4.5 Article

Hamiltonian energy and coexistence of hidden firing patterns from bidirectional coupling between two different neurons

期刊

COGNITIVE NEURODYNAMICS
卷 16, 期 4, 页码 899-916

出版社

SPRINGER
DOI: 10.1007/s11571-021-09747-1

关键词

Hindmarsh-Rose neuron; FitzHugh-Nagumo neuron; Asymmetric electrical synapse; Hamilton energy; Coexistence of hidden firing patterns; Pspice; Microcontroller implementation

资金

  1. Polish National Science Centre [OPUS 14 2017/27/B/ST8/01330]

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This paper investigates bidirectional-coupled neurons through an asymmetric electrical synapse, exploring different firing patterns under various electrical synaptic weights. It analyzes the equilibria, stabilities, and Hamiltonian energy of the model, as well as conducts Pspice simulations and digital implementation using an STM32F407ZE microcontroller development board to support the numerical results.
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.

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