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

Yasuhiro Hatori, Tatsuroh Mashita, Ko Sakai

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings
ページ327-334
ページ数8
DOI
出版ステータスPublished - 2013
外部発表はい
イベント23rd International Conference on Artificial Neural Networks, ICANN 2013 - Sofia, Bulgaria
継続期間: 2013 9月 102013 9月 13

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8131 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

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

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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