Skip to main content
Log in

A power law in the exploratory behavior of the Physarum plasmodium

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

The plasmodium of Physarum polycephalum is a unicellular giant amoeba that grows up to macroscopic scale under appropriate condition, and is known to its computational abilities. In this study, we tried to observe the long-term exploratory behavior of the plasmodium in an open environment and to evaluate its efficiency. For this purpose, we developed an experimental system with an extendable substrate. As a result of the experiment, we found that the frequency distribution of the speed of plasmodial locomotion is fitted by power function. By simulation, we further tried to estimate the efficiency of the exploration implemented with the patterns from the plasmodium, and confirmed that it is actually effective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Nakagaki T, Yamada H, Tóth Á (2000) Maze-solving by an amoeboid organism. Nature 407:479

    Article  Google Scholar 

  2. Shirakawa T, Gunji YP (2007) Emergence of morphological order in the network formation of Physarum polycephalum. Biophys Chem 128:253–260

    Article  Google Scholar 

  3. Aono M, Hara M (2008) Spontaneous Deadlock breaking on amoeba-based neurocomputer. Biosystems 91:83–93

    Article  Google Scholar 

  4. Shirakawa T, Gunji YP, Miyake M (2011) An associative learning experiment using the plasmodium of Physarum polycephalum. Nano Commun Netw 2:99–105

    Article  Google Scholar 

  5. Wolke A, Niemeyer F, Achenbach F (1987) Geotactic behavior of the acellular myxomycete Physarum polycephalum. Cell Biol Int Rep 11:525–528

    Article  Google Scholar 

  6. Anderson JD (1951) Galvanotaxis of slime mold. J Gen Physiol 35:1–16

    Article  Google Scholar 

  7. Shirakawa T, Konagano R, Inoue K (2012) Novel taxis of the Physarum plasmodium and a taxis-based simulation of Physarum swarm. In: Proceeding of the joint 6th international conference on soft computing and intelligent systems and 13th international symposium on advanced intelligent systems, Kobe, Hyogo Japan, Nov 20–24, 2012, pp 296–300

  8. Tero A, Saigusa T, Ito K, Bebber DP, Fricker MD, Yumiki K, Kobayashi R, Nakagaki T (2010) Rules for biologically inspired adaptive network design. J Theor Biol 256:29–44

    MathSciNet  MATH  Google Scholar 

  9. Viswanathan GM, Bartumeus F, Buldyrev SV, Catalan J, Fulco UL, Havlin S, Luz MGE, Lyra ML, Raposo EP, Stanley HE (2002) Lévy flight random searches in biological phenomena. Phys A 314:208–213

    Article  MathSciNet  MATH  Google Scholar 

  10. Rasband WS (1997–2012) ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA. http://imagej.nih.gov/ij/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tomohiro Shirakawa.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shirakawa, T., Sato, H. & Nishida, M. A power law in the exploratory behavior of the Physarum plasmodium. Artif Life Robotics 21, 195–200 (2016). https://doi.org/10.1007/s10015-016-0269-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-016-0269-6

Keywords

Navigation