Dynamic entrainment: A deep learning and data-driven process approach for synchronization in the Hodgkin-Huxley model

Document Type

Article

Publication Date

10-1-2024

Abstract

Resonance and synchronized rhythm are significant phenomena observed in dynamical systems in nature, particularly in biological contexts. These phenomena can either enhance or disrupt system functioning. Numerous examples illustrate the necessity for organs within the human body to maintain their rhythmic patterns for proper operation. For instance, in the brain, synchronized or desynchronized electrical activities can contribute to neurodegenerative conditions like Huntington’s disease. In this paper, we utilize the well-established Hodgkin-Huxley (HH) model, which describes the propagation of action potentials in neurons through conductance-based mechanisms. Employing a “data-driven” approach alongside the outputs of the HH model, we introduce an innovative technique termed “dynamic entrainment.” This technique leverages deep learning methodologies to dynamically sustain the system within its entrainment regime. Our findings show that the results of the dynamic entrainment technique match with the outputs of the mechanistic (HH) model.

Identifier

85208082469 (Scopus)

Publication Title

Chaos

External Full Text Location

https://doi.org/10.1063/5.0219848

e-ISSN

10897682

ISSN

10541500

PubMed ID

39470595

Issue

10

Volume

34

Grant

DMS-2152115

Fund Ref

Georgia State University

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