基于忆阻器的混沌、存储器及神经网络电路研究进展

王春华; 蔺海荣; 孙晶如; 周玲; 周超; 邓全利

doi:10.11999/JEIT190821

基于忆阻器的混沌、存储器及神经网络电路研究进展

doi: 10.11999/JEIT190821

1.
湖南大学信息科学与工程学院长沙 410082
2.
湖南科技学院电子与信息工程学院永州 425199

基金项目: 国家自然科学基金重大研究计划项目(91964108)，国家自然科学基金(61971185)，湖南省高校重点实验室开放基金(18K010)

详细信息

作者简介:
王春华：男，1963年生，教授，研究方向为模拟/混合集成电路设计、混沌电路与系统、神经网络与类脑智能、混沌图像加密

蔺海荣：男，1988年生，博士生，研究方向为基于忆阻的神经网络模型设计、动力学分析以及电路实现

孙晶如：女，1977年生，助理教授，研究方向为基于忆阻的存储器技术、混沌图像加密技术、基于神经网络的交通流预测

周玲：女，1980年生，讲师，研究方向为混沌电路与系统、图像处理与加密

周超：男，1991年生，博士生，研究方向为复杂网络、基于忆阻神经网络同步与控制

邓全利：男，1993年生，硕士生，研究方向为基于忆阻器的混沌系统及基于忆阻器的神经网络

通讯作者:
王春华　wch1227164@hnu.edu.cn

中图分类号: TN601; TN710.2
计量
- 文章访问数: 7302
- HTML全文浏览量: 1770
- PDF下载量: 892
- 被引次数: 0
出版历程
- 收稿日期: 2019-10-25
- 修回日期: 2020-01-10
- 网络出版日期: 2020-01-21
- 刊出日期: 2020-06-04

Research Progress on Chaos, Memory and Neural Network Circuits Based on Memristor

1.
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
2.
School of Electronics and Information Engineering, Hunan University of Science and Engeneering, Yongzhou 425199, China

Funds: The Major Research Project of the National Natural Science Foundation of China (91964108), The National Natural Science Foundation of China (61971185), The Open Fund Project of Key Laboratory in Hunan Universities (18K010)

摘要

摘要: 忆阻器是除电阻、电容、电感之外发现的第4种基本电子元件，它是一种具有记忆特性的非线性器件，可用于混沌、存储器、神经网络等电路与系统的实现。该文对基于忆阻器的混沌电路、存储器、神经网络电路的设计与神经动力学的国内外研究进行了综述，并给出了对它们的研究展望。
- 忆阻器 /
- 混沌电路 /
- 存储器 /
- 神经网络
Abstract: Memristor is the fourth basic electronic component in addition to resistor, capacitor and inductor. It is a nonlinear device with memory characteristics, which can be used to design chaotic circuits, memory devices and neural networks. The design of memristor-based chaos circuits, memory and neural systems, and some research of neural dynamics in this field are reviewed, and their research prospects are also given.
- Memristor /
- Chaos circuit /
- Memory /
- Neural network

HTML全文

图 1 HP忆阻器的结构图

下载: 全尺寸图片幻灯片

图 2 基于1T2M存储单元的多值存储器原理图

下载: 全尺寸图片幻灯片

图 3 基于忆阻器突触的电路结构图

下载: 全尺寸图片幻灯片

图 4 电磁辐射忆阻神经模型

下载: 全尺寸图片幻灯片

图 5 忆阻突触神经模型

下载: 全尺寸图片幻灯片

参考文献(160)

CHUA L O. Memristor-the missing circuit element[J]. IEEE Transactions on Circuit Theory, 1971, 18(5): 507–519. doi: 10.1109/tct.1971.1083337

STRUKOV D B, SNIDER G S, STEWART D R, et al. The missing memristor found[J]. Nature, 2008, 453(7191): 80–83. doi: 10.1038/nature06932

KIM H, SAH M P, YANG C J, et al. Memristor emulator for memristor circuit applications[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2012, 59(10): 2422–2431. doi: 10.1109/tcsi.2012.2188957

WANG Xiaobin, CHEN Yiran, XI Haiwen, et al. Spintronic memristor through spin-torque-induced magnetization motion[J]. IEEE Electron Device Letters, 2009, 30(3): 294–297. doi: 10.1109/LED.2008.2012270

HU Miao, LI Hai, CHEN Yiran, et al. Geometry variations analysis of TiO₂ thin-film and spintronic memristors[C]. The 16th Asia and South Pacific Design Automation Conference, Yokohama, Japan, 2011: 25–30. doi: 10.1109/ASPDAC.2011.5722193.

VOURKAS I, and SIRAKOULIS G C. A novel design and modeling paradigm for memristor-based crossbar circuits[J]. IEEE Transactions on Nanotechnology, 2012, 11(6): 1151–1159. doi: 10.1109/TNANO.2012.2217153

KVATINSKY S, FRIEDMAN E G, KOLODNY A, et al. TEAM: Threshold adaptive memristor model[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2013, 60(1): 211–221. doi: 10.1109/TCSI.2012.2215714

KVATINSKY S, RAMADAN M, FRIEDMAN E G, et al. VTEAM: A general model for voltage-controlled memristors[J]. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2015, 62(8): 786–790. doi: 10.1109/TCSⅡ.2015.2433536

ZHANG Yang, LI Yi, WANG Xiaoping, et al. Synaptic characteristics of Ag/AgInSbTe/Ta-based memristor for pattern recognition applications[J]. IEEE Transactions on Electron Devices, 2017, 64(4): 1806–1811. doi: 10.1109/TED.2017.2671433

