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Erschienen in: Journal of Computational Neuroscience 1/2011

01.02.2011

Synaptic information transfer in computer models of neocortical columns

verfasst von: Samuel A. Neymotin, Kimberle M. Jacobs, André A. Fenton, William W. Lytton

Erschienen in: Journal of Computational Neuroscience | Ausgabe 1/2011

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Abstract

Understanding the direction and quantity of information flowing in neuronal networks is a fundamental problem in neuroscience. Brains and neuronal networks must at the same time store information about the world and react to information in the world. We sought to measure how the activity of the network alters information flow from inputs to output patterns. Using neocortical column neuronal network simulations, we demonstrated that networks with greater internal connectivity reduced input/output correlations from excitatory synapses and decreased negative correlations from inhibitory synapses, measured by Kendall’s τ correlation. Both of these changes were associated with reduction in information flow, measured by normalized transfer entropy (nTE). Information handling by the network reflected the degree of internal connectivity. With no internal connectivity, the feedforward network transformed inputs through nonlinear summation and thresholding. With greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. This dynamic contribution amounts to added information drawn from that stored in the network. At still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through. The association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing.

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Metadaten
Titel
Synaptic information transfer in computer models of neocortical columns
verfasst von
Samuel A. Neymotin
Kimberle M. Jacobs
André A. Fenton
William W. Lytton
Publikationsdatum
01.02.2011
Verlag
Springer US
Erschienen in
Journal of Computational Neuroscience / Ausgabe 1/2011
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI
https://doi.org/10.1007/s10827-010-0253-4

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