• DocumentCode
    2715380
  • Title

    Unsupervised learning for the visual cortex (layer IV): model and simulations

  • Author

    Mougeot, Mathilde ; Azencott, Robert

  • Author_Institution
    Ecole Normale Superieure, Paris, France
  • fYear
    1991
  • fDate
    8-14 Jul 1991
  • Firstpage
    613
  • Abstract
    The authors present a statistical model to study the evolution of orientation columns in layer IV of the visual cortex after birth by simulating, on the retina, the first visual experiences of an animal (darkness, biased, and normal visual environments). The results are consistent with biological experiments. The authors show the necessity of introducing a new nonlinear model in which both vertical and intracortical connections evolve simultaneously as soon as the visual experience begins. The model sets up intracortical connections and achieves the maturation of orientation columns. It takes into account recent neurobiological observations for the development of intracortical connections. Simulations reproduce the results of experiments which observe excitative intracortical connections between cells of similar orientation in different hypercolumns
  • Keywords
    brain models; learning systems; neural nets; neurophysiology; vision; biased; darkness; intracortical connections; layer IV; model; neurobiological observations; nonlinear model; normal visual environments; orientation columns; retina; simulations; statistical model; unsupervised learning; visual cortex; Animals; Biological system modeling; Brain modeling; Convergence; Evolution (biology); Gaussian distribution; Retina; Unsupervised learning; Visual system; White noise;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    0-7803-0164-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.1991.155405
  • Filename
    155405