Second-Order Moments of Activity in Large Neural Network Models
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
2020-05-06Defense Date
2020-02-26CIP Code
- 27.9999
Research Director(s)
Robert J. RosenbaumCommittee Members
Daniele Schiavazzi Alexandra JilkineDegree
- Doctor of Philosophy
Degree Level
- Doctoral Dissertation
Alternate Identifier
1153265412Library Record
5498847OCLC Number
1153265412Program Name
- Applied and Computational Mathematics and Statistics