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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Pulse neural network Chaotic pattern alternations Multistable perception Binocular rivalry Perceptual ambiguity Dominance duration 

References

  1. 1.
    Lehky, S.R.: Binocular rivalry is not chaotic. Proc. R. Soc. Lond. B 259, 71–76 (1995)CrossRefGoogle Scholar
  2. 2.
    Blake, R.: A primer on binocular rivalry, including current controversies. Brain Mind 2, 5–38 (2001)CrossRefGoogle Scholar
  3. 3.
    Alais, D., Blake, R.: Binocular rivalry and perceptual ambiguity. In: Wagemans, J. (ed.) The Oxford Handbook of Perceptual Organization. Oxford University Press, Oxford (2015)Google Scholar
  4. 4.
    Adachi, M., Aihara, K.: Associative dynamics in a chaotic neural network. Neural Netw. 10, 83–98 (1997)CrossRefGoogle Scholar
  5. 5.
    Aihara, K., Takabe, T., Toyoda, M.: Chaotic neural networks. Phys. Lett. A 144, 333–340 (1990)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Tsuda, I.: Dynamic link of memory - chaotic memory map in nonequilibrium neural networks. Neural Netw. 5, 313–326 (1992)CrossRefGoogle Scholar
  7. 7.
    Ermentrout, G.B., Kopell, N.: Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM J. Appl. Math. 46, 233–253 (1986)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Izhikevich, E.M.: Class 1 neural excitability, conventional synapses, weakly connected networks, and mathematical foundations of pulse-coupled models. IEEE Trans. Neural Netw. 10, 499–507 (1999)CrossRefGoogle Scholar
  9. 9.
    Kanamaru, T., Sekine, M.: Synchronized firings in the networks of class 1 excitable neurons with excitatory and inhibitory connections and their dependences on the forms of interactions. Neural Comput. 17, 1315–1338 (2005)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Kanamaru, T.: Chaotic pattern transitions in pulse neural networks. Neural Netw. 20, 781–790 (2007)CrossRefMATHGoogle Scholar
  11. 11.
    Kanamaru, T., Fujii, H., Aihara, K.: Deformation of attractor landscape via cholinergic presynaptic modulations: a computational study using a phase neuron model. PLoS ONE 8, e53854 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Mechanical Science and Engineering, School of Advanced EngineeringKogakuin UniversityHachioji-city, TokyoJapan

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