Journal
VISION RESEARCH
Volume 41, Issue 18, Pages 2413-2423Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0042-6989(01)00114-6
Keywords
cortex; independent component analysis; natural images; neural networks; spatial vision
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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. (C) 2001 Elsevier Science Ltd. All rights reserved.
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