title: Applying Slow Feature Analysis to Image Sequences Yields a Rich Repertoire of Complex Cell Properties
creator: Berkes, Pietro
creator: Wiskott, Laurenz
subject: Neural Modelling
subject: Computational Neuroscience
subject: Machine Vision
subject: Theoretical Biology
description: We apply Slow Feature Analysis (SFA) to image sequences generated from natural images using a range of spatial transformations. An analysis of the resulting receptive fields shows that they have a rich spectrum of invariances and share many properties with complex and hypercomplex cells of the primary visual cortex. Furthermore, the dependence of the solutions on the statistics of the transformations is investigated. 
publisher: Springer Verlag
contributor: Dorronsoro, José R.
date: 2002
type: Conference Paper
type: PeerReviewed
format: application/postscript
identifier: http://cogprints.org/2706/1/I0220.ps
identifier:   Berkes, Pietro and Wiskott, Laurenz  (2002) Applying Slow Feature Analysis to Image Sequences Yields a Rich Repertoire of Complex Cell Properties.  [Conference Paper]     
relation: http://cogprints.org/2706/