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International Symposium on Neural Networks

ISNN 2017: Advances in Neural Networks - ISNN 2017 pp 371–378Cite as

The Mixed States of Associative Memories Realize Unimodal Distribution of Dominance Durations in Multistable Perception

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10261))

Abstract

We propose a pulse neural network that exhibits chaotic pattern alternations among stored patterns as a model of multistable perception, which is reflected in phenomena such as binocular rivalry and perceptual ambiguity. When we regard the mixed state of patterns as a part of each pattern, the durations of the retrieved pattern obey unimodal distributions. The mixed states of the patterns are essential to obtain the results that are consistent with psychological studies. Based on these results, it is proposed that many pre-existing attractors in the brain might relate to the general category of multistable phenomena, such as binocular rivalry and perceptual ambiguity.

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Authors and Affiliations

  1. Department of Mechanical Science and Engineering, School of Advanced Engineering, Kogakuin University, 2665-1 Nakano, Hachioji-city, Tokyo, 192-0015, Japan

    Takashi Kanamaru

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  1. Takashi Kanamaru
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Correspondence to Takashi Kanamaru .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Kanamaru, T. (2017). The Mixed States of Associative Memories Realize Unimodal Distribution of Dominance Durations in Multistable Perception. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_44

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  • DOI: https://doi.org/10.1007/978-3-319-59072-1_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59071-4

  • Online ISBN: 978-3-319-59072-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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