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Über dieses Buch

The unconventional computing is a niche for interdisciplinary science, cross-bred of computer science, physics, mathematics, chemistry, electronic engineering, biology, material science and nanotechnology. The aims of this book are to uncover and exploit principles and mechanisms of information processing in and functional properties of physical, chemical and living systems to develop efficient algorithms, design optimal architectures and manufacture working prototypes of future and emergent computing devices.

This second volume presents experimental laboratory prototypes and applied computing implementations. Emergent molecular computing is presented by enzymatic logical gates and circuits, and DNA nano-devices. Reaction-diffusion chemical computing is exemplified by logical circuits in Belousov-Zhabotinsky medium and geometrical computation in precipitating chemical reactions. Logical circuits realised with solitons and impulses in polymer chains show advances in collision-based computing. Photo-chemical and memristive devices give us a glimpse on hot topics of a novel hardware. Practical computing is represented by algorithms of collective and immune-computing and nature-inspired optimisation. Living computing devices are implemented in real and simulated cells, regenerating organisms, plant roots and slime mould.

The book is the encyclopedia, the first ever complete authoritative account, of the theoretical and experimental findings in the unconventional computing written by the world leaders in the field. All chapters are self-contains, no specialist background is required to appreciate ideas, findings, constructs and designs presented. This treatise in unconventional computing appeals to readers from all walks of life, from high-school pupils to university professors, from mathematicians, computers scientists and engineers to chemists and biologists.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Implementing Molecular Logic Gates, Circuits, and Cascades Using DNAzymes

The programmable nature of DNA chemistry makes it an attractive framework for the implementation of unconventional computing systems. Our early work in this area was among the first to use oligonucleotide-based logic gates to perform computations in a bulk solution. In this chapter we chart the development of this technology over the course of almost 15 years. We review our work on the implementation of DNA-based logic gates and circuits, which we have used to demonstrate digital logic circuits, autonomous game-playing automata, trainable systems and, more recently, decision-making circuits with potential diagnostic applications.

Matthew R. Lakin, Milan N. Stojanovic, Darko Stefanovic

Chapter 2. Enzyme-Based Reversible Logic Gates Operated in Flow Cells

Reversible logic gates, such as Feynman gate (Controlled NOT), Double Feynman gate, Toffoli gate and Peres gate, with 2-input/2-output and 3-input/3-output channels, were realized using reactions biocatalyzed by enzymes and performed in flow systems. The flow devices were constructed using a modular approach, where each flow cell was modified with one enzyme that biocatalyzed one chemical reaction. Assembling the biocatalytic flow cells in different networks, with different pathways for transporting the reacting species, allowed the multi-step processes mimicking various reversible logic gates. The chapter emphasizes “logic” reversibility but not the “physical” reversibility of the constructed systems. Their advantages and disadvantages are discussed and potential use in biosensing systems, rather than in computing devices, is suggested.

Evgeny Katz, Brian E. Fratto

Chapter 3. Modeling and Modifying Response of Biochemical Processes for Biocomputing and Biosensing Signal Processing

Processes involving multi-input multi-step reaction cascades are used in developing novel biosensing, biocomputing, and decision making systems. In various applications different changes in responses of the constituent processing steps (reactions) in a cascade are desirable in order to allow control of the system’s response. Here we consider conversion of convex response to sigmoid by “intensity filtering,” as well as “threshold filtering,” and we offer a general overview of this field of research. Specifically, we survey rate equation modelling that has been used for enzymatic reactions. This allows us to design modified biochemical processes as “network components” with responses desirable in applications.

Sergii Domanskyi, Vladimir Privman

Chapter 4. Sensing Parameters of a Time Dependent Inflow with an Enzymatic Reaction

Functionality of living organisms is based on decision making. Chemical reactions stand behind information processing in biological systems. Therefore, it is interesting to consider reaction models that show ability to make decisions by evolving towards significantly different states, depending on conditions at which those reactions proceed. It has been recently demonstrated that a system exhibiting cooperative or sigmoidal response with respect to the input exhibits the potential to function as a discriminator of the amplitude or the frequency of its external periodic perturbation. Here we consider a few models of allosteric enzymatic reactions and discuss their applicability for sensing the frequency or the amplitude of the time dependent input in a form of reagent inflow. The output is coded in a product oscillation type. On the basis of numerical simulations we compare results for a full reaction model with its reduced, easier to analyze version.

