5,926 research outputs found
Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings, in vivo
Understanding the function of complex cortical circuits requires the
simultaneous recording of action potentials from many neurons in awake and
behaving animals. Practically, this can be achieved by extracellularly
recording from multiple brain sites using single wire electrodes. However, in
densely packed neural structures such as the human hippocampus, a single
electrode can record the activity of multiple neurons. Thus, analytic
techniques that differentiate action potentials of different neurons are
required. Offline spike sorting approaches are currently used to detect and
sort action potentials after finishing the experiment. Because the
opportunities to record from the human brain are relatively rare, it is
desirable to analyze large numbers of simultaneous recordings quickly using
online sorting and detection algorithms. In this way, the experiment can be
optimized for the particular response properties of the recorded neurons. Here
we present and evaluate a method that is capable of detecting and sorting
extracellular single-wire recordings in realtime. We demonstrate the utility of
the method by applying it to an extensive data set we acquired from
chronically-implanted depth electrodes in the hippocampus of human epilepsy
patients. This dataset is particularly challenging because it was recorded in a
noisy clinical environment. This method will allow the development of
closed-loop experiments, which immediately adapt the experimental stimuli
and/or tasks to the neural response observed.Comment: 9 figures, 2 tables. Journal of Neuroscience Methods 2006 (in press).
Journal of Neuroscience Methods, 2006 (in press
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery
PCA is one of the most widely used dimension reduction techniques. A related
easier problem is "subspace learning" or "subspace estimation". Given
relatively clean data, both are easily solved via singular value decomposition
(SVD). The problem of subspace learning or PCA in the presence of outliers is
called robust subspace learning or robust PCA (RPCA). For long data sequences,
if one tries to use a single lower dimensional subspace to represent the data,
the required subspace dimension may end up being quite large. For such data, a
better model is to assume that it lies in a low-dimensional subspace that can
change over time, albeit gradually. The problem of tracking such data (and the
subspaces) while being robust to outliers is called robust subspace tracking
(RST). This article provides a magazine-style overview of the entire field of
robust subspace learning and tracking. In particular solutions for three
problems are discussed in detail: RPCA via sparse+low-rank matrix decomposition
(S+LR), RST via S+LR, and "robust subspace recovery (RSR)". RSR assumes that an
entire data vector is either an outlier or an inlier. The S+LR formulation
instead assumes that outliers occur on only a few data vector indices and hence
are well modeled as sparse corruptions.Comment: To appear, IEEE Signal Processing Magazine, July 201
PPF - A Parallel Particle Filtering Library
We present the parallel particle filtering (PPF) software library, which
enables hybrid shared-memory/distributed-memory parallelization of particle
filtering (PF) algorithms combining the Message Passing Interface (MPI) with
multithreading for multi-level parallelism. The library is implemented in Java
and relies on OpenMPI's Java bindings for inter-process communication. It
includes dynamic load balancing, multi-thread balancing, and several
algorithmic improvements for PF, such as input-space domain decomposition. The
PPF library hides the difficulties of efficient parallel programming of PF
algorithms and provides application developers with the necessary tools for
parallel implementation of PF methods. We demonstrate the capabilities of the
PPF library using two distributed PF algorithms in two scenarios with different
numbers of particles. The PPF library runs a 38 million particle problem,
corresponding to more than 1.86 GB of particle data, on 192 cores with 67%
parallel efficiency. To the best of our knowledge, the PPF library is the first
open-source software that offers a parallel framework for PF applications.Comment: 8 pages, 8 figures; will appear in the proceedings of the IET Data
Fusion & Target Tracking Conference 201
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