3,488 research outputs found
Dynamic reconfiguration of human brain networks during learning
Human learning is a complex phenomenon requiring flexibility to adapt
existing brain function and precision in selecting new neurophysiological
activities to drive desired behavior. These two attributes -- flexibility and
selection -- must operate over multiple temporal scales as performance of a
skill changes from being slow and challenging to being fast and automatic. Such
selective adaptability is naturally provided by modular structure, which plays
a critical role in evolution, development, and optimal network function. Using
functional connectivity measurements of brain activity acquired from initial
training through mastery of a simple motor skill, we explore the role of
modularity in human learning by identifying dynamic changes of modular
organization spanning multiple temporal scales. Our results indicate that
flexibility, which we measure by the allegiance of nodes to modules, in one
experimental session predicts the relative amount of learning in a future
session. We also develop a general statistical framework for the identification
of modular architectures in evolving systems, which is broadly applicable to
disciplines where network adaptability is crucial to the understanding of
system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4
figures, 3 table
Louisville Ridge subduction at the Tonga-Kermadec trench: preliminary velocity models from wide-angle seismics
Robust Detection of Dynamic Community Structure in Networks
We describe techniques for the robust detection of community structure in
some classes of time-dependent networks. Specifically, we consider the use of
statistical null models for facilitating the principled identification of
structural modules in semi-decomposable systems. Null models play an important
role both in the optimization of quality functions such as modularity and in
the subsequent assessment of the statistical validity of identified community
structure. We examine the sensitivity of such methods to model parameters and
show how comparisons to null models can help identify system scales. By
considering a large number of optimizations, we quantify the variance of
network diagnostics over optimizations (`optimization variance') and over
randomizations of network structure (`randomization variance'). Because the
modularity quality function typically has a large number of nearly-degenerate
local optima for networks constructed using real data, we develop a method to
construct representative partitions that uses a null model to correct for
statistical noise in sets of partitions. To illustrate our results, we employ
ensembles of time-dependent networks extracted from both nonlinear oscillators
and empirical neuroscience data.Comment: 18 pages, 11 figure
Massless Metric Preheating
Can super-Hubble metric perturbations be amplified exponentially during
preheating ? Yes. An analytical existence proof is provided by exploiting the
conformal properties of massless inflationary models. The traditional conserved
quantity \zeta is non-conserved in many regions of parameter space. We include
backreaction through the homogeneous parts of the inflaton and preheating
fields and discuss the role of initial conditions on the post-preheating
power-spectrum. Maximum field variances are strongly underestimated if metric
perturbations are ignored. We illustrate this in the case of strong
self-interaction of the decay products. Without metric perturbations,
preheating in this case is very inefficient. However, metric perturbations
increase the maximum field variances and give alternative channels for the
resonance to proceed. This implies that metric perturbations can have a large
impact on calculations of relic abundances of particles produced during
preheating.Comment: 8 pages, 4 colour figures. Version to appear in Phys. Rev. D.
Contains substantial new analysis of the ranges of parameter space for which
large changes to the inflation-produced power spectrum are expecte
Louisville Ridge subduction at the Tonga-Kermadec trench: preliminary models to compare pre- and post collision zone crustal velocity structure
Spectral mapping of brain functional connectivity from diffusion imaging.
Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions. Although some studies provide evidence that whole-brain functional connectivity is shaped by the underlying anatomy, the observed relationship between function and structure is weak, and the rules by which anatomy constrains brain dynamics remain elusive. In this article, we introduce a methodology to map the functional connectivity of a subject at rest from his or her structural graph. Using our methodology, we are able to systematically account for the role of structural walks in the formation of functional correlations. Furthermore, in our empirical evaluations, we observe that the eigenmodes of the mapped functional connectivity are associated with activity patterns associated with different cognitive systems
A new twist to preheating
Metric perturbations typically strengthen field resonances during preheating.
In contrast we present a model in which the super-Hubble field resonances are
completely {\em suppressed} when metric perturbations are included. The model
is the nonminimal Fakir-Unruh scenario which is exactly solvable in the
long-wavelength limit when metric perturbations are included, but exhibits
exponential growth of super-Hubble modes in their absence. This gravitationally
enhanced integrability is exceptional, both for its rarity and for the power
with which it illustrates the importance of including metric perturbations in
consistent studies of preheating. We conjecture a no-go result - there exists
no {\em single-field} model with growth of cosmologically-relevant metric
perturbations during preheating.Comment: 6 pages, 3 figures, Version to appear in Physical Review
Preheating of the nonminimally coupled inflaton field
We investigate preheating of an inflaton field coupled nonminimally to
a spacetime curvature. In the case of a self-coupling inflaton potential
, the dynamics of preheating changes by the effect of
the negative . We find that the nonminimal coupling works in two ways.
First, since the initial value of inflaton field for reheating becomes
smaller with the increase of , the evolution of the inflaton quanta is
delayed for fixed . Second, the oscillation of the inflaton field is
modified and the nonadiabatic change around occurs significantly. That
makes the resonant band of the fluctuation field wider. Especially for strong
coupling regimes , the growth of the inflaton flutuation is
dominated by the resonance due to the nonminimal coupling, which leads to the
significant enhancement of low momentum modes. Although the final variance of
the inflaton fluctuation does notchange significantly compared with the
minimally coupled case, we have found that the energy transfer from the
homogeneous inflaton to created particles efficiently occurs for .Comment: 13pages, 11figure
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