title: Eigenvalue spectra of random matrices for neural networks.
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.
authors.affiliation: Center for Neurobiology and Behavior, Columbia University,, College of Physicians and Surgeons, New York, New York 10032, USA.
authors.forename: Kanaka
authors.initials: K
authors.lastname: Rajan
authors.forename: L F
authors.initials: LF
authors.lastname: Abbott
mesh_terms: D008962:Models, Theoretical; D009434:Neural Pathways; D009474:Neurons