supported by NIH grant U01EB017695, NIH R01EB022903 and NIH R01MH086638.
P307 Membrane resonance and oscillation preferences of a multi-compartment model pyramidal
neuron
Melvin A. Felton Jr1, Alfred B. Yu2, David L. Boothe2, Kelvin S. Oie2, Piotr J. Franaszczuk2,
3
1U. S. Army Research Laboratory, Adelphi, MD 20783, USA; 2U. S. Army Research Laboratory,
Aberdeen Proving Ground, MD 21005, USA; 3Department of Neurology, Johns Hopkins University
School of Medicine, Baltimore, MD 21287, USA
Correspondence: Melvin A. Felton Jr (melvin.a.felton.civ@mail.mil)
BMC Neuroscience 2017, 18 (Suppl 1):P307
In the soma of neocortical neurons, near-threshold depolarizations have been shown
to induce subthreshold membrane potential oscillations that contribute to network
oscillations by enhancing or hindering neuronal responses to synaptic inputs in specific
frequency bands [1]. The frequency of these subthreshold membrane potential oscillations
coincides with the peak resonance of the neuronal membrane. While differential responses
within the soma of neocortical neurons to inputs of varying frequency have been well
studied, the dendritic contribution within these same neurons is less clear [2], [3].
In addition, the differential impact on neuronal response properties of afferent inputs
to different areas or “zones” of pyramidal neurons is not well understood.
We characterize resonance and membrane potential oscillation characteristics of a
biophysically-realistic compartmental model of a neocortical layer 5 pyramidal neuron
[4]. We simulated injected currents with varying temporal properties, including both
step currents and sinusoidal currents with linearly increasing frequency (chirp currents),
to determine the resonant properties of individual model compartments that were parameterized
to reflect known differences in the properties of functionally distinct zones. We
computed changes in membrane potential under different conditions of input current,
and calculated the input and transfer impedance in the soma, initial axon segment,
and along the dendrites. In addition, we calculated resonance strength and phase relationship
between input current and output membrane potential.
The model showed that preferred oscillation frequency depends critically on parameters
defining the ionic conductance of neuronal compartments that are active in the subthreshold
range, which drive currents that contribute to the total membrane potential. These
ionic currents include: hyperpolarization-activated anomalous rectifier, low-threshold
inactivating calcium, persistent sodium, transient inactivating potassium, and muscarinic
potassium (M current). For instance, resonance at low frequencies (1-4 Hz) can be
produced by low-threshold calcium at near-rest/hyperpolarized potentials, resonance
at intermediate frequencies (4-10 Hz) can be produced by anomalous rectifier at near-rest/hyperpolarized
potentials, and resonance at intermediate and faster frequencies (4-30 Hz) is produced
by M current from resting to more depolarized potentials.
The existence of perisomatic and distal dendritic zones whose intrinsic properties
preferentially enhance or impede resonance in specific frequency ranges coincides
with differential afferent connectivity [5]. Understanding the relationship between
the intrinsic properties of these zones and the areas of the brain that target them
could reveal additional insight about functional connectivity. Furthermore, the oscillatory
behavior of the zones may modulate action potential initiation at the soma/axon hillock
of a neuron which results in variability in overall network activity.
References
1. Erchova I, Kreck G, Heinemann U, Herz AVM: Dynamics of rat entorhinal cortex layer
II and III cells: characteristics of membrane potential resonance at rest predict
oscillation properties near threshold, J Physiol 2004, 560.1: 89–110.
2. Yoshida M, Giocomo LM, Boardman I, Hasselmo ME: Frequency of subthreshold oscillations
at different membrane potential voltages in neurons at different anatomical positions
on the dorso-ventral axis in the rat medial entorhinal cortex, J Neurosci 2011, 31(35):
12683–12694.
3. Remme MWH, Lengyel M, Gutkin BS: The role of ongoing dendritic oscillations in
single-neuron dynamics, PLoS Comput Biol 2009, 5(9): e1000493.
4. Bower JM, Beeman D: The Book of Genesis, 2nd Edition. New York: Springer-Verlag;
1998.
5. Zhuchkova E, Remme MWH, Schreiber S: Somatic versus dendritic resonance: differential
filtering of inputs through non-uniform distributions of active conductances, PLOS
One 2013, 8(11): e78908.
