Skip to main content
Article thumbnail
Location of Repository

Slow feature analysis yields a rich repertoire of complex cell properties

By Pietro Berkes and Laurenz Wiskott

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

Topics: Computational Neuroscience, Machine Vision
Year: 2003
OAI identifier: oai:cogprints.org:2804
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://cogprints.org/2804/1/Be... (external link)
  • http://cogprints.org/2804/2/Be... (external link)
  • http://cogprints.org/2804/ (external link)
  • Suggested articles

    Citations

    1. (2001). Independent component analysis of temporal sequences subject to constraints by lateral geniculate nucleus inputs yields all three major cell types of the primary visual cortex.
    2. (1998). Independent component filters of natural images compared with simple cells in primary visual cortex.
    3. (1991). Learning invariance from transformation sequences.
    4. (2002). Slow feature analysis: Unsupervised learning of invariances.
    5. (1997). The ‘independent components’ of natural scenes are edge filters.
    6. (1982). The orientation and direction selectivity of cells in macaque visual cortex. Vision Res.

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.