Recent advances in the deep CNN neocognitron

Abstract

Deep convolutional neural networks (deep CNN) show a large power for robust recognition of visual patterns. The neocognitron, which was first proposed by Fukushima (1979), is a network classified to this category. Its architecture was suggested by neurophysiological findings on the visual systems of mammals. It acquires the ability to recognize visual patterns robustly through learning. Although the neocognitron has a long history, improvements of the network are still continuing. This paper discusses the recent neocognitron, focusing on differences from the conventional deep CNN. Some other functions of the visual system can also be realized by networks extended from the neocognitron, for example, recognition of partly occluded patterns, the mechanism of selective attention, and so on.