Inference of Time-Evolving Coupled Dynamical Systems in the Presence of Noise

Abstract

A new method is introduced for analysis of interactions between time-dependent coupled oscillators, based on the signals they generate. It distinguishes unsynchronized dynamics from noise-induced phase slips, and enables the evolution of the coupling functions and other parameters to be followed. It is based on phase dynamics, with Bayesian inference of the time-evolving parameters achieved by shaping the prior densities to incorporate knowledge of previous samples. The method is tested numerically and applied to reveal and quantify the time-varying nature of cardiorespiratory interactions.

5 pages, 3 figures, accepted for Physical Review Letters

References in corpus (2)

• Kernel method for nonlinear Granger causality
• Spurious detection of phase synchronization in coupled nonlinear oscillators

Cited by in corpus (6)

• Coupling functions: Universal insights into dynamical interaction mechanisms
• Dynamical Bayesian Inference of Time-evolving Interactions: From a Pair of Coupled Oscillators to Networks of Oscillators
• Reconstruction of a random phase dynamics network from observations
• Identifying delayed directional couplings with symbolic transfer entropy
• Time-varying coupling functions: dynamical inference and cause of synchronization transitions
• Generalized chronotaxic systems: time-dependent oscillatory dynamics stable under continuous perturbation