P308 Network cloning using DNA barcodes
Sergey A. Shuvaev, Batuhan Başerdem, Anthony Zador, Alexei A. Koulakov
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
Correspondence: Alexei A. Koulakov (koulakov@cshl.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P308
The ability to measure or manipulate network connectivity is the main challenge in
the field of connectomics. Recently, a set of approaches has been developed that takes
advantage of next generation DNA sequencing to scan connections between neurons into
a set of DNA barcodes. Individual DNA sequences called markers represent single neurons,
while pairs of markers, called barcodes contain information about connections. Here
we propose a strategy for ‘copying’ or ‘cloning’ connectivity contained in barcodes
into a clean slate tabula rasa network. We show that a one marker one cell (OMOC)
rule, which forces all markers with the same sequence to condense into the same neuron,
leads to fast and reliable formation of desired connectivity in a new network. We
show that OMOC rule yields convergence in a number of steps given by a power law function
of the network size. We thus propose that copying network connectivity from one network
to another is theoretically possible. Most current implementations of artificial neural
networks are on digital computers and GPUs [1]. On these architectures, connections
are stored explicitly and therefore straightforward to extract and copy into a new
network. However, in biological networks, there is no central repository for connections,
so reading out the connections of a network and copying them into a new network represents
a difficult challenge.
Figure 1. Network cloning as a way to copy connectivity from one network to another.
The original network is read out into a set of barcodes carrying information about
connections. Each half of the barcode (marker) represents one of the cells that are
connected, while the link represents the direction of the connections. These barcodes
are then introduced into a tabula rasa network that has no structure. Barcodes are
capable to shape the tabula rasa network to match the target connectivity
We have recently proposed a new way to read out neuronal connections using DNA barcodes
[2, 3]. In this strategy, individual neurons produce distinguishable pseudo-random
DNA identifiers called markers. Pairs of markers, called here barcodes, represent
individual synaptic connections (Figure 1). Barcodes are read out using high-throughput
sequencing technology, either in situ [4] or ex vivo after individual neurons are
disassociated. This strategy allows to convert connections between neurons into an
ensemble of DNA barcodes that can be identified using sequencing methods. Here we
formulate a different question: Given an ensemble of connections represented by barcodes,
can we copy them into a new network? In other words, can original network be cloned?
We explore a computational model that simulates the behavior of barcodes introduced
into a tabula rasa network with unstructured connectivity and test its ability to
recreate target connectivity in such networks (Fig. 1). We require that the underlying
mechanisms be purely local, i.e. the behavior of each cell and barcode is based on
the information available in this cell or in its synapses only. The particular form
of dynamics that we considered is described by one marker one cell rule (OMOC), which
favors positioning of a single type of marker DNA sequence in a single neuron. We
showed that OMOC dynamics leads to fast and reliable recreation of desired connectivity
in the new network. The formation of new connectivity is achieved in a number of steps
given by a power law of the network size. Thus, copying connectivity from one neural
network to another using DNA barcodes is theoretically possible.
References
1. LeCun, Y., Y. Bengio, and G. Hinton: Deep learning. Nature 2015, 521: 436–44.
2. Kebschull, J.M., et al.: High-Throughput Mapping of Single-Neuron Projections by
Sequencing of Barcoded RNA. Neuron 2016, 91: 975–87.
3. Zador, A.M., et al.: Sequencing the connectome. PLoS Biol 2012, 10: e1001411.
4. Lee, J.H., et al.: Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression
profiling in intact cells and tissues. Nat Protoc 2015, 10: 442–58.
