Neocognitron
The neocognitron is a hierarchical, multilayered
artificial neural network proposed by Kunihiko Fukushima in 1979.[1] It has been used for Japanese handwritten character recognition and other pattern recognition tasks, and served as the inspiration for convolutional neural networks.[2]
The neocognitron was inspired by the model proposed by Hubel & Wiesel in 1959. They found two types of cells in the visual primary cortex called simple cell and complex cell, and also proposed a cascading model of these two types of cells for use in pattern recognition tasks.[3][4]
The neocognitron is a natural extension of these cascading models. The neocognitron consists of multiple types of cells, the most important of which are called S-cells and C-cells. method.
There are various kinds of neocognitron.selective attention.[8]
See also
- Artificial neural network
- Deep learning
- Pattern recognition
- Receptive field
- Self-organizing map
- Unsupervised learning
Notes
- ^ Fukushima, Kunihiko (October 1979). "位置ずれに影響されないパターン認識機構の神経回路のモデル --- ネオコグニトロン ---" [Neural network model for a mechanism of pattern recognition unaffected by shift in position — Neocognitron —]. Trans. IECE (in Japanese). J62-A (10): 658–665.
- S2CID 3074096.
- ^
David H. Hubel and Torsten N. Wiesel (2005). Brain and visual perception: the story of a 25-year collaboration. Oxford University Press US. p. 106. ISBN 978-0-19-517618-6.
- PMID 14403679.
- ^ Fukushima 1987, p. 83.
- ^ Fukushima 1987, p. 84.
- ^ Fukushima 2007.
- ^ Fukushima 1987, pp. 81, 85.
References
- Fukushima, Kunihiko (April 1980). "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position". Biological Cybernetics. 36 (4): 193–202. S2CID 206775608.
- Fukushima, Kunihiko; Miyake, S.; Ito, T. (1983). "Neocognitron: a neural network model for a mechanism of visual pattern recognition". IEEE Transactions on Systems, Man, and Cybernetics. SMC-13 (3): 826–834. S2CID 8235461.
- Fukushima, Kunihiko (1987). "A hierarchical neural network model for selective attention". In Eckmiller, R.; Von der Malsburg, C. (eds.). Neural computers. Springer-Verlag. pp. 81–90.
- Fukushima, Kunihiko (2007). "Neocognitron". Scholarpedia. 2 (1): 1717. .
- Hubel, D.H.; Wiesel, T.N. (1959). "Receptive fields of single noreones in the cat's striate cortex". J Physiol. 148 (3): 574–591. PMID 14403679.
External links
- Neocognitron on Scholarpedia
- NeoCognitron by Ing. Gabriel Minarik - application (C#) and video
- Neocognitron resources at Visiome Platform - includes MATLAB environment
- Beholder - a Neocognitron simulator
 | This artificial intelligence-related article is a stub. You can help Wikipedia by expanding it. |