• DocumentCode
    1209303
  • Title

    Spike timing dependent plasticity (STDP) can ameliorate process variations in neuromorphic VLSI

  • Author

    Cameron, Katherine ; Boonsobhak, Vasin ; Murray, Alan ; Renshaw, David

  • Author_Institution
    Sch. of Eng. & Electron., Univ. of Edinburgh, UK
  • Volume
    16
  • Issue
    6
  • fYear
    2005
  • Firstpage
    1626
  • Lastpage
    1637
  • Abstract
    A transient-detecting very large scale integration (VLSI) pixel is described, suitable for use in a visual-processing, depth-recovery algorithm based upon spike timing. A small array of pixels is coupled to an adaptive system, based upon spike timing dependent plasticity (STDP), that aims to reduce the effect of VLSI process variations on the algorithm´s performance. Results from 0.35 μm CMOS temporal differentiating pixels and STDP circuits show that the system is capable of adapting to substantially reduce the effects of process variations without interrupting the algorithm´s natural processes. The concept is generic to all spike timing driven processing algorithms in a VLSI.
  • Keywords
    CMOS integrated circuits; VLSI; digital signal processing chips; neural nets; video coding; CMOS; adaptive system; depth-recovery algorithm; neuromorphic VLSI; spike timing dependent plasticity; very large scale integration; visual-processing; Animals; CMOS process; Circuits; Educational institutions; Error correction; Image motion analysis; Neuromorphics; Neurons; Timing; Very large scale integration; Active pixel; CMOS integrated circuits; focal-plane sensor; neuromorphic analogue very large scale integration (VLSI); spike timing dependent plasticity (STDP); temporal processing; transistor mismatch; Action Potentials; Algorithms; Artificial Intelligence; Biomimetics; Equipment Design; Equipment Failure Analysis; Image Enhancement; Image Interpretation, Computer-Assisted; Neuronal Plasticity; Pattern Recognition, Automated; Quality Control; Semiconductors; Time Factors; Transducers; Video Recording;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2005.852238
  • Filename
    1528538