Eigenvalue spectra of asymmetric random matrices for multi-component neural networks

TL;DR

This paper extends the understanding of eigenvalue spectra in large multi-component neural networks with random synaptic matrices, proving independence from mean values and deriving explicit spectral formulas.

Contribution

It generalizes previous models to multiple neuron types with correlated synaptic strengths and provides explicit spectral formulas using diagrammatic techniques.

Findings

Eigenvalue spectra are independent of mean synaptic strengths.

Derived explicit formulas for spectra of multi-component neural networks.

Extended previous two-component models to multiple types with correlations.

Abstract

This paper focuses on large neural networks whose synaptic connectivity matrices are randomly chosen from certain random matrix ensembles. The dynamics of these networks can be characterized by the eigenvalue spectra of their connectivity matrices. In reality, neurons in a network do not necessarily behave in a similar way, but may belong to several different categories. The first study of the spectra of two-component neural networks was carried out by Rajan and Abbott. In their model, neurons are either 'excitatory' or 'inhibitory', and strengths of synapses from different types of neurons have Gaussian distributions with different means and variances. A surprising finding by Rajan and Abbott is that the eigenvalue spectra of these types of random synaptic matrices do not depend on the mean values of their elements. In this paper we prove that this is true even for a much more general type of random neural network, where there is a finite number of types of neurons, and their synaptic strengths have correlated distributions. Furthermore, using the diagrammatic techniques, we calculate the explicit formula for the spectra of synaptic matrices of multi-component neural networks.