Academic Journal
Collective dynamics of a coupled Hindmarsh-Rose neurons with locally active memristor.
| Title: | Collective dynamics of a coupled Hindmarsh-Rose neurons with locally active memristor. |
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| Authors: | Kanagaraj S; Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, India., Durairaj P; Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, India., Sampath S; Department of Electronics and Communication Engineering, Presidency University, Bengaluru, Karnataka, India., Karthikeyan A; Department of Electronics and Communications Engineering, University Centre for Research and Development, Chandigarh University, Mohali, 140 413, Punjab, India; Department of Electronics and Communication Engineering, Vemu Institute of Technology, Chitoor, Andhra Pradesh 517112, India., Rajagopal K; Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, India. Electronic address: rkarthiekeyan@gmail.com. |
| Source: | Bio Systems [Biosystems] 2023 Oct; Vol. 232, pp. 105010. Date of Electronic Publication: 2023 Aug 24. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Elsevier Science Ireland Country of Publication: Ireland NLM ID: 0430773 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8324 (Electronic) Linking ISSN: 03032647 NLM ISO Abbreviation: Biosystems Subsets: MEDLINE |
| Imprint Name(s): | Publication: Limerick : Elsevier Science Ireland Original Publication: Amsterdam, North-Holland Pub. Co. |
| MeSH Terms: | Neurons* , Mass Behavior*, Cluster Analysis ; Synapses |
| Abstract: | A Locally active memristors can mimic neural synapses, resulting in rich neuro-morphological dynamics in biological neurons. To illustrate the impact of a local active memristive synapse, we consider coupled Hindmarsh-Rose (HR) neurons. Firstly, the dynamical transitions of the proposed system are investigated using bifurcation analysis and Lyapunov exponents, and we find that the transition between periodic and chaotic states depends on the input currents and memristive coupling strength. By performing the two-parameter analysis, the existence of periodic and chaotic regions is revealed. The collective behavior is then examined by expanding the network to include memristive coupled HR neurons under different network connectivities. We show that the system achieves synchronization behavior for all network connectivities, including regular, random, and small-world, when the strength of the memristive coupling is increased. (Copyright © 2023 Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Chaotic state; Chimera; Memristor; Synchronization |
| Entry Date(s): | Date Created: 20230826 Date Completed: 20230914 Latest Revision: 20230914 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.biosystems.2023.105010 |
| PMID: | 37633485 |
| Database: | MEDLINE |
| ISSN: | 1872-8324 |
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| DOI: | 10.1016/j.biosystems.2023.105010 |