A multi-layer sparse coding network learns contour coding from natural images

被引：106

作者：

Hoyer, PO ^{[1
]}

Hyvärinen, A ^{[1
]}

机构：

[1] Aalto Univ, Neural Networks Res Ctr, FIN-02015 Helsinki, Finland

来源：

VISION RESEARCH | 2002年 / 42卷 / 12期

基金：

芬兰科学院;

关键词：

natural images; neural networks; contours; cortex; independent component analysis;

D O I：

10.1016/S0042-6989(02)00017-2

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous studies have shown how many basic properties of the primary visual cortex, such as the receptive fields of simple and complex cells and the spatial organization (topography) of the cells, can be understood as efficient coding of natural images. Here we extend the framework by considering how the responses of complex cells could be sparsely represented by a higher-order neural layer. This leads to contour coding and end-stopped receptive fields. In addition, contour integration could be interpreted as top-down inference in the presented model. (C) 2002 Elsevier Science Ltd. All rights reserved.

引用

页码：1593 / 1605

页数：13

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