%0 Journal Article %1 Fukushima1980 %A Fukushima, Kunihiko %D 1980 %J Biological Cybernetics %K machinelearning neuralnetwork %N 4 %P 193--202 %R 10.1007/BF00344251 %T Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position %U https://doi.org/10.1007/BF00344251 %V 36 %X 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 consits 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-cell 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.

@article{Fukushima1980, 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 consits 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-cell 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.}, added-at = {2020-09-04T21:06:04.000+0200}, author = {Fukushima, Kunihiko}, biburl = {https://www.bibsonomy.org/bibtex/24c1c851c79dc9b184fa46ab215014b00/cpankow}, day = 01, description = {Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position | SpringerLink}, doi = {10.1007/BF00344251}, interhash = {303975e6400e477e91c91e7dc2c47544}, intrahash = {4c1c851c79dc9b184fa46ab215014b00}, issn = {1432-0770}, journal = {Biological Cybernetics}, keywords = {machinelearning neuralnetwork}, month = apr, number = 4, pages = {193--202}, timestamp = {2020-09-04T21:06:04.000+0200}, title = {Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position}, url = {https://doi.org/10.1007/BF00344251}, volume = 36, year = 1980 }