• DocumentCode
    258945
  • Title

    IC implementation of spike-timing-dependent synaptic plasticity model using low capacitance value

  • Author

    Yamashita, Daichi ; Saeki, Katsutoshi ; Sekine, Yoshifumi

  • Author_Institution
    Graduated Sch. of Sci. & Technol., Nihon Univ., Funabashi, Japan
  • fYear
    2014
  • fDate
    17-20 Nov. 2014
  • Firstpage
    221
  • Lastpage
    224
  • Abstract
    A number of recent studies on neural networks have been conducted with the purpose of applying engineering to the brain. An artificial neural networks (ANNs) were created that focus on how learning is achieved. In this paper, we focus on spike-timing-dependent synaptic plasticity (STDP) and construct an STDP model using semiconductors. We propose a new STDP model using a low capacitance value with CMOS technology, and show that the proposed integrated circuit has similar characteristics to biological neural networks.
  • Keywords
    CMOS integrated circuits; brain models; learning (artificial intelligence); neural nets; neurophysiology; ANN; CMOS technology; IC implementation; STDP model; artificial neural networks; biological neural networks; brain; integrated circuit; low capacitance value; semiconductors; spike-timing-dependent synaptic plasticity model; Biological neural networks; Biological system modeling; Brain modeling; Capacitance; Educational institutions; Integrated circuit modeling; Semiconductor device modeling; CMOS; artificial neural network nonlinear electronics circuit; integrated circuit; spike timing plasticity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems (APCCAS), 2014 IEEE Asia Pacific Conference on
  • Conference_Location
    Ishigaki
  • Type

    conf

  • DOI
    10.1109/APCCAS.2014.7032759
  • Filename
    7032759