搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

具有突触特性忆阻模型的改进与模型经验学习特性机理

邵楠 张盛兵 邵舒渊

引用本文:
Citation:

具有突触特性忆阻模型的改进与模型经验学习特性机理

邵楠, 张盛兵, 邵舒渊

Modification of memristor model with synaptic characteristics and mechanism analysis of the model's learning-experience behavior

Shao Nan, Zhang Sheng-Bing, Shao Shu-Yuan
PDF
导出引用
  • 许多忆阻器都具有与生物神经突触功能相似的特性, 这些特性包括记忆与遗忘特性、经验学习特性等. 文献[17]根据记忆与遗忘特性建立了这类忆阻器的模型, 文献[19, 20]在对该模型的仿真研究中发现该模型也具有描述经验学习特性的能力. 在关于这一模型已有研究的基础上, 本文对该模型状态方程的特性与机理给出进一步的分析. 分析中发现原模型的窗口函数的设计和使用存在问题, 并且原模型建模时对于实验现象的解读不够准确. 针对这些问题对原模型的状态方程进行了改进, 完善了模型功能. 对于该模型能够描述经验学习特性的机理, 分别利用对于模型的状态方程的分析以及周期脉冲信号作用下的状态方程解析分析, 对该机理给出定性和定量的讨论. 利用机理分析所得的相关结论, 设计了基于经验学习实验的模型状态方程中的参数和函数的估计方法, 方便了该模型在这一类忆阻器的实验研究中的应用.
    Many memristors fabricated by different materials share the characteristics which are similar to the memory and learning functions of synapse in biological systems. These characteristics include memorizing and forgetting function and learning-experience behavior. A memristor model was proposed in the published paper [Chen L, Li C D, Huang T W, Chen Y R, Wen S P, Qi J T 2013 Phys. Lett. A 377 3260] to describe the memorizing and forgetting function of this kind of memristor. This model includes three state variables , and . The change of w describes the variation of the conductance of the memristor, a function fE () is used to the input voltage's influence on the change of , and are used to describe the its forgetting effect. The simulation analyses of this model in the published papers [Chen L, Li C D, Huang T W, Hu X F, Chen Y R 2016 Neurocomputing 171 1637] and [Meng F Y, Duan S K, Wang L D, Hu X F, Dong Z K 2015 Acta Phys. Sin. 64 148501] showed that this model can also describe the learning-experience behavior. This model is further studied in this paper to show its detailed characteristics. The analyses of the state equations of the original model show that these state equations cannot restrict the state variables in their permissible interval because the window function is not appropriately used in all the state equations, and the original window function cannot force the state equation to be identical to zero either when corresponding state variable reaches its bound. An improved window function is introduced and the appropriate utilization of this window function is discussed to deal with this problem. The upper bound of is defined in the modified model to describe the saturation of that has been observed in the experimental studies of this kind of memristor. The behaviors of the modified state equations are different from those of the original ones only when the state variables reach their bounds, and this modified model has the same ability to describe the memristor's memorizing and forgetting function and learning-experience behavior as original one. The behaviors of the model when the input voltage is not negative are discussed based on the state equations and their analytical solution when the input is the repeated voltage pulses, and the results of the discussion are used to explain how a model designed according to the memorizing and forgetting function can also describe the learning-experience behavior. The analysis shows that the increased rising speed of the state variable w in the stimulating process is caused by increasing the values of and , and the learning-experience behavior described by this model would also be influenced by the value of :a smaller initial value of state variable in the learning-experience experiment would lead to a more obvious learning-experience behavior. The analytical results are also used to design an estimation method based on the learning-experience experiment to estimate the parameters and function in the state equation. The further discussion shows that this proposed estimation method can also be used to verify the reasonability of the assumption used in the state equations that the derivatives of and are proportional to fE (V).
      通信作者: 邵楠, shao@mail.nwpu.edu.cn
      Corresponding author: Shao Nan, shao@mail.nwpu.edu.cn
    [1]

    Chang T, Jo S H, Lu W 2011 ACS Nano 5 7669

    [2]

    Yang R, Terabe K, Yao Y P, Tsuruoka T, Hasegawa T, Gimzewski J K, Aono M 2013 Nanotechnology 24 384003

    [3]

    Wang Z Q, Xu H Y, Li X H, Yu H, Liu Y C, Zhu X J 2012 Adv. Funct. Mater. 22 2759

    [4]

