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Synaptic information transfer in computer models of neocortical columns

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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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Acknowledgements

We thank the anonymous reviewers for their insightful comments and suggestions on improvement of the manuscript.

We also thank Michael Hines and Ted Carnevale (Yale) for continuing support and assistance with the NEURON simulator, Andrey Olypher (Emory) for helpful discussions, Matthew Lazenka (VCU) for assistance with development of the neocortical model, and Larry Eberle (SUNY Downstate) for administration and network support at the Neurosimulation laboratory.

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Authors and Affiliations

  1. Biomedical Engineering, SUNY Downstate Medical Center, 450 Clarkson Avenue, P.O. Box 31, Brooklyn, NY, 11203-2098, USA

    Samuel A. Neymotin, André A. Fenton & William W. Lytton

  2. Departments of Anatomy and Neurobiology, Virginia Commonwealth University, Richmond, VA, 23284, USA

    Kimberle M. Jacobs

  3. Department of Physiology and Pharmacology, SUNY Downstate Medical Center, 450 Clarkson Avenue, P.O. Box 31, Brooklyn, NY, 11203-2098, USA

    André A. Fenton & William W. Lytton

  4. Center for Neural Science, New York University, 4 Washington Place, New York, NY, 10003-6621, USA

    André A. Fenton

  5. Department of Neurology, SUNY Downstate Medical Center, 450 Clarkson Avenue, P.O. Box 31, Brooklyn, NY, 11203-2098, USA

    William W. Lytton

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  1. Samuel A. Neymotin
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  2. Kimberle M. Jacobs
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  3. André A. Fenton
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  4. William W. Lytton
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Correspondence to Samuel A. Neymotin.

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Action Editor: P. Dayan

This research was supported by NIH grants MH082417 and MH057068 and by DARPA grant N66001-10-C-2008.

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Neymotin, S.A., Jacobs, K.M., Fenton, A.A. et al. Synaptic information transfer in computer models of neocortical columns. J Comput Neurosci 30, 69–84 (2011). https://doi.org/10.1007/s10827-010-0253-4

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