A method for estimating of synaptic connectivity from spike data of multiple neurons

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Author(s)

    • Kobayashi Ryota
    • Principles of Informatics Research Division, National Institute of Informatics|Department of Informatics, SOKENDAI (The Graduate University for Advanced Studies)

Abstract

Investigation of synaptic connectivity between neurons is essential for understanding information processing mechanisms in the brain because this connectivity determines how spikes propagate in a neural circuit. Recent advances in experimental technology have enabled simultaneous recording of the spiking activity from hundreds of neurons. In this study, we propose a method for estimating synaptic connectivity using the spike data from multiple neurons. We first demonstrated that this method can perfectly estimate synaptic connectivity using synthetic spike data in an ideal condition. Subsequently, this method is applied to more realistic spike data generated by a large-scale network of Hodgkin-Huxley neurons. The results suggest that our estimation method is superior to a conventional method in identifying synaptic connectivity when the spiking activity from a large number of neurons is available for the analysis.

Journal

  • Nonlinear Theory and Its Applications, IEICE

    Nonlinear Theory and Its Applications, IEICE 7(2), 156-163, 2016

    The Institute of Electronics, Information and Communication Engineers

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