Jerzy Gorecki, Joanna N. Gorecka, Bogdan Nowakowski, Hiroshi Ueno, Tatsuaki Tsuruyama, Kenichi Yoshikawa

Chapter 5. Combinational Logic Circuit Based on BZ Reaction

As a basic unit of large scale integration, combinational logic circuit is very important in the development of digital computer. Chemical computation should have the ability to replicate the basic function of combinational logic circuit in BZ medium, in order to realize chemical computer. In this chapter, we design and implement different types of combinational logic circuits from two perspectives. On one hand, based on the basic chemical processors and logic gates, the cascade method is applied to achieve the functions of multi-bit combinational logic by using low-bit logic circuits. On the other hand, a universal method is put forward to construct combinational logic circuits according to their sum-of-products expressions. Simulation results demonstrate the effectiveness of the two construction methods, as well as all the combinational logical circuits designed in this chapter. We believe that the realization of combinational logical circuits will be helpful to fulfil other logic and arithmetic functions, and ultimately can bring great potential applications for the implementation of chemical computer and other intelligent systems.

Mingzhu Sun, Xin Zhao

Chapter 6. Associative Memory in Reaction-Diffusion Chemistry

Unconventional computing paradigms are typically very difficult to program. By implementing efficient parallel control architectures such as artificial neural networks, we show that it is possible to program unconventional paradigms with relative ease. The work presented implements correlation matrix memories (a form of artificial neural network based on associative memory) in reaction-diffusion chemistry, and shows that implementations of such artificial neural networks can be trained and act in a similar way to conventional implementations.

James Stovold, Simon O’Keefe

Chapter 7. Calculating Voronoi Diagrams Using Chemical Reactions

This chapter overviews work on the use of simple chemical reactions to calculate Voronoi diagrams and undertake other related geometric calculations. This work highlights that this type of specialised chemical processor is a model example of a parallel processor. For example increasing the complexity of the input data within a given area does not increase the computation time. These processors are also able to calculate two or more Voronoi diagrams in parallel. Due to the specific chemical reactions involved and the relative strength of reaction with the substrate (and cross-reactivity with the products) these processors are also capable of calculating Voronoi diagrams sequentially from distinct chemical inputs. The chemical processors are capable of calculating a range of generalised Voronoi diagrams (either from circular drops of chemical or other geometric shapes made from adsorbent substrates soaked in reagent), skeletonisation of planar shapes and weighted Voronoi diagrams (e.g. additively weighted Voronoi diagrams, multiplicatively weighted crystal growth Voronoi diagrams). The chapter also discusses some limitations of these processors. These chemical processors constitute a class of pattern forming reactions which have parallels with those observed in natural systems. It is possible that specialised chemical processors of this general type could be useful for synthesising functional structured materials.

Ben De Lacy Costello, Andrew Adamatzky

Chapter 8. Light-Sensitive Belousov–Zhabotinsky Computing Through Simulated Evolution

Many forms of unconventional computing, i.e., massively parallel non-linear computers, can be realised through simulated evolution. That is, the behaviour of non-linear media can be controlled automatically and the structural design of the media optimized through the nature-inspired machine learning approach. This chapter describes work using the Belousov–Zhabotinsky reaction as a non-linear chemical medium in which to realise computation. A checkerboard image comprising of varying light intensity cells is projected onto the surface of a catalyst-loaded gel resulting in rich spatio-temporal chemical wave behaviour. Cellular automata are evolved to control the chemical activity through dynamic adjustment of the light intensity, implementing a number of Boolean functions in both simulation and experimentation.

Larry Bull, Rita Toth, Chris Stone, Ben De Lacy Costello, Andrew Adamatzky

Chapter 9. On Synthesis and Solutions of Nonlinear Differential Equations—A Bio-Inspired Approach

This chapter discusses an alternative approach for mathematical-physical problems solution by means of bio-inspired methods, especially by evolutionary algorithms. Two different approaches are demonstrated here. The first one is the use of evolutionary algorithms on design, parameter estimation and control of the chemical reactor that is represented by 5 nonlinear and mutually joined differential equations, the second one is the use of analytic programming (method of the same class as genetic programming or grammatical evolution) to solve two different differential equations (4th and 2nd order), that represent problems from civil engineering by appropriate function synthesis. Theoretical background as well as applications are discusses here.

