基于SBT忆阻器元件的神经突触设计
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摘要
利用SBT(Sr0.95Ba0.05TiO3)忆阻器进行神经突触设计,并分析其模拟的神经突触的性能。首先,采用分周期积累建模的方法对测得的忆阻器电压、电流数据进行建模,得到SBT忆阻器忆导值随时间的变化;其次,将忆导值作为突触权重,拟合到忆阻器普遍适用的模型中,更好地描述SBT忆阻突触的记忆及遗忘特性;最后,通过数值仿真实验对SBT忆阻突触的脉冲突触可塑性、长/短时记忆可塑性及"学习经验式"行为进行分析。实验结果表明,SBT忆阻突触具有良好的突触可塑性,能够实现类脑学习、记忆过程。
A class of artificial synapse is designed by utilizing SBT(Sr0.95 Ba0.05 TiO3)memristor,and the corresponding characteristics of the SBT-based synapse is analyzed.First,the model is established via periodic subsection accumulation for the measured voltage and current data.The variation of memductance with time can be obtained from the model.Second,memductances are applied to a universal memristor model,acting as weights of the synapse.By this way,the characteristics of memory and forgetting are described preferably.Finally,the features of SBT memristive synapse,including pulsed synaptic plasticity,long/short term memory plasticity and‘learning experiential'behavior,are analyzed via a series of simulation experiments.The experiment results indicate that the SBT memristive synapse possesses excellent synaptic plasticity,and can realize brain-like learning and memory processes.
A class of artificial synapse is designed by utilizing SBT(Sr0.95 Ba0.05 TiO3)memristor,and the corresponding characteristics of the SBT-based synapse is analyzed.First,the model is established via periodic subsection accumulation for the measured voltage and current data.The variation of memductance with time can be obtained from the model.Second,memductances are applied to a universal memristor model,acting as weights of the synapse.By this way,the characteristics of memory and forgetting are described preferably.Finally,the features of SBT memristive synapse,including pulsed synaptic plasticity,long/short term memory plasticity and‘learning experiential'behavior,are analyzed via a series of simulation experiments.The experiment results indicate that the SBT memristive synapse possesses excellent synaptic plasticity,and can realize brain-like learning and memory processes.
引文
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[2]SHIN S,CHOI J M,CHO S,et al.Small-area and compact CMOS emulator circuit for CMOS/nanoscale memristor co-design[J].Nanoscale Research Letters,2013,8(1):454.
[3]段书凯,胡小方,王丽丹,等.忆阻器阻变随机存取存储器及其在信息存储中的应用[J].中国科学(信息科学),2012,42(6):754-769.DUAN Shukai,HU Xiaofang,WANG Lidan,et al.Memristor-based RRAM with applications[J].Scientia Sinica Informationis,2012,42(6):754-769.(in Chinese)
[4]王雨桑,王敬蕊.忆阻器在人工神经网络方面的研究应用[J].通讯世界,2016,2(3):280-281.
[5]张琳琳,张烁,常文亭,等.基于蔡氏对偶电路的四阶忆阻混沌电路[J].中国科技论文,2017,12(8):946-951.ZHANG Linlin,ZHANG Shuo,CHANG Wenting,et al.A new fourth-order memristor chaotic circuit based on Chua’s dual circuit[J].China Sciencepaper,2017,12(8):946-951.(in Chinese)
[6]王国权,刘亮.忆阻器应用于人工神经网络的前景与展望[J].中国新技术新产品,2009,137(6):18-19.
[7]PEREDA A E.Electrical synapses and their functional interactions with chemical synapses[J].Nature Reviews Neuroscience,2014,15(4):250-263.
[8]CHANG T,JO S H,KIM K H,et al.Synaptic behaviors and modeling of a metal oxide memristive device[J].Applied Physics A,2011,102(4):857-863
[9]PERSHIN Y V,DI VENTRA M.Spin memristive systems:spin memory effects in semiconductor spintronic[J].Physical Review B,2008,78(11):3309-3312.
[10]DRISCOLL T,KIM H T,CHAE B G,et al.Phasetransition driven memristive system[J].Applied Physics Letters,2009,95(4):043505.
[11]HASEGAWA T,OHNO T,TERABE K,et al.Learning abilities achieved by a single solid-state atomic switch[J].Advanced Materials,2010,22(16):1831-1834.
[12]WANG Weidong,YU Qin,XU Chunxiang,et al.Study of filter characteristics based on PWL memristor[C]∥International Conference on Communications,Circuits and Systems.Milpitas:IEEE,2009:969-973.
[13]BAO Bocheng,XU Jianzhou,ZHOU Guohua,et al.Chaotic memristive circuit:equivalent circuit realization and dynamical analysis[J].Chinese Physics B,2011,20(12):109-115.
[14]NAFEA S F,DESSOUKI A A S,EI-RABAIE S,et al.Area-efficient read/write circuit for spintronic memristor based memories[C]∥International Midwest Symposium on Circuits&Systems.Boston:IEEE,2017:1544-1547.
[15]田晓波.钛氧化物忆阻器导电机理与阻抗控制研究[D].长沙:国防科技大学,2014.TIAN Xiaobo.Research on conductive mechanisms and resistance control of titanium oxide memristors[D].Changsha:National University of Defense Technology,2014.(in Chinese)
[16]臧新风,沈谅平,王浩叶,等.基于阈值电压的忆阻器模型研究[J].信息通信,2018,189(9):26-29.ZANG Xinfeng,SHEN Liangping,WANG Haoye,et al.Study of memristor model based on threshold voltage[J].Information&Communications,2018,189(9):26-29.(in Chinese)
[17]ZHANG Yuman,DOU Gang,SUN Zhao,et al.Establishment of physical and mathematical models for Sr0.95Ba0.05TiO3 memristor[J].International Journal of Bifurcation&Chaos,2017,27(9):1750148.
[18]DOU Gang,YU Yang,GUO Mei,et al.Memristive behavior based on Ba-doped SrTiO3films[J].Chinese Physics Letters,2017,34(3):126-129.
[19]WHITLOCK J R,HEYNEN A J,SHULER M G,et al.Learning induces long-term potentiation in the hippocampus[J].Science,2006,313(5790):1093-1097.
[20]LEVY W B,STEWARD O.Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus[J].Neuroscience,1983,8(4):791-797.
[21]孟凡一,段书凯,王丽丹,等.一种改进的WOx忆阻器模型及其突触特性分析[J].物理学报,2015,64(14):359-369.MENG Fanyi,DUAN Shukai,WANG Lidan,et al.An improved WOx memristor model and its synaptic characteristics analysis[J].Acta Physica Sinica,2015,64(14):359-369.(in Chinese)
[22]BI G Q,POO M M.Synaptic modifications in cultured hippocampal neurons:dependence on spike timing,synaptic strength,and postsynaptic cell type[J].Journal of Neuroscience,1998,18(24):10464-10472.
[23]COLLINGRIDGE G L,ISAAC J T,WANG Yutian.Receptor trafficking and synaptic plasticity[J].Nature Reviews Neuroscience,2004,5(12):952-962.
[24]BLISS T V,GARDNER A R.Long-lasting potentiation of synaptic transmission in the dentate area of the unanaestetized rabbit following stimulation of the perforant path[J].Journal of Physiology,1973,232(2):331-356.