The type II phase resetting curve is optimal for stochastic synchrony

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.

💡 Research Summary

The paper investigates how the shape of a neuron’s phase‑resetting curve (PRC) influences stochastic synchrony among uncoupled oscillators driven by noisy inputs. Traditional studies have shown that PRCs are powerful predictors of dynamics in weakly coupled deterministic networks, but real cortical neurons are constantly bombarded by synaptic noise, and effective connectivity often arises from correlations in these noisy streams rather than from direct synaptic coupling. To address this gap, the authors model two identical neural oscillators receiving a common noisy drive plus independent private noise. The phase dynamics are described by the stochastic differential equation dθ = ω dt + Z(θ)