Local Dirac Synchronization on Networks

TL;DR

This paper introduces Local Dirac Synchronization, a novel method using the Dirac operator to analyze coupled node dynamics on networks, revealing discontinuous transitions and rhythmic phases linked to network topology.

Contribution

The paper presents a new synchronization framework based on the Dirac operator, connecting network topology with non-linear dynamics and brain rhythms.

Findings

Discontinuous synchronization transitions observed.

Emergence of rhythmic coherent phase with slow frequency oscillations.

Topology influences synchronization and brain rhythm onset.

Abstract

We propose Local Dirac Synchronization which uses the Dirac operator to capture the dynamics of coupled nodes and link signals on an arbitrary network. In Local Dirac Synchronization, the harmonic modes of the dynamics oscillate freely while the other modes are interacting non-linearly, leading to a collectively synchronized state when the coupling constant of the model is increased. Local Dirac Synchronization is characterized by discontinuous transitions and the emergence of a rhythmic coherent phase. In this rhythmic phase, one of the two complex order parameters oscillates in the complex plane at a slow frequency (called emergent frequency) in the frame in which the intrinsic frequencies have zero average. Our theoretical results obtained within the annealed approximation are validated by extensive numerical results on fully connected networks and sparse Poisson and scale-free networks. Local Dirac Synchronization on both random and real networks, such as the connectome of Caenorhabditis Elegans, reveals the interplay between topology (Betti numbers and harmonic modes) and non-linear dynamics. This unveils how topology might play a role in the onset of brain rhythms.