场耦合忆阻神经网络的电活动

doi:10.19596/j.cnki.1001-246x.8168

计算物理 ›› 2020, Vol. 37 ›› Issue (6): 750-756.DOI: 10.19596/j.cnki.1001-246x.8168

• • 上一篇

场耦合忆阻神经网络的电活动

周倩, 韦笃取

广西师范大学电子工程学院, 广西桂林 541004

收稿日期:2019-11-08 修回日期:2020-01-17 出版日期:2020-11-25 发布日期:2020-11-25
通讯作者: 韦笃取(1975-),博士,教授,主要研究方向为复杂神经网络动力学行为分析与控制,E-mail:weiduqu@mailbox.gxnu.edu.cn
作者简介:周倩(1996-),硕士研究生,主要研究方向为神经信息分析
基金资助:
国家自然科学基金（11562004）、广西研究生教育创新计划项目（XYCSZ2020052、XJGY2020002）资助

Firing Activity of Memristive Neural Network Under Field Coupling

ZHOU Qian, WEI Duqu

College of Electronic Engineering, Guangxi Normal University, Guilin, Guangxi 541004, China

Received:2019-11-08 Revised:2020-01-17 Online:2020-11-25 Published:2020-11-25

摘要/Abstract

摘要： 神经元之间除了突触耦合，还存在磁通耦合.因此在传统的神经元模型中引入磁通量，并研究场耦合下神经网络的放电活动具有实际意义.建立一个含场耦合的Hodgkin-Huxley忆阻神经网络，引入神经元节点之间的距离权重，用磁通量描述时变电磁场，采用磁控忆阻器实现膜电位和磁通量之间的耦合.探讨距离权重和系统大小对神经网络放电活动的影响.研究发现，随着权重增大，神经网络放电活动增强，且系统规模越大，诱导神经元兴奋性的权重阈值越大，系统大小不影响神经网络活性随距离权重变化的规律.在不同的权重值下，神经网络活性随系统大小变化的规律明显不同.研究表明，距离权重和系统大小对含场耦合的忆阻神经网络放电活动有重要影响，其中距离权重起主导作用.

关键词: 忆阻神经网络, 场耦合, 距离权重, 系统大小

Abstract: In reality, in addition to synapse coupling, magnetic flux coupling between neurons exists. Therefore, it is of practical significance to investigate the firing activity of neural network under field coupling. A Hodgkin-Huxley memristive neural model under field coupling is constructed, in which a distance-weighte between neurons is introduced. Then, effect of time-varying electromagnetic field is described with magnetic flux and a flux-controlled memristor is used to realize coupling between membrane potential and magnetic flux. The influence of distance-weighted and system size on the firing activity of memristive neural model under field coupling is studied. Experimental results show that with the increase of weight, the firing activity of neural network is enhanced. Besides, in order to induce neuronal excitability, threshold of weight increases as system becomes larger. Nevertheless, the evolution behavior of neural network activity with different weight is not affected by the system size. On the other hand, with different weight the evolution behavior of neural network activity of different systems is obviously different. These phenomena demonstrate that the weight and system size are important for the firing activity of memristive neural network under field coupling, and especially, the distance-weighte plays a leading role.

Key words: memristive neural network, field coupling, distance-weighted, system size

中图分类号:

TM341

周倩, 韦笃取. 场耦合忆阻神经网络的电活动[J]. 计算物理, 2020, 37(6): 750-756.

ZHOU Qian, WEI Duqu. Firing Activity of Memristive Neural Network Under Field Coupling[J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2020, 37(6): 750-756.

