Noise-induced chimera states in a neural network

TL;DR

This paper demonstrates that noise can induce chimera patterns in neural networks, leading to coherence-resonance chimeras that exhibit alternating coherent and incoherent domains, highlighting noise's constructive role in complex neural dynamics.

Contribution

It introduces the concept of coherence-resonance chimeras, showing how noise induces and sustains these patterns with alternating behaviors in neural networks.

Findings

Noise induces chimera patterns in neural networks.

Coherence-resonance chimeras exhibit alternating coherent and incoherent domains.

Patterns occur at intermediate noise levels, demonstrating constructive noise effects.

Abstract

We show that chimera patterns can be induced by noise in nonlocally coupled neural networks in the excitable regime. In contrast to classical chimeras, occurring in noise-free oscillatory networks, they have features of two phenomena: coherence resonance and chimera states. Therefore, we call them coherence-resonance chimeras. These patterns demonstrate the constructive role of noise and appear for intermediate values of noise intensity, which is a characteristic feature of coherence resonance. In the coherence-resonance chimera state a neural network of identical elements splits into two coexisting domains with different behavior: spatially coherent and spatially incoherent, a typical property of chimera states. Moreover, these noise-induced chimera states are characterized by alternating behavior: coherent and incoherent domains switch periodically their location. We show that this alternating switching can be explained by analyzing the coupling functions.