P309 Triads of synchronized theta cycles boost Cross-Frequency Coupling during novelty
exploration
Víctor J. López-Madrona1, Ernesto Pereda2, Claudio R. Mirasso3, and Santiago Canals1
1Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad
Miguel Hernández, Sant Joan d’Alacant 03550, Spain; 2Departamento de Ingeniería Industrial,
Escuela Superior de Ingeniería y Tecnología, Universidad de La Laguna Avda. Astrofísico
Fco. Sanchez, s/n, La Laguna, Tenerife 38205, Spain; 3Instituto de Física Interdisciplinar
y Sistemas Complejos, CSIC-UIB, Campus Universitat de les Illes Balears E-07122, Palma
de Mallorca, Spain
Correspondence: Víctor J. López-Madrona (v.lopez@umh.es)
BMC Neuroscience 2017, 18 (Suppl 1):P309
Introduction: A prominent feature of brain activity is the presence of oscillations
in neuronal recordings. Interactions between brain rhythms have been demonstrated
at different space and time scales, and are believed to play an important role in
neuronal communication. One such interactions is the cross-frequency coupling (CFC)
between the phase of the theta rhythm and the amplitude of gamma oscillations in the
hippocampus. However, neither the cellular mechanisms supporting this interaction,
nor its behavioral correlates, are well understood. In the present work, we study
the causal interactions between both frequency bands and the features maximizing this
interaction in the hippocampus of behaving animals.
Methods: Sprague-Dawley rats (n = 5) were used for multi-site, multichannel electrophysiological
recordings in freely moving conditions. Local field potentials (LFP) were recorded
across the CA1 and dentate gyrus (DG) regions of the dorsal hippocampus. We applied
an independent component analysis to dissect the local generators of the LFP signals.
CFC between theta phase and gamma amplitude was computed and the directionality of
this modulation measured with phase transfer entropy (PhTE). Alterations on the level
of coupling were estimated as a function of the synchronization between the theta
phases of the generators and in relation to the exploration of new vs. known environments
(novelty test) or new vs. known object locations in a familiar environment (novel
object location task or NOL).
Results: Phase differences between theta oscillations across hippocampal subfields
were patent, as expected from previous literature. PhTE analysis indicates a causal
link between theta and gamma bands in each component, suggesting that theta phases
modulate gamma amplitudes and no otherwise. We show that CFC in each LFP generator
is predominantly found when theta phases are synchronized (locked phase-differences)
across hippocampal subfields, at least during three consecutive cycles. However, maximal
gamma amplitude is found from the first cycle in the triad. The transitions between
synchronization states were analyzed through Markov chains and found a significantly
higher probability of phase-locking in CA1 and CA3 previous to a global synchronization
state. Importantly, quantification of the modulation index during behavior demonstrates
maximal theta-gamma coupling when the subject is exposed to a novel environment or
when the animal explores a new object location in the NOL task.
Conclusions: Our findings suggest that CFC is a communication mechanism in the hippocampus
for encoding new information into memory. Efficient coupling requires precise and
sustained synchronization across all subfields, suggesting global integration.
P310 Simulations of synaptic integration in a detailed Purkinje cell model
Stefano Masoli1, Egidio D’Angelo1,2
1Department of Brain and Behavioural Science, Neurophysiology and Neurocomputation
Unit, University of Pavia, Via Forlanini 6, I-27100, Pavia, Italy; 2Brain Connectivity
Center, Istituto Neurologico IRCCS C. Mondino, Pavia, I-27100, Italy
Correspondence: Stefano Masoli (stefano.masoli@unipv.it)
BMC Neuroscience 2017, 18 (Suppl 1):P310
The Purkinje cell (PC) is one of the most complex neurons of the brain and integrates
more than 100000 synaptic inputs coming from granule cell (GrC) ascending axons (aa),
parallel fibers (pf), climbing fibers (cf) and molecular layer interneurons (mli).
The synapses are distributed, with zone specific limitations, on a large dendritic
tree and exploit different neurotransmission mechanisms to modulate the PC discharge
in way that remains largely unknown. Here we have explored this wide synaptic computational
space using a detailed PC model built in Python - NEURON [1]. The GrC inputs generated
the characteristic burst-pause in the simple spike (SS) responses that were accentuated
by MLI inhibition [2]. The cf inputs elicited complex spikes (CS) followed by pauses
in SS firing that inversely correlated with the number of spikes in cf bursts [3].
Ephaptic coupling between MLIs and PCs depressed SS firing, especially when associated
to GABA-A receptor-mediated synaptic inputs to the PC soma. The activation of GABA-B
receptors and Kir channels generated a permanent downstate [4]. The PC discharge patterns
depended on the excitatory/inhibitory balance, efficacy, dendritic location, Zebrin
(Z + vs. Z-) phenotype, and input patterns of the synapses in a way that matched a
large set of experimental observations [5]. The model thus anticipates how a large
set of electroresponsive patterns could emerge from the complexity of PCs synaptic
organization generating the specific outputs to be transmitted to DCN [6].