参考文献

[1] PERC M, JORDAN J J, RAND D G, et al. Statistical physics of human cooperation[J]. Physics Reports, 2017, 687:1-51.
[2] MA J, TANG J. A review for dynamics in neuron and neuronal network[J].Nonlinear Dynamics, 2017, 89(3):1569-1578.
[3] 于文婷, 张娟, 唐军. 动态突触:神经耦合与时间延迟对神经元发放的影响[J].物理学报, 2017, 66(20):200201.
[4] XU Y, JIA Y, MA J, et al. Synchronization between neuros coupled by memristor[J]. Chaos, Solitons & Fractals, 2017, 104:435-442.
[5] USHA K, SUBHA P A.Hindmarsh-Rose neuron model with memristors[J]. Biosystems, 2019, 178:1-9.
[6] HUANG Z J, BAI J, TANG G N. Mechanism of spontaneous formation of spiral wave in neuronal network with unidirectional coupling[J].Chinese Journal of Computational Physics, 2020, 37(5):612-622.
[7] LU L, KIRUNDA J B, XU Y, et al. Effects of temperature and electromagnetic induction on action potential of Hodgkin-Huxley model[J]. The European Physical Journal Special Topics, 2018, 227(7-9):767-776.
[8] LI C G, CHEN G R. Local stability and Hopf bifurcation in small-world delayed networks[J]. Chaos, Solitons & Fractals, 2004, 20(2):353-361.
[9] YUAN W J, LUO X S, WANG B H, et al. Excitation properties of the biological neurons with side-inhibition mechanism in small-world networks[J]. Chinese Physics Letters, 2006, 23(11):3115.
[10] GE M Y, JIA Y, XU Y, et al. Wave propagation and synchronization induced by chemical autapse in chain Hindmarsh-Rose neural network[J]. Applied Mathematics and Computation, 2019, 352:136-145.
[11] XU Y, JIA Y, MA J, et al. Collective responses in electrical activities of neurons under field coupling[J]. Scientific reports, 2018, 8(1):1349.
[12] WEI D Q, LUO X S, ZOU Y L. Coupling strength and system size induce firing activity of globally coupled neural network[J]. Communications in Theoretical Physics, 2008, 50(1):267.
[13] ZHANG J Q, WANG C D, WANG M S, et al. Firing patterns transition induced by system size in coupled Hindmarsh-Rose neural system[J]. Neurocomputing, 2011, 74(17):2961-2966.
[14] SONG X L, WANG C N, MA J, et al. Transition of electric activity of neurons induced by chemical and electric autapses[J]. Science China:Technological Sciences, 2015, 58(6):1007-1014.
[15] WANG Q, GONG Y B, XIE H J. Mutual and intermittent enhancements of synchronization transitions by autaptic and synaptic delay in scale-free neuron networks[J]. The European Physical Journal B, 2016, 89(5):129.
[16] WU K J, WANG T J, WANG C L, et al. Study on electrical synapse coupling synchronization of Hindmarsh-Rose neurons under Gaussian white noise[J]. Neural Computing and Applications, 2018, 30(2):551-561.
[17] GUO S L, XU Y, WANG C N, et al. Collective response, synapse coupling and field coupling in neuronal network[J]. Chaos, Solitons & Fractals, 2017, 105:120-127.
[18] MA J, MI L, ZHOU P, et al. Phase synchronization between two neurons induced by coupling of electromagnetic field[J]. Applied Mathematics and Computation, 2017, 307:321-328.
[19] LV M, MA J, YAO Y G, et al. Synchronization and wave propagation in neuronal network under field coupling[J]. Science China:Technological Sciences, 2019, 62(3):448-457.
[20] CHUA L. Memristor-the missing circuit element[J]. IEEE Trans on Circuit Theory, 1971, 18(5):507-519.
[21] STRUKOV D B, SNIDER G S, STEWART D R, et al. The missing memristor found[J]. Nature, 2008, 453(7191):80.
[22] TOUR J M, HE T. The fourth element[J]. Nature, 2008, 453(7191):42-43.
[23] BORGHETTI J, SNIDER G S, KUEKES P J, et al. ‘Memristive’ switches enable ‘stateful’ logic operations via material implication[J]. Nature, 2010, 464(7290):873.
[24] 包伯成, 史国栋, 许建平, 等. 含两个忆阻器混沌电路的动力学分析[J]. 中国科学:技术科学, 2011, 41(8):1135-1142.
[25] LI C B, THIO W J-C, IU H H-C, et al. A memristive chaotic oscillator with increasing amplitude and frequency[J]. IEEE Transactions on Circuit Theory, 2018, 6:12945-12950.
[26] TAN Z P, YANG H J, LIU Q N, et al. Analysis and implementation of a simplest charge-controlled memristor chaotic circuit[J]. Chinese Journal of Computational Physics, 2015, 32(4):496-504.
[27] YU S C, ZENG Y C, LI Z J. A quadratic memristor-based fourth-order chaotic circuit[J]. Chinese Journal of Computational Physics, 2015, 32(6):735-743.
[28] XU Y, JIA Y, MA J, et al. Synchronization between neurons coupled by memristor[J]. Chaos, Solitons & Fractals, 2017, 104:435-442.
[29] TAKEMBO C N, MVOGO A, FOUDA H P E, et al. Effect of electromagnetic radiation on the dynamics of spatiotemporal patterns in memristor-based neuronal network[J]. Nonlinear Dynamics, 2019, 95(2):1067-1078.
[30] 刘少宝, 吴莹, 郝忠文, 等. 钠离子和钾离子通道噪声扰动对神经网络时空模式的影响[J].物理学报, 2012, 61(2):20503-020503.

场耦合忆阻神经网络的电活动

Firing Activity of Memristive Neural Network Under Field Coupling

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

作者中心

审稿中心

期刊浏览

期刊介绍