Simulation of Neurocomputing Based on Photophobic Reactions of Euglena: Toward Microbe–Based Neural Network Computing

  • Kazunari Ozasa
  • Masashi Aono
  • Mizuo Maeda
  • Masahiko Hara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5715)


In order to develop an adaptive computing system, we investigate microscopic optical feedback to a group of microbes (Euglena gracilis in this study) with a neural network algorithm, expecting that the unique characteristics of microbes, especially their strategies to survive/adapt against unfavorable environmental stimuli, will explicitly determine the temporal evolution of the microbe-based feedback system. The photophobic reactions of Euglena are extracted from experiments, and built in the Monte-Carlo simulation of a microbe-based neurocomputing. The simulation revealed a good performance of Euglena-based neurocomputing. Dynamic transition among the solutions is discussed from the viewpoint of feedback instability.


Microbe Euglena gracilis Feedback instability Neural network Oscillation Neurocomputing simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Whittakera, J., Garsidea, S., Lindveldb, K.: Tracking and predicting a network traffic process. Int. J. Forecasting 13, 51–61 (1997)CrossRefGoogle Scholar
  2. 2.
    Jozsa, B.G., Makai, M.: On the solution of reroute sequence planning problem in MPLS networks. Comput. Networks 42, 199–210 (2003)CrossRefzbMATHGoogle Scholar
  3. 3.
    Hopfield, J.J., Tank, D.W.: Computing with Neural Circuits: A model. Science 233, 625–633 (1986)CrossRefzbMATHGoogle Scholar
  4. 4.
    Wasserman, P.D.: Neural Computing: Theory and Practice. Van Nostrand Reinhold Co., New York (1989)Google Scholar
  5. 5.
    Egmont-Petersen, M., Ridder, D., Handels, H.: Image processing with neural networks - a review. Pattern Recognition 35, 2279–2301 (2002)CrossRefzbMATHGoogle Scholar
  6. 6.
    Nakagaki, T., Yamada, H., Toth, A.: Intelligence: Maze-Solving by an Amoeboid Organism. Nature 407, 470–470 (2000)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Aono, M., Gunji, Y.-P.: Beyond Input-Output Computings: Error-Driven Emergence with Parallel No-Distributed Slime Mold Computer. BioSystems 71, 257–287 (2003)CrossRefGoogle Scholar
  9. 9.
    Aono, M., Hara, M., Aihara, K.: Amoeba-based Neurocomputing with Chaotic Dynamics. Commum. ACM 50, 69–72 (2007)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Diehn, B.: Phototaxis and Sensory Transduction in Euglena. Science 181, 1009–1015 (1973)CrossRefGoogle Scholar
  12. 12.
    Creutz, C., Colombetti, G., Diehn, B.: Photophobic Behavioral Responses of Euglena in a Light Intensity Gradient and the Kinetics of Photoreceptor Pigment Interconversions. Photochem. Photobiol. 27, 611–616 (1978)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kazunari Ozasa
    • 1
  • Masashi Aono
    • 1
  • Mizuo Maeda
    • 1
  • Masahiko Hara
    • 1
  1. 1.RIKEN (The Institute of Physical and Chemical Research)SaitamaJapan

Personalised recommendations