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      Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

      Biological Cybernetics
      Springer Science and Business Media LLC

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          Abstract

          A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by "learning without a teacher", and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname "neocognitron". After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consists of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of "S-cells", which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of "C-cells" similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any "teacher" during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cells of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern.

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          RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT.

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            Ferrier Lecture: Functional Architecture of Macaque Monkey Visual Cortex

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              • Article: not found

              (Invited) An Advanced MOS-IC Process Technology Using Oxidation of Oxygen-Doped Polycrystalline Silicon Films

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                Author and article information

                Journal
                Biological Cybernetics
                Biol. Cybernetics
                Springer Science and Business Media LLC
                0340-1200
                1432-0770
                April 1980
                April 1980
                : 36
                : 4
                : 193-202
                Article
                10.1007/BF00344251
                7370364
                2f5fb128-7e4c-42d0-b896-920a1e83f0d1
                © 1980

                http://www.springer.com/tdm

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