A Non-Negative Sparse Coding Network Learns Contour Coding and Integration From Natural Images (2001)
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BibTeX
@MISC{Hoyer01anon-negative,
author = {Patrik O. Hoyer and Aapo Hyvärinen},
title = {A Non-Negative Sparse Coding Network Learns Contour Coding and Integration From Natural Images},
year = {2001}
}
OpenURL
Abstract
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 eciently coded by a higher-order neural layer. This leads to contour coding and end-stopped receptive fields. Interestingly, contour integration can in this framework be seen as a direct result of top-down noise reduction, suggesting such a role for cortico-cortical feedback connections in the visual cortex.







