Amoeba-based computing for traveling salesman problem: Long-term correlations between spatially separated individual cells of Physarum polycephalum

BioSystems

Volumn 112, Issue 1, 2013, Pages 1-10

  Zhu, Liping ^a   Aono, Masashi ^a,b   Kim, Song Ju ^b   Hara, Masahiko ^a,b  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^b RIKEN   (Japan)

Author keywords

Chaos; Combinatorial optimization; Coupled oscillators; Natural computing; Neural network; Spatiotemporal dynamics

Indexed keywords

ABSORPTION; ARTIFICIAL NEURAL NETWORK; CELL ORGANELLE; COMPUTER SIMULATION; FUNGUS; MEMORY; OPTIMIZATION; PROTOZOAN; RISK FACTOR; SPATIAL ANALYSIS; SPATIOTEMPORAL ANALYSIS;

ABSORPTION; AMOEBA BASED COMPUTING; ARTICLE; CELL DIVISION; FEEDBACK SYSTEM; ILLUMINATION; LONG TERM MEMORY; MATHEMATICAL ANALYSIS; MICROBIAL IDENTIFICATION; NONHUMAN; OSCILLATION; PHYSARUM POLYCEPHALUM; PROBABILITY; PROCESS OPTIMIZATION;

MYXOGASTRIA; PHYSARUM POLYCEPHALUM; PSEUDOPODA;

EID: 84875762392     PISSN: 03032647     EISSN: 18728324     Source Type: Journal    
DOI: 10.1016/j.biosystems.2013.01.008     Document Type: Article

References (39)

