Collective chaos in pulse-coupled neural networks

TL;DR

This paper investigates the complex collective dynamics of two coupled neural populations, revealing symmetry-breaking, chimera states, and chaos through Lyapunov analysis, highlighting high-dimensional microscopic behavior.

Contribution

It introduces the study of collective chaos and symmetry-breaking transitions in pulse-coupled neural networks with excitatory coupling, expanding understanding of neural synchronization phenomena.

Findings

Identification of symmetry-breaking transitions leading to chimera states

Detection of chaotic dynamics via Lyapunov exponents

Evidence of high-dimensional microscopic behavior

Abstract

We study the dynamics of two symmetrically coupled populations of identical leaky integrate-and-fire neurons characterized by an excitatory coupling. Upon varying the coupling strength, we find symmetry-breaking transitions that lead to the onset of various chimera states as well as to a new regime, where the two populations are characterized by a different degree of synchronization. Symmetric collective states of increasing dynamical complexity are also observed. The computation of the the finite-amplitude Lyapunov exponent allows us to establish the chaoticity of the (collective) dynamics in a finite region of the phase plane. The further numerical study of the standard Lyapunov spectrum reveals the presence of several positive exponents, indicating that the microscopic dynamics is high-dimensional.