Machine learning techniques to identify synchronization patterns in multiple timescale dynamical systems networks

doi:10.1016/j.physd.2025.135082

Machine learning techniques to identify synchronization patterns in multiple timescale dynamical systems networks

2026-01-21

0

PRE

AI

A

Alejandro Bandera Moreno

S

Soledad Fernández-García *

M

Macarena Gómez Mármol

A

Alexandre Vidal

DOI：10.1016/j.physd.2025.135082

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Abstract

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We present a novel methodology that combines machine learning techniques with dynamical analysis to classify and interpret the behavior distribution of network models of coupled dynamical systems. Our methodology determines the optimal number of distinct behaviors and classifies them based on time-series features, allowing for an interpretable and automated partition of the parameter space. Applying this approach to a homogeneous two-clusters model of intracellular calcium concentration dynamics, we identify nine different long-term behaviors, including complex and chaotic regimes, mapping experimental data available in the literature. The results highlight the complementarity between data-driven classification and classical dynamical analysis in capturing rich synchronization patterns and detecting subtle transitions in multiple timescale biological systems.

Keywords:

Multiple timescales

Synchronization patterns

Mixed mode oscillations

Dynamics of neural networks

Intracellular calcium concentration oscillations

Machine learning techniques to identify synchronization patterns in multiple timescale dynamical systems networks

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