Encoding Cortical Dynamics in Sparse Features
Khan, Sheraz; Lefèvre, Julien; Baillet, Sylvain; Michmizos, Konstantinos P.; Ganesan, Santosh; Kitzbichler, Manfred G.; Zetino, Manuel; Hämäläinen, Matti S.; Papadelis, Christos; Kenet, Tal
2014-01-01
Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz–Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical
Encoding cortical dynamics in sparse features.
Khan, Sheraz; Lefèvre, Julien; Baillet, Sylvain; Michmizos, Konstantinos P; Ganesan, Santosh; Kitzbichler, Manfred G; Zetino, Manuel; Hämäläinen, Matti S; Papadelis, Christos; Kenet, Tal
2014-01-01
Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz-Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical
Sparse distributed memory overview
NASA Technical Reports Server (NTRS)
Raugh, Mike
1990-01-01
The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.
NASA Technical Reports Server (NTRS)
Kanerva, Pentti
1988-01-01
Theoretical models of the human brain and proposed neural-network computers are developed analytically. Chapters are devoted to the mathematical foundations, background material from computer science, the theory of idealized neurons, neurons as address decoders, and the search of memory for the best match. Consideration is given to sparse memory, distributed storage, the storage and retrieval of sequences, the construction of distributed memory, and the organization of an autonomous learning system.
Kanerva, P.
1988-01-01
Theoretical models of the human brain and proposed neural-network computers are developed analytically. Chapters are devoted to the mathematical foundations, background material from computer science, the theory of idealized neurons, neurons as address decoders, and the search of memory for the best match. Consideration is given to sparse memory, distributed storage, the storage and retrieval of sequences, the construction of distributed memory, and the organization of an autonomous learning system. 63 refs.
NASA Technical Reports Server (NTRS)
Denning, Peter J.
1989-01-01
Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines - e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, continuation of a sequence of events when given a cue from the middle, knowing that one doesn't know, or getting stuck with an answer on the tip of one's tongue. These behaviors are now within reach of machines that can be incorporated into the computing systems of robots capable of seeing, talking, and manipulating. Kanerva's theory is a break with the Western rationalistic tradition, allowing a new interpretation of learning and cognition that respects biology and the mysteries of individual human beings.
Sparse asynchronous cortical generators can produce measurable scalp EEG signals.
von Ellenrieder, Nicolás; Dan, Jonathan; Frauscher, Birgit; Gotman, Jean
2016-09-01
We investigate to what degree the synchronous activation of a smooth patch of cortex is necessary for observing EEG scalp activity. We perform extensive simulations to compare the activity generated on the scalp by different models of cortical activation, based on intracranial EEG findings reported in the literature. The spatial activation is modeled as a cortical patch of constant activation or as random sets of small generators (0.1 to 3cm(2) each) concentrated in a cortical region. Temporal activation models for the generation of oscillatory activity are either equal phase or random phase across the cortical patches. The results show that smooth or random spatial activation profiles produce scalp electric potential distributions with the same shape. Also, in the generation of oscillatory activity, multiple cortical generators with random phase produce scalp activity attenuated on average only 2 to 4 times compared to generators with equal phase. Sparse asynchronous cortical generators can produce measurable scalp EEG. This is a possible explanation for seemingly paradoxical observations of simultaneous disorganized intracranial activity and scalp EEG signals. Thus, the standard interpretation of scalp EEG might constitute an oversimplification of the underlying brain activity. PMID:27262240
Sparse cortical source localization using spatio-temporal atoms.
Korats, Gundars; Ranta, Radu; Le Cam, Steven; Louis-Dorr, Valérie
2015-08-01
This paper addresses the problem of sparse localization of cortical sources from scalp EEG recordings. Localization algorithms use propagation model under spatial and/or temporal constraints, but their performance highly depends on the data signal-to-noise ratio (SNR). In this work we propose a dictionary based sparse localization method which uses a data driven spatio-temporal dictionary to reconstruct the measurements using Single Best Replacement (SBR) and Continuation Single Best Replacement (CSBR) algorithms. We tested and compared our methods with the well-known MUSIC and RAP-MUSIC algorithms on simulated realistic data. Tests were carried out for different noise levels. The results show that our method has a strong advantage over MUSIC-type methods in case of synchronized sources. PMID:26737185
Distributed memory compiler design for sparse problems
NASA Technical Reports Server (NTRS)
Wu, Janet; Saltz, Joel; Berryman, Harry; Hiranandani, Seema
1991-01-01
A compiler and runtime support mechanism is described and demonstrated. The methods presented are capable of solving a wide range of sparse and unstructured problems in scientific computing. The compiler takes as input a FORTRAN 77 program enhanced with specifications for distributing data, and the compiler outputs a message passing program that runs on a distributed memory computer. The runtime support for this compiler is a library of primitives designed to efficiently support irregular patterns of distributed array accesses and irregular distributed array partitions. A variety of Intel iPSC/860 performance results obtained through the use of this compiler are presented.
Statistical prediction with Kanerva's sparse distributed memory
NASA Technical Reports Server (NTRS)
Rogers, David
1989-01-01
A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory.
Notes on implementation of sparsely distributed memory
NASA Technical Reports Server (NTRS)
Keeler, J. D.; Denning, P. J.
1986-01-01
The Sparsely Distributed Memory (SDM) developed by Kanerva is an unconventional memory design with very interesting and desirable properties. The memory works in a manner that is closely related to modern theories of human memory. The SDM model is discussed in terms of its implementation in hardware. Two appendices discuss the unconventional approaches of the SDM: Appendix A treats a resistive circuit for fast, parallel address decoding; and Appendix B treats a systolic array for high throughput read and write operations.
A view of Kanerva's sparse distributed memory
NASA Technical Reports Server (NTRS)
Denning, P. J.
1986-01-01
Pentti Kanerva is working on a new class of computers, which are called pattern computers. Pattern computers may close the gap between capabilities of biological organisms to recognize and act on patterns (visual, auditory, tactile, or olfactory) and capabilities of modern computers. Combinations of numeric, symbolic, and pattern computers may one day be capable of sustaining robots. The overview of the requirements for a pattern computer, a summary of Kanerva's Sparse Distributed Memory (SDM), and examples of tasks this computer can be expected to perform well are given.
Integer sparse distributed memory: analysis and results.
Snaider, Javier; Franklin, Stan; Strain, Steve; George, E Olusegun
2013-10-01
Sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean vectors. Here we present an extension of the original SDM, the Integer SDM that uses modular arithmetic integer vectors rather than binary vectors. This extension preserves many of the desirable properties of the original SDM: auto-associativity, content addressability, distributed storage, and robustness over noisy inputs. In addition, it improves the representation capabilities of the memory and is more robust over normalization. It can also be extended to support forgetting and reliable sequence storage. We performed several simulations that test the noise robustness property and capacity of the memory. Theoretical analyses of the memory's fidelity and capacity are also presented. PMID:23747569
Sparse distributed memory and related models
NASA Technical Reports Server (NTRS)
Kanerva, Pentti
1992-01-01
Described here is sparse distributed memory (SDM) as a neural-net associative memory. It is characterized by two weight matrices and by a large internal dimension - the number of hidden units is much larger than the number of input or output units. The first matrix, A, is fixed and possibly random, and the second matrix, C, is modifiable. The SDM is compared and contrasted to (1) computer memory, (2) correlation-matrix memory, (3) feet-forward artificial neural network, (4) cortex of the cerebellum, (5) Marr and Albus models of the cerebellum, and (6) Albus' cerebellar model arithmetic computer (CMAC). Several variations of the basic SDM design are discussed: the selected-coordinate and hyperplane designs of Jaeckel, the pseudorandom associative neural memory of Hassoun, and SDM with real-valued input variables by Prager and Fallside. SDM research conducted mainly at the Research Institute for Advanced Computer Science (RIACS) in 1986-1991 is highlighted.
Sparse distributed memory: Principles and operation
NASA Technical Reports Server (NTRS)
Flynn, M. J.; Kanerva, P.; Bhadkamkar, N.
1989-01-01
Sparse distributed memory is a generalized random access memory (RAM) for long (1000 bit) binary words. Such words can be written into and read from the memory, and they can also be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original write address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech recognition and scene analysis, in signal detection and verification, and in adaptive control of automated equipment, in general, in dealing with real world information in real time. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. Major design issues were resolved which were faced in building the memories. The design is described of a prototype memory with 256 bit addresses and from 8 to 128 K locations for 256 bit words. A key aspect of the design is extensive use of dynamic RAM and other standard components.
Sparse distributed memory prototype: Principles of operation
NASA Technical Reports Server (NTRS)
Flynn, Michael J.; Kanerva, Pentti; Ahanin, Bahram; Bhadkamkar, Neal; Flaherty, Paul; Hickey, Philip
1988-01-01
Sparse distributed memory is a generalized random access memory (RAM) for long binary words. Such words can be written into and read from the memory, and they can be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original right address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech and scene analysis, in signal detection and verification, and in adaptive control of automated equipment. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. The research is aimed at resolving major design issues that have to be faced in building the memories. The design of a prototype memory with 256-bit addresses and from 8K to 128K locations for 256-bit words is described. A key aspect of the design is extensive use of dynamic RAM and other standard components.
Sparsey™: event recognition via deep hierarchical sparse distributed codes
Rinkus, Gerard J.
2014-01-01
The visual cortex's hierarchical, multi-level organization is captured in many biologically inspired computational vision models, the general idea being that progressively larger scale (spatially/temporally) and more complex visual features are represented in progressively higher areas. However, most earlier models use localist representations (codes) in each representational field (which we equate with the cortical macrocolumn, “mac”), at each level. In localism, each represented feature/concept/event (hereinafter “item”) is coded by a single unit. The model we describe, Sparsey, is hierarchical as well but crucially, it uses sparse distributed coding (SDC) in every mac in all levels. In SDC, each represented item is coded by a small subset of the mac's units. The SDCs of different items can overlap and the size of overlap between items can be used to represent their similarity. The difference between localism and SDC is crucial because SDC allows the two essential operations of associative memory, storing a new item and retrieving the best-matching stored item, to be done in fixed time for the life of the model. Since the model's core algorithm, which does both storage and retrieval (inference), makes a single pass over all macs on each time step, the overall model's storage/retrieval operation is also fixed-time, a criterion we consider essential for scalability to the huge (“Big Data”) problems. A 2010 paper described a nonhierarchical version of this model in the context of purely spatial pattern processing. Here, we elaborate a fully hierarchical model (arbitrary numbers of levels and macs per level), describing novel model principles like progressive critical periods, dynamic modulation of principal cells' activation functions based on a mac-level familiarity measure, representation of multiple simultaneously active hypotheses, a novel method of time warp invariant recognition, and we report results showing learning/recognition of spatiotemporal
BIRD: A general interface for sparse distributed memory simulators
NASA Technical Reports Server (NTRS)
Rogers, David
1990-01-01
Kanerva's sparse distributed memory (SDM) has now been implemented for at least six different computers, including SUN3 workstations, the Apple Macintosh, and the Connection Machine. A common interface for input of commands would both aid testing of programs on a broad range of computer architectures and assist users in transferring results from research environments to applications. A common interface also allows secondary programs to generate command sequences for a sparse distributed memory, which may then be executed on the appropriate hardware. The BIRD program is an attempt to create such an interface. Simplifying access to different simulators should assist developers in finding appropriate uses for SDM.
Two demonstrators and a simulator for a sparse, distributed memory
NASA Technical Reports Server (NTRS)
Brown, Robert L.
1987-01-01
Described are two programs demonstrating different aspects of Kanerva's Sparse, Distributed Memory (SDM). These programs run on Sun 3 workstations, one using color, and have straightforward graphically oriented user interfaces and graphical output. Presented are descriptions of the programs, how to use them, and what they show. Additionally, this paper describes the software simulator behind each program.
Investigation of wall-bounded turbulence over sparsely distributed roughness
NASA Astrophysics Data System (ADS)
Placidi, Marco; Ganapathisubramani, Bharath
2011-11-01
The effects of sparsely distributed roughness elements on the structure of a turbulent boundary layer are examined by performing a series of Particle Image Velocimetry (PIV) experiments in a wind tunnel. From the literature, the best way to characterise a rough wall, especially one where the density of roughness elements is sparse, is unclear. In this study, rough surfaces consisting of sparsely and uniformly distributed LEGO® blocks are used. Five different patterns are adopted in order to examine the effects of frontal solidity (λf, frontal area of the roughness elements per unit wall-parallel area), plan solidity (λp, plan area of roughness elements per unit wall-parallel area) and the geometry of the roughness element (square and cylindrical elements), on the turbulence structure. The Karman number, Reτ , has been matched, at the value of approximately 2300, in order to compare across the different cases. In the talk, we will present detailed analysis of mean and rms velocity profiles, Reynolds stresses and quadrant decomposition.
EPR Oximetry in Three Spatial Dimensions using Sparse Spin Distribution
Som, Subhojit; Potter, Lee C.; Ahmad, Rizwan; Vikram, Deepti S.; Kuppusamy, Periannan
2008-01-01
A method is presented to use continuous wave electron paramagnetic resonance imaging for rapid measurement of oxygen partial pressure in three spatial dimensions. A particulate paramagnetic probe is employed to create a sparse distribution of spins in a volume of interest. Information encoding location and spectral linewidth is collected by varying the spatial orientation and strength of an applied magnetic gradient field. Data processing exploits the spatial sparseness of spins to detect voxels with nonzero spin and to estimate the spectral linewidth for those voxels. The parsimonious representation of spin locations and linewidths permits an order of magnitude reduction in data acquisition time, compared to four-dimensional tomographic reconstruction using traditional spectral-spatial imaging. The proposed oximetry method is experimentally demonstrated for a lithium octa-n-butoxy naphthalocyanine (LiNc-BuO) probe using an L-band EPR spectrometer. PMID:18538600
Kanerva's sparse distributed memory with multiple hamming thresholds
NASA Technical Reports Server (NTRS)
Pohja, Seppo; Kaski, Kimmo
1992-01-01
If the stored input patterns of Kanerva's Sparse Distributed Memory (SDM) are highly correlated, utilization of the storage capacity is very low compared to the case of uniformly distributed random input patterns. We consider a variation of SDM that has a better storage capacity utilization for correlated input patterns. This approach uses a separate selection threshold for each physical storage address or hard location. The selection of the hard locations for reading or writing can be done in parallel of which SDM implementations can benefit.
Weir, Keiko; Blanquie, Oriane; Kilb, Werner; Luhmann, Heiko J.; Sinning, Anne
2015-01-01
Primary neuronal cultures share many typical features with the in vivo situation, including similarities in distinct electrical activity patterns and synaptic network interactions. Here, we use multi-electrode array (MEA) recordings from spontaneously active cultures of wildtype and glutamic acid decarboxylase 67 (GAD67)-green fluorescent protein (GFP) transgenic mice to evaluate which spike parameters differ between GABAergic interneurons and principal, putatively glutamatergic neurons. To analyze this question we combine MEA recordings with optical imaging in sparse cortical cultures to assign individual spikes to visually-identified single neurons. In our culture system, excitatory and inhibitory neurons are present at a similar ratio as described in vivo, and spike waveform characteristics and firing patterns are fully developed after 2 weeks in vitro. Spike amplitude, but not other spike waveform parameters, correlated with the distance between the recording electrode and the location of the assigned neuron’s soma. Cluster analysis of spike waveform properties revealed no particular cell population that may be assigned to putative inhibitory or excitatory neurons. Moreover, experiments in primary cultures from transgenic GAD67-GFP mice, which allow optical identification of GABAergic interneurons and thus unambiguous assignment of extracellular signals, did not reveal any significant difference in spike timing and spike waveform parameters between inhibitory and excitatory neurons. Despite of our detailed characterization of spike waveform and temporal spiking properties we could not identify an unequivocal electrical parameter to discriminate between individual excitatory and inhibitory neurons in vitro. Our data suggest that under in vitro conditions cellular classifications of single neurons on the basis of their extracellular firing properties should be treated with caution. PMID:25642167
Multiple peaks of species abundance distributions induced by sparse interactions.
Obuchi, Tomoyuki; Kabashima, Yoshiyuki; Tokita, Kei
2016-08-01
We investigate the replicator dynamics with "sparse" symmetric interactions which represent specialist-specialist interactions in ecological communities. By considering a large self-interaction u, we conduct a perturbative expansion which manifests that the nature of the interactions has a direct impact on the species abundance distribution. The central results are all species coexistence in a realistic range of the model parameters and that a certain discrete nature of the interactions induces multiple peaks in the species abundance distribution, providing the possibility of theoretically explaining multiple peaks observed in various field studies. To get more quantitative information, we also construct a non-perturbative theory which becomes exact on tree-like networks if all the species coexist, providing exact critical values of u below which extinct species emerge. Numerical simulations in various different situations are conducted and they clarify the robustness of the presented mechanism of all species coexistence and multiple peaks in the species abundance distributions. PMID:27627322
Improved load distribution in parallel sparse Cholesky factorization
NASA Technical Reports Server (NTRS)
Rothberg, Edward; Schreiber, Robert
1994-01-01
Compared to the customary column-oriented approaches, block-oriented, distributed-memory sparse Cholesky factorization benefits from an asymptotic reduction in interprocessor communication volume and an asymptotic increase in the amount of concurrency that is exposed in the problem. Unfortunately, block-oriented approaches (specifically, the block fan-out method) have suffered from poor balance of the computational load. As a result, achieved performance can be quite low. This paper investigates the reasons for this load imbalance and proposes simple block mapping heuristics that dramatically improve it. The result is a roughly 20% increase in realized parallel factorization performance, as demonstrated by performance results from an Intel Paragon system. We have achieved performance of nearly 3.2 billion floating point operations per second with this technique on a 196-node Paragon system.
NASA Technical Reports Server (NTRS)
Rogers, David
1988-01-01
The advent of the Connection Machine profoundly changes the world of supercomputers. The highly nontraditional architecture makes possible the exploration of algorithms that were impractical for standard Von Neumann architectures. Sparse distributed memory (SDM) is an example of such an algorithm. Sparse distributed memory is a particularly simple and elegant formulation for an associative memory. The foundations for sparse distributed memory are described, and some simple examples of using the memory are presented. The relationship of sparse distributed memory to three important computational systems is shown: random-access memory, neural networks, and the cerebellum of the brain. Finally, the implementation of the algorithm for sparse distributed memory on the Connection Machine is discussed.
Two-dimensional shape recognition using sparse distributed memory
NASA Technical Reports Server (NTRS)
Kanerva, Pentti; Olshausen, Bruno
1990-01-01
Researchers propose a method for recognizing two-dimensional shapes (hand-drawn characters, for example) with an associative memory. The method consists of two stages: first, the image is preprocessed to extract tangents to the contour of the shape; second, the set of tangents is converted to a long bit string for recognition with sparse distributed memory (SDM). SDM provides a simple, massively parallel architecture for an associative memory. Long bit vectors (256 to 1000 bits, for example) serve as both data and addresses to the memory, and patterns are grouped or classified according to similarity in Hamming distance. At the moment, tangents are extracted in a simple manner by progressively blurring the image and then using a Canny-type edge detector (Canny, 1986) to find edges at each stage of blurring. This results in a grid of tangents. While the technique used for obtaining the tangents is at present rather ad hoc, researchers plan to adopt an existing framework for extracting edge orientation information over a variety of resolutions, such as suggested by Watson (1987, 1983), Marr and Hildreth (1980), or Canny (1986).
In-Memory Computing Architectures for Sparse Distributed Memory.
Kang, Mingu; Shanbhag, Naresh R
2016-08-01
This paper presents an energy-efficient and high-throughput architecture for Sparse Distributed Memory (SDM)-a computational model of the human brain [1]. The proposed SDM architecture is based on the recently proposed in-memory computing kernel for machine learning applications called Compute Memory (CM) [2], [3]. CM achieves energy and throughput efficiencies by deeply embedding computation into the memory array. SDM-specific techniques such as hierarchical binary decision (HBD) are employed to reduce the delay and energy further. The CM-based SDM (CM-SDM) is a mixed-signal circuit, and hence circuit-aware behavioral, energy, and delay models in a 65 nm CMOS process are developed in order to predict system performance of SDM architectures in the auto- and hetero-associative modes. The delay and energy models indicate that CM-SDM, in general, can achieve up to 25 × and 12 × delay and energy reduction, respectively, over conventional SDM. When classifying 16 × 16 binary images with high noise levels (input bad pixel ratios: 15%-25%) into nine classes, all SDM architectures are able to generate output bad pixel ratios (Bo) ≤ 2%. The CM-SDM exhibits negligible loss in accuracy, i.e., its Bo degradation is within 0.4% as compared to that of the conventional SDM. PMID:27305686
Sparse Solution of Fiber Orientation Distribution Function by Diffusion Decomposition
Yeh, Fang-Cheng; Tseng, Wen-Yih Isaac
2013-01-01
Fiber orientation is the key information in diffusion tractography. Several deconvolution methods have been proposed to obtain fiber orientations by estimating a fiber orientation distribution function (ODF). However, the L2 regularization used in deconvolution often leads to false fibers that compromise the specificity of the results. To address this problem, we propose a method called diffusion decomposition, which obtains a sparse solution of fiber ODF by decomposing the diffusion ODF obtained from q-ball imaging (QBI), diffusion spectrum imaging (DSI), or generalized q-sampling imaging (GQI). A simulation study, a phantom study, and an in-vivo study were conducted to examine the performance of diffusion decomposition. The simulation study showed that diffusion decomposition was more accurate than both constrained spherical deconvolution and ball-and-sticks model. The phantom study showed that the angular error of diffusion decomposition was significantly lower than those of constrained spherical deconvolution at 30° crossing and ball-and-sticks model at 60° crossing. The in-vivo study showed that diffusion decomposition can be applied to QBI, DSI, or GQI, and the resolved fiber orientations were consistent regardless of the diffusion sampling schemes and diffusion reconstruction methods. The performance of diffusion decomposition was further demonstrated by resolving crossing fibers on a 30-direction QBI dataset and a 40-direction DSI dataset. In conclusion, diffusion decomposition can improve angular resolution and resolve crossing fibers in datasets with low SNR and substantially reduced number of diffusion encoding directions. These advantages may be valuable for human connectome studies and clinical research. PMID:24146772
Is cortical distribution of spectral power a stable individual characteristic?
Knyazev, Gennady G
2009-05-01
General understanding in EEG research is that cortical distribution of spectral power varies as a function of time, frequency, state, and experimental condition. There are findings, however, which show that individual-specific patterns of cortical spectral power distribution could be amazingly stable, at least in some experimental conditions. In this study two different experimental datasets were used to analyze stability and variability of individual pattern of cortical spectral power distribution across time, experimental conditions, and frequency bands. First experiment consisted of presentation of pictures of emotional facial expressions. Second experiment was an auditory stop-signal task. In both experiments a number of psychometric measures were obtained from each participant. It has been shown that in spite of high short-term variability, individual-specific patterns of cortical spectral power distribution are remarkably stable across frequency bands, long periods of time, and experimental conditions. These patterns are related to state and trait participant's characteristics. The antero-posterior spectral power gradient emerged as the most prominent feature associated with important personality dimensions. Relatively higher oscillatory activity in the frontal cortical region relates to female gender and Behavioral Inhibition tendencies. Relatively higher activity at posterior sites is associated with Extraversion. Significant differences in event-related spectral perturbations upon presentation of emotionally loaded stimuli were found between high and low antero-posterior gradient participants. These data show that cortical distribution of oscillatory activity may be seen as a relatively stable individual characteristic. Enhanced or diminished oscillatory activity of some cortical regions, such as the prefrontal cortex, may play an important role in organization of human behavior. PMID:19047002
Storing structured sparse memories in a multi-modular cortical network model.
