Control of synchronization in coupled neural systems by time-delayed feedback

TL;DR

This paper explores how time-delayed feedback control can modulate synchronization in coupled neural systems modeled by FitzHugh-Nagumo equations, demonstrating robustness with increased memory parameters.

Contribution

It introduces a novel control method using extended time-delayed feedback to influence synchronization in coupled neural populations.

Findings

Control method effectively alters synchronization measures.

Robustness of control improves with higher memory parameter.

Local control can steer global neural dynamics.

Abstract

We discuss the synchronization of coupled neurons which are modelled as FitzHugh-Nagumo systems. As smallest entity in a larger network, we focus on two diffusively coupled subsystems, which can be interpreted as two mutually interacting neural populations. Each system is prepared in the excitable regime and subject to independent random fluctuations. In order to modify their cooperative dynamics, we apply a local external stimulus in form of an extended time-delayed feedback loop that involves multiple delays weighted by a memory parameter and investigate if local control applied to a subsystem can allow one to steer the global cooperative dynamics. Depending on the choice of this new control parameter, we investigate different measures to quantify the influence on synchronization: ratio of interspike intervals, power spectrum, interspike interval distribution, and phase synchronization intervals. We show that the control method is more robust for increasing memory parameter.