Zhan Feibiao, Liu Shenquan
School of Mathematics, South China University of Technology, Guangzhou, China.
Front Comput Neurosci. 2017 Nov 21;11:107. doi: 10.3389/fncom.2017.00107. eCollection 2017.
Electrical activities are ubiquitous neuronal bioelectric phenomena, which have many different modes to encode the expression of biological information, and constitute the whole process of signal propagation between neurons. Therefore, we focus on the electrical activities of neurons, which is also causing widespread concern among neuroscientists. In this paper, we mainly investigate the electrical activities of the Morris-Lecar (M-L) model with electromagnetic radiation or Gaussian white noise, which can restore the authenticity of neurons in realistic neural network. First, we explore dynamical response of the whole system with electromagnetic induction (EMI) and Gaussian white noise. We find that there are slight differences in the discharge behaviors via comparing the response of original system with that of improved system, and electromagnetic induction can transform bursting or spiking state to quiescent state and vice versa. Furthermore, we research bursting transition mode and the corresponding periodic solution mechanism for the isolated neuron model with electromagnetic induction by using one-parameter and bi-parameters bifurcation analysis. Finally, we analyze the effects of Gaussian white noise on the original system and coupled system, which is conducive to understand the actual discharge properties of realistic neurons.
电活动是普遍存在的神经元生物电现象,它有许多不同的模式来编码生物信息的表达,并构成神经元之间信号传播的全过程。因此,我们关注神经元的电活动,这也引起了神经科学家的广泛关注。在本文中,我们主要研究具有电磁辐射或高斯白噪声的莫里斯 - 莱卡(M - L)模型的电活动,其能够在现实神经网络中恢复神经元的真实性。首先,我们探索具有电磁感应(EMI)和高斯白噪声的整个系统的动力学响应。通过比较原始系统和改进系统的响应,我们发现放电行为存在细微差异,并且电磁感应可以将爆发或尖峰状态转变为静止状态,反之亦然。此外,我们通过单参数和双参数分岔分析研究具有电磁感应的孤立神经元模型的爆发转变模式和相应的周期解机制。最后,我们分析高斯白噪声对原始系统和耦合系统的影响,这有助于理解现实神经元的实际放电特性。