Learning in linear feature-discovery networks

Todd K. Leen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We describe the dynamics of learning in unsupervised linear feature-discovery networks that have recurrent lateral connections. Bifurcation theory provides a description of the location of multiple equilibria and limit cycles in the weight-space dynamics.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsSimon Haykin
PublisherPubl by Int Soc for Optical Engineering
Number of pages10
ISBN (Print)0819406937
StatePublished - 1991
Externally publishedYes
EventAdaptive Signal Processing - San Diego, CA, USA
Duration: Jul 22 1991Jul 24 1991

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherAdaptive Signal Processing
CitySan Diego, CA, USA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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