• DocumentCode
    1809013
  • Title

    Self-organization of shift-invariant receptive fields

  • Author

    Fukushima, Kunihiko

  • Author_Institution
    Univ. of Electro-Commun., Tokyo, Japan
  • Volume
    2
  • fYear
    1999
  • fDate
    36342
  • Firstpage
    1087
  • Abstract
    This paper proposes a new learning rule by which cells with shift-invariant receptive fields are self-organized. Namely, cells similar to simple and complex cells in the primary visual cortex are generated in a network trained by the new leaning rule. To demonstrate the new learning rule, we simulate a three-layered network that consists of an input layer (retina), a layer of S-cells (simple cells), and a layer of C-cells (complex cells). During the learning, straight lines of various orientations sweep across the input layer Both S- and C-cells are created through competition. Although S-cells compete depending on their instantaneous outputs, C-cells compete depending on the traces (or temporal averages) of their outputs. For the self-organization of S-cells, only winner S-cells have LTP (long term potentiation) in their input connections. For the self-organization of S-cells, however, loser S-cells have LTD (long term depression) in their input connections, while winners have LTP. Both S- and C-cells are accompanied by inhibitory cells. Modification of inhibitory connections together with excitatory connections is important for creation of C-cells as well as S-cells
  • Keywords
    learning (artificial intelligence); multilayer perceptrons; physiological models; self-organising feature maps; visual perception; C-cells; LTD; LTP; S-cells; excitatory connections; inhibitory cells; inhibitory connections; learning rule; long-term depression; long-term potentiation; output temporal averages; output traces; primary visual cortex; retina; self-organization; shift-invariant receptive fields; three-layered network; Brain modeling; Neural networks; Pattern recognition; Retina; Robustness; Unsupervised learning; Visual system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.831107
  • Filename
    831107