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International Conference on Artificial Neural Networks

ICANN 2014: Artificial Neural Networks and Machine Learning – ICANN 2014 pp 467–474Cite as

Analysis of Neural Circuit for Visual Attention Using Lognormally Distributed Input

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8681)

Abstract

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.

Keywords

  • Visual Attention
  • Neural Network Model
  • Spontaneous Activity
  • Lognormal Distribution

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  • DOI: 10.1007/978-3-319-11179-7_59
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Author information

Authors and Affiliations

  1. Faculty of Environment and Information Studies, Keio University, 5322 Endo, Fujisawa-shi, Kanagawa, 252-0882, Japan

    Yoshihiro Nagano & Atsushi Aoyama

  2. School of Computer Science, Tokyo University of Technology, 1404-1 Katakura-cho, Hachioji-shi, Tokyo, 192-0982, Japan

    Norifumi Watanabe

Authors
  1. Yoshihiro Nagano
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  2. Norifumi Watanabe
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  3. Atsushi Aoyama
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Editor information

Editors and Affiliations

  1. Department of Informatics, University of Hamburg, Vogt-Kölln-Straße 30, 22527, Hamburg, Germany

    Stefan Wermter, Cornelius Weber & Sven Magg,  & 

  2. Department of Informatics, Nicolaus Compernicus University, ul. Grudziądzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Modern Languages, University of Helsinki, P.O. Box 24, 00014, Helsinki, Finland

    Timo Honkela

  4. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl. 25A, 1113, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  5. Institute of Neural Information Processing, University of Ulm, 89069, Oberer Eselsberg, Ulm, Germany

    Günther Palm

  6. Department of Information Systems, Quartier UNIL-Dorigny, Bâtiment Internef, University of Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

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© 2014 Springer International Publishing Switzerland

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Nagano, Y., Watanabe, N., Aoyama, A. (2014). Analysis of Neural Circuit for Visual Attention Using Lognormally Distributed Input. In: , et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_59

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  • DOI: https://doi.org/10.1007/978-3-319-11179-7_59

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11178-0

  • Online ISBN: 978-3-319-11179-7

  • eBook Packages: Computer ScienceComputer Science (R0)