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.[5] 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 classified in the higher layers.[6] The idea of local feature integration is found in several other models, such as the Convolutional Neural Network model, the SIFT method, and the HoG method.
There are various kinds of neocognitron.[7] For example, some types of neocognitron can detect multiple patterns in the same input by using backward signals to achieve selective attention.[8]
^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.
^
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.
^Hubel, DH; Wiesel, TN (October 1959). "Receptive fields of single neurones in the cat's striate cortex". J. Physiol. 148 (3): 574–91. doi:10.1113/jphysiol.1959.sp006308. PMC 1363130. PMID 14403679.
The neocognitron is a hierarchical, multilayered artificial neural network proposed by Kunihiko Fukushima in 1979. It has been used for Japanese handwritten...
Systems Institute in Fukuoka, Japan. In 1980, Fukushima published the neocognitron, the original deep convolutional neural network (CNN) architecture. Fukushima...
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shift in position — Neocognitron —]. Trans. IECE (in Japanese). J62-A (10): 658–665. Fukushima, Kunihiko (April 1980). "Neocognitron: A self-organizing...
of SNNs are the OSFA spatial neural networks, SVANNs and GWNNs. The neocognitron is a hierarchical, multilayered network that was modeled after the visual...
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proposed by WA Little [de] for cognition 1980 Fukushima introduces the neocognitron, which is later called a convolutional neural network. It is mostly used...
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London: Springer. ISBN 978-1-85233-532-8. Fukushima, Kunihiko (1980). "Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition...