Implications of neuronal diversity on population coding.

Full Abstract

In many cortical and subcortical areas, neurons are known to modulate their average firing rate in response to certain external stimulus features. It is widely believed that information about the stimulus features is coded by a weighted average of the neural responses. Recent theoretical studies have shown that the information capacity of such a coding scheme is very limited in the presence of the experimentally observed pairwise correlations. However, central to the analysis of these studies was the assumption of a homogeneous population of neurons. Experimental findings show a considerable measure of heterogeneity in the response properties of different neurons. In this study, we investigate the effect of neuronal heterogeneity on the information capacity of a correlated population of neurons. We show that information capacity of a heterogeneous network is not limited by the correlated noise, but scales linearly with the number of cells in the population. This information cannot be extracted by the population vector readout, whose accuracy is greatly suppressed by the correlated noise. On the other hand, we show that an optimal linear readout that takes into account the neuronal heterogeneity can extract most of this information. We study analytically the nature of the dependence of the optimal linear readout weights on the neuronal diversity. We show that simple online learning can generate readout weights with the appropriate dependence on the neuronal diversity, thereby yielding efficient readout.

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Author information

Author/s: Shamir, Maoz (M); Sompolinsky, Haim (H);

Affiliation: Center for BioDynamics, Boston University, Boston, MA 02215, U.S.A. shamir(-atsign-)bu.edu

Journal and publication information

Publication Type: Letter; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.

Journal: Neural computation (Neural Comput), published in United States. (Language: eng)

Reference: 2006-Aug; vol 18 (issue 8) : pp 1951-86

Dates: Created 2006/06/14; Completed 2006/08/24; Revised 2006/11/15;

PMID: 16771659, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Action Potentials - physiology
Animals
Artifacts
Brain - physiology
Humans
Linear Models

Models, Neurological
Nerve Net - physiology
Neural Networks (Computer)
Neural Pathways - physiology
Neurons - physiology
Synaptic Transmission - physiology

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