Dubreuil, Alexis M; Brunel, Nicolas
2016-04-01
We study the memory performance of a class of modular attractor neural networks, where modules are potentially fully-connected networks connected to each other via diluted long-range connections. On this anatomical architecture we store memory patterns of activity using a Willshaw-type learning rule. P patterns are split in categories, such that patterns of the same category activate the same set of modules. We first compute the maximal storage capacity of these networks. We then investigate their error-correction properties through an exhaustive exploration of parameter space, and identify regions where the networks behave as an associative memory device. The crucial parameters that control the retrieval abilities of the network are (1) the ratio between the number of synaptic contacts of long- and short-range origins (2) the number of categories in which a module is activated and (3) the amount of local inhibition. We discuss the relationship between our model and networks of cortical patches that have been observed in different cortical areas. PMID:26852335
Cortical bone distribution in the femoral neck of strepsirhine primates.
Demes, B; Jungers, W L; Walker, C
2000-10-01
The thickness of the inferior and superior cortices of the femoral neck was measured on X-rays of 181 strepsirhine primate femora representing 24 species. Neck length, neck depth and neck-shaft angle were also measured. The strength of the femoral neck in frontal bending was estimated by modeling the neck as a hollow cylinder, with neck depth as the outer diameter and cortical thickness representing the superior and inferior shell dimensions. Results indicate that the inferior cortex is always thicker than the superior cortex. The ratio of superior to inferior cortical thickness is highly variable but distinguishes two of the three locomotor groups in the sample. Vertical clingers and leapers have higher ratios (i.e., a more even distribution of cortical bone) than quadrupeds. The slow climbers tend to have the lowest ratios, although they do not differ significantly from the leapers and quadrupeds. These results do not confirm prior theoretical expectations and reported data for anthropoid primates that link greater asymmetry of the cortical shell to more stereotypical hip excursions. The ratio of superior to inferior cortical thickness is unrelated to body mass, femoral neck length, and neck-shaft angle, calling into question whether the short neck of strepsirhine primates acts as a cantilever beam in bending. On the other hand, the estimated section moduli are highly correlated with body mass and neck length, a correlation that is driven primarily by body mass. In conclusion, we believe that an alternative interpretation to the cantilever beam model is needed to explain the asymmetry in bone distribution in the femoral neck, at least in strepsirhine primates (e.g., a thicker inferior cortex is required to reinforce the strongly curved inferior surface). As in prior studies of cross-sectional geometry of long bones, we found slightly positive allometry of cortical dimensions with body mass. PMID:11006046
SuperLU{_}DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems
Li, Xiaoye S.; Demmel, James W.
2002-03-27
In this paper, we present the main algorithmic features in the software package SuperLU{_}DIST, a distributed-memory sparse direct solver for large sets of linear equations. We give in detail our parallelization strategies, with focus on scalability issues, and demonstrate the parallel performance and scalability on current machines. The solver is based on sparse Gaussian elimination, with an innovative static pivoting strategy proposed earlier by the authors. The main advantage of static pivoting over classical partial pivoting is that it permits a priori determination of data structures and communication pattern for sparse Gaussian elimination, which makes it more scalable on distributed memory machines. Based on this a priori knowledge, we designed highly parallel and scalable algorithms for both LU decomposition and triangular solve and we show that they are suitable for large-scale distributed memory machines.
NASA Technical Reports Server (NTRS)
Kanerva, P.
1986-01-01
To determine the relation of the sparse, distributed memory to other architectures, a broad review of the literature was made. The memory is called a pattern memory because they work with large patterns of features (high-dimensional vectors). A pattern is stored in a pattern memory by distributing it over a large number of storage elements and by superimposing it over other stored patterns. A pattern is retrieved by mathematical or statistical reconstruction from the distributed elements. Three pattern memories are discussed.
An empirical investigation of sparse distributed memory using discrete speech recognition
NASA Technical Reports Server (NTRS)
Danforth, Douglas G.
1990-01-01
Presented here is a step by step analysis of how the basic Sparse Distributed Memory (SDM) model can be modified to enhance its generalization capabilities for classification tasks. Data is taken from speech generated by a single talker. Experiments are used to investigate the theory of associative memories and the question of generalization from specific instances.
A class of designs for a sparse distributed memory
NASA Technical Reports Server (NTRS)
Jaeckel, Louis A.
1989-01-01
A general class of designs for a space distributed memory (SDM) is described. The author shows that Kanerva's original design and the selected-coordinate design are related, and that there is a series of possible intermediate designs between those two designs. In each such design, the set of addresses that activate a memory location is a sphere in the address space. We can also have hybrid designs, in which the memory locations may be a mixture of those found in the other designs. In some applications, the bits of the read and write addresses that will actually be used might be mostly zeros; that is, the addresses might lie on or near z hyperplane in the address space. The author describes a hyperplane design which is adapted to this situation and compares it to an adaptation of Kanerva's design. To study the performance of these designs, he computes the expected number of memory locations activated by both of two addresses.
Learning to read aloud: A neural network approach using sparse distributed memory
NASA Technical Reports Server (NTRS)
Joglekar, Umesh Dwarkanath
1989-01-01
An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.
NASA Technical Reports Server (NTRS)
Jaeckel, Louis A.
1988-01-01
In Kanerva's Sparse Distributed Memory, writing to and reading from the memory are done in relation to spheres in an n-dimensional binary vector space. Thus it is important to know how many points are in the intersection of two spheres in this space. Two proofs are given of Wang's formula for spheres of unequal radii, and an integral approximation for the intersection in this case.
Analysis, tuning and comparison of two general sparse solvers for distributed memory computers
Amestoy, P.R.; Duff, I.S.; L'Excellent, J.-Y.; Li, X.S.
2000-06-30
We describe the work performed in the context of a Franco-Berkeley funded project between NERSC-LBNL located in Berkeley (USA) and CERFACS-ENSEEIHT located in Toulouse (France). We discuss both the tuning and performance analysis of two distributed memory sparse solvers (superlu from Berkeley and mumps from Toulouse) on the 512 processor Cray T3E from NERSC (Lawrence Berkeley National Laboratory). This project gave us the opportunity to improve the algorithms and add new features to the codes. We then quite extensively analyze and compare the two approaches on a set of large problems from real applications. We further explain the main differences in the behavior of the approaches on artificial regular grid problems. As a conclusion to this activity report, we mention a set of parallel sparse solvers on which this type of study should be extended.
Künzle, H; Rehkämper, G
1992-01-01
Using retrograde axonal flow and wheatgerm agglutinin conjugated to horseradish peroxidase, we studied the distribution of cortical neurons giving rise to spinal and dorsal column nuclear projections, and correlated the regions involved in the projections with the cytoarchitectonic areas recently identified in the lesser hedgehog tenrec, Echinops telfairi (Insectivora). Labeled cortical neurons were most numerous following injections of tracer into higher cervical segments, whereas almost none were found following thoracic injections. The cortical labeling appeared more prominent ipsilaterally than contralaterally after spinal injections, although it was more prominent on the contralateral side after injection into the dorsal column nuclear complex. The majority of labeled neurons found in lamina V occupied the neocortex adjacent to the interhemispheric fissure along the rostrocaudal extent of the small corpus callosum. This location corresponded to an intermediate rostrocaudal portion of the hemisphere, and particularly to area 2 of Rehkämper. In some cases, adjacent portions of areas 1 and 3 were also involved, as well as neocortical regions of the lateral hemisphere. The present data did not suggest a somatotopic organization of the projections; likewise, evidence for the presence of more than one somatosensorimotor representation was sparse. PMID:1414117
Oryspayev, Dossay; Aktulga, Hasan Metin; Sosonkina, Masha; Maris, Pieter; Vary, James P.
2015-07-14
In this article, sparse matrix vector multiply (SpMVM) is an important kernel that frequently arises in high performance computing applications. Due to its low arithmetic intensity, several approaches have been proposed in literature to improve its scalability and efficiency in large scale computations. In this paper, our target systems are high end multi-core architectures and we use messaging passing interface + open multiprocessing hybrid programming model for parallelism. We analyze the performance of recently proposed implementation of the distributed symmetric SpMVM, originally developed for large sparse symmetric matrices arising in ab initio nuclear structure calculations. We also study important features of this implementation and compare with previously reported implementations that do not exploit underlying symmetry. Our SpMVM implementations leverage the hybrid paradigm to efficiently overlap expensive communications with computations. Our main comparison criterion is the "CPU core hours" metric, which is the main measure of resource usage on supercomputers. We analyze the effects of topology-aware mapping heuristic using simplified network load model. Furthermore, we have tested the different SpMVM implementations on two large clusters with 3D Torus and Dragonfly topology. Our results show that the distributed SpMVM implementation that exploits matrix symmetry and hides communication yields the best value for the "CPU core hours" metric and significantly reduces data movement overheads.
Oryspayev, Dossay; Aktulga, Hasan Metin; Sosonkina, Masha; Maris, Pieter; Vary, James P.
2015-07-14
In this article, sparse matrix vector multiply (SpMVM) is an important kernel that frequently arises in high performance computing applications. Due to its low arithmetic intensity, several approaches have been proposed in literature to improve its scalability and efficiency in large scale computations. In this paper, our target systems are high end multi-core architectures and we use messaging passing interface + open multiprocessing hybrid programming model for parallelism. We analyze the performance of recently proposed implementation of the distributed symmetric SpMVM, originally developed for large sparse symmetric matrices arising in ab initio nuclear structure calculations. We also study important featuresmore » of this implementation and compare with previously reported implementations that do not exploit underlying symmetry. Our SpMVM implementations leverage the hybrid paradigm to efficiently overlap expensive communications with computations. Our main comparison criterion is the "CPU core hours" metric, which is the main measure of resource usage on supercomputers. We analyze the effects of topology-aware mapping heuristic using simplified network load model. Furthermore, we have tested the different SpMVM implementations on two large clusters with 3D Torus and Dragonfly topology. Our results show that the distributed SpMVM implementation that exploits matrix symmetry and hides communication yields the best value for the "CPU core hours" metric and significantly reduces data movement overheads.« less
Xu, Dong; Huang, Yi; Zeng, Zinan; Xu, Xinxing
2012-01-01
In this paper, we propose a new patch distribution feature (PDF) (i.e., referred to as Gabor-PDF) for human gait recognition. We represent each gait energy image (GEI) as a set of local augmented Gabor features, which concatenate the Gabor features extracted from different scales and different orientations together with the X-Y coordinates. We learn a global Gaussian mixture model (GMM) (i.e., referred to as the universal background model) with the local augmented Gabor features from all the gallery GEIs; then, each gallery or probe GEI is further expressed as the normalized parameters of an image-specific GMM adapted from the global GMM. Observing that one video is naturally represented as a group of GEIs, we also propose a new classification method called locality-constrained group sparse representation (LGSR) to classify each probe video by minimizing the weighted l(1, 2) mixed-norm-regularized reconstruction error with respect to the gallery videos. In contrast to the standard group sparse representation method that is a special case of LGSR, the group sparsity and local smooth sparsity constraints are both enforced in LGSR. Our comprehensive experiments on the benchmark USF HumanID database demonstrate the effectiveness of the newly proposed feature Gabor-PDF and the new classification method LGSR for human gait recognition. Moreover, LGSR using the new feature Gabor-PDF achieves the best average Rank-1 and Rank-5 recognition rates on this database among all gait recognition algorithms proposed to date. PMID:21724511
Short-Term Variations in Response Distribution to Cortical Stimulation
ERIC Educational Resources Information Center
Lesser, Ronald P.; Lee, Hyang Woon; Webber, W. R. S.; Prince, Barry; Crone, Nathan E.; Miglioretti, Diana L.
2008-01-01
Patterns of responses in the cerebral cortex can vary, and are influenced by pre-existing cortical function, but it is not known how rapidly these variations can occur in humans. We investigated how rapidly response patterns to electrical stimulation can vary in intact human brain. We also investigated whether the type of functional change…
Sparse distributed memory: understanding the speed and robustness of expert memory.
Brogliato, Marcelo S; Chada, Daniel M; Linhares, Alexandre
2014-01-01
How can experts, sometimes in exacting detail, almost immediately and very precisely recall memory items from a vast repertoire? The problem in which we will be interested concerns models of theoretical neuroscience that could explain the speed and robustness of an expert's recollection. The approach is based on Sparse Distributed Memory, which has been shown to be plausible, both in a neuroscientific and in a psychological manner, in a number of ways. A crucial characteristic concerns the limits of human recollection, the "tip-of-tongue" memory event-which is found at a non-linearity in the model. We expand the theoretical framework, deriving an optimization formula to solve this non-linearity. Numerical results demonstrate how the higher frequency of rehearsal, through work or study, immediately increases the robustness and speed associated with expert memory. PMID:24808842
Comparison between sparsely distributed memory and Hopfield-type neural network models
NASA Technical Reports Server (NTRS)
Keeler, James D.
1986-01-01
The Sparsely Distributed Memory (SDM) model (Kanerva, 1984) is compared to Hopfield-type neural-network models. A mathematical framework for comparing the two is developed, and the capacity of each model is investigated. The capacity of the SDM can be increased independently of the dimension of the stored vectors, whereas the Hopfield capacity is limited to a fraction of this dimension. However, the total number of stored bits per matrix element is the same in the two models, as well as for extended models with higher order interactions. The models are also compared in their ability to store sequences of patterns. The SDM is extended to include time delays so that contextual information can be used to cover sequences. Finally, it is shown how a generalization of the SDM allows storage of correlated input pattern vectors.
Using data tagging to improve the performance of Kanerva's sparse distributed memory
NASA Technical Reports Server (NTRS)
Rogers, David
1988-01-01
The standard formulation of Kanerva's sparse distributed memory (SDM) involves the selection of a large number of data storage locations, followed by averaging the data contained in those locations to reconstruct the stored data. A variant of this model is discussed, in which the predominant pattern is the focus of reconstruction. First, one architecture is proposed which returns the predominant pattern rather than the average pattern. However, this model will require too much storage for most uses. Next, a hybrid model is proposed, called tagged SDM, which approximates the results of the predominant pattern machine, but is nearly as efficient as Kanerva's original formulation. Finally, some experimental results are shown which confirm that significant improvements in the recall capability of SDM can be achieved using the tagged architecture.
Sparse distributed memory: understanding the speed and robustness of expert memory
Brogliato, Marcelo S.; Chada, Daniel M.; Linhares, Alexandre
2014-01-01
How can experts, sometimes in exacting detail, almost immediately and very precisely recall memory items from a vast repertoire? The problem in which we will be interested concerns models of theoretical neuroscience that could explain the speed and robustness of an expert's recollection. The approach is based on Sparse Distributed Memory, which has been shown to be plausible, both in a neuroscientific and in a psychological manner, in a number of ways. A crucial characteristic concerns the limits of human recollection, the “tip-of-tongue” memory event—which is found at a non-linearity in the model. We expand the theoretical framework, deriving an optimization formula to solve this non-linearity. Numerical results demonstrate how the higher frequency of rehearsal, through work or study, immediately increases the robustness and speed associated with expert memory. PMID:24808842
Bachega, Leonardo R; Hariharan, Srikanth; Bouman, Charles A; Shroff, Ness B
2015-12-01
This paper introduces the vector sparse matrix transform (vector SMT), a new decorrelating transform suitable for performing distributed processing of high-dimensional signals in sensor networks. We assume that each sensor in the network encodes its measurements into vector outputs instead of scalar ones. The proposed transform decorrelates a sequence of pairs of vector outputs, until these vectors are decorrelated. In our experiments, we simulate distributed anomaly detection by a network of cameras, monitoring a spatial region. Each camera records an image of the monitored environment from its particular viewpoint and outputs a vector encoding the image. Our results, with both artificial and real data, show that the proposed vector SMT transform effectively decorrelates image measurements from the multiple cameras in the network while maintaining low overall communication energy consumption. Since it enables joint processing of the multiple vector outputs, our method provides significant improvements to anomaly detection accuracy when compared with the baseline case when the images are processed independently. PMID:26415179
NASA Technical Reports Server (NTRS)
Jaeckel, Louis A.
1990-01-01
Previously, a method was described of representing a class of simple visual images so that they could be used with a Sparse Distributed Memory (SDM). Herein, two possible implementations are described of a SDM, for which these images, suitably encoded, will serve both as addresses to the memory and as data to be stored in the memory. A key feature of both implementations is that a pattern that is represented as an unordered set with a variable number of members can be used as an address to the memory. In the 1st model, an image is encoded as a 9072 bit string to be used as a read or write address; the bit string may also be used as data to be stored in the memory. Another representation, in which an image is encoded as a 256 bit string, may be used with either model as data to be stored in the memory, but not as an address. In the 2nd model, an image is not represented as a vector of fixed length to be used as an address. Instead, a rule is given for determining which memory locations are to be activated in response to an encoded image. This activation rule treats the pieces of an image as an unordered set. With this model, the memory can be simulated, based on a method of computing the approximate result of a read operation.
Parks, N.J.; Jee, W.S.; Dell, R.B.; Miller, G.E.
1986-07-01
The distribution of bone calcium between morphologically identifiable cortical and trabecular bone obtained by dissection and quantitated by neutron activation analysis (NAA) is described. The skeleton of a female beagle dog was dissected into approximately 400 pieces and assayed for /sup 49/Ca produced in the University of California, Irvine TRIGA reactor. For each of the skeletal sections, we give the initial weight of the alcohol-fixed tissue, which includes cortical bone, trabecular bone, marrow, and cartilage, and a final tissue weight after the marrow and trabecular bone have been dissected away; total section and cortical section calcium weights are reported. The level of detail is represented, for example, by the vertebrae, which were divided into three parts (body, spine, and transverse processes) and by the long bones, which were divided into 10-12 parts such that characterization of the epiphysis, metaphysis, and diaphysis was accomplished. The median percentage cortical calcium values for cervical, thoracic, and lumbar vertebrae were 82%, 56%, and 66%, respectively; however, variation within these groups and among individual vertebral sections was about a factor of 2. For long bones, the median percentage cortical calcium varied from 90-100% in the midshaft to below 50% in the proximal and distal sections. The final calculated cortical tissue-to-calcium mass ratio (TCR) varied from about 4.5 for midshafts of the long bones to about 9 for thoracic vertebral bodies and indicated that the mineral fraction of cortical bone is not constant throughout the skeleton. The ratio of cortical to trabecular calcium in the skeleton was 79.6:20.4.
Alix regulates cortical actin and the spatial distribution of endosomes.
Cabezas, Alicia; Bache, Kristi G; Brech, Andreas; Stenmark, Harald
2005-06-15
Alix/AIP1 is a proline-rich protein that has been implicated in apoptosis, endocytic membrane trafficking and viral budding. To further elucidate the functions of Alix, we used RNA interference to specifically suppress its expression. Depletion of Alix caused a striking redistribution of early endosomes from a peripheral to a perinuclear location. The redistribution of endosomes did not affect transferrin recycling or degradation of endocytosed epidermal growth factor receptors, although the uptake of transferrin was mildly reduced when Alix was downregulated. Quantitative immunoelectron microscopy showed that multivesicular endosomes of Alix-depleted cells contained normal amounts of CD63, whereas their levels of lysobisphosphatidic acid were reduced. Alix depletion also caused an accumulation of unusual actin structures that contained clathrin and cortactin, a protein that couples membrane dynamics to the cortical actin cytoskeleton. Our results suggest that Alix functions in the actin-dependent intracellular positioning of endosomes, but that it is not essential for endocytic recycling or for trafficking of membrane proteins between early and late endosomes in non-polarised cells. PMID:15914539
Herculano-Houzel, Suzana; Watson, Charles; Paxinos, George
2013-01-01
How are neurons distributed along the cortical surface and across functional areas? Here we use the isotropic fractionator (Herculano-Houzel and Lent, 2005) to analyze the distribution of neurons across the entire isocortex of the mouse, divided into 18 functional areas defined anatomically. We find that the number of neurons underneath a surface area (the N/A ratio) varies 4.5-fold across functional areas and neuronal density varies 3.2-fold. The face area of S1 contains the most neurons, followed by motor cortex and the primary visual cortex. Remarkably, while the distribution of neurons across functional areas does not accompany the distribution of surface area, it mirrors closely the distribution of cortical volumes—with the exception of the visual areas, which hold more neurons than expected for their volume. Across the non-visual cortex, the volume of individual functional areas is a shared linear function of their number of neurons, while in the visual areas, neuronal densities are much higher than in all other areas. In contrast, the 18 functional areas cluster into three different zones according to the relationship between the N/A ratio and cortical thickness and neuronal density: these three clusters can be called visual, sensory, and, possibly, associative. These findings are remarkably similar to those in the human cerebral cortex (Ribeiro et al., 2013) and suggest that, like the human cerebral cortex, the mouse cerebral cortex comprises two zones that differ in how neurons form the cortical volume, and three zones that differ in how neurons are distributed underneath the cortical surface, possibly in relation to local differences in connectivity through the white matter. Our results suggest that beyond the developmental divide into visual and non-visual cortex, functional areas initially share a common distribution of neurons along the parenchyma that become delimited into functional areas according to the pattern of connectivity established later
Ryynänen, Outi R M; Hyttinen, Jari A K; Laarne, Päivi H; Malmivuo, Jaakko A
2004-09-01
The purpose of the present study was to examine the spatial resolution of electroencephalography (EEG) by means of inverse cortical EEG solution. The main interest was to study how the number of measurement electrodes and the amount of measurement noise affects the spatial resolution. A three-layer spherical head model was used to obtain the source-field relationship of cortical potentials and scalp EEG field. Singular value decomposition was used to evaluate the spatial resolution with various measurement noise estimates. The results suggest that as the measurement noise increases the advantage of dense electrode systems is decreased. With low realistic measurement noise, a more accurate inverse cortical potential distribution can be obtained with an electrode system where the distance between two electrodes is as small as 16 mm, corresponding to as many as 256 measurement electrodes. In clinical measurement environments, it is always beneficial to have at least 64 measurement electrodes. PMID:15376503
Reinsberger, Claus; Tanaka, Naoaki; Cole, Andrew J.; Woo Lee, Jong; Dworetzky, Barbara A.; Bromfield, Edward B.; Hamiwka, Lorie; Bourgeois, Blaise F.; Golby, Alexandra J.; Madsen, Joseph R.; Stufflebeam, Steven M.
2011-01-01
To evaluate cortical architecture in mesial temporal lobe epilepsy (MTLE) with respect to electrophysiology, we analyze both magnetic resonance imaging (MRI) and magnetoencephalography (MEG) in 19 patients with left MTLE. We divide the patients into two groups: 9 patients (Group A) had vertically oriented antero-medial equivalent current dipoles (ECDs). 10 patients (Group B) had ECDs that were diversely oriented and widely distributed. Group analysis of MRI data showed widespread cortical thinning in Group B compared with Group A, in the left hemisphere involving the cingulate, supramarginal, occipito-temporal and parahippocampal gyri, precuneus and parietal lobule, and in the right hemisphere involving the fronto-medial, -central and -basal gyri and the precuneus. These results suggest that regardless of the presence of hippocampal sclerosis, in a subgroup of patients with MTLE a large cortical network is affected. This finding may, in part, explain the unfavorable outcome in some MTLE patients after epilepsy surgery. PMID:20472073
Assessment of water distribution changes in human cortical bone by nuclear magnetic resonance
NASA Astrophysics Data System (ADS)
Ni, Qingwen; Nyman, Jeffry S.; Wang, Xiaodu; DeLos Santos, Armondo; Nicolella, Daniel P.
