Yang Huilan, Tian Shuxiang, Zhu Haijun, Xu Guizhi
School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China.
School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Oct 25;40(5):859-866. doi: 10.7507/1001-5515.202209021.
Electromagnetic stimulation is an important neuromodulation technique that modulates the electrical activity of neurons and affects cortical excitability for the purpose of modulating the nervous system. The phenomenon of inverse stochastic resonance is a response mechanism of the biological nervous system to external signals and plays an important role in the signal processing of the nervous system. In this paper, a small-world neural network with electrical synaptic connections was constructed, and the inverse stochastic resonance of the small-world neural network under electromagnetic stimulation was investigated by analyzing the dynamics of the neural network. The results showed that: the Levy channel noise under electromagnetic stimulation could cause the occurrence of inverse stochastic resonance in small-world neural networks; the characteristic index and location parameter of the noise had significant effects on the intensity and duration of the inverse stochastic resonance in neural networks; the larger the probability of randomly adding edges and the number of nearest neighbor nodes in small-world networks, the more favorable the anti-stochastic resonance was; by adjusting the electromagnetic stimulation parameters, a dual regulation of the inverse stochastic resonance of the neural network can be achieved. The results of this study provide some theoretical support for exploring the regulation mechanism of electromagnetic nerve stimulation technology and the signal processing mechanism of nervous system.
电磁刺激是一种重要的神经调节技术，它调节神经元的电活动并影响皮层兴奋性，以达到调节神经系统的目的。逆随机共振现象是生物神经系统对外界信号的一种响应机制，在神经系统的信号处理中起着重要作用。本文构建了具有电突触连接的小世界神经网络，并通过分析神经网络的动力学来研究电磁刺激下小世界神经网络的逆随机共振。结果表明：电磁刺激下的列维通道噪声可导致小世界神经网络中逆随机共振的发生；噪声的特征指数和位置参数对神经网络中逆随机共振的强度和持续时间有显著影响；小世界网络中随机添加边的概率和最近邻节点数越大，越有利于反随机共振；通过调节电磁刺激参数，可以实现对神经网络逆随机共振的双重调节。本研究结果为探索电磁神经刺激技术的调节机制和神经系统的信号处理机制提供了一定的理论支持。
