SLOW FEATURE ANALYSIS YIELDS
A RICH REPERTOIRE OF COMPLEX-CELL PROPERTIES
by Pietro Berkes and Laurenz Wiskott
In this study, we investigate temporal slowness as a learning principle for
receptive fields using slow feature analysis, a new algorithm to determine
functions that extract slowly varying signals from the input data. We find
that the learned functions trained on image sequences develop many
properties found also experimentally in complex cells of primary visual
cortex, such as direction selectivity, non-orthogonal inhibition,
end-inhibition and side-inhibition. Our results demonstrate that a single
unsupervised learning principle can account for such a rich repertoire of
receptive field properties.
Berkes, P. and Wiskott, L. (2003).
Slow feature analysis yields a rich repertoire of complex-cell properties.
Cognitive Sciences EPrint Archive (CogPrint) 2785,
(<add date of your document download here>).
-- Dr. Laurenz Wiskott, Institute for Theoretical Biology, Berlin http://itb.biologie.hu-berlin.de/~wiskott/ email@example.com
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