Properties of networks with partially structured and partially random connectivity

TL;DR

This paper derives formulas for the eigenvalue density and transient dynamics of large, partially structured and random matrices, with applications to neurobiological network models and stability analysis.

Contribution

It provides a general eigenvalue density formula for matrices combining deterministic and random components, extending understanding of nonnormal matrix spectra and dynamics.

Findings

Eigenvalue density formula for matrices of the form A = M + LJR.

Analytical results for transient activity and response in linear systems.

Identification of conditions for outlying eigenvalues persistence in large matrices.

Abstract

We provide a general formula for the eigenvalue density of large random $N\times N$ matrices of the form $A = M + LJR$, where $M$, $L$ and $R$ are arbitrary deterministic matrices and $J$ is a random matrix of zero-mean independent and identically distributed elements. For $A$ nonnormal, the eigenvalues do not suffice to specify the dynamics induced by $A$, so we also provide general formulae for the transient evolution of the magnitude of activity and frequency power spectrum in an $N$-dimensional linear dynamical system with a coupling matrix given by $A$. These quantities can also be thought of as characterizing the stability and the magnitude of the linear response of a nonlinear network to small perturbations about a fixed point. We derive these formulae and work them out analytically for some examples of $M$, $L$ and $R$ motivated by neurobiological models. We also argue that the persistence as $N\rightarrow\infty$ of a finite number of randomly distributed outlying eigenvalues outside the support of the eigenvalue density of $A$, as previously observed, arises in regions of the complex plane $\Omega$ where there are nonzero singular values of $L^{-1} (z\mathbf{1} - M) R^{-1}$ (for $z\in\Omega$) that vanish as $N\rightarrow\infty$. When such singular values do not exist and $L$ and $R$ are equal to the identity, there is a correspondence in the normalized Frobenius norm (but not in the operator norm) between the support of the spectrum of $A$ for $J$ of norm $\sigma$ and the $\sigma$-pseudospectrum of $M$.