• DocumentCode
    727034
  • Title

    Triplet spike time dependent plasticity in a floating-gate synapse

  • Author

    Gopalakrishnan, Roshan ; Basu, Arindam

  • Author_Institution
    Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
  • fYear
    2015
  • fDate
    24-27 May 2015
  • Firstpage
    710
  • Lastpage
    713
  • Abstract
    Synapses plays an important role of learning in a neural network; the learning rules which modify the synaptic strength based on the timing difference between the pre- and post-synaptic spike occurrence is termed as Spike Time Dependent Plasticity (STDP). This paper describes the compact implementation of a synapse using single floating-gate (FG) transistor (and two additional high voltage transistors) that can store a weight in a non-volatile manner and demonstrate the triplet STDP (T-STDP) learning rule developed to explain biologically observed plasticity. We describe a mathematical procedure to obtain control voltages for the FG device for T-STDP and also show measurement results, from a FG synapse fabricated in TSMC 0.35μm CMOS process to support the theory.
  • Keywords
    CMOS integrated circuits; MOSFET; learning (artificial intelligence); neural nets; neurophysiology; CMOS process; FG transistor; STDP; T-STDP; TSMC; complementary metal oxide semiconductor; floating-gate synapse; floating-gate transistor; learning; neural network; size 0.35 mum; timing difference; triplet spike time dependent plasticity; Biological system modeling; Logic gates; Mathematical model; Protocols; Timing; Tunneling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems (ISCAS), 2015 IEEE International Symposium on
  • Conference_Location
    Lisbon
  • Type

    conf

  • DOI
    10.1109/ISCAS.2015.7168732
  • Filename
    7168732