Compact Dynamical Mean-Field Theory of Oscillator Networks

TL;DR

This paper develops a compact dynamical mean-field theory for large networks of coupled phase oscillators, enabling predictions of synchronization based on single-neuron phase response data.

Contribution

It introduces a new DMFT framework that handles arbitrary periodic coupling functions and connects single-neuron phase response curves to network synchronization.

Findings

Reproduces Ott--Antonsen reduction in the absence of disorder.

Provides quantitative predictions for synchronization thresholds in neuron models.

Accommodates biophysical neuron coupling functions derived from phase response curves.

Abstract

We present a compact dynamical mean-field theory (DMFT) for large networks of coupled phase oscillators whose phases live on the circle $S^1$ and interact with both coherent mean-field coupling and quenched randomness. Starting from wrapped Langevin dynamics, we build a path-integral representation that keeps the $2\pi$-periodicity of the phases explicit. After averaging over the disorder in the thermodynamic limit, this construction reduces to a single-oscillator stochastic equation driven by a deterministic mean field and a self-consistent colored Gaussian noise, whose covariance is fixed by a circular two-time correlator. In the limit of vanishing disorder, the formalism reproduces the Ott--Antonsen reduction and recovers standard Kuramoto and theta-neuron neural-mass equations. The same framework accommodates arbitrary $2\pi$-periodic coupling functions, including those obtained from infinitesimal phase response curves (iPRCs) of biophysical neuron models. As an example, we show that for adaptive exponential integrate-and-fire neurons, inserting an iPRC-fitted coupling into the compact DMFT yields quantitative predictions for synchronization thresholds, providing a direct route from single-neuron phase response data to network-level mean-field predictions for arbitrary phase-reducible oscillators.