Slow feature analysis yields a rich repertoire of complex cell properties

Abstract

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.

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• Slow feature analysis yields a rich repertoire of complex cell properties. (deposited 22 Feb 2003) [Currently Displayed]
  • Slow feature analysis yields a rich repertoire of complex cell properties. (deposited 04 Mar 2003)