SHERI A M, HWANG H, JEON M, et al. Neuromorphic character recognition system with two PCMO memristors as a synapse[J]. IEEE Transactions on Industrial Electronics, 2014, 61(6): 2933–2941. doi: 10.1109/tie.2013.2275966

CHOI S, SHERIDAN P, and LU W D. Data clustering using memristor networks[J]. Scientific Reports, 2015, 5: 10492. doi: 10.1038/srep10492

DOU Gang, YU Yang, GOU Mei, et al. Memristive behavior based on Ba-doped SrTiO3 films[J]. Chinese Physics Letters, 2017, 34(3): 038502. doi: 10.1088/0256-307X/34/3/038502

CORINTO F and ASCOLI A. Memristive diode bridge with LCR filter[J]. Electronics Letters, 2012, 48(14): 824–825. doi: 10.1049/el.2012.1480

SANCHEZ-LÓPEZ C, MENDOZA-LÓPEZ J, CARRASCO-AGUILAR M A, et al. A floating analog memristor emulator circuit[J]. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2014, 61(5): 309–313. doi: 10.1109/TCSⅡ.2014.2312806

BABACAN Y and KAÇAR F. Floating memristor emulator with subthreshold region[J]. Analog Integrated Circuits and Signal Processing, 2017, 90(2): 471–475. doi: 10.1007/s10470-016-0888-9

ADHIKARI S P, SAH M P, KIM H, et al. Three fingerprints of memristor[J]. IEEE Transactions on Circuits and Systems-I, 2013, 60(11): 3008–3021. doi: 10.1109/TCSI.2013.2256171

CHUA L O. If it’s pinched it’s a memristor[J]. Semiconductor Science and Technology, 2014, 29(10): 1040001–1040042. doi: 10.1088/0268-1242/29/10/104001

BAO Bocheng, YU Jingjing, HU Fengwei, et al. Generalized memristor consisting of diode bridge with first order parallel RC filter[J]. International Journal of Bifurcation and Chaos, 2014, 24(11): 1450143. doi: 10.1142/S0218127414501430

YUAN Fang and LI Yuxia. A chaotic circuit constructed by a memristor, a memcapacitor and a meminductor[J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019, 29(10): 101101. doi: 10.1063/1.5125673

ITOH M and CHUA L O. Memristor oscillators[J]. International Journal of Bifurcation and Chaos, 2008, 18(11): 3183–3206. doi: 10.1142/S0218127408022354

FITCH A L, YU Dongsheng, IU H H C, et al. Hyperchaos in a memristor-based modified canonical chua's circuit[J]. International Journal of Bifurcation and Chaos, 2012, 22(6): 1250133. doi: 10.1142/S0218127412501337

BAO Han, JIANG Tao, CHU Kaibin, et al. Memristor-based canonical Chua’s circuit: Extreme multistability in voltage-current domain and its controllability in flux-charge domain[J]. Complexity, 2018, 2018: 5935637. doi: 10.1155/2018/5935637

AHAMED A I and LAKSHMANAN M. Nonsmooth bifurcations, transient hyperchaos and hyperchaotic beats in a memristive murali-lakshmanan-chua circuit[J]. International Journal of Bifurcation and Chaos, 2013, 23(6): 1350098. doi: 10.1142/S0218127413500983

ZHAO Qing, WANG Chunhua, and ZHANG Xin. A universal emulator for memristor, memcapacitor, and meminductor and its chaotic circuit[J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019, 29(1): 013141. doi: 10.1063/1.5081076

BUSCARINO A, FORTUNA L, FRASCA M, et al. A chaotic circuit based on Hewlett-Packard memristor[J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2012, 22(2): 023136. doi: 10.1063/1.4729135

BAO Bocheng, JIANG Tao, WANG Guangyi, et al. Two-memristor-based Chua’s hyperchaotic circuit with plane equilibrium and its extreme multistability[J]. Nonlinear Dynamics, 2017, 89(2): 1157–1171. doi: 10.1007/s11071-017-3507-0

BAO Bocheng, JIANG Pan, WU Huagan, et al. Complex transient dynamics in periodically forced memristive Chua’s circuit[J]. Nonlinear Dynamics, 2015, 79(4): 2333–2343. doi: 10.1007/s11071-014-1815-1

洪庆辉, 曾以成, 李志军. 含磁控和荷控两种忆阻器的混沌电路设计与仿真[J]. 物理学报, 2013, 62(23): 230502. doi: 10.7498/aps.62.230502

HONG Qinghui, ZENG Yicheng, and LI Zhijun. Design and simulation of chaotic circuit for flux-controlled memristor and charge-controlled memristor[J]. Acta Physica Sinica, 2013, 62(23): 230502. doi: 10.7498/aps.62.230502

XU Quan, LIN Yi, BAO Bocheng, et al. Multiple attractors in a non-ideal active voltage- controlled memristor based Chua's circuit[J]. Chaos, Solitons & Fractals, 2016, 83: 186–200. doi: 10.1016/j.chaos.2015.12.007

CHEN Mo, LI Mengyuan, YU Qing, et al. Dynamics of self-excited attractors and hidden attractors in generalized memristor-based Chua’s circuit[J]. Nonlinear Dynamics, 2016, 81(1/2): 215–226. doi: 10.1007/s11071-015-1983-7

WANG Chunhua, LIU Xiaoming, and XIA Hu. Multi-piecewise quadratic nonlinearity memristor and its 2N-scroll and 2N+ 1-scroll chaotic attractors system[J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017, 27(3): 033114. doi: 10.1063/1.4979039

