University of Notre Dame
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Second-Order Moments of Activity in Large Neural Network Models

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posted on 2020-03-09, 00:00 authored by Cody Baker

Understanding the magnitude and structure of interneuronal correlations and their relationship to synaptic connectivity structure is an important and difficult problem in computational neuroscience. Early studies show that neuronal network models with excitatory-inhibitory balance naturally create very weak spike train correlations, defining the “asynchronous state.” Later work showed that, under some connectivity structures, balanced networks can produce larger correlations between some neuron pairs, even when the average correlation is very small. All of these previous studies assume that the local network receives feedforward synaptic input from a population of uncorrelated spike trains. We show that when spike trains providing feedforward input are correlated, the downstream recurrent network produces much larger correlations. We provide an in-depth analysis of the resulting “correlated state” in balanced networks and show that, unlike the asynchronous state, it produces a tight excitatory-inhibitory balance consistent with in vivo cortical recordings.

History

Date Modified

2020-05-06

Defense Date

2020-02-26

CIP Code

  • 27.9999

Research Director(s)

Robert J. Rosenbaum

Committee Members

Daniele Schiavazzi Alexandra Jilkine

Degree

  • Doctor of Philosophy

Degree Level

  • Doctoral Dissertation

Alternate Identifier

1153265412

Library Record

5498847

OCLC Number

1153265412

Program Name

  • Applied and Computational Mathematics and Statistics

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