20.500.12556/DKUM-88200 Synchronization in simplicial complexes of memristive Rulkov neurons 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. simplicial complex higher-order network memristive Rulkov synchronization cluster synchronization kompleks višjega reda omrežje višjega reda Rulkov nevron sinhronizacija sinhronizacija skupin true true true Frontiers Media Angleški jezik Slovenski jezik Članek v reviji 2024-04-11 03:56:49 2024-09-11 11:48:31 2024-09-12 03:30:22 0000-00-00 00:00:00 2023 0 0 12 str. [article no.] ǂ1248976 Vol. 17 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-06-27 2023-08-11 2023-08-31 53 163917827 10.3389/fncom.2023.1248976 1662-5188 RAZ_Mehrabbeik_Mahtab_2023.pdf RAZ_Mehrabbeik_Mahtab_2023.pdf 1 0CCB99765C6F9C69BDDE156770F2792E 777b0db20438d6c43a9be47e6200731ee623938c8366065ce74b3684e3681fc1 d47ee5f2-7021-11ef-bcb8-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=184929 https://doi.org/10.3389/fncom.2023.1248976 0 29867e07-7022-11ef-bcb8-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=184930 Fakulteta za naravoslovje in matematiko 0 0 0