On the Higher-Order Smallest Ring-Star Network of Chialvo Neurons Under Diffusive Couplings

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aut.relation.issue7
aut.relation.journalChaos: An Interdisciplinary Journal of Nonlinear Science
aut.relation.volume34
dc.contributor.authorNair, Anjana S
dc.contributor.authorGhosh, Indranil
dc.contributor.authorFatoyinbo, Hammed O
dc.contributor.authorMuni, Sishu S
dc.date.accessioned2024-07-25T22:11:39Z
dc.date.available2024-07-25T22:11:39Z
dc.date.issued2024-07-18
dc.description.abstractNetwork dynamical systems with higher-order interactions are a current trending topic, pervasive in many applied fields. However, our focus in this work is neurodynamics. We numerically study the dynamics of the smallest higher-order network of neurons arranged in a ring-star topology. The dynamics of each node in this network is governed by the Chialvo neuron map, and they interact via linear diffusive couplings. This model is perceived to imitate the nonlinear dynamical properties exhibited by a realistic nervous system where the neurons transfer information through multi-body interactions. We deploy the higher-order coupling strength as the primary bifurcation parameter. We start by analyzing our model using standard tools from dynamical systems theory: fixed point analysis, Jacobian matrix, and bifurcation patterns. We observe the coexistence of disparate chaotic attractors. We also observe an interesting route to chaos from a fixed point via period-doubling and the appearance of cyclic quasiperiodic closed invariant curves. Furthermore, we numerically observe the existence of codimension-1 bifurcation points: saddle-node, period-doubling, and Neimark–Sacker. We also qualitatively study the typical phase portraits of the system, and numerically quantify chaos and complexity using the 0–1 test and sample entropy measure, respectively. Finally, we study the synchronization behavior among the neurons using the cross correlation coefficient and the Kuramoto order parameter. We conjecture that unfolding these patterns and behaviors of the network model will help us identify different states of the nervous system, further aiding us in dealing with various neural diseases and nervous disorders.
dc.identifier.citationChaos: An Interdisciplinary Journal of Nonlinear Science, ISSN: 1054-1500 (Print); 1089-7682 (Online), AIP Publishing, 34(7). doi: 10.1063/5.0217017
dc.identifier.doi10.1063/5.0217017
dc.identifier.issn1054-1500
dc.identifier.issn1089-7682
dc.identifier.urihttp://hdl.handle.net/10292/17822
dc.languageen
dc.publisherAIP Publishing
dc.relation.urihttps://pubs.aip.org/aip/cha/article/34/7/073135/3303765/On-the-higher-order-smallest-ring-star-network-of
dc.rights© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject0102 Applied Mathematics
dc.subject0103 Numerical and Computational Mathematics
dc.subject0299 Other Physical Sciences
dc.subjectFluids & Plasmas
dc.subject4901 Applied mathematics
dc.subject5199 Other physical sciences
dc.titleOn the Higher-Order Smallest Ring-Star Network of Chialvo Neurons Under Diffusive Couplings
dc.typeJournal Article
pubs.elements-id563096
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