Eigenvalue spectra of random matrices for neural networks

Kanaka Rajan, L. F. Abbott

Research output: Contribution to journalArticlepeer-review

173 Scopus citations

Abstract

The dynamics of neural networks is influenced strongly by the spectrum of eigenvalues of the matrix describing their synaptic connectivity. In large networks, elements of the synaptic connectivity matrix can be chosen randomly from appropriate distributions, making results from random matrix theory highly relevant. Unfortunately, classic results on the eigenvalue spectra of random matrices do not apply to synaptic connectivity matrices because of the constraint that individual neurons are either excitatory or inhibitory. Therefore, we compute eigenvalue spectra of large random matrices with excitatory and inhibitory columns drawn from distributions with different means and equal or different variances.

Original languageEnglish
Article number188104
JournalPhysical Review Letters
Volume97
Issue number18
DOIs
StatePublished - 2006
Externally publishedYes

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