References
1. Masoli S, Solinas S, D’Angelo E. Action potential processing in a detailed Purkinje
cell model reveals a critical role for axonal compartmentalization. Front. Cell. Neurosci.
2015; 9:1–22.
2. Steuber V, Mittmann W, Hoebeek FE, Silver RA, De Zeeuw CI, Häusser M, et al. Cerebellar
LTD and pattern recognition by Purkinje cells. Neuron. 2007; 54:121–36.
3. Yartsev MM, Givon-Mayo R, Maller M, Donchin O. Pausing purkinje cells in the cerebellum
of the awake cat. Front. Syst. Neurosci. 2009; 3:2.
4. Loewenstein Y, Mahon S, Chadderton P, Kitamura K, Sompolinsky H, Yarom Y, et al.
Bistability of cerebellar Purkinje cells modulated by sensory stimulation. Nat. Neurosci.
2005; 202–11.
5. Zhou H, Voges K, Lin Z, Ju C, Schonewille M. Differential Purkinje cell simple
spike activity and pausing behavior related to cerebellar modules. J. Neurophysiol.
2015; 113:2524–36.
6. Dykstra S, Turner RW. Determinants of rebound burst responses in rat cerebellar
nuclear neurons to physiological stimuli Steven. Mol. Microbiol. 2015; 82:1496–514.
P311 A model for tactile stimuli processing in cuneate nucleus
Udaya B. Rongala1, Alberto Mazzoni1†, Anton Spanne2, Henrik Jorntell2†, Calogero M.
Oddo1†
1The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Pisa 56025, Italy;
2Neural Basis of Sensorimotor Control, Department of Experimental Medical Science,
Lund University, Lund, Sweden
Correspondence: Alberto Mazzoni (alberto.mazzoni@santannapisa.it)
†equal last author contribution
BMC Neuroscience 2017, 18 (Suppl 1):P311
Our understanding of tactile information processing in humans made critical advancements
in the recent years [1] These studies fruitfully interacted with those aiming at the
development of neuromorphic devices [2]. Here we tackle this issue combining computational
neuroscience and neuroengineering. We modelled tactile sensors and neurons from both
the periphery and the cuneate nucleus taking advantage of the existing neurophysiology
knowledge. We injected them with inputs coming from electronic hardware sensors presented
with a variety of artificial and naturalistic textures. This approach offers rewards
in making robots efficient [3], and contributing for better understanding of neural
mechanisms of sensory processing [4].
First, we generated artificial mechanoreceptor-like output injecting the output of
our biomimetic tactile sensor into an Izhikevich regular spiking neuron. We injected
the normalized output and its derivative to reproduce the dynamics of Slowly Adaptive
and Fast Adaptive neurons respectively [1]. We mimicked the rich information content
in primary afferent sensors by presenting 10 naturalistic textures (Glass, BioSkin,
Textiles, etc.) to our tactile sensor, in a passive touch protocol. We have achieved
accuracy as high as 97% in classifying these 10 textures using a kNN decoding based
on Victor-Purpura distances [5]. As a second step, we have simulated a second layer
of neurons receiving the output of mechanosensors with conduction delays mimicking
the peripheral nerve fibers that transmit primary afferent signals onto the cuneate
neurons (CNs). The CNs are the second order neuron structure present in the brain
stem [1, 2], that is responsible in segregating the PA information based on different
tactile input features [6]. The conduction delays generated a structure of coincident
inputs that could encode stimuli orientation [7]. We modeled then the learning of
stimuli segregation in CNs, based on recent neurophysiology studies [6, 8]. We developed
a model of synaptic learning plasticity able to reproduce the sparseness of CNs encoding
of information from primary afferent. We tested this model by presenting a broad spectrum
of high & low frequency inputs (textures & shape stimuli, using sliding & indentation
protocol respectively) in a pseudo random fashion to induce a realistic rearrangement
of synaptic weights, studying the evolution of connectivity with stimulation history.
