• DocumentCode
    1797550
  • Title

    STDP learning rule based on memristor with STDP property

  • Author

    Ling Chen ; Chuandong Li ; Tingwen Huang ; Xing He ; Hai Li ; Yiran Chen

  • Author_Institution
    Coll. of Comput., Chongqing Univ., Chongqing, China
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Spike-timing-dependent plasticity (STDP) learning ability has been observed in physical memristors, but whether the STDP is caused by the neuron or the memristor is unclear. In this paper, we proved the STDP property in the model for both symmetric and asymmetric memristor. We also employed the symmetric/asymmetric memristors with STDP property and the simplified neurons to perform the STDP learning ability. At last, the sequence learning experiment of the memritive neural network (MNN) with the symmetric memristor synapse further verifies the STDP learning ability of the memristor.
  • Keywords
    memristors; neural nets; MNN; STDP learning ability; STDP learning rule; STDP property; memritive neural network; physical memristors; simplified neurons; spike timing dependent plasticity; Analytical models; Biological neural networks; Educational institutions; Electric potential; Memristors; Multi-layer neural network; Neurons;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), 2014 International Joint Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6627-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2014.6889506
  • Filename
    6889506