112,518 research outputs found

    Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

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    Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

    Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots.

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    One of the key innovations during the evolution of life on earth has been the emergence of efficient communication systems, yet little is known about the causes and consequences of the great diversity within and between species. By conducting experimental evolution in 20 independently evolving populations of cooperatively foraging simulated robots, we found that historical contingency in the occurrence order of novel phenotypic traits resulted in the emergence of two distinct communication strategies. The more complex foraging strategy was less efficient than the simpler strategy. However, when the 20 populations were placed in competition with each other, the populations with the more complex strategy outperformed the populations with the less complex strategy. These results demonstrate a tradeoff between communication efficiency and robustness and suggest that stochastic events have important effects on signal evolution and the outcome of competition between distinct populations

    A stochastic model of catalytic reaction networks in protocells

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    Protocells are supposed to have played a key role in the self-organizing processes leading to the emergence of life. Existing models either (i) describe protocell architecture and dynamics, given the existence of sets of collectively self-replicating molecules for granted, or (ii) describe the emergence of the aforementioned sets from an ensemble of random molecules in a simple experimental setting (e.g. a closed system or a steady-state flow reactor) that does not properly describe a protocell. In this paper we present a model that goes beyond these limitations by describing the dynamics of sets of replicating molecules within a lipid vesicle. We adopt the simplest possible protocell architecture, by considering a semi-permeable membrane that selects the molecular types that are allowed to enter or exit the protocell and by assuming that the reactions take place in the aqueous phase in the internal compartment. As a first approximation, we ignore the protocell growth and division dynamics. The behavior of catalytic reaction networks is then simulated by means of a stochastic model that accounts for the creation and the extinction of species and reactions. While this is not yet an exhaustive protocell model, it already provides clues regarding some processes that are relevant for understanding the conditions that can enable a population of protocells to undergo evolution and selection.Comment: 20 pages, 5 figure

    Can surface flux transport account for the weak polar field in cycle 23?

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    To reproduce the weak magnetic field on the polar caps of the Sun observed during the declining phase of cycle 23 poses a challenge to surface flux transport models since this cycle has not been particularly weak. We use a well-calibrated model to evaluate the parameter changes required to obtain simulated polar fields and open flux that are consistent with the observations. We find that the low polar field of cycle 23 could be reproduced by an increase of the meridional flow by 55% in the last cycle. Alternatively, a decrease of the mean tilt angle of sunspot groups by 28% would also lead to a similarly low polar field, but cause a delay of the polar field reversals by 1.5 years in comparison to the observations.Comment: 9 pages, 8 figures, Space Science Reviews, accepte

    On the complexity and the information content of cosmic structures

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    The emergence of cosmic structure is commonly considered one of the most complex phenomena in Nature. However, this complexity has never been defined nor measured in a quantitative and objective way. In this work we propose a method to measure the information content of cosmic structure and to quantify the complexity that emerges from it, based on Information Theory. The emergence of complex evolutionary patterns is studied with a statistical symbolic analysis of the datastream produced by state-of-the-art cosmological simulations of forming galaxy clusters. This powerful approach allows us to measure how many bits of information are necessary to predict the evolution of energy fields in a statistical way, and it offers a simple way to quantify when, where and how the cosmic gas behaves in complex ways. The most complex behaviors are found in the peripheral regions of galaxy clusters, where supersonic flows drive shocks and large energy fluctuations over a few tens of million years. Describing the evolution of magnetic energy requires at least a twice as large amount of bits than for the other energy fields. When radiative cooling and feedback from galaxy formation are considered, the cosmic gas is overall found to double its degree of complexity. In the future, Cosmic Information Theory can significantly increase our understanding of the emergence of cosmic structure as it represents an innovative framework to design and analyze complex simulations of the Universe in a simple, yet powerful way.Comment: 15 pages, 14 figures. MNRAS accepted, in pres

    Magnetic Flux Transport at the Solar Surface

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    After emerging to the solar surface, the Sun's magnetic field displays a complex and intricate evolution. The evolution of the surface field is important for several reasons. One is that the surface field, and its dynamics, sets the boundary condition for the coronal and heliospheric magnetic fields. Another is that the surface evolution gives us insight into the dynamo process. In particular, it plays an essential role in the Babcock-Leighton model of the solar dynamo. Describing this evolution is the aim of the surface flux transport model. The model starts from the emergence of magnetic bipoles. Thereafter, the model is based on the induction equation and the fact that after emergence the magnetic field is observed to evolve as if it were purely radial. The induction equation then describes how the surface flows -- differential rotation, meridional circulation, granular, supergranular flows, and active region inflows -- determine the evolution of the field (now taken to be purely radial). In this paper, we review the modeling of the various processes that determine the evolution of the surface field. We restrict our attention to their role in the surface flux transport model. We also discuss the success of the model and some of the results that have been obtained using this model.Comment: 39 pages, 15 figures, accepted for publication in Space Sci. Re
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