Spectrum density of large sparse random matrices associated to neural networks

TL;DR

This paper derives exact and approximate formulas for the eigenvalue spectrum density of large sparse neural network matrices, considering Dale's law and network sparsity, and explores eigenvector localization properties.

Contribution

It provides the first self-consistent expressions for the spectrum density of sparse neural network matrices, extending previous dense matrix results and analyzing eigenvector localization.

Findings

Exact spectrum density formulas for sparse matrices

Effective numerical approximations for spectrum density

Insights into eigenvector localization phenomena

Abstract

The eigendecomposition of the coupling matrix of large biological networks is central to the study of the dynamics of these networks. For neural networks, this matrix should reflect the topology of the network and conform with Dale's law which states that a neuron can have only all excitatory or only all inhibitory output connections, i.e., coefficients of one column of the coupling matrix must all have the same sign. The eigenspectrum density has been determined before for dense matrices $J_{ij}$, when several populations are considered. However, the expressions were derived under the assumption of dense connectivity, whereas neural circuits have sparse connections. Here, we followed mean-field approaches in order to come up with exact self-consistent expressions for the spectrum density in the limit of sparse matrices for both symmetric and neural network matrices. Furthermore we introduced approximations that allow for good numerical evaluation of the density. Finally, we studied the phenomenology of localization properties of the eigenvectors.