Artificial convolution neural network for medical image pattern recognition
Neural Networks - Special issue: automatic target recognition
Recognition of rotated patterns using a neocognitron
Knowledge-based intelligent techniques in character recognition
Backpropagation applied to handwritten zip code recognition
Neural Computation
Optimal training of thresholded linear correlation classifiers
IEEE Transactions on Neural Networks
Increased robustness against background noise: pattern recognition by a neocognitron
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Neocognitron trained by winner-kill-loser with triple threshold
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Multigraphical membrane systems revisited
CMC'12 Proceedings of the 13th international conference on Membrane Computing
Training multi-layered neural network neocognitron
Neural Networks
Noise tolerance in a Neocognitron-like network
Neural Networks
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The neocognitron, which was proposed by Fukushima (1980), is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It acquires the ability to recognize patterns through learning. This paper proposes a new rule for competitive learning, named winner-kill-loser, and apply it to the neocognitron. The winner-kill-loser rule resembles the winner-take-all rule. Every time when a training stimulus is presented, non-silent cells compete with each other. The winner, however, not only takes all, but also kills losers. In other words, the winner learns the training stimulus, and losers are removed from the network. If all cells are silent, a new cell is generated and it learns the training stimulus. Thus feature-extracting cells gradually come to distribute uniformly in the feature space. The use of winner-kill-loser rule is not limited to the neocognitron. It is useful for various types of competitive learning, in general. This paper also proposes several improvements made on the neocognitron: such as, disinhibition to the inhibitory surround in the connections to C-cells (or complex cells) from S-cells (or simple cells); and square root shaped saturation in the input-to-output characteristics of C-cells. As a result of these improvements, the recognition rate of the neocognitron has been largely increased.