    Ohno T, Hasegawa T, Tsuruoka T, Terabe K, Gimzewski J K, Aono M 2011 Nat. Mater. 10 591

    [5]

    Li S Z, Zeng F, Chen C, Liu H Y, Tang G S, Gao S, Song C, Lin Y S, Pan F, Guo D 2013 J. Mater. Chem. C 1 5292

    [6]

    Lei Y, Liu Y, Xia Y D, Gao X, Xu B, Wang S D, Yin J, Liu Z G 2014 AIP Adv. 4 077105

    [7]

    Chang T, Yang Y C, Lu W 2013 IEEE Circ. Syst. Mag. 13 56

    [8]

    Adhikari S P, Yang C, Kim H, Chua L O 2012 IEEE Trans. Neur. Net. Lear. Syst. 23 1426

    [9]

    Adhikari S P, Kim H, Budhathoki R K, Yang C J, Chua L O 2015 IEEE Trans. Circ. Syst. I 62 215

    [10]

    Wang G, Shen Y, Yin Q 2013 Chin. Phys. B 22 050504

    [11]

    Duan S K, Hu X F, Dong Z K, Wang L D, Mazumder P 2015 IEEE Trans. Neur. Net. Lear. Syst. 26 1202

    [12]

    Zhou J, Huang D 2012 Chin. Phys. B 21 048401

    [13]

    Liu H J, Li Z W, Bu K, Sun Z L, Nie H S 2014 Chin. Phys. B 23 048401

    [14]

    Min G Q, Wang L D, Duan S K 2015 Acta Phys. Sin. 64 210507 (in Chinese) [闵国旗, 王丽丹, 段书凯 2015 物理学报 64 210507]

    [15]

    Yuan F, Wang G Y, Wang X Y 2015 Chin. Phys. B 24 060506

    [16]

    Chang T, Jo S H, Kim K H, Sheridan P, Gaba S, Lu W 2011 Appl. Phys. A 102 857

    [17]

    Chen L, Li C D, Huang T W, Chen Y R, Wen S P, Qi J T 2013 Phys. Lett. A 377 3260

    [18]

    Chen L, Li C D, Huang T W, Ahmad H G, Chen Y R 2014 Phys. Lett. A 378 2924

    [19]

    Chen L, Li C D, Huang T W, Hu X F, Chen Y R 2016 Neurocomputing 171 1637

    [20]

    Meng F Y, Duan S K, Wang L D, Hu X F, Dong Z K 2015 Acta Phys. Sin. 64 148501 (in Chinese) [孟凡一, 段书凯, 王丽丹, 胡小方, 董哲康 2015 物理学报 64 148501]

    [21]

    Strukov D B, Snider G S, Stewart D R, Williams R S 2008 Nature 453 80

    [22]

    Biolek Z, Biolek D, Biolkova V 2009 Radioengineering 18 210

    [23]

    Kvatinsky S, Friedman E G, Kolodny A, Weiser U C 2013 IEEE Trans. Circ. Syst. I-Regul. Pap. 60 211

    [24]

    Ascoli A, Corinto F, Tetzlaff R 2016 Int. J. Circ. Theo. Appl. 44 60

    [25]

    Corinto F, Ascoli A 2012 IEEE Trans. Circ. Syst. I-Regul. Pap. 59 2713

    [26]

    Joglekar Y N, Wolf S J 2009 Eur. J. Phys. 30 661

    [27]

    Prodromakis T, Peh B P, Papavassiliou C, Toumazou C 2011 IEEE Trans. Electron Dev. 58 3099

    [28]

    Takahashi Y, Sekine T, Yokoyama M 2015 IEICE Electron. Express 12 1

    [29]

    Mu X M, Yu J T, Wang S N 2015 Int. J. Numer. Model. 28 96

    [30]

    Yu J T, Mu X M, Xi X M, Wang S N 2013 Radioengineering 22 969

  • [1]

    Chang T, Jo S H, Lu W 2011 ACS Nano 5 7669

    [2]

    Yang R, Terabe K, Yao Y P, Tsuruoka T, Hasegawa T, Gimzewski J K, Aono M 2013 Nanotechnology 24 384003

    [3]

