__NOTOC__ The neocognitron is a hierarchical, multilayered

artificial neural network Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected unit ...

proposed by

Kunihiko Fukushima Kunihiko Fukushima ( Japanese: 福島邦彦, born 16 March 1936) is a Japanese computer scientist, most noted for his work on artificial neural networks and deep learning. He is currently working part-time as a Senior Research Scientist at the F ...

in 1979. It has been used for Japanese handwritten character recognition and other

pattern recognition Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics ...

tasks, and served as the inspiration for

convolutional neural network In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...

s. 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 A simple cell in the primary visual cortex is a cell that responds primarily to oriented edges and gratings (bars of particular orientations). These cells were discovered by Torsten Wiesel and David Hubel in the late 1950s. Such cells are tune ...

'' and '' complex cell'', and also proposed a cascading model of these two types of cells for use in pattern recognition tasks. 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.'' 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. The idea of local feature integration is found in several other models, such as the ''

Convolutional Neural Network In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...

'' model, the '' SIFT'' method, and the '' HoG'' method. There are various kinds of neocognitron. For example, some types of neocognitron can detect multiple patterns in the same input by using backward signals to achieve selective attention.Fukushima 1987, pp.81, 85

Notes

References

* * * Kunihiko 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. * *

External links

Neocognitron
on

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NeoCognitron by Ing. Gabriel Minarik
- application (C#) and video
Neocognitron resources at Visiome Platform
- includes MATLAB environment
Beholder
- a Neocognitron simulator Artificial neural networks {{compu-AI-stub

See also

Notes

References

External links