• DocumentCode
    908772
  • Title

    An adaptive neural processing node

  • Author

    Donald, James ; Akers, Lex

  • Author_Institution
    Center for Solid State Electron. Res., Arizona State Univ., Tempe, AZ, USA
  • Volume
    4
  • Issue
    3
  • fYear
    1993
  • fDate
    5/1/1993 12:00:00 AM
  • Firstpage
    413
  • Lastpage
    426
  • Abstract
    The design and test results for two analog adaptive VLSI processing chips are described. These chips use pulse coded signals for communication between processing nodes and analog weights for information storage. The weight modification rule, implemented on chip, uses concepts developed by E. Oja (1982) and later extended by T. Leen et al. (1989) and T. Sanger (1989). Experimental results demonstrate that the network produces linearly separable outputs that correspond to dominant features of the inputs. Such representations allow for efficient additional neural processing. Part of the adaptation rule also includes a small number of fixed inputs and a variable lateral inhibition mechanism. Experimental results from the first chip show the operation of function blocks that make a single processing node. These function blocks include forward transfer function, weight modification, and inhibition. Experimental results from the second chip show the ability of an array of processing elements to extract important features from the input data
  • Keywords
    VLSI; analogue processing circuits; neural chips; adaptation rule; adaptive VLSI processing chips; adaptive neural processing node; analog weights; analogue processing circuits; fixed inputs; forward transfer function; information storage; linearly separable outputs; pulse coded signals; variable lateral inhibition mechanism; weight modification; Adaptive control; Control systems; Delay lines; Feature extraction; Neural networks; Neurons; Signal processing; Testing; Transfer functions; Very large scale integration;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.217183
  • Filename
    217183