2008
DOI: 10.1142/s0218127408021750
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Abstract: Using the examples of an excitable chemical system (Belousov-Zhabotinsky medium) and plasmodium of Physarum polycephalum we show that universal computation in a geometrically unconstrained medium is only possible when resources (excitability or concentration of nutrients) are limited. In situations of limited resources the systems studied develop travelling localizations. The localizations are elementary units of dynamical logical circuits in collision-based computing architectures. Keywords: unconventional co… Show more

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Cited by 60 publications
(49 citation statements)
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“…Ideas of the present Letter emerged from our early computational and laboratory experiments on developing unconventional computing circuits using operations with wave-fragments in BZ medium [2,9] and the fact that wave-fragments in sub-excitable BZ medium behave similarly to pseudopodia of the plasmodium cultivated on a nutrient-poor substrate [7].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Ideas of the present Letter emerged from our early computational and laboratory experiments on developing unconventional computing circuits using operations with wave-fragments in BZ medium [2,9] and the fact that wave-fragments in sub-excitable BZ medium behave similarly to pseudopodia of the plasmodium cultivated on a nutrient-poor substrate [7].…”
Section: Discussionmentioning
confidence: 99%
“…A substrate was a wet filter Letter. We preferred the filter Letter not 2% agar gel, because the Letter offers less favoured conditions for the plasmodium growth, and thus less branching of the propagating pseudopodia is observed (see details in [7,27]). The Petri dishes with plasmodia were kept in darkness and only exposed to light during observation and recording of images.…”
Section: Methodsmentioning
confidence: 99%
“…More recent efforts include the notion of memory for position during regeneration 108 and development 109 , learning models of diabetic electrophysiology in pancreas 110 , excitable cortex memory models of pseudopod dynamics 111 , and neural network models of chemical signaling 112 (which showed formal isomorphisms between gene regulation networks and Hebbian learning in neural nets) 113 . In addition to classical neuroscience concepts, more exotic group cognition models have been applied to patterning 114 , while a few recent studies investigated the decision-making and formal computational capabilities of reaction-diffusion systems – a chemical signaling modality often used to model morphogenesis 115 , which is now known to be Turing-complete 116 and to support semantic interpretations 117 .…”
Section: Broader Implications: Homologies Between Neural Informatimentioning
confidence: 99%
“…In other words, both the diffusion reaction (microscopic level) and changing boundary conditions (macroscopic level) interplay to create an adaptive network. In this context, the interplay can reveal the transition of the Physarum graph that can be used as memory storage (Adamatzky et al, 2008;Adamatzky and Jones, 2010).…”
Section: Introductionmentioning
confidence: 99%