Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices

被引:22
|
作者
Zarudnyi, Konstantin [1 ]
Mehonic, Adnan [1 ]
Montesi, Luca [1 ]
Buckwell, Mark [1 ]
Hudziak, Stephen [1 ]
Kenyon, Anthony J. [1 ]
机构
[1] UCL, Dept Elect & Elect Engn, London, England
基金
英国工程与自然科学研究理事会;
关键词
resistive switching; resistance switching; STDP; RRAM; machine learning; neuromorphic systems; NEURAL-NETWORKS; SYSTEMS; VLSI; SYNAPSES; NEURONS;
D O I
10.3389/fnins.2018.00057
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Resistance switching, or Resistive RAM (RRAM) devices show considerable potential for application in hardware spiking neural networks (neuro-inspired computing) bymimicking some of the behavior of biological synapses, and hence enabling non-von Neumann computer architectures. Spike-timing dependent plasticity (STDP) is one such behavior, and one example of several classes of plasticity that are being examined with the aim of finding suitable algorithms for application in many computing tasks such as coincidence detection, classification and image recognition. In previous work we have demonstrated that the neuromorphic capabilities of silicon-rich silicon oxide (SiOx) resistance switching devices extend beyond plasticity to include thresholding, spiking, and integration. We previously demonstrated such behaviors in devices operated in the unipolar mode, opening up the question of whether we could add plasticity to the list of features exhibited by our devices. Here we demonstrate clear STDP in unipolar devices. Significantly, we show that the response of our devices is broadly similar to that of biological synapses. This work further reinforces the potential of simple two-terminal RRAM devices to mimic neuronal functionality in hardware spiking neural networks.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] Motion detection and prediction through spike-timing dependent plasticity
    Shon, AP
    Rao, RPN
    Sejnowski, TJ
    NETWORK-COMPUTATION IN NEURAL SYSTEMS, 2004, 15 (03) : 179 - 198
  • [22] Neural connectivity inference with spike-timing dependent plasticity network
    John MOON
    Yuting WU
    Xiaojian ZHU
    Wei D.LU
    Science China(Information Sciences), 2021, 64 (06) : 70 - 79
  • [23] Spike-timing dependent and homeostatic plasticity from an optimality viewpoint
    Toyoizumi, Taro
    Pfister, Jean-Pascal
    Aihara, Kazuyuki
    Gerstner, Wulfram
    NEUROSCIENCE RESEARCH, 2006, 55 : S22 - S22
  • [24] Dynamic regulation of spike-timing dependent plasticity in electrosensory processing
    Roberts, PD
    Lafferriere, G
    Sawtell, N
    Williams, A
    Bell, CC
    NEUROCOMPUTING, 2006, 69 (10-12) : 1195 - 1198
  • [25] CMOL implementation of spiking neurons and spike-timing dependent plasticity
    Afifi, Ahmad
    Ayatollahi, Ahmad
    Raissi, Farshid
    INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, 2011, 39 (04) : 357 - 372
  • [26] The dopaminergic contribution to spike-timing dependent plasticity in the corticostriatal pathway
    Shindou, Tomomi
    Ochi-Shindou, Mayumi
    Wickens, Jeffrey R.
    JOURNAL OF PHYSIOLOGICAL SCIENCES, 2010, 60 : S47 - S47
  • [27] Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity
    Badoual, Mathilde
    Zou, Quan
    Davison, Andrew P.
    Rudolph, Michael
    Bal, Thierry
    Fregnac, Yves
    Destexhe, Alain
    INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2006, 16 (02) : 79 - 97
  • [28] Intrinsic Stability of Temporally Shifted Spike-Timing Dependent Plasticity
    Babadi, Baktash
    Abbott, L. F.
    PLOS COMPUTATIONAL BIOLOGY, 2010, 6 (11)
  • [29] Predictive coding, cortical feedback, and spike-timing dependent plasticity
    Rao, RPN
    Sejnowski, TJ
    PROBABILISTIC MODELS OF THE BRAIN: PERCEPTION AND NEURAL FUNCTION, 2002, : 297 - 315
  • [30] Spike-timing dependent plasticity as a mechanism for ocular dominance shift
    Siegler, BA
    Ritchey, M
    Rubin, J
    NEUROCOMPUTING, 2005, 65 : 181 - 188