Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

doi:10.1088/1674-1056/ad84c5

中国物理B ›› 2024, Vol. 33 ›› Issue (12): 128701-128701.doi: 10.1088/1674-1056/ad84c5

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Zhi-Jun Li(李志军)† and Jing Zhang(张晶)

School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China

收稿日期:2024-07-11 修回日期:2024-09-10 接受日期:2024-10-09 出版日期:2024-12-15 发布日期:2024-11-29
通讯作者: Zhi-Jun Li E-mail:lizhijun@xtu.edu.cn
基金资助:
Project supported by the National Natural Science Foundations of China (Grant Nos. 62171401 and 62071411).

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Zhi-Jun Li(李志军)† and Jing Zhang(张晶)

School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China

Received:2024-07-11 Revised:2024-09-10 Accepted:2024-10-09 Online:2024-12-15 Published:2024-11-29
Contact: Zhi-Jun Li E-mail:lizhijun@xtu.edu.cn
Supported by:
Project supported by the National Natural Science Foundations of China (Grant Nos. 62171401 and 62071411).

摘要/Abstract

摘要： Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant seven-dimensional (7D) neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum, and two-dimensional (2D) dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

关键词: synaptic plasticity, memristive synapse, electromagnetic radiation, hidden attractor, phase synchronization

Abstract: Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant seven-dimensional (7D) neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum, and two-dimensional (2D) dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

Key words: synaptic plasticity, memristive synapse, electromagnetic radiation, hidden attractor, phase synchronization

中图分类号: (Models of single neurons and networks)

87.19.ll

87.19.lj (Neuronal network dynamics) 05.45.Pq (Numerical simulations of chaotic systems) 05.45.Xt (Synchronization; coupled oscillators)

Zhi-Jun Li(李志军) and Jing Zhang(张晶). Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network[J]. 中国物理B, 2024, 33(12): 128701-128701.

Zhi-Jun Li(李志军) and Jing Zhang(张晶). Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network[J]. Chin. Phys. B, 2024, 33(12): 128701-128701.

