中国物理B ›› 2025, Vol. 34 ›› Issue (10): 100502-100502.doi: 10.1088/1674-1056/adde39

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

Minglin Ma(马铭磷)1, Zhiyi Yuan(袁芷依)1, Umme Kalsoom2, Weizheng Deng(邓为政)1, and Shaobo He(贺少波)1,†   

  1. 1 School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China;
    2 Department of Computer Science University of Narowal, Pakistan
  • 收稿日期:2025-05-06 修回日期:2025-05-23 接受日期:2025-05-29 发布日期:2025-09-29
  • 通讯作者: Shaobo He E-mail:heshaobo@xtu.edu.cn
  • 基金资助:
    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).

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

Minglin Ma(马铭磷)1, Zhiyi Yuan(袁芷依)1, Umme Kalsoom2, Weizheng Deng(邓为政)1, and Shaobo He(贺少波)1,†   

  1. 1 School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China;
    2 Department of Computer Science University of Narowal, Pakistan
  • Received:2025-05-06 Revised:2025-05-23 Accepted:2025-05-29 Published:2025-09-29
  • Contact: Shaobo He E-mail:heshaobo@xtu.edu.cn
  • Supported by:
    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).

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摘要： 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.

关键词: Izhikevich neurons, memristor, synchronization, electromagnetic radiation

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.

Key words: Izhikevich neurons, memristor, synchronization, electromagnetic radiation

中图分类号:  (Synchronization; coupled oscillators)

  • 05.45.Xt
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)