Mechanisms inducing parallel computation in a model of physarum polycephalum transport networks

The giant amoeboid organism true slime mould Physarum polycephalum dynamically adapts its body plan in response to changing environmental conditions and its protoplasmic transport network is used to distribute nutrients within the organism. These networks are efficient in terms of network length and network resilience and are parallel approximations of a range of proximity graphs and plane division problems. The complex parallel distributed computation exhibited by this simple organism has since served as an inspiration for intensive research into distributed computing and robotics within the last decade. P. polycephalum may be considered as a spatially represented parallel unconventional computing substrate, but how can this ‘computer’ be programmed? In this paper we examine and catalogue individual low-level mechanisms which may be used to induce network formation and adaptation in a multi-agent model of P. polycephalum. These mechanisms include those intrinsic to the model (particle sensor angle, rotation angle, and scaling parameters) and those mediated by the environment (stimulus location, distance, angle, concentration, engulfment and consumption of nutrients, and the presence of simulated light irradiation, repellents and obstacles). The mechanisms induce a concurrent integration of chemoattractant and chemorepellent gradients diffusing within the 2D lattice upon which the agent population resides, stimulating growth, movement, morphological adaptation and network minimisation. Chemoattractant gradients, and their modulation by the engulfment and consumption of nutrients by the model population, represent an efficient outsourcing of spatial computation. The mechanisms may prove useful in understanding the search strategies and adaptation of distributed organisms within their environment, in understanding the minimal requirements for complex adaptive behaviours, and in developing methods of spatially programming parallel unconventional computers and robotic devices

Representation of shape mediated by environmental stimuli in Physarum polycephalum and a multi-agent model

© 2015 Taylor & Francis. The slime mould Physarum polycephalum is known to construct protoplasmic transport networks which approximate proximity graphs by foraging for nutrients during its plasmodial life cycle stage. In these networks, nodes are represented by nutrients and edges are represented by protoplasmic tubes. These networks have been shown to be efficient in terms of length and resilience of the overall network to random damage. However, relatively little research has been performed in the potential for Physarum transport networks to approximate the overall shape of a data-set. In this paper we distinguish between connectivity and shape of a planar point data-set and demonstrate, using scoping experiments with plasmodia of P. polycephalum and a multi-agent model of the organism, how we can generate representations of the external and internal shapes of a set of points. As with proximity graphs formed by P. polycephalum, the behaviour of the plasmodium (real and model) is mediated by environmental stimuli. We further explore potential morphological computation approaches with the multi-agent model, presenting methods which approximate the Convex Hull and the Concave Hull. We demonstrate how a growth parameter in the model can be used to transition between Convex and Concave Hulls. These results suggest novel mechanisms of morphological computation mediated by environmental stimuli

Maze solvers demystified and some other thoughts

There is a growing interest towards implementation of maze solving in spatially-extended physical, chemical and living systems. Several reports of prototypes attracted great publicity, e.g. maze solving with slime mould and epithelial cells, maze navigating droplets. We show that most prototypes utilise one of two phenomena: a shortest path in a maze is a path of the least resistance for fluid and current flow, and a shortest path is a path of the steepest gradient of chemoattractants. We discuss that substrates with so-called maze-solving capabilities simply trace flow currents or chemical diffusion gradients. We illustrate our thoughts with a model of flow and experiments with slime mould. The chapter ends with a discussion of experiments on maze solving with plant roots and leeches which show limitations of the chemical diffusion maze-solving approach.Comment: This is a preliminary version of the chapter to be published in Adamatzky A. (Ed.) Shortest path solvers. From software to wetware. Springer, 201

A morphological adaptation approach to path planning inspired by slime mould

Path planning is a classic problem in computer science and robotics which has recently been implemented in unconventional computing substrates such as chemical reaction–diffusion computers. These novel computing schemes utilise the parallel spatial propagation of information and often use a two-stage method involving diffusive propagation to discover all paths and a second stage to highlight or visualise the path between two particular points in the arena. The true slime mould Physarum polycephalum is known to construct efficient transport networks between nutrients in its environment. These networks are continuously remodelled as the organism adapts its body plan to changing spatial stimuli. It can be guided towards attractant stimuli (nutrients, warm regions) and it avoids locations containing hazardous stimuli (light irradiation, repellents, or regions occupied by predatory threats). Using a particle model of slime mould we demonstrate scoping experiments which explore how path planning may be performed by morphological adaptation. We initially demonstrate simple path planning by a shrinking blob of virtual plasmodium between two attractant sources within a polygonal arena. We examine the case where multiple paths are required and the subsequent selection of a single path from multiple options. Collision-free paths are implemented via repulsion from the borders of the arena. Finally, obstacle avoidance is implemented by repulsion from obstacles as they are uncovered by the shrinking blob. These examples show proof-of-concept results of path planning by morphological adaptation which complement existing research on path planning in novel computing substrates