Resource-Competing Oscillator Network as a Model of Amoeba-Based Neurocomputer

  • Masashi Aono
  • Yoshito Hirata
  • Masahiko Hara
  • Kazuyuki Aihara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5715)


An amoeboid organism, Physarum, exhibits rich spatiotemporal oscillatory behavior and various computational capabilities. Previously, the authors created a recurrent neurocomputer incorporating the amoeba as a computing substrate to solve optimization problems. In this paper, considering the amoeba to be a network of oscillators coupled such that they compete for constant amounts of resources, we present a model of the amoeba-based neurocomputer. The model generates a number of oscillation modes and produces not only simple behavior to stabilize a single mode but also complex behavior to spontaneously switch among different modes, which reproduces well the experimentally observed behavior of the amoeba. To explore the significance of the complex behavior, we set a test problem used to compare computational performances of the oscillation modes. The problem is a kind of optimization problem of how to allocate a limited amount of resource to oscillators such that conflicts among them can be minimized. We show that the complex behavior enables to attain a wider variety of solutions to the problem and produces better performances compared with the simple behavior.


Physarum Amoeba-based Computing Resource Allocation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nakagaki, T., Yamada, H., Toth, A.: Maze-Solving by an Amoeboid Organism. Nature 407, 470 (2000)CrossRefGoogle Scholar
  2. 2.
    Tero, A., Kobayashi, R., Nakagaki, T.: Physarum solver: A biologically inspired method of road-network navigation. Physica A 363, 115–119 (2006)CrossRefGoogle Scholar
  3. 3.
    Nakagaki, T., Iima, M., Ueda, T., Nishiura, Y., Saigusa, T., Tero, A., Kobayashi, R., Showalter, K.: Minimum-risk path finding by an adaptive amoebal network. Phys. Rev. Lett. 99, 068104 (2007)CrossRefGoogle Scholar
  4. 4.
    Saigusa, T., Tero, A., Nakagaki, T., Kuramoto, Y.: Amoebae anticipate periodic events. Phys. Rev. Lett. 100, 018101 (2008)CrossRefGoogle Scholar
  5. 5.
    Takamatsu, A., Fujii, T., Endo, I.: Time delay effect in a living coupled oscillator system with the plasmodium of Physarum polycephalum. Phys. Rev. Lett. 85, 2026–2029 (2000)CrossRefGoogle Scholar
  6. 6.
    Takamatsu, A., Tanaka, R., Yamada, H., Nakagaki, T., Fujii, T., Endo, I.: Spatiotemporal symmetry in rings of coupled biological oscillators of Physarum plasmodial slime mold. Phys. Rev. Lett. 87, 078102 (2001)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    Aono, M., Hara, M.: Amoeba-based Nonequilibrium Neurocomputer Utilizing Fluctuations and Instability. In: Aki, S.G., Calude, C.S., Dinneen, M.J., Rozenberg, G., Wareham, H.T. (eds.) UC 2007. LNCS, vol. 4618, pp. 41–54. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Aono, M., Hara, M., Aihara, K.: Amoeba-based Neurocomputing with Chaotic Dynamics. Commun. ACM 50(9), 69–72 (2007)CrossRefGoogle Scholar
  11. 11.
    Aono, M., Hara, M.: Spontaneous deadlock breaking on amoeba-based neurocomputer. BioSystems 91, 83–93 (2008)CrossRefGoogle Scholar
  12. 12.
    Aono, M., Hirata, Y., Hara, M., Aihara, K.: Amoeba-based chaotic neurocomputing: Combinatorial optimization by coupled biological oscillators. New Generation Computing 27, 129–157 (2009)CrossRefzbMATHGoogle Scholar
  13. 13.
    Aono, M., Hara, M., Aihara, K., Munakata, T.: Amoeba-based emergent computing: Combinatorial optimization and autonomous meta-problem solving. International Journal of Unconventional Computing (in press)Google Scholar
  14. 14.
    Kuznetsov, Y.A.: Elements of applied bifurcation theory. Springer, New York (2004)CrossRefzbMATHGoogle Scholar
  15. 15.
    Hirata, Y., Aono, M., Hara, M., Aihara, K.: Spontaneous mode switching in coupled oscillators competing for constant amounts of resources (submitted)Google Scholar
  16. 16.
    Hopfield, J.J., Tank, D.W.: Computing with Neural Circuits: A model. Science 233, 625–633 (1986)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Masashi Aono
    • 1
  • Yoshito Hirata
    • 2
  • Masahiko Hara
    • 1
  • Kazuyuki Aihara
    • 2
    • 3
  1. 1.Flucto-Order Functions Asian Collaboration Team, Advanced Science InstituteRIKENWakoJapan
  2. 2.Institute of Industrial ScienceThe University of Tokyo, Meguro-kuTokyoJapan
  3. 3.ERATO Aihara Complexity Modelling Project, JST, Shibuya-kuTokyoJapan

Personalised recommendations