The slime mold Physarum polycephalum is a huge single cell that has proved to be a fruitful material for designing novel computing architectures. The slime mold is capable of sensing tactile, chemical, and optical stimuli and converting them to characteristic patterns of its electrical potential oscillations. The electrical responses to stimuli may propagate along protoplasmic tubes for distances exceeding tens of centimeters, as impulses in neural pathways do. A slime mold makes decisions about its propagation direction based on information fusion from thousands of spatially extended protoplasmic loci, similarly to a neuron collecting information from its dendritic tree. The analogy is distant yet inspiring. We speculate on whether alternative—would-be—nervous systems can be developed and practically implemented from the slime mold. We uncover analogies between the slime mold and neurons, and demonstrate that the slime mold can play the roles of primitive mechanoreceptors, photoreceptors, and chemoreceptors; we also show how the Physarum neural pathways develop. The results constituted the first step towards experimental laboratory studies of nervous system implementation in slime molds.

The plasmodium of the acellular slime mold Physarum polycephalum is a gigantic single cell visible to the unaided eye. The cell shows a rich spectrum of behavioral patterns in response to environmental conditions. In a series of simple experiments we demonstrate how to make computing, sensing, and actuating devices from the slime mold. We show how to program living slime mold machines by configurations of repelling and attracting gradients and demonstrate the workability of the living machines on tasks of computational geometry, logic, and arithmetic.

We propose that the behavior of nonlinear media can be controlled automatically through evolutionary learning. By extension, forms of unconventional computing (viz., massively parallel nonlinear computers) can be realized by such an approach. In this initial study a light-sensitive subexcitable Belousov-Zhabotinsky reaction in which a checkerboard image, composed of cells of varying light intensity projected onto the surface of a thin silica gel impregnated with a catalyst and indicator, is controlled using a learning classifier system. Pulses of wave fragments are injected into the checkerboard grid, resulting in rich spatiotemporal behavior, and a learning classifier system is shown to be able to direct the fragments to an arbitrary position through dynamic control of the light intensity within each cell in both simulated and real chemical systems. Similarly, a learning classifier system is shown to be able to control the electrical stimulation of cultured neuronal networks so that they display elementary learning. Results indicate that the learned stimulation protocols identify seemingly fundamental properties of in vitro neuronal networks. Use of another learning scheme presented in the literature confirms that such fundamental behavioral characteristics of a given network must be considered in training experiments.