We found that highly specialized CNs tended to pick up diverse features in the input
spike patterns and hence in tactile stimuli. Our model provides a candidate mechanism
for feature extraction in CNs and might pave the way to neuromorphic algorithms able
to learn to segregate tactile inputs.
Acknowledgements
This work was supported by institutional funds from Scuola Superiore Sant’Anna, and
by the Ministero degli Affari Esteri e della Cooperazione Internazionale, via the
Italy-Sweden bilateral research projectSE14GR4.
References
1. Johansson RS, Flanagan JR: Coding and use of tactile signals from the fingertips
in object manipulation tasks. Nature Reviews Neuroscience2009, 10: 345–359.
2. Bologna LL, Pinoteau J, Passot JB, Garrido JA, Vogel J, Vidal ER, Arleo A:A closed-loop
neurobotic system for fine touch sensing. Journal of neural engineering 2013, 10(4):046019.
3. ServiceRF: Minds of their own. Science2014, 346.6206:182-183.
4. Oddo CM, Raspopovic S, Artoni F, Mazzoni A, Spigler G, Petrini F, Giambattistelli
F, Vecchio F, Miraglia F, Zollo L, et al.: Intraneural stimulation elicits discrimination
of textural features by artificial fingertip in intact and amputee humans. Elife 2016,
5:e09148.
5. Rongala UB, Mazzoni A, Oddo CM: Neuromorphic artificial touch for categorization
of naturalistic textures. IEEE transactions on neural networks and learning systems
2015, PP(99): 1–1
6. Jörntell H, Bengtsson F, Geborek P, Spanne A, Terekhov AV, Hayward V: Segregation
of tactile input features in neurons of the cuneate nucleus. Neuron 2014, 83(6):1444–52.
7. Rongala UB, Mazzoni A, Camboni D, Carrozza MC, Oddo CM: Neuromorphic artificial
sense of touch: Bridging robotics and neuroscience. Robotics Research, Springer Proceedings
in Advanced Robotics 2017.
8. Bengtsson F, Brasselet R, Johansson RS, Arleo A, Jörntell H: Integration of sensory
quanta in cuneate nucleus neurons in vivo. PloS one 2013, 8(2):e56630.
P312 A new method of system visualization of cognitive functioning for fMRI
Alexander V. Vartanov1, Anastasia K. Neklyudova1, Stanislav A. Kozlovskiy1, Andrey
A. Kiselnikov1, Julia A. Marakshina1,2
1Department of Psychology, Lomonosov Moscow State University, Moscow, Russia; 2Psychological
Institute of Russian Academy of Education, Moscow, Russia
Correspondence: Alexander V. Vartanov (a_v_vartanov@mail.ru)
BMC Neuroscience 2017, 18 (Suppl 1):P312
There are two main ways of neural networks identification in human brain using fMRI:
based on searching for dependency of voxel activity on the task performance and based
on finding interdependency of voxel activity among each other (i.e., resting state)
[1]. The disadvantage of the first method is that it is practically impossible to
select an experimental task which only one cognitive process is involved in, and that
is why it is difficult to talk about specificity of the obtained network to some particular
function. Because of this item, it is necessary to conduct a system of tasks. Brain
activity which was obtained using the second way also connects not only with certain
cognitive processes, but reflects primarily default state of the brain. Furthermore,
another serious problem is artifacts and physiological noise [2]. A new method of
system visualization of cognitive functioning for fMRI (Russian Federation #2016149614
patent pending) and corresponding software (Fact-fMRI) for complex factor analysis
for several individual datasets is presented (Fig. 1). Compared to the ICA method,
the new method allows to estimate the number of functional systems by orthogonal factors
which can be additionally rotated. This method allows to formalize identification
of brain systems which are involved in executing of different cognitive tasks. A proposed
method includes the following stages:
Normalization of voxel activity in a given time slice in a specific task;
Obtaining a matrix of voxel activity in time slices in each of the tasks;
Calculating cross-correlation between different time slices within one task and among
the others and the following factorization (Q factor analysis);
Dimensionality estimation of this matrix based on eigenvalues;
Obtaining the orthogonal system of factors (or factor loadings) as the result of different
types of rotation;
Computing a corresponding matrix of factor scores.
