Dynamical behavior of ring-star neural networks with small-world characteristics

doi:10.1088/1674-1056/adde39

Abstract This paper proposes a ring-star neural network with small-world characteristics (RS-SWNN) based on the classical ring-star network, and combines the Izhikevich neuron model. RS-SWNN incorporates small-world characteristics, better mimicking the non-uniform connectivity of biological neural networks. According to the different coupling strength settings of $D_{\rm ring}$ and $D_{\rm star}$, the dynamical behavior of the network is studied, and the synchronicity differences of the network under different coupling strengths are revealed. In addition, a discrete memristor is used to simulate the effects of electromagnetic radiation. The modulation effects of varying radiation intensities on the network synchronization are further analyzed. The study shows that the electromagnetic radiation effect significantly impacts the neuronal synchronization behavior, especially in its modulation of network synchronization under varying coupling strengths. Numerical simulation is carried out using MATLAB software, and the corresponding results are obtained.

Keywords: Izhikevich neurons memristor synchronization electromagnetic radiation

Received: 06 May 2025
Revised: 23 May 2025
Accepted manuscript online: 29 May 2025

PACS:	05.45.Xt	(Synchronization; coupled oscillators)
	87.85.dq	(Neural networks)
	05.45.-a	(Nonlinear dynamics and chaos)
	05.90.+m	(Other topics in statistical physics, thermodynamics, and nonlinear dynamical systems)

Fund: This work was supported by the Key Projects of Hunan Provincial Department of Education (Grant No. 23A0133) and the National Natural Science Foundation of China (Grant No. 62171401).

Corresponding Authors: Shaobo He
E-mail: heshaobo@xtu.edu.cn

Cite this article:

Minglin Ma(马铭磷), Zhiyi Yuan(袁芷依), Umme Kalsoom, Weizheng Deng(邓为政), and Shaobo He(贺少波) Dynamical behavior of ring-star neural networks with small-world characteristics 2025 Chin. Phys. B 34 100502

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Dynamical behavior of ring-star neural networks with small-world characteristics

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