Ivan Zelinka

Chapter 10. Marangoni Flow Driven Maze Solving

Algorithmic approaches to maze solving problems and finding shortest paths are generally NP-hard (Non-deterministic Polynomial-time hard) and thus, at best, computationally expensive. Unconventional computational methods, which often utilize non-local information about the geometry at hand, provide an alternative to solving such problems much more efficiently. In the past few decades several chemical, physical and other methods have been proposed to tackle this issue. In this chapter we discuss a novel chemical method for maze solving which relies on the Marangoni flow induced by a surface tension gradient due to a pH gradient imposed between the entrance and exit of the maze. The solutions of the maze problem are revealed by paths of a passive dye which is transported on the surface of the liquid in the direction of the acidic area, which is chosen to be the exit of the maze. The shortest path is visualized first, as the Marangoni flow advecting the dye particles is the most intense along the shortest path. The longer paths, which also solve the maze, emerge subsequently as they are associated with weaker branches of the chemically-induced Marangoni flow which is key to the proposed method.

Kohta Suzuno, Daishin Ueyama, Michal Branicki, Rita Tóth, Artur Braun, István Lagzi

Chapter 11. Chemotaxis and Chemokinesis of Living and Non-living Objects

One of the fundamental properties of living organisms is the ability to sense and respond to changes in their environment by movement. If a motile cell senses soluble molecules and follows along a concentration gradient to the source, or if it moves away from a source of undesirable chemicals, e.g. repellent, toxin, it is displaying a directional movement called positive or negative chemotaxis, respectively. This phenomenon is well-known to biologists and intensively studied in living systems. In contrast chemokinesisChemokinesis is a change in movement due to environmental input but the resulting movement is non-vectorial and can be considered directionally random. Recently, in the last ten years, few laboratories started to focus on the movement properties of artificial constructs, including the directional movement of non-living objects in chemical gradients. This chapter will focus on chemotaxis and chemokinesis of natural and synthetic systems that may provide chemical platforms for unconventional computing.

Jitka Čejková, Silvia Holler, To Quyen Nguyenová, Christian Kerrigan, František Štěpánek, Martin M. Hanczyc

Chapter 12. Computing with Classical Soliton Collisions

We review work on computing with solitons, from the discovery of solitons in cellular automata, to an abstract model for particle computation, information transfer in collisions of optical solitons, state transformations in collisions of vector solitons, a proof of the universality of blinking spatial solitons, and the demonstration of multistable collision cycles and their application to state-restoring logic. We conclude by discussing open problems and the prospects for practical computing applications using optical soliton collisions in photo-refractive crystals and fibers.

Mariusz H. Jakubowski, Ken Steiglitz, Richard Squier

Chapter 13. Soliton-Guided Quantum Information Processing

We describe applications of solitons and soliton collisions to the transport, transfer, and beam-splitting of qubits carried by optical photons. The transport and transfer realize the “flying qubits” necessary for quantum information processing, and the beam-splitting leads, in theory, to an implementation of quantum computing using linear optics. These proposed applications are embedded in a uniform optical fiber and require no special device fabrication, no cooling, and no vacuum.

Ken Steiglitz

Chapter 14. Models of Computing on Actin Filaments

Actin is a filament-forming protein forming cytoskeleton and information processing network of eukaryotic cells. To speculate about a range of computing operation that could be implemented in actin filaments we design quantum automata, non-linear electrical circuits and one-dimensional latices of nodes with Morse interaction models of the actin filaments. In numerical experiments we implement quantum gates, one-bit binary adder, multi-valued logic gates, gates based on (un)forced pulses, and collision-based soliton circuits.

