Optimal Colored Noise for Estimating Phase Response Curves

TL;DR

This paper introduces an optimal colored noise approach to improve the accuracy of phase response curve estimation from limited biological data, addressing limitations of previous white-noise methods.

Contribution

It proposes a novel method using colored noise to more accurately estimate PRCs with fewer samples, enhancing biological oscillation analysis.

Findings

Colored noise improves PRC estimation accuracy.

Optimal noise parameters depend on system dynamics.

Numerical simulations validate the method's effectiveness.

Abstract

The phase response curve (PRC) is an important measure representing the interaction between oscillatory elements. To understand synchrony in biological systems, many research groups have sought to measure PRCs directly from biological cells including neurons. Ermentrout et al. and Ota et al. showed that PRCs can be identified through measurement of white-noise spike-triggered averages. The disadvantage of this method is that one has to collect more than ten-thousand spikes to ensure the accuracy of the estimate. In this paper, to achieve a more accurate estimation of PRCs with a limited sample size, we use colored noise, which has recently drawn attention because of its unique effect on dynamical systems. We numerically show that there is an optimal colored noise to estimate PRCs in the most rigorous fashion.