P. O. Hoyer and A. Hyvärinen.
A multi-layer sparse coding network learns contour coding
from natural images.
Vision Research, in press.
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PostScript
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 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.
Computational Neuroscience Group