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9 - Distributed population codes in sensory and memory representations of the neocortex

Published online by Cambridge University Press:  14 August 2009

By
Matthias Munk
Edited by
Christian Holscher  and
Matthias Munk
Christian Holscher
Affiliation:
University of Ulster
Matthias Munk
Affiliation:
Max-Planck-Institut für biologische Kybernetik, Tübingen
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Summary

Distributed representations are the inevitable consequence of devoting large neuronal circuits to a detailed and adaptive analysis of complex information. The neocortical sheet with its extensive cortico-cortical connectivity is characterized by ubiquitous massive divergence and convergence, sparseness and reciprocity of the vast majority of connections. It therefore appears as an optimized dynamical structure for detailed and adaptive analysis on the one hand and the operation of multiple parallel neuronal processes required for optimizing speed and accuracy of information processing on the other hand. The established concepts of information coding in the cortex are based on tuning functions of many individual neurons thought to express their stimulus specificity in an independent way. A second level of organization is usually attributed to the spatial relations of neurons in topographically organized representations like those in sensory areas and the convergence of neuronal signals carrying information from different modalities into “higher” areas which are more involved in executive functions or the formation of complex memory representations. It has been argued that the collection of neuronal signals from consecutive recording sessions can be used to reconstruct population codes as it has been done with the “population vector” analysis in motor, sensory, and memory areas of the cortex. It is clear that the success of this method relies on fixed neuronal response properties which have been consolidated in cortical circuits over long periods of time such that the spatial pattern and the mixture of neurons contributing to the population response is reasonably stable.

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Information Processing by Neuronal Populations , pp. 192 - 223
Publisher: Cambridge University Press
Print publication year: 2008

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