GUO Mei, YANG Wenyan, XUE Youbao, et al. Multistability in a physical memristor-based modified Chua’s circuit[J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019, 29(4): 043114. doi: 10.1063/1.5089293

LI Qingdu, ZENG Hongzheng, and LI Jing. Hyperchaos in a 4D memristive circuit with infinitely many stable equilibria[J]. Nonlinear Dynamics, 2015, 79(4): 2295–2308. doi: 10.1007/s11071-014-1812-4

MA Jian, CHEN Zengqiang, WANG Zhonglin, et al. A four-wing hyper-chaotic attractor generated from a 4-D memristive system with a line equilibrium[J]. Nonlinear Dynamics, 2015, 81(3): 1275–1288. doi: 10.1007/s11071-015-2067-4

ZHOU Ling, WANG Chunhua, and ZHOU Lili. Generating four-wing hyperchaotic attractor and two-wing, three-wing, and four-wing chaotic attractors in 4D memristive system[J]. International Journal of Bifurcation and Chaos, 2017, 27(2): 1750027. doi: 10.1142/S0218127417500274

ZHOU Ling, WANG Chunhua, and ZHOU Lili. A novel no-equilibrium hyperchaotic multi-wing system via introducing memristor[J]. International Journal of Circuit Theory and Applications, 2018, 46(1): 84–98. doi: 10.1002/cta.2339

WANG Chunhua, XIA Hu, and ZHOU Ling. A memristive hyperchaotic multiscroll jerk system with controllable scroll numbers[J]. International Journal of Bifurcation and Chaos, 2017, 27(6): 1750091. doi: 10.1142/S0218127417500912

BAO Han, WANG Ning, BAO Bocheng, et al. Initial condition-dependent dynamics and transient period in memristor-based hypogenetic jerk system with four line equilibria[J]. Communications in Nonlinear Science and Numerical Simulation, 2018, 57: 264–275. doi: 10.1016/j.cnsns.2017.10.001

阮静雅, 孙克辉, 牟骏. 基于忆阻器反馈的Lorenz超混沌系统及其电路实现[J]. 物理学报, 2016, 65(19): 190502. doi: 10.7498/aps.65.190502

RUAN Jingya, SUN Kehui, and MOU Jun. Memristor-based Lorenz hyper-chaotic system and its circuit implementation[J]. Acta Physica Sinica, 2016, 65(19): 190502. doi: 10.7498/aps.65.190502

ZHOU Ling, WANG Chunhua, and ZHOU Lili. Generating hyperchaotic multi-wing attractor in a 4D memristive circuit[J]. Nonlinear Dynamics, 2016, 85(4): 2653–2663. doi: 10.1007/s11071-016-2852-8

俞清, 包伯成, 胡丰伟, 等. 基于一阶广义忆阻器的文氏桥混沌振荡器研究[J]. 物理学报, 2014, 63(24): 240505. doi: 10.7498/aps.63.240505

YU Qing, BAO Bocheng, HU Fengwei, et al. Wien-bridge chaotic oscillator based on fisrt-order generalized memristor[J]. Acta Physica Sinica, 2014, 63(24): 240505. doi: 10.7498/aps.63.240505

WU Huagan, BAO Bocheng, LIU Zhong, et al. Chaotic and periodic bursting phenomena in a memristive Wien-bridge oscillator[J]. Nonlinear Dynamics, 2016, 83(1/2): 893–903. doi: 10.1007/s11071-015-2375-8

XU Birong, WANG Guangyi, WANG Xiaoyuan, et al. A third-order memristive Wien-bridge circuit and its integrable deformation[J]. Pramana, 2019, 93(42): 1–14. doi: 10.1007/s12043-019-1807-2

LI Zhijun and ZHENG Yicheng. A memristor oscillator based on a twin-T network[J]. Chinese Physics B, 2013, 22(4): 040502. doi: 10.1088/1674-1056/22/4/040502

ZHOU Ling, WANG Chunhua, ZHANG Xin, et al. Various attractors, coexisting attractors and antimonotonicity in a simple fourth-order memristive twin-t oscillator[J]. International Journal of Bifurcation and Chaos, 2018, 28(4): 1850050. doi: 10.1142/s0218127418500505

BAO Bocheng, JIANG Tao, XU Quan, et al. Coexisting infinitely many attractors in active band-pass filter-based memristive circuit[J]. Nonlinear Dynamics, 2016, 86(3): 1711–1723. doi: 10.1007/s11071-016-2988-6

BAO Bocheng, WANG Ning, XU Quan, et al. A simple third-order memristive band pass filter chaotic circuit[J]. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2017, 64(8): 977–981. doi: 10.1109/TCSⅡ.2016.2641008

WANG Ning, ZHANG Guoshan, and BAO Han. Bursting oscillations and coexisting attractors in a simple memristor-capacitor-based chaotic circuit[J]. Nonlinear Dynamics, 2019, 97(2): 1477–1494. doi: 10.1007/s11071-019-05067-6

VARSHNEY V, SABARATHINAM S, PRASAD A, et al. Infinite number of hidden attractors in memristor-based autonomous duffing oscillator[J]. International Journal of Bifurcation and Chaos, 2018, 28(1): 1850013. doi: 10.1142/S021812741850013X

TENG Lin, IU H H C, WANG Xingyuan, et al. Chaotic behavior in fractional-order memristor-based simplest chaotic circuit using fourth degree polynomial[J]. Nonlinear Dynamics, 2014, 77(1/2): 231–241. doi: 10.1007/s11071-014-1286-4

