Initial-induced coexisting and synchronous firing activities in memristor synapse-coupled Morris-Lecar bi-neuron network

被引:77
作者
Bao, Bocheng [1 ]
Yang, Qinfeng [1 ]
Zhu, Dong [1 ]
Zhang, Yunzhen [2 ]
Xu, Quan [1 ]
Chen, Mo [1 ]
机构
[1] Changzhou Univ, Sch Informat Sci & Engn, Changzhou 213164, Jiangsu, Peoples R China
[2] Nanjing Univ Sci & Technol, Dept Elect Engn, Nanjing 210094, Peoples R China
基金
中国国家自然科学基金;
关键词
Memristor synapse; Bi-neuron network; Initial value; Coexisting activity; Synchronous activity; ELECTROMAGNETIC INDUCTION; SYNCHRONIZATION; MODEL; PROPAGATION; DYNAMICS; PATTERNS;
D O I
10.1007/s11071-019-05395-7
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
A memristor synapse with threshold memductance is employed to couple two neurons for representation of the electromagnetic induction effect triggered by their membrane potential difference. This paper presents a memristor synapse-coupled bi-neuron network by bidirectionally coupling two three-dimensional heterogeneous or homogeneous Morris-Lecar neurons with such a memristor synapse. The memristive bi-neuron network possesses a line equilibrium set with its stability related to the induction coefficient and memristor initial value. Coexisting firing activities in the heterogeneous memristive bi-neuron network are explored using bifurcation plots, phase plots, and time sequences, upon which the initial-induced infinitely many firing patterns including hyperchaotic, chaotic, and periodic bursting and tonic-spiking patterns are disclosed, indicating the emergence of the initial-induced extreme multistability. Furthermore, synchronous firing activities in homogeneous memristive bi-neuron network are investigated using the time sequences, synchronization transition states, and mean synchronized errors. The results demonstrate that the synchronous firing activities are associated with the induction coefficient and specially associated with the initial values of memristor synapse and coupling neurons. Finally, an FPGA-based electronic bi-neuron network is designed to experimentally confirm the memristor initial-induced coexisting firing activities.
引用
收藏
页码:2339 / 2354
页数:16
相关论文
共 56 条
[1]  
[Anonymous], 2002, Control of Nonlinear Systems
[2]   Chaotic Bursting Dynamics and Coexisting Multistable Firing Patterns in 3D Autonomous Morris-Lecar Model and Microcontroller-Based Validations [J].
Bao, Bocheng ;
Yang, Qinfeng ;
Zhu, Lei ;
Bao, Han ;
Xu, Quan ;
Yu, Yajuan ;
Chen, Mo .
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, 2019, 29 (10)
[3]   AC-induced coexisting asymmetric bursters in the improved Hindmarsh-Rose model [J].
Bao, Bocheng ;
Hu, Aihuang ;
Xu, Quan ;
Bao, Han ;
Wu, Huagan ;
Chen, Mo .
NONLINEAR DYNAMICS, 2018, 92 (04) :1695-1706
[4]   Three-Dimensional Memristive Hindmarsh-Rose Neuron Model with Hidden Coexisting Asymmetric Behaviors [J].
Bao, Bocheng ;
Hu, Aihuang ;
Bao, Han ;
Xu, Quan ;
Chen, Mo ;
Wu, Huagan .
COMPLEXITY, 2018,
[5]   Hidden extreme multistability and dimensionality reduction analysis for an improved non-autonomous memristive FitzHugh-Nagumo circuit [J].
Bao, Han ;
Liu, Wenbo ;
Chen, Mo .
NONLINEAR DYNAMICS, 2019, 96 (03) :1879-1894
[6]   Hidden Bursting Firings and Bifurcation Mechanisms in Memristive Neuron Model With Threshold Electromagnetic Induction [J].
Bao, Han ;
Hu, Aihuang ;
Liu, Wenbo ;
Bao, Bocheng .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 31 (02) :502-511
[7]   Coexisting multiple firing patterns in two adjacent neurons coupled by memristive electromagnetic induction [J].
Bao, Han ;
Liu, Wenbo ;
Hu, Aihuang .
NONLINEAR DYNAMICS, 2019, 95 (01) :43-56
[8]   Experimental evidence for phase synchronization transitions in the human cardiorespiratory system [J].
Bartsch, Ronny ;
Kantelhardt, Jan W. ;
Penzel, Thomas ;
Havlin, Shlomo .
PHYSICAL REVIEW LETTERS, 2007, 98 (05)
[9]   Evidence for plateau potentials in tail motoneurons of awake chronic spinal rats with spasticity [J].
Bennett, DJ ;
Li, YR ;
Harvey, PJ ;
Gorassini, M .
JOURNAL OF NEUROPHYSIOLOGY, 2001, 86 (04) :1972-1982
[10]   Speeding Up Cellular Neural Network Processing Ability by Embodying Memristors [J].
Bilotta, E. ;
Pantano, P. ;
Vena, S. .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (05) :1228-1232