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Biological Cybernetics

, Volume 20, Issue 3–4, pp 121–136 | Cite as

Cognitron: A self-organizing multilayered neural network

  • Kunihiko Fukushima
Article

Abstract

A new hypothesis for the organization of synapses between neurons is proposed: “The synapse from neuron x to neuron y is reinforced when x fires provided that no neuron in the vicinity of y is firing stronger than y”. By introducing this hypothesis, a new algorithm with which a multilayered neural network is effectively organized can be deduced. A self-organizing multilayered neural network, which is named “cognitron”, is constructed following this algorithm, and is simulated on a digital computer. Unlike the organization of a usual brain models such as a three-layered perceptron, the self-organization of a cognitron progresses favorably without having a “teacher” which instructs in all particulars how the individual cells respond. After repetitive presentations of several stimulus patterns, the cognitron is self-organized in such a way that the receptive fields of the cells become relatively larger in a deeper layer. Each cell in the final layer integrates the information from whole parts of the first layer and selectively responds to a specific stimulus pattern or a feature.

Keywords

Neural Network Individual Cell Deep Layer Receptive Field Final Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 1975

Authors and Affiliations

  • Kunihiko Fukushima
    • 1
  1. 1.NHK Broadcasting Science Research LaboratoriesTokyoJapan

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