中国物理B ›› 2018, Vol. 27 ›› Issue (4): 40501-040501.doi: 10.1088/1674-1056/27/4/040501

• GENERAL • 上一篇    下一篇

Stochastic resonance and synchronization behaviors of excitatory-inhibitory small-world network subjected to electromagnetic induction

Xiao-Han Zhang(张晓函), Shen-Quan Liu(刘深泉)   

  1. School of Mathematics, South China University of Technology, Guangzhou 510640, China
  • 收稿日期:2017-10-23 修回日期:2018-01-09 出版日期:2018-04-05 发布日期:2018-04-05
  • 通讯作者: Shen-Quan Liu E-mail:mashqliu@scut.edu.cn
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 11172103).

Stochastic resonance and synchronization behaviors of excitatory-inhibitory small-world network subjected to electromagnetic induction

Xiao-Han Zhang(张晓函), Shen-Quan Liu(刘深泉)   

  1. School of Mathematics, South China University of Technology, Guangzhou 510640, China
  • Received:2017-10-23 Revised:2018-01-09 Online:2018-04-05 Published:2018-04-05
  • Contact: Shen-Quan Liu E-mail:mashqliu@scut.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 11172103).

摘要: The phenomenon of stochastic resonance and synchronization on some complex neuronal networks have been investigated extensively. These studies are of great significance for us to understand the weak signal detection and information transmission in neural systems. Moreover, the complex electrical activities of a cell can induce time-varying electromagnetic fields, of which the internal fluctuation can change collective electrical activities of neuronal networks. However, in the past there have been a few corresponding research papers on the influence of the electromagnetic induction among neurons on the collective dynamics of the complex system. Therefore, modeling each node by imposing electromagnetic radiation on the networks and investigating stochastic resonance in a hybrid network can extend the interest of the work to the understanding of these network dynamics. In this paper, we construct a small-world network consisting of excitatory neurons and inhibitory neurons, in which the effect of electromagnetic induction that is considered by using magnetic flow and the modulation of magnetic flow on membrane potential is described by using memristor coupling. According to our proposed network model, we investigate the effect of induced electric field generated by magnetic stimulation on the transition of bursting phase synchronization of neuronal system under electromagnetic radiation. It is shown that the intensity and frequency of the electric field can induce the transition of the network bursting phase synchronization. Moreover, we also analyze the effect of magnetic flow on the detection of weak signals and stochastic resonance by introducing a subthreshold pacemaker into a single cell of the network and we find that there is an optimal electromagnetic radiation intensity, where the phenomenon of stochastic resonance occurs and the degree of response to the weak signal is maximized. Simulation results show that the extension of the subthreshold pacemaker in the network also depends greatly on coupling strength. The presented results may have important implications for the theoretical study of magnetic stimulation technology, thus promoting further development of transcranial magnetic stimulation (TMS) as an effective means of treating certain neurological diseases.

关键词: electromagnetic induction, synchronization, stochastic resonance, small-world network

Abstract: The phenomenon of stochastic resonance and synchronization on some complex neuronal networks have been investigated extensively. These studies are of great significance for us to understand the weak signal detection and information transmission in neural systems. Moreover, the complex electrical activities of a cell can induce time-varying electromagnetic fields, of which the internal fluctuation can change collective electrical activities of neuronal networks. However, in the past there have been a few corresponding research papers on the influence of the electromagnetic induction among neurons on the collective dynamics of the complex system. Therefore, modeling each node by imposing electromagnetic radiation on the networks and investigating stochastic resonance in a hybrid network can extend the interest of the work to the understanding of these network dynamics. In this paper, we construct a small-world network consisting of excitatory neurons and inhibitory neurons, in which the effect of electromagnetic induction that is considered by using magnetic flow and the modulation of magnetic flow on membrane potential is described by using memristor coupling. According to our proposed network model, we investigate the effect of induced electric field generated by magnetic stimulation on the transition of bursting phase synchronization of neuronal system under electromagnetic radiation. It is shown that the intensity and frequency of the electric field can induce the transition of the network bursting phase synchronization. Moreover, we also analyze the effect of magnetic flow on the detection of weak signals and stochastic resonance by introducing a subthreshold pacemaker into a single cell of the network and we find that there is an optimal electromagnetic radiation intensity, where the phenomenon of stochastic resonance occurs and the degree of response to the weak signal is maximized. Simulation results show that the extension of the subthreshold pacemaker in the network also depends greatly on coupling strength. The presented results may have important implications for the theoretical study of magnetic stimulation technology, thus promoting further development of transcranial magnetic stimulation (TMS) as an effective means of treating certain neurological diseases.

Key words: electromagnetic induction, synchronization, stochastic resonance, small-world network

中图分类号:  (Fluctuation phenomena, random processes, noise, and Brownian motion)

  • 05.40.-a
05.45.-a (Nonlinear dynamics and chaos) 87.85.Ng (Biological signal processing) 87.85.dq (Neural networks)