Neuronal heterogeneity modulates phase-synchronization between unidirectionally coupled populations with excitation-inhibition balance

TL;DR

This study models how neuronal heterogeneity influences phase synchronization between coupled brain regions, reproducing experimental phase relations and revealing diverse synchronization regimes while maintaining excitation-inhibition balance.

Contribution

It introduces a model demonstrating how neuronal variability affects phase relations between coupled populations, explaining experimental observations of synchronization phenomena.

Findings

The model reproduces anticipated, delayed, and zero-lag synchronization regimes.

Neuronal heterogeneity determines the phase relation between populations.

Diversity in phase relations is maintained with excitation-inhibition balance.

Abstract

Several experiments and models have highlighted the importance of neuronal heterogeneity in brain dynamics and function. However, how such a cell-to-cell diversity can affect cortical computation, synchronization, and neuronal communication is still under debate. Previous studies have focused on the effect of neuronal heterogeneity in one neuronal population. Here we are specifically interested in the effect of neuronal variability on the phase relations between two populations, which can be related to different cortical communication hypotheses. It has been recently shown that two spiking neuron populations unidirectionally connected in a sender-receiver configuration can exhibit anticipated synchronization (AS), which is characterized by a negative phase-lag. This phenomenon has been reported in electrophysiological data of non-human primates and human EEG during a visual discrimination cognitive task. In experiments, the unidirectional coupling could be accessed by Granger causality and can be accompanied by both positive or negative phase difference between cortical areas. Here we propose a model of two coupled populations in which the neuronal heterogeneity can determine the dynamical relation between the sender and the receiver and can reproduce phase relations reported in experiments. Depending on the distribution of parameters characterizing the neuronal firing patterns, the system can exhibit both AS and the usual delayed synchronization regime (DS, with positive phase) as well as a zero-lag synchronization regime and phase bistability between AS and DS. Furthermore, we show that our network can present diversity in their phase relations maintaining the excitation-inhibition balance.