54 research outputs found
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
Exploiting Environmental Computation in a Multi-Agent Model of Slime Mould
Very simple organisms, such as the single-celled amoeboid slime mould
Physarum polycephalum possess no neural tissue yet, despite this, are known to
exhibit complex biological and computational behaviour. Given such limited
resources, can environmental stimuli play a role in generating the complexity
of slime mould behaviour? We use a multi-agent collective model of slime mould
to explore a two-way mechanism where the collective behaviour is influenced by
simulated chemical concentration gradient fields and, in turn, this behaviour
alters the spatial pattern of the concentration gradients. This simple
mechanism yields complex behaviour amid the dynamically changing gradient
profiles and suggests how the apparently intelligent response of the slime
mould could possibly be due to outsourcing of computation to the environment.Comment: 2014 ABBII International Symposium on Artificial, Biological and
Bio-Inspired Intelligence, 27-28th September, Rhodes, Greec
When the path is never shortest: a reality check on shortest path biocomputation
Shortest path problems are a touchstone for evaluating the computing
performance and functional range of novel computing substrates. Much has been
published in recent years regarding the use of biocomputers to solve minimal
path problems such as route optimisation and labyrinth navigation, but their
outputs are typically difficult to reproduce and somewhat abstract in nature,
suggesting that both experimental design and analysis in the field require
standardising. This chapter details laboratory experimental data which probe
the path finding process in two single-celled protistic model organisms,
Physarum polycephalum and Paramecium caudatum, comprising a shortest path
problem and labyrinth navigation, respectively. The results presented
illustrate several of the key difficulties that are encountered in categorising
biological behaviours in the language of computing, including biological
variability, non-halting operations and adverse reactions to experimental
stimuli. It is concluded that neither organism examined are able to efficiently
or reproducibly solve shortest path problems in the specific experimental
conditions that were tested. Data presented are contextualised with biological
theory and design principles for maximising the usefulness of experimental
biocomputer prototypes.Comment: To appear in: Adamatzky, A (Ed.) Shortest path solvers. From software
to wetware. Springer, 201
Towards a Physarum learning chip
Networks of protoplasmic tubes of organism Physarum polycehpalum are macro-scale structures which optimally span multiple food sources to avoid repellents yet maximize coverage of attractants. When data are presented by configurations of attractants and behaviour of the slime mould is tuned by a range of repellents, the organism preforms computation. It maps given data configuration into a protoplasmic network. To discover physical means of programming the slime mould computers we explore conductivity of the protoplasmic tubes; proposing that the network connectivity of protoplasmic tubes shows pathway-dependent plasticity. To demonstrate this we encourage the slime mould to span a grid of electrodes and apply AC stimuli to the network. Learning and weighted connections within a grid of electrodes is produced using negative and positive voltage stimulation of the network at desired nodes; low frequency (10 Hz) sinusoidal (0.5 V peak-to-peak) voltage increases connectivity between stimulated electrodes while decreasing connectivity elsewhere, high frequency (1000 Hz) sinusoidal (2.5 V peak-to-peak) voltage stimulation decreases network connectivity between stimulated electrodes. We corroborate in a particle model. This phenomenon may be used for computation in the same way that neural networks process information and has the potential to shed light on the dynamics of learning and information processing in non-neural metazoan somatic cell networks
Evaluation of French motorway network in relation to slime mould transport networks
© The Author(s) 2016. France has developed a high quality motorway system that has been rapidly rationalised and matured in the late 20th century yet has been founded on ancient, Roman infrastructures. The development of the motorway system is thus an iterative method associated with hierarchical ‘top-down’ processes taking into consideration factors such as population density, network demand, location of natural resources, civil engineering challenges and population growth. At the opposite extreme to this approach is the development of transport networks within simple biological systems which are typically decentralised, dynamic and emerge from simple, local and ‘bottom-up’ interactions. We examine the notion, and to what extent, that the structure of a complex motorway network could be predicted by the transport network of the single-celled slime mould Physarum polycephalum. This comparison is explored through its ability to ‘deduce’ the French motorway network in a series of analogue and digital experiments. We compare Physarum network and motorway network topology in relation to proximity graphs and assess the trade-off between connectivity and minimal network length with a bottom-up model of a virtual plasmodium. We demonstrate that despite the apparent complexity of the challenge Physarum can successfully apply its embodied intelligence to rationalise the motorway topology. We also demonstrate that such calculations prove challenging in the face of significant obstacles such as, mountainous terrain and may account for the missing route between Nice, Grenoble Avignon and Lyon. Finally, we discuss the topological findings with respect to circle and spoke city planning infrastructures and certain species of web-building spiders
Computers from plants we never made. Speculations
We discuss possible designs and prototypes of computing systems that could be
based on morphological development of roots, interaction of roots, and analog
electrical computation with plants, and plant-derived electronic components. In
morphological plant processors data are represented by initial configuration of
roots and configurations of sources of attractants and repellents; results of
computation are represented by topology of the roots' network. Computation is
implemented by the roots following gradients of attractants and repellents, as
well as interacting with each other. Problems solvable by plant roots, in
principle, include shortest-path, minimum spanning tree, Voronoi diagram,
-shapes, convex subdivision of concave polygons. Electrical properties
of plants can be modified by loading the plants with functional nanoparticles
or coating parts of plants of conductive polymers. Thus, we are in position to
make living variable resistors, capacitors, operational amplifiers,
multipliers, potentiometers and fixed-function generators. The electrically
modified plants can implement summation, integration with respect to time,
inversion, multiplication, exponentiation, logarithm, division. Mathematical
and engineering problems to be solved can be represented in plant root networks
of resistive or reaction elements. Developments in plant-based computing
architectures will trigger emergence of a unique community of biologists,
electronic engineering and computer scientists working together to produce
living electronic devices which future green computers will be made of.Comment: The chapter will be published in "Inspired by Nature. Computing
inspired by physics, chemistry and biology. Essays presented to Julian Miller
on the occasion of his 60th birthday", Editors: Susan Stepney and Andrew
Adamatzky (Springer, 2017
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