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
Neocognitron and the Map Transformation Cascade
Neural Networks
Neocognitron trained with winner-kill-loser rule
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
Optimal training of thresholded linear correlation classifiers
IEEE Transactions on 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
Spike-timing-dependent construction
Neural Computation
Noise tolerance in a Neocognitron-like network
Neural Networks
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The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It has been demonstrated that recent versions of the neocognitron exhibit excellent performance for recognizing handwritten digits. When characters are written on a noisy background, however, recognition rate was not always satisfactory. To find out the causes of vulnerability to noise, this paper analyzes the behavior of feature-extracting S-cells. It then proposes the use of subtractive inhibition to S-cells from V-cells, which calculate the average of input signals to the S-cells with a root-mean-square. Together with this, several modifications have also been applied to the neocognitron. Computer simulation shows that the new neocognitron is much more robust against background noise than the conventional ones.