Stefano Siccardi, Andrew Adamatzky

Chapter 15. Modeling DNA Nanodevices Using Graph Rewrite Systems

DNA based nanostructures and devices are becoming ubiquitous in nanotechnology with rapid advancements in theory and experiments in DNA self-assembly which have led to a myriad of DNA nanodevices. However, the modeling methods used by researchers in the field for design and analysis of DNA nanostructures and nanodevices have not progressed at the same rate. Specifically, there does not exist a formal system that can capture the spectrum of the most frequently intended chemical reactions on DNA nanostructures and nanodevices which have branched and pseudo-knotted structures. In this paper we introduce a graph rewriting system for modeling DNA nanodevices. We define pseudo-DNA nanostructures ($$\mathbf {PDN}$$PDNs), which describe the sequence information and secondary structure of DNA nanostructures, but exclude modeling of tertiary structures. We define a class of labeled graphs called DNA graphs, that provide a graph theoretic representation of PDNs. We introduce a set of graph rewrite rules that operate on DNA graphs. Our DNA graphs and graph rewrite rules provide a powerful and expressive way to model DNA nanostructures and their reactions. These rewrite rules model most conventional reactions on DNA nanostructures, which include hybridization, dehybridization, base-stacking, and a large family of enzymatic reactions. A subset of these rewrite rules would likely be used for a basic graph rewrite system modeling most DNA devices, which use just DNA hybridization reactions, whereas other of our rewrite rules could be incorporated as needed for DNA devices for example enzymic reactions. To ensure consistency of our systems, we define a subset of DNA graphs which we call well-formed DNA graphs, whose strands have consistent $$5^\prime $$5′ to $$3^\prime $$3′ polarity. We show that if we start with an input set of well-formed DNA graphs, our rewrite rules produce only well-formed DNA graphs. We give four detailed example applications of our graph rewriting system on (1) Yurke et al. [82] DNA tweezer system, (2) Yurke et al. [77] catalytic hairpin-based triggered branched junctions, (3) Dirks and Pierce [17] HCR, and (4) Qian and Winfree [59] scalable circuit of seesaw gates. Finally, we have a working software prototype (DAGRS) that we have used to generate automatically well-formed DNA graphs using a basic rewriting rule set for some of the examples mentioned.

Reem Mokhtar, Sudhanshu Garg, Harish Chandran, Hieu Bui, Tianqi Song, John Reif

Chapter 16. Computational Matter: Evolving Computational Functions in Nanoscale Materials

Natural evolution has been manipulating the properties of proteins for billions of years. This ‘design process’ is completely different to conventional human design which assembles well-understood smaller parts in a highly principled way. In evolution-in-materio (EIM), researchers use evolutionary algorithms to define configurations and magnitudes of physical variables (e.g. voltages) which are applied to material systems so that they carry out useful computation. One of the advantages of this is that artificial evolution can exploit physical effects that are either too complex to understand or hitherto unknown. An EU funded project in Unconventional Computation called NASCENCE: Nanoscale Engineering of Novel Computation using Evolution, has the aim to model, understand and exploit the behaviour of evolved configurations of nanosystems (e.g. networks of nanoparticles, carbon nanotubes, liquid crystals) to solve computational problems. The project showed that it is possible to use materials to help find solutions to a number of well-known computational problems (e.g. TSP, Bin-packing, Logic gates, etc.).

Hajo Broersma, Julian F. Miller, Stefano Nichele

Chapter 17. Unconventional Computing Realized with Hybrid Materials Exhibiting the PhotoElectrochemical Photocurrent Switching (PEPS) Effect

Increasing demand for high computational power and high density memories enforces rapid development of microelectronic technologies. However, classical, silicon-based electronic elements cannot be miniaturized infinitely. Therefore, in order to sustain rapid development of information processing devices, new approaches towards future computing devices are needed. These approaches encompass either search for new material technologies or new information processing paradigms. In this chapter we present our contribution to the field including both approaches. We introduce classical, Boolean logic devices based on different materials and nanoscale implementations of ternary logic, fuzzy logic and neuromimetic computing.

Kacper Pilarczyk, Przemysław Kwolek, Agnieszka Podborska, Sylwia Gawęda, Marek Oszajca, Konrad Szaciłowski

Chapter 18. Organic Memristor Based Elements for Bio-inspired Computing

Bio-based/bio-inspired systems are attracting the interest of many studies even if we are far from reproducing the simplest living cell property. The concept of memory is particularly well suited for mimicking learning behavior in biosystems and in information processing systems being capable of coupling inherently memory and logic capabilities. Bio-electronics is another challenging platform, mostly if we consider organic devices based on conductive and biocompatible polymers. This chapter deals with several examples of devices developed by joining unconventional computing, organic memristors and living being. Starting from organic memristors we realized logic gates with memory and a single layer perceptron. We developed hybrid systems based on living beings as key elements for the proper device working, in particular with Phyarum polycephalum and neurons. These devices enable new and unexplored opportunities in such emerging field of research.

