Document Detail


Slow feature analysis yields a rich repertoire of complex cell properties.
MedLine Citation:
PMID:  16097870     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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 a good qualitative and quantitative match between the set of learned functions trained on image sequences and the population of complex cells in the primary visual cortex (V1). The functions show many properties found also experimentally in complex cells, 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.
Authors:
Pietro Berkes; Laurenz Wiskott
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2005-07-20
Journal Detail:
Title:  Journal of vision     Volume:  5     ISSN:  1534-7362     ISO Abbreviation:  J Vis     Publication Date:  2005  
Date Detail:
Created Date:  2005-08-15     Completed Date:  2006-01-27     Revised Date:  2008-04-29    
Medline Journal Info:
Nlm Unique ID:  101147197     Medline TA:  J Vis     Country:  United States    
Other Details:
Languages:  eng     Pagination:  579-602     Citation Subset:  IM    
Affiliation:
Institute for Theoretical Biology, Humboldt University, Berlin, Germany. p.berkes@biologie.hu-berlin.de
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Computer Simulation
Humans
Learning
Mathematics
Neural Inhibition*
Neurons / physiology*
Visual Cortex / physiology*
Visual Perception / physiology*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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