Reconstructing Network Dynamics of Coupled Discrete Chaotic Units from Data

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Date

2023

Authors

Eroğlu, Deniz
Eroglu, Deniz

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Amer Physical Soc

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Abstract

Reconstructing network dynamics from data is crucial for predicting the changes in the dynamics of complex systems such as neuron networks; however, previous research has shown that the reconstruction is possible under strong constraints such as the need for lengthy data or small system size. Here, we present a recovery scheme blending theoretical model reduction and sparse recovery to identify the governing equations and the interactions of weakly coupled chaotic maps on complex networks, easing unrealistic constraints for real-world applications. Learning dynamics and connectivity lead to detecting critical transitions for parameter changes. We apply our technique to realistic neuronal systems with and without noise on a real mouse neocortex and artificial networks.

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3

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Source

Physical Review Letters

Volume

130

Issue

11

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