Analyzing synchronized clusters in neuron networks

TL;DR

This paper introduces a comprehensive framework for analyzing synchronized clusters in realistic neuron networks, accounting for delays, diverse neuron types, and synapses, and applies it to macaque cortex data.

Contribution

It presents a novel, general method for studying cluster synchronization in biologically realistic neuron models, extending beyond previous simplified approaches.

Findings

Framework successfully explains functional mechanisms in macaque cortex

Analysis reveals stability conditions for synchronized clusters

Results align with biological data despite model simplifications

Abstract

The presence of synchronized clusters in neuron networks is a hallmark of information transmission and processing. The methods commonly used to study cluster synchronization in networks of coupled oscillators ground on simplifying assumptions, which often neglect key biological features of neuron networks. Here we propose a general framework to study presence and stability of synchronous clusters in more realistic models of neuron networks, characterized by the presence of delays, different kinds of neurons and synapses. Application of this framework to the directed network of the macaque cerebral cortex provides an interpretation key to explain known functional mechanisms emerging from the combination of anatomy and neuron dynamics. The cluster synchronization analysis is carried out also by changing parameters and studying bifurcations. Despite some simplifications with respect to the real network, the obtained results are in good agreement with previously reported biological data.