Sparseness controls the receptive field characteristics of V4 neurons: Generation of curvature selectivity in V4

Yasuhiro Hatori, Tatsuroh Mashita, Ko Sakai

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

1 Citation (Scopus)

Abstract

Physiological studies have reported that the intermediate-level visual area represents primitive shape by the selectivity to curvature and its direction. However, it has not been revealed that what coding scheme underlies the construction of the selectivity with complex characteristics. We propose that sparse representation is crucial for the construction so that a sole control of sparseness is capable of generating physiological characteristics. To test the proposal, we applied component analysis with sparseness constraint to activities of model neurons, and investigated whether the computed bases reproduce the characteristics of the selectivity. To evaluate the learned bases quantitatively, we computed the tuning properties of single bases and the population, as similar to the physiological reports. The basis functions reproduced the physiological characteristics when sparseness was medium (0.6-0.8). These results indicate that sparse representation is crucial for the curvature selectivity, and that a sole control of sparseness is capable of constructing the representation.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings
Pages327-334
Number of pages8
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event23rd International Conference on Artificial Neural Networks, ICANN 2013 - Sofia, Bulgaria
Duration: 2013 Sep 102013 Sep 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8131 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on Artificial Neural Networks, ICANN 2013
CountryBulgaria
CitySofia
Period13/9/1013/9/13

Keywords

  • computational model
  • curvature
  • shape representation
  • sparse coding

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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