Dynamics, synchronization and traveling wave patterns of flux coupled network of Chay neurons.

Arnold tongue Chay neuron Imperfect chimera Lyapunov stability theory Master stability function

Journal

Bio Systems
ISSN: 1872-8324
Titre abrégé: Biosystems
Pays: Ireland
ID NLM: 0430773

Informations de publication

Date de publication:
28 Dec 2023
Historique:
received: 11 10 2023
revised: 22 12 2023
accepted: 24 12 2023
medline: 2 1 2024
pubmed: 2 1 2024
entrez: 30 12 2023
Statut: aheadofprint

Résumé

Studies in the literature have demonstrated the significance of the synchronization of neuronal electrical activity for signal transmission and information encoding. In light of this importance, we investigate the synchronization of the Chay neuron model using both theoretical analysis and numerical simulations. The Chay model is chosen for its comprehensive understanding of neuronal behavior and computational efficiency. Additionally, we explore the impact of electromagnetic induction, leading to the magnetic flux Chay neuron model. The single neuron model exhibits rich and complex dynamics for various parameter choices. We explore the bifurcation structure of the model through bifurcation diagrams and Lyapunov exponents. Subsequently, we extend our study to two coupled magnetic flux Chay neurons, identifying mode locking and structures reminiscent of Arnold's tongue. We evaluate the stability of the synchronized manifold using Lyapunov theory and confirm our findings through simulations. Expanding our study to networks of diffusively coupled flux Chay neurons, we observe coherent, incoherent, and imperfect chimera patterns. Our investigation of three network types highlights the impact of network topology on the emergent dynamics of the Chay neuron network. Regular networks exhibit diverse patterns, small-world networks demonstrate a critical transition to coherence, and random networks showcase synchronization at specific coupling strengths. These findings significantly contribute to our understanding of the synchronization patterns exhibited by the magnetic flux Chay neuron. To assess the synchronization stability of the Chay neuron network, we employ master stability function analysis.

Identifiants

pubmed: 38159671
pii: S0303-2647(23)00288-5
doi: 10.1016/j.biosystems.2023.105113
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105113

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Karthikeyan Rajagopal reports financial support was provided by Chennai Institute of Technology.

Auteurs

Dianavinnarasi Joseph (D)

Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, India. Electronic address: dianavinarasij@citchennai.net.

Rakshanaa Kumar (R)

Department of Information Technology, Chennai Institute of Technology, Chennai 600069, India. Electronic address: rakshanaak.it2020@citchennai.net.

Anitha Karthikeyan (A)

Department of Electronics and Communication Engineering, Vemu Institute of Technology, Chitoor, Andhra Pradesh 517112, India; Department of Electronics and Communications Engineering and University Centre for Research & Development, Chandigarh University, Mohali 140413, India. Electronic address: mrs.anithakarthikeyan@gmail.com.

Karthikeyan Rajagopal (K)

Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, India. Electronic address: karthikeyan.rajagopal@citchennai.net.

Classifications MeSH