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
  • [41] Pulse-type neuro devices with spike timing dependent synaptic plasticity
    Saeki, Katsutoshi
    Hayashi, Yugo
    Sekine, Yoshifumi
    BIODEVICES 2008: PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON BIOMEDICAL ELECTRONICS AND DEVICES, VOL 1, 2008, : 264 - 268
  • [42] Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs
    Du, Nan
    Kiani, Mahdi
    Mayr, Christian G.
    You, Tiangui
    Buerger, Danilo
    Skorupa, Ilona
    Schmidt, Oliver G.
    Schmidt, Heidemarie
    FRONTIERS IN NEUROSCIENCE, 2015, 9
  • [43] Modeling triplet spike-timing-dependent plasticity using memristive devices
    Aghnout, Soraya
    Karimi, Gholamreza
    Azghadi, Mostafa Rahimi
    JOURNAL OF COMPUTATIONAL ELECTRONICS, 2017, 16 (02) : 401 - 410
  • [44] Self-organization through spike-timing dependent plasticity using localized synfire-chain patterns
    Akimitsu, Toshio
    Okabe, Yoichi
    Hirose, Akira
    NEURAL PROCESSING LETTERS, 2007, 25 (01) : 79 - 89
  • [45] Spectral Analysis of Input Spike Trains by Spike-Timing-Dependent Plasticity
    Gilson, Matthieu
    Fukai, Tomoki
    Burkitt, Anthony N.
    PLOS COMPUTATIONAL BIOLOGY, 2012, 8 (07)
  • [46] A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity
    Wang, Quan
    Rothkopf, Constantin A.
    Triesch, Jochen
    PLOS COMPUTATIONAL BIOLOGY, 2017, 13 (08) : e1005632
  • [47] Spike-timing dependent plasticity with release probability supported to eliminate weight boundaries and to balance the excitation of Hebbian neurons
    Fernando, Subha
    Yamada, Koichi
    6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS, 2012, : 1052 - 1058
  • [48] Self-Organization through Spike-Timing Dependent Plasticity Using localized Synfire-Chain Patterns
    Toshio Akimitsu
    Yoichi Okabe
    Akira Hirose
    Neural Processing Letters, 2007, 25 : 79 - 89
  • [49] BP-STDP: Approximating backpropagation using spike timing dependent plasticity
    Tavanaei, Amirhossein
    Maida, Anthony
    NEUROCOMPUTING, 2019, 330 : 39 - 47
  • [50] Calcium and Spike Timing-Dependent Plasticity
    Inglebert, Yanis
    Debanne, Dominique
    FRONTIERS IN CELLULAR NEUROSCIENCE, 2021, 15