The dimensionality of the obtained matrix is interpreted as an amount of functional
systems involved in all conducted cognitive tasks. Factor loadings are interpreted
as characteristic of dynamics of each system as a whole in different tasks. Factor
scores reveal the localization of each brain system.
Figure 1. A scheme of the Fact-fMRI method. N - a number of cognitive tasks; m - a
general number of scans within N tasks; n - a number of analyzing voxels in the brain;
Z - a normalized matrix of initial data; K - a number of obtained factors, which interpreted
as an amount of functional systems included in all cognitive tasks; A (rot) - a matrix
of factor loadings after rotation, describing dynamics of brain activity for each
of functional systems; P - factor scores, revealing the localization of each brain
system
Thus, the proposed method allows to combine data which show conducting several cognitive
tasks by a person in a single model. Furthermore, it allows to identify and characterize
corresponding functional brain systems.
Acknowledgements
The research was supported by the Russian Science Foundation, project № 16-18-00066.
References
1. Biswal BB: Resting state fMRI: a personal history. Neuroimage, 2012, 62(2): 938–944.
2. Birn RM, Murphy K, Bandettini PA: The effect of respiration variations on independent
component analysis results of resting state functional connectivity. Human brain mapping,
2008, 29(7): 740–750.
P313 Synaptic distribution predicts unitary LFP fields in the hippocampus and in the
neocortex
Maria Teleńczuk1,2, Bartosz Teleńczuk1,2, Alain Destexhe1,2
1European Institute for Theoretical Neuroscience, Paris, France; 2Unité de Neurosciences,
Information et Complexité, Gif-sur-Yvette, France
Correspondence: Maria Teleńczuk (maria@telenczuk.pl)
BMC Neuroscience 2017, 18 (Suppl 1):P313
The local field potential (LFP) is a widely-used signal to monitor the activity of
neural populations. It is usually considered to be generated by the synaptic currents
triggered by pre-synaptic action potentials. Nevertheless, the magnitude and spatial
distribution of LFP depends greatly on the anatomy of recorded region of the brain,
including the neuron morphology, arrangement of different neuron types and the distribution
of excitatory and inhibitory synapses.
In hippocampus, the pyramidal cells are aligned with somas placed in the stratum pyramidale
(s.p), basal dendrites in stratum oriens (s.o) and apical dendrites stretching through
stratum lucidum (s.l), stratum radiatum (s.r) and stratum lacunosum moleculare (s.lm).
Distribution of synapses terminating on the pyramidal cells is also framed. Synapses
of the basket cells terminate mostly on or near to the soma of pyramidal cells forming
basket-like-looking dense axonal structures [1, 2]. Pyramidal cells, on the other
hand, stretch their axons much further from the cell body and terminate their synapses
in the s.r and s.o. Extracellular recordings from the pyramidal cell layer, besides
spiking activity, show very distinct inhibitory fields generated by single interneurons
[3, 4].
In neocortex, on the other hand, neurons are positioned in parallel, they are shifted
in the vertical axis. Although, inhibitory synapses are still placed on nearby somas,
they generate effectively closed-field symmetry, which does not produce large far-field
potentials. Recently, we have shown, however, that the electric field following a
single interneuron spike dominates the on-going LFP. Excitatory neurons contribute
to the LFP with longer latencies suggesting that their contribution is di-synaptic,
mediated by an intermediary interneuron [5].
In the present study, we are investigating this discrepancy using computational modelling.
We place pyramidal neurons according to their distribution in the hippocampus and
neocortex and activate inhibitory or excitatory synapses on them. We follow the distributions
of neurons and synapses found in the literature and realistic morphology downloaded
from online databases (neuromorpho.org). The simulation of the model is performed
in NEURON simulator through its Python interface and the extracellular field is calculated
using the NeuroEAP library [6]. We reproduce the findings of Bazelot et al. [4] in
the hippocampus and Telenczuk et al. [5] in the neocortex and show that the difference
in the field magnitude originate from the differences in the distribution of synaptic
target of inhibitory and excitatory neurons. Importantly, we find that the magnitude
of the LFP generated by the synapses and the relative contribution of excitatory vs.
inhibitory pre-synaptic neurons depend on the cortical layer and the source of feedforward
activation.
