Stimulus onset quenches neural variability: a widespread cortical phenomenon

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Neural responses are typically characterized by computing the mean firing rate. Yet response variability can exist across trials. Many studies have examined the impact of a stimulus on the mean response, yet few have examined the impact on response variability. We measured neural variability in 13 extracellularly-recorded datasets and one intracellularly-recorded dataset from 7 areas spanning the four cortical lobes. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observable in membrane potential recordings, in the spiking of individual neurons, and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving, or anaesthetized. This widespread variability decline suggests a rather general property of cortex: that its state is stabilized by an input.

Related collections

Most cited references 47

Record: found
Abstract: found
Article: not found

The variable discharge of cortical neurons: implications for connectivity, computation, and information coding.

Michael N. Shadlen, William Newsome (1998)

Cortical neurons exhibit tremendous variability in the number and temporal distribution of spikes in their discharge patterns. Furthermore, this variability appears to be conserved over large regions of the cerebral cortex, suggesting that it is neither reduced nor expanded from stage to stage within a processing pathway. To investigate the principles underlying such statistical homogeneity, we have analyzed a model of synaptic integration incorporating a highly simplified integrate and fire mechanism with decay. We analyzed a "high-input regime" in which neurons receive hundreds of excitatory synaptic inputs during each interspike interval. To produce a graded response in this regime, the neuron must balance excitation with inhibition. We find that a simple integrate and fire mechanism with balanced excitation and inhibition produces a highly variable interspike interval, consistent with experimental data. Detailed information about the temporal pattern of synaptic inputs cannot be recovered from the pattern of output spikes, and we infer that cortical neurons are unlikely to transmit information in the temporal pattern of spike discharge. Rather, we suggest that quantities are represented as rate codes in ensembles of 50-100 neurons. These column-like ensembles tolerate large fractions of common synaptic input and yet covary only weakly in their spike discharge. We find that an ensemble of 100 neurons provides a reliable estimate of rate in just one interspike interval (10-50 msec). Finally, we derived an expression for the variance of the neural spike count that leads to a stable propagation of signal and noise in networks of neurons-that is, conditions that do not impose an accumulation or diminution of noise. The solution implies that single neurons perform simple algebra resembling averaging, and that more sophisticated computations arise by virtue of the anatomical convergence of novel combinations of inputs to the cortical column from external sources.

0 comments Cited 464 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Generating coherent patterns of activity from chaotic neural networks.

David Sussillo, L.F. Abbott (2009)

Neural circuits display complex activity patterns both spontaneously and when responding to a stimulus or generating a motor output. How are these two forms of activity related? We develop a procedure called FORCE learning for modifying synaptic strengths either external to or within a model neural network to change chaotic spontaneous activity into a wide variety of desired activity patterns. FORCE learning works even though the networks we train are spontaneously chaotic and we leave feedback loops intact and unclamped during learning. Using this approach, we construct networks that produce a wide variety of complex output patterns, input-output transformations that require memory, multiple outputs that can be switched by control inputs, and motor patterns matching human motion capture data. Our results reproduce data on premovement activity in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid and powerful modulator of network activity than generally appreciated.

0 comments Cited 353 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Chaos in neuronal networks with balanced excitatory and inhibitory activity.

C. van Vreeswijk, H. Sompolinsky (1996)

Neurons in the cortex of behaving animals show temporally irregular spiking patterns. The origin of this irregularity and its implications for neural processing are unknown. The hypothesis that the temporal variability in the firing of a neuron results from an approximate balance between its excitatory and inhibitory inputs was investigated theoretically. Such a balance emerges naturally in large networks of excitatory and inhibitory neuronal populations that are sparsely connected by relatively strong synapses. The resulting state is characterized by strongly chaotic dynamics, even when the external inputs to the network are constant in time. Such a network exhibits a linear response, despite the highly nonlinear dynamics of single neurons, and reacts to changing external stimuli on time scales much smaller than the integration time constant of a single neuron.

0 comments Cited 318 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-journal-id): 9809671

Journal ID (pubmed-jr-id): 21092

Journal ID (nlm-ta): Nat Neurosci

Title: Nature neuroscience

ISSN (Print): 1097-6256

ISSN (Electronic): 1546-1726

Publication date Nihms-submitted: 28 January 2010

Publication date (Electronic): 21 February 2010

Publication date (Print): March 2010

Publication date PMC-release: 1 September 2010

Volume: 13

Issue: 3

Pages: 369-378

Affiliations

[1 ]Dept. of Electrical Engineering, Stanford University, Stanford CA, 94305, USA.

[2 ]Neurosciences Program, Stanford University, Stanford CA, 94305, USA.

[3 ]Howard Hughes Medical Institute, Stanford University, Stanford CA, 94305, USA.

[4 ]Department of Neurobiology, Stanford University School of Medicine, Stanford University, Stanford CA, 94305, USA.

[5 ]Department of Neurosurgery, Stanford University School of Medicine, Stanford University, Stanford CA, 94305, USA.

[6 ]Department of Psychology and Committee on Computational Neuroscience, University of Chicago, Chicago, IL 60637, USA.

[7 ]Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.

[8 ]Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA.

[9 ]Center for Neural Science, New York University, New York, NY 10003, USA.

[10 ]Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO, USA.

[11 ]Howard Hughes Medical Institute, W.M. Keck Foundation Center for Integrative Neuroscience and Department of Physiology, University of California San Francisco, San Francisco, California, USA.

[12 ]Section of Neurobiology, School of Biological Sciences, University of Texas at Austin, Austin, Texas, USA.

[13 ]Department of Neurobiology and Physiology, Northwestern University, Evanston, Illinois, USA.

[14 ]Department of Neurosurgery, Stanford University School of Medicine, Stanford University, Stanford, California, USA.

[15 ]Department of Bioengineering, Stanford University, Stanford, California, USA.

Author notes

Correspondence should be addressed to M.M.C. ( church@ 123456stanford.edu )

[16]

These authors contributed equally to this work.

Article

Manuscript ID: nihpa170585

DOI: 10.1038/nn.2501

PMC ID: 2828350

PubMed ID: 20173745

SO-VID: 77b5ee05-41fb-45e5-8382-578268b5546a

License:

Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms