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Slow feature analysis yields a rich repertoire of complex cell properties

Berkes, Pietro and Wiskott, Laurenz (2003) Slow feature analysis yields a rich repertoire of complex cell properties. [Preprint]

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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.

Item Type:Preprint
Keywords:complex cells, slow feature analysis, temporal slowness, model, spatio-temporal, receptive fields
Subjects:Neuroscience > Computational Neuroscience
ID Code:2785
Deposited By:Berkes, Pietro
Deposited On:22 Feb 2003
Last Modified:11 Mar 2011 08:55

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