2007-03-01
A NMR spin-spin (T2) relaxation technique has been described for determining water distribution changes in human cortical bone tissue. The advantages of using NMR T2 relaxation techniques for bone water distribution are illustrated. The CPMG T2 relaxation data can be inverted to T2 relaxation distribution and this distribution then can be transformed to a pore size distribution with the longer relaxation times corresponding to larger pores. The FID T2 relaxation data can be inverted to T2 relaxation distribution and this distribution then can be transformed to bound- and mobile-water distribution with the longest relaxation time corresponding to mobile water and the middle relaxation time corresponding to bound water. The technique is applied to quantify apparent changes in porosity, bound and mobile water in cortical bone. Overall bone porosity is determined using the calibrated NMR fluid volume from the proton relaxation data divided by overall bone volume. The NMR bound and mobile water changes were determined from cortical bone specimens obtained from male and female donors of different ages. Differences in water distribution were found between specimens from male and female donors. Furthermore, the distribution of water within a single specimen was found to be non-homogeneous. Our results show that the ratio of the average bound to mobile water in bone from male donors is higher than in bone from female donors when the bone porosities are similar between male and female groups. We also show that the average bone porosity multiplied by the ratio of bound to mobile water is constant for both male and female bone groups. This parameter may be used as a measure of bone quality describing both porosity and water content, both of which may be important determinants of bone strength and fracture resistance.
Sparse regression analysis of task-relevant information distribution in the brain
NASA Astrophysics Data System (ADS)
Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle; Baliki, Marwan N.; Apkarian, A. Vania
2012-02-01
One of key topics in fMRI analysis is discovery of task-related brain areas. We focus on predictive accuracy as a better relevance measure than traditional univariate voxel activations that miss important multivariate voxel interactions. We use sparse regression (more specifically, the Elastic Net1) to learn predictive models simultaneously with selection of predictive voxel subsets, and to explore transition from task-relevant to task-irrelevant areas. Exploring the space of sparse solutions reveals a much wider spread of task-relevant information in the brain than it is typically suggested by univariate correlations. This happens for several tasks we considered, and is most noticeable in case of complex tasks such as pain rating; however, for certain simpler tasks, a clear separation between a small subset of relevant voxels and the rest of the brain is observed even with multivariate approach to measuring relevance.
Rasanen, Okko J; Saarinen, Jukka P
2016-09-01
Modeling and prediction of temporal sequences is central to many signal processing and machine learning applications. Prediction based on sequence history is typically performed using parametric models, such as fixed-order Markov chains ( n -grams), approximations of high-order Markov processes, such as mixed-order Markov models or mixtures of lagged bigram models, or with other machine learning techniques. This paper presents a method for sequence prediction based on sparse hyperdimensional coding of the sequence structure and describes how higher order temporal structures can be utilized in sparse coding in a balanced manner. The method is purely incremental, allowing real-time online learning and prediction with limited computational resources. Experiments with prediction of mobile phone use patterns, including the prediction of the next launched application, the next GPS location of the user, and the next artist played with the phone media player, reveal that the proposed method is able to capture the relevant variable-order structure from the sequences. In comparison with the n -grams and the mixed-order Markov models, the sparse hyperdimensional predictor clearly outperforms its peers in terms of unweighted average recall and achieves an equal level of weighted average recall as the mixed-order Markov chain but without the batch training of the mixed-order model. PMID:26285224
Xiao, Youping; Rao, Ravi; Cecchi, Guillermo; Kaplan, Ehud
2008-01-01
The early visual cortices represent information of several stimulus attributes, such as orientation and color. To understand the coding mechanisms of these attributes in the brain, and the functional organization of the early visual cortices, it is necessary to determine whether different attributes are represented by different compartments within each cortex. Previous studies addressing this question have focused on the information encoded by the response amplitude of individual neurons or cortical columns, and have reached conflicting conclusions. Given the correlated variability in response amplitude across neighboring columns, it is likely that the spatial pattern of responses across these columns encodes the attribute information more reliably than the response amplitude does. Here we present a new method of mapping the spatial distribution of information that is encoded by both the response amplitude and spatial pattern. This new method is based on a statistical learning approach, the Support Vector Machine (SVM). Application of this new method to our optical imaging data suggests that information about stimulus orientation and color are distributed differently in the striate cortex, and this observation is consistent with the hypothesis of segregated representations of orientation and color in this area. We also demonstrate that SVM can be used to extract ‘single-condition’ activation maps from noisy images of intrinsic optical signals. PMID:18249089
Sparse Distributed Representation of Odors in a Large-scale Olfactory Bulb Circuit
Yu, Yuguo; McTavish, Thomas S.; Hines, Michael L.; Shepherd, Gordon M.; Valenti, Cesare; Migliore, Michele
2013-01-01
In the olfactory bulb, lateral inhibition mediated by granule cells has been suggested to modulate the timing of mitral cell firing, thereby shaping the representation of input odorants. Current experimental techniques, however, do not enable a clear study of how the mitral-granule cell network sculpts odor inputs to represent odor information spatially and temporally. To address this critical step in the neural basis of odor recognition, we built a biophysical network model of mitral and granule cells, corresponding to 1/100th of the real system in the rat, and used direct experimental imaging data of glomeruli activated by various odors. The model allows the systematic investigation and generation of testable hypotheses of the functional mechanisms underlying odor representation in the olfactory bulb circuit. Specifically, we demonstrate that lateral inhibition emerges within the olfactory bulb network through recurrent dendrodendritic synapses when constrained by a range of balanced excitatory and inhibitory conductances. We find that the spatio-temporal dynamics of lateral inhibition plays a critical role in building the glomerular-related cell clusters observed in experiments, through the modulation of synaptic weights during odor training. Lateral inhibition also mediates the development of sparse and synchronized spiking patterns of mitral cells related to odor inputs within the network, with the frequency of these synchronized spiking patterns also modulated by the sniff cycle. PMID:23555237
Basermann, A.
1994-12-31
For the solution of discretized ordinary or partial differential equations it is necessary to solve systems of equations or eigenproblems with coefficient matrices of different sparsity pattern, depending on the discretization method; using the finite element method (FE) results in largely unstructured systems of equations. Sparse eigenproblems play particularly important roles in the analysis of elastic solids and structures. In the corresponding FE models, the natural frequencies and mode shapes of free vibration are determined as are buckling loads and modes. Another class of problems is related to stability analysis, e.g. of electrical networks. Moreover, approximations of extreme eigenvalues are useful for solving sets of linear equations, e.g. for determining condition numbers of symmetric positive definite matrices or for conjugate gradients methods with polynomial preconditioning. Iterative methods for solving linear systems and eigenproblems mainly consist of matrix-vector products and vector-vector operations; the main work in each iteration is usually the computation of matrix-vector products. Therein, accessing the vector is determined by the sparsity pattern and the storage scheme of the matrix.
NASA Technical Reports Server (NTRS)
Keeler, James D.
1988-01-01
The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used here, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.
Liang, Yujie; Ying, Rendong; Lu, Zhenqi; Liu, Peilin
2014-01-01
In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach. PMID:25420150
Liang, Yujie; Ying, Rendong; Lu, Zhenqi; Liu, Peilin
2014-01-01
In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach. PMID:25420150
NASA Astrophysics Data System (ADS)
George, Jacob
The present study deals with the effects of sparsely distributed three-dimensional elements on two-dimensional (2-D) and three-dimensional (3-D) turbulent boundary layers (TBL) such as those that occur on submarines, ship hulls, etc. This study was achieved in three parts: Part 1 dealt with the cylinders when placed individually in the turbulent boundary layers, thereby considering the effect of a single perturbation on the TBL; Part 2 considered the effects when the same individual elements were placed in a sparse and regular distribution, thus studying the response of the flow to a sequence of perturbations; and in Part 3, the distributions were subjected to 3-D turbulent boundary layers, thus examining the effects of streamwise and spanwise pressure gradients on the same perturbed flows as considered in Part 2. The 3-D turbulent boundary layers were generated by an idealized wing-body junction flow. Detailed 3-velocity-component Laser-Doppler Velocimetry (LDV) and other measurements were carried out to understand and describe the rough-wall flow structure. The measurements include mean velocities, turbulence quantities (Reynolds stresses and triple products), skin friction, surface pressure and oil flow visualizations in 2-D and 3-D rough-wall flows for Reynolds numbers, based on momentum thickness, greater than 7000. Very uniform circular cylindrical roughness elements of 0.38mm, 0.76mm and 1.52mm height (k) were used in square and diagonal patterns, yielding six different roughness geometries of rough-wall surface. For the 2-D rough-wall flows, the roughness Reynolds numbers, k +, based on the element height (k) and the friction velocity (Utau), range from 26 to 131. Results for the 2-D rough-wall flows reveal that the velocity-defect law is similar for both smooth and rough surfaces, and the semi-logarithmic velocity-distribution curve is shifted by an amount DeltaU/U, depending on the height of the roughness element, showing that Delta U/Utau is a function
NASA Technical Reports Server (NTRS)
Hof, P. R.; Vogt, B. A.; Bouras, C.; Morrison, J. H.; Bloom, F. E. (Principal Investigator)
1997-01-01
In recent years, the existence of visual variants of Alzheimer's disease characterized by atypical clinical presentation at onset has been increasingly recognized. In many of these cases post-mortem neuropathological assessment revealed that correlations could be established between clinical symptoms and the distribution of neurodegenerative lesions. We have analyzed a series of Alzheimer's disease patients presenting with prominent visual symptomatology as a cardinal sign of the disease. In these cases, a shift in the distribution of pathological lesions was observed such that the primary visual areas and certain visual association areas within the occipito-parieto-temporal junction and posterior cingulate cortex had very high densities of lesions, whereas the prefrontal cortex had fewer lesions than usually observed in Alzheimer's disease. Previous quantitative analyses have demonstrated that in Alzheimer's disease, primary sensory and motor cortical areas are less damaged than the multimodal association areas of the frontal and temporal lobes, as indicated by the laminar and regional distribution patterns of neurofibrillary tangles and senile plaques. The distribution of pathological lesions in the cerebral cortex of Alzheimer's disease cases with visual symptomatology revealed that specific visual association pathways were disrupted, whereas these particular connections are likely to be affected to a less severe degree in the more common form of Alzheimer's disease. These data suggest that in some cases with visual variants of Alzheimer's disease, the neurological symptomatology may be related to the loss of certain components of the cortical visual pathways, as reflected by the particular distribution of the neuropathological markers of the disease.
Roxin, Alex
2011-01-01
Neuronal network models often assume a fixed probability of connection between neurons. This assumption leads to random networks with binomial in-degree and out-degree distributions which are relatively narrow. Here I study the effect of broad degree distributions on network dynamics by interpolating between a binomial and a truncated power-law distribution for the in-degree and out-degree independently. This is done both for an inhibitory network (I network) as well as for the recurrent excitatory connections in a network of excitatory and inhibitory neurons (EI network). In both cases increasing the width of the in-degree distribution affects the global state of the network by driving transitions between asynchronous behavior and oscillations. This effect is reproduced in a simplified rate model which includes the heterogeneity in neuronal input due to the in-degree of cells. On the other hand, broadening the out-degree distribution is shown to increase the fraction of common inputs to pairs of neurons. This leads to increases in the amplitude of the cross-correlation (CC) of synaptic currents. In the case of the I network, despite strong oscillatory CCs in the currents, CCs of the membrane potential are low due to filtering and reset effects, leading to very weak CCs of the spike-count. In the asynchronous regime of the EI network, broadening the out-degree increases the amplitude of CCs in the recurrent excitatory currents, while CC of the total current is essentially unaffected as are pairwise spiking correlations. This is due to a dynamic balance between excitatory and inhibitory synaptic currents. In the oscillatory regime, changes in the out-degree can have a large effect on spiking correlations and even on the qualitative dynamical state of the network. PMID:21556129
Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis.
Anton-Sanchez, Laura; Bielza, Concha; Merchán-Pérez, Angel; Rodríguez, José-Rodrigo; DeFelipe, Javier; Larrañaga, Pedro
2014-01-01
The biggest problem when analyzing the brain is that its synaptic connections are extremely complex. Generally, the billions of neurons making up the brain exchange information through two types of highly specialized structures: chemical synapses (the vast majority) and so-called gap junctions (a substrate of one class of electrical synapse). Here we are interested in exploring the three-dimensional spatial distribution of chemical synapses in the cerebral cortex. Recent research has showed that the three-dimensional spatial distribution of synapses in layer III of the neocortex can be modeled by a random sequential adsorption (RSA) point process, i.e., synapses are distributed in space almost randomly, with the only constraint that they cannot overlap. In this study we hypothesize that RSA processes can also explain the distribution of synapses in all cortical layers. We also investigate whether there are differences in both the synaptic density and spatial distribution of synapses between layers. Using combined focused ion beam milling and scanning electron microscopy (FIB/SEM), we obtained three-dimensional samples from the six layers of the rat somatosensory cortex and identified and reconstructed the synaptic junctions. A total volume of tissue of approximately 4500μm(3) and around 4000 synapses from three different animals were analyzed. Different samples, layers and/or animals were aggregated and compared using RSA replicated spatial point processes. The results showed no significant differences in the synaptic distribution across the different rats used in the study. We found that RSA processes described the spatial distribution of synapses in all samples of each layer. We also found that the synaptic distribution in layers II to VI conforms to a common underlying RSA process with different densities per layer. Interestingly, the results showed that synapses in layer I had a slightly different spatial distribution from the other layers. PMID:25206325
Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis
Anton-Sanchez, Laura; Bielza, Concha; Merchán-Pérez, Angel; Rodríguez, José-Rodrigo; DeFelipe, Javier; Larrañaga, Pedro
2014-01-01
The biggest problem when analyzing the brain is that its synaptic connections are extremely complex. Generally, the billions of neurons making up the brain exchange information through two types of highly specialized structures: chemical synapses (the vast majority) and so-called gap junctions (a substrate of one class of electrical synapse). Here we are interested in exploring the three-dimensional spatial distribution of chemical synapses in the cerebral cortex. Recent research has showed that the three-dimensional spatial distribution of synapses in layer III of the neocortex can be modeled by a random sequential adsorption (RSA) point process, i.e., synapses are distributed in space almost randomly, with the only constraint that they cannot overlap. In this study we hypothesize that RSA processes can also explain the distribution of synapses in all cortical layers. We also investigate whether there are differences in both the synaptic density and spatial distribution of synapses between layers. Using combined focused ion beam milling and scanning electron microscopy (FIB/SEM), we obtained three-dimensional samples from the six layers of the rat somatosensory cortex and identified and reconstructed the synaptic junctions. A total volume of tissue of approximately 4500μm3 and around 4000 synapses from three different animals were analyzed. Different samples, layers and/or animals were aggregated and compared using RSA replicated spatial point processes. The results showed no significant differences in the synaptic distribution across the different rats used in the study. We found that RSA processes described the spatial distribution of synapses in all samples of each layer. We also found that the synaptic distribution in layers II to VI conforms to a common underlying RSA process with different densities per layer. Interestingly, the results showed that synapses in layer I had a slightly different spatial distribution from the other layers. PMID:25206325
Bazzo, João Paulo; Pipa, Daniel Rodrigues; da Silva, Erlon Vagner; Martelli, Cicero; Cardozo da Silva, Jean Carlos
2016-01-01
This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure. PMID:27618040
Efficient packing of patterns in sparse distributed memory by selective weighting of input bits
NASA Technical Reports Server (NTRS)
Kanerva, Pentti
1991-01-01
When a set of patterns is stored in a distributed memory, any given storage location participates in the storage of many patterns. From the perspective of any one stored pattern, the other patterns act as noise, and such noise limits the memory's storage capacity. The more similar the retrieval cues for two patterns are, the more the patterns interfere with each other in memory, and the harder it is to separate them on retrieval. A method is described of weighting the retrieval cues to reduce such interference and thus to improve the separability of patterns that have similar cues.
Inferring learning rules from distributions of firing rates in cortical neurons.
Lim, Sukbin; McKee, Jillian L; Woloszyn, Luke; Amit, Yali; Freedman, David J; Sheinberg, David L; Brunel, Nicolas
2015-12-01
Information about external stimuli is thought to be stored in cortical circuits through experience-dependent modifications of synaptic connectivity. These modifications of network connectivity should lead to changes in neuronal activity as a particular stimulus is repeatedly encountered. Here we ask what plasticity rules are consistent with the differences in the statistics of the visual response to novel and familiar stimuli in inferior temporal cortex, an area underlying visual object recognition. We introduce a method that allows one to infer the dependence of the presumptive learning rule on postsynaptic firing rate, and we show that the inferred learning rule exhibits depression for low postsynaptic rates and potentiation for high rates. The threshold separating depression from potentiation is strongly correlated with both mean and s.d. of the firing rate distribution. Finally, we show that network models implementing a rule extracted from data show stable learning dynamics and lead to sparser representations of stimuli. PMID:26523643
Distributed cortical adaptation during learning of a brain–computer interface task
Wander, Jeremiah D.; Blakely, Timothy; Miller, Kai J.; Weaver, Kurt E.; Johnson, Lise A.; Olson, Jared D.; Fetz, Eberhard E.; Rao, Rajesh P. N.; Ojemann, Jeffrey G.
2013-01-01
The majority of subjects who attempt to learn control of a brain–computer interface (BCI) can do so with adequate training. Much like when one learns to type or ride a bicycle, BCI users report transitioning from a deliberate, cognitively focused mindset to near automatic control as training progresses. What are the neural correlates of this process of BCI skill acquisition? Seven subjects were implanted with electrocorticography (ECoG) electrodes and had multiple opportunities to practice a 1D BCI task. As subjects became proficient, strong initial task-related activation was followed by lessening of activation in prefrontal cortex, premotor cortex, and posterior parietal cortex, areas that have previously been implicated in the cognitive phase of motor sequence learning and abstract task learning. These results demonstrate that, although the use of a BCI only requires modulation of a local population of neurons, a distributed network of cortical areas is involved in the acquisition of BCI proficiency. PMID:23754426
Misof, B M; Dempster, D W; Zhou, Hua; Roschger, P; Fratzl-Zelman, N; Fratzl, P; Silverberg, S J; Shane, E; Cohen, A; Stein, E; Nickolas, T L; Recker, R R; Lappe, J; Bilezikian, J P; Klaushofer, K
2014-10-01
Bone mineralization density distribution (BMDD) is an important determinant of bone mechanical properties. The most available skeletal site for access to the BMDD is the iliac crest. Compared to cancellous bone much less information on BMDD is available for cortical bone. Hence, we analyzed complete transiliac crest bone biopsy samples from premenopausal women (n = 73) aged 25-48 years, clinically classified as healthy, by quantitative backscattered electron imaging for cortical (Ct.) and cancellous (Cn.) BMDD. The Ct.BMDD was characterized by the arithmetic mean of the BMDD of the cortical plates. We found correlations between Ct. and Cn. BMDD variables with correlation coefficients r between 0.42 and 0.73 (all p < 0.001). Additionally to this synchronous behavior of cortical and cancellous compartments, we found that the heterogeneity of mineralization densities (Ct.Ca(Width)), as well as the cortical porosity (Ct.Po) was larger for a lower average degree of mineralization (Ct.Ca(Mean)). Moreover, Ct.Po correlated negatively with the percentage of highly mineralized bone areas (Ct.Ca(High)) and positively with the percentage of lowly mineralized bone areas (Ct.Ca(Low)). In conclusion, the correlation of cortical with cancellous BMDD in the iliac crest of the study cohort suggests coordinated regulation of bone turnover between both bone compartments. Only in a few cases, there was a difference in the degree of mineralization of >1wt % between both cortices suggesting a possible modeling situation. This normative dataset of healthy premenopausal women will provide a reference standard by which disease- and treatment-specific effects can be assessed at the level of cortical bone BMDD. PMID:25134800
NASA Technical Reports Server (NTRS)
Jaeckel, Louis A.
1989-01-01
To study the problems of encoding visual images for use with a Sparse Distributed Memory (SDM), I consider a specific class of images- those that consist of several pieces, each of which is a line segment or an arc of a circle. This class includes line drawings of characters such as letters of the alphabet. I give a method of representing a segment of an arc by five numbers in a continuous way; that is, similar arcs have similar representations. I also give methods for encoding these numbers as bit strings in an approximately continuous way. The set of possible segments and arcs may be viewed as a five-dimensional manifold M, whose structure is like a Mobious strip. An image, considered to be an unordered set of segments and arcs, is therefore represented by a set of points in M - one for each piece. I then discuss the problem of constructing a preprocessor to find the segments and arcs in these images, although a preprocessor has not been developed. I also describe a possible extension of the representation.
Chen, Xin; Yang, Meihua; Sun, Feiji; Liang, Chao; Wei, Yujia; Wang, Lukang; Yue, Jiong; Chen, Bing; Li, Song; Liu, Shiyong; Yang, Hui
2016-04-01
Cortical tubers in patients with tuberous sclerosis complex (TSC) are highly associated with intractable epilepsy. Recent evidence has shown that transient receptor potential vanilloid 4 (TRPV4) has direct effects on both neurons and glial cells. To understand the role of TRPV4 in pathogenesis of cortical tubers, we investigated the expression patterns of TRPV4 in cortical tubers of TSC compared with normal control cortex (CTX). We found that TRPV4 was clearly up-regulated in cortical tubers at the protein levels. Immunostaining indicated that TRPV4 was specially distributed in abnormal cells, including dysplastic neurons (DNs) and giant cells (GCs). In addition, double immunofluorescent staining revealed that TRPV4 was localized on neurofilament proteins (NF200) positive neurons and glial fibrillary acidic portein (GFAP) positive reactive astrocytes. Moreover, TRPV4 co-localized with both glutamatergic and GABAergic neurons. Furthermore, protein levels of protein kinase C (PKC), but not protein kinase A (PKA), the important upstream factors of the TRPV4, were significantly increased in cortical tubers. Taken together, the overexpression and distribution patterns of TRPV4 may be linked with the intractable epilepsy caused by TSC. PMID:26874068
Distribution of cortical granules in bovine oocytes classified by cumulus complex.
Hosoe, M; Shioya, Y
1997-11-01
The present study was conducted to examine distributional changes of cortical granules (CGs) during meiotic maturation and fertilisation in vitro and the developmental ability in bovine oocytes classified by cumulus cells. The oocytes were classified by the morphology of their cumulus cell layers as follows: class A, compact and thick; class B, compact but thin; class C, naked; and class D, expanded. Some of the oocytes were stained with Lens curinalis agglutinin (LCA) before and after maturation in vitro and after insemination, and then stained with orcein to observe their nuclear stages. The others were left in culture. Distributional patterns of the CGs were classified into four types: type I, CGs distributed in clusters; type II, CGs dispersed and partly clustered; type III, all CGs dispersed; and type IV, no CGs. Most of the oocytes before culture showed a type I pattern, but this decreased after maturation culture, whereas type III increased in class A. The oocytes of class B showed similar changes while the oocytes of class C did not. In class C, many oocytes showed type I after culture, indicating that cytoplasmic maturation was not completed. In class D, 80.4% of the oocytes exhibited type III before maturation culture, indicating that their cytoplasmic maturation was different from classes A-C. And about 70% of the class D oocytes were at the nuclear stage of germinal vesicle breakdown (GVBD) before culture. The developmental rates to blastocysts in classes A-D were 28.7%, 23.1%, 0.5% and 3.4% respectively. PMID:9563685
Distribution of the lingual foramina in mandibular cortical bone in Koreans
Kim, Dae Hyun; Kim, Moon Yong
2013-01-01
Objectives The interforminal region, between the mandibular foramen, is known as a relatively safe area that is free of anatomic structures, such as inferior alveolar nerve, submandibular fossa, and lingual side of the mandible is occasionally neglected for its low clinical importance. Even in the case of a severely constricted alveolus, perforation of the lingual cortical bone had been intended. However, anterior extension of the inferior alveolar canal, important anatomic structure, such as concavity of lingual bone, lingual foramina, and lingual canal, has recently been reported through various studies, and untypical bleeding by perforation of the lingual plate on implantation has also been reported. Therefore, in this study, we performed radiographic and statistical analysis on distribution and appearance frequencies of the lingual foramina that causes perforation of the mandibular lingual cortical bone to prevent complications, such as untypical bleeding, during surgical procedure. Materials and Methods We measured the horizontal length from a midline of the mandible to the lingual foramina, as well as the horizontal length from the alveolar crest to the lingual foramina and from the lingual foramina to the mandibular border by multi-detector computed tomography of 187 patients, who visited Dankook University Dental Hospital for various reasons from January 1, 2008 to August 31, 2012. Results From a total of 187 human mandibles, 110 (58.8%) mandibles had lingual foramina; 39 (20.9%) had bilateral lingual foramen; 34 (18.2%) had the only left lingual foramen; and 37 (19.8%) had the only right lingual foramen. Conclusion When there is consistent bleeding during a surgical procedure, clinicians must consider damages on the branches of the sublingual artery, which penetrate the lingual foramina. Also, when there is a lingual foramina larger than 1 mm in diameter on a pre-implantation computed tomography, clinicians must beware of vessel damage. In order to prevent
Grassmannian sparse representations
NASA Astrophysics Data System (ADS)
Azary, Sherif; Savakis, Andreas
2015-05-01
We present Grassmannian sparse representations (GSR), a sparse representation Grassmann learning framework for efficient classification. Sparse representation classification offers a powerful approach for recognition in a variety of contexts. However, a major drawback of sparse representation methods is their computational performance and memory utilization for high-dimensional data. A Grassmann manifold is a space that promotes smooth surfaces where points represent subspaces and the relationship between points is defined by the mapping of an orthogonal matrix. Grassmann manifolds are well suited for computer vision problems because they promote high between-class discrimination and within-class clustering, while offering computational advantages by mapping each subspace onto a single point. The GSR framework combines Grassmannian kernels and sparse representations, including regularized least squares and least angle regression, to improve high accuracy recognition while overcoming the drawbacks of performance and dependencies on high dimensional data distributions. The effectiveness of GSR is demonstrated on computationally intensive multiview action sequences, three-dimensional action sequences, and face recognition datasets.
