  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.
