Nonclassical Computation — A Dynamical Systems Perspective

Stepney, Susan

doi:10.1007/978-3-540-92910-9_59

Cited by 23 publications

(15 citation statements)

References 90 publications

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“…Trajectories may correspond to computations and attractors may correspond to computational results [46]; new attractors arising from bifurcations may correspond to emergent properties [20,51].…”

Section: Dynamicsmentioning

confidence: 99%

“…In a continuous physical dynamical system, that rule is given by the relevant physical laws. Classical computation can be described in discrete dynamical systems terms [46], where the relevant rule is defined by the computer program. Hence it seems possible that a dynamical systems approach could provide a route to an unconventional computational view of physically embodied systems exploiting the natural dynamics of their material substrates (such as exhibited by Pask's ear [47,48], Thompson's FPGA tone discriminator [49], and Harding's in materio computation [50]).…”

Section: Dynamicsmentioning

confidence: 99%

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Programming Unconventional Computers: Dynamics, Development, Self-Reference

Stepney

2012

Entropy

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Classical computing has well-established formalisms for specifying, refining, composing, proving, and otherwise reasoning about computations. These formalisms have matured over the past 70 years or so. Unconventional Computing includes the use of novel kinds of substrates–from black holes and quantum effects, through to chemicals, biomolecules, even slime moulds–to perform computations that do not conform to the classical model. Although many of these unconventional substrates can be coerced into performing classical computation, this is not how they “naturally” compute. Our ability to exploit unconventional computing is partly hampered by a lack of corresponding programming formalisms: we need models for building, composing, and reasoning about programs that execute in these substrates. What might, say, a slime mould programming language look like? Here I outline some of the issues and properties of these unconventional substrates that need to be addressed to find “natural” approaches to programming them. Important concepts include embodied real values, processes and dynamical systems, generative systems and their meta-dynamics, and embodied self-reference

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Section: Dynamicsmentioning

confidence: 99%

Section: Dynamicsmentioning

confidence: 99%

Programming Unconventional Computers: Dynamics, Development, Self-Reference

Stepney

2012

Entropy

Self Cite

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“…What's the role of bifurcations and chaos? (Stepney, 2012) Finding natural models of computation would pave the way for exploiting physical systems directly for information processing. Such devices would be highly efficient, since computation happens "for free" directly in the substrate as the result of intrinsic physical properties.…”

Section: Introductionmentioning

confidence: 99%

Reservoir computing with a chaotic circuit

Jensen¹,

Tufte²

2017

Proceedings of the 14th European Conference on Artificial Life ECAL 2017

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Reservoir Computing has been highlighted as a promising methodology to perform computation in dynamical systems. This makes Reservoir Computing particularly interesting for exploiting physical systems directly as computing substrates, where the computation happens "for free" in the rich physical domain. In this work we consider a simple chaotic circuit as a reservoir: the Driven Chua's circuit. Its rich variety of available dynamics makes it versatile as a reservoir. At the same time, its simplicity offers insight into what physical properties can be useful for computation. We demonstrate both through simulation and in-circuit experiments, that such a simple circuit can be readily exploited for computation. Our results show excellent performance on two non-temporal tasks. The fact that such a simple nonlinear circuit can be used, suggests that a wide variety of physical systems can be viewed as potential reservoirs.

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“…In the interest of a potential embodied implementation, we approach the use of reactiondiffusion for computation from a chemical computing (Dittrich 2005) perspective: chemical reactions can be regarded as computation steps, and the overall computation process can be viewed as a movement in the state space of a dynamical system (Stepney 2010), which in our case is represented by the system of chemical reactions combined with the set of processors that use chemical signaling as source of information. For this purpose, we focus on the chemical implementation of the reaction-diffusion systems 1 covered here, that is, we explicitly derive the set of chemical reactions that lead to the desired patterns, as opposed to the more widespread approach of using the corresponding differential equations directly.…”

Section: Introductionmentioning

confidence: 99%

Recovery properties of distributed cluster head election using reaction–diffusion

Yamamoto

Miorandi²,

Collet

et al. 2011

Swarm Intell

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Chemical reaction-diffusion is a basic component of morphogenesis, and can be used to obtain interesting and unconventional self-organizing algorithms for swarms of autonomous agents, using natural or artificial chemistries. However, the performance of these algorithms in the face of disruptions has not been sufficiently studied. In this paper we evaluate the use of reaction-diffusion for the morphogenetic engineering of distributed coordination algorithms, in particular, cluster head election in a distributed computer system. We consider variants of reaction-diffusion systems around the activator-inhibitor model, able to produce spot patterns. We evaluate the robustness of these models against the deletion of activator peaks that signal the location of cluster heads, and the destruction of large patches of chemicals. Three models are selected for evaluation: the Gierer-Meinhardt ActivatorInhibitor model, the Activator-Substrate Depleted model, and the Gray-Scott model. Our results reveal a trade-off between these models. The Gierer-Meinhardt model is more stable, with rare failures, but is slower to recover from disruptions. The Gray-Scott model is able to recover more quickly, at the expense of more frequent failures. The Activator-Substrate model lies somewhere in the middle.

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Nonclassical Computation — A Dynamical Systems Perspective

Cited by 23 publications

References 90 publications

Programming Unconventional Computers: Dynamics, Development, Self-Reference

Programming Unconventional Computers: Dynamics, Development, Self-Reference

Reservoir computing with a chaotic circuit

Recovery properties of distributed cluster head election using reaction–diffusion

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