Learning multiple feature representations from natural image sequences

Wolfgang Einhäuser, Christoph Kayser, Konrad P. Körding, Peter König

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

2 Scopus citations

Abstract

Hierarchical neural networks require the parallel extraction of multiple features. This raises the question how a subpopulation of cells can become specific to one feature and invariant to another, while a different subpopulation becomes invariant to the first but specific to the second feature. Using a colour image sequence recorded by a camera mounted to a cat's head, we train a population of neurons to achieve optimally stable responses. We find that colour sensitive cells emerge. Adding the additional objective of decorrelating the neurons' outputs leads a subpopulation to develop achromatic receptive fields. The colour sensitive cells tend to be non-oriented, while the achromatic cells are orientation-tuned, in accordance with physiological findings. The proposed objective thus successfully separates cells which are specific for orientation and invariant to colour from orientation invariant colour cells.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks, ICANN 2002 - International Conference, Proceedings
PublisherSpringer Verlag
Pages21-26
Number of pages6
ISBN (Print)9783540440741
StatePublished - Jan 1 2002
Event2002 International Conference on Artificial Neural Networks, ICANN 2002 - Madrid, Spain
Duration: Aug 28 2002Aug 30 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2415 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2002 International Conference on Artificial Neural Networks, ICANN 2002
CountrySpain
CityMadrid
Period8/28/028/30/02

Keywords

  • Colour UTN:I0109
  • Learning
  • Natural stimuli
  • Temporal coherence
  • Visual cortex

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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