LOFAR sparse image reconstruction
NASA Astrophysics Data System (ADS)
Garsden, H.; Girard, J. N.; Starck, J. L.; Corbel, S.; Tasse, C.; Woiselle, A.; McKean, J. P.; van Amesfoort, A. S.; Anderson, J.; Avruch, I. M.; Beck, R.; Bentum, M. J.; Best, P.; Breitling, F.; Broderick, J.; Brüggen, M.; Butcher, H. R.; Ciardi, B.; de Gasperin, F.; de Geus, E.; de Vos, M.; Duscha, S.; Eislöffel, J.; Engels, D.; Falcke, H.; Fallows, R. A.; Fender, R.; Ferrari, C.; Frieswijk, W.; Garrett, M. A.; Grießmeier, J.; Gunst, A. W.; Hassall, T. E.; Heald, G.; Hoeft, M.; Hörandel, J.; van der Horst, A.; Juette, E.; Karastergiou, A.; Kondratiev, V. I.; Kramer, M.; Kuniyoshi, M.; Kuper, G.; Mann, G.; Markoff, S.; McFadden, R.; McKay-Bukowski, D.; Mulcahy, D. D.; Munk, H.; Norden, M. J.; Orru, E.; Paas, H.; Pandey-Pommier, M.; Pandey, V. N.; Pietka, G.; Pizzo, R.; Polatidis, A. G.; Renting, A.; Röttgering, H.; Rowlinson, A.; Schwarz, D.; Sluman, J.; Smirnov, O.; Stappers, B. W.; Steinmetz, M.; Stewart, A.; Swinbank, J.; Tagger, M.; Tang, Y.; Tasse, C.; Thoudam, S.; Toribio, C.; Vermeulen, R.; Vocks, C.; van Weeren, R. J.; Wijnholds, S. J.; Wise, M. W.; Wucknitz, O.; Yatawatta, S.; Zarka, P.; Zensus, A.
2015-03-01
Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods. Aims: Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework. Methods: We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data. Results: We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN images. Conclusions: Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SKA.
A scalable 2-D parallel sparse solver
Kothari, S.C.; Mitra, S.
1995-12-01
Scalability beyond a small number of processors, typically 32 or less, is known to be a problem for existing parallel general sparse (PGS) direct solvers. This paper presents a parallel general sparse PGS direct solver for general sparse linear systems on distributed memory machines. The algorithm is based on the well-known sequential sparse algorithm Y12M. To achieve efficient parallelization, a 2-D scattered decomposition of the sparse matrix is used. The proposed algorithm is more scalable than existing parallel sparse direct solvers. Its scalability is evaluated on a 256 processor nCUBE2s machine using Boeing/Harwell benchmark matrices.
Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.; Makedonska, Nataliia; Karra, Satish
2016-08-25
We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less
NASA Astrophysics Data System (ADS)
Kumar, Manoj; Agarwal, Rupali; Bhutani, Ravi; Shakher, Chandra
2016-05-01
An application of digital speckle pattern interferometry (DSPI) for the measurement of deformations and strain-field distributions developed in cortical bone around orthodontic miniscrew implants inserted into the human maxilla is presented. The purpose of this study is to measure and compare the strain distribution in cortical bone/miniscrew interface of human maxilla around miniscrew implants of different diameters, different implant lengths, and implants of different commercially available companies. The technique is also used to measure tilt/rotation of canine caused due to the application of retraction springs. The proposed technique has high sensitivity and enables the observation of deformation/strain distribution. In DSPI, two specklegrams are recorded corresponding to pre- and postloading of the retraction spring. The DSPI fringe pattern is observed by subtracting these two specklegrams. Optical phase was extracted using Riesz transform and the monogenic signal from a single DSPI fringe pattern. The obtained phase is used to calculate the parameters of interest such as displacement/deformation and strain/stress. The experiment was conducted on a dry human skull fulfilling the criteria of intact dental arches and all teeth present. Eight different miniscrew implants were loaded with an insertion angulation of 45 deg in the inter-radicular region of the maxillary second premolar and molar region. The loading of miniscrew implants was done with force level (150 gf) by nickel-titanium closed-coil springs (9 mm). The obtained results from DSPI reveal that implant diameter and implant length affect the displacement and strain distribution in cortical bone layer surrounding the miniscrew implant.
NASA Astrophysics Data System (ADS)
Teramae, Jun-nosuke
2016-06-01
Neurons in the cortical circuit continuous to generate irregular spike firing with extremely low firing rate (about 1-2 Hz) even when animals neither receive any external stimuli nor they do not show any significant motor movement. The ongoing activity is often called neuronal noise because measured spike trains are often highly irregular and also spike timings are highly asynchronous among neurons. Many experiments imply that neural networks themselves must generate the noisy activity as an intrinsic property of cortical circuit. However, how a network of neurons sustains the irregular spike firings with low firing rate remains unclear. Recently, by focusing on long-tailed distribution of amplitude of synaptic connections or EPSP (Excitatory Post-Synaptic Potential), we successfully revealed that due to coexistence of a few extremely strong synaptic connections and majority of weak synapses, nonlinear dynamics of population of spiking neurons can have a nontrivial stable state that corresponding to the intrinsic ongoing fluctuation of the cortical circuit. We also found that due to the fluctuation fidelity of spike transmission between neurons are optimized. Here, we report our recent findings of the ongoing fluctuation from viewpoints of mathematical and computational side.
Taylor, Sabrina R.; Smith, Colin M.; Keeley, Kristen L.; McGuone, Declan; Dodge, Carter P.; Duhaime, Ann-Christine; Costine, Beth A.
2016-01-01
Cortical contusions are a common type of traumatic brain injury (TBI) in children. Current knowledge of neuroblast response to cortical injury arises primarily from studies utilizing aspiration or cryoinjury in rodents. In infants and children, cortical impact affects both gray and white matter and any neurogenic response may be complicated by the large expanse of white matter between the subventricular zone (SVZ) and the cortex, and the large number of neuroblasts in transit along the major white matter tracts to populate brain regions. Previously, we described an age-dependent increase of neuroblasts in the SVZ in response to cortical impact in the immature gyrencephalic brain. Here, we investigate if neuroblasts target the injury, if white matter injury influences repair efforts, and if postnatal population of brain regions are disrupted. Piglets received a cortical impact to the rostral gyrus cortex or sham surgery at postnatal day (PND) 7, BrdU 2 days prior to (PND 5 and 6) or after injury (PND 7 and 8), and brains were collected at PND 14. Injury did not alter the number of neuroblasts in the white matter between the SVZ and the rostral gyrus. In the gray matter of the injury site, neuroblast density was increased in cavitated lesions, and the number of BrdU+ neuroblasts was increased, but comprised less than 1% of all neuroblasts. In the white matter of the injury site, neuroblasts with differentiating morphology were densely arranged along the cavity edge. In a ventral migratory stream, neuroblast density was greater in subjects with a cavitated lesion, indicating that TBI may alter postnatal development of regions supplied by that stream. Cortical impact in the immature gyrencephalic brain produced complicated and variable lesions, increased neuroblast density in cavitated gray matter, resulted in potentially differentiating neuroblasts in the white matter, and may alter the postnatal population of brain regions utilizing a population of neuroblasts that
Taylor, Sabrina R; Smith, Colin M; Keeley, Kristen L; McGuone, Declan; Dodge, Carter P; Duhaime, Ann-Christine; Costine, Beth A
2016-01-01
Cortical contusions are a common type of traumatic brain injury (TBI) in children. Current knowledge of neuroblast response to cortical injury arises primarily from studies utilizing aspiration or cryoinjury in rodents. In infants and children, cortical impact affects both gray and white matter and any neurogenic response may be complicated by the large expanse of white matter between the subventricular zone (SVZ) and the cortex, and the large number of neuroblasts in transit along the major white matter tracts to populate brain regions. Previously, we described an age-dependent increase of neuroblasts in the SVZ in response to cortical impact in the immature gyrencephalic brain. Here, we investigate if neuroblasts target the injury, if white matter injury influences repair efforts, and if postnatal population of brain regions are disrupted. Piglets received a cortical impact to the rostral gyrus cortex or sham surgery at postnatal day (PND) 7, BrdU 2 days prior to (PND 5 and 6) or after injury (PND 7 and 8), and brains were collected at PND 14. Injury did not alter the number of neuroblasts in the white matter between the SVZ and the rostral gyrus. In the gray matter of the injury site, neuroblast density was increased in cavitated lesions, and the number of BrdU(+) neuroblasts was increased, but comprised less than 1% of all neuroblasts. In the white matter of the injury site, neuroblasts with differentiating morphology were densely arranged along the cavity edge. In a ventral migratory stream, neuroblast density was greater in subjects with a cavitated lesion, indicating that TBI may alter postnatal development of regions supplied by that stream. Cortical impact in the immature gyrencephalic brain produced complicated and variable lesions, increased neuroblast density in cavitated gray matter, resulted in potentially differentiating neuroblasts in the white matter, and may alter the postnatal population of brain regions utilizing a population of neuroblasts that
Krieg, Sandro M; Sollmann, Nico; Tanigawa, Noriko; Foerschler, Annette; Meyer, Bernhard; Ringel, Florian
2016-05-01
Navigated transcranial magnetic stimulation (nTMS) gains increasing importance in presurgical language mapping. Although bipolar direct cortical stimulation (DCS) is regarded as the gold standard for intraoperative mapping of language-related areas, it cannot be used to map the healthy human brain due to its invasive character. Therefore, the present study employed a non-invasive virtual-lesion modality to provide a causality-confirmed cortical language map of the healthy human brain by repetitive nTMS (rTMS) with functional specifications beyond language-positive/language-negative distinction. Fifty right-handed healthy volunteers underwent rTMS language mapping of the left hemisphere combined with an object-naming task. The induced errors were categorized and frequency maps were calculated. Moreover, a principal component analysis (PCA) was performed on the basis of language-positive cortical regions for each error category. The left hemisphere was stimulated at 258-789 sites (median: 361.5 sites), and 12-241 naming errors (median: 72.5 errors) were observed. In male subjects, a total number of 2091 language errors were elicited by 9579 stimulation trains, which is equal to an error rate of 21.8 %. Within females, 10,238 stimulation trains elicited 2032 language errors (19.8 %). PCA revealed that the inferior parietal lobe (IPL) and middle frontal gyrus (MFG) were causally involved in object naming as a semantic center and an executive control center. For the first time, this study provides causality-based data and a model that approximates the distribution of language-related cortical areas grouped for different functional aspects of single-word production processes by PCA. PMID:25894631
Janovic, Aleksa; Saveljic, Igor; Vukicevic, Arso; Nikolic, Dalibor; Rakocevic, Zoran; Jovicic, Gordana; Filipovic, Nenad; Djuric, Marija
2015-01-01
Understanding of the occlusal load distribution through the mid-facial skeleton in natural dentition is essential because alterations in magnitude and/or direction of occlusal forces may cause remarkable changes in cortical and trabecular bone structure. Previous analyses by strain gauge technique, photoelastic and, more recently, finite element (FE) methods provided no direct evidence for occlusal load distribution through the cortical and trabecular bone compartments individually. Therefore, we developed an improved three-dimensional FE model of the human skull in order to clarify the distribution of occlusal forces through the cortical and trabecular bone during habitual masticatory activities. Particular focus was placed on the load transfer through the anterior and posterior maxilla. The results were presented in von Mises stress (VMS) and the maximum principal stress, and compared to the reported FE and strain gauge data. Our qualitative stress analysis indicates that occlusal forces distribute through the mid-facial skeleton along five vertical and two horizontal buttresses. We demonstrated that cortical bone has a priority in the transfer of occlusal load in the anterior maxilla, whereas both cortical and trabecular bone in the posterior maxilla are equally involved in performing this task. Observed site dependence of the occlusal load distribution may help clinicians in creating strategies for implantology and orthodontic treatments. Additionally, the magnitude of VMS in our model was significantly lower in comparison to previous FE models composed only of cortical bone. This finding suggests that both cortical and trabecular bone should be modeled whenever stress will be quantitatively analyzed. PMID:25458179
Künzle, H
1995-01-01
Retrograde tracer substances were injected into either the inferior or the superior colliculus in the Madagascan hedgehog tenrec, Echinops telfairi (Insectivora), to reveal the laminar and regional distribution of corticotectal cells and to correlate the labeled areas with architectural data. The tenrecs, taken from our breeding colony, have one of the least differentiated cerebral cortices among mammals, and experimental investigations of such brains are important for the understanding of the evolution and intrinsic organization of the more highly differentiated cerebral cortex in other placental mammals. Following injections into the inferior colliculus, cortical neurons were labeled bilaterally, with an ipsilateral predominance. Most labeled cells were found in the caudolateral hemisphere, area 4 as defined by Rehkämper (1981); some were in the somatosensorimotor cortex, as defined in a previous study. The labeled neurons in area 4 were located in layers V and VI, forming two bands of cells separated from each other by a poorly labeled interspace. A further subdivision of this presumed auditory region could not be achieved. This entire area was also weakly labeled following tracer injections into the superior colliculus. The labeled cells, however, were restricted to layer V of the ipsilateral side. The most consistent sites of labeled cells following injections into the superior colliculus were found in layer V of the ipsilateral caudomedial hemisphere, Rehkämper's caudal area 3, and the transitional zone adjacent to the retrosplenial cortex. This area is small in comparison to the entire region that was found in this study to project to the superior colliculus. The superior colliculus also receives projections from the ipsilateral sensorimotor and cingulate cortices. The latter projections are particularly striking in comparison to other mammals because they originate from along the entire rostrocaudal extent of the cingulate/retrosplenial region. PMID
Sparse encoding of automatic visual association in hippocampal networks.
Hulme, Oliver J; Skov, Martin; Chadwick, Martin J; Siebner, Hartwig R; Ramsøy, Thomas Z
2014-11-15
Intelligent action entails exploiting predictions about associations between elements of ones environment. The hippocampus and mediotemporal cortex are endowed with the network topology, physiology, and neurochemistry to automatically and sparsely code sensori-cognitive associations that can be reconstructed from single or partial inputs. Whilst acquiring fMRI data and performing an attentional task, participants were incidentally presented with a sequence of cartoon images. By assigning subjects a post-scan free-association task on the same images we assayed the density of associations triggered by these stimuli. Using multivariate Bayesian decoding, we show that human hippocampal and temporal neocortical structures host sparse associative representations that are automatically triggered by visual input. Furthermore, as predicted theoretically, there was a significant increase in sparsity in the Cornu Ammonis subfields, relative to the entorhinal cortex. Remarkably, the sparsity of CA encoding correlated significantly with associative memory performance over subjects; elsewhere within the temporal lobe, entorhinal, parahippocampal, perirhinal and fusiform cortices showed the highest model evidence for the sparse encoding of associative density. In the absence of reportability or attentional confounds, this charts a distribution of visual associative representations within hippocampal populations and their temporal lobe afferent fields, and demonstrates the viability of retrospective associative sampling techniques for assessing the form of reflexive associative encoding. PMID:25038440
Duckham, Rachel L; Rantalainen, Timo; Ducher, Gaele; Hill, Briony; Telford, Richard D; Telford, Rohan M; Daly, Robin M
2016-07-01
Targeted weight-bearing activities during the pre-pubertal years can improve cortical bone mass, structure and distribution, but less is known about the influence of habitual physical activity (PA) and fitness. This study examined the effects of contrasting habitual PA and fitness levels on cortical bone density, geometry and mass distribution in pre-pubertal children. Boys (n = 241) and girls (n = 245) aged 7-9 years had a pQCT scan to measure tibial mid-shaft total, cortical and medullary area, cortical thickness, density, polar strength strain index (SSIpolar) and the mass/density distribution through the bone cortex (radial distribution divided into endo-, mid- and pericortical regions) and around the centre of mass (polar distribution). Four contrasting PA and fitness groups (inactive-unfit, inactive-fit, active-unfit, active-fit) were generated based on daily step counts (pedometer, 7-days) and fitness levels (20-m shuttle test and vertical jump) for boys and girls separately. Active-fit boys had 7.3-7.7 % greater cortical area and thickness compared to inactive-unfit boys (P < 0.05), which was largely due to a 6.4-7.8 % (P < 0.05) greater cortical mass in the posterior-lateral, medial and posterior-medial 66 % tibial regions. Cortical area was not significantly different across PA-fitness categories in girls, but active-fit girls had 6.1 % (P < 0.05) greater SSIpolar compared to inactive-fit girls, which was likely due to their 6.7 % (P < 0.05) greater total bone area. There was also a small region-specific cortical mass benefit in the posterior-medial 66 % tibia cortex in active-fit girls. Higher levels of habitual PA-fitness were associated with small regional-specific gains in 66 % tibial cortical bone mass in pre-pubertal children, particularly boys. PMID:26983726
Ozawa, Shota; Ueda, Shuko; Imamura, Hiromi; Mori, Kiyoshi; Asanuma, Katsuhiko; Yanagita, Motoko; Nakagawa, Takahiko
2015-01-01
Differentiated podocytes, a type of renal glomerular cells, require substantial levels of energy to maintain glomerular physiology. Mitochondria and glycolysis are two major producers of ATP, but the precise roles of each in podocytes remain unknown. This study evaluated the roles of mitochondria and glycolysis in differentiated and differentiating podocytes. Mitochondria in differentiated podocytes are located in the central part of cell body while blocking mitochondria had minor effects on cell shape and migratory ability. In contrast, blocking glycolysis significantly reduced the formation of lamellipodia, a cortical area of these cells, decreased the cell migratory ability and induced the apoptosis. Consistently, the local ATP production in lamellipodia was predominantly regulated by glycolysis. In turn, synaptopodin expression was ameliorated by blocking either mitochondrial respiration or glycolysis. Similar to differentiated podocytes, the differentiating podocytes utilized the glycolysis for regulating apoptosis and lamellipodia formation while synaptopodin expression was likely involved in both mitochondrial OXPHOS and glycolysis. Finally, adult mouse podocytes have most of mitochondria predominantly in the center of the cytosol whereas phosphofructokinase, a rate limiting enzyme for glycolysis, was expressed in foot processes. These data suggest that mitochondria and glycolysis play parallel but distinct roles in differentiated and differentiating podocytes. PMID:26677804
Nieto-Diego, Javier; Malmierca, Manuel S.
2016-01-01
Stimulus-specific adaptation (SSA) in single neurons of the auditory cortex was suggested to be a potential neural correlate of the mismatch negativity (MMN), a widely studied component of the auditory event-related potentials (ERP) that is elicited by changes in the auditory environment. However, several aspects on this SSA/MMN relation remain unresolved. SSA occurs in the primary auditory cortex (A1), but detailed studies on SSA beyond A1 are lacking. To study the topographic organization of SSA, we mapped the whole rat auditory cortex with multiunit activity recordings, using an oddball paradigm. We demonstrate that SSA occurs outside A1 and differs between primary and nonprimary cortical fields. In particular, SSA is much stronger and develops faster in the nonprimary than in the primary fields, paralleling the organization of subcortical SSA. Importantly, strong SSA is present in the nonprimary auditory cortex within the latency range of the MMN in the rat and correlates with an MMN-like difference wave in the simultaneously recorded local field potentials (LFP). We present new and strong evidence linking SSA at the cellular level to the MMN, a central tool in cognitive and clinical neuroscience. PMID:26950883
ERIC Educational Resources Information Center
Leech, Robert; Saygin, Ayse Pinar
2011-01-01
Using functional MRI, we investigated whether auditory processing of both speech and meaningful non-linguistic environmental sounds in superior and middle temporal cortex relies on a complex and spatially distributed neural system. We found that evidence for spatially distributed processing of speech and environmental sounds in a substantial…
Lee, Nancy Raitano; Raznahan, Armin; Wallace, Gregory L; Alexander-Bloch, Aaron; Clasen, Liv S; Lerch, Jason P; Giedd, Jay N
2014-05-01
Patient lesion and functional magnetic resonance imaging (fMRI) studies have provided convincing evidence that a distributed brain network subserves word knowledge. However, little is known about the structural correlates of this network within the context of typical development and whether anatomical coupling in linguistically relevant regions of cortex varies as a function of vocabulary skill. Here we investigate the association between vocabulary and anatomical coupling in 235 typically developing youth (ages 6-19 years) using structural MRI. The study's primary aim was to evaluate whether higher vocabulary performance was associated with greater vertex-level cortical thickness covariation in distributed regions of cortex known to be associated with word knowledge. Results indicate that better vocabulary skills are associated with greater anatomical coupling in several linguistically relevant regions of cortex, including the left inferior parietal (temporal-parietal junction), inferior temporal, middle frontal, and superior frontal gyri and the right inferior frontal and precentral gyri. Furthermore, in high vocabulary scorers, stronger coupling is found among these regions. Thus, complementing patient and fMRI studies, this is the first investigation to highlight the relevance of anatomical covariance within the cortex to vocabulary skills in typically developing youth, further elucidating the distributed nature of neural systems subserving word knowledge. PMID:23728856
Liewald, Daniel; Miller, Robert; Logothetis, Nikos; Wagner, Hans-Joachim; Schüz, Almut
2014-10-01
The aim of this study was to obtain information on the axonal diameters of cortico-cortical fibres in the human brain, connecting distant regions of the same hemisphere via the white matter. Samples for electron microscopy were taken from the region of the superior longitudinal fascicle and from the transitional white matter between temporal and frontal lobe where the uncinate and inferior occipitofrontal fascicle merge. We measured the inner diameter of cross sections of myelinated axons. For comparison with data from the literature on the human corpus callosum, we also took samples from that region. For comparison with well-fixed material, we also included samples from corresponding regions of a monkey brain (Macaca mulatta). Fibre diameters in human brains ranged from 0.16 to 9 μm. Distributions of diameters were similar in the three systems of cortico-cortical fibres investigated, both in humans and the monkey, with most of the average values below 1 μm diameter and a small population of much thicker fibres. Within individual human brains, the averages were larger in the superior longitudinal fascicle than in the transitional zone between temporal and frontal lobe. An asymmetry between left and right could be found in one of the human brains, as well as in the monkey brain. A correlation was also found between the thickness of the myelin sheath and the inner axon diameter for axons whose calibre was greater than about 0.6 μm. The results are compared to white matter data in other mammals and are discussed with respect to conduction velocity, brain size, cognition, as well as diffusion weighted imaging studies. PMID:25142940
Marzagalli, R; Leggio, G M; Bucolo, C; Pricoco, E; Keay, K A; Cardile, V; Castorina, S; Salomone, S; Drago, F; Castorina, A
2016-03-01
Dopamine D3 receptors (D3Rs) are implicated in several aspects of cognition, but their role in aversive conditioning has only been marginally uncovered. Investigations have reported that blockade of D3Rs enhances the acquisition of fear memories, a phenomenon tightly linked to the neuropeptide pituitary adenylate cyclase-activating peptide (PACAP). However, the impact of D3R ablation on the PACAPergic system in regions critical for the formation of new memories remains unexplored. To address this issue, levels of PACAP and its receptors were compared in the hippocampus and cerebral cortex (CX) of mice devoid of functional D3Rs (D3R(-/-)) and wild-types (WTs) using a series of comparative immunohistochemical and biochemical analyses. Morphometric and stereological data revealed increased hippocampal area and volume in D3R(-/-) mice, and augmented neuronal density in CA1 and CA2/3 subfields. PACAP levels were increased in the hippocampus of D3R(-/-) mice. Expression of PACAP receptors was also heightened in mutant mice. In the CX, PACAP immunoreactivity (IR), was restricted to cortical layer V in WTs, but was distributed throughout layers IV-VI in D3R(-/-) mice, along with increased mRNAs, protein concentration and staining scores. Consistently, PAC1, VPAC1 and VPAC2 IRs were variably redistributed in CX, with a general upregulation in cortical layers II-IV in knockout animals. Our interpretation of these findings is that disturbed dopamine neurotransmission due to genetic D3R blockade may enhance the PACAP/PAC1-VPAC axis, a key endogenous system for the processing of fear memories. This could explain, at least in part, the facilitated acquisition and consolidation of aversive memories in D3R(-/-) mice. PMID:26718601
Sparse Regression as a Sparse Eigenvalue Problem
NASA Technical Reports Server (NTRS)
Moghaddam, Baback; Gruber, Amit; Weiss, Yair; Avidan, Shai
2008-01-01
We extend the l0-norm "subspectral" algorithms for sparse-LDA [5] and sparse-PCA [6] to general quadratic costs such as MSE in linear (kernel) regression. The resulting "Sparse Least Squares" (SLS) problem is also NP-hard, by way of its equivalence to a rank-1 sparse eigenvalue problem (e.g., binary sparse-LDA [7]). Specifically, for a general quadratic cost we use a highly-efficient technique for direct eigenvalue computation using partitioned matrix inverses which leads to dramatic x103 speed-ups over standard eigenvalue decomposition. This increased efficiency mitigates the O(n4) scaling behaviour that up to now has limited the previous algorithms' utility for high-dimensional learning problems. Moreover, the new computation prioritizes the role of the less-myopic backward elimination stage which becomes more efficient than forward selection. Similarly, branch-and-bound search for Exact Sparse Least Squares (ESLS) also benefits from partitioned matrix inverse techniques. Our Greedy Sparse Least Squares (GSLS) generalizes Natarajan's algorithm [9] also known as Order-Recursive Matching Pursuit (ORMP). Specifically, the forward half of GSLS is exactly equivalent to ORMP but more efficient. By including the backward pass, which only doubles the computation, we can achieve lower MSE than ORMP. Experimental comparisons to the state-of-the-art LARS algorithm [3] show forward-GSLS is faster, more accurate and more flexible in terms of choice of regularization
NASA Astrophysics Data System (ADS)
McNeill, A.; Fitzsimmons, A.; Jedicke, R.; Wainscoat, R.; Denneau, L.; Vereš, P.; Magnier, E.; Chambers, K. C.; Kaiser, N.; Waters, C.
