Modeling and hardware implementation of an amoeba-like cellular automaton.

Over the last few years, an increasing number of publications has shown that living organisms are very effective in finding solutions to complex mathematical problems which usually demand large computation resources. The plasmodium of the slime mould Physarum polycephalum is a successful example that has been used to solve path-finding problems on graphs and combinatorial problems. Cellular automata (CAs) computational model can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally (emergent computation). We developed a CA that models exactly the Physarum's behavior and applied it in finding the minimum-length path between two points in a labyrinth, as well as in solving a path-planning problem by guiding the development of adaptive networks, as in the case of the actual rail network of Tokyo. The CA results are in very good agreement with the computation results produced by the living organism experiments in both cases. Moreover, our CA hardware implementation results in faster and more effective computation performance, because of its inherent parallel nature. Consequently, our CA, implemented both in software and hardware, can serve as a powerful and low-cost virtual laboratory that models the slime mould Physarum's computation behavior.