Neocognitron

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The neocognitron is a hierarchical multilayered neural network proposed by Professor Kunihiko Fukushima. It has been used for handwritten character recognition and other pattern recognition tasks.

The neocognitron is inspired by the model proposed by Hubel & Wiesel in 1959. They found two types of cells in visual primary cortex called simple cell and complex cell, and also proposed a cascading model of these two type of cells.[citation needed]

The neocognitron is a natural extension of these cascading models. In the neocognitron, which consists of multiple types of cells the most important of which are called S-cells and C-cells,[1] the local features are extracted by S-cells, and these features' deformation, such as local shifts, are tolerated by C-cells. Local features in the input are integrated gradually and classifying in the higher layers.[2] The idea of local feature integration is in several other models such as LeNet and SIFT model.

There are multiple kinds of neocognitron.[3] For example, some types of neocognitron can detect multiple patterns in the same input by using backward signals to achieve selective attention.[4]

These networks give results that are a bit low for an unsupervised learning network.

See also

Notes

  1. ↑ Fukushima 1987, p. 83.
  2. ↑ Fukushima 1987, p. 84.
  3. ↑ Fukushima 2007
  4. ↑ Fukushima 1987, pp.81, 85

References

  • K. Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4): 93-202, 1980.
  • K. Fukushima, S. Miyake, and T. Ito. Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-13(Nb. 3):pp. 826—834, September/October 1983.
  • K. Fukushima. "A hierarchical neural network model for selective attention." In Eckmiller, R. & Von der Malsburg, C. eds. Neural computers, Springer-Verlag. pp. 81-90. 1987.
  • K. Fukushima. "Neocognitron." Scholarpedia, 2(1):1717. 2007.

External links


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