2016-07-01
The rotational state of asteroids is controlled by various physical mechanisms including collisions, internal damping and the Yarkovsky-O'Keefe-Radzievskii-Paddack effect. We have analysed the changes in magnitude between consecutive detections of ˜60 000 asteroids measured by the Panoramic Survey Telescope and Rapid Response System (PanSTARRS) 1 survey during its first 18 months of operations. We have attempted to explain the derived brightness changes physically and through the application of a simple model. We have found a tendency towards smaller magnitude variations with decreasing diameter for objects of 1 < D < 8 km. Assuming the shape distribution of objects in this size range to be independent of size and composition our model suggests a population with average axial ratios 1 : 0.85 ± 0.13 : 0.71 ± 0.13, with larger objects more likely to have spin axes perpendicular to the orbital plane.
NASA Astrophysics Data System (ADS)
McNeill, A.; Fitzsimmons, A.; Jedicke, R.; Wainscoat, R.; Denneau, L.; Vereš, P.; Magnier, E.; Chambers, K. C.; Kaiser, N.; Waters, C.
2016-04-01
The rotational state of asteroids is controlled by various physical mechanisms including collisions, internal damping and the Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) effect. We have analysed the changes in magnitude between consecutive detections of ˜ 60,000 asteroids measured by the PanSTARRS 1 survey during its first 18 months of operations. We have attempted to explain the derived brightness changes physically and through the application of a simple model. We have found a tendency toward smaller magnitude variations with decreasing diameter for objects of 1 < D < 8 km. Assuming the shape distribution of objects in this size range to be independent of size and composition our model suggests a population with average axial ratios 1: 0.85 ± 0.13: 0.71 ± 0.13, with larger objects more likely to have spin axes perpendicular to the orbital plane.
Si, Weijian; Zhao, Pinjiao; Qu, Zhiyu
2016-01-01
This paper presents an L-shaped sparsely-distributed vector sensor (SD-VS) array with four different antenna compositions. With the proposed SD-VS array, a novel two-dimensional (2-D) direction of arrival (DOA) and polarization estimation method is proposed to handle the scenario where uncorrelated and coherent sources coexist. The uncorrelated and coherent sources are separated based on the moduli of the eigenvalues. For the uncorrelated sources, coarse estimates are acquired by extracting the DOA information embedded in the steering vectors from estimated array response matrix of the uncorrelated sources, and they serve as coarse references to disambiguate fine estimates with cyclical ambiguity obtained from the spatial phase factors. For the coherent sources, four Hankel matrices are constructed, with which the coherent sources are resolved in a similar way as for the uncorrelated sources. The proposed SD-VS array requires only two collocated antennas for each vector sensor, thus the mutual coupling effects across the collocated antennas are reduced greatly. Moreover, the inter-sensor spacings are allowed beyond a half-wavelength, which results in an extended array aperture. Simulation results demonstrate the effectiveness and favorable performance of the proposed method. PMID:27258271
Si, Weijian; Zhao, Pinjiao; Qu, Zhiyu
2016-01-01
This paper presents an L-shaped sparsely-distributed vector sensor (SD-VS) array with four different antenna compositions. With the proposed SD-VS array, a novel two-dimensional (2-D) direction of arrival (DOA) and polarization estimation method is proposed to handle the scenario where uncorrelated and coherent sources coexist. The uncorrelated and coherent sources are separated based on the moduli of the eigenvalues. For the uncorrelated sources, coarse estimates are acquired by extracting the DOA information embedded in the steering vectors from estimated array response matrix of the uncorrelated sources, and they serve as coarse references to disambiguate fine estimates with cyclical ambiguity obtained from the spatial phase factors. For the coherent sources, four Hankel matrices are constructed, with which the coherent sources are resolved in a similar way as for the uncorrelated sources. The proposed SD-VS array requires only two collocated antennas for each vector sensor, thus the mutual coupling effects across the collocated antennas are reduced greatly. Moreover, the inter-sensor spacings are allowed beyond a half-wavelength, which results in an extended array aperture. Simulation results demonstrate the effectiveness and favorable performance of the proposed method. PMID:27258271
Caballero-Lima, David; Kaneva, Iliyana N.; Watton, Simon P.
2013-01-01
In the hyphal tip of Candida albicans we have made detailed quantitative measurements of (i) exocyst components, (ii) Rho1, the regulatory subunit of (1,3)-β-glucan synthase, (iii) Rom2, the specialized guanine-nucleotide exchange factor (GEF) of Rho1, and (iv) actin cortical patches, the sites of endocytosis. We use the resulting data to construct and test a quantitative 3-dimensional model of fungal hyphal growth based on the proposition that vesicles fuse with the hyphal tip at a rate determined by the local density of exocyst components. Enzymes such as (1,3)-β-glucan synthase thus embedded in the plasma membrane continue to synthesize the cell wall until they are removed by endocytosis. The model successfully predicts the shape and dimensions of the hyphae, provided that endocytosis acts to remove cell wall-synthesizing enzymes at the subapical bands of actin patches. Moreover, a key prediction of the model is that the distribution of the synthase is substantially broader than the area occupied by the exocyst. This prediction is borne out by our quantitative measurements. Thus, although the model highlights detailed issues that require further investigation, in general terms the pattern of tip growth of fungal hyphae can be satisfactorily explained by a simple but quantitative model rooted within the known molecular processes of polarized growth. Moreover, the methodology can be readily adapted to model other forms of polarized growth, such as that which occurs in plant pollen tubes. PMID:23666623
Mestres-Missé, Anna; Trampel, Robert; Turner, Robert; Kotz, Sonja A
2016-04-01
A key aspect of optimal behavior is the ability to predict what will come next. To achieve this, we must have a fairly good idea of the probability of occurrence of possible outcomes. This is based both on prior knowledge about a particular or similar situation and on immediately relevant new information. One question that arises is: when considering converging prior probability and external evidence, is the most probable outcome selected or does the brain represent degrees of uncertainty, even highly improbable ones? Using functional magnetic resonance imaging, the current study explored these possibilities by contrasting words that differ in their probability of occurrence, namely, unbalanced ambiguous words and unambiguous words. Unbalanced ambiguous words have a strong frequency-based bias towards one meaning, while unambiguous words have only one meaning. The current results reveal larger activation in lateral prefrontal and insular cortices in response to dominant ambiguous compared to unambiguous words even when prior and contextual information biases one interpretation only. These results suggest a probability distribution, whereby all outcomes and their associated probabilities of occurrence-even if very low-are represented and maintained. PMID:25523107
Sparse Biclustering of Transposable Data
Tan, Kean Ming
2013-01-01
We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. We apply an ℓ1 penalty to the means of the biclusters in order to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of k-means clustering. We show that k-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for bi-clustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression data set. This article has supplementary material online. PMID:25364221
Sparse Biclustering of Transposable Data.
Tan, Kean Ming; Witten, Daniela M
2014-01-01
We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. We apply an ℓ1 penalty to the means of the biclusters in order to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of k-means clustering. We show that k-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for bi-clustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression data set. This article has supplementary material online. PMID:25364221
SparsePZ: Sparse Representation of Photometric Redshift PDFs
NASA Astrophysics Data System (ADS)
Carrasco Kind, Matias; Brunner, R. J.
2015-11-01
SparsePZ uses sparse basis representation to fully represent individual photometric redshift probability density functions (PDFs). This approach requires approximately half the parameters for the same multi-Gaussian fitting accuracy, and has the additional advantage that an entire PDF can be stored by using a 4-byte integer per basis function. Only 10-20 points per galaxy are needed to reconstruct both the individual PDFs and the ensemble redshift distribution, N(z), to an accuracy of 99.9 per cent when compared to the one built using the original PDFs computed with a resolution of δz = 0.01, reducing the required storage of 200 original values by a factor of 10-20. This basis representation can be directly extended to a cosmological analysis, thereby increasing computational performance without losing resolution or accuracy.
Estimating sparse precision matrices
NASA Astrophysics Data System (ADS)
Padmanabhan, Nikhil; White, Martin; Zhou, Harrison H.; O'Connell, Ross
2016-08-01
We apply a method recently introduced to the statistical literature to directly estimate the precision matrix from an ensemble of samples drawn from a corresponding Gaussian distribution. Motivated by the observation that cosmological precision matrices are often approximately sparse, the method allows one to exploit this sparsity of the precision matrix to more quickly converge to an asymptotic 1/sqrt{N_sim} rate while simultaneously providing an error model for all of the terms. Such an estimate can be used as the starting point for further regularization efforts which can improve upon the 1/sqrt{N_sim} limit above, and incorporating such additional steps is straightforward within this framework. We demonstrate the technique with toy models and with an example motivated by large-scale structure two-point analysis, showing significant improvements in the rate of convergence. For the large-scale structure example, we find errors on the precision matrix which are factors of 5 smaller than for the sample precision matrix for thousands of simulations or, alternatively, convergence to the same error level with more than an order of magnitude fewer simulations.
Estimating sparse precision matrices
NASA Astrophysics Data System (ADS)
Padmanabhan, Nikhil; White, Martin; Zhou, Harrison H.; O'Connell, Ross
2016-05-01
We apply a method recently introduced to the statistical literature to directly estimate the precision matrix from an ensemble of samples drawn from a corresponding Gaussian distribution. Motivated by the observation that cosmological precision matrices are often approximately sparse, the method allows one to exploit this sparsity of the precision matrix to more quickly converge to an asymptotic 1/√{N_sim} rate while simultaneously providing an error model for all of the terms. Such an estimate can be used as the starting point for further regularization efforts which can improve upon the 1/√{N_sim} limit above, and incorporating such additional steps is straightforward within this framework. We demonstrate the technique with toy models and with an example motivated by large-scale structure two-point analysis, showing significant improvements in the rate of convergence. For the large-scale structure example we find errors on the precision matrix which are factors of 5 smaller than for the sample precision matrix for thousands of simulations or, alternatively, convergence to the same error level with more than an order of magnitude fewer simulations.
Estimating sparse precision matrices
NASA Astrophysics Data System (ADS)
Padmanabhan, Nikhil; White, Martin; Zhou, Harrison H.; O'Connell, Ross
2016-08-01
We apply a method recently introduced to the statistical literature to directly estimate the precision matrix from an ensemble of samples drawn from a corresponding Gaussian distribution. Motivated by the observation that cosmological precision matrices are often approximately sparse, the method allows one to exploit this sparsity of the precision matrix to more quickly converge to an asymptotic 1/√{N_sim} rate while simultaneously providing an error model for all of the terms. Such an estimate can be used as the starting point for further regularization efforts which can improve upon the 1/√{N_sim} limit above, and incorporating such additional steps is straightforward within this framework. We demonstrate the technique with toy models and with an example motivated by large-scale structure two-point analysis, showing significant improvements in the rate of convergence. For the large-scale structure example, we find errors on the precision matrix which are factors of 5 smaller than for the sample precision matrix for thousands of simulations or, alternatively, convergence to the same error level with more than an order of magnitude fewer simulations.
Sparse representation with kernels.
Gao, Shenghua; Tsang, Ivor Wai-Hung; Chia, Liang-Tien
2013-02-01
Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks. PMID:23014744
Highly parallel sparse Cholesky factorization
NASA Technical Reports Server (NTRS)
Gilbert, John R.; Schreiber, Robert
1990-01-01
Several fine grained parallel algorithms were developed and compared to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special purpose algorithms in which the matrix structure conforms to the connection structure of the machine, the focus is on matrices with arbitrary sparsity structure. The most promising algorithm is one whose inner loop performs several dense factorizations simultaneously on a 2-D grid of processors. Virtually any massively parallel dense factorization algorithm can be used as the key subroutine. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and it is used to analyze the algorithms.
Structured Multifrontal Sparse Solver
Energy Science and Technology Software Center (ESTSC)
2014-05-01
StruMF is an algebraic structured preconditioner for the interative solution of large sparse linear systems. The preconditioner corresponds to a multifrontal variant of sparse LU factorization in which some dense blocks of the factors are approximated with low-rank matrices. It is algebraic in that it only requires the linear system itself, and the approximation threshold that determines the accuracy of individual low-rank approximations. Favourable rank properties are obtained using a block partitioning which is amore » refinement of the partitioning induced by nested dissection ordering.« less
Singer, Wolf
2013-12-01
Recent discoveries on the organisation of the cortical connectome together with novel data on the dynamics of neuronal interactions require an extension of classical concepts on information processing in the cerebral cortex. These new insights justify considering the brain as a complex, self-organised system with nonlinear dynamics in which principles of distributed, parallel processing coexist with serial operations within highly interconnected networks. The observed dynamics suggest that cortical networks are capable of providing an extremely high-dimensional state space in which a large amount of evolutionary and ontogenetically acquired information can coexist and be accessible to rapid parallel search. PMID:24139950
Modeling cortical source dynamics and interactions during seizure.
Mullen, Tim; Acar, Zeynep Akalin; Worrell, Gregory; Makeig, Scott
2011-01-01
Mapping the dynamics and spatial topography of brain source processes critically involved in initiating and propagating seizure activity is critical for effective epilepsy diagnosis, intervention, and treatment. In this report we analyze neuronal dynamics before and during epileptic seizures using adaptive multivariate autoregressive (VAR) models applied to maximally-independent (ICA) sources of intracranial EEG (iEEG, ECoG) data recorded from subdural electrodes implanted in a human patient for evaluation of surgery for epilepsy. We visualize the spatial distribution of causal sources and sinks of ictal activity on the cortical surface (gyral and sulcal) using a novel combination of multivariate Granger-causal and graph-theoretic metrics combined with distributed source localization by Sparse Bayesian Learning applied to a multi-scale patch basis. This analysis reveals and visualizes distinct, seizure stage-dependent shifts in inter-component spatiotemporal dynamics and connectivity including the clinically-identified epileptic foci. PMID:22254582
Ducommun, Christine Y; Michel, Christoph M; Clarke, Stephanie; Adriani, Michela; Seeck, Margitta; Landis, Theodor; Blanke, Olaf
2004-09-16
The extent to which the auditory system, like the visual system, processes spatial stimulus characteristics such as location and motion in separate specialized neuronal modules or in one homogeneously distributed network is unresolved. Here we present a patient with a selective deficit for the perception and discrimination of auditory motion following resection of the right anterior temporal lobe and the right posterior superior temporal gyrus (STG). Analysis of stimulus identity and location within the auditory scene remained intact. In addition, intracranial auditory evoked potentials, recorded preoperatively, revealed motion-specific responses selectively over the resected right posterior STG, and electrical cortical stimulation of this region was experienced by the patient as incoming moving sounds. Collectively, these data present a patient with cortical motion deafness, providing evidence that cortical processing of auditory motion is performed in a specialized module within the posterior STG. PMID:15363389
2016-01-01
Purpose This study investigated the effects of bone density and crestal cortical bone thickness at the implant-placement site on micromotion (relative displacement between the implant and bone) and the peri-implant bone strain distribution under immediate-loading conditions. Methods A three-dimensional finite element model of the posterior mandible with an implant was constructed. Various bone parameters were simulated, including low or high cancellous bone density, low or high crestal cortical bone density, and crestal cortical bone thicknesses ranging from 0.5 to 2.5 mm. Delayed- and immediate-loading conditions were simulated. A buccolingual oblique load of 200 N was applied to the top of the abutment. Results The maximum extent of micromotion was approximately 100 μm in the low-density cancellous bone models, whereas it was under 30 μm in the high-density cancellous bone models. Crestal cortical bone thickness significantly affected the maximum micromotion in the low-density cancellous bone models. The minimum principal strain in the peri-implant cortical bone was affected by the density of the crestal cortical bone and cancellous bone to the same degree for both delayed and immediate loading. In the low-density cancellous bone models under immediate loading, the minimum principal strain in the peri-implant cortical bone decreased with an increase in crestal cortical bone thickness. Conclusions Cancellous bone density may be a critical factor for avoiding excessive micromotion in immediately loaded implants. Crestal cortical bone thickness significantly affected the maximum extent of micromotion and peri-implant bone strain in simulations of low-density cancellous bone under immediate loading. PMID:27382504
González-Hernández, J A; Cedeño, I; Pita-Alcorta, C; Galán, L; Aubert, E; Figueredo-Rodríguez, P
2003-04-01
Prefrontal dysfunction has been associated with schizophrenia. Activation during Wisconsin card sorting test (WCST) is a common approach used in functional neuroimaging to address this failure. Equally, current knowledge states that oscillations are basic forms of cells-assembly communications during mental activity. Promising results were revealed in a previous study assessing healthy subjects, WCST and oscillations. However, those previous studies failed to meet the functional integration of the network during the WCST in schizophrenics, based on the induced oscillations and their distributed cortical sources. In this research, we utilized the brain electrical tomography (variable-resolution brain electromagnetic tomography) technique to accomplish this goal. Task specific delta, theta, alpha and beta-2 oscillations were induced and simultaneously synchronized over large extensions of cortex, encompassing prefrontal, temporal and posterior regions as in healthy subjects. Every frequency had a well-defined network involving a variable number of areas and sharing some of them. Oscillations at 11.5, 5.0 and 30 Hz seem to reflect an abnormal increase or decrease, being located at supplementary motor area (SMA), left occipitotemporal region (OT), and right frontotemporal subregions (RFT), respectively. Three cortical areas appeared to be critical, that may lead to difficulties either in coordinating/sequencing the input/output of the prefrontal networks-SMA, and retention of information in memory-RFT, both preceded or paralleled by a deficient visual information processing-OT. PMID:12694897
Sparse subspace clustering: algorithm, theory, and applications.
Elhamifar, Ehsan; Vidal, René
2013-11-01
Many real-world problems deal with collections of high-dimensional data, such as images, videos, text, and web documents, DNA microarray data, and more. Often, such high-dimensional data lie close to low-dimensional structures corresponding to several classes or categories to which the data belong. In this paper, we propose and study an algorithm, called sparse subspace clustering, to cluster data points that lie in a union of low-dimensional subspaces. The key idea is that, among the infinitely many possible representations of a data point in terms of other points, a sparse representation corresponds to selecting a few points from the same subspace. This motivates solving a sparse optimization program whose solution is used in a spectral clustering framework to infer the clustering of the data into subspaces. Since solving the sparse optimization program is in general NP-hard, we consider a convex relaxation and show that, under appropriate conditions on the arrangement of the subspaces and the distribution of the data, the proposed minimization program succeeds in recovering the desired sparse representations. The proposed algorithm is efficient and can handle data points near the intersections of subspaces. Another key advantage of the proposed algorithm with respect to the state of the art is that it can deal directly with data nuisances, such as noise, sparse outlying entries, and missing entries, by incorporating the model of the data into the sparse optimization program. We demonstrate the effectiveness of the proposed algorithm through experiments on synthetic data as well as the two real-world problems of motion segmentation and face clustering. PMID:24051734
Sparse inpainting and isotropy
Feeney, Stephen M.; McEwen, Jason D.; Peiris, Hiranya V.; Marinucci, Domenico; Cammarota, Valentina; Wandelt, Benjamin D. E-mail: marinucc@axp.mat.uniroma2.it E-mail: h.peiris@ucl.ac.uk E-mail: cammarot@axp.mat.uniroma2.it
2014-01-01
Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.
Sparse matrix test collections
Duff, I.
1996-12-31
This workshop will discuss plans for coordinating and developing sets of test matrices for the comparison and testing of sparse linear algebra software. We will talk of plans for the next release (Release 2) of the Harwell-Boeing Collection and recent work on improving the accessibility of this Collection and others through the World Wide Web. There will only be three talks of about 15 to 20 minutes followed by a discussion from the floor.
Optical sparse aperture imaging.
