Chimera in a neuronal network model of the cat brain

TL;DR

This paper investigates the emergence of chimera states in a neuronal network model of the cat brain, using the Hindmarsh-Rose model and the cat's cortical connectivity, revealing conditions for their occurrence and robustness.

Contribution

It introduces a novel analysis of chimera states in a biologically realistic cat brain network model based on actual cortical connectivity data.

Findings

Chimera states can be observed with both desynchronized spikes and bursts.

Chimera states with desynchronized bursts are more noise-resistant.

Coupling intensity influences the stability of chimera states.

Abstract

Neuronal systems have been modeled by complex networks in different description levels. Recently, it has been verified that networks can simultaneously exhibit one coherent and other incoherent domain, known as chimera states. In this work, we study the existence of chimera states in a network considering the connectivity matrix based on the cat cerebral cortex. The cerebral cortex of the cat can be separated in 65 cortical areas organised into the four cognitive regions: visual, auditory, somatosensory-motor and frontolimbic. We consider a network where the local dynamics is given by the Hindmarsh-Rose model. The Hindmarsh-Rose equations are a well known model of neuronal activity that has been considered to simulate membrane potential in neuron. Here, we analyse under which conditions chimera states are present, as well as the affects induced by intensity of coupling on them. We observe the existence of chimera states in that incoherent structure can be composed of desynchronised spikes or desynchronised bursts. Moreover, we find that chimera states with desynchronised bursts are more robust to neuronal noise than with desynchronised spikes.