# Critical synchronization dynamics of the Kuramoto model on connectome and small world graphs

## TL;DR

This study investigates the synchronization behavior of the Kuramoto model on a large human connectome network, revealing critical-like dynamics and power-law synchronization durations that resemble brain activity patterns.

## Contribution

It demonstrates the presence of critical synchronization dynamics on a large connectome network and explores the effects of network modifications, advancing understanding of brain-like criticality in complex networks.

## Key findings

- Identified a crossover transition with power-law synchronization durations.
- Observed exponents consistent with human brain experimental data.
- Found similar critical behavior in networks with inhibitory interactions.

## Abstract

The hypothesis, that cortical dynamics operates near criticality also suggests, that it exhibits universal critical exponents which marks the Kuramoto equation, a fundamental model for synchronization, as a prime candidate for an underlying universal model. Here, we determined the synchronization behavior of this model by solving it numerically on a large, weighted human connectome network, containing 804092 nodes, in an assumed homeostatic state. Since this graph has a topological dimension $d < 4$, a real synchronization phase transition is not possible in the thermodynamic limit, still we could locate a transition between partially synchronized and desynchronized states. At this crossover point we observe power-law--tailed synchronization durations, with $\tau_t \simeq 1.2(1)$, away from experimental values for the brain. For comparison, on a large two-dimensional lattice, having additional random, long-range links, we obtain a mean-field value: $\tau_t \simeq 1.6(1)$. However, below the transition of the connectome we found global coupling control-parameter dependent exponents $1 < \tau_t \le 2$, overlapping with the range of human brain experiments. We also studied the effects of random flipping of a small portion of link weights, mimicking a network with inhibitory interactions, and found similar results. The control-parameter dependent exponent suggests extended dynamical criticality below the transition point.