参考文献

[1] Xu F, Zhang J, Fang T, Huang S and Wang M 2018 Nonlinear Dyn. 92 1395
[2] Dias C, Castro D, Aroso M, Ventura J and Aguiar P 2022 ACS Appl. Electron. Mater. 4 2380
[3] Mariano P M and Spadini M 2024 Physica D 458 133993
[4] Hodgkin A L and Huxley A F 1952 J. Physiol. 117 500
[5] Hindmarsh J L and Rose R M 1982 Nature 296 162
[6] Izhikevich E M 2003 IEEE Trans. Neural Network 14 1569
[7] Izhikevich E M 2006 Scholarpedia 1 1349
[8] Huang L,Wang S, Lei T, Huang K and Li C 2024 Int. J. Bifurcat. Chaos 34 2450022
[9] Njitacke Z T, Kengne J and Fotsin H B 2019 Int. J. Dyn. Control 7 36
[10] Lai Q, Wan Z, Zhang H and Chen G 2023 IEEE Trans. Neural Networks Learn. Syst. 34 7824
[11] Zhang S, Zheng J,Wang X and Zeng Z 2021 Chaos, Solitons and Fractals 145 110761
[12] Ge M, Xu Y, Lu L, Zhao Y, Yang L, Zhan X and Jia Y 2018 IET Syst. Biol. 12 177
[13] Wu K, Wang T, Wang C, Du T and Lu H 2018 Neural Comput. Appl. 30 551
[14] Li Z and Chen K 2023 Chaos, Solitons and Fractals 175 114017
[15] Chua L 1971 IEEE Trans. Circuit Syst. 18 507
[16] Innocenti G, Tesi A, Di Marco M and Forti M 2022 Emerging Sel. Top. Circuits Syst. 12 735
[17] Ascoli A, Slesazeck S, Mähne H, Tetzlaff R and Mikolajick T 2015 IEEE Trant. Circuits-I 62 1165
[18] Bao H, Chen M, Wu H G and Bao B C 2020 Sci. China-Technol. Sci. 63 603
[19] Li C L, Li Z Y, FengW, Tong Y N, Du J R andWei D Q 2019 AEU-INT J. Electron. C 110 152861
[20] Bao B, Peol M A, Bao H, Chen M, Li H and Chen B 2021 Chaos, Solitons and Fractals 144 110744
[21] Du C, Liu L, Zhang Z and Yu S 2021 Chaos, Solitons and Fractals 148 111023
[22] Chen M, Luo X, Suo Y, Xu Q and Wu H 2023 Nonlinear Dyn. 111 7773
[23] Ma M L, Xie X H, Yang Y, Li Z J and Sun Y C 2023 Chin. Phys. B 32 058701
[24] Peng C, Li Z J,WangMJ and MaML 2023 Nonlinear Dyn. 111 16527
[25] Yu X, Bao H, Chen M and Bao B C 2023 Chaos, Solitons and Fractals 171 113442
[26] Bao H, Yu X, Zhang Y, Liu X and Chen M 2023 Chaos, Solitons and Fractals 177 114167
[27] Chen M, Luo X, Suo Y, Xu Q and Wu H 2023 Nonlinear Dyn. 111 7773
[28] Lin H, Wang C, Sun J, Zhang X, Sun Y and Iu H H 2023 Chaos, Solitons and Fractals 166 112905
[29] Takembo C N, Mvogo A, Ekobena Fouda H P and Kofan eT C 2019 Nonlinear Dyn. 95 1067
[30] Vinaya M and Ignatius R P 2019 Int. J. Mod. Phys. C 30 1950047
[31] An X, Xiong L, Shi Q, Qiao S and Zhang L 2023 Nonlinear Dyn. 111 9509
[32] Lai Q and Yang L 2024 Chaos 34 013145
[33] Vinaya M and Ignatius R P 2020 Nonlinear Dyn. 101 2369
[34] Kourosh-Arami M, Komaki A, Gholami M, Marashi S H and Hejazi S 2023 J. Physiol. Sci. 73 33
[35] Magee J C and Grienberger C 2020 Annu. Rev. Neurosci. 43 95
[36] Sargsyan A R, Melkonyan A A, Papatheodoropoulos C, Mkrtchian H H and Kostopoulos G K 2003 Neural Networks 16 1161
[37] Oner M, Cheng P T, Wang H Y, Chen M C and Lin H 2024 Biochem. Biophys. Res. Commun. 710 149874
[38] Madadi Asl M, Valizadeh A and Tass P A 2018 Sci. Rep. 8 12068
[39] Madadi Asl M and Ramezani Akbarabadi S 2021 Plos One 16 e0257228
[40] Madadi Asl M and Ramezani Akbarabadi S 2023 Cogn. Neurodynamics 17 523
[41] Chen L, Li C, Huang T, Chen Y,Wen S and Qi J 2013 Phys. Lett. A 377 3260
[42] ZhouW,Wen S, Liu Y, Liu L, Liu X and Chen L 2023 Neural Networks 158 293
[43] Chen L, Zhou W, Li C and Huang J 2021 Neurocomputing 456 126
[44] Zhang J and Li Z J 2024 Nonlinear Dyn. 112 6647
[45] Li C, Zhang X, Chen P, Zhou K, Yu J and Wu G 2023 iScience 26 106315
[46] Wang Y, Wang G, Shen Y and Iu H H C 2020 Circuits Syst. Signal Process 39 3496
[47] Mannan Z I, Kim H and Chua L 2021 Sensors 21 644
[48] Zhang X, Liu S, Zhao X,Wu F,Wu Q and Wang W2017 IEEE Electron Dev. Lett. 38 1208
[49] Wu L, Liu H, Lin J and Wang S 2021 IEEE Trans. Electron Dev. 68 1622
[50] Chua L 2018 Appl. Phys. A 124 563
[51] Deperrois N and Graupner M 2020 PLoS Comput. Biol. 16 e1008265
[52] Wen L and Ong C K 2024 Int. J. Bifurcation Chaos 34 2450040
[53] Shakib M A, Gao Z and Lamuta C 2023 ACS Appl. Electron. Mater. 5 4875
[54] Chua L 2014 Semicond. Sci. Technol. 29 104001
[55] Dudkowski D, Jafari S, Kapitaniak T, Kuznetsov N V, Leonov G A and Prasad A 2016 Phys. Rep. 637 1
[56] Shavikloo M, Esmaeili A, Valizadeh A and Madadi Asl M 2023 Cogn. Neurodynamics 18 631
[57] Schmalz J and Kumar G 2019 Front. Comput. Neurosci. 13 61
[58] Hu X, Jiang B, Chen J and Liu C 2022 Eur. Phys. J. Plus 137 895
[59] Wang H X, Lu Q S and Shi X 2010 Chin. Phys. B 19 060509
[60] Njitacke Z T, Tsafack N, Ramakrishnan B, Rajagopal K, Kengne J and Awrejcewicz J 2021 Chaos, Solitons and Fractals 153 111577

