Refereed full papers (journals, book chapters, international conferences)

1996

@article{OlF1996a,
	vgclass =	{refpap},
	vgproject =	{nn},
	author =	{B. A. Olshausen and D. J. Field},
	title =	{Emergence of simple-cell receptive field properties by
	learning a sparse code for natural images},
	journal =	{Nature},
	volume =	{381},
	pages =	{607--609},
	year =	{1996},
	abstract =	{The receptive fields of simple cells in mammalian primary
	visual cortex can be characterized as being spatially localized,
	oriented(1-4) and bandpass (selective to structure at different spatial
	scales), comparable to the basis functions of wavelet transforms(5,6),
	One approach to understanding such response properties of visual
	neurons has been to consider their relationship to the statistical
	structure of natural images in terms of efficient coding(7-12), Along
	these lines, a number of studies have attempted to train unsupervised
	learning algorithms on natural images in the hope of developing
	receptive fields with similar properties(13-18), but none has succeeded
	in producing a full set that spans the image space and contains all
	three of the above properties. Here we investigate the proposal(8,12)
	that a coding strategy that maximizes sparseness is sufficient to
	account for these properties, We show that a learning algorithm that
	attempts to find sparse linear codes for natural scenes will develop a
	complete family of localized, oriented, bandpass receptive fields,
	similar to those found in the primary visual cortex, The resulting
	sparse image code provides a more efficient representation for later
	stages of processing because it possesses a higher degree of
	statistical independence among its outputs.},
}