Insights into oscillator network dynamics using a phase-isostable framework

TL;DR

This paper develops a phase-isostable framework for analyzing the dynamics of networks of coupled nonlinear oscillators, demonstrating its improved accuracy over traditional phase reduction methods in capturing bifurcations and stability of phase-locked states.

Contribution

It extends the phase-isostable approach to arbitrary networks, providing conditions for stability and showing its superior accuracy compared to higher-order phase reductions.

Findings

Phase-isostable equations accurately predict bifurcations.

Framework captures dynamics missed by first-order phase models.

Qualitative agreement with full system simulations in neuron networks.

Abstract

Networks of coupled nonlinear oscillators can display a wide range of emergent behaviours under variation of the strength of the coupling. Network equations for pairs of coupled oscillators where the dynamics of each node is described by the evolution of its phase and slowest decaying isostable coordinate have previously been shown to capture bifurcations and dynamics of the network which cannot be explained through standard phase reduction. An alternative framework using isostable coordinates to obtain higher-order phase reductions has also demonstrated a similar descriptive ability for two oscillators. In this work we consider the phase-isostable network equations for an arbitrary but finite number of identical coupled oscillators, obtaining conditions required for stability of phase-locked states including synchrony. For the mean-field complex Ginzburg-Landau equation where the solutions of the full system are known, we compare the accuracy of the phase-isostable network equations and higher-order phase reductions in capturing bifurcations of phase-locked states. We find the former to be the more accurate and therefore employ this to investigate the dynamics of globally linearly coupled networks of Morris-Lecar neuron models (both two and many nodes). We observe qualitative correspondence between results from numerical simulations of the full system and the phase-isostable description demonstrating that in both small and large networks the phase-isostable framework is able to capture dynamics that the first-order phase description cannot.