68 research outputs found
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
Localization dynamics in a binary two-dimensional cellular automaton: the Diffusion Rule
We study a two-dimensional cellular automaton (CA), called Diffusion Rule
(DR), which exhibits diffusion-like dynamics of propagating patterns. In
computational experiments we discover a wide range of mobile and stationary
localizations (gliders, oscillators, glider guns, puffer trains, etc), analyze
spatio-temporal dynamics of collisions between localizations, and discuss
possible applications in unconventional computing.Comment: Accepted to Journal of Cellular Automat
Approximating Mexican highways with slime mould
Plasmodium of Physarum polycephalum is a single cell visible by unaided eye.
During its foraging behavior the cell spans spatially distributed sources of
nutrients with a protoplasmic network. Geometrical structure of the
protoplasmic networks allows the plasmodium to optimize transport of nutrients
between remote parts of its body. Assuming major Mexican cities are sources of
nutrients how much structure of Physarum protoplasmic network correspond to
structure of Mexican Federal highway network? To find an answer undertook a
series of laboratory experiments with living Physarum polycephalum. We
represent geographical locations of major cities by oat flakes, place a piece
of plasmodium in Mexico city area, record the plasmodium's foraging behavior
and extract topology of nutrient transport networks. Results of our experiments
show that the protoplasmic network formed by Physarum is isomorphic, subject to
limitations imposed, to a network of principle highways. Ideas and results of
the paper may contribute towards future developments in bio-inspired road
planning
Computing Naturally in the Billiard Ball Model
Fredkin's Billiard Ball Model (BBM) is considered one of the fundamental
models of collision-based computing, and it is essentially based on elastic
collisions of mobile billiard balls. Moreover, fixed mirrors or reflectors are
brought into the model to deflect balls to complete the computation. However,
the use of fixed mirrors is "physically unrealistic" and makes the BBM not
perfectly momentum conserving from a physical point of view, and it imposes an
external architecture onto the computing substrate which is not consistent with
the concept of "architectureless" in collision-based computing. In our initial
attempt to reduce mirrors in the BBM, we present a class of gates: the
m-counting gate, and show that certain circuits can be realized with few
mirrors using this gate. We envisage that our findings can be useful in future
research of collision-based computing in novel chemical and optical computing
substrates.Comment: 10 pages, 7 figure
A physarum-inspired approach to supply chain network design
A supply chain is a system which moves products from a supplier to customers, which plays a very important role in all economic activities. This paper proposes a novel algorithm for a supply chain network design inspired by biological principles of nutrients’ distribution in protoplasmic networks of slime mould Physarum polycephalum. The algorithm handles supply networks where capacity investments and product flows are decision variables, and the networks are required to satisfy product demands. Two features of the slime mould are adopted in our algorithm. The first is the continuity of flux during the iterative process, which is used in real-time updating of the costs associated with the supply links. The second feature is adaptivity. The supply chain can converge to an equilibrium state when costs are changed. Numerical examples are provided to illustrate the practicality and flexibility of the proposed method algorithm
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
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
Evolving spiking networks with variable resistive memories
Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types. © 2014 by the Massachusetts Institute of Technology
Maze Solving Using Fatty Acid Chemistry
This
study demonstrates that the Marangoni flow in a channel network
can solve maze problems such as exploring and visualizing the shortest
path and finding all possible solutions in a parallel fashion. The
Marangoni flow is generated by the pH gradient in a maze filled with
an alkaline solution of a fatty acid by introducing a hydrogel block
soaked with an acid at the exit. The pH gradient changes the protonation
rate of fatty acid molecules, which translates into the surface tension
gradient at the liquid–air interface through the maze. Fluid
flow maintained by the surface tension gradient (Marangoni flow) can
drag water-soluble dye particles toward low pH (exit) at the liquid–air
interface. Dye particles placed at the entrance of the maze dissolve
during this motion, thus exhibiting and finding the shortest path
and all possible paths in a maze
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