The type II phase resetting curve is optimal for stochastic synchrony

TL;DR

This paper demonstrates that Type II phase-resetting curves are optimal for achieving stochastic synchrony in neural oscillators receiving noisy inputs, highlighting the importance of PRC shape in neural synchronization.

Contribution

It proves that Type II PRCs facilitate greater stochastic synchrony compared to Type I PRCs using constrained optimization and perturbation methods.

Findings

Type II PRCs promote more robust stochastic synchrony.

PRC shape critically influences neural oscillator synchronization.

Type I PRCs are less effective for noise-driven synchrony.

Abstract

The phase-resetting curve (PRC) describes the response of a neural oscillator to small perturbations in membrane potential. Its usefulness for predicting the dynamics of weakly coupled deterministic networks has been well characterized. However, the inputs to real neurons may often be more accurately described as barrages of synaptic noise. Effective connectivity between cells may thus arise in the form of correlations between the noisy input streams. We use constrained optimization and perturbation methods to prove that PRC shape determines susceptibility to synchrony among otherwise uncoupled noise-driven neural oscillators. PRCs can be placed into two general categories: Type I PRCs are non-negative while Type II PRCs have a large negative region. Here we show that oscillators with Type II PRCs receiving common noisy input sychronize more readily than those with Type I PRCs.