17,290 research outputs found
From modular to centralized organization of synchronization in functional areas of the cat cerebral cortex
Recent studies have pointed out the importance of transient synchronization
between widely distributed neural assemblies to understand conscious
perception. These neural assemblies form intricate networks of neurons and
synapses whose detailed map for mammals is still unknown and far from our
experimental capabilities. Only in a few cases, for example the C. elegans, we
know the complete mapping of the neuronal tissue or its mesoscopic level of
description provided by cortical areas. Here we study the process of transient
and global synchronization using a simple model of phase-coupled oscillators
assigned to cortical areas in the cerebral cat cortex. Our results highlight
the impact of the topological connectivity in the developing of
synchronization, revealing a transition in the synchronization organization
that goes from a modular decentralized coherence to a centralized synchronized
regime controlled by a few cortical areas forming a Rich-Club connectivity
pattern.Comment: 24 pages, 8 figures. Final version published in PLoS On
Multiscale Phenomenology of the Cosmic Web
We analyze the structure and connectivity of the distinct morphologies that
define the Cosmic Web. With the help of our Multiscale Morphology Filter (MMF),
we dissect the matter distribution of a cosmological CDM N-body
computer simulation into cluster, filaments and walls. The MMF is ideally
suited to adress both the anisotropic morphological character of filaments and
sheets, as well as the multiscale nature of the hierarchically evolved cosmic
matter distribution. The results of our study may be summarized as follows:
i).- While all morphologies occupy a roughly well defined range in density,
this alone is not sufficient to differentiate between them given their overlap.
Environment defined only in terms of density fails to incorporate the intrinsic
dynamics of each morphology. This plays an important role in both linear and
non linear interactions between haloes. ii).- Most of the mass in the Universe
is concentrated in filaments, narrowly followed by clusters. In terms of
volume, clusters only represent a minute fraction, and filaments not more than
9%. Walls are relatively inconspicous in terms of mass and volume. iii).- On
average, massive clusters are connected to more filaments than low mass
clusters. Clusters with M h have on average
two connecting filaments, while clusters with M
h have on average five connecting filaments. iv).- Density profiles
indicate that the typical width of filaments is 2\Mpch. Walls have less well
defined boundaries with widths between 5-8 Mpc h. In their interior,
filaments have a power-law density profile with slope ,
corresponding to an isothermal density profile.Comment: 28 pages, 22 figures, accepted for publication in MNRAS. For a
high-res version see http://www.astro.rug.nl/~weygaert/webmorph_mmf.pd
The New Horizon Run Cosmological N-Body Simulations
We present two large cosmological N-body simulations, called Horizon Run 2
(HR2) and Horizon Run 3 (HR3), made using 6000^3 = 216 billions and 7210^3 =
374 billion particles, spanning a volume of (7.200 Gpc/h)^3 and (10.815
Gpc/h)^3, respectively. These simulations improve on our previous Horizon Run 1
(HR1) up to a factor of 4.4 in volume, and range from 2600 to over 8800 times
the volume of the Millennium Run. In addition, they achieve a considerably
finer mass resolution, down to 1.25x10^11 M_sun/h, allowing to resolve
galaxy-size halos with mean particle separations of 1.2 Mpc/h and 1.5 Mpc/h,
respectively. We have measured the power spectrum, correlation function, mass
function and basic halo properties with percent level accuracy, and verified
that they correctly reproduce the LCDM theoretical expectations, in excellent
agreement with linear perturbation theory. Our unprecedentedly large-volume
N-body simulations can be used for a variety of studies in cosmology and
astrophysics, ranging from large-scale structure topology, baryon acoustic
oscillations, dark energy and the characterization of the expansion history of
the Universe, till galaxy formation science - in connection with the new
SDSS-III. To this end, we made a total of 35 all-sky mock surveys along the
past light cone out to z=0.7 (8 from the HR2 and 27 from the HR3), to simulate
the BOSS geometry. The simulations and mock surveys are already publicly
available at http://astro.kias.re.kr/Horizon-Run23/.Comment: 18 pages, 10 figures. Added clarification on Fig 6. Published in the
Journal of the Korean Astronomical Society (JKAS). The paper with
high-resolution figures is available at
http://jkas.kas.org/journals/2011v44n6/v44n6.ht
Percolation Analysis of a Wiener Reconstruction of the IRAS 1.2 Jy Redshift Catalog
We present percolation analyses of Wiener Reconstructions of the IRAS 1.2 Jy
Redshift Survey. There are ten reconstructions of galaxy density fields in real
space spanning the range to , where
, is the present dimensionless density and
is the bias factor. Our method uses the growth of the largest cluster
statistic to characterize the topology of a density field, where Gaussian
randomized versions of the reconstructions are used as standards for analysis.
For the reconstruction volume of radius, Mpc,
percolation analysis reveals a slight `meatball' topology for the real space,
galaxy distribution of the IRAS survey.
cosmology-galaxies:clustering-methods:numericalComment: Revised version accepted for publication in The Astrophysical
Journal, January 10, 1997 issue, Vol.47
The persistent cosmic web and its filamentary structure II: Illustrations
The recently introduced discrete persistent structure extractor (DisPerSE,
Soubie 2010, paper I) is implemented on realistic 3D cosmological simulations
and observed redshift catalogues (SDSS); it is found that DisPerSE traces
equally well the observed filaments, walls, and voids in both cases. In either
setting, filaments are shown to connect onto halos, outskirt walls, which
circumvent voids. Indeed this algorithm operates directly on the particles
without assuming anything about the distribution, and yields a natural
(topologically motivated) self-consistent criterion for selecting the
significance level of the identified structures. It is shown that this
extraction is possible even for very sparsely sampled point processes, as a
function of the persistence ratio. Hence astrophysicists should be in a
position to trace and measure precisely the filaments, walls and voids from
such samples and assess the confidence of the post-processed sets as a function
of this threshold, which can be expressed relative to the expected amplitude of
shot noise. In a cosmic framework, this criterion is comparable to friend of
friend for the identifications of peaks, while it also identifies the connected
filaments and walls, and quantitatively recovers the full set of topological
invariants (Betti numbers) {\sl directly from the particles} as a function of
the persistence threshold. This criterion is found to be sufficient even if one
particle out of two is noise, when the persistence ratio is set to 3-sigma or
more. The algorithm is also implemented on the SDSS catalogue and used to locat
interesting configurations of the filamentary structure. In this context we
carried the identification of an ``optically faint'' cluster at the
intersection of filaments through the recent observation of its X-ray
counterpart by SUZAKU. The corresponding filament catalogue will be made
available online.Comment: A higher resolution version is available at
http://www.iap.fr/users/sousbie together with complementary material (movie
and data). Submitted to MNRA
Topology regulates pattern formation capacity of binary cellular automata on graphs
We study the effect of topology variation on the dynamic behavior of a system
with local update rules. We implement one-dimensional binary cellular automata
on graphs with various topologies by formulating two sets of degree-dependent
rules, each containing a single parameter. We observe that changes in graph
topology induce transitions between different dynamic domains (Wolfram classes)
without a formal change in the update rule. Along with topological variations,
we study the pattern formation capacities of regular, random, small-world and
scale-free graphs. Pattern formation capacity is quantified in terms of two
entropy measures, which for standard cellular automata allow a qualitative
distinction between the four Wolfram classes. A mean-field model explains the
dynamic behavior of random graphs. Implications for our understanding of
information transport through complex, network-based systems are discussed.Comment: 16 text pages, 13 figures. To be published in Physica
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