    Wang Z Q, Xu H Y, Li X H, Yu H, Liu Y C, Zhu X J 2012 Adv. Funct. Mater. 22 2759

    [4]

    Ohno T, Hasegawa T, Tsuruoka T, Terabe K, Gimzewski J K, Aono M 2011 Nat. Mater. 10 591

    [5]

    Li S Z, Zeng F, Chen C, Liu H Y, Tang G S, Gao S, Song C, Lin Y S, Pan F, Guo D 2013 J. Mater. Chem. C 1 5292

    [6]

    Lei Y, Liu Y, Xia Y D, Gao X, Xu B, Wang S D, Yin J, Liu Z G 2014 AIP Adv. 4 077105

    [7]

    Chang T, Yang Y C, Lu W 2013 IEEE Circ. Syst. Mag. 13 56

    [8]

    Adhikari S P, Yang C, Kim H, Chua L O 2012 IEEE Trans. Neur. Net. Lear. Syst. 23 1426

    [9]

    Adhikari S P, Kim H, Budhathoki R K, Yang C J, Chua L O 2015 IEEE Trans. Circ. Syst. I 62 215

    [10]

    Wang G, Shen Y, Yin Q 2013 Chin. Phys. B 22 050504

    [11]

    Duan S K, Hu X F, Dong Z K, Wang L D, Mazumder P 2015 IEEE Trans. Neur. Net. Lear. Syst. 26 1202

    [12]

    Zhou J, Huang D 2012 Chin. Phys. B 21 048401

    [13]

    Liu H J, Li Z W, Bu K, Sun Z L, Nie H S 2014 Chin. Phys. B 23 048401

    [14]

    Min G Q, Wang L D, Duan S K 2015 Acta Phys. Sin. 64 210507 (in Chinese) [闵国旗, 王丽丹, 段书凯 2015 物理学报 64 210507]

    [15]

    Yuan F, Wang G Y, Wang X Y 2015 Chin. Phys. B 24 060506

    [16]

    Chang T, Jo S H, Kim K H, Sheridan P, Gaba S, Lu W 2011 Appl. Phys. A 102 857

    [17]

    Chen L, Li C D, Huang T W, Chen Y R, Wen S P, Qi J T 2013 Phys. Lett. A 377 3260

    [18]

    Chen L, Li C D, Huang T W, Ahmad H G, Chen Y R 2014 Phys. Lett. A 378 2924

    [19]

    Chen L, Li C D, Huang T W, Hu X F, Chen Y R 2016 Neurocomputing 171 1637

    [20]

    Meng F Y, Duan S K, Wang L D, Hu X F, Dong Z K 2015 Acta Phys. Sin. 64 148501 (in Chinese) [孟凡一, 段书凯, 王丽丹, 胡小方, 董哲康 2015 物理学报 64 148501]

    [21]

    Strukov D B, Snider G S, Stewart D R, Williams R S 2008 Nature 453 80

    [22]

    Biolek Z, Biolek D, Biolkova V 2009 Radioengineering 18 210

    [23]

    Kvatinsky S, Friedman E G, Kolodny A, Weiser U C 2013 IEEE Trans. Circ. Syst. I-Regul. Pap. 60 211

    [24]

    Ascoli A, Corinto F, Tetzlaff R 2016 Int. J. Circ. Theo. Appl. 44 60

    [25]

    Corinto F, Ascoli A 2012 IEEE Trans. Circ. Syst. I-Regul. Pap. 59 2713

    [26]

    Joglekar Y N, Wolf S J 2009 Eur. J. Phys. 30 661

    [27]

    Prodromakis T, Peh B P, Papavassiliou C, Toumazou C 2011 IEEE Trans. Electron Dev. 58 3099

    [28]

    Takahashi Y, Sekine T, Yokoyama M 2015 IEICE Electron. Express 12 1

    [29]

    Mu X M, Yu J T, Wang S N 2015 Int. J. Numer. Model. 28 96

    [30]

