Amoeba-Based Nonequilibrium Neurocomputer Utilizing Fluctuations and Instability

  • Masashi Aono
  • Masahiko Hara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4618)


We employ a photosensitive amoeboid cell known as a model organism for studying cellular information processing, and construct an experimental system for exploring the amoeba’s processing ability of information on environmental light stimuli. The system enables to examine the amoeba’s solvability of various problems imposed by an optical feedback, as the feedback is implemented with a neural network algorithm. We discovered that the amoeba solves the problems by positively exploiting fluctuations and instability of its components. Thus, our system works as a neurocomputer having flexible properties. The elucidation of the amoeba’s dynamics may lead to the development of unconventional computing devices based on nonequilibrium media to utilize fluctuations and instability.


Bottom-up technology Physarum Optimization Chaos 


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  1. 1.
    Whitesides, G.M., Grzybowski, B.A.: Self-Assembly at all Scales. Science 295, 2418–2421 (2002)CrossRefGoogle Scholar
  2. 2.
    Adamatzky, A., De Lacy Costello, B., Asai, T.: Reaction-Diffusion Computers. Elsevier, UK (2005)Google Scholar
  3. 3.
    Steinbock, O., Toth, A., Showalter, K.: Navigating Complex Labyrinths: Optimal Paths from Chemical Waves. Science 267, 868–871 (1995)CrossRefGoogle Scholar
  4. 4.
    Motoike, I., Yoshikawa, K.: Information operations with an excitable field. Phys. Rev. E 59, 5354–5360 (1999)CrossRefGoogle Scholar
  5. 5.
    Nakagaki, T., Yamada, H., Ueda, T.: Interaction between cell shape and contraction pattern. Biophys. Chem. 84, 195–204 (2000)CrossRefGoogle Scholar
  6. 6.
    Nakagaki, T., Yamada, H., Toth, A.: Maze-solving by an amoeboid organism. Nature 407, 470 (2000)CrossRefGoogle Scholar
  7. 7.
    Ueda, T., Mori, Y., Nakagaki, T., Kobatake, Y.: Action spectra for superoxide generation and UV and visible light photoavoidance in plasmodia of Physarum polycephalum. Photochem. Photobiol. 48, 705–709 (1988)CrossRefGoogle Scholar
  8. 8.
    Hopfield, J.J., Tank, D.W.: Computing with neural circuits: A model. Science 233, 625–633 (1986)CrossRefGoogle Scholar
  9. 9.
    Aono, M., Gunji, Y.P.: Beyond input-output computings: Error-driven emergence with parallel non-distributed slime mold computer. BioSystems 71, 257–287 (2003)CrossRefGoogle Scholar
  10. 10.
    Aono, M., Gunji, Y.P.: Resolution of infinite-loop in hyperincursive and nonlocal cellular automata: Introduction to slime mold computing. In: Dubois, D.M. (ed.) Computing Anticipatory Systems: CASYS 2003. AIP conference proceedings, vol. 718, pp. 177–187 (2004)Google Scholar
  11. 11.
    Aono, M., Gunji, Y.P.: Material implementation of hyperincursive field on slime mold computer. In: Dubois, D.M. (ed.) Computing Anticipatory Systems: CASYS 2003. AIP conference proceedings, vol. 718, pp. 188–203 (2004)Google Scholar
  12. 12.
    Takamatsu, A., et al.: Time delay effect in a living coupled oscillator system with the plasmodium of Physarum polycephalum. Phys. Rev. Lett. 85, 2026 (2000)CrossRefGoogle Scholar
  13. 13.
    Takamatsu, A., et al.: Spatiotemporal symmetry in rings of coupled biological oscillators of Physarum plasmodial slime mold. Phys. Rev. Lett. 87, 078102 (2001)CrossRefGoogle Scholar
  14. 14.
    Takamatsu, A.: Spontaneous switching among multiple spatio-temporal patterns in three-oscillator systems constructed with oscillatory cells of true slime mold. Physica D 223, 180–188 (2006)CrossRefGoogle Scholar
  15. 15.
    Kaneko, K., Tsuda, I.: Complex Systems: Chaos and Beyond - A Constructive Approach with Applications in Life Sciences. Springer, New York (2001)zbMATHGoogle Scholar
  16. 16.
    Arbib, M.A. (ed.): The Handbook of Brain Theory and Neural Networks. The MIT Press, Cambridge, Massachusetts (2003)zbMATHGoogle Scholar
  17. 17.
    Aihara, K., Takabe, T., Toyoda, M.: Chaotic Neural Networks. Phys. Lett. A 144, 333–340 (1990)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Hasegawa, M., Ikeguchi, T., Aihara, K.: Combination of Chaotic Neurodynamics with the 2-opt Algorithm to Solve Traveling Salesman Problems. Phys. Rev. Lett. 79, 2344–2347 (1997)CrossRefGoogle Scholar
  19. 19.
    Tsuda, S., Zauner, K.P., Gunji, Y.P.: Robot Control with Biological Cells. In: Proceedings of Sixth International Workshop on Information Processing in Cells and Tissues, pp. 202–216 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Masashi Aono
    • 1
  • Masahiko Hara
    • 1
  1. 1.Local Spatio-temporal Functions Lab., Frontier Research System, RIKEN (The Institute of Physical and Chemical Research), Wako, Saitama 351-0198Japan

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