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Hierarchical Spatio-Temporal Dynamics Of A Chaotic Neural Network For Multistable Binocular Rivalry

Author

Listed:
  • YUTA KAKIMOTO

    (Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba-ken 277-8561, Japan)

  • KAZUYUKI AIHARA

    (Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan;
    Aihara Complexity Modelling Project, ERATO, Japan Science and Technology Agency (JST), 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan)

Abstract

Binocular rivalry is perceptual alternation that occurs when different visual images are presented to each eye. Despite the intensive studies, the mechanism of binocular rivalry still remains unclear. In multistable binocular rivalry, which is a special case of binocular rivalry, it is known that the perceptual alternation between paired patterns is more frequent than that between unpaired patterns. This result suggests that perceptual transition in binocular rivalry is not a simple random process, and the memories stored in the brain can play an important role in the perceptual transition. In this study, we propose a hierarchical chaotic neural network model for multistable binocular rivalry and show that our model reproduces some characteristic features observed in multistable binocular rivalry.

Suggested Citation

  • Yuta Kakimoto & Kazuyuki Aihara, 2009. "Hierarchical Spatio-Temporal Dynamics Of A Chaotic Neural Network For Multistable Binocular Rivalry," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 123-134.
    • Handle: RePEc:wsi:nmncxx:v:05:y:2009:i:01:n:s1793005709001301
    DOI: 10.1142/S1793005709001301
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