The Phi-Bot: A Robot Controlled by a Slime Mould

Tsuda, Soichiro; Artmann, Stefan; Zauner, Klaus-Peter

doi:10.1007/978-1-84882-530-7_10

Cited by 18 publications

(16 citation statements)

References 29 publications

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“…As the title suggests, a more general focus of this paper is on the use of evolved computational dynamical systems to control dynamical systems. Previous work in this area has suggested that a wide variety of computational dynamical systems can be used to perform control [5], [7], [27], [28], [31], [32]. Our results build upon this by demonstrating that a certain class of computational dynamical systems can be evolved to control dynamical systems that exhibit a wide range of properties, notably discrete, continuous and hybrid-time dynamics; dissipative and conservative dynamics; and ordered and chaotic dynamics.…”

Section: Discussionsupporting

confidence: 71%

“…In addition to relatively well known in silico algorithms, such as cellular automata (CAs) and recurrent neural networks (RNNs), these include in vitro approaches such as reaction-diffusion computers, and in vivo methods, especially if biological organisms are considered as dynamical systems [41]. An example of the latter is the use of the slime mould physarum polycephalum for robotic control [31].…”

Section: B Computational Dynamical Systemsmentioning

confidence: 99%

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Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

2014

IEEE Trans. Evol. Computat.

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Biological organisms exist within environments in which complex, non-linear dynamics are ubiquitous. They are coupled to these environments via their own complex, dynamical networks of enzyme-mediated reactions, known as biochemical networks. These networks, in turn, control the growth and behaviour of an organism within its environment. In this paper, we consider computational models whose structure and function are motivated by the organisation of biochemical networks. We refer to these as artificial biochemical networks, and show how they can be evolved to control trajectories within three behaviourally diverse complex dynamical systems: the Lorenz system, Chirikov's standard map, and legged robot locomotion. More generally, we consider the notion of evolving dynamical systems to control dynamical systems, and discuss the advantages and disadvantages of using higher order coupling and configurable dynamical modules (in the form of discrete maps) within artificial biochemical networks. We find both approaches to be advantageous in certain situations, though note that the relative trade-offs between different models of artificial biochemical network strongly depend on the type of dynamical systems being controlled.

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Section: Discussionsupporting

confidence: 71%

Section: B Computational Dynamical Systemsmentioning

confidence: 99%

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

2014

IEEE Trans. Evol. Computat.

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“…Fourth, we could argue that, from the perspective of biochemical networks at least, the brain 6 The idea of deriving computational systems from models of biochemical processes is certainly not new. The resulting computational systems include in silico models, such as cellular automata [107], membrane systems [86], Boolean networks [47] and artificial chemistries [6], in vitro processes such as chemical [1] and DNA [2] computers, and even in vivo approaches, such as the use of a slime mould to control a robot [101]. The application of concepts from biochemical networks to connectionist architectures is also not new [27,73], and many of the approaches listed above can be readily mapped to a connectionist perspective.…”

Section: Biochemical Connectionismmentioning

confidence: 99%

Biochemical connectionism

et al. 2013

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In this paper, we discuss computational architectures that are motivated by connectionist patterns that occur in biochemical networks, and speculate about how this biochemical approach to connectionism might complement conventional neural approaches. In particular, we focus on three features of biochemical networks that make them distinct from neural networks: their diverse, complex nodal processes, their emergent organisation, and the dynamical behaviours that result from higher-order, self-modifying processes. We also consider the growing use of evolutionary algorithms in the design of connectionist systems, noting how this enables us to explore a wider range of connectionist architectures, and how the close relationship between biochemical networks and biological evolution can guide us in this endeavour.

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“…Biochemistry can be used to engineer novel types of computers based on biological components. Examples include, DNA based computers [2,3], robots controlled by slimemolds [4], or logic gates implemented in living cells [5,6,7,8]. Beside this technological importance of biochemical computers, there is now also an increasing appreciation that information processing may be an important fitness contributing function for natural organisms [9,10].…”

Section: Introductionmentioning

confidence: 99%

Performance limits and trade-offs in entropy-driven biochemical computers

Chu

2018

Journal of Theoretical Biology

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Chu, Dominique (2018) Performance limits and trade-offs in entropy-driven biochemical computers.Journal As of yet, research in this topic is based on case-studies. There exists no generally accepted criterion to distinguish between chemical processes that compute and those that do not. Here, the concept of entropy driven computer (EDC) is proposed as a general model of chemical computation. It is found that entropy driven computation is subject to a trade-off between accuracy and entropy production, but unlike many biological systems, there are no trade-offs involving time. The latter only arise when it is taken into account that the observation of the state of the EDC is not energy neutral, but comes at a cost. The significance of this conclusion in relation to biological systems is discussed. Three examples of biological computers, including an implementation of a neural network as an EDC are given.

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The Phi-Bot: A Robot Controlled by a Slime Mould

Cited by 18 publications

References 29 publications

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

Biochemical connectionism

Performance limits and trade-offs in entropy-driven biochemical computers

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