CANG Shijian, WU Aiguo, WANG Zenghui, et al. Birth of one-to-four-wing chaotic attractors in a class of simplest three-dimensional continuous memristive systems[J]. Nonlinear Dynamics, 2016, 83(4): 1987–2001. doi: 10.1007/s11071-015-2460-z

WANG Chunhua, XIA Hu, and ZHOU Ling. Implementation of a new memristor-based multiscroll hyperchaotic system[J]. Pramana, 2017, 88(34): 1–7. doi: 10.1007/s12043-016-1342-3

HO Y, HUANG G M, and LI Peng. Nonvolatile memristor memory: Device characteristics and design implications[C]. 2009 IEEE/ACM International Conference on Computer-aided Design-digest of Technical Papers, San Jose, USA, 2009, 485–490. doi: 10.1145/1687399.1687491.

HO Y, HUANG G M, and LI Peng. Dynamical properties and design analysis for nonvolatile memristor memories[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2011, 58(4): 724–736. doi: 10.1109/TCSI.2010.2078710

KIM S, JEONG H Y, KIM S K, et al. Flexible memristive memory array on plastic substrates[J]. Nano Letters, 2011, 11(12): 5438–5442. doi: 10.1021/nl203206h

ZANGENEH M and JOSHI A. Design and optimization of nonvolatile multibit 1T1R resistive RAM[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2014, 22(8): 1815–1828. doi: 10.1109/TVLSI.2013.2277715

ZHANG Yang, SHEN Yi, WANG Xiaoping, et al. A novel design for memristor-based logic switch and crossbar circuits[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2015, 62(5): 1402–1411. doi: 10.1109/TCSI.2015.2407436

ZIDAN M A, FAHMY H A H, HUSSAIN M M, et al. Memristor-based memory: The sneak paths problem and solutions[J]. Microelectronics Journal, 2013, 44(2): 176–183. doi: 10.1016/j.mejo.2012.10.001

LEE M J, PARK Y, KANG B S, et al. 2-stack 1D-1R cross-point structure with oxide diodes as switch elements for high density resistance RAM applications[C]. 2007 IEEE International Electron Devices Meeting, Washington, USA, 2007: 771–774. doi: 10.1109/IEDM.2007.4419061.

MANEM H and ROSE G S. A read-monitored write circuit for 1T1M multi-level memristor memories[C]. 2011 IEEE International Symposium of Circuits and Systems, Rio de Janeiro, Brazil, 2011, 2938–2941. doi: 10.1109/ISCAS.2011.5938207.

HAMDIOUI S, AZIZA H, and SIRAKOULIS G C. Memristor based memories: Technology, design and test[C]. The 9th IEEE International Conference on Design & Technology of Integrated Systems in Nanoscale Era, Santorini, Greece, 2014: 1–7. doi: 10.1109/DTIS.2014.6850647.

SAKIB M N, HASSAN R, and BISWAS S. Design a memristor-based hybrid memory cell having faster bidirectional storage operation[C]. The 5th International Conference on Informatics, Electronics and Vision, Dhaka, Bangladesh, 2016. doi: 10.1109/ICIEV.2016.7760089.

RABBANI P, DEHGHANI R, and SHAHPARI N. A multilevel memristor-CMOS memory cell as a ReRAM[J]. Microelectronics Journal, 2015, 46(12): 1283–1290. doi: 10.1016/j.mejo.2015.10.006

SHEU S S, KUO C C, CHANG M F, et al. A ReRAM integrated 7T2R non-volatile SRAM for normally-off computing application[C]. 2013 IEEE Asian Solid-state Circuits Conference, Singapore, 2013: 245–248. doi: 10.1109/ASSCC.2013.6691028.

HO P W C, ALMURIB H A F, and KUMAR T N. Memristive SRAM cell of seven transistors and one memristor[J]. Journal of Semiconductors, 2016, 37(10): 104002. doi: 10.1088/1674-4926/37/10/104002

TEIMOORI M, AMIRSOLEIMANI A, AHMADI A, et al. A 2M1M Crossbar Architecture: Memory[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2018, 26(12): 2608–2618. doi: 10.1109/TVLSI.2018.2799951

MAAN A K, JAYADEVI D A, JAMES A P. A survey of memristive threshold logic circuits[J]. IEEE Transaactions on Neural Networks and Lerning System, 2016, 28(8): 1734–1746. doi: 10.1109/TNNLS.2016.2547842

EMARA A, GHONEIMA M, and EL-DESSOUKY M. Differential 1T2M memristor memory cell for single/multi-bit RRAM modules[C]. The 6th Computer Science and Electronic Engineering Conference, Colchester, UK, 2014: 69–72. doi: 10.1109/ceec.2014.6958557.

YILMAZ Y and MAZUMDER P. A drift-tolerant read/write scheme for multilevel memristor memory[J]. IEEE Transactions on Nanotechnology, 2017, 16(6): 1016–1027. doi: 10.1109/TNANO.2017.2741504

SAKIB M N, HASSAN R, BISWAS S N, et al. Memristor-based high-speed memory cell with stable successive read operation[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018, 37(5): 1037–1049. doi: 10.1109/TCAD.2017.2729464

WANG Xiaoping, LI Shuai, LIU Hui, et al. A compact scheme of reading and writing for memristor-based multivalued memory[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018, 37(7): 1505–1509. doi: 10.1109/TCAD.2017.2753199

EMARA A and GHONEIMA M. A reference-less multilevel memristor based RRAM module[C]. The 58th IEEE International Midwest Symposium on Circuits and Systems, Fort Collins, 2015: 1–4. doi: 10.1109/MWSCAS.2015.7282147.

