Dynamical rewiring promotes synchronization in memristive FitzHugh-Nagumo neuronal networks

Xueyan Hu, Qianming Ding, Yong Wu, Weifang Huang, Lijian Yang and Ya Jia

Chaos, Solitons & Fractals, 2024, vol. 184, issue C

Abstract: Dynamical rewiring widely exists in complex systems, however the impact of dynamical rewiring in the synchronization of neural systems is currently unknown. In this paper, we use memristive FitzHugh-Nagumo neurons to construct random, small-world and scale-free networks in which the connections between neurons can be rewired, and investigate the influence of rewiring on the synchronization of neural networks in with/without Gaussian white noise, and comparing it to the corresponding static networks. We found that dynamical rewiring enhances the synchronization of the network, and the degree of synchronization will be higher when the rewiring period is shorter and the rewiring proportion is larger. In addition, the synchronization of the network gradually diminishes as the coupling strength decreases and the noise intensity increases, and rewiring networks always exhibit superior synchronization to static networks since the dynamical rewiring enhances the interaction between neurons. Our study shows that neural network models with dynamically changing topology are more suitable and realistic network models, which may reveal the profound significance of dynamic rewiring for the multifaceted dynamic flexibility and adaptability of neural systems.

Keywords: Dynamical rewiring; Memristive neurons; Complex networks; Synchronization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:184:y:2024:i:c:s096007792400599x

DOI: 10.1016/j.chaos.2024.115047

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