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      Axonal Noise as a Source of Synaptic Variability

      research-article
      1 , * , 1 , 2 , 3
      PLoS Computational Biology
      Public Library of Science

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          Abstract

          Post-synaptic potential (PSP) variability is typically attributed to mechanisms inside synapses, yet recent advances in experimental methods and biophysical understanding have led us to reconsider the role of axons as highly reliable transmission channels. We show that in many thin axons of our brain, the action potential (AP) waveform and thus the Ca ++ signal controlling vesicle release at synapses will be significantly affected by the inherent variability of ion channel gating. We investigate how and to what extent fluctuations in the AP waveform explain observed PSP variability. Using both biophysical theory and stochastic simulations of central and peripheral nervous system axons from vertebrates and invertebrates, we show that channel noise in thin axons (<1 µm diameter) causes random fluctuations in AP waveforms. AP height and width, both experimentally characterised parameters of post-synaptic response amplitude, vary e.g. by up to 20 mV and 0.5 ms while a single AP propagates in C-fibre axons. We show how AP height and width variabilities increase with a ¾ power-law as diameter decreases and translate these fluctuations into post-synaptic response variability using biophysical data and models of synaptic transmission. We find for example that for mammalian unmyelinated axons with 0.2 µm diameter (matching cerebellar parallel fibres) axonal noise alone can explain half of the PSP variability in cerebellar synapses. We conclude that axonal variability may have considerable impact on synaptic response variability. Thus, in many experimental frameworks investigating synaptic transmission through paired-cell recordings or extracellular stimulation of presynaptic neurons, causes of variability may have been confounded. We thereby show how bottom-up aggregation of molecular noise sources contributes to our understanding of variability observed at higher levels of biological organisation.

          Author Summary

          The fundamental signal of the nervous system is the action potential: an electrical spike propagated along neurons and transmitted between them via synapses. Once triggered, action potentials are generally assumed to be robust to noise, and the variability observed at all levels of the nervous system is primarily attributed to synapses. However, this view is based on data from classically studied axons, which are very large compared to the average diameter of axons in the mammalian nervous system, and even larger when compared to the thinnest axons. As the effects of thermodynamic noise affecting the proteins responsible for the initiation and propagation of action potentials are much bigger in thin axons, the assumption does not necessarily hold for very thin axons. We show that the action potentials waveform in thin axons is subject to random variability. Fluctuations in this waveform result in fluctuations in synaptic ionic currents, and account for a significant portion of the variability observed at the synapse.

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          Most cited references73

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          Noise in the nervous system.

          Noise--random disturbances of signals--poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.
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            Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model.

            Fast neuronal oscillations (gamma, 20-80 Hz) have been observed in the neocortex and hippocampus during behavioral arousal. Using computer simulations, we investigated the hypothesis that such rhythmic activity can emerge in a random network of interconnected GABAergic fast-spiking interneurons. Specific conditions for the population synchronization, on properties of single cells and the circuit, were identified. These include the following: (1) that the amplitude of spike afterhyperpolarization be above the GABAA synaptic reversal potential; (2) that the ratio between the synaptic decay time constant and the oscillation period be sufficiently large; (3) that the effects of heterogeneities be modest because of a steep frequency-current relationship of fast-spiking neurons. Furthermore, using a population coherence measure, based on coincident firings of neural pairs, it is demonstrated that large-scale network synchronization requires a critical (minimal) average number of synaptic contacts per cell, which is not sensitive to the network size. By changing the GABAA synaptic maximal conductance, synaptic decay time constant, or the mean external excitatory drive to the network, the neuronal firing frequencies were gradually and monotonically varied. By contrast, the network synchronization was found to be high only within a frequency band coinciding with the gamma (20-80 Hz) range. We conclude that the GABAA synaptic transmission provides a suitable mechanism for synchronized gamma oscillations in a sparsely connected network of fast-spiking interneurons. In turn, the interneuronal network can presumably maintain subthreshold oscillations in principal cell populations and serve to synchronize discharges of spatially distributed neurons.
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              Wiring optimization can relate neuronal structure and function.

              We pursue the hypothesis that neuronal placement in animals minimizes wiring costs for given functional constraints, as specified by synaptic connectivity. Using a newly compiled version of the Caenorhabditis elegans wiring diagram, we solve for the optimal layout of 279 nonpharyngeal neurons. In the optimal layout, most neurons are located close to their actual positions, suggesting that wiring minimization is an important factor. Yet some neurons exhibit strong deviations from "optimal" position. We propose that biological factors relating to axonal guidance and command neuron functions contribute to these deviations. We capture these factors by proposing a modified wiring cost function.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                May 2014
                8 May 2014
                : 10
                : 5
                : e1003615
                Affiliations
                [1 ]Department of Bioengineering, Imperial College London, London, United Kingdom
                [2 ]Department of Computing, Imperial College London, London, United Kingdom
                [3 ]MRC Clinical Sciences Centre, London, United Kingdom
                University College London, United Kingdom
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: AAF. Performed the experiments: AN AAF. Analyzed the data: AN AAF. Wrote the paper: AN AAF.

                Article
                PCOMPBIOL-D-13-01788
                10.1371/journal.pcbi.1003615
                4014398
                24809823
                2c958fb6-0ca1-4aac-9248-3981a743e265
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 October 2013
                : 26 March 2014
                Page count
                Pages: 16
                Funding
                AN is supported by the Engineering and Physical Sciences Research Council, UK. AAF is supported by Human Frontiers in Science program grant (No: RPG00022/2012). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Computational Neuroscience
                Neuroscience

                Quantitative & Systems biology
                Quantitative & Systems biology

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