SHAARAWY N, EMARA A, EL-NAGGAR A M, et al. Design and analysis of 2T2M hybrid CMOS-memristor based RRAM[J]. Microelectronics Journal, 2018, 73: 75–85. doi: 10.1016/j.mejo.2018.01.001

KIM H, SAH M P, YANG Changju, et al. Memristor bridge synapses[J]. Proceedings of the IEEE, 2012, 100(6): 2061–2070. doi: 10.1109/JPROC.2011.2166749

ADHIKARI S P, YANG Changju, KIM H, et al. Memristor bridge synapse-based neural network and its learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(9): 1426–1435. doi: 10.1109/tnnls.2012.2204770

ADHIKARI S P, KIM H, BUDHATHOKI R K, et al. A circuit-based learning architecture for multilayer neural networks with memristor bridge synapses[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2015, 62(1): 215–223. doi: 10.1109/TCSI.2014.2359717

冯广, 段书凯, 王丽丹. 双极性脉冲忆阻桥电子突触神经网络及图像处理[J]. 中国科学: 信息科学, 2017, 47(3): 385–400. doi: 10.1360/N112016-00061

FENG Guang, DUAN Shukai, and WANG Lidan. Neural networks based on doublet generator synapses and its applications in image processing[J]. Scientia Sinica Informationis, 2017, 47(3): 385–400. doi: 10.1360/N112016-00061

ALIBART F, ZAMANIDOOST E, and STRUKOV D B. Pattern classification by memristive crossbar circuits using ex situ and in situ training[J]. Nature Communications, 2013, 4(1): 1–7. doi: 10.1038/ncomms3072

YAKOPCIC C, HASAN R, TAHA T M, et al. Memristor-based neuron circuit and method for applying learning algorithm in SPICE[J]. Electronics Letters, 2014, 50(7): 492–494. doi: 10.1049/el.2014.0464

TRUONG S N and MIN K S. New memristor-based crossbar array architecture with 50-% area reduction and 48-% power saving for matrix-vector multiplication of analog neuromorphic computing[J]. Journal of Semiconductor Technology and Science, 2014, 14(3): 356–363. doi: 10.5573/jsts.2014.14.3.356

SOUDRY D, DI CASTRO D, GAL A, et al. Memristor-based multilayer neural networks with online gradient descent training[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(10): 2408–2421. doi: 10.1109/tnnls.2014.2383395

HASAN R, TAHA T M, and YAKOPCIC C. On-chip training of memristor crossbar based multi-layer neural networks[J]. Microelectronics Journal, 2017, 66: 31–40. doi: 10.1016/j.mejo.2017.05.005

ZHANG Yang, WANG Xiaoping, LI Yi, et al. Memristive model for synaptic circuits[J]. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2017, 64(7): 767–771. doi: 10.1109/TCSⅡ.2016.2605069

WANG Chunhua, XIONG Lin, SUN Jingru, et al. Memristor-based neural networks with weight simultaneous perturbation training[J]. Nonlinear Dynamics, 2019, 95(4): 2893–2906. doi: 10.1007/s11071-018-4730-z

HODGKIN A L and HUXLEY A F. A quantitative description of membrane current and its application to conduction and excitation in nerve[J]. The Journal of physiology, 1952, 117(4): 500–544. doi: 10.1113/jphysiol.1952.sp004764

NAGUMO J, ARIMOTO S, and YOSHIZAWA S. An active pulse transmission line simulating nerve axon[J]. Proceedings of the IRE, 1962, 50(10): 2061–2070. doi: 10.1109/JRPROC.1962.288235

IZHIKEVICH E M. Simple model of spiking neurons[J]. IEEE Transactions on Neural Networks, 2003, 14(6): 1569–1572. doi: 10.1109/TNN.2003.820440

HINDMARSH J L and ROSE R M. A model of the nerve impulse using two first-order differential equations[J]. Nature, 1982, 296(5853): 162–164. doi: 10.1038/296162a0

HINDMARSH J L and ROSE R M. A model of neuronal bursting using three coupled first order differential equations[J]. Proceedings of the Royal Society B: Biological Sciences, 1984, 221(1222): 87–102. doi: 10.1098/rspb.1984.0024

KASLIK E. Analysis of two- and three-dimensional fractional-order Hindmarsh-Rose type neuronal models[J]. Fractional Calculus and Applied Analysis, 2017, 20(3): 623–645. doi: 10.1515/fca-2017-0033

LAKSHMANAN S, LIM C P, NAHAVANDI S, et al. Dynamical analysis of the Hindmarsh-Rose neuron with time delays[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(8): 1953–1958. doi: 10.1109/TNNLS.2016.2557845

THOTTIL S K and IGNATIUS R P. Nonlinear feedback coupling in Hindmarsh-Rose neurons[J]. Nonlinear Dynamics, 2017, 87(3): 1879–1899. doi: 10.1007/s11071-016-3160-z

CHUA L O and YANG L. Cellular neural networks: Theory[J]. IEEE Transactions on Circuits and Systems, 1988, 35(10): 1257–1272. doi: 10.1109/31.7600

HOPFIELD J J. Neurons with graded response have collective computational properties like those of two-state neurons[J]. Proceedings of the National Academy of Sciences of the United States of America, 1984, 81(10): 3088–3092. doi: 10.1073/pnas.81.10.3088