Miller, Nicholas J; Dierking, Matthew P; Duncan, Bradley D
2007-08-10
The resolution of a conventional diffraction-limited imaging system is proportional to its pupil diameter. A primary goal of sparse aperture imaging is to enhance resolution while minimizing the total light collection area; the latter being desirable, in part, because of the cost of large, monolithic apertures. Performance metrics are defined and used to evaluate several sparse aperture arrays constructed from multiple, identical, circular subapertures. Subaperture piston and/or tilt effects on image quality are also considered. We selected arrays with compact nonredundant autocorrelations first described by Golay. We vary both the number of subapertures and their relative spacings to arrive at an optimized array. We report the results of an experiment in which we synthesized an image from multiple subaperture pupil fields by masking a large lens with a Golay array. For this experiment we imaged a slant edge feature of an ISO12233 resolution target in order to measure the modulation transfer function. We note the contrast reduction inherent in images formed through sparse aperture arrays and demonstrate the use of a Wiener-Helstrom filter to restore contrast in our experimental images. Finally, we describe a method to synthesize images from multiple subaperture focal plane intensity images using a phase retrieval algorithm to obtain estimates of subaperture pupil fields. Experimental results from synthesizing an image of a point object from multiple subaperture images are presented, and weaknesses of the phase retrieval method for this application are discussed. PMID:17694146
Glennon, Mark; Keane, Michael A; Elliott, Mark A; Sauseng, Paul
2016-05-01
Attentional blink (AB) describes a visuo-perceptual phenomenon in which the second of 2 targets within a rapid serial visual presentation stream is not detected. There are several cognitive models attempting to explain the fundamentals of this information processing bottleneck. Here, we used electroencephalographic recordings and the analysis of interregional phase synchronization of rhythmical brain activity to investigate the neural bases of the AB. By investigating the time course of interregional phase synchronization separately for trials in which participants failed to report the second target correctly (AB trials) and trials in which no AB occurred, and by clustering interregional connections based on their functional similarity, it was possible to define several distinct cortical networks. Analyzing these networks comprising phase synchronization-over a large spectrum of brain frequencies from theta to gamma activity-it was possible to identify neural correlates for cognitive subfunctions involved in the AB, such as the encoding of targets into working memory, tuning of attentional filters, and the recruitment of general cognitive resources. This parallel activation of functionally distinct neural processes substantiates the eligibility of several cognitive models on the AB. PMID:25750255
Sprecher, Christoph M.; Schmidutz, Florian; Helfen, Tobias; Richards, R. Geoff; Blauth, Michael; Milz, Stefan
2015-01-01
Abstract Osteoporosis is a systemic disorder predominantly affecting postmenopausal women but also men at an advanced age. Both genders may suffer from low-energy fractures of, for example, the proximal humerus when reduction of the bone stock or/and quality has occurred. The aim of the current study was to compare the amount of bone in typical fracture zones of the proximal humerus in osteoporotic and non-osteoporotic individuals. The amount of bone in the proximal humerus was determined histomorphometrically in frontal plane sections. The donor bones were allocated to normal and osteoporotic groups using the T-score from distal radius DXA measurements of the same extremities. The T-score evaluation was done according to WHO criteria. Regional thickness of the subchondral plate and the metaphyseal cortical bone were measured using interactive image analysis. At all measured locations the amount of cancellous bone was significantly lower in individuals from the osteoporotic group compared to the non-osteoporotic one. The osteoporotic group showed more significant differences between regions of the same bone than the non-osteoporotic group. In both groups the subchondral cancellous bone and the subchondral plate were least affected by bone loss. In contrast, the medial metaphyseal region in the osteoporotic group exhibited higher bone loss in comparison to the lateral side. This observation may explain prevailing fracture patterns, which frequently involve compression fractures and certainly has an influence on the stability of implants placed in this medial region. It should be considered when planning the anchoring of osteosynthesis materials in osteoporotic patients with fractures of the proximal humerus. PMID:26705200
Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering.
Wang, Changqing; Kipping, Judy; Bao, Chenglong; Ji, Hui; Qiu, Anqi
2016-01-01
The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC). SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem. The dictionary is comprised of an over-complete set of time course signals, with which a sparse representation of rs-fMRI signals can be constructed. Cerebellar functional regions were then identified using k-means clustering based on the sparse representation of rs-fMRI signals. We solved SDLC using a multi-block hybrid proximal alternating method that guarantees strong convergence. We evaluated the reliability of SDLC and benchmarked its classification accuracy against other clustering techniques using simulated data. We then demonstrated that SDLC can identify biologically reasonable functional regions of the cerebellum as estimated by their cerebello-cortical functional connectivity. We further provided new insights into the cerebello-cortical functional organization in children. PMID:27199650
Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering
Wang, Changqing; Kipping, Judy; Bao, Chenglong; Ji, Hui; Qiu, Anqi
2016-01-01
The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC). SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem. The dictionary is comprised of an over-complete set of time course signals, with which a sparse representation of rs-fMRI signals can be constructed. Cerebellar functional regions were then identified using k-means clustering based on the sparse representation of rs-fMRI signals. We solved SDLC using a multi-block hybrid proximal alternating method that guarantees strong convergence. We evaluated the reliability of SDLC and benchmarked its classification accuracy against other clustering techniques using simulated data. We then demonstrated that SDLC can identify biologically reasonable functional regions of the cerebellum as estimated by their cerebello-cortical functional connectivity. We further provided new insights into the cerebello-cortical functional organization in children. PMID:27199650
Energy Science and Technology Software Center (ESTSC)
2007-04-12
The Sparse Image Format (SIF) is a file format for storing spare raster images. It works by breaking an image down into tiles. Space is savid by only storing non-uniform tiles, i.e. tiles with at least two different pixel values. If a tile is completely uniform, its common pixel value is stored instead of the complete tile raster. The software is a library in the C language used for manipulating files in SIF format. Itmore » supports large files (> 2GB) and is designed to build in Windows and Linux environments.« less
Eads, Damian Ryan
2007-04-12
The Sparse Image Format (SIF) is a file format for storing spare raster images. It works by breaking an image down into tiles. Space is savid by only storing non-uniform tiles, i.e. tiles with at least two different pixel values. If a tile is completely uniform, its common pixel value is stored instead of the complete tile raster. The software is a library in the C language used for manipulating files in SIF format. It supports large files (> 2GB) and is designed to build in Windows and Linux environments.
and Drayton Munster, Miroslav Stoyanov
2013-09-20
Sparse Grids are the family of methods of choice for multidimensional integration and interpolation in low to moderate number of dimensions. The method is to select extend a one dimensional set of abscissas, weights and basis functions by taking a subset of all possible tensor products. The module provides the ability to create global and local approximations based on polynomials and wavelets. The software has three components, a library, a wrapper for the library that provides a command line interface via text files ad a MATLAB interface via the command line tool.
Energy Science and Technology Software Center (ESTSC)
2013-09-20
Sparse Grids are the family of methods of choice for multidimensional integration and interpolation in low to moderate number of dimensions. The method is to select extend a one dimensional set of abscissas, weights and basis functions by taking a subset of all possible tensor products. The module provides the ability to create global and local approximations based on polynomials and wavelets. The software has three components, a library, a wrapper for the library thatmore » provides a command line interface via text files ad a MATLAB interface via the command line tool.« less
Peel, Andrew D; Averof, Michalis
2010-11-01
The localization of maternal mRNAs during oogenesis plays a central role in axial specification in some insects. Here we describe a polar body-associated asymmetry in maternal transcript distribution in pre-blastoderm eggs of the beetle Tribolium castaneum. Since the position of the polar body marks the future dorsal side of the embryo, we have investigated whether this asymmetry in mRNA distribution plays a role in dorsal-ventral axis specification. Whilst our results suggest polar body-associated transcripts do not play a significant role in specifying the DV axis, at least during early embryogenesis, we do find that the polar body is closely associated with a cortical microtubule network (CMN), which may play a role in the localization of transcripts during oogenesis. Transcripts of the gene T.c.pangolin co-localize with the CMN at the time of their anterior localization during oogenesis and their anterior localization is disrupted by the microtubule-depolymerizing agent colcemid. PMID:20857499
Neonatal Atlas Construction Using Sparse Representation
Shi, Feng; Wang, Li; Wu, Guorong; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang
2014-01-01
Atlas construction generally includes first an image registration step to normalize all images into a common space and then an atlas building step to fuse the information from all the aligned images. Although numerous atlas construction studies have been performed to improve the accuracy of the image registration step, unweighted or simply weighted average is often used in the atlas building step. In this article, we propose a novel patch-based sparse representation method for atlas construction after all images have been registered into the common space. By taking advantage of local sparse representation, more anatomical details can be recovered in the built atlas. To make the anatomical structures spatially smooth in the atlas, the anatomical feature constraints on group structure of representations and also the overlapping of neighboring patches are imposed to ensure the anatomical consistency between neighboring patches. The proposed method has been applied to 73 neonatal MR images with poor spatial resolution and low tissue contrast, for constructing a neonatal brain atlas with sharp anatomical details. Experimental results demonstrate that the proposed method can significantly enhance the quality of the constructed atlas by discovering more anatomical details especially in the highly convoluted cortical regions. The resulting atlas demonstrates superior performance of our atlas when applied to spatially normalizing three different neonatal datasets, compared with other start-of-the-art neonatal brain atlases. PMID:24638883
Compressed Sampling of Spectrally Sparse Signals Using Sparse Circulant Matrices
NASA Astrophysics Data System (ADS)
Xu, Guangjie; Wang, Huali; Sun, Lei; Zeng, Weijun; Wang, Qingguo
2014-11-01
Circulant measurement matrices constructed by partial cyclically shifts of one generating sequence, are easier to be implemented in hardware than widely used random measurement matrices; however, the diminishment of randomness makes it more sensitive to signal noise. Selecting a deterministic sequence with optimal periodic autocorrelation property (PACP) as generating sequence, would enhance the noise robustness of circulant measurement matrix, but this kind of deterministic circulant matrices only exists in the fixed periodic length. Actually, the selection of generating sequence doesn't affect the compressive performance of circulant measurement matrix but the subspace energy in spectrally sparse signals. Sparse circulant matrices, whose generating sequence is a sparse sequence, could keep the energy balance of subspaces and have similar noise robustness to deterministic circulant matrices. In addition, sparse circulant matrices have no restriction on length and are more suitable for the compressed sampling of spectrally sparse signals at arbitrary dimensionality.
Percolation on Sparse Networks
NASA Astrophysics Data System (ADS)
Karrer, Brian; Newman, M. E. J.; Zdeborová, Lenka
2014-11-01
We study percolation on networks, which is used as a model of the resilience of networked systems such as the Internet to attack or failure and as a simple model of the spread of disease over human contact networks. We reformulate percolation as a message passing process and demonstrate how the resulting equations can be used to calculate, among other things, the size of the percolating cluster and the average cluster size. The calculations are exact for sparse networks when the number of short loops in the network is small, but even on networks with many short loops we find them to be highly accurate when compared with direct numerical simulations. By considering the fixed points of the message passing process, we also show that the percolation threshold on a network with few loops is given by the inverse of the leading eigenvalue of the so-called nonbacktracking matrix.
NASA Astrophysics Data System (ADS)
Jurgens, B. C.; Bohlke, J. K.; Voss, S.; Fram, M. S.; Esser, B.
2015-12-01
Tracer-based, lumped parameter models (LPMs) are an appealing way to estimate the distribution of age for groundwater because the cost of sampling wells is often less than building numerical groundwater flow models sufficiently complex to provide groundwater age distributions. In practice, however, tracer datasets are often incomplete because of anthropogenic or terrigenic contamination of tracers, or analytical limitations. While age interpretations using such datsets can have large uncertainties, it may still be possible to identify key parts of the age distribution if LPMs are carefully chosen to match hydrogeologic conceptualization and the degree of age mixing is reasonably estimated. We developed a systematic approach for evaluating groundwater age distributions using LPMs with a large but incomplete set of tracer data (3H, 3Hetrit, 14C, and CFCs) from 535 wells, mostly used for public supply, in the Central Valley, California, USA that were sampled by the USGS for the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment or the USGS National Water Quality Assessment Programs. In addition to mean ages, LPMs gave estimates of unsaturated zone travel times, recharge rates for pre- and post-development groundwater, the degree of age mixing in wells, proportion of young water (<60 yrs), and the depth of the boundary between post-development and predevelopment groundwater throughout the Central Valley. Age interpretations were evaluated by comparing past nitrate trends with LPM predicted trends, and whether the presence or absence of anthropogenic organic compounds was consistent with model results. This study illustrates a practical approach for assessing groundwater age information at a large scale to reveal important characteristics about the age structure of a major aquifer, and of the water supplies being derived from it.
Reconstructing spatially extended brain sources via enforcing multiple transform sparseness.
Zhu, Min; Zhang, Wenbo; Dickens, Deanna L; Ding, Lei
2014-02-01
Accurate estimation of location and extent of neuronal sources from EEG/MEG remain challenging. In the present study, a new source imaging method, i.e. variation and wavelet based sparse source imaging (VW-SSI), is proposed to better estimate cortical source locations and extents. VW-SSI utilizes the L1-norm regularization method with the enforcement of transform sparseness in both variation and wavelet domains. The performance of the proposed method is assessed by both simulated and experimental MEG data, obtained from a language task and a motor task. Compared to L2-norm regularizations, VW-SSI demonstrates significantly improved capability in reconstructing multiple extended cortical sources with less spatial blurredness and less localization error. With the use of transform sparseness, VW-SSI overcomes the over-focused problem in classic SSI methods. With the use of two transformations, VW-SSI further indicates significantly better performance in estimating MEG source locations and extents than other SSI methods with single transformations. The present experimental results indicate that VW-SSI can successfully estimate neural sources (and their spatial coverage) located in close areas while responsible for different functions, i.e. temporal cortical sources for auditory and language processing, and sources on the pre-bank and post-bank of the central sulcus. Meantime, all other methods investigated in the present study fail to recover these phenomena. Precise estimation of cortical source locations and extents from EEG/MEG is of significance for applications in neuroscience and neurology. PMID:24103850
James, Gareth M.; Sabatti, Chiara; Zhou, Nengfeng; Zhu, Ji
2011-01-01
In many organisms the expression levels of each gene are controlled by the activation levels of known “Transcription Factors” (TF). A problem of considerable interest is that of estimating the “Transcription Regulation Networks” (TRN) relating the TFs and genes. While the expression levels of genes can be observed, the activation levels of the corresponding TFs are usually unknown, greatly increasing the difficulty of the problem. Based on previous experimental work, it is often the case that partial information about the TRN is available. For example, certain TFs may be known to regulate a given gene or in other cases a connection may be predicted with a certain probability. In general, the biology of the problem indicates there will be very few connections between TFs and genes. Several methods have been proposed for estimating TRNs. However, they all suffer from problems such as unrealistic assumptions about prior knowledge of the network structure or computational limitations. We propose a new approach that can directly utilize prior information about the network structure in conjunction with observed gene expression data to estimate the TRN. Our approach uses L1 penalties on the network to ensure a sparse structure. This has the advantage of being computationally efficient as well as making many fewer assumptions about the network structure. We use our methodology to construct the TRN for E. coli and show that the estimate is biologically sensible and compares favorably with previous estimates. PMID:21625366
Completeness for sparse potential scattering
Shen, Zhongwei
2014-01-15
The present paper is devoted to the scattering theory of a class of continuum Schrödinger operators with deterministic sparse potentials. We first establish the limiting absorption principle for both modified free resolvents and modified perturbed resolvents. This actually is a weak form of the classical limiting absorption principle. We then prove the existence and completeness of local wave operators, which, in particular, imply the existence of wave operators. Under additional assumptions on the sparse potential, we prove the completeness of wave operators. In the context of continuum Schrödinger operators with sparse potentials, this paper gives the first proof of the completeness of wave operators.
The sparseness of neuronal responses in ferret primary visual cortex.
Tolhurst, David J; Smyth, Darragh; Thompson, Ian D
2009-02-25
Various arguments suggest that neuronal coding of natural sensory stimuli should be sparse (i.e., individual neurons should respond rarely but should respond reliably). We examined sparseness of visual cortical neurons in anesthetized ferret to flashed natural scenes. Response behavior differed widely between neurons. The median firing rate of 4.1 impulses per second was slightly higher than predicted from consideration of metabolic load. Thirteen percent of neurons (12 of 89) responded to <5% of the images, but one-half responded to >25% of images. Multivariate analysis of the range of sparseness values showed that 67% of the variance was accounted for by differing response patterns to moving gratings. Repeat presentation of images showed that response variance for natural images exaggerated sparseness measures; variance was scaled with mean response, but with a lower Fano factor than for the responses to moving gratings. This response variability and the "soft" sparse responses (Rehn and Sommer, 2007) raise the question of what constitutes a reliable neuronal response and imply parallel signaling by multiple neurons. We investigated whether the temporal structure of responses might be reliable enough to give additional information about natural scenes. Poststimulus time histogram shape was similar for "strong" and "weak" stimuli, with no systematic change in first-spike latency with stimulus strength. The variance of first-spike latency for repeat presentations of the same image was greater than the latency variance between images. In general, responses to flashed natural scenes do not seem compatible with a sparse encoding in which neurons fire rarely but reliably. PMID:19244512
Boer, Karin; Troost, Dirk; Spliet, Wim G. M.; van Rijen, Peter C.; Gorter, Jan A.
2008-01-01
Members of the vascular endothelial growth factor (VEGF) family are key signaling proteins in the induction and regulation of angiogenesis, both during development and in pathological conditions. However, signaling mediated through VEGF family proteins and their receptors has recently been shown to have direct effects on neurons and glial cells. In the present study, we immunocytochemically investigated the expression and cellular distribution of VEGFA, VEGFB, and their associated receptors (VEGFR-1 and VEGFR-2) in focal cortical dysplasia (FCD) type IIB from patients with medically intractable epilepsy. Histologically normal temporal cortex and perilesional regions displayed neuronal immunoreactivity (IR) for VEGFA, VEGFB, and VEGF receptors (VEGFR-1 and VEGFR-2), mainly in pyramidal neurons. Weak IR was observed in blood vessels and there was no notable glial IR within the grey and white matter. In all FCD specimens, VEGFA, VEGFB, and both VEGF receptors were highly expressed in dysplastic neurons. IR in astroglial and balloon cells was observed for VEGFA and its receptors. VEGFR-1 displayed strong endothelial staining in FCD. Double-labeling also showed expression of VEGFA, VEGFB and VEGFR-1 in cells of the microglia/macrophage lineage. The neuronal expression of both VEGFA and VEGFB, together with their specific receptors in FCD, suggests autocrine/paracrine effects on dysplastic neurons. These autocrine/paracrine effects could play a role in the development of FCD, preventing the death of abnormal neuronal cells. In addition, the expression of VEGFA and its receptors in glial cells within the dysplastic cortex indicates that VEGF-mediated signaling could contribute to astroglial activation and associated inflammatory reactions. PMID:18317782
Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation.
Aram, Parham; Kadirkamanathan, Visakan; Anderson, Sean R
2015-11-01
We present a framework for the identification of spatiotemporal linear dynamical systems. We use a state-space model representation that has the following attributes: 1) the number of spatial observation locations are decoupled from the model order; 2) the model allows for spatial heterogeneity; 3) the model representation is continuous over space; and 4) the model parameters can be identified in a simple and sparse estimation procedure. The model identification procedure we propose has four steps: 1) decomposition of the continuous spatial field using a finite set of basis functions where spatial frequency analysis is used to determine basis function width and spacing, such that the main spatial frequency contents of the underlying field can be captured; 2) initialization of states in closed form; 3) initialization of state-transition and input matrix model parameters using sparse regression-the least absolute shrinkage and selection operator method; and 4) joint state and parameter estimation using an iterative Kalman-filter/sparse-regression algorithm. To investigate the performance of the proposed algorithm we use data generated by the Kuramoto model of spatiotemporal cortical dynamics. The identification algorithm performs successfully, predicting the spatiotemporal field with high accuracy, whilst the sparse regression leads to a compact model. PMID:25647667
Threaded Operations on Sparse Matrices
Sneed, Brett
2015-09-01
We investigate the use of sparse matrices and OpenMP multi-threading on linear algebra operations involving them. Several sparse matrix data structures are presented. Implementation of the multi- threading primarily occurs in the level one and two BLAS functions used within the four algorithms investigated{the Power Method, Conjugate Gradient, Biconjugate Gradient, and Jacobi's Method. The bene ts of launching threads once per high level algorithm are explored.
Representation-Independent Iteration of Sparse Data Arrays
NASA Technical Reports Server (NTRS)
James, Mark
2007-01-01
An approach is defined that describes a method of iterating over massively large arrays containing sparse data using an approach that is implementation independent of how the contents of the sparse arrays are laid out in memory. What is unique and important here is the decoupling of the iteration over the sparse set of array elements from how they are internally represented in memory. This enables this approach to be backward compatible with existing schemes for representing sparse arrays as well as new approaches. What is novel here is a new approach for efficiently iterating over sparse arrays that is independent of the underlying memory layout representation of the array. A functional interface is defined for implementing sparse arrays in any modern programming language with a particular focus for the Chapel programming language. Examples are provided that show the translation of a loop that computes a matrix vector product into this representation for both the distributed and not-distributed cases. This work is directly applicable to NASA and its High Productivity Computing Systems (HPCS) program that JPL and our current program are engaged in. The goal of this program is to create powerful, scalable, and economically viable high-powered computer systems suitable for use in national security and industry by 2010. This is important to NASA for its computationally intensive requirements for analyzing and understanding the volumes of science data from our returned missions.
Mean-field sparse optimal control
Fornasier, Massimo; Piccoli, Benedetto; Rossi, Francesco
2014-01-01
We introduce the rigorous limit process connecting finite dimensional sparse optimal control problems with ODE constraints, modelling parsimonious interventions on the dynamics of a moving population divided into leaders and followers, to an infinite dimensional optimal control problem with a constraint given by a system of ODE for the leaders coupled with a PDE of Vlasov-type, governing the dynamics of the probability distribution of the followers. In the classical mean-field theory, one studies the behaviour of a large number of small individuals freely interacting with each other, by simplifying the effect of all the other individuals on any given individual by a single averaged effect. In this paper, we address instead the situation where the leaders are actually influenced also by an external policy maker, and we propagate its effect for the number N of followers going to infinity. The technical derivation of the sparse mean-field optimal control is realized by the simultaneous development of the mean-field limit of the equations governing the followers dynamics together with the Γ-limit of the finite dimensional sparse optimal control problems. PMID:25288818
NASA Astrophysics Data System (ADS)
Lu, Yiliang; Yan, Yan; Chai, Xinyu; Ren, Qiushi; Chen, Yao; Li, Liming
2013-06-01
Objective. A visual prosthesis based on penetrating electrode stimulation within the optic nerve (ON) is a potential way to restore partial functional vision for blind patients. We investigated the retinotopic organization of ON stimulation and its spatial resolution. Approach. A five-electrode array was inserted perpendicularly into the ON or a single electrode was advanced to different depths within the ON (˜1-2 mm behind the eyeball, 13 cats). A sparse noise method was used to map ON electrode position and the visual cortex. Cortical responses were recorded by a 5 × 6 array. The visuotopic correspondence between the retinotopic position of the ON electrode was compared with the visual evoked cortical map and the electrical evoked potentials elicited in response to ON stimulation. Main results. Electrical stimulation with penetrating ON electrodes elicited cortical responses in visuotopographically corresponding areas of the cortex. Stimulation of the temporal side of the ON elicited cortical responses corresponding to the central visual field. The visual field position shifted from the lower to central visual field as the electrode penetrated through the depth of the ON. A spatial resolution of ˜ 2° to 3° within a limited cortical visuotopic representation could be obtained by this approach. Significance. Visuotopic electrical stimulation with a relatively fine spatial resolution can be accomplished using penetrating electrodes implanted at multiple sites and at different depths within the ON just behind the globe. This study also provides useful experimental data for the design of electrode density and the distribution of penetrating ON electrodes for a visual prosthesis.
Wavelet Sparse Approximate Inverse Preconditioners
NASA Technical Reports Server (NTRS)
Chan, Tony F.; Tang, W.-P.; Wan, W. L.
