A hierarchical view on Evolution-In-Materio computations

Laketic, Dragana; Tufte, Gunnar

doi:10.1109/ssci.2016.7850185

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…The knowledge about the role(s) quantum processes play in complex systems evolved in nature may be applied to the challenges in man-made systems where quantum dynamics plays a significant role. The manipulation of the quantum dynamics towards the desired goal in a bottom-up fashion may find applications in the field of nanotechnology and even some less conventional computing paradigms (Laketić and Tufte, 2016). Such investigations may be extended in the future.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Hyperdescriptions of Quantum Dynamics - A Case Study for Avian Magnetoreception

Laketic¹

2021

The 2021 Conference on Artificial Life

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A previously established information theoretic approach for dynamical hierarchies is hereby revisited for the case when a higher hierarchical level, which is observable in the realm of classical physics, arises from quantum dynamics. The focus of the paper is on the description of information transfer from quantum to classical level. As an example of complex functionality emerging at a higher level from quantum dynamics in a biological system, a case study of coherent spin dynamics, which is hypothesised to be one of the mechanisms of avian magnetoreception, is presented within such an approach. The findings may be useful for better understanding of the phenomena emergent from quantum processes which occur in biological complex systems. Moreover, the lessons learned may provide valuable knowledge for nature-inspired engineering in a bottom-up fashion within the field of nanotechnology.

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Section: Discussion and Future Workmentioning

confidence: 99%

Hyperdescriptions of Quantum Dynamics - A Case Study for Avian Magnetoreception

Laketic¹

2021

The 2021 Conference on Artificial Life

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“…Given such state space descriptions, the following equations were shown to hold (Laketić and Tufte, 2016):…”

Section: Eim Systems and Hierarchies Within Themmentioning

confidence: 99%

Evolution-in-materio computations: Hierarchies rising from electron dynamics in carbon nanotubes

Laketic

Tufte²

2017

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

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Previously, Evolution-In-Materio (EIM), an unconventional computing paradigm, was addressed as a computing system which exhibits dynamical hierarchies. For different conceptual domains identified within an EIM system, a corresponding hierarchical level was defined and the state space description provided. Entropic relations established between such system descriptions show that one hyperdescribes another in an information theoretical sense. Hereby we report on those findings and revisit entropic relations between the level of material physics and the level of measurements via simulations of the addressed physical phenomenon. Evolution-In-Materio (EIM) (Miller et al., 2014), an unconventional computing approach, exploits physical properties of materials for computations. Materials are considered as bulks, unorganised matter, and manipulated under the guidance of an Evolutionary Algorithm (EA) towards achieving solution to a given computational problem. EA is run on a digital computer. Interaction with material physics (analogue) is realised via an interface board. For such a computing scenario different conceptual domains have been identified as shown in Figure 1. For each domain, a system description is provided in a form of a discrete state space description, i.e., a set of system states and a transfer function, as summarised in Table 1. The description is parameterised in the sense given by the system theory (Ashby, 1960). Since each state has a pertaining probability, the system is entropic. Also, description functions are defined which map states from a lower to a higher level in a sense described in (McGregor and Fernando, 2005). EIM Systems and Hierarchies Within ThemGiven such state space descriptions, the following equations were shown to hold (Laketić and Tufte, 2016):where indices a and b refer to a lower and higher level respectively. Such entropic relations are equivalent to distinctness and state-dependence as defined in (McGregor and Fernando, 2005) being less than 1 thereby providing a proof that recognised hierarchical levels exhibit novelty and loss of information which are prominent characteristics of dynamical hierarchies. For the level of material physics, we use carbon nanotubes (CNTs) since the physical phenomenon manipulated for achieving computations in most of our experiments within NASCENCE project (Broersma et al., 2012) was the change of CNT conductivity due to the changes in the electric field.

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“…The approach in [30] aims at modeling conductivity dependence on the concentration of carbon nanotubes and varying electric potential in the material. The approach is based on two main paradigms: dynamical hierarchies [57] and cellular computation [10].…”

Section: Model Of Conductivity Based On Dynamical Hierarchies and Cel...mentioning

confidence: 99%

Computational Matter: Evolving Computational Functions in Nanoscale Materials

Broersma

Miller

Nichele

2016

Emergence, Complexity and Computation

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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.).

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A hierarchical view on Evolution-In-Materio computations

Cited by 3 publications

References 35 publications

Hyperdescriptions of Quantum Dynamics - A Case Study for Avian Magnetoreception

Hyperdescriptions of Quantum Dynamics - A Case Study for Avian Magnetoreception

Evolution-in-materio computations: Hierarchies rising from electron dynamics in carbon nanotubes

Computational Matter: Evolving Computational Functions in Nanoscale Materials

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