112,518 research outputs found
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
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.
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
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?
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
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
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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