详细信息

Synchronization in memristive HR neurons with hidden coexisting firing and lower energy under electrical and magnetic coupling  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:Synchronization in memristive HR neurons with hidden coexisting firing and lower energy under electrical and magnetic coupling

作者:Wu, Fuqiang[1,4];Wang, Rubin[2,3]

机构:[1]Ningxia Univ, Sch Math & Stat, Yinchuan 750021, Peoples R China;[2]East China Univ Sci & Technol, Inst Cognit Neurodynam, Shanghai 200237, Peoples R China;[3]Hangzhou Dianzi Univ, Sch Comp Sci, Hangzhou, Peoples R China;[4]Ningxia Univ, Ningxia Basic Sci Res Ctr Math, Yinchuan 750021, Peoples R China

年份:2023

卷号:126

外文期刊名:COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION

收录:;EI(收录号:20233614691667);WOS:【SCI-EXPANDED(收录号:WOS:001052034400001)】;

基金:The authors thank the editor and anonymous reviewers for their valuable comments and suggestions that helped to improve the paper. This study was funded by the National Natural Science Foundation of China (Nos. 11472104, 11872180, 12072113) .

语种:英文

外文关键词:Electromagnetic induction; Energy; Synchronization; Hidden firing

摘要:Employing memristors to mimic biological synapses and to describe electromagnetic induction effects attracts much attention in current researches. In this paper, a memris-tive Hindmarsh-Rose neuron model is proposed by using an electromagnetic inductive current involving quadratic memconductance to replace the adaptive current in the classical 3-D HR model. The memristive neuron shows hidden firing and coexisting state. The energy function of memristive neuron is deduced by the generalized Hamiltonian formalism. In particular, two memristive neurons with irregular firing reach phase synchronization under the electrical coupling, while achieve complete synchronization under the magnetic coupling. The coupled neurons operate with economy energy in the region of whether complete synchronization or phase synchronization. Corresponding results are further demonstrated by designing digital circuit. These theoretical investiga-tions can better choose appropriate model to explore dynamics of larger-scale neuronal network.& COPY; 2023 Elsevier B.V. All rights reserved.

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