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	Minglin Ma(马铭磷), Kangling Xiong(熊康灵), Zhijun Li(李志军), and Shaobo He(贺少波). Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk[J]. 中国物理B, 2024, 33(2): 28706-028706.
[2]	Peng-Chong Zhao(赵鹏翀), Hao-Juan Wei(卫皓娟), Zhen-Kun Xu(徐振坤), Di-Yi Chen(陈帝伊), Bei-Bei Xu(许贝贝), and Yu-Meng Wang(王雨萌). Existence of hidden attractors in nonlinear hydro-turbine governing systems and its stability analysis[J]. 中国物理B, 2023, 32(9): 90503-090503.
[3]	Yong-Tao Yu(于永涛) and Xiao-Li Yang(杨晓丽). Inverse stochastic resonance in modular neural network with synaptic plasticity[J]. 中国物理B, 2023, 32(3): 30201-030201.
[4]	Zhan-Hong Guo(郭展宏), Zhi-Jun Li(李志军), Meng-Jiao Wang(王梦蛟), and Ming-Lin Ma(马铭磷). Hopf bifurcation and phase synchronization in memristor-coupled Hindmarsh-Rose and FitzHugh-Nagumo neurons with two time delays[J]. 中国物理B, 2023, 32(3): 38701-038701.
[5]	Fan Yang(杨帆) and Feng Liu(刘锋). Effect of short-term plasticity on working memory[J]. 中国物理B, 2023, 32(11): 118706-118706.
[6]	Yang Yang(杨洋), Chuanyu Fu(傅传玉), Shuo Ke(柯硕), Hangyuan Cui(崔航源), Xiao Fang(方晓), Changjin Wan(万昌锦), and Qing Wan(万青). W-doped In₂O₃ nanofiber optoelectronic neuromorphic transistors with synergistic synaptic plasticity[J]. 中国物理B, 2023, 32(11): 118101-118101.
[7]	Xiaodong Jiao(焦晓东), Mingfeng Yuan(袁明峰), Jin Tao(陶金), Hao Sun(孙昊), Qinglin Sun(孙青林), and Zengqiang Chen(陈增强). Memristor hyperchaos in a generalized Kolmogorov-type system with extreme multistability[J]. 中国物理B, 2023, 32(1): 10507-010507.
[8]	Li-Ping Zhang(张丽萍), Yang Liu(刘洋), Zhou-Chao Wei(魏周超),Hai-Bo Jiang(姜海波), and Qin-Sheng Bi(毕勤胜). A class of two-dimensional rational maps with self-excited and hidden attractors[J]. 中国物理B, 2022, 31(3): 30503-030503.
[9]	Li-Ping Zhang(张丽萍), Yang Liu(刘洋), Zhou-Chao Wei(魏周超), Hai-Bo Jiang(姜海波), Wei-Peng Lyu(吕伟鹏), and Qin-Sheng Bi(毕勤胜). Extremely hidden multi-stability in a class of two-dimensional maps with a cosine memristor[J]. 中国物理B, 2022, 31(10): 100503-100503.
[10]	Gang Dou(窦刚), Ming-Long Dou(窦明龙), Ren-Yuan Liu(刘任远), and Mei Guo(郭梅). Artificial synaptic behavior of the SBT-memristor[J]. 中国物理B, 2021, 30(7): 78401-078401.
[11]	Rui Liu(刘锐), Yongli He(何勇礼), Shanshan Jiang(姜珊珊), Li Zhu(朱力), Chunsheng Chen(陈春生), Ying Zhu(祝影), and Qing Wan(万青). Synaptic plasticity and classical conditioning mimicked in single indium-tungsten-oxide based neuromorphic transistor[J]. 中国物理B, 2021, 30(5): 58102-058102.
[12]	Li-Lian Huang(黄丽莲), Shuai Liu(刘帅), Jian-Hong Xiang(项建弘), and Lin-Yu Wang(王霖郁). Design and multistability analysis of five-value memristor-based chaotic system with hidden attractors[J]. 中国物理B, 2021, 30(10): 100506-100506.
[13]	Adel Ouannas, Amina Aicha Khennaoui, Shaher Momani, Viet-Thanh Pham, Reyad El-Khazali. Hidden attractors in a new fractional-order discrete system: Chaos, complexity, entropy, and control[J]. 中国物理B, 2020, 29(5): 50504-050504.
[14]	钟海, 孙勤超, 李果, 杜剑宇, 黄河意, 郭尔佳, 何萌, 王灿, 杨国桢, 葛琛, 金奎娟. High-performance synaptic transistors for neuromorphic computing[J]. 中国物理B, 2020, 29(4): 40703-040703.
[15]	于海涛, 蔡立辉, 武欣昱, 王江, 刘静, 张宏. Cross-frequency network analysis of functional brain connectivity in temporal lobe epilepsy[J]. 中国物理B, 2019, 28(4): 48702-048702.