Bifurcation analysis and parameter identification of HR neurons under electric field(电场作用下HR神经元的分岔分析及参数辨识)

XIAORan(肖冉); ANXinlei(安新磊); QIHuimin(祁慧敏); QIAOShuai(乔帅)

doi:10.3785/j.issn.1008-9497.2022.06.007

Zhejiang Daxue xuebao. Lixue ban (Nov 2022)

Bifurcation analysis and parameter identification of HR neurons under electric field(电场作用下HR神经元的分岔分析及参数辨识)

XIAORan(肖冉),
ANXinlei(安新磊),
QIHuimin(祁慧敏),
QIAOShuai(乔帅)

Affiliations

XIAORan(肖冉): ORCiD; School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China(兰州交通大学　数理学院，甘肃　兰州　730070)
ANXinlei(安新磊): ORCiD
QIHuimin(祁慧敏): School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China(兰州交通大学　数理学院，甘肃　兰州　730070)
QIAOShuai(乔帅): School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China(兰州交通大学　数理学院，甘肃　兰州　730070)

DOI: https://doi.org/10.3785/j.issn.1008-9497.2022.06.007
Journal volume & issue: Vol. 49, no. 6
pp. 691 – 697

Abstract

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详细分析了电场作用下四维Hindmarsh-Rose（HR）神经元模型的分岔模式及放电行为。通过数值仿真得到该神经元模型的多组双参数分岔图、最大Lyapunov指数图、峰峰间期分岔图等，发现该模型在双参数平面上存在倍周期分岔、加周期分岔等模式及“锯齿状”混沌结构。通过构建合适的目标函数，提出了自适应混合粒子群遗传算法，将神经元模型的参数辨识转化为最优化问题。数值仿真结果表明，算法对神经元模型的参数辨识效果较好，能更准确地辨识未知参数，具有一定优越性。

Published in Zhejiang Daxue xuebao. Lixue ban

ISSN: 1008-9497 (Print)
Publisher: Zhejiang University Press
Country of publisher: China
LCC subjects: Science: Mathematics: Instruments and machines: Electronic computers. Computer science; Science: Physics
Website: https://www.zjujournals.com/sci/EN/1008-9497/home.shtml

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