62,387 research outputs found
Two-photon imaging and analysis of neural network dynamics
The glow of a starry night sky, the smell of a freshly brewed cup of coffee
or the sound of ocean waves breaking on the beach are representations of the
physical world that have been created by the dynamic interactions of thousands
of neurons in our brains. How the brain mediates perceptions, creates thoughts,
stores memories and initiates actions remains one of the most profound puzzles
in biology, if not all of science. A key to a mechanistic understanding of how
the nervous system works is the ability to analyze the dynamics of neuronal
networks in the living organism in the context of sensory stimulation and
behaviour. Dynamic brain properties have been fairly well characterized on the
microscopic level of individual neurons and on the macroscopic level of whole
brain areas largely with the help of various electrophysiological techniques.
However, our understanding of the mesoscopic level comprising local populations
of hundreds to thousands of neurons (so called 'microcircuits') remains
comparably poor. In large parts, this has been due to the technical
difficulties involved in recording from large networks of neurons with
single-cell spatial resolution and near- millisecond temporal resolution in the
brain of living animals. In recent years, two-photon microscopy has emerged as
a technique which meets many of these requirements and thus has become the
method of choice for the interrogation of local neural circuits. Here, we
review the state-of-research in the field of two-photon imaging of neuronal
populations, covering the topics of microscope technology, suitable fluorescent
indicator dyes, staining techniques, and in particular analysis techniques for
extracting relevant information from the fluorescence data. We expect that
functional analysis of neural networks using two-photon imaging will help to
decipher fundamental operational principles of neural microcircuits.Comment: 36 pages, 4 figures, accepted for publication in Reports on Progress
in Physic
A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam
A Short-Baseline Neutrino (SBN) physics program of three LAr-TPC detectors
located along the Booster Neutrino Beam (BNB) at Fermilab is presented. This
new SBN Program will deliver a rich and compelling physics opportunity,
including the ability to resolve a class of experimental anomalies in neutrino
physics and to perform the most sensitive search to date for sterile neutrinos
at the eV mass-scale through both appearance and disappearance oscillation
channels. Using data sets of 6.6e20 protons on target (P.O.T.) in the LAr1-ND
and ICARUS T600 detectors plus 13.2e20 P.O.T. in the MicroBooNE detector, we
estimate that a search for muon neutrino to electron neutrino appearance can be
performed with ~5 sigma sensitivity for the LSND allowed (99% C.L.) parameter
region. In this proposal for the SBN Program, we describe the physics analysis,
the conceptual design of the LAr1-ND detector, the design and refurbishment of
the T600 detector, the necessary infrastructure required to execute the
program, and a possible reconfiguration of the BNB target and horn system to
improve its performance for oscillation searches.Comment: 209 pages, 129 figure
Event Analysis of Pulse-reclosers in Distribution Systems Through Sparse Representation
The pulse-recloser uses pulse testing technology to verify that the line is
clear of faults before initiating a reclose operation, which significantly
reduces stress on the system components (e.g. substation transformers) and
voltage sags on adjacent feeders. Online event analysis of pulse-reclosers are
essential to increases the overall utility of the devices, especially when
there are numerous devices installed throughout the distribution system. In
this paper, field data recorded from several devices were analyzed to identify
specific activity and fault locations. An algorithm is developed to screen the
data to identify the status of each pole and to tag time windows with a
possible pulse event. In the next step, selected time windows are further
analyzed and classified using a sparse representation technique by solving an
l1-regularized least-square problem. This classification is obtained by
comparing the pulse signature with the reference dictionary to find a set that
most closely matches the pulse features. This work also sheds additional light
on the possibility of fault classification based on the pulse signature. Field
data collected from a distribution system are used to verify the effectiveness
and reliability of the proposed method.Comment: Accepted in: 19th International Conference on Intelligent System
Application to Power Systems (ISAP), San Antonio, TX, 201
Applied sensor fault detection, identification and data reconstruction based on PCA and SOMNN for industrial systems
The paper presents two readily implementable approaches for Sensor Fault Detection, Identification (SFD/I) and faulted sensor data reconstruction in complex systems, in real-time. Specifically, Principal Component Analysis (PCA) and Self-Organizing Map Neural Networks (SOMNNs) are demonstrated for use on industrial turbine systems. In the first approach, Squared Prediction Error (SPE) based on the PCA residual space is used for SFD. SPE contribution plot is employed for SFI. A missing value approach from an extension of PCA is applied for faulted sensor data reconstruction. In the second approach, SFD is performed by SOMNN based Estimation Error (EE), and SFI is achieved by EE contribution plot. Data reconstruction is based on an extension of the SOMNN algorithm. The results are compared in each examining stage. The validation of both approaches is demonstrated through experimental data during the commissioning of an industrial 15MW turbine
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