Nonlocal mechanism for cluster synchronization in neural circuits

TL;DR

This paper introduces a nonlocal GCD-based mechanism explaining cluster synchronization in neural circuits, linking network topology to activity modes, supported by simulations and offering new insights into cortical activity and information processing.

Contribution

It proposes a novel GCD-based nonlocal mechanism for cluster synchronization in neural networks, connecting network loops to synchronized activity modes.

Findings

GCD determines cluster synchronization patterns

External stimuli influence the number of synchronized clusters

Simulation results support the GCD mechanism in neural dynamics

Abstract

The interplay between the topology of cortical circuits and synchronized activity modes in distinct cortical areas is a key enigma in neuroscience. We present a new nonlocal mechanism governing the periodic activity mode: the greatest common divisor (GCD) of network loops. For a stimulus to one node, the network splits into GCD-clusters in which cluster neurons are in zero-lag synchronization. For complex external stimuli, the number of clusters can be any common divisor. The synchronized mode and the transients to synchronization pinpoint the type of external stimuli. The findings, supported by an information mixing argument and simulations of Hodgkin Huxley population dynamic networks with unidirectional connectivity and synaptic noise, call for reexamining sources of correlated activity in cortex and shorter information processing time scales.