    Yu J T, Mu X M, Xi X M, Wang S N 2013 Radioengineering 22 969

  • [1] 吴朝俊, 方礼熠, 杨宁宁. 含有偏置电压源的非齐次分数阶忆阻混沌电路动力学分析与实验研究. 物理学报, 2024, 73(1): 010501. doi: 10.7498/aps.73.20231211
    [2] 王璇, 杜健嵘, 李志军, 马铭磷, 李春来. 串扰忆阻突触异质离散神经网络的共存放电与同步行为. 物理学报, 2024, 0(0): . doi: 10.7498/aps.73.20231972
    [3] 胡炜, 廖建彬, 杜永乾. 一种适用于大规模忆阻网络的忆阻器单元解析建模策略. 物理学报, 2021, 70(17): 178505. doi: 10.7498/aps.70.20210116
    [4] 邓文, 汪礼胜, 刘嘉宁, 余雪玲, 陈凤翔. 光电协控多层MoS2记忆晶体管的阻变行为与机理研究. 物理学报, 2021, 70(21): 217302. doi: 10.7498/aps.70.20210750
    [5] 史晨阳, 闵光宗, 刘向阳. 蛋白质基忆阻器研究进展. 物理学报, 2020, 69(17): 178702. doi: 10.7498/aps.69.20200617
    [6] 邵楠, 张盛兵, 邵舒渊. 具有感觉记忆的忆阻器模型. 物理学报, 2019, 68(1): 018501. doi: 10.7498/aps.68.20181577
    [7] 邵楠, 张盛兵, 邵舒渊. 具有经验学习特性的忆阻器模型分析. 物理学报, 2019, 68(19): 198502. doi: 10.7498/aps.68.20190808
    [8] 余志强, 刘敏丽, 郎建勋, 钱楷, 张昌华. 基于Au/TiO2/FTO结构忆阻器的开关特性与机理研究. 物理学报, 2018, 67(15): 157302. doi: 10.7498/aps.67.20180425
    [9] 俞亚娟, 王在华. 一个分数阶忆阻器模型及其简单串联电路的特性. 物理学报, 2015, 64(23): 238401. doi: 10.7498/aps.64.238401
    [10] 孟凡一, 段书凯, 王丽丹, 胡小方, 董哲康. 一种改进的WOx忆阻器模型及其突触特性分析. 物理学报, 2015, 64(14): 148501. doi: 10.7498/aps.64.148501
    [11] 郭羽泉, 段书凯, 王丽丹. 纳米级尺寸参数对钛氧化物忆阻器的特性影响. 物理学报, 2015, 64(10): 108502. doi: 10.7498/aps.64.108502
    [12] 刘东青, 程海峰, 朱玄, 王楠楠, 张朝阳. 忆阻器及其阻变机理研究进展. 物理学报, 2014, 63(18): 187301. doi: 10.7498/aps.63.187301
    [13] 洪庆辉, 李志军, 曾金芳, 曾以成. 基于电流反馈运算放大器的忆阻混沌电路设计与仿真. 物理学报, 2014, 63(18): 180502. doi: 10.7498/aps.63.180502
    [14] 董哲康, 段书凯, 胡小方, 王丽丹. 两类纳米级非线性忆阻器模型及串并联研究. 物理学报, 2014, 63(12): 128502. doi: 10.7498/aps.63.128502
    [15] 徐晖, 田晓波, 步凯, 李清江. 温度改变对钛氧化物忆阻器导电特性的影响. 物理学报, 2014, 63(9): 098402. doi: 10.7498/aps.63.098402
    [16] 田晓波, 徐晖, 李清江. 横截面积参数对钛氧化物忆阻器导电特性的影响. 物理学报, 2014, 63(4): 048401. doi: 10.7498/aps.63.048401
    [17] 梁燕, 于东升, 陈昊. 基于模拟电路的新型忆感器等效模型. 物理学报, 2013, 62(15): 158501. doi: 10.7498/aps.62.158501
    [18] 李智炜, 刘海军, 徐欣. 忆阻逾渗导电模型中的初态影响. 物理学报, 2013, 62(9): 096401. doi: 10.7498/aps.62.096401
    [19] 贾林楠, 黄安平, 郑晓虎, 肖志松, 王玫. 界面效应调制忆阻器研究进展. 物理学报, 2012, 61(21): 217306. doi: 10.7498/aps.61.217306
    [20] 包伯成, 胡文, 许建平, 刘中, 邹凌. 忆阻混沌电路的分析与实现. 物理学报, 2011, 60(12): 120502. doi: 10.7498/aps.60.120502
计量
  • 文章访问数:  5022
  • PDF下载量:  365
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-01-19
  • 修回日期:  2016-03-18
  • 刊出日期:  2016-06-05

/

返回文章
返回