Amoeba-based Chaotic Neurocomputing: Combinatorial Optimization by Coupled Biological Oscillators
- 180 Downloads
We demonstrate a neurocomputing system incorporating an amoeboid unicellular organism, the true slime mold Physarum, known to exhibit rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. Introducing optical feedback applied according to a recurrent neural network model, we induce that the amoeba’s photosensitive branches grow or degenerate in a network-patterned chamber in search of an optimal solution to the traveling salesman problem (TSP), where the solution corresponds to the amoeba’s stably relaxed configuration (shape), in which its body area is maximized while the risk of being illuminated is minimized.Our system is capable of reaching the optimal solution of the four-city TSP with a high probability. Moreover, our system can find more than one solution, because the amoeba can coordinate its branches’ oscillatory movements to perform transitional behavior among multiple stable configurations by spontaneously switching between the stabilizing and destabilizing modes. We show that the optimization capability is attributable to the amoeba’s fluctuating oscillatory movements. Applying several surrogate data analyses, we present results suggesting that the amoeba can be characterized as a set of coupled chaotic oscillators.
Keywords:Multilevel Self-Organization Coupled Oscillators Chaotic Neural Network Chaotic Itinerancy Self-Disciplined Computing
Unable to display preview. Download preview PDF.
- 10.Aono, M. and Hara, M. “Dynamic Transition among Memories on Neurocomputer Composed of Amoeboid Cell with Optical Feedback,” in Proceedings of The 2006 International Symposium on Nonlinear Theory and its Applications, pp. 763-766, 2006.Google Scholar
- 11.Aono, M. and Hara, M., “Amoeba-based Nonequilibrium Neurocomputer Utilizing Fluctuations and Instability,” in UC 2007, LNCS, 4618 (Aki, S. G., et al. eds.), pp. 41-54. Springer-Verlag, Berlin, 2007.Google Scholar
- 14.Aono, M., Hara, M., Aihara, K. and Munakata, T, “Amoeba-Based Emergent Computing: Combinatorial Optimization and Autonomous Meta-Problem Solving,” to appear in International Journal of Unconventional Computing, 2009.Google Scholar
- 16.Tsuda, S., Zauner, K. P. and 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
- 19.Ohl, C. and Stockem, W., “Distribution and Function of Myosin II as a Main Constituent of the Microfilament System in Physarum Polycephalum,” Europ. J. Protistol, 31, pp. 208-222, 1995.Google Scholar
- 24.Arbib, M. A. (ed.). The Handbook of Brain Theory and Neural Networks (Second Edition), The MIT Press, Cambridge, Massachusetts, 2003.Google Scholar
- 26.Holland, J. H., Adaptation in Natural and Artificial Systems (Second Edition), The MIT Press, Cambridge, Massachusetts, 1992.Google Scholar
- 28.Munakata, T.,Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More (Second Edition), Springer-Verlag, Berlin, 2008.Google Scholar
- 29.Garey, M. R. and Johnson, D. S., Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman and co., New York, 1979.Google Scholar
- 31.Ott, E., Chaos in Dynamical Systems (2nd edition), Cambridge University Press, Cambridge, 2002.Google Scholar
- 47.Adamatzky, A., De Lacy Costello, B. and Asai, T. Reaction-Diffusion Computers, Elsevier, Amsterdam, 2005.Google Scholar