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      Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex

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          Abstract

          It is well known that neural activity exhibits variability, in the sense that identical sensory stimuli produce different responses, but it has been difficult to determine what this variability means. Is it noise, or does it carry important information – about, for example, the internal state of the organism? We address this issue from the bottom up, by asking whether small perturbations to activity in cortical networks are amplified. Based on in vivo whole-cell recordings in rat barrel cortex, we find that a perturbation consisting of a single extra spike in one neuron produces ~28 additional spikes in its postsynaptic targets, and we show, using simultaneous intra- and extra-cellular recordings, that a single spike produces a detectable increase in firing rate in the local network. Theoretical analysis indicates that this amplification leads to intrinsic, stimulus-independent variations in membrane potential on the order of ±2.2 - 4.5 mV – variations that are pure noise, and so carry no information at all. Therefore, for the brain to perform reliable computations, it must either use a rate code, or generate very large, fast depolarizing events, such as those proposed by the theory of synfire chains – yet in our in vivo recordings, we found that such events were very rare. Our findings are consistent with the idea that cortex is likely to use primarily a rate code.

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          Most cited references28

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          Dendritic computation.

          One of the central questions in neuroscience is how particular tasks, or computations, are implemented by neural networks to generate behavior. The prevailing view has been that information processing in neural networks results primarily from the properties of synapses and the connectivity of neurons within the network, with the intrinsic excitability of single neurons playing a lesser role. As a consequence, the contribution of single neurons to computation in the brain has long been underestimated. Here we review recent work showing that neuronal dendrites exhibit a range of linear and nonlinear mechanisms that allow them to implement elementary computations. We discuss why these dendritic properties may be essential for the computations performed by the neuron and the network and provide theoretical and experimental examples to support this view.
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            Chaos in neuronal networks with balanced excitatory and inhibitory activity.

            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.
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              Polychronization: computation with spikes.

              We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. Due to the interplay between the delays and STDP, the spiking neurons spontaneously self-organize into groups and generate patterns of stereotypical polychronous activity. To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. We speculate on the significance of polychrony to the theory of neuronal group selection (TNGS, neural Darwinism), cognitive neural computations, binding and gamma rhythm, mechanisms of attention, and consciousness as "attention to memories."
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                0028-0836
                1476-4687
                21 April 2010
                1 July 2010
                1 January 2011
                : 466
                : 7302
                : 123-127
                Affiliations
                [1 ]Wolfson Institute for Biomedical Research and Department of Neuroscience, Physiology and Pharmacology, University College London, Gower Street, London WC1E 6BT, UK
                [2 ]Gatsby Computational Neuroscience Unit University College London, Gower Street, London WC1E 6BT, UK
                Author notes
                Address for editorial correspondence: Peter E. Latham Gatsby Computational Neuroscience Unit University College London 17 Queen Square London WC1N 3AR UK tel. +44-(0)20-7679-1178 fax +44-(0)20-7679-1173 pel@ 123456gatsby.ucl.ac.uk
                Article
                nihpa196781
                10.1038/nature09086
                2898896
                20596024
                9fe0905c-8b64-4448-a8ed-1e6f8753c833

                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

                History
                Funding
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH062447-08 ||MH
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