Analysis of neural circuit for visual attention using lognormally distributed input

Yoshihiro Nagano, Norifumi Watanabe, Atsushi Aoyama

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

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings
PublisherSpringer Verlag
Pages467-474
Number of pages8
ISBN (Print)9783319111780
DOIs
Publication statusPublished - 2014
Event24th International Conference on Artificial Neural Networks, ICANN 2014 - Hamburg, Germany
Duration: 2014 Sep 152014 Sep 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8681 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

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

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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