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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

Topics: Computational Neuroscience, Machine Vision
Year: 2003
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