WU Kaijun, LUO Tianqi, LU Huaiwei, et al. Bifurcation study of neuron firing activity of the modified Hindmarsh-Rose model[J]. Neural Computing and Applications, 2016, 27(3): 739–747. doi: 10.1007/s00521-015-1892-1

MINEEJA K K and IGNATIUS R P. Spatiotemporal activities of a pulse-coupled biological neural network[J]. Nonlinear Dynamics, 2018, 92(4): 1881–1897. doi: 10.1007/s11071-018-4169-2

YAO Yuangen and MA Jun. Weak periodic signal detection by sine-Wiener-noise-induced resonance in the FitzHugh-Nagumo neuron[J]. Cognitive Neurodynamics, 2018, 12(3): 343–349. doi: 10.1007/s11571-018-9475-3

MA Jun, ZHANG Ge, HAYAT T, et al. Model electrical activity of neuron under electric field[J]. Nonlinear Dynamics, 2019, 95(2): 1585–1598. doi: 10.1007/s11071-018-4646-7

MA Jun and TANG Jun. A review for dynamics in neuron and neuronal network[J]. Nonlinear Dynamics, 2017, 89(3): 1569–1578. doi: 10.1007/s11071-017-3565-3

KUMAR S, STRACHAN J P, and WILLIAMS R S. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing[J]. Nature, 2017, 548(7667): 318–321. doi: 10.1038/nature23307

WANG Zhongrui, JOSHI S, SAVEL’EV S E, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing[J]. Nature Materials, 2017, 16(1): 101–108. doi: 10.1038/nmat4756

SERB A, BILL J, KHIAT A, et al. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses[J]. Nature Communications, 2016, 7: 12611. doi: 10.1038/ncomms12611

PREZIOSO M, MERRIKH-BAYAT F, HOSKINS B D, et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors[J]. Nature, 2015, 521(7550): 61–64. doi: 10.1038/nature14441

RECH P C. Chaos and hyperchaos in a Hopfield neural network[J]. Neurocomputing, 2011, 74(17): 3361–3364. doi: 10.1016/j.neucom.2011.05.016

LIN Hairong, WANG Chunhua, and TAN Yumei. Hidden extreme multistability with hyperchaos and transient chaos in a Hopfield neural network affected by electromagnetic radiation[J]. Nonlinear Dynamics, 2019, 99: 2369–2386. doi: org/10.1007/s11071-019-05408-5

ZHAO Liang, HONG Qinghui, and WANG Xiaoping. Novel designs of spiking neuron circuit and STDP learning circuit based on memristor[J]. Neurocomputing, 2018, 314: 207–214. doi: 10.1016/j.neucom.2018.06.062

LÜ Mi and MA Jun. Multiple modes of electrical activities in a new neuron model under electromagnetic radiation[J]. Neurocomputing, 2016, 205: 375–381. doi: 10.1016/j.neucom.2016.05.004

LI Jiajia, LIU Shaobao, LIU Weiming, et al. Suppression of firing activities in neuron and neurons of network induced by electromagnetic radiation[J]. Nonlinear Dynamics, 2016, 83(1/2): 801–810. doi: 10.1007/s11071-015-2368-7

XU Ying, YING Heping, JIA Ya, et al. Autaptic regulation of electrical activities in neuron under electromagnetic induction[J]. Scientific Reports, 2017, 7: 43452. doi: 10.1038/srep43452

WU Fuqiang, WANG Chunni, JIN Wuyin, et al. Dynamical responses in a new neuron model subjected to electromagnetic induction and phase noise[J]. Physica A: Statistical Mechanics and its Applications, 2017, 469: 81–88. doi: 10.1016/j.physa.2016.11.056

WU Juan, XU Yong, and MA Jun. Lévy noise improves the electrical activity in a neuron under electromagnetic radiation[J]. PLoS One, 2017, 12(3): e0174330. doi: 10.1371/journal.pone.0174330

LU Lulu, JIA Ya, LIU Wangheng, et al. Mixed stimulus-induced mode selection in neural activity driven by high and low frequency current under electromagnetic radiation[J]. Complexity, 2017, 2017: 7628537. doi: 10.1155/2017/7628537

MA Jun, LÜ Mi, ZHOU Ping, et al. Phase synchronization between two neurons induced by coupling of electromagnetic field[J]. Applied Mathematics and Computation, 2017, 307: 321–328. doi: 10.1016/j.amc.2017.03.002

REN Guodong, XU Ying, and WANG Chunni. Synchronization behavior of coupled neuron circuits composed of memristors[J]. Nonlinear Dynamics, 2017, 88(2): 893–901. doi: 10.1007/s11071-016-3283-2

MA Jun, WU Fuqiang, and WANG Chunni. Synchronization behaviors of coupled neurons under electromagnetic radiation[J]. International Journal of Modern Physics B, 2017, 31(2): 1650251. doi: 10.1142/S0217979216502519

GE Mengyan, JIA Ya, XU Ying, et al. Mode transition in electrical activities of neuron driven by high and low frequency stimulus in the presence of electromagnetic induction and radiation[J]. Nonlinear Dynamics, 2018, 91(1): 515–523. doi: 10.1007/s11071-017-3886-2

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. doi: 10.1007/s11071-018-4616-0

FENG Peihua, WU Ying, and ZHANG Jiazhong. A route to chaotic behavior of single neuron exposed to external electromagnetic radiation[J]. Frontiers in Computational Neuroscience, 2017, 11(94): 1–9. doi: 10.3389/fncom.2017.00094

