Abstract

This paper discusses the history of the appearance and development of the concept of convolutional artificial neural networks, which, because they use a learning technology based on back-propagation of the error signal, have become one of the most efficient tools of automatic image classification. Along with the possibilities of modern convolutional neural networks in the area of shape classification of objects, the features of their use in analyzing the information of other hierarchical levels have also been analyzed—from the classification of textures to the structural decomposition of images, based on the formation of attention zones. Convolutional networks are considered in close association with a description of their natural analogs, found in the neural ensembles of living visual systems.

© 2015 Optical Society of America

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