Acknowledgements
We acknowledge support from the European Commission (The Human Brain Project, grant
number: H2020-720270) and Centre National de la Recherche Scientifique (CNRS, France).
References
1. Miles R, Tóth K, Gulyás AI, Hájos N, Freund TF. Differences between somatic and
dendritic inhibition in the hippocampus. Neuron 1996; 16:815–23.
2. Le Duigou C, Simonnet J, Telenczuk M, Fricker D, Miles RM. Recurrent synapses and
circuits in the CA3 region of the hippocampus: an associative network. Front. Cell.
Neurosci. 2014; 7:262.
3. Glickfeld LL, Roberts JD, Somogyi P, Scanziani M. Interneurons hyperpolarize pyramidal
cells along their entire somatodendritic axis. Nat. Neurosci. 2009; 12:21–3.
4. Bazelot M, Dinocourt C, Cohen I, Miles R. Unitary inhibitory field potentials in
the CA3 region of rat hippocampus. J. Physiol. 2010; 588:2077–90.
5. Teleńczuk B, Dehghani N, Quyen MLV, Cash SS, Halgren E, Hatsopoulos NG, et al.
Local field potentials primarily reflect inhibitory neuron activity in human and monkey
cortex. Sci. Rep. 2017; 7:40211.
6. Telenczuk B, Telenczuk M. NeuronEAP library. Zenodo. 2016.
P314 Differential tuning of the low- and high-frequency components of the neurophonic
spectrum reveals the spike contribution of barn owl’s nucleus laminaris neurons
Paula T. Kuokkanen1,2,3, Anna Kraemer3, Thomas McColgan1, Catherine E. Carr3, Richard
Kempter1,2
1Inst. For Theoretical Biology, Humboldt-Universitaet Zu Berlin, 10115 Berlin, Germany;
2Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany; 3Dept.
of Biol., Univ. of Maryland, College Park, MD 20742, USA
Correspondence: Paula T. Kuokkanen (p.kuokkanen@biologie.hu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P314
In-vivo neural activity gives rise to transmembrane currents that can be recorded
as an extracellular field potential. These potentials are often challenging to interpret
due to thousands of contributing sources. We aim at revealing the neural sources of
the “neurophonic”. The neurophonic is a frequency-following extracellular potential
that can be recorded in the network formed by the nucleus magnocellularis (NM) and
the nucleus laminaris (NL) in the brainstem of the barn owl. NL anatomy is well understood,
and putative generators of the neurophonic are the activity of afferent axons from
NM, the synaptic activation onto NL neurons, and spikes of NL neurons.
We recorded the neurophonic in response to binaural high-frequency tones (3-7 kHz)
close to the recording site’s best frequency, and we varied the interaural time difference
(ITD). The mean activity of the monaural inputs to NL does not change with ITD. However,
their relative phase does, causing cancellation or summation of input signals. The
activity of the binaurally sensitive output of NL, i.e., firing rate of NL neurons,
strongly depends on ITD. Our recordings contained both of these signals, and we analysed
the broadband power spectrum of the response (0-18 kHz).
The low-frequency component (LFc, 200-700 Hz) of the neurophonic spectrum depended
on ITD. The spectrum of extracellularly recorded NL neurons’ action potentials closely
resembled this component. Thus, the LFc reflects the contribution of action potentials
initiated in NL neurons. The spectral component at the stimulus frequency (SFc) was
much stronger than the LFc. The SFc also depended on ITD, reflecting the activity
of the inputs and their relative phase change with ITD. The power spectrum at other
frequencies did not depend on ITD. We used the LFc as a proxy for NL neurons’ local
population activity, and the SFc as a proxy for NM axons’ local population activity.
We compared the ITD and frequency tunings of these proxies at each recording site.
The best ITDs of the LFc and the SFc were independent. Also, the tuning to stimulus
frequency was different: LFcs showed typically a 400 Hz lower best frequency than
SFcs. Both findings indicate that the LFc might originate from NL neurons’ axons in
the vicinity of the electrode. Related NL neurons can be located tens to hundreds
of micrometers away. The findings are consistent with the known anatomy of NL. Our
analysis thus reveals the small contribution of NL neurons to the neurophonic, improving
our understanding of the extracellular field potential in the auditory brainstem.
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