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Intermittent chimera-like and bi-stable synchronization states in network of distinct Izhikevich neurons

Gabriel Marghoti, Thiago de Lima Prado, Arturo Cagnato Conte, Fabiano Alan Serafim Ferrari and Sergio Roberto Lopes

Chaos, Solitons & Fractals, 2022, vol. 162, issue C

Abstract: Phase synchronization phenomena of neuronal networks are one of many features depicted by real networks that can be studied using computational models. Here, we proceed with numerical simulations of a globally connected network composed of non-identical (distinct) Izhikevich neuron model to study clustered phase synchronization. We investigate the case in which, once coupled, there exist two main neuron clusters in the network: one of them is bi-stable, depicting phase-synchronized or unsynchronized states, depending on the initial conditions; and the second one shows just an unsynchronized state. For the set of initial conditions that lead the first cluster to the synchronized regime, we observe a chimera-like pattern of the network. For small networks, the dynamics can also present intermittent chimera-like scenarios. In this context, the mechanism for intermittent chimera states is based on two features: the coexistence of a synchronized cluster with an unsynchronized one; and the capability of one cluster to display bi-stability depending on the signal trait by which it is forced. We conclude with an understanding of intermittent chimera-like dynamics as the limit case where bi-stability is not maintained, which occurs due to the loss of uniformity in the neuron input synaptic currents.

Keywords: Phase synchronization; Bi-stable network; Intermittent transitions; Globally connected network (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:162:y:2022:i:c:s0960077922006117

DOI: 10.1016/j.chaos.2022.112401

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