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{Is slowness a learning principle of the visual cortex?}
Type of publication: Article
Citation: Wiskott2003
Journal: Zoology (Jena, Germany)
Volume: 106
Number: 4
Year: 2003
Month: January
Pages: 373--82
ISSN: 0944-2006
URL: http://www.ncbi.nlm.nih.gov/pu...
DOI: 10.1078/0944-2006-00132
Abstract: Slow feature analysis is an algorithm for extracting slowly varying features from a quickly varying signal. It has been shown in network simulations on one-dimensional stimuli that visual invariances to shift and other transformations can be learned in an unsupervised fashion based on slow feature analysis. More recently, we have shown that slow feature analysis applied to image sequences generated from natural images using a range of spatial transformations results in units that share many properties with complex and hypercomplex cells of the primary visual cortex. We find cells responsive to Gabor stimuli with phase invariance, sharpened or widened orientation or frequency tuning, secondary response lobes, end-stopping, and cells selective for direction of motion. These results indicate that slowness may be an important principle of self-organization in the visual cortex.
Userfields: bdsk-url-1={http://www.ncbi.nlm.nih.gov/pubmed/16351921}, bdsk-url-2={http://dx.doi.org/10.1078/0944-2006-00132}, date-added={2012-09-23 10:50:23 +0200}, date-modified={2012-09-23 10:50:23 +0200}, file={:home/jim/Desktop/sortedLiterature/sfa+invariances/Is slowness a learning principle of the visual cortex? .pdf:pdf}, pmid={16351921}, project={fremdliteratur},
Keywords: complex cells, invariances, receptive fields, Slow Feature Analysis, visual system
Authors Wiskott, Laurenz
Berkes, Pietro
  • http://www.ncbi.nlm.nih.gov/pu...
  • http://dx.doi.org/10.1078/0944...