1996-01-01
There is an increasing interest in using sparse approximate inverses as preconditioners for Krylov subspace iterative methods. Recent studies of Grote and Huckle and Chow and Saad also show that sparse approximate inverse preconditioner can be effective for a variety of matrices, e.g. Harwell-Boeing collections. Nonetheless a drawback is that it requires rapid decay of the inverse entries so that sparse approximate inverse is possible. However, for the class of matrices that, come from elliptic PDE problems, this assumption may not necessarily hold. Our main idea is to look for a basis, other than the standard one, such that a sparse representation of the inverse is feasible. A crucial observation is that the kind of matrices we are interested in typically have a piecewise smooth inverse. We exploit this fact, by applying wavelet techniques to construct a better sparse approximate inverse in the wavelet basis. We shall justify theoretically and numerically that our approach is effective for matrices with smooth inverse. We emphasize that in this paper we have only presented the idea of wavelet approximate inverses and demonstrated its potential but have not yet developed a highly refined and efficient algorithm.
Learning Sparse Representations of Depth
NASA Astrophysics Data System (ADS)
Tosic, Ivana; Olshausen, Bruno A.; Culpepper, Benjamin J.
2011-09-01
This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, typically obtained by laser range scanners or structured light depth cameras. Sparse representations are learned from the Middlebury database disparity maps and then exploited in a two-layer graphical model for inferring depth from stereo, by including a sparsity prior on the learned features. Since they capture higher-order dependencies in the depth structure, these priors can complement smoothness priors commonly used in depth inference based on Markov Random Field (MRF) models. Inference on the proposed graph is achieved using an alternating iterative optimization technique, where the first layer is solved using an existing MRF-based stereo matching algorithm, then held fixed as the second layer is solved using the proposed non-stationary sparse coding algorithm. This leads to a general method for improving solutions of state of the art MRF-based depth estimation algorithms. Our experimental results first show that depth inference using learned representations leads to state of the art denoising of depth maps obtained from laser range scanners and a time of flight camera. Furthermore, we show that adding sparse priors improves the results of two depth estimation methods: the classical graph cut algorithm by Boykov et al. and the more recent algorithm of Woodford et al.
Sparse modeling of spatial environmental variables associated with asthma
Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.
2014-01-01
Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437
Sparse modeling of spatial environmental variables associated with asthma.
Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W
2015-02-01
Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437
Sparsely-synchronized brain rhythm in a small-world neural network
NASA Astrophysics Data System (ADS)
Kim, Sang-Yoon; Lim, Woochang
2013-07-01
Sparsely-synchronized cortical rhythms, associated with diverse cognitive functions, have been observed in electric recordings of brain activity. At the population level, cortical rhythms exhibit small-amplitude fast oscillations while at the cellular level, individual neurons show stochastic firings sparsely at a much lower rate than the population rate. We study the effect of network architecture on sparse synchronization in an inhibitory population of subthreshold Morris-Lecar neurons (which cannot fire spontaneously without noise). Previously, sparse synchronization was found to occur for cases of both global coupling ( i.e., regular all-to-all coupling) and random coupling. However, a real neural network is known to be non-regular and non-random. Here, we consider sparse Watts-Strogatz small-world networks which interpolate between a regular lattice and a random graph via rewiring. We start from a regular lattice with only short-range connections and then investigate the emergence of sparse synchronization by increasing the rewiring probability p for the short-range connections. For p = 0, the average synaptic path length between pairs of neurons becomes long; hence, only an unsynchronized population state exists because the global efficiency of information transfer is low. However, as p is increased, long-range connections begin to appear, and global effective communication between distant neurons may be available via shorter synaptic paths. Consequently, as p passes a threshold p th (}~ 0.044), sparsely-synchronized population rhythms emerge. However, with increasing p, longer axon wirings become expensive because of their material and energy costs. At an optimal value p* DE (}~ 0.24) of the rewiring probability, the ratio of the synchrony degree to the wiring cost is found to become maximal. In this way, an optimal sparse synchronization is found to occur at a minimal wiring cost in an economic small-world network through trade-off between synchrony and
Sparsely sampling the sky: Regular vs. random sampling
NASA Astrophysics Data System (ADS)
Paykari, P.; Pires, S.; Starck, J.-L.; Jaffe, A. H.
2015-09-01
Aims: The next generation of galaxy surveys, aiming to observe millions of galaxies, are expensive both in time and money. This raises questions regarding the optimal investment of this time and money for future surveys. In a previous work, we have shown that a sparse sampling strategy could be a powerful substitute for the - usually favoured - contiguous observation of the sky. In our previous paper, regular sparse sampling was investigated, where the sparse observed patches were regularly distributed on the sky. The regularity of the mask introduces a periodic pattern in the window function, which induces periodic correlations at specific scales. Methods: In this paper, we use a Bayesian experimental design to investigate a "random" sparse sampling approach, where the observed patches are randomly distributed over the total sparsely sampled area. Results: We find that in this setting, the induced correlation is evenly distributed amongst all scales as there is no preferred scale in the window function. Conclusions: This is desirable when we are interested in any specific scale in the galaxy power spectrum, such as the matter-radiation equality scale. As the figure of merit shows, however, there is no preference between regular and random sampling to constrain the overall galaxy power spectrum and the cosmological parameters.
Galaxy redshift surveys with sparse sampling
Chiang, Chi-Ting; Wullstein, Philipp; Komatsu, Eiichiro; Jee, Inh; Jeong, Donghui; Blanc, Guillermo A.; Ciardullo, Robin; Gronwall, Caryl; Hagen, Alex; Schneider, Donald P.; Drory, Niv; Fabricius, Maximilian; Landriau, Martin; Finkelstein, Steven; Jogee, Shardha; Cooper, Erin Mentuch; Tuttle, Sarah; Gebhardt, Karl; Hill, Gary J.
2013-12-01
Survey observations of the three-dimensional locations of galaxies are a powerful approach to measure the distribution of matter in the universe, which can be used to learn about the nature of dark energy, physics of inflation, neutrino masses, etc. A competitive survey, however, requires a large volume (e.g., V{sub survey} ∼ 10Gpc{sup 3}) to be covered, and thus tends to be expensive. A ''sparse sampling'' method offers a more affordable solution to this problem: within a survey footprint covering a given survey volume, V{sub survey}, we observe only a fraction of the volume. The distribution of observed regions should be chosen such that their separation is smaller than the length scale corresponding to the wavenumber of interest. Then one can recover the power spectrum of galaxies with precision expected for a survey covering a volume of V{sub survey} (rather than the volume of the sum of observed regions) with the number density of galaxies given by the total number of observed galaxies divided by V{sub survey} (rather than the number density of galaxies within an observed region). We find that regularly-spaced sampling yields an unbiased power spectrum with no window function effect, and deviations from regularly-spaced sampling, which are unavoidable in realistic surveys, introduce calculable window function effects and increase the uncertainties of the recovered power spectrum. On the other hand, we show that the two-point correlation function (pair counting) is not affected by sparse sampling. While we discuss the sparse sampling method within the context of the forthcoming Hobby-Eberly Telescope Dark Energy Experiment, the method is general and can be applied to other galaxy surveys.
Iterative Sparse Approximation of the Gravitational Potential
NASA Astrophysics Data System (ADS)
Telschow, R.
2012-04-01
In recent applications in the approximation of gravitational potential fields, several new challenges arise. We are concerned with a huge quantity of data (e.g. in case of the Earth) or strongly irregularly distributed data points (e.g. in case of the Juno mission to Jupiter), where both of these problems bring the established approximation methods to their limits. Our novel method, which is a matching pursuit, however, iteratively chooses a best basis out of a large redundant family of trial functions to reconstruct the signal. It is independent of the data points which makes it possible to take into account a much higher amount of data and, furthermore, handle irregularly distributed data, since the algorithm is able to combine arbitrary spherical basis functions, i.e., global as well as local trial functions. This additionaly results in a solution, which is sparse in the sense that it features more basis functions where the signal has a higher local detail density. Summarizing, we get a method which reconstructs large quantities of data with a preferably low number of basis functions, combining global as well as several localizing functions to a sparse basis and a solution which is locally adapted to the data density and also to the detail density of the signal.
Amesos2 Templated Direct Sparse Solver Package
Energy Science and Technology Software Center (ESTSC)
2011-05-24
Amesos2 is a templated direct sparse solver package. Amesos2 provides interfaces to direct sparse solvers, rather than providing native solver capabilities. Amesos2 is a derivative work of the Trilinos package Amesos.
Ryynänen, Outi R M; Hyttinen, Jari A K; Malmivuo, Jaakko A
2006-09-01
The purpose of the present theoretical study was to examine the spatial resolution of electroencephalography (EEG) by means of the accuracy of the inverse cortical EEG solution. The study focused on effect of the amount of measurement noise and the number of electrodes on the spatial resolution with different resistivity ratios for the scalp, skull and brain. The results show that if the relative skull resistivity is lower than earlier believed, the spatial resolution of different electrode systems is less sensitive to the measurement noise. Furthermore, there is then also greater advantage to be obtained with high-resolution EEG at realistic noise levels. PMID:16941841
P-SPARSLIB: A parallel sparse iterative solution package
Saad, Y.
1994-12-31
Iterative methods are gaining popularity in engineering and sciences at a time where the computational environment is changing rapidly. P-SPARSLIB is a project to build a software library for sparse matrix computations on parallel computers. The emphasis is on iterative methods and the use of distributed sparse matrices, an extension of the domain decomposition approach to general sparse matrices. One of the goals of this project is to develop a software package geared towards specific applications. For example, the author will test the performance and usefulness of P-SPARSLIB modules on linear systems arising from CFD applications. Equally important is the goal of portability. In the long run, the author wishes to ensure that this package is portable on a variety of platforms, including SIMD environments and shared memory environments.
Sparse Sensing of Aerodynamic Loads on Insect Wings
NASA Astrophysics Data System (ADS)
Manohar, Krithika; Brunton, Steven; Kutz, J. Nathan
2015-11-01
We investigate how insects use sparse sensors on their wings to detect aerodynamic loading and wing deformation using a coupled fluid-structure model given periodically flapping input motion. Recent observations suggest that insects collect sensor information about their wing deformation to inform control actions for maneuvering and rejecting gust disturbances. Given a small number of point measurements of the chordwise aerodynamic loads from the sparse sensors, we reconstruct the entire chordwise loading using sparsesensing - a signal processing technique that reconstructs a signal from a small number of measurements using l1 norm minimization of sparse modal coefficients in some basis. We compare reconstructions from sensors randomly sampled from probability distributions biased toward different regions along the wing chord. In this manner, we determine the preferred regions along the chord for sensor placement and for estimating chordwise loads to inform control decisions in flight.
Finding communities in sparse networks
Singh, Abhinav; Humphries, Mark D.
2015-01-01
Spectral algorithms based on matrix representations of networks are often used to detect communities, but classic spectral methods based on the adjacency matrix and its variants fail in sparse networks. New spectral methods based on non-backtracking random walks have recently been introduced that successfully detect communities in many sparse networks. However, the spectrum of non-backtracking random walks ignores hanging trees in networks that can contain information about their community structure. We introduce the reluctant backtracking operators that explicitly account for hanging trees as they admit a small probability of returning to the immediately previous node, unlike the non-backtracking operators that forbid an immediate return. We show that the reluctant backtracking operators can detect communities in certain sparse networks where the non-backtracking operators cannot, while performing comparably on benchmark stochastic block model networks and real world networks. We also show that the spectrum of the reluctant backtracking operator approximately optimises the standard modularity function. Interestingly, for this family of non- and reluctant-backtracking operators the main determinant of performance on real-world networks is whether or not they are normalised to conserve probability at each node. PMID:25742951
Precise Feature Based Time Scales and Frequency Decorrelation Lead to a Sparse Auditory Code
Chen, Chen; Read, Heather L.; Escabí, Monty A.
2012-01-01
Sparse redundancy reducing codes have been proposed as efficient strategies for representing sensory stimuli. A prevailing hypothesis suggests that sensory representations shift from dense redundant codes in the periphery to selective sparse codes in cortex. We propose an alternative framework where sparseness and redundancy depend on sensory integration time scales and demonstrate that the central nucleus of the inferior colliculus (ICC) of cats encodes sound features by precise sparse spike trains. Direct comparisons with auditory cortical neurons demonstrate that ICC responses were sparse and uncorrelated as long as the spike train time scales were matched to the sensory integration time scales relevant to ICC neurons. Intriguingly, correlated spiking in the ICC was substantially lower than predicted by linear or nonlinear models and strictly observed for neurons with best frequencies within a “critical band,” the hallmark of perceptual frequency resolution in mammals. This is consistent with a sparse asynchronous code throughout much of the ICC and a complementary correlation code within a critical band that may allow grouping of perceptually relevant cues. PMID:22723685
Sparse Matrices in MATLAB: Design and Implementation
NASA Technical Reports Server (NTRS)
Gilbert, John R.; Moler, Cleve; Schreiber, Robert
1992-01-01
The matrix computation language and environment MATLAB is extended to include sparse matrix storage and operations. The only change to the outward appearance of the MATLAB language is a pair of commands to create full or sparse matrices. Nearly all the operations of MATLAB now apply equally to full or sparse matrices, without any explicit action by the user. The sparse data structure represents a matrix in space proportional to the number of nonzero entries, and most of the operations compute sparse results in time proportional to the number of arithmetic operations on nonzeros.
Optimized sparse-particle aerosol representations for modeling cloud-aerosol interactions
NASA Astrophysics Data System (ADS)
Fierce, Laura; McGraw, Robert
2016-04-01
Sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the method of moments. Given a set of moment constraints, we show how linear programming can be used to identify collections of sparse particles that approximately maximize distributional entropy. The collections of sparse particles derived from this approach reproduce CCN activity of the exact model aerosol distributions with high accuracy. Additionally, the linear programming techniques described in this study can be used to bound key aerosol properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy moment-based approach is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a new aerosol simulation scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.
A performance study of sparse Cholesky factorization on INTEL iPSC/860
NASA Technical Reports Server (NTRS)
Zubair, M.; Ghose, M.
1992-01-01
The problem of Cholesky factorization of a sparse matrix has been very well investigated on sequential machines. A number of efficient codes exist for factorizing large unstructured sparse matrices. However, there is a lack of such efficient codes on parallel machines in general, and distributed machines in particular. Some of the issues that are critical to the implementation of sparse Cholesky factorization on a distributed memory parallel machine are ordering, partitioning and mapping, load balancing, and ordering of various tasks within a processor. Here, we focus on the effect of various partitioning schemes on the performance of sparse Cholesky factorization on the Intel iPSC/860. Also, a new partitioning heuristic for structured as well as unstructured sparse matrices is proposed, and its performance is compared with other schemes.
Murray, John D.; McCormick, David A.
2016-01-01
Abstract Cortical feedback pathways are hypothesized to distribute context-dependent signals during flexible behavior. Recent experimental work has attempted to understand the mechanisms by which cortical feedback inputs modulate their target regions. Within the mouse whisker sensorimotor system, cortical feedback stimulation modulates spontaneous activity and sensory responsiveness, leading to enhanced sensory representations. However, the cellular mechanisms underlying these effects are currently unknown. In this study we use a simplified neural circuit model, which includes two recurrent excitatory populations and global inhibition, to simulate cortical modulation. First, we demonstrate how changes in the strengths of excitation and inhibition alter the input–output processing responses of our model. Second, we compare these responses with experimental findings from cortical feedback stimulation. Our analyses predict that enhanced inhibition underlies the changes in spontaneous and sensory evoked activity observed experimentally. More generally, these analyses provide a framework for relating cellular and synaptic properties to emergent circuit function and dynamic modulation. PMID:27595137
Zagha, Edward; Murray, John D; McCormick, David A
2016-01-01
Cortical feedback pathways are hypothesized to distribute context-dependent signals during flexible behavior. Recent experimental work has attempted to understand the mechanisms by which cortical feedback inputs modulate their target regions. Within the mouse whisker sensorimotor system, cortical feedback stimulation modulates spontaneous activity and sensory responsiveness, leading to enhanced sensory representations. However, the cellular mechanisms underlying these effects are currently unknown. In this study we use a simplified neural circuit model, which includes two recurrent excitatory populations and global inhibition, to simulate cortical modulation. First, we demonstrate how changes in the strengths of excitation and inhibition alter the input-output processing responses of our model. Second, we compare these responses with experimental findings from cortical feedback stimulation. Our analyses predict that enhanced inhibition underlies the changes in spontaneous and sensory evoked activity observed experimentally. More generally, these analyses provide a framework for relating cellular and synaptic properties to emergent circuit function and dynamic modulation. PMID:27595137
Dose-shaping using targeted sparse optimization
Sayre, George A.; Ruan, Dan
2013-07-15
Purpose: Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method.Methods: In designing the energy minimization objective (E{sub tot}{sup sparse}), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L{sub 1} norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E{sub tot
Scenario generation for stochastic optimization problems via the sparse grid method
Chen, Michael; Mehrotra, Sanjay; Papp, David
2015-04-19
We study the use of sparse grids in the scenario generation (or discretization) problem in stochastic programming problems where the uncertainty is modeled using a continuous multivariate distribution. We show that, under a regularity assumption on the random function involved, the sequence of optimal objective function values of the sparse grid approximations converges to the true optimal objective function values as the number of scenarios increases. The rate of convergence is also established. We treat separately the special case when the underlying distribution is an affine transform of a product of univariate distributions, and show how the sparse grid method can be adapted to the distribution by the use of quadrature formulas tailored to the distribution. We numerically compare the performance of the sparse grid method using different quadrature rules with classic quasi-Monte Carlo (QMC) methods, optimal rank-one lattice rules, and Monte Carlo (MC) scenario generation, using a series of utility maximization problems with up to 160 random variables. The results show that the sparse grid method is very efficient, especially if the integrand is sufficiently smooth. In such problems the sparse grid scenario generation method is found to need several orders of magnitude fewer scenarios than MC and QMC scenario generation to achieve the same accuracy. As a result, it is indicated that the method scales well with the dimension of the distribution--especially when the underlying distribution is an affine transform of a product of univariate distributions, in which case the method appears scalable to thousands of random variables.
ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES
Fan, Jianqing; Rigollet, Philippe; Wang, Weichen
2016-01-01
High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓr norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics. PMID:26806986
Fast generation of sparse random kernel graphs
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less
Fast generation of sparse random kernel graphs
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.
Fast Generation of Sparse Random Kernel Graphs
2015-01-01
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most 𝒪(n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity. PMID:26356296
Cortical Reorganization following Injury Early in Life
Artzi, Moran; Shiran, Shelly Irene; Weinstein, Maya; Myers, Vicki; Tarrasch, Ricardo; Schertz, Mitchell; Fattal-Valevski, Aviva; Miller, Elka; Gordon, Andrew M.; Green, Dido; Ben Bashat, Dafna
2016-01-01
The brain has a remarkable capacity for reorganization following injury, especially during the first years of life. Knowledge of structural reorganization and its consequences following perinatal injury is sparse. Here we studied changes in brain tissue volume, morphology, perfusion, and integrity in children with hemiplegia compared to typically developing children, using MRI. Children with hemiplegia demonstrated reduced total cerebral volume, with increased cerebrospinal fluid (CSF) and reduced total white matter volumes, with no differences in total gray matter volume, compared to typically developing children. An increase in cortical thickness at the hemisphere contralateral to the lesion (CLH) was detected in motor and language areas, which may reflect compensation for the gray matter loss in the lesion area or retention of ipsilateral pathways. In addition, reduced cortical thickness, perfusion, and surface area were detected in limbic areas. Increased CSF volume and precentral cortical thickness and reduced white matter volume were correlated with worse motor performance. Brain reorganization of the gray matter within the CLH, while not necessarily indicating better outcome, is suggested as a response to neuronal deficits following injury early in life. PMID:27298741
Mesoscopic Patterns of Neural Activity Support Songbird Cortical Sequences
Guitchounts, Grigori; Velho, Tarciso; Lois, Carlos; Gardner, Timothy J.
2015-01-01
Time-locked sequences of neural activity can be found throughout the vertebrate forebrain in various species and behavioral contexts. From “time cells” in the hippocampus of rodents to cortical activity controlling movement, temporal sequence generation is integral to many forms of learned behavior. However, the mechanisms underlying sequence generation are not well known. Here, we describe a spatial and temporal organization of the songbird premotor cortical microcircuit that supports sparse sequences of neural activity. Multi-channel electrophysiology and calcium imaging reveal that neural activity in premotor cortex is correlated with a length scale of 100 µm. Within this length scale, basal-ganglia–projecting excitatory neurons, on average, fire at a specific phase of a local 30 Hz network rhythm. These results show that premotor cortical activity is inhomogeneous in time and space, and that a mesoscopic dynamical pattern underlies the generation of the neural sequences controlling song. PMID:26039895
Mesoscopic patterns of neural activity support songbird cortical sequences.
Markowitz, Jeffrey E; Liberti, William A; Guitchounts, Grigori; Velho, Tarciso; Lois, Carlos; Gardner, Timothy J
2015-06-01
Time-locked sequences of neural activity can be found throughout the vertebrate forebrain in various species and behavioral contexts. From "time cells" in the hippocampus of rodents to cortical activity controlling movement, temporal sequence generation is integral to many forms of learned behavior. However, the mechanisms underlying sequence generation are not well known. Here, we describe a spatial and temporal organization of the songbird premotor cortical microcircuit that supports sparse sequences of neural activity. Multi-channel electrophysiology and calcium imaging reveal that neural activity in premotor cortex is correlated with a length scale of 100 µm. Within this length scale, basal-ganglia-projecting excitatory neurons, on average, fire at a specific phase of a local 30 Hz network rhythm. These results show that premotor cortical activity is inhomogeneous in time and space, and that a mesoscopic dynamical pattern underlies the generation of the neural sequences controlling song. PMID:26039895
Sparse Coding for Alpha Matting
NASA Astrophysics Data System (ADS)
Johnson, Jubin; Varnousfaderani, Ehsan Shahrian; Cholakkal, Hisham; Rajan, Deepu
2016-07-01
Existing color sampling based alpha matting methods use the compositing equation to estimate alpha at a pixel from pairs of foreground (F) and background (B) samples. The quality of the matte depends on the selected (F,B) pairs. In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples. A non-parametric probabilistic segmentation provides a certainty measure on the pixel belonging to foreground or background, based on which a dictionary is formed for use in sparse coding. By removing the restriction to conform to (F,B) pairs, this method allows for better alpha estimation from multiple F and B samples. The same framework is extended to videos, where the requirement of temporal coherence is handled effectively. Here, the dictionary is formed by samples from multiple frames. A multi-frame graph model, as opposed to a single image as for image matting, is proposed that can be solved efficiently in closed form. Quantitative and qualitative evaluations on a benchmark dataset are provided to show that the proposed method outperforms current state-of-the-art in image and video matting.
NASA Astrophysics Data System (ADS)
Fang, Jun; Zhang, Lizao; Duan, Huiping; Huang, Lei; Li, Hongbin
2016-05-01
The application of sparse representation to SAR/ISAR imaging has attracted much attention over the past few years. This new class of sparse representation based imaging methods present a number of unique advantages over conventional range-Doppler methods, the basic idea behind these works is to formulate SAR/ISAR imaging as a sparse signal recovery problem. In this paper, we propose a new two-dimensional pattern-coupled sparse Bayesian learning(SBL) method to capture the underlying cluster patterns of the ISAR target images. Based on this model, an expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Experimental results demonstrate that the proposed method is able to achieve a substantial performance improvement over existing algorithms, including the conventional SBL method.
Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing
Yoshida, Ryo; West, Mike
2010-01-01
We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance matrices, also induce conditional independence structures via zeros in the implied precision matrices. We describe the models and their use for robust estimation of sparse latent factor structure and data/signal reconstruction. We develop computational algorithms for model exploration and posterior mode search, addressing the hard combinatorial optimization involved in the search over a huge space of potential sparse configurations. A mean-field variational technique coupled with annealing is developed to successively generate “artificial” posterior distributions that, at the limiting temperature in the annealing schedule, define required posterior modes in the GFM parameter space. Several detailed empirical studies and comparisons to related approaches are discussed, including analyses of handwritten digit image and cancer gene expression data. PMID:20890391
Image fusion using sparse overcomplete feature dictionaries
Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt
2015-10-06
Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.
