Optimized brute-force algorithms for the bifurcation analysis of a spin-glass-like neural network model

Abstract

Bifurcation theory is a powerful tool for studying how the dynamics of a neural network model depends on its underlying neurophysiological parameters. However, bifurcation theory has been developed mostly for smooth dynamical systems and for continuous-time non-smooth models, which prevents us from understanding the changes of dynamics in some widely used classes of artificial neural network models. This article is an attempt to fill this gap, through the introduction of algorithms that perform a semi-analytical bifurcation analysis of a spin-glass-like neural network model with binary firing rates and discrete-time evolution. Our approach is based on a numerical brute-force search of the stationary and oscillatory solutions of the spin-glass model, from which we derive analytical expressions of its bifurcation structure by means of the state-to-state transition probability matrix. The algorithms determine how the network parameters affect the degree of multistability, the emergence and the period of the neural oscillations, and the formation of symmetry-breaking in the neural populations. While this technique can be applied to networks with arbitrary (generally asymmetric) connectivity matrices, in particular we introduce a highly efficient algorithm for the bifurcation analysis of sparse networks. We also provide some examples of the obtained bifurcation diagrams and a Python implementation of the algorithms.