Bao Han, Yu Xihong, Xu Quan, Wu Huagan, Bao Bocheng
School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164 People's Republic of China.
Cogn Neurodyn. 2023 Aug;17(4):1079-1092. doi: 10.1007/s11571-022-09871-6. Epub 2022 Aug 27.
To characterize the magnetic induction flow induced by neuron membrane potential, a three-dimensional (3D) memristive Morris-Lecar (ML) neuron model is proposed in this paper. It is achieved using a memristor induction current to replace the slow modulation current in the existing 3D ML neuron model with fast-slow structure. The magnetic induction effects on firing activities are explained by the spiking/bursting firings with period-adding bifurcation and periodic/chaotic spiking-bursting patterns, and the bifurcation mechanisms of the bursting patterns are elaborated using the fast-slow analysis method to create two bifurcation sets. In particular, the 3D memristive ML model can also exhibit the homogeneous coexisting bursting patterns when switching the memristor initial states, which are effectively illustrated by the theoretical analysis and numerical simulations. Finally, a digitally FPGA-based hardware platform is developed for the 3D memristive ML model and the experimentally measured results well verify the numerical ones.
为了表征由神经元膜电位引起的磁感应流,本文提出了一种三维(3D)忆阻Morris-Lecar(ML)神经元模型。它是通过使用忆阻器感应电流来替代现有具有快-慢结构的3D ML神经元模型中的慢调制电流来实现的。通过具有周期加性分岔和周期性/混沌尖峰-爆发模式的尖峰/爆发放电来解释磁感应对放电活动的影响,并使用快-慢分析方法阐述爆发模式的分岔机制以创建两个分岔集。特别地,当切换忆阻器初始状态时,3D忆阻ML模型还可以呈现均匀共存的爆发模式,理论分析和数值模拟有效地说明了这一点。最后,为3D忆阻ML模型开发了基于数字FPGA的硬件平台,实验测量结果很好地验证了数值结果。