Silvia Battistoni, Alice Dimonte, Victor Erokhin

Chapter 19. Memristors in Unconventional Computing: How a Biomimetic Circuit Element Can be Used to Do Bioinspired Computation

Memristors differ from resistors by possessing a memory, and both synapses and neurons have been discussed as biological memristors. The short-term memory of the memristor (or spiking profile) is similar in form to neural spikes. Thus, memristors are obvious candidates for building biomimetic circuits and computers. In this chapter, we review some recent experimental results in the area of memritor-based spike computing. We demonstrate how memristor spikes are a real-world memristor model of an inhibitory neuron, then we demonstrate the complex emergent behaviour from networks of memristors which resembles neural dynamics, we then expose these memristor networks to living neural cells, where the memristor state is altered by cellular action. Further investigation of the memristor spiking process allows us to elucidate design rules for spiking logic gates, and we demonstrate a novel full adder instantiated in a single memristor. Spiking memristor computation might be the best route to truly neuromorphic computers.

Ella Gale

Chapter 20. Nature-Inspired Computation: An Unconventional Approach to Optimization

Nature-inspired computation plays an increasingly important role in many areas such as computational intelligence, optimization and data mining. From the perspective of traditional algorithms, such nature-inspired, iterative problem-solving methods are an unconventional approach to optimization. Both the number of algorithms and the popularity have increased significantly in recent years. This chapter provides a critical analysis of some nature-inspired algorithms and strives to identify the most essential characteristics among these algorithms. We also look at different algorithmic structures and ways of generating new solutions in a mathematical framework, which will provide some insight into these algorithms. We also discuss some key open problems concerning nature-inspired metaheuristics.

Xin-She Yang

Chapter 21. On Hybrid Classical and Unconventional Computing for Guiding Collective Movement

Collective movement in living systems typically displays complex dynamics which cannot be described by the component parts themselves. Plasmodium of slime mould Physarum polycephalum exhibits complex amoeboid movement during its foraging and hazard avoidance which may be influenced by the local placement of attractants, repellents and light irradiation stimuli. Slime mould is a useful inspiration to soft-robotics due to its simple component parts and the distributed nature of its control and locomotion mechanisms. However, it is challenging to interface classical computing devices to a distributed system which utilises self-organised and emergent properties. In this chapter we investigate potential hybrid approaches to the task of automatically guiding collective robotics devices, using a multi-agent model of slime mould. We demonstrate a variety of simple open-loop guidance methods. We then describe a hybrid classical/unconventional computing approach using a closed-loop feedback mechanism with attractant and repellent stimuli. Both stimulus types were capable of successful automatic guidance, but we found that repellent stimuli (a light illumination mask) provided faster and more accurate guidance than attractant sources, which were found to exhibit overshooting phenomena at path turns. The method allows traversal of convoluted arenas with challenging obstacles such as narrow channels and complex gratings, and provides an insight into how unconventional computing substrates may be hybridised with classical computing methods to take advantage of the mutual benefits of both approaches.

Jeff Jones

Chapter 22. Cellular Automata Ants

During the last decades much attention was given to bio-inspired techniques able to successfully handle really complex algorithmic problems. As such Ant Colony Optimization (ACO) algorithmsAnt colony optimization have been introduced as a metaheuristic optimization technique arriving from the swarm intelligence methods family and applied to several computational and combinatorial optimization problems. However, long before ACO, Cellular Automata (CA)Cellular automata have been proposed as a powerful parallel computational tool where space and time are discrete and interactions are local. It has been proven that CA are ubiquitous: they are mathematical models of computation and computer models of natural systems and their research in interdisciplinary topics leads to new theoretical constructs, novel computational solutions and elegant powerful models. As a result, in this chapter we step forward presenting a combination of CA with ant colonies aiming at the introduction of an unconventional computational model, namely “Cellular Automata Ants”. This rather theoretical approach is stressed in rather competitive field, namely clusteringClustering. It is well known that the spread of data for almost all areas of life has rapidly increased during the last decades. Nevertheless, the overall process of discovering true knowledge from data demands more powerful clustering techniques to ensure that some of those data are useful and some are not. In this chapter it is presented that Cellular Automata Ants can provide efficient, robust and low cost solutions to data clustering problems using quite small amount of computational resources.

