• DocumentCode
    285265
  • Title

    An examination of real-time neuronal models in a classical conditioning framework

  • Author

    Cheung, John Y. ; Chance, David C. ; Lawton, Asa

  • Author_Institution
    Oklahoma Univ., Norman, OK, USA
  • Volume
    3
  • fYear
    1992
  • fDate
    7-11 Jun 1992
  • Firstpage
    504
  • Abstract
    The focus is on the unsupervised spatiotemporal single-neuron models of R.S. Sutton and A.G. Barto (1981, 1982), R.A. Rescorla and A.R. Wagner (1972), and A. Klopf. The preliminary results of the learning and activation rules of these and other single-neuron models are presented. Computer simulations of the models were studied within the framework of modern Pavlovian associative learning. The simulation environment was a set of C programs representing the models and an interactive environment which allowed the user to select one of seven different learning strategies
  • Keywords
    neural nets; unsupervised learning; activation rules; classical conditioning framework; modern Pavlovian associative learning; real-time neuronal models; unsupervised spatiotemporal single-neuron models; Biological system modeling; Computer simulation; Context modeling; Current measurement; Frequency measurement; Mathematical model; Modems; Neurons; Predictive models; Time measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1992. IJCNN., International Joint Conference on
  • Conference_Location
    Baltimore, MD
  • Print_ISBN
    0-7803-0559-0
  • Type

    conf

  • DOI
    10.1109/IJCNN.1992.227124
  • Filename
    227124