The role of the fitness model in the suppression of neuronal synchronous behavior with three-stage switching control in clustered networks

A.S. Reis, E.L. Brugnago, R.L. Viana, A.M. Batista, K.C. Iarosz, F.A.S. Ferrari and I.L. Caldas

Chaos, Solitons & Fractals, 2023, vol. 167, issue C

Abstract: In this work, we investigate the synchronization of neuronal activity through a model of a clustered network formed by scale-free subnetworks. These simulate the areas of the cerebral cortex and capture the spatial distribution of the vertices. The growth of the scale-free subnetworks takes place according to the fitness model, and the architecture of the clustered network presents internal and external links to simulate connections inside and between cortical areas. The corticocortical connections are established according to a human connectivity matrix obtained through experimental data. The model also considers electrical and chemical synapses. A two-dimensional map, in the bursting regime, simulates the dynamic behavior of the neuron. The high synchronization of neuronal activity is revealed by the Kuramoto order parameter. To corroborate the analyses using the order parameter, we calculate a suppression measure. In order to suppress this synchronization, we propose a three-stage switching control as a function of the delayed mean-field in each subnetwork. This suppressor agent is effective in two scenarios, whether applied according to the spatial distribution of neurons or in the emitting hubs of the subnetworks. Our results show that the fitness model has a relevant role in the study of neuronal activity suppression, allied to the application of a three-stage switching control by means of a time-delayed feedback method being an efficient way to suppress synchronization in clustered networks.

Keywords: Neuronal network; Scale-free; Fitness model; Synchronization; Suppression (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:167:y:2023:i:c:s0960077923000231

DOI: 10.1016/j.chaos.2023.113122

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