Robust universal approach to identify travelling chimeras and synchronized clusters in spiking networks

TL;DR

This paper introduces a universal, adaptive method for detecting and characterizing various complex dynamical states, including stationary and travelling chimeras, in spiking neural networks, enhancing analysis robustness and automation.

Contribution

The paper presents a novel, robust, and automatic approach for identifying multiple dynamical states and clusters in spiking networks, including a new speed calculation for travelling chimeras.

Findings

Successfully identifies diverse dynamical states in neural networks

Determines the number of clusters in synchronization regimes

Extends applicability to phase and relaxation oscillator networks

Abstract

We propose a robust universal approach to identify multiple dynamical states, including stationary and travelling chimera states based on an adaptive coherence measure. Our approach allows automatic disambiguation of synchronized clusters, travelling waves, chimera states, and asynchronous regimes. In addition, our method can determine the number of clusters in the case of cluster synchronization. We further couple our approach with a new speed calculation method for travelling chimeras. We validate our approach by an example of a ring network of type II Morris-Lecar neurons with asymmetrical nonlocal inhibitory connections where we identify a rich repertoire of coherent and wave states. We propose that the method is robust for the networks of phase oscillators and extends to a general class of relaxation oscillator networks.