Chimera-like states in modular neural networks

TL;DR

This paper investigates chimera-like states in a modular neural network modeled after C. elegans, revealing how mixed synchronized and desynchronized activity can emerge due to electrical and chemical synapse interactions.

Contribution

It demonstrates the emergence of chimera-like states in a biologically inspired modular neural network with mixed synaptic coupling, expanding understanding of complex neural dynamics.

Findings

Chimera-like states occur in C. elegans neural network model.

Electrical and chemical synapses jointly influence synchronization patterns.

Similar phenomena are observed in other modular networks.

Abstract

Chimera states, namely the coexistence of coherent and incoherent behavior, were previously analyzed in complex networks. However, they have not been extensively studied in modular networks. Here, we consider the neural network of the \textit{C.elegans} soil worm, organized into six interconnected communities, where neurons obey chaotic bursting dynamics. Neurons are assumed to be connected with electrical synapses within their communities and with chemical synapses across them. As our numerical simulations reveal, the coaction of these two types of coupling can shape the dynamics in such a way that chimera-like states can happen. They consist of a fraction of synchronized neurons which belong to the larger communities, and a fraction of desynchronized neurons which are part of smaller communities. In addition to the Kuramoto order parameter $\rho$, we also employ other measures of coherence, such as the chimera-like $\chi$ and metastability $\lambda$ indices, which quantify the degree of synchronization among communities and along time, respectively. We perform the same analysis for networks that share common features with the \textit{C.elegans} neural network. Similar results suggest that under certain assumptions, chimera-like states are prominent phenomena in modular networks, and might provide insight for the behavior of more complex modular networks.