Njitacke Zeric Tabekoueng, Koumetio Bernard Nzoko, Ramakrishnan Balamurali, Leutcho Gervais Dolvis, Fozin Theophile Fonzin, Tsafack Nestor, Rajagopal Kartikeyan, Kengne Jacques
Department of Electrical and Electronic Engineering, College of Technology (COT), University of Buea, P.O. Box 63, Buea, Cameroon.
Research Unit of Automation and Applied Computer (URAIA), Electrical Engineering Department of IUT-FV, University of Dschang, P.O. Box 134, Bandjoun, Cameroon.
Cogn Neurodyn. 2022 Aug;16(4):899-916. doi: 10.1007/s11571-021-09747-1. Epub 2021 Dec 2.
In this paper, bidirectional-coupled neurons through an asymmetric electrical synapse are investigated. These coupled neurons involve 2D Hindmarsh-Rose (HR) and 2D FitzHugh-Nagumo (FN) neurons. The equilibria of the coupled neurons model are investigated, and their stabilities have revealed that, for some values of the electrical synaptic weight, the model under consideration can display either self-excited or hidden firing patterns. In addition, the hidden coexistence of chaotic bursting with periodic spiking, chaotic spiking with period spiking, chaotic bursting with a resting pattern, and the coexistence of chaotic spiking with a resting pattern are also found for some sets of electrical synaptic coupling. For all the investigated phenomena, the Hamiltonian energy of the model is computed. It enables the estimation of the amount of energy released during the transition between the various electrical activities. Pspice simulations are carried out based on the analog circuit of the coupled neurons to support our numerical results. Finally, an STM32F407ZE microcontroller development board is exploited for the digital implementation of the proposed coupled neurons model.
本文研究了通过非对称电突触双向耦合的神经元。这些耦合神经元包括二维Hindmarsh-Rose(HR)神经元和二维FitzHugh-Nagumo(FN)神经元。研究了耦合神经元模型的平衡点,其稳定性表明,对于电突触权重的某些值,所考虑的模型可以显示自激或隐藏的放电模式。此外,对于某些电突触耦合集,还发现了混沌爆发与周期性尖峰的隐藏共存、混沌尖峰与周期尖峰的共存、混沌爆发与静息模式的共存以及混沌尖峰与静息模式的共存。对于所有研究的现象,计算了模型的哈密顿能量。这使得能够估计在各种电活动之间转换期间释放的能量量。基于耦合神经元的模拟电路进行了Pspice仿真,以支持我们的数值结果。最后,利用STM32F407ZE微控制器开发板对所提出的耦合神经元模型进行数字实现。