A. Hyvärinen and P. O. Hoyer.
A two-layer sparse coding model learns simple and complex
cell receptive fields and topography from natural images
.
Vision Research, 41(18):2413-2423, 2001.
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PostScript
Abstract:
The classical receptive fields of simple cells in the visual cortex
have been shown to emerge from the statistical properties of natural
images by forcing the cell responses to be maximally sparse, i.e.\
significantly activated only rarely. Here, we show that this single
principle of sparseness can also lead to emergence of topography
(columnar organization) and complex cell properties as well. These
are obtained by maximizing the sparsenesses of locally pooled
energies, which correspond to complex cell outputs. Thus we obtain a
highly parsimonious model of how these properties of the visual cortex
are adapted to the characteristics of the natural input.
Computational Neuroscience Group