896 research outputs found
Understanding the brain through its spatial structure
The spatial location of cells in neural tissue can be easily extracted from many imaging modalities, but the information contained in spatial relationships between cells is seldom utilized. This is because of a lack of recognition of the importance of spatial relationships to some aspects of brain function, and the reflection in spatial statistics of other types of information. The mathematical tools necessary to describe spatial relationships are also unknown to many neuroscientists, and biologists in general.
We analyze two cases, and show that spatial relationships can be used to understand the role of a particular type of cell, the astrocyte, in Alzheimer's disease, and that the geometry of axons in the brain's white matter sheds light on the process of establishing connectivity between areas of the brain.
Astrocytes provide nutrients for neuronal metabolism, and regulate the chemical environment of the brain, activities that require manipulation of spatial distributions (of neurotransmitters, for example). We first show, through the use of a correlation function, that inter-astrocyte forces determine the size of independent regulatory domains in the cortex. By examining the spatial distribution of astrocytes in a mouse model of Alzheimer's Disease, we determine that astrocytes are not actively transported to fight the disease, as was previously thought.
The paths axons take through the white matter determine which parts of the brain are connected, and how quickly signals are transmitted. The rules that determine these paths (i.e. shortest distance) are currently unknown. By measurement of axon orientation distributions using three-point correlation functions and the statistics of axon turning and branching, we reveal that axons are restricted to growth in three directions, like a taxicab traversing city blocks, albeit in three-dimensions. We show how geometric restrictions at the small scale are related to large-scale trajectories. Finally we discuss the implications of this finding for experimental and theoretical connectomics
Universal Organization of Resting Brain Activity at the Thermodynamic Critical Point
Thermodynamic criticality describes emergent phenomena in a wide variety of
complex systems. In the mammalian brain, the complex dynamics that
spontaneously emerge from neuronal interactions have been characterized as
neuronal avalanches, a form of critical branching dynamics. Here, we show that
neuronal avalanches also reflect that the brain dynamics are organized close to
a thermodynamic critical point. We recorded spontaneous cortical activity in
monkeys and humans at rest using high-density intracranial microelectrode
arrays and magnetoencephalography, respectively. By numerically changing a
control parameter equivalent to thermodynamic temperature, we observed typical
critical behavior in cortical activities near the actual physiological
condition, including the phase transition of an order parameter, as well as the
divergence of susceptibility and specific heat. Finite-size scaling of these
quantities allowed us to derive robust critical exponents highly consistent
across monkey and humans that uncover a distinct, yet universal organization of
brain dynamics
New Identification and Decoding Techniques for Low-Density Parity-Check Codes
Error-correction coding schemes are indispensable for high-capacity high data-rate communication systems nowadays. Among various channel coding schemes, low-density parity-check (LDPC) codes introduced by pioneer Robert G. Gallager are prominent due to the capacity-approaching and superior error-correcting properties. There is no hard constraint on the code rate of LDPC codes. Consequently, it is ideal to incorporate LDPC codes with various code rate and codeword length in the adaptive modulation and coding (AMC) systems which change the encoder and the modulator adaptively to improve the system throughput. In conventional AMC systems, a dedicated control channel is assigned to coordinate the encoder/decoder changes. A questions then rises: if the AMC system still works when such a control channel is absent. This work gives positive answer to this question by investigating various scenarios consisting of different modulation schemes, such as quadrature-amplitude modulation (QAM), frequency-shift keying (FSK), and different channels, such as additive white Gaussian noise (AWGN) channels and fading channels. On the other hand, LDPC decoding is usually carried out by iterative belief-propagation (BP) algorithms. As LDPC codes become prevalent in advanced communication and storage systems, low-complexity LDPC decoding algorithms are favored in practical applications. In the conventional BP decoding algorithm, the stopping criterion is to check if all the parities are satisfied. This single rule may not be able to identify the undecodable blocks, as a result, the decoding time and power consumption are wasted for executing unnecessary iterations. In this work, we propose a new stopping criterion to identify the undecodable blocks in the early stage of the iterative decoding process. Furthermore, in the conventional BP decoding algorithm, the variable (check) nodes are updated in parallel. It is known that the number of iterations can be reduced by the serial scheduling algorithm. The informed dynamic scheduling (IDS) algorithms were proposed in the existing literatures to further reduce the number of iterations. However, the computational complexity involved in finding the update node in the existing IDS algorithms would not be neglected. In this work, we propose a new efficient IDS scheme which can provide better performance-complexity trade-off compared to the existing IDS ones. In addition, the iterative decoding threshold, which is used for differentiating which LDPC code is better, is investigated in this work. A family of LDPC codes, called LDPC convolutional codes, has drawn a lot of attentions from researchers in recent years due to the threshold saturation phenomenon. The IDT for an LDPC convolutional code may be computationally demanding when the termination length goes to thousand or even approaches infinity, especially for AWGN channels. In this work, we propose a fast IDT estimation algorithm which can greatly reduce the complexity of the IDT calculation for LDPC convolutional codes with arbitrary large termination length (including infinity). By utilizing our new IDT estimation algorithm, the IDTs for LDPC convolutional codes with arbitrary large termination length (including infinity) can be quickly obtained