HU Xiaoyu, LIU Chongxin, LIU Ling, et al. Chaotic dynamics in a neural network under electromagnetic radiation[J]. Nonlinear Dynamics, 2018, 91(3): 1541–1554. doi: 10.1007/s11071-017-3963-6

LIN Hairong and WANG Chunhua. Influences of electromagnetic radiation distribution on chaotic dynamics of a neural network[J]. Applied Mathematics and Computation, 2020, 369: 124840. doi: 10.1016/j.amc.2019.124840

BAO Bocheng, HU Aihuang, BAO Han, et al. Three-dimensional memristive Hindmarsh-Rose neuron model with hidden coexisting asymmetric behaviors[J]. Complexity, 2018, 2018: 3872573. doi: 10.1155/2018/3872573

BAO Han, LIU Wenbo, and HU Aihuang. Coexisting multiple firing patterns in two adjacent neurons coupled by memristive electromagnetic induction[J]. Nonlinear Dynamics, 2019, 95(1): 43–56. doi: 10.1007/s11071-018-4549-7

BAO Han, HU Aihuang, LIU Wenbo, et al. Hidden bursting firings and bifurcation mechanisms in memristive neuron model with threshold electromagnetic induction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(2): 502–511. doi: 10.1109/TNNLS.2019.2905137

ZHANG Jihong and LIAO Xiaofeng. Synchronization and chaos in coupled memristor-based FitzHugh-Nagumo circuits with memristor synapse[J]. AEU-International Journal of Electronics and Communications, 2017, 75: 82–90. doi: 10.1016/j.aeue.2017.03.003

XU Ying, JIA Ya, MA Jun, et al. Synchronization between neurons coupled by memristor[J]. Chaos, Solitons & Fractals, 2017, 104: 435–442. doi: 10.1016/j.chaos.2017.09.002

ZHANG Ge, WANG Chunni, ALZAHRANI F, et al. Investigation of dynamical behaviors of neurons driven by memristive synapse[J]. Chaos, Solitons & Fractals, 2018, 108: 15–27. doi: 10.1016/j.chaos.2018.01.017

BAO Han, LIU Wenbo, and CHEN Mo. Hidden extreme multistability and dimensionality reduction analysis for an improved non-autonomous memristive FitzHugh-Nagumo circuit[J]. Nonlinear Dynamics, 2019, 96(3): 1879–1894. doi: 10.1007/s11071-019-04890-1

CHEN Mo, QI Jianwei, XU Quan, et al. Quasi-period, periodic bursting and bifurcations in memristor-based Fitzhugh-Nagumo circuit[J]. AEU-International Journal of Electronics and Communications, 2019, 110: 152840. doi: 10.1016/j.aeue.2019.152840

LI Qingdu, TANG Song, ZENG Hongzheng, et al. On hyperchaos in a small memristive neural network[J]. Nonlinear Dynamics, 2014, 78(2): 1087–1099. doi: 10.1007/s11071-014-1498-7

PHAM V T, JAFARI S, VAIDYANATHAN S, et al. A novel memristive neural network with hidden attractors and its circuitry implementation[J]. Science China Technological Sciences, 2016, 59(3): 358–363. doi: 10.1007/s11431-015-5981-2

BAO Bocheng, QIAN Hui, XU Quan, et al. Coexisting behaviors of asymmetric attractors in hyperbolic-type memristor based Hopfield neural network[J]. Frontiers in Computational Neuroscience, 2017, 11(81): 1–14. doi: 10.3389/fncom.2017.00081

NJITACKE Z T, KENGNE J, and FOTSIN H B. A plethora of behaviors in a memristor based Hopfield neural networks (HNNs)[J]. International Journal of Dynamics and Control, 2019, 7(1): 36–52. doi: 10.1007/s40435-018-0435-x

XU Quan, SONG Zhe, BAO Han, et al. Two-neuron-based non-autonomous memristive Hopfield neural network: Numerical analyses and hardware experiments[J]. AEU-International Journal of Electronics and Communications, 2018, 96: 66–74. doi: 10.1016/j.aeue.2018.09.017

CHEN Chengjie, CHEN Jingqi, BAO Han, et al. Coexisting multi-stable patterns in memristor synapse-coupled Hopfield neural network with two neurons[J]. Nonlinear Dynamics, 2019, 95(4): 3385–3399. doi: 10.1007/s11071-019-04762-8

HU Jin, and WANG Jun. Global uniform asymptotic stability of memristor-based recurrent neural networks with time delays[C]. 2010 International Joint Conference on Neural Networks, Barcelona, Spain, 2010: 1–8. doi: 10.1109/IJCNN.2010.5596359.

WU Ailong and ZENG Zhigang. Exponential stabilization of memristive neural networks with time delays[J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(12): 1919–1929. doi: 10.1109/TNNLS.2012.2219554

WU Huaiqin, LI Ruoxia, WEI Hongzhi, et al. Synchronization of a class of memristive neural networks with time delays via sampled-data control[J]. International Journal of Machine Learning and Cybernetics, 2015, 6(3): 365–373. doi: 10.1007/s13042-014-0271-z

ZHANG Guodong, HU Junhao, and SHEN Yi. Exponential lag synchronization for delayed memristive recurrent neural networks[J]. Neurocomputing, 2015, 154: 86–93. doi: 10.1016/j.neucom.2014.12.016

DING Sanbo, WANG Zhanshan, NIU Haisha, et al. Stop and go adaptive strategy for synchronization of delayed memristive recurrent neural networks with unknown synaptic weights[J]. Journal of the Franklin Institute, 2017, 354(12): 4989–5010. doi: 10.1016/j.jfranklin.2017.05.011

