Synaptic information transfer in computer models of neocortical columns

Samuel A. Neymotin, Kimberle M. Jacobs, Andre Fenton, William W. Lytton

Research output: Contribution to journalArticle

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 Kendalls 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.

Original languageEnglish (US)
Pages (from-to)69-84
Number of pages16
JournalJournal of Computational Neuroscience
Volume30
Issue number1
DOIs
StatePublished - Feb 2011

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Information Services
Computer Simulation
Synapses
Entropy
Neurosciences
Automatic Data Processing
Brain

Keywords

  • Information transfer
  • Modeling
  • Neuronal networks
  • Simulation

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience
  • Sensory Systems

Cite this

Synaptic information transfer in computer models of neocortical columns. / Neymotin, Samuel A.; Jacobs, Kimberle M.; Fenton, Andre; Lytton, William W.

In: Journal of Computational Neuroscience, Vol. 30, No. 1, 02.2011, p. 69-84.

Research output: Contribution to journalArticle

Neymotin, Samuel A. ; Jacobs, Kimberle M. ; Fenton, Andre ; Lytton, William W. / Synaptic information transfer in computer models of neocortical columns. In: Journal of Computational Neuroscience. 2011 ; Vol. 30, No. 1. pp. 69-84.
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