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An analytical model of divisive normalization in disparity-tuned complex cells

  • Technical University of Munich
  • University of Tübingen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Based on the energy model for disparity-tuned neurons, we calculate probability density functions of complex cell activity for random-dot stimuli. We investigate the effects of normalization and give analytical expressions for the disparity tuning curve and its variance. We show that while normalized and non-normalized complex cells have similar tuning curves, the variance is significantly lower for normalized complex cells, which makes disparity estimation more reliable. The results of the analytical calculations are compared to computer simulations.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings
PublisherSpringer Verlag
Pages776-787
Number of pages12
EditionPART 1
ISBN (Print)9783540746898
DOIs
StatePublished - 2007
Event17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto, Portugal
Duration: 9 Sep 200713 Sep 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4668 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Artificial Neural Networks, ICANN 2007
Country/TerritoryPortugal
CityPorto
Period9/09/0713/09/07

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