WU Hongjuan, FENG Yuming, TU Zhengwen, et al. Exponential synchronization of memristive neural networks with time delays[J]. Neurocomputing, 2018, 297: 1–7. doi: 10.1016/j.neucom.2018.01.017

CAI Shuiming, LI Xiaojing, ZHOU Peipei, et al. Aperiodic intermittent pinning control for exponential synchronization of memristive neural networks with time-varying delays[J]. Neurocomputing, 2019, 332: 249–258. doi: 10.1016/j.neucom.2018.12.070

WANG Xin, SHE Kun, ZHONG Shouming, et al. Exponential synchronization of memristor-based neural networks with time-varying delay and stochastic perturbation[J]. Neurocomputing, 2017, 242: 131–139. doi: 10.1016/j.neucom.2017.02.059

LIU Dan, ZHU Song, and SUN Kaili. Global anti-synchronization of complex-valued memristive neural networks with time delays[J]. IEEE Transactions on Cybernetics, 2018, 49(5): 1735–1747. doi: 10.1109/TCYB.2018.2812708

ZHANG Weiwei, ZHANG Hai, CAO Jinde, et al. Synchronization in uncertain fractional-order memristive complex-valued neural networks with multiple time delays[J]. Neural Networks, 2019, 110: 186–198. doi: 10.1016/j.neunet.2018.12.004

YAO Wei, WANG Chunhua, CAO Jinde, et al. Hybrid multisynchronization of coupled multistable memristive neural networks with time delays[J]. Neurocomputing, 2019, 363: 281–294. doi: 10.1016/j.neucom.2019.07.014

TANG Yu. Terminal sliding mode control for rigid robots[J]. Automatica, 1998, 34(1): 51–56. doi: 10.1016/s0005-1098(97)00174-x

ABDURAHMAN A, JIANG Haijun, and TENG Zhidong. Finite-time synchronization for memristor-based neural networks with time-varying delays[J]. Neural Networks, 2015, 69: 20–28. doi: 10.1016/j.neunet.2015.04.015

ZHAO Hui, LI Lixiang, PENG Haipeng, et al. Finite-time robust synchronization of memrisive neural network with perturbation[J]. Neural Processing Letters, 2018, 47(2): 509–533. doi: 10.1007/s11063-017-9664-9

GUO Zhenyuan, GONG Shuqing, and HUANG Tingwen. Finite-time synchronization of inertial memristive neural networks with time delay via delay-dependent control[J]. Neurocomputing, 2018, 293: 100–107. doi: 10.1016/j.neucom.2018.03.004

HUANG Dasong, JIANG Minghui, and JIAN Jigui. Finite-time synchronization of inertial memristive neural networks with time-varying delays via sampled-date control[J]. Neurocomputing, 2017, 266: 527–539. doi: 10.1016/j.neucom.2017.05.075

YANG Xinsong. Can neural networks with arbitrary delays be finite-timely synchronized?[J]. Neurocomputing, 2014, 143: 275–281. doi: 10.1016/j.neucom.2014.05.064

VELMURUGAN G, RAKKIYAPPAN R, and CAO Jinde. Finite-time synchronization of fractional-order memristor-based neural networks with time delays[J]. Neural Networks, 2016, 73: 36–46. doi: 10.1016/j.neunet.2015.09.012

ZHENG Mingwen, LI Lixiang, PENG Haipeng, et al. Finite-time projective synchronization of memristor-based delay fractional-order neural networks[J]. Nonlinear Dynamics, 2017, 89(4): 2641–2655. doi: 10.1007/s11071-017-3613-z

ZHENG Mingwen, LI Lixiang, PENG Haipeng, et al. Finite-time stability and synchronization for memristor-based fractional-order Cohen-Grossberg neural network[J]. The European Physical Journal B, 2016, 89(9): 1–11. doi: 10.1140/epjb/e2016-70337-6

HUI Meng, LUO Ni, WU Qisheng, et al. New results of finite-time synchronization via piecewise control for memristive cohen-grossberg neural networks with time-varying delays[J]. IEEE Access, 2019, 7: 79173–79185. doi: 10.1109/access.2019.2922973

POLYAKOV A. Nonlinear feedback design for fixed-time stabilization of linear control systems[J]. IEEE Transactions on Automatic Control, 2011, 57(8): 2106–2110. doi: 10.1109/tac.2011.2179869

CAO Jinde and LI Ruoxia. Fixed-time synchronization of delayed memristor-based recurrent neural networks[J]. Science China Information Sciences, 2017, 60(3): 032201. doi: 10.1007/s11432-016-0555-2

WANG Shiqin, GUO Zhenyuan, WEN Shiping, et al. Finite/fixed-time synchronization of delayed memristive reaction-diffusion neural networks[J]. Neurocomputing, 2020, 375: 1–8. doi: 10.1016/j.neucom.2019.06.092

LI Ruoxia, CAO Jinde, ALSAEDI A, et al. Exponential and fixed-time synchronization of Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms[J]. Applied Mathematics and Computation, 2017, 313: 37–51. doi: 10.1016/j.amc.2017.05.073

CHEN Chuan, LI Lixiang, PENG Haipeng, et al. Fixed-time projective synchronization of memristive neural networks with discrete delay[J]. Physica A: Statistical Mechanics and its Applications, 2019, 534: 122248. doi: 10.1016/j.physa.2019.122248

施引文献

资源附件(0)

访问统计