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      The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding

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          Abstract

          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.

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          Author and article information

          Journal
          J Neurosci
          J. Neurosci
          jneuro
          jneurosci
          J. Neurosci
          The Journal of Neuroscience
          Society for Neuroscience
          0270-6474
          1529-2401
          15 May 1998
          : 18
          : 10
          : 3870-3896
          Affiliations
          [ 1 ]Department of Physiology and Biophysics and Regional Primate Research Center, University of Washington, Seattle, Washington 98195-7290, and
          [ 2 ]Howard Hughes Medical Institute and Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305
          Article
          PMC6793166 PMC6793166 6793166 2000
          10.1523/JNEUROSCI.18-10-03870.1998
          6793166
          9570816
          f8a35cd9-aeb9-4f6f-8057-3c277cd81353
          Copyright © 1998 Society for Neuroscience
          Categories
          Article
          Custom metadata
          5.00

          rate code,temporal coding,correlation,interspike interval,spike count variance,synaptic integration,response variability,visual cortex,neural model,noise

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