Wide-Angle Multistatic Synthetic Aperture Radar: Focused Image Formation and Aliasing Artifact Mitigation
Traditional monostatic Synthetic Aperture Radar (SAR) platforms force the user to choose between two image types: larger, low resolution images or smaller, high resolution images. Switching to a Wide-Angle Multistatic Synthetic Aperture Radar (WAM-SAR) approach allows formation of large high-resolution images. Unfortunately, WAM-SAR suffers from two significant implementation problems. First, wavefront curvature effects, non-linear flight paths, and warped ground planes lead to image defocusing with traditional SAR processing methods. A new 3-D monostatic/bistatic image formation routine solves the defocusing problem, correcting for all relevant wide-angle effects. Inverse SAR (ISAR) imagery from a Radar Cross Section (RCS) chamber validates this approach. The second implementation problem stems from the large Doppler spread in the wide-angle scene, leading to severe aliasing problems. This research effort develops a new anti-aliasing technique using randomized Stepped-Frequency (SF) waveforms to form Doppler filter nulls coinciding with aliasing artifact locations. Both simulation and laboratory results demonstrate effective performance, eliminating more than 99% of the aliased energy
Early warning signals for critical transitions in complex systems
In this review, we present the different measures of early warning signals
that can indicate the occurrence of critical transitions in complex systems. We
start with the mechanisms that trigger critical transitions, how they relate to
warning signals and the methods used to detect early warning signals (EWS) for
sudden transitions or tipping. We discuss briefly a few applications in real
systems in this context, like transitions in ecology, climate and environment,
medicine, epidemics, finance and engineering. Towards the end, we mention the
issues in detecting EWS in specific applications and our perspective on future
trends in this area, especially related to sudden transitions in the dynamics
of connected systems on complex networks.Comment: 35 pages, 11 figure
Fractal analysis applied to light curves of Scuti stars
Fractal behaviour, i.e. scale invariance in spatio-temporal dynamics, have
been found to describe and model many systems in nature, in particular fluid
mechanics and geophysical related geometrical objects, like the convective
boundary layer of cumulus cloud fields, topographic landscapes, solar
granulation patterns, and observational astrophysical time series, like light
curves of pulsating stars. The main interest in the study of fractal properties
in such physical phenomena lies in the close relationships they have with
chaotic and turbulent dynamic. In this work we introduce some statistical tools
for fractal analysis of light curves: Rescaled Range Analysis (R/S),
Multifractal Spectra Analysis, and Coarse Graining Spectral Analysis (CGSA), an
FFT based algorithm, which can discriminate in a time series the stochastic
fractal power spectra from the harmonic one. An interesting application of
fractal analysis in asteroseismology concerns the joint use of all these tools
in order to develop classification criteria and algorithms for {\delta}-Scuti
pulsating stars. In fact from the fractal and multi-fractal fingerprints in
background noise of light curves we could infer on different mechanism of
stellar dynamic, among them rotation, modes excitation and magnetic activity.Comment: 13 pages, 10 figure
Maximum likelihood, parametric component separation and CMB B-mode detection in suborbital experiments
We investigate the performance of the parametric Maximum Likelihood component
separation method in the context of the CMB B-mode signal detection and its
characterization by small-scale CMB suborbital experiments. We consider
high-resolution (FWHM=8') balloon-borne and ground-based observatories mapping
low dust-contrast sky areas of 400 and 1000 square degrees, in three frequency
channels, 150, 250, 410 GHz, and 90, 150, 220 GHz, with sensitivity of order 1
to 10 micro-K per beam-size pixel. These are chosen to be representative of
some of the proposed, next-generation, bolometric experiments. We study the
residual foreground contributions left in the recovered CMB maps in the pixel
and harmonic domain and discuss their impact on a determination of the
tensor-to-scalar ratio, r. In particular, we find that the residuals derived
from the simulated data of the considered balloon-borne observatories are
sufficiently low not to be relevant for the B-mode science. However, the
ground-based observatories are in need of some external information to permit
satisfactory cleaning. We find that if such information is indeed available in
the latter case, both the ground-based and balloon-borne experiments can detect
the values of r as low as ~0.04 at 95% confidence level. The contribution of
the foreground residuals to these limits is found to be then subdominant and
these are driven by the statistical uncertainty due to CMB, including E-to-B
leakage, and noise. We emphasize that reaching such levels will require a
sufficient control of the level of systematic effects present in the data.Comment: 18 pages, 12 figures, 6 table
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