22,110 research outputs found
Modeling sparse connectivity between underlying brain sources for EEG/MEG
We propose a novel technique to assess functional brain connectivity in
EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA),
can overcome the problem of volume conduction by modeling neural data
innovatively with the following ingredients: (a) the EEG is assumed to be a
linear mixture of correlated sources following a multivariate autoregressive
(MVAR) model, (b) the demixing is estimated jointly with the source MVAR
parameters, (c) overfitting is avoided by using the Group Lasso penalty. This
approach allows to extract the appropriate level cross-talk between the
extracted sources and in this manner we obtain a sparse data-driven model of
functional connectivity. We demonstrate the usefulness of SCSA with simulated
data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure
Simultaneous inference for misaligned multivariate functional data
We consider inference for misaligned multivariate functional data that
represents the same underlying curve, but where the functional samples have
systematic differences in shape. In this paper we introduce a new class of
generally applicable models where warping effects are modeled through nonlinear
transformation of latent Gaussian variables and systematic shape differences
are modeled by Gaussian processes. To model cross-covariance between sample
coordinates we introduce a class of low-dimensional cross-covariance structures
suitable for modeling multivariate functional data. We present a method for
doing maximum-likelihood estimation in the models and apply the method to three
data sets. The first data set is from a motion tracking system where the
spatial positions of a large number of body-markers are tracked in
three-dimensions over time. The second data set consists of height and weight
measurements for Danish boys. The third data set consists of three-dimensional
spatial hand paths from a controlled obstacle-avoidance experiment. We use the
developed method to estimate the cross-covariance structure, and use a
classification setup to demonstrate that the method outperforms
state-of-the-art methods for handling misaligned curve data.Comment: 44 pages in total including tables and figures. Additional 9 pages of
supplementary material and reference
Transformation of stimulus correlations by the retina
Redundancies and correlations in the responses of sensory neurons seem to
waste neural resources but can carry cues about structured stimuli and may help
the brain to correct for response errors. To assess how the retina negotiates
this tradeoff, we measured simultaneous responses from populations of ganglion
cells presented with natural and artificial stimuli that varied greatly in
correlation structure. We found that pairwise correlations in the retinal
output remained similar across stimuli with widely different spatio-temporal
correlations including white noise and natural movies. Meanwhile, purely
spatial correlations tended to increase correlations in the retinal response.
Responding to more correlated stimuli, ganglion cells had faster temporal
kernels and tended to have stronger surrounds. These properties of individual
cells, along with gain changes that opposed changes in effective contrast at
the ganglion cell input, largely explained the similarity of pairwise
correlations across stimuli where receptive field measurements were possible.Comment: author list corrected in metadat
Post-Nonlinear Mixtures and Beyond
Although sources in general nonlinear mixturm arc not separable iising only statistical
independence, a special and realistic case of nonlinear mixtnres, the post nonlinear
(PNL) mixture is separable choosing a suited separating system. Then, a natural approach is
based on the estimation of tho separating Bystem parameters by minimizing an indcpendence
criterion, like estimated mwce mutual information. This class of methods requires higher
(than 2) order statistics, and cannot separate Gaarsian sources. However, use of [weak) prior,
like source temporal correlation or nonstationarity, leads to other source separation Jgw
rithms, which are able to separate Gaussian sourra, and can even, for a few of them, works
with second-order statistics. Recently, modeling time correlated s011rces by Markov models,
we propose vcry efficient algorithms hmed on minimization of the conditional mutual information.
Currently, using the prior of temporally correlated sources, we investigate the fesihility
of inverting PNL mixtures with non-bijectiw non-liacarities, like quadratic functions. In this
paper, we review the main ICA and BSS results for riunlinear mixtures, present PNL models
and algorithms, and finish with advanced resutts using temporally correlated snu~s
Universal features of correlated bursty behaviour
Inhomogeneous temporal processes, like those appearing in human
communications, neuron spike trains, and seismic signals, consist of
high-activity bursty intervals alternating with long low-activity periods. In
recent studies such bursty behavior has been characterized by a fat-tailed
inter-event time distribution, while temporal correlations were measured by the
autocorrelation function. However, these characteristic functions are not
capable to fully characterize temporally correlated heterogenous behavior. Here
we show that the distribution of the number of events in a bursty period serves
as a good indicator of the dependencies, leading to the universal observation
of power-law distribution in a broad class of phenomena. We find that the
correlations in these quite different systems can be commonly interpreted by
memory effects and described by a simple phenomenological model, which displays
temporal behavior qualitatively similar to that in real systems
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