413 research outputs found
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
Electroencephalograph (EEG) signal processing techniques for motor imagery Brain Computer interface systems
Brain-Computer Interface (BCI) system provides a channel for the brain to
control external devices using electrical activities of the brain without using the
peripheral nervous system. These BCI systems are being used in various medical
applications, for example controlling a wheelchair and neuroprosthesis devices for
the disabled, thereby assisting them in activities of daily living. People suffering
from Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis and completely locked
in are unable to perform any body movements because of the damage of the
peripheral nervous system, but their cognitive function is still intact. BCIs operate
external devices by acquiring brain signals and converting them to control
commands to operate external devices. Motor-imagery (MI) based BCI systems, in
particular, are based on the sensory-motor rhythms which are generated by the
imagination of body limbs. These signals can be decoded as control commands in
BCI application. Electroencephalogram (EEG) is commonly used for BCI applications
because it is non-invasive. The main challenges of decoding the EEG signal are
because it is non-stationary and has a low spatial resolution. The common spatial
pattern algorithm is considered to be the most effective technique for
discrimination of spatial filter but is easily affected by the presence of outliers.
Therefore, a robust algorithm is required for extraction of discriminative features
from the motor imagery EEG signals.
This thesis mainly aims in developing robust spatial filtering criteria which
are effective for classification of MI movements. We have proposed two approaches
for the robust classification of MI movements. The first approach is for the
classification of multiclass MI movements based on the thinICA (Independent
Component Analysis) and mCSP (multiclass Common Spatial Pattern Filter) method.
The observed results indicate that these approaches can be a step towards the
development of robust feature extraction for MI-based BCI system.
The main contribution of the thesis is the second criterion, which is based on
Alpha- Beta logarithmic-determinant divergence for the classification of two class
MI movements. A detailed study has been done by obtaining a link between the AB
log det divergence and CSP criterion. We propose a scaling parameter to enable a
similar way for selecting the respective filters like the CSP algorithm. Additionally,
the optimization of the gradient of AB log-det divergence for this application was
also performed. The Sub-ABLD (Subspace Alpha-Beta Log-Det divergence)
algorithm is proposed for the discrimination of two class MI movements. The
robustness of this algorithm is tested with both the simulated and real data from BCI
competition dataset. Finally, the resulting performances of the proposed algorithms
have been favorably compared with other existing algorithms
Three more Decades in Array Signal Processing Research: An Optimization and Structure Exploitation Perspective
The signal processing community currently witnesses the emergence of sensor
array processing and Direction-of-Arrival (DoA) estimation in various modern
applications, such as automotive radar, mobile user and millimeter wave indoor
localization, drone surveillance, as well as in new paradigms, such as joint
sensing and communication in future wireless systems. This trend is further
enhanced by technology leaps and availability of powerful and affordable
multi-antenna hardware platforms. The history of advances in super resolution
DoA estimation techniques is long, starting from the early parametric
multi-source methods such as the computationally expensive maximum likelihood
(ML) techniques to the early subspace-based techniques such as Pisarenko and
MUSIC. Inspired by the seminal review paper Two Decades of Array Signal
Processing Research: The Parametric Approach by Krim and Viberg published in
the IEEE Signal Processing Magazine, we are looking back at another three
decades in Array Signal Processing Research under the classical narrowband
array processing model based on second order statistics. We revisit major
trends in the field and retell the story of array signal processing from a
modern optimization and structure exploitation perspective. In our overview,
through prominent examples, we illustrate how different DoA estimation methods
can be cast as optimization problems with side constraints originating from
prior knowledge regarding the structure of the measurement system. Due to space
limitations, our review of the DoA estimation research in the past three
decades is by no means complete. For didactic reasons, we mainly focus on
developments in the field that easily relate the traditional multi-source
estimation criteria and choose simple illustrative examples.Comment: 16 pages, 8 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Scaling Multidimensional Inference for Big Structured Data
In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications [151]. In a
world of increasing sensor modalities, cheaper storage, and more data oriented questions, we are quickly passing the limits of tractable computations using traditional statistical analysis
methods. Methods which often show great results on simple data have difficulties processing complicated multidimensional data. Accuracy alone can no longer justify unwarranted memory
use and computational complexity. Improving the scaling properties of these methods for multidimensional data is the only way to make these methods relevant. In this work we explore methods for improving the scaling properties of parametric and nonparametric
