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

A single-celled, multi-nucleated amoeboid organism, a plasmodium of the true slime mold Physarum polycephalum, can perform sophisticated computing by exhibiting complex spatiotemporal oscillatory dynamics while deforming its amorphous body. We previously devised an "amoeba-based computer (ABC)" to quantitatively evaluate the optimization capability of the amoeboid organism in searching for a solution to the traveling salesman problem (TSP) under optical feedback control. In ABC, the organism changes its shape to find a high quality solution (a relatively shorter TSP route) by alternately expanding and contracting its pseudopod-like branches that exhibit local photoavoidance behavior. The quality of the solution serves as a measure of the optimality of which the organism maximizes its global body area (nutrient absorption) while minimizing the risk of being illuminated (exposure to aversive stimuli). ABC found a high quality solution for the 8-city TSP with a high probability. However, it remains unclear whether intracellular communication among the branches of the organism is essential for computing. In this study, we conducted a series of control experiments using two individual cells (two single-celled organisms) to perform parallel searches in the absence of intercellular communication. We found that ABC drastically lost its ability to find a solution when it used two independent individuals. However, interestingly, when two individuals were prepared by dividing one individual, they found a solution for a few tens of minutes. That is, the two divided individuals remained correlated even though they were spatially separated. These results suggest the presence of a long-term memory in the intrinsic dynamics of this organism and its significance in performing sophisticated computing.

[1]  T. Ueda,et al.  Emergence and transitions of dynamic patterns of thickness oscillation of the plasmodium of the true slime mold Physarum polycephalum , 2008 .

[2]  T Fujii,et al.  Time delay effect in a living coupled oscillator system with the plasmodium of Physarum polycephalum. , 2000, Physical review letters.

[3]  Kazuyuki Aihara,et al.  Amoeba-based Chaotic Neurocomputing: Combinatorial Optimization by Coupled Biological Oscillators , 2009, New Generation Computing.

[4]  Atsuko Takamatsu,et al.  Spontaneous switching among multiple spatio-temporal patterns in three-oscillator systems constructed with oscillatory cells of true slime mold , 2006 .

[5]  Jeff Jones Approximating the Behaviours of Physarum polycephalum for the Construction and Minimisation of Synthetic Transport Networks , 2009, UC.

[6]  Kazuyuki Aihara,et al.  Greedy versus social: resource-competing oscillator network as a model of amoeba-based neurocomputer , 2011, Natural Computing.

[7]  A. Tero,et al.  Rules for Biologically Inspired Adaptive Network Design , 2010, Science.

[8]  Kazuyuki Aihara,et al.  Resource-Competing Oscillator Network as a Model of Amoeba-Based Neurocomputer , 2009, UC.

[9]  Masashi Aono,et al.  Beyond input-output computings: error-driven emergence with parallel non-distributed slime mold computer. , 2003, Bio Systems.

[10]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[11]  Toshiyuki Nakagaki,et al.  Physarum solver: A biologically inspired method of road-network navigation , 2006 .

[12]  D. Kessler,et al.  CHAPTER 5 – Plasmodial Structure and Motility , 1982 .

[13]  K. Aihara,et al.  Spontaneous mode switching in coupled oscillators competing for constant amounts of resources. , 2010, Chaos.

[14]  A. Tero,et al.  Minimum-risk path finding by an adaptive amoebal network. , 2007, Physical review letters.

[15]  Kazuyuki Aihara,et al.  Amoeba-based neurocomputing with chaotic dynamics , 2007, CACM.

[16]  Rolf Herken,et al.  The Universal Turing Machine: A Half-Century Survey , 1992 .

[17]  Klaus-Peter Zauner,et al.  Robot control with biological cells , 2007, Biosyst..

[18]  T. Nakagaki,et al.  Intelligence: Maze-solving by an amoeboid organism , 2000, Nature.

[19]  Michael Conrad,et al.  The price of programmability , 1988 .

[20]  Masashi Aono,et al.  Amoeba-based Neurocomputing for 8-City Traveling Salesman Problem , 2011, Int. J. Unconv. Comput..

[21]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[22]  Michael Conrad,et al.  On design principles for a molecular computer , 1985, CACM.

[23]  Tomonobu M. Watanabe,et al.  Input-output relationship in galvanotactic response of Dictyostelium cells , 2007, Biosyst..

[24]  E. F. Haskins,et al.  Cell Biology of Physarum and Didymium , 1983 .

[25]  M Conrad,et al.  Microscopic-macroscopic interface in biological information processing. , 1983, Bio Systems.

[26]  Masashi Aono,et al.  Robust and emergent Physarum logical-computing. , 2004, Bio Systems.

[27]  Werner Baumgarten,et al.  Plasmodial vein networks of the slime mold Physarum polycephalum form regular graphs. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[29]  Masashi Aono,et al.  Spontaneous deadlock breaking on amoeba-based neurocomputer , 2008, Biosyst..

[30]  Andrew Adamatzky,et al.  Developing Proximity Graphs by Physarum polycephalum: Does the Plasmodium Follow the Toussaint Hierarchy? , 2009, Parallel Process. Lett..

[31]  A. Dussutour,et al.  Amoeboid organism solves complex nutritional challenges , 2010, Proceedings of the National Academy of Sciences.

[32]  Song-Ju Kim,et al.  Tug-of-war model for the two-bandit problem: Nonlocally-correlated parallel exploration via resource conservation , 2010, Biosyst..

[33]  T. Ueda,et al.  ACTION SPECTRA FOR SUPEROXIDE GENERATION AND UV AND VISIBLE LIGHT PHOTOAVOIDANCE IN PLASMODIA OF Physarum polycephalum , 1988 .

[34]  Toshiyuki Nakagaki,et al.  Amoebae anticipate periodic events. , 2008, Physical review letters.

[35]  Masahiro Ueda,et al.  Noise generation, amplification and propagation in chemotactic signaling systems of living cells , 2008, Biosyst..

[36]  T. Fujii,et al.  Spatiotemporal symmetry in rings of coupled biological oscillators of Physarum plasmodial slime mold. , 2001, Physical review letters.

[37]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[38]  Tomohiro Shirakawa,et al.  An associative learning experiment using the plasmodium of Physarum polycephalum , 2011, Nano Commun. Networks.