BANDERA MORENO, ALEJANDRO, Fernández-García, S. , Gómez-Mármol, M. , Vidal, A.
No
PHYSICA D
Article
Científica
01/01/2026
2-s2.0-105025424305
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. © 2025 The Author(s)
Artificial intelligence; Biological systems; Classification (of information); Dynamical systems; Learning algorithms; Learning systems; Pattern recognition; Dynamic of neural network; Dynamical analysis; Intracellular calcium concentration; Intracellular calcium concentration oscillation; Machine learning techniques; Mixed mode oscillations; Multiple time scale; Neural-networks; Synchronization patterns; Systems networks; Synchronization