Nikolaos P. Bitsakidis, Nikolaos I. Dourvas, Savvas A. Chatzichristofis, Georgios Ch. Sirakoulis

Chapter 23. Rough Set Description of Strategy Games on Physarum Machines

A Physarum machine is a biological computing device implemented in the plasmodium of Physarum polycephalum or Badhamia utricularis which are one-cell organisms able to build complex networks for solving different computational tasks. The plasmodial stage of such organisms is a natural transition system which can be considered the medium for bio-inspired strategy games. In the paper, we describe a rough set approach for description of a strategy game created on the Physarum machine. The strategies of such a game are approximated on the basis of a rough set model, describing behavior of the Physarum machine, created according to the VPRSM (Variable Precision Rough Set Model) approach. Theoretical foundations given in the paper are supplemented with description of a specialized programming language for Physarum machines, as well as a software tool developed, among others, for simulation of Physarum games.

Krzysztof Pancerz, Andrew Schumann

Chapter 24. Computing a Worm: Reverse-Engineering Planarian Regeneration

In order to understand and control complex biological systems, we need to unravel the information processing and computations required to regulate their dynamics. The development of a complete organism from a single cell or the restoration of lost structures and body parts after amputations require the coordination of millions of cells exchanging and processing information. Understanding these dynamic processes from the results of biological perturbation experiments represent an outstanding challenge due to the characteristic non-linear dynamics and feed-back loops of their molecular and biophysical regulatory mechanisms—an inverse problem with no analytical or computationally tractable solutions. To bridge the gap between molecular-level mechanistic data and systems-level outcomes, we have developed a computational methodology based on heuristic algorithms to automatically reverse-engineer dynamic regulatory networks directly from experimental results. Using this method, applied to problems of pattern regulation during metazoan regeneration, we inferred the first comprehensive regulatory network of planarian regeneration, capable of explaining the most relevant experiments of anterior-posterior specification during regeneration. Here we summarize our results and study the dynamics of the inferred regulatory model, unraveling the information processing and computations required to regenerate a correct morphology.

Daniel Lobo, Michael Levin

Chapter 25. An Integrated In Silico Simulation and Biomatter Compilation Approach to Cellular Computation

Recent advances Synthetic Biology are ushering a new practical computational substrate based on programmable information processing via biological cells. Due to the difficulties in orchestrating complex programmes using myriads of relatively simple, limited and highly stochastic processors such as living cells, robust computational technology to specify, simulate, analyse and compile cellular programs are in demand. We provide the Infobiotics Workbench (Ibw) tool, a software platform developed to model and analyse stochastic compartmentalized systems, which permits using various computational techniques, such as modelling, simulation, verification and biocompilation. We report here the details of our work for modelling, simulation and, for the first time, biocompilation, while verification is reported elsewhere in this book. We consider some basic genetic logic gates to illustrate the main features of the Ibw platform. Our results show that membrane computing provides a suitable formalism for building synthetic biology models. The software platform we developed permits analysing biological systems through the computational methods integrated into the workbench, providing significant advantages in terms of time, and enhanced understanding of biological functionality.

Savas Konur, Harold Fellermann, Larentiu Marian Mierla, Daven Sanassy, Christophe Ladroue, Sara Kalvala, Marian Gheorghe, Natalio Krasnogor

Chapter 26. Plant Roots as Excellent Pathfinders: Root Navigation Based on Plant Specific Sensory Systems and Sensorimotor Circuits

Roots are underground plant organs hidden in the soil and coping with many environmental challenges. The root system forms ultimately complex networks of roots with numerous root apices at the distal ends of all roots. All these root apices move away from the plant body, being pushed via the elongation region in which cells rapidly elongate. Each root apex acts as an autonomous sensory organ receiving information from numerous sensory systems feeding into the root apex transition zone. The latter is acting as command center navigating growing root apices through very complex underground environment. New root apices are formed continuously behind the growth zone in endogenous manner, initiated at the stele-cortex interface via cell divisions in the pericycle and endodermis. All this allows exploratory root systems to effectively explore and exploit large areas of heterogeneous soil. In order to find out the underlying biological mechanisms, root behavior can be observed and manipulated in laboratory. Roots use their plant-specific cognition and problem-solving apparatus which allows them to exploit heterogeneous soil for water and mineral nutrition. Plant-specific memory and processing of sensory information are discussed also from the perspective of plant-specific unconventional computing. We hope that our better understanding of root behavior will be relevant for the bio-inspired robotics.

