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.
Users
Please
log in to take part in the discussion (add own reviews or comments).