Analysis of neural circuit for visual attention using lognormally distributed input

Yoshihiro Nagano, Norifumi Watanabe, Atsushi Aoyama

研究成果: Conference contribution

抄録

Visual attention has recently been reported to modulate neural activity of narrow spiking and broad spiking neurons in V4, with increased firing rate and less inter-trial variations. We simulated these physiological phenomena using a neural network model based on spontaneous activity, assuming that the visual attention modulation could be achieved by a change in variance of input firing rate distributed with a lognormal distribution. Consistent with the physiological studies, an increase in firing rate and a decrease in inter-trial variance was simultaneously obtained in the simulation by increasing variance of input firing rate distribution. These results indicate that visual attention forms strong sparse and weak dense input or a 'winner-take-all' state, to improve the signal-to-noise ratio of the target information.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings
出版社Springer Verlag
ページ467-474
ページ数8
ISBN(印刷版)9783319111780
DOI
出版ステータスPublished - 2014
イベント24th International Conference on Artificial Neural Networks, ICANN 2014 - Hamburg, Germany
継続期間: 2014 9 152014 9 19

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)
8681 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other24th International Conference on Artificial Neural Networks, ICANN 2014
CountryGermany
CityHamburg
Period14/9/1514/9/19

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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