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

1997

@article{BeS1997,
	vgclass =	{refpap},
	author =	{Anthony J. Bell and Terrence J. Sejnowski},
	title =	{The ``independent components'' of natural scenes are edge
	filters},
	journal =	{Vision Research},
	volume =	{37},
	number =	{23},
	pages =	{3327--3338},
	month =	{December},
	year =	{1997},
	url =	{http://dx.doi.org/10.1016/S0042-6989(97)00121-1},
	abstract =	{It has previously been suggested that neurons with line
	and edge selectivities found in primary visual cortex of cats and
	monkeys form a sparse, distributed representation of natural scenes,
	and it has been reasoned that such responses should emerge from an
	unsupervised learning algorithm that attempts to find a factorial code
	of independent visual features. We show here that a new unsupervised
	learning algorithm based on information maximization, a nonlinear
	"infomax" network, when applied to an ensemble of natural scenes
	produces sets of visual filters that are localized and oriented. Some
	of these filters are Gabor-like and resemble those produced by the
	sparseness-maximization network. In addition, the outputs of these
	filters are as independent as possible, since this infomax network
	performs Independent Components Analysis or ICA, for sparse
	(super-gaussian) component distributions. We compare the resulting ICA
	filters and their associated basis functions, with other decorrelating
	filters produced by Principal Components Analysis (PCA) and zero-phase
	whitening filters (ZCA). The ICA filters have more sparsely distributed
	(kurtotic) outputs on natural scenes. They also resemble the receptive
	fields of simple cells in visual cortex, which suggests that these
	neurons form a natural, information-theoretic coordinate system for
	natural images.},
}