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      Synchronization in simplicial complexes of memristive Rulkov neurons

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          Abstract

          Simplicial complexes are mathematical constructions that describe higher-order interactions within the interconnecting elements of a network. Such higher-order interactions become increasingly significant in neuronal networks since biological backgrounds and previous outcomes back them. In light of this, the current research explores a higher-order network of the memristive Rulkov model. To that end, the master stability functions are used to evaluate the synchronization of a network with pure pairwise hybrid (electrical and chemical) synapses alongside a network with two-node electrical and multi-node chemical connections. The findings provide good insight into the impact of incorporating higher-order interaction in a network. Compared to two-node chemical synapses, higher-order interactions adjust the synchronization patterns to lower multi-node chemical coupling parameter values. Furthermore, the effect of altering higher-order coupling parameter value on the dynamics of neurons in the synchronization state is researched. It is also shown how increasing network size can enhance synchronization by lowering the value of coupling parameters whereby synchronization occurs. Except for complete synchronization, cluster synchronization is detected for higher electrical coupling strength values wherein the neurons are out of the completed synchronization state.

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          Most cited references60

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          Impulses and Physiological States in Theoretical Models of Nerve Membrane

          Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model" has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a "physiological state diagram," with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.
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            Master Stability Functions for Synchronized Coupled Systems

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              Complex networks: Structure and dynamics

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                Author and article information

                Contributors
                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Media S.A.
                1662-5188
                31 August 2023
                2023
                : 17
                : 1248976
                Affiliations
                [1] 1Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic) , Tehran, Iran
                [2] 2Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic) , Tehran, Iran
                [3] 3Faculty of Natural Sciences and Mathematics, University of Maribor , Maribor, Slovenia
                [4] 4Department of Medical Research, China Medical University Hospital, China Medical University , Taichung, Taiwan
                [5] 5Alma Mater Europaea , Maribor, Slovenia
                [6] 6Complexity Science Hub Vienna , Vienna, Austria
                [7] 7Department of Physics, Kyung Hee University , Seoul, Republic of Korea
                Author notes

                Edited by: Thanos Manos, Université de Cergy-Pontoise, France

                Reviewed by: Sorinel A. Oprisan, College of Charleston, United States; Kun Li, Hebei University of Technology, China

                *Correspondence: Matjaž Perc matjaz.perc@ 123456gmail.com
                Article
                10.3389/fncom.2023.1248976
                10501309
                37720251
                0cb9c3c8-5bee-4684-b55e-45a08773d47c
                Copyright © 2023 Mehrabbeik, Jafari and Perc.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 27 June 2023
                : 11 August 2023
                Page count
                Figures: 9, Tables: 0, Equations: 10, References: 60, Pages: 12, Words: 6796
                Funding
                MP was supported by the Slovenian Research and Innovation Agency (Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije) (Grant Nos. P1-0403, J1-2457, and N1-0232).
                Categories
                Neuroscience
                Original Research

                Neurosciences
                simplicial complex,higher-order network,memristive rulkov,synchronization,cluster synchronization

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