Gaussian Process Phase Interpolation for estimating the asymptotic phase of a limit cycle oscillator from time series data

TL;DR

This paper introduces Gaussian Process Phase Interpolation (GPPI), a novel method for accurately estimating the asymptotic phase of limit cycle oscillators from noisy time series data, applicable to complex biological systems.

Contribution

The paper presents a new Gaussian process-based method for phase estimation that works effectively with high-dimensional, nonlinear, and noisy data, advancing data-driven analysis of oscillatory systems.

Findings

GPPI accurately estimates phase in noisy conditions

Effective for high-dimensional and nonlinear oscillators

Enables data-driven phase control applications

Abstract

Rhythmic activity commonly observed in biological systems, occurring from the cellular level to the organismic level, is typically modeled as limit cycle oscillators. Phase reduction theory serves as a useful analytical framework for elucidating the synchronization mechanism of these oscillators. Essentially, this theory describes the dynamics of a multi-dimensional nonlinear oscillator using a single variable called asymptotic phase. In order to understand and control the rhythmic phenomena in the real world, it is crucial to estimate the asymptotic phase from the observed data. In this study, we propose a new method, Gaussian Process Phase Interpolation (GPPI), for estimating the asymptotic phase from time series data. The GPPI method first evaluates the asymptotic phase on the limit cycle and subsequently estimates the asymptotic phase outside the limit cycle employing Gaussian process regression. Thanks to the high expressive power of Gaussian processes, the GPPI is capable of capturing a variety of functions. Furthermore, it is easily applicable even when the dimension of the system increases. The performance of the GPPI is tested by using simulation data from the Stuart-Landau oscillator and the Hodgkin-Huxley oscillator. The results demonstrate that the GPPI can accurately estimate the asymptotic phase even in the presence of high observation noise and strong nonlinearity. Additionally, the GPPI is demonstrated as an effective tool for data-driven phase control of a Hodgkin-Huxley oscillator. Thus, the proposed GPPI will facilitate the data-driven modeling of the limit cycle oscillators.