432 research outputs found
Further results on why a point process is effective for estimating correlation between brain regions
Signals from brain functional magnetic resonance imaging (fMRI) can be efficiently represented by a sparse spatiotemporal point process, according to a recently introduced heuristic signal processing scheme. This approach has already been validated for relevant conditions, demonstrating that it preserves and compresses a surprisingly large fraction of the signal information. Here we investigated the conditions necessary for such an approach to succeed, as well as the underlying reasons, using real fMRI data and a simulated dataset. The results show that the key lies in the temporal correlation properties of the time series under consideration. It was found that signals with slowly decaying autocorrelations are particularly suitable for this type of compression, where inflection points contain most of the information.Fil: Cifre, I.. Universitat Ramon Llull; EspañaFil: Zarepour Nasir Abadi, Mahdi. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - CĂłrdoba. Instituto de FĂsica Enrique Gaviola. Universidad Nacional de CĂłrdoba. Instituto de FĂsica Enrique Gaviola; ArgentinaFil: Horovitz, S. G.. National Institutes of Health; Estados UnidosFil: Cannas, Sergio Alejandro. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - CĂłrdoba. Instituto de FĂsica Enrique Gaviola. Universidad Nacional de CĂłrdoba. Instituto de FĂsica Enrique Gaviola; ArgentinaFil: Chialvo, Dante Renato. Universidad Nacional de San MartĂn. Escuela de Ciencia y TecnologĂa; Argentin
Rate-dependent propagation of cardiac action potentials in a one-dimensional fiber
Action potential duration (APD) restitution, which relates APD to the
preceding diastolic interval (DI), is a useful tool for predicting the onset of
abnormal cardiac rhythms. However, it is known that different pacing protocols
lead to different APD restitution curves (RCs). This phenomenon, known as APD
rate-dependence, is a consequence of memory in the tissue. In addition to APD
restitution, conduction velocity restitution also plays an important role in
the spatiotemporal dynamics of cardiac tissue. We present new results
concerning rate-dependent restitution in the velocity of propagating action
potentials in a one-dimensional fiber. Our numerical simulations show that,
independent of the amount of memory in the tissue, waveback velocity exhibits
pronounced rate-dependence and the wavefront velocity does not. Moreover, the
discrepancy between waveback velocity RCs is most significant for small DI. We
provide an analytical explanation of these results, using a system of coupled
maps to relate the wavefront and waveback velocities. Our calculations show
that waveback velocity rate-dependence is due to APD restitution, not memory.Comment: 17 pages, 7 figure
Unstable Dynamics, Nonequilibrium Phases and Criticality in Networked Excitable Media
Here we numerically study a model of excitable media, namely, a network with
occasionally quiet nodes and connection weights that vary with activity on a
short-time scale. Even in the absence of stimuli, this exhibits unstable
dynamics, nonequilibrium phases -including one in which the global activity
wanders irregularly among attractors- and 1/f noise while the system falls into
the most irregular behavior. A net result is resilience which results in an
efficient search in the model attractors space that can explain the origin of
certain phenomenology in neural, genetic and ill-condensed matter systems. By
extensive computer simulation we also address a relation previously conjectured
between observed power-law distributions and the occurrence of a "critical
state" during functionality of (e.g.) cortical networks, and describe the
precise nature of such criticality in the model.Comment: 18 pages, 9 figure
Statistical mixing and aggregation in Feller diffusion
We consider Feller mean-reverting square-root diffusion, which has been
applied to model a wide variety of processes with linearly state-dependent
diffusion, such as stochastic volatility and interest rates in finance, and
neuronal and populations dynamics in natural sciences. We focus on the
statistical mixing (or superstatistical) process in which the parameter related
to the mean value can fluctuate - a plausible mechanism for the emergence of
heavy-tailed distributions. We obtain analytical results for the associated
probability density function (both stationary and time dependent), its
correlation structure and aggregation properties. Our results are applied to
explain the statistics of stock traded volume at different aggregation scales.Comment: 16 pages, 3 figures. To be published in Journal of Statistical
Mechanics: Theory and Experimen
Brownian motors: Joint effect of non-Gaussian noise and time asymmetric forcing
Previous works have shown that time asymmetric forcing on the one hand, as
well as non-Gaussian noises on the other, can separately enhance the efficiency
and current of a Brownian motor. Here, we study the result of subjecting a
Brownian motor to both effects simultaneously. Our results have been compared
with those obtained for the Gaussian white noise regime in the adiabatic limit.
We find that, although the inclusion of the time asymmetry parameter increases
the efficiency value up to a certain extent, for the present case this increase
is much less appreciable than in the white noise case. We also present a
comparative study of the transport coherence in the context of colored noise.
Though the efficiency in some cases becomes higher for the non-Gaussian case,
the P\'eclet number is always higher in the Gaussian colored noise case than in
the white noise as well as non-Gaussian colored noise cases.Comment: 18 page
Noise Induced Intermittency in a Superconducting Microwave Resonator
We experimentally and numerically study a NbN superconducting stripline
resonator integrated with a microbridge. We find that the response of the
system to monochromatic excitation exhibits intermittency, namely,
noise-induced jumping between coexisting steady-state and limit-cycle
responses. A theoretical model that assumes piecewise linear dynamics yields
partial agreement with the experimental findings
Scale-free brain functional networks
Functional magnetic resonance imaging (fMRI) is used to extract {\em
functional networks} connecting correlated human brain sites. Analysis of the
resulting networks in different tasks shows that: (a) the distribution of
functional connections, and the probability of finding a link vs. distance are
both scale-free, (b) the characteristic path length is small and comparable
with those of equivalent random networks, and (c) the clustering coefficient is
orders of magnitude larger than those of equivalent random networks. All these
properties, typical of scale-free small world networks, reflect important
functional information about brain states.Comment: 4 pages, 5 figures, 2 table
Extremal Optimization for Graph Partitioning
Extremal optimization is a new general-purpose method for approximating
solutions to hard optimization problems. We study the method in detail by way
of the NP-hard graph partitioning problem. We discuss the scaling behavior of
extremal optimization, focusing on the convergence of the average run as a
function of runtime and system size. The method has a single free parameter,
which we determine numerically and justify using a simple argument. Our
numerical results demonstrate that on random graphs, extremal optimization
maintains consistent accuracy for increasing system sizes, with an
approximation error decreasing over runtime roughly as a power law t^(-0.4). On
geometrically structured graphs, the scaling of results from the average run
suggests that these are far from optimal, with large fluctuations between
individual trials. But when only the best runs are considered, results
consistent with theoretical arguments are recovered.Comment: 34 pages, RevTex4, 1 table and 20 ps-figures included, related papers
available at http://www.physics.emory.edu/faculty/boettcher
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