models. Namely, we focus on the structure of the data to lower the complexity of a specific family of problems. The two types of structures considered in this work are distributive
optimization with separable constraints (Chapters 2-3), and scaling Gaussian processes for multidimensional lattice input (Chapters 4-5). By improving the scaling of these methods, we can expand their use to a wide range of applications which were previously intractable
open the door to new research questions
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Identification and Validation of Structures in Neural Population Responses
A fundamental challenge of neuroscience is to understand how interconnected populations of neurons give rise to the remarkable computational abilities of our brains. Large neural datasets offer promise, but they are perilous: they are too complex to be studied with traditional single-neuron analyses, and thus require new analyses that can uncover structure at the level of the population. However, since these analyses operate on large datasets, our intuition whether structure is significant breaks down. Hence, we run the risk of over-interpreting structure from the population data that may have a simple explanation. Thus, with population analysis methods, there is also a need for methods that can validate the significance of structure identified. In this dissertation, I discuss topics covering both the identification and the validation of structure in population data. In the first part, I discuss novel methods for uncovering the computational strategy employed by the motor cortex to flexibly switch between different neural computations. I demonstrate that collective activity patterns of motor cortex neurons related to different computations are orthogonal yet can still be linked, indicating a degree of flexibility that was not displayed or predicted by existing cortical models. In the second part, I discuss a novel analytical framework to rigorously test the novelty of population-level findings, given a specified set of primary features such as correlations across time, neurons and experimental conditions. This framework provides a general tool for validating population findings across the brain and across population-level hypotheses
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Statistical Machine Learning & Deep Neural Networks Applied to Neural Data Analysis
Computational neuroscience seeks to discover the underlying mechanisms by which neural activity is generated. With the recent advancement in neural data acquisition methods, the bottleneck of this pursuit is the analysis of ever-growing volume of neural data acquired in numerous labs from various experiments. These analyses can be broadly divided into two categories. First, extraction of high quality neuronal signals from noisy large scale recordings. Second, inference for statistical models aimed at explaining the neuronal signals and underlying processes that give rise to them. Conventionally, majority of the methodologies employed for this effort are based on statistics and signal processing. However, in recent years recruiting Artificial Neural Networks (ANN) for neural data analysis is gaining traction. This is due to their immense success in computer vision and natural language processing, and the stellar track record of ANN architectures generalizing to a wide variety of problems. In this work we investigate and improve upon statistical and ANN machine learning methods applied to multi-electrode array recordings and inference for dynamical systems that play critical roles in computational neuroscience.
In the first and second part of this thesis, we focus on spike sorting problem. The analysis of large-scale multi-neuronal spike train data is crucial for current and future of neuroscience research. However, this type of data is not available directly from recordings and require further processing to be converted into spike trains. Dense multi-electrode arrays (MEA) are standard methods for collecting such recordings. The processing needed to extract spike trains from these raw electrical signals is carried out by ``spike sorting'' algorithms. We introduce a robust and scalable MEA spike sorting pipeline YASS (Yet Another Spike Sorter) to address many challenges that are inherent to this task. We primarily pay attention to MEA data collected from the primate retina for important reasons such as the unique challenges and available side information that ultimately assist us in scoring different spike sorting pipelines. We also introduce a Neural Network architecture and an accompanying training scheme specifically devised to address the challenging task of deconvolution in MEA recordings.
In the last part, we shift our attention to inference for non-linear dynamics. Dynamical systems are the governing force behind many real world phenomena and temporally correlated data. Recently, a number of neural network architectures have been proposed to address inference for nonlinear dynamical systems. We introduce two different methods based on normalizing flows for posterior inference in latent non-linear dynamical systems. We also present gradient-based amortized posterior inference approaches using the auto-encoding variational Bayes framework that can be applied to a wide range of generative models with nonlinear dynamics. We call our method (FNF). FNF performs favorably against state-of-the-art inference methods in terms of accuracy of predictions and quality of uncovered codes and dynamics on synthetic data
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