3,010 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
Bump formation in a binary attractor neural network
This paper investigates the conditions for the formation of local bumps in
the activity of binary attractor neural networks with spatially dependent
connectivity. We show that these formations are observed when asymmetry between
the activity during the retrieval and learning is imposed. Analytical
approximation for the order parameters is derived. The corresponding phase
diagram shows a relatively large and stable region, where this effect is
observed, although the critical storage and the information capacities
drastically decrease inside that region. We demonstrate that the stability of
the network, when starting from the bump formation, is larger than the
stability when starting even from the whole pattern. Finally, we show a very
good agreement between the analytical results and the simulations performed for
different topologies of the network.Comment: about 14 page
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Tracking Target Signal Strengths on a Grid using Sparsity
Multi-target tracking is mainly challenged by the nonlinearity present in the
measurement equation, and the difficulty in fast and accurate data association.
To overcome these challenges, the present paper introduces a grid-based model
in which the state captures target signal strengths on a known spatial grid
(TSSG). This model leads to \emph{linear} state and measurement equations,
which bypass data association and can afford state estimation via
sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of
the novel model, two types of sparsity-cognizant TSSG-KF trackers are
developed: one effects sparsity through -norm regularization, and the
other invokes sparsity as an extra measurement. Iterative extended KF and
Gauss-Newton algorithms are developed for reduced-complexity tracking, along
with accurate error covariance updates for assessing performance of the
resultant sparsity-aware state estimators. Based on TSSG state estimates, more
informative target position and track estimates can be obtained in a follow-up
step, ensuring that track association and position estimation errors do not
propagate back into TSSG state estimates. The novel TSSG trackers do not
require knowing the number of targets or their signal strengths, and exhibit
considerably lower complexity than the benchmark hidden Markov model filter,
especially for a large number of targets. Numerical simulations demonstrate
that sparsity-cognizant trackers enjoy improved root mean-square error
performance at reduced complexity when compared to their sparsity-agnostic
counterparts.Comment: Submitted to IEEE Trans. on Signal Processin
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