Combinatorial Optimization by Amoeba-Based Neurocomputer with Chaotic Dynamics

We demonstrate a computing system based on an amoeba of a true slime mold Physarum capable of producing rich spatiotemporal oscillatory behavior. Our system operates as a neurocomputer because an optical feedback control in accordance with a recurrent neural network algorithm leads the amoeba’s photosensitive branches to search for a stable configuration concurrently. We show our system’s capability of solving the traveling salesman problem. Furthermore, we apply various types of nonlinear time series analysis to the amoeba’s oscillatory behavior in the problem-solving process. The results suggest that an individual amoeba might be characterized as a set of coupled chaotic oscillators.

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