Recognition of rotated patterns using a neocognitron
Knowledge-based intelligent techniques in character recognition
Recognition and Segmentation of Components of a Face by a Multi-Resolution Neural Network
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Neocognitron capable of incremental learning
Neural Networks
Restoring partly occluded patterns: a neural network model
Neural Networks
Interpolating vectors for robust pattern recognition
Neural Networks
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
Neocognitron trained with winner-kill-loser rule
Neural Networks
Neocognitron trained by winner-kill-loser with triple threshold
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Training multi-layered neural network neocognitron
Neural Networks
The learning problem of multi-layer neural networks
Neural Networks
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The neocognitron is a neural network model proposed by Fukushima (1980). Its architecture was suggested by neurophysiological findings on the visual systems of mammals. It is a hierarchical multi-layered network. It acquires the ability to robustly recognize visual patterns through learning. Although the neocognitron has a long history, modifications of the network to improve its performance are still going on. For example, a recent neocognitron uses a new learning rule, named add-if-silent, which makes the learning process much simpler and more stable. Nevertheless, a high recognition rate can be kept with a smaller scale of the network. Referring to the history of the neocognitron, this paper discusses recent advances in the neocognitron. We also show that various new functions can be realized by, for example, introducing top-down connections to the neocognitron: mechanism of selective attention, recognition and completion of partly occluded patterns, restoring occluded contours, and so on.