Show Reference: "Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position"

Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position Biological Cybernetics In Biological Cybernetics, Vol. 36, No. 4. (1 April 1980), pp. 193-202, doi:10.1007/bf00344251 by Kunihiko Fukushima
@article{fukushima-1980,
    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.},
    author = {Fukushima, Kunihiko},
    booktitle = {Biological Cybernetics},
    day = {1},
    doi = {10.1007/bf00344251},
    issn = {0340-1200},
    journal = {Biological Cybernetics},
    keywords = {ann, learning, self-organization, unsupervised-learning, visual-processing},
    month = apr,
    number = {4},
    pages = {193--202},
    posted-at = {2013-08-15 04:59:53},
    priority = {2},
    publisher = {Springer-Verlag},
    title = {Neocognitron: A {Self-Organizing} Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position},
    url = {http://dx.doi.org/10.1007/bf00344251},
    volume = {36},
    year = {1980}
}

See the CiteULike entry for more info, PDF links, BibTex etc.

ANN implementing DBN have been around for a long time (they go back at least to Fukushima's Neocognitron).