Stabilizing synchrony by inhomogeneity

TL;DR

This paper demonstrates that inhomogeneity in neuronal oscillators can stabilize synchrony and improve spike timing precision, counteracting instability caused by identical oscillators and noise.

Contribution

It reveals that small inhomogeneity can stabilize phase locking in neuronal models, enhancing spike train correlation.

Findings

Inhomogeneity stabilizes synchrony in neuronal oscillators.

Weak noise can disrupt synchrony in identical neurons.

Inhomogeneity increases spike train correlation.

Abstract

We show that for two identical neuronal oscillators with strictly positive phase resetting curve, isochronous synchrony is an unstable attractor and arbitrarily weak noise can destroy entrainment and generate intermittent phase slips. Small inhomogeneity (mismatch in the intrinsic firing rate of the neurons) can stabilize the phase locking and lead to more precise relative spike timing of the two neurons. The results can explain how for a class of neuronal models, including leaky integrate-fire model, inhomogeneity can increase correlation of spike trains when the neurons are synaptically connected.