Simulation of neurocomputing based on the photophobic reactions of Euglena with optical feedback stimulation

To explore possible forms of unconventional computers that have high capacities for adaptation and exploration, we propose a new approach to developing a biocomputer based on the photophobic reactions of microbes (Euglena gracilis), and perform the Monte-Carlo simulation of Euglena-based neural network computing, involving virtual optical feedback to the Euglena cells. The photophobic reactions of Euglena are obtained experimentally, and incorporated in the simulation, together with a feedback algorithm with a modified Hopfield-Tank model for solving a 4-city traveling salesman problem. The simulation shows high performances in terms of (1) reaching one of the best solutions of the problem, and (2) searching for a number of solutions via dynamic transition among the solutions. This dynamic transition is attributed to the fluctuation of state variables, global oscillation through feedback instability, and the one-by-one change of state variables.

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