1. Adamatzky A. Developing proximity graphs by Physarum polycephalum: does the plasmodium follow Toussaint hierarchy?. Parallel Process. Lett. 2008, 19:105-127.
2. Aono M., Gunji Y.-P. Beyond input-output computings: error-driven emergence with parallel non-distributed slime mold computer. Biosystems 2003, 71:257-287.
3. Aono M., Hara M., Aihara K. Amoeba-based neurocomputing with chaotic dynamics. Commun. ACM 2007, 50(9):69-72.
4. Aono M., Hara M. Spontaneous deadlock breaking on amoeba-based neurocomputer. Biosystems 2008, 91:83-93.
5. Aono M., Hirata Y., Hara M., Aihara K. Amoeba-based chaotic neurocomputing: combinatorial optimization by coupled biological oscillators. New Gener. Comput. 2009, 27:129-157.
6. Aono, M., Hirata, Y., Hara, Y., Aihara, K., 2009. Resource-competing oscillator network as a model of amoeba-based neurocomputer. In: Calude, C. (Ed.), Unconventional Computation. Lecture Notes in Computer Science 5715, pp. 56-69.
7. Aono M., Zhu L., Hara M. Amoeba-based neurocomputing for 8-city traveling salesman problem. Int. J. Unconventional Comput. 2011, 7(6):463-480.
8. Aono M., Zhu L., Kim S.-J., Hara M. Performance enhancement of amoeba-based neurocomputer for 8-city traveling salesman problem. Proceedings of the 2011 International Symposium on Nonlinear Theory and its Applications (NOLTA 2011) 2011, 104-107.
9. Aono M., Hirata Y., Hara M., Aihara K. Greedy versus social: resource-competing oscillator network as a model of amoeba-based neurocomputer. Nat. Comput. 2011, 10:1219-1244.
10. The Handbook of Brain Theory and Neural Networks 2003, The MIT Press, Cambridge, MA. 2nd ed. M.A. Arbib (Ed.).
11. Baumgarten W., Ueda T., Hauser M.J.B. Plasmodial vein networks of the slime mold Physarum polycephalum form regular graphs. Phys. Rev. E 2010, 82:046113.
12. Conrad M. Microscopic-macroscopic interface in biological information processing. Biosystems 1984, 16:345-363.
13. Conrad M. On design principles for a molecular computer. Commun. ACM 1985, 28(5):464-480.
14. Conrad, M., 1994. The price of programmability. In: Rolf Herken (Ed.), The Universal Turing Machine A Half-Century Survey. Springer-Verlag, Wien, pp. 261-281.
15. Dussutoura A., Lattyb T., Beekmanb M., Simpson S.J. Amoeboid organism solves complex nutritional challenges. Proc. Natl. Acad. Sci. U.S.A. 2011, 107(10):4607-4611.
16. Garey M.R., Johnson D.S. Computers and Intractability: A Guide to the Theory of NP-Completeness 1979, W. H. Freeman and Co., New York.
17. Hirata Y., Aono M., Hara M., Aihara K. Spontaneous mode switching in coupled oscillators competing for constant amounts of resources. Chaos 2010, 20:013117.
18. Hopfield J.J., Tank D.W. Computing with neural circuits: a model. Science 1986, 233:625-633.
19. Jones, J., 2009. Approximating the behaviours of Physarum polycephalum for the construction and minimisation of synthetic transport networks. In: Unconventional Computation. Lecture Notes in Computer Science 5715, Springer, pp. 191-208.
20. Kessler D. Plasmodial Structure and Motility. Cell Biology of Physarum and Didymium, vol. 1. 1982, 145-208. Academic Press, Inc., New York. H.C. Aldrich, J.W. Daniel (Eds.).
21. Kim S.-J., Aono M., Hara M. Tug-of-war model for two-bandit problem: nonlocally correlated parallel exploration via resource conservation. Biosystems 2010, 101:29-36.
22. Lin S., Kernighan B. An effective heuristic algorithm for the traveling-salesman problem. Oper. Res. 1973, 498-516.
23. Nakagaki T., Yamada H., Toth A. Maze-solving by an amoeboid organism. Nature 2000, 407:470.
24. 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. 2007, 99:068104.
25. Or, I., 1967. Traveling salesman-type combinatorial problems and their relation to the logistics of regional blood banking, Ph.D. Thesis. Department of Industrial Engineering and Management Science, Northwestern University, Evanston, IL.
26. Saigusa T., Tero A., Nakagaki T., Kuramoto Y. Amoebae anticipate periodic events. Phys. Rev. Lett. 2008, 100:018101.
27. Sato M.J., Ueda M., Takagi H., Watanabe T.M., Yanagida T., Ueda M. Input-output relationship in galvanotactic response of Dictyostelium cells. Biosystems 2007, 88:261-270.
28. Shibata T., Ueda M. Noise generation, amplification and propagation in chemotactic signaling systems of living cells. Biosystems 2008, 93:126-132.
29. Shirakawa T., Gunji Y.-P., Miyake Y. An associative learning experiment using the plasmodium of Physarum polycephalum. Nano Commun. Networks 2011, 2:99-105.
30. Takagi S., Ueda T. Emergence and transitions of dynamic patterns of thickness oscillation of the plasmodium of the true slime mold Physarum polycephalum. Physica D 2008, 237:420-427.
31. Takamatsu A., Fujii T., Endo I. Time delay effect in a living coupled oscillator system with the plasmodium of Physarum polycephalum. Phys. Rev. Lett. 2000, 85(9):2026-2029.
32. 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. 2001, 87(7):078102.
33. Takamatsu A. Spontaneous switching among multiple spatio-temporal patterns in three-oscillator systems constructed with oscillatory cells of true slime mold. Physica D 2006, 223:180-188.
34. Tero A., Kobayashi R., Nakagaki T. Physarum solver: a biologically inspired method of road-network navigation. Physica A 2006, 363:115-119.
35. Tero A., Takagi S., Saigusa T., Ito K., Bebber D.P., Fricker M.D., Yumiki K., Kobayashi R., Nakagaki T. Rules for biologically inspired adaptive network design. Science 2010, 327(5964):439-442.
36. Tsuda S., Aono M., Gunji Y.-P. Robust and emergent Physarum logical-computing. Biosystems 2004, 73:45-55.
37. Tsuda S., Zauner K.P., Gunji Y.-P. Robot control with biological cells. Biosystems 2007, 87:215-223.
38. 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. 1988, 48:705-709.
39. Zhu L., Aono M., Kim S.-J., Hara M. Problem-size scalability of amoeba-based neurocomputer for traveling salesman problem. Proceedings of the 2011 International Symposium on Nonlinear Theory and its Applications (NOLTA 2011) 2011, 108-111.