Ken Yokawa, František Baluška

Chapter 27. Soft Plant Robotic Solutions: Biological Inspiration and Technological Challenges

Plants have a sessile lifestyle, and, as a consequence, their structures have a modular organization to ensure surviving in case of environmental damage or predation. Moreover, they developed strategies for efficiently use the resources available in their surroundings, and a well-organized sensing system that allows them to explore the environment and react rapidly to potentially dangerous circumstances. In particular plant roots behaviour emerges from the complex and dynamic interaction between their morphology, sensory-motor control, and environment. Despite the richness of behaviours, mechanisms and features shown, only in recent years scientists and engineers started to consider plants as a possible source of inspiration for developing new technological solutions. In this chapter we highlight how plants and plant roots represent a new model in bioinspired soft robotics and technologies, reporting on few examples of solutions inspired by plants, including growing robots, osmosis-based actuators, controllable hygromorphic materials, and mechanoperceptive systems.

B. Mazzolai, V. Mattoli, L. Beccai

Chapter 28. Thirty Seven Things to Do with Live Slime Mould

Slime mould Physarum polycephalum is a large single cell capable for distributed sensing, concurrent information processing, parallel computation and decentralised actuation. The ease of culturing and experimenting with Physarum makes this slime mould an ideal substrate for real-world implementations of unconventional sensing and computing devices. In the last decade the Physarum became a swiss knife of the unconventional computing: give the slime mould a problem it will solve it. We provide a concise summary of what exact computing and sensing operations are implemented with live slime mould. The Physarum devices discussed range from morphological processors for computational geometry to experimental archeology tools, from self-routing wires to memristors, from devices approximating a shortest path to analog physical models of space exploration.

Andrew Adamatzky

Chapter 29. Experiments in Musical Biocomputing: Towards New Kinds of Processors for Audio and Music

The emerging field of Unconventional Computing is developing new algorithms and computing architectures inspired by or implemented in biological, physical and chemical systems. We are investigating how Unconventional Computing may benefit the future of the music industry and related audio engineering technologies. In this chapter, after a brief introduction to Unconventional Computing, we present our research into harnessing the behaviour of a slime mould called Physarum polycephalum to build new kinds of processors for audio and music. The plasmodium of Physarum polycephalum is a large single cell with a myriad of diploid nuclei, which moves like a giant amoeba in its pursuit for food. The organism is amorphous, and although without a brain or any serving centre of control, can respond to the environmental conditions that surround it. As our research progressed, we have successfully harnessed the organism to implement a sound synthesiser and a musical sequencer, grow biological audio wires, and build an interactive biocomputer that can listen and produce musical responses in real-time.

Eduardo Reck Miranda, Edward Braund

Chapter 30. Immunocomputing and Baltic Indicator of Global Warming

UnconventionalImmunocomputingBaltic indicatorGlobal warming computing of sea surface temperature (SST)Sea surface temperature was once featured by NASA as a unique merger of science and art. Our approach led to a discovery that just one geographical point could be sufficient to track global anomalies of SST based on El Niño Southern Oscillation (ENSO).El Niño Such single point in the Pacific Ocean off of the island of Isabella in the Galapagos Islands was named the Galapagos indicator. Now we show that a single point in the Baltic Sea off of the coast of Göteborg could be also sufficient to track ENSO. We propose to name it the Baltic indicator.Baltic indicator We also demonstrate that two crisis falls of oil price in 2008 and 2014 followed just after the local maximums of Baltic indicator. However, Baltic and Galapagos indicators do not confirm any settled global warming from the beginning of this century.

Alexander O. Tarakanov, Alla V. Borisova

Chapter 31. Experimental Architecture and Unconventional Computing

This chapter examines aspects of unconventional computing from a design perspective through the practice of architecture. It reflects on how non-scientific forms of investigation may help develop cultural and economic frameworks for design thinking and scientific innovation, by building public and commercial interest in the field.

Rachel Armstrong

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