Neocognitron: A neural network model for a mechanism of visual pattern recognition

@article{Fukushima1983NeocognitronAN,
  title={Neocognitron: A neural network model for a mechanism of visual pattern recognition},
  author={Kunihiko Fukushima and Sei Miyake and Takayuki Ito},
  journal={IEEE Transactions on Systems, Man, and Cybernetics},
  year={1983},
  volume={SMC-13},
  pages={826-834}
}
A recognition with a large-scale network is simulated on a PDP-11/34 minicomputer and is shown to have a great capability for visual pattern recognition. The model consists of nine layers of cells. The authors demonstrate that the model can be trained to recognize handwritten Arabic numerals even with considerable deformations in shape. A learning-with-a-teacher process is used for the reinforcement of the modifiable synapses in the new large-scale model, instead of the learning-without-a… 
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    IEEE Transactions on Systems, Man, and Cybernetics: Systems
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This paper is the first to carry out quantitative analysis of the neuron responses for gray scale image classification, and an analogy is shown between unsupervised Hebbian learning and PCA when applied to the neocognitron model.
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An approach to pattern recognition that is based on a concept involving an invariance net and a trainable classifier and some numerical results are illustrated to show the effectiveness of the present algorithm for pattern recognition.
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TLDR
This may be the first time that a neurobiological model, faithful to the physiology and the anatomy of visual cortex, not only competes with some of the best computer vision systems thus providing a realistic alternative to engineered artificial vision systems, but also achieves performance close to that of humans in a categorization task involving complex natural images.
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References

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Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
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TLDR
A layer structured system suitable for pattern recognition which operates similar to the afferent nervous system of vertebrates and corresponds approximately to the human capability for this task is described.
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TLDR
This method is used to examine receptive fields of a more complex type and to make additional observations on binocular interaction and this approach is necessary in order to understand the behaviour of individual cells, but it fails to deal with the problem of the relationship of one cell to its neighbours.
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Analysis of a Four-Layer Series-Coupled Perceptron. II
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