Neural network spectral robustness under perturbations of the underlying graph

TL;DR

This paper investigates how the spectral properties of graph adjacency and Laplacian matrices respond to structural perturbations in simple network models, with implications for understanding brain network dynamics and robustness.

Contribution

It provides analytical and numerical insights into the spectral sensitivity of small, modular networks to edge density changes and perturbations, relevant for brain circuit modeling.

Findings

Spectral robustness varies with intra- and inter-modular edge density.

Perturbations in edge configuration affect the spectrum significantly.

Network size influences spectral properties and robustness.

Abstract

Recent studies have been using graph theoretical approaches to model complex networks (such as social, infrastructural or biological networks), and how their hardwired circuitry relates to their dynamic evolution in time. Understanding how configuration reflects on the coupled behavior in a system of dynamic nodes can be of great importance, for example in the context of how the brain connectome is affecting brain function. However, the connectivity patterns that appear in brain networks, and their individual effects on network dynamics, are far from being fully understood. We study the connections between edge configuration and dynamics in a simple oriented network composed of two interconnected cliques (representative of brain feedback regulatory circuitry). In this paper, our main goal is to study the spectra of the graph adjacency and Laplacian matrices, with a focus on three aspects in particular: (1) the sensitivity/robustness the spectrum in response to varying the intra and inter-modular edge density, (2) the effects on the spectrum of perturbing the edge configuration, while keeping the densities fixed and (3) the effects of increasing the network size. We study some tractable aspects analytically, then simulate more general results numerically. This paper aims to clarify, from analytical and modeling perspectives, the underpinnings of our related work, which further addresses how graph properties affect the network's temporal dynamics and phase transitions. We propose that this type of results may be helpful when studying small networks such as macroscopic brain circuits. We suggest potential applications to understanding synaptic restructuring in learning networks, and the effects of network configuration to function of emotion-regulatory neural circuits.