Miniature Laboratory for Detecting Sparse Biomolecules
NASA Technical Reports Server (NTRS)
Lin, Ying; Yu, Nan
2005-01-01
A miniature laboratory system has been proposed for use in the field to detect sparsely distributed biomolecules. By emphasizing concentration and sorting of specimens prior to detection, the underlying system concept would make it possible to attain high detection sensitivities without the need to develop ever more sensitive biosensors. The original purpose of the proposal is to aid the search for signs of life on a remote planet by enabling the detection of specimens as sparse as a few molecules or microbes in a large amount of soil, dust, rocks, water/ice, or other raw sample material. Some version of the system could prove useful on Earth for remote sensing of biological contamination, including agents of biological warfare. Processing in this system would begin with dissolution of the raw sample material in a sample-separation vessel. The solution in the vessel would contain floating microscopic magnetic beads coated with substances that could engage in chemical reactions with various target functional groups that are parts of target molecules. The chemical reactions would cause the targeted molecules to be captured on the surfaces of the beads. By use of a controlled magnetic field, the beads would be concentrated in a specified location in the vessel. Once the beads were thus concentrated, the rest of the solution would be discarded. This procedure would obviate the filtration steps and thereby also eliminate the filter-clogging difficulties of typical prior sample-concentration schemes. For ferrous dust/soil samples, the dissolution would be done first in a separate vessel before the solution is transferred to the microbead-containing vessel.
Pharmacology of cortical inhibition
Krnjević, K.; Randić, Mirjana; Straughan, D. W.
1966-01-01
1. We have studied the effects of various pharmacological agents on the cortical inhibitory process described in the previous two papers (Krnjević, Randić & Straughan, 1966a, b); the drugs were mostly administered directly by iontophoresis from micropipettes and by systemic injection (I.V.). 2. Strychnine given by iontophoresis or by the application of a strong solution to the cortical surface potentiated excitatory effects, but very large iontophoretic doses also depressed neuronal firing. Subconvulsive and even convulsive systemic doses had little or no effect at the cortical level. There was no evidence, with any method of application, that strychnine directly interferes with the inhibitory process. 3. Tetanus toxin, obtained from two different sources and injected into the cortex 12-48 hr previously, also failed to block cortical inhibition selectively. As with strychnine, there was some evidence of increased responses to excitatory inputs. 4. Other convulsant drugs which failed to block cortical inhibition included picrotoxin, pentamethylene tetrazole, thiosemicarbazide, longchain ω-amino acids and morphine. 5. The inhibition was not obviously affected by cholinomimetic agents or by antagonists of ACh. 6. α- and β-antagonists of adrenergic transmission were also ineffective. 7. Cortical inhibition was fully developed in the presence of several general anaesthetics, including ether, Dial, pentobarbitone, Mg and chloralose. A temporary reduction in inhibition which is sometimes observed after systemic doses of pentobarbitone, is probably secondary to a fall in blood pressure. 8. Several central excitants such as amphetamine, caffeine and lobeline also failed to show any specific antagonistic action on cortical inhibition. 9. In view of the possibility that GABA is the chemical agent mediating cortical inhibition, an attempt was made to find a selective antagonist of its depressant action on cortical neurones. None of the agents listed above, nor any other
Varying Coefficient Models for Sparse Noise-contaminated Longitudinal Data
2014-01-01
Summary In this paper we propose a varying coefficient model for highly sparse longitudinal data that allows for error-prone time-dependent variables and time-invariant covariates. We develop a new estimation procedure, based on covariance representation techniques, that enables effective borrowing of information across all subjects in sparse and irregular longitudinal data observed with measurement error, a challenge in which there is no adequate solution currently. More specifically, sparsity is addressed via a functional analysis approach that considers the observed longitudinal data as noise contaminated realizations of a random process that produces smooth trajectories. This approach allows for estimation based on pooled data, borrowing strength from all subjects, in targeting the mean functions and auto- and cross-covariances to overcome sparse noisy designs. The resulting estimators are shown to be uniformly consistent. Consistent prediction for the response trajectories are also obtained via conditional expectation under Gaussian assumptions. Asymptotic distribution of the predicted response trajectories are derived, allowing for construction of asymptotic pointwise confidence bands. Efficacy of the proposed method is investigated in simulation studies and compared to the commonly used local polynomial smoothing method. The proposed method is illustrated with a sparse longitudinal data set, examining the age-varying relationship between calcium absorption and dietary calcium. Prediction of individual calcium absorption curves as a function of age are also examined. PMID:25589822
Scaled norm-based Euclidean projection for sparse speaker adaptation
NASA Astrophysics Data System (ADS)
Kim, Younggwan; Kim, Myung Jong; Kim, Hoirin
2015-12-01
To reduce data storage for speaker adaptive (SA) models, in our previous work, we proposed a sparse speaker adaptation method which can efficiently reduce the number of adapted parameters by using Euclidean projection onto the L 1-ball (EPL1) while maintaining recognition performance comparable to maximum a posteriori (MAP) adaptation. In the EPL1-based sparse speaker adaptation framework, however, the adapted Gaussian mean vectors are mostly concentrated on dimensions having large variances because of assuming unit variance for all dimensions. To make EPL1 more flexible, in this paper, we propose scaled norm-based Euclidean projection (SNEP) which can consider dimension-specific variances. By using SNEP, we also propose a new sparse speaker adaptation method which can consider the variances of a speaker-independent model. Our experiments show that the adapted components of mean vectors are evenly distributed in all dimensions, and we can obtain sparsely adapted models with no loss of phone recognition performance from the proposed method compared with MAP adaptation.
Sparse representation for vehicle recognition
NASA Astrophysics Data System (ADS)
Monnig, Nathan D.; Sakla, Wesam
2014-06-01
The Sparse Representation for Classification (SRC) algorithm has been demonstrated to be a state-of-the-art algorithm for facial recognition applications. Wright et al. demonstrate that under certain conditions, the SRC algorithm classification performance is agnostic to choice of linear feature space and highly resilient to image corruption. In this work, we examined the SRC algorithm performance on the vehicle recognition application, using images from the semi-synthetic vehicle database generated by the Air Force Research Laboratory. To represent modern operating conditions, vehicle images were corrupted with noise, blurring, and occlusion, with representation of varying pose and lighting conditions. Experiments suggest that linear feature space selection is important, particularly in the cases involving corrupted images. Overall, the SRC algorithm consistently outperforms a standard k nearest neighbor classifier on the vehicle recognition task.
Sparse and stable Markowitz portfolios
Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace
2009-01-01
We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio. PMID:19617537
Sparsely Sampled Phase-Insensitive Ultrasonic Transducer Arrays
NASA Technical Reports Server (NTRS)
Johnston, Patrick H.
1992-01-01
Three methods of interpretation of outputs from sparsely sampled two-dimensional array of receiving ultrasonic transducers used in transmission experiments investigated. Methods are: description of sampled beam in terms of first few spatial moments of sampled distribution of energy; use of signal-dependent cutoff to limit extent of effective receiver aperture; and use of spatial interpolation to increase apparent density of sampling during computation. Methods reduce errors in computations of shapes of ultrasonic beams.
Approximate Orthogonal Sparse Embedding for Dimensionality Reduction.
Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Yang, Jian; Zhang, David
2016-04-01
Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L1-norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes. PMID:25955995
Sparse representation for the ISAR image reconstruction
NASA Astrophysics Data System (ADS)
Hu, Mengqi; Montalbo, John; Li, Shuxia; Sun, Ligang; Qiao, Zhijun G.
2016-05-01
In this paper, a sparse representation of the data for an inverse synthetic aperture radar (ISAR) system is provided in two dimensions. The proposed sparse representation motivates the use a of a Convex Optimization that recovers the image with far less samples, which is required by Nyquist-Shannon sampling theorem to increases the efficiency and decrease the cost of calculation in radar imaging.
Approximate inverse preconditioners for general sparse matrices
Chow, E.; Saad, Y.
1994-12-31
Preconditioned Krylov subspace methods are often very efficient in solving sparse linear matrices that arise from the discretization of elliptic partial differential equations. However, for general sparse indifinite matrices, the usual ILU preconditioners fail, often because of the fact that the resulting factors L and U give rise to unstable forward and backward sweeps. In such cases, alternative preconditioners based on approximate inverses may be attractive. We are currently developing a number of such preconditioners based on iterating on each column to get the approximate inverse. For this approach to be efficient, the iteration must be done in sparse mode, i.e., we must use sparse-matrix by sparse-vector type operatoins. We will discuss a few options and compare their performance on standard problems from the Harwell-Boeing collection.
Large-scale sparse singular value computations
NASA Technical Reports Server (NTRS)
Berry, Michael W.
1992-01-01
Four numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture are presented. Lanczos and subspace iteration-based methods for determining several of the largest singular triplets (singular values and corresponding left and right-singular vectors) for sparse matrices arising from two practical applications: information retrieval and seismic reflection tomography are emphasized. The target architectures for implementations are the CRAY-2S/4-128 and Alliant FX/80. The sparse SVD problem is well motivated by recent information-retrieval techniques in which dominant singular values and their corresponding singular vectors of large sparse term-document matrices are desired, and by nonlinear inverse problems from seismic tomography applications which require approximate pseudo-inverses of large sparse Jacobian matrices.
Rawal, Seema; Morisseau, Christophe; Hammock, Bruce D.; Shivachar, Amruthesh C
2013-01-01
The microsomal epoxide hydrolase (mEH) and soluble epoxide hydrolase (sEH) enzymes exist in a variety of cells and tissues, including liver, kidney and testis. However, very little is known about brain epoxide hydrolases. Here we report the expression, localization and subcellular distribution of mEH and sEH in cultured neonatal rat cortical astrocytes by immunocytochemistry, subcellular fractionation, western blotting and radiometric enzyme assays. Our results showed a diffused immunofluorescence pattern for mEH, which co-localized with the astroglial cytoskeletal marker, glial fibrillary acidic protein (GFAP). The GFAP-positive cells also expressed sEH which was mainly localized in the cytoplasm especially in and around the nucleus. Western blot analyses, revealed a distinct protein band with a molecular mass of ~50 kDa, the signal intensity of which increased about 1.5-fold in the microsomal fraction over the whole cell lysate and other subcellular fractions. The polyclonal anti-human sEH rabbit serum recognized a protein band with a molecular mass similar to that of purified sEH protein (~62 kDa), and the signal intensity increased 1.7-fold in the 105,000×g supernatant fraction over the cell lysate. Although the corresponding mEH enzyme activities generally corroborated with the immunocytochemical and western blotting data a low sEH enzyme activity was detected especially in the total cell lysate and in the soluble fractions. These results suggest that rat brain cortical astrocytes differentially co-express mEH and sEH enzymes. The differential subcellular localization of mEH and sEH may play a role in the cerebrovascular functions that are known to be affected by brain-derived vasoactive epoxides. PMID:18711743
Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data
Han, Fang; Liu, Han
2014-01-01
We propose a semiparametric method for conducting scale-invariant sparse principal component analysis (PCA) on high dimensional non-Gaussian data. Compared with sparse PCA, our method has weaker modeling assumption and is more robust to possible data contamination. Theoretically, the proposed method achieves a parametric rate of convergence in estimating the parameter of interests under a flexible semiparametric distribution family; Computationally, the proposed method exploits a rank-based procedure and is as efficient as sparse PCA; Empirically, our method outperforms most competing methods on both synthetic and real-world datasets. PMID:24932056
Towards robust and effective shape modeling: sparse shape composition.
Zhang, Shaoting; Zhan, Yiqiang; Dewan, Maneesh; Huang, Junzhou; Metaxas, Dimitris N; Zhou, Xiang Sean
2012-01-01
Organ shape plays an important role in various clinical practices, e.g., diagnosis, surgical planning and treatment evaluation. It is usually derived from low level appearance cues in medical images. However, due to diseases and imaging artifacts, low level appearance cues might be weak or misleading. In this situation, shape priors become critical to infer and refine the shape derived by image appearances. Effective modeling of shape priors is challenging because: (1) shape variation is complex and cannot always be modeled by a parametric probability distribution; (2) a shape instance derived from image appearance cues (input shape) may have gross errors; and (3) local details of the input shape are difficult to preserve if they are not statistically significant in the training data. In this paper we propose a novel Sparse Shape Composition model (SSC) to deal with these three challenges in a unified framework. In our method, a sparse set of shapes in the shape repository is selected and composed together to infer/refine an input shape. The a priori information is thus implicitly incorporated on-the-fly. Our model leverages two sparsity observations of the input shape instance: (1) the input shape can be approximately represented by a sparse linear combination of shapes in the shape repository; (2) parts of the input shape may contain gross errors but such errors are sparse. Our model is formulated as a sparse learning problem. Using L1 norm relaxation, it can be solved by an efficient expectation-maximization (EM) type of framework. Our method is extensively validated on two medical applications, 2D lung localization in X-ray images and 3D liver segmentation in low-dose CT scans. Compared to state-of-the-art methods, our model exhibits better performance in both studies. PMID:21963296
Anderson, Lauren G; Meeker, Rick B; Poulton, Winona E; Huang, David Y
2010-09-01
Carboxy terminus of Hsc70-interacting protein (CHIP) is thought to be a cytoprotective protein with protein quality control roles in neurodegenerative diseases and myocardial ischemia. This study describes the localization of CHIP expression in normal rodent brain and the early CHIP response in primary cultures of cortical neurons following ischemic stress models: heat stress (HS) and oxygen-glucose deprivation (OGD). CHIP was highly expressed throughout the brain, predominantly in neurons. The staining pattern was primarily cytoplasmic, although small amounts were seen in the nucleus. More intense nuclear staining was observed in primary cultured neurons which increased with stress. Nuclear accumulation of CHIP occurred within 5-10 min of HS and decreased to baseline levels or lower by 30-60 min. Decrease in nuclear CHIP at 30-60 min of HS was associated with a sharp increase in delayed cell death. While no changes in cytoplasmic CHIP were observed immediately following OGD, nuclear levels of CHIP increased slightly in response to OGD durations of 30 to 240 min. OGD-induced increases in nuclear CHIP decreased slowly during post-ischemic recovery. Nuclear CHIP decreased earlier in recovery following 120 min of OGD (4 h) than 30 min of OGD (12 h). Significant cell death first appeared between 12 and 24 h after OGD, again suggesting that delayed cell death follows closely behind the disappearance of nuclear CHIP. The ability of CHIP to translocate to and accumulate in the nucleus may be a limiting variable that determines how effectively cells respond to external stressors to facilitate cell survival. Using primary neuronal cell cultures, we were able to demonstrate rapid translocation of CHIP to the nucleus within minutes of heat stress and oxygen-glucose deprivation. An inverse relationship between nuclear CHIP and delayed cell death at 24 h suggests that the decrease in nuclear CHIP following extreme stress is linked to delayed cell death. Our findings of acute
Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents.
Yoshimura, Natsue; Nishimoto, Atsushi; Belkacem, Abdelkader Nasreddine; Shin, Duk; Kambara, Hiroyuki; Hanakawa, Takashi; Koike, Yasuharu
2016-01-01
With the goal of providing assistive technology for the communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach for EEG-based brain-computer interface spellers. EEG cortical currents were estimated with a variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data as a hierarchical prior. EEG and fMRI data were recorded from ten healthy participants during covert articulation of Japanese vowels /a/ and /i/, as well as during a no-imagery control task. Applying a sparse logistic regression (SLR) method to classify the three tasks, mean classification accuracy using EEG cortical currents was significantly higher than that using EEG sensor signals and was also comparable to accuracies in previous studies using electrocorticography. SLR weight analysis revealed vertices of EEG cortical currents that were highly contributive to classification for each participant, and the vertices showed discriminative time series signals according to the three tasks. Furthermore, functional connectivity analysis focusing on the highly contributive vertices revealed positive and negative correlations among areas related to speech processing. As the same findings were not observed using EEG sensor signals, our results demonstrate the potential utility of EEG cortical currents not only for engineering purposes such as brain-computer interfaces but also for neuroscientific purposes such as the identification of neural signaling related to language processing. PMID:27199638
Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents
Yoshimura, Natsue; Nishimoto, Atsushi; Belkacem, Abdelkader Nasreddine; Shin, Duk; Kambara, Hiroyuki; Hanakawa, Takashi; Koike, Yasuharu
2016-01-01
With the goal of providing assistive technology for the communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach for EEG-based brain-computer interface spellers. EEG cortical currents were estimated with a variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data as a hierarchical prior. EEG and fMRI data were recorded from ten healthy participants during covert articulation of Japanese vowels /a/ and /i/, as well as during a no-imagery control task. Applying a sparse logistic regression (SLR) method to classify the three tasks, mean classification accuracy using EEG cortical currents was significantly higher than that using EEG sensor signals and was also comparable to accuracies in previous studies using electrocorticography. SLR weight analysis revealed vertices of EEG cortical currents that were highly contributive to classification for each participant, and the vertices showed discriminative time series signals according to the three tasks. Furthermore, functional connectivity analysis focusing on the highly contributive vertices revealed positive and negative correlations among areas related to speech processing. As the same findings were not observed using EEG sensor signals, our results demonstrate the potential utility of EEG cortical currents not only for engineering purposes such as brain-computer interfaces but also for neuroscientific purposes such as the identification of neural signaling related to language processing. PMID:27199638
The cortical and cerebellar representation of the lumbar spine.
Boendermaker, Bart; Meier, Michael L; Luechinger, Roger; Humphreys, B Kim; Hotz-Boendermaker, Sabina
2014-08-01
Eight decades after Penfield's discovery of the homunculus only sparse evidence exists on the cortical representation of the lumbar spine. The aim of our investigation was the description of the lumbar spine's cortical representation in healthy subjects during the application of measured manual pressure. Twenty participants in the prone position were investigated during functional magnetic resonance imaging (fMRI). An experienced manual therapist applied non-painful, posterior-to-anterior (PA) pressure on three lumbar spinous processes (L1, L3, and L5). The pressure (30 N) was monitored and controlled by sensors. The randomized stimulation protocol consisted of 68 pressure stimuli of 5 s duration. Blood oxygenation level dependent (BOLD) responses were analyzed in relation to the lumbar stimulations. The results demonstrate that controlled PA pressure on the lumbar spine induced significant activation patterns. The major new finding was a strong and consistent activation bilaterally in the somatosensory cortices (S1 and S2). In addition, bilateral activation was located medially in the anterior cerebellum. The activation pattern also included other cortical areas probably related to anticipatory postural adjustments. These revealed stable somatosensory maps of the lumbar spine in healthy subjects can subsequently be used as a baseline to investigate cortical and subcortical reorganization in low back pain patients. PMID:24464423
Synaptogenesis in Purified Cortical Subplate Neurons
Shatz, Carla J.
2009-01-01
An ideal preparation for investigating events during synaptogenesis would be one in which synapses are sparse, but can be induced at will using a rapid, exogenous trigger. We describe a culture system of immunopurified subplate neurons in which synaptogenesis can be triggered, providing the first homogeneous culture of neocortical neurons for the investigation of synapse development. Synapses in immunopurified rat subplate neurons are sparse, and can be induced by a 48-h exposure to feeder layers of neurons and glia, an induction more rapid than any previously reported. Induced synapses are electrophysiologically functional and ultrastructurally normal. Microarray and real-time PCR experiments reveal a new program of gene expression accompanying synaptogenesis. Surprisingly few known synaptic genes are upregulated during the first 24 h of synaptogenesis; Gene Ontology annotation reveals a preferential upregulation of synaptic genes only at a later time. In situ hybridization confirms that some of the genes regulated in cultures are also expressed in the developing cortex. This culture system provides both a means of studying synapse formation in a homogeneous population of cortical neurons, and better synchronization of synaptogenesis, permitting the investigation of neuron-wide events following the triggering of synapse formation. PMID:19029062
Harris, Kenneth D.; Thiele, Alexander
2012-01-01
Preface The brain continuously adapts its processing machinery to behavioural demands. To achieve this it rapidly modulates the operating mode of cortical circuits, controlling the way information is transformed and routed. This article will focus on two experimental approaches by which the control of cortical information processing has been investigated: the study of state-dependent cortical processing in rodents, and attention in the primate visual system. Both processes involve a modulation of low-frequency activity fluctuations and spiking correlation, and are mediated by common receptor systems. We suggest that selective attention involves processes similar to state change, operating at a local columnar level to enhance the representation of otherwise nonsalient features while suppressing internally generated activity patterns. PMID:21829219
Prefrontal cortical minicolumn: from executive control to disrupted cognitive processing
Casanova, Manuel F.
2014-01-01
The prefrontal cortex of the primate brain has a modular architecture based on the aggregation of neurons in minicolumnar arrangements having afferent and efferent connections distributed across many brain regions to represent, select and/or maintain behavioural goals and executive commands. Prefrontal cortical microcircuits are assumed to play a key role in the perception to action cycle that integrates relevant information about environment, and then selects and enacts behavioural responses. Thus, neurons within the interlaminar microcircuits participate in various functional states requiring the integration of signals across cortical layers and the selection of executive variables. Recent research suggests that executive abilities emerge from cortico-cortical interactions between interlaminar prefrontal cortical microcircuits, whereas their disruption is involved in a broad spectrum of neurologic and psychiatric disorders such as autism, schizophrenia, Alzheimer’s and drug addiction. The focus of this review is on the structural, functional and pathological approaches involving cortical minicolumns. Based on recent technological progress it has been demonstrated that microstimulation of infragranular cortical layers with patterns of microcurrents derived from supragranular layers led to an increase in cognitive performance. This suggests that interlaminar prefrontal cortical microcircuits are playing a causal role in improving cognitive performance. An important reason for the new interest in cortical modularity comes from both the impressive progress in understanding anatomical, physiological and pathological facets of cortical microcircuits and the promise of neural prosthetics for patients with neurological and psychiatric disorders. PMID:24531625
Automatic parcellation of longitudinal cortical surfaces
NASA Astrophysics Data System (ADS)
Alassaf, Manal H.; Hahn, James K.
2015-03-01
We present a novel automatic method to parcellate the cortical surfaces of the neonatal brain longitudinal atlas at different stages of development. A labeled brain atlas of newborn at 41 weeks gestational age (GA) is used to propagate labels of anatomical regions of interest to an unlabeled spatio-temporal atlas, which provides a dynamic model of brain development at each week between 28-44 GA weeks. First, labels from the cortical volume of the labeled newborn brain are propagated to an age-matched cortical surface from the spatio-temporal atlas. Then, labels are propagated across the cortical surfaces of each week of the spatio-temporal atlas by registering successive cortical surfaces using a novel approach and an energy optimization function. This procedure incorporates local and global, spatial and temporal information when assigning the labels to each surface. The result is a complete parcellation of 17 neonatal brain surfaces of the spatio-temporal atlas with similar points per labels distributions across weeks.
Sparse Spectrotemporal Coding of Sounds
NASA Astrophysics Data System (ADS)
Klein, David J.; König, Peter; Körding, Konrad P.
2003-12-01
Recent studies of biological auditory processing have revealed that sophisticated spectrotemporal analyses are performed by central auditory systems of various animals. The analysis is typically well matched with the statistics of relevant natural sounds, suggesting that it produces an optimal representation of the animal's acoustic biotope. We address this topic using simulated neurons that learn an optimal representation of a speech corpus. As input, the neurons receive a spectrographic representation of sound produced by a peripheral auditory model. The output representation is deemed optimal when the responses of the neurons are maximally sparse. Following optimization, the simulated neurons are similar to real neurons in many respects. Most notably, a given neuron only analyzes the input over a localized region of time and frequency. In addition, multiple subregions either excite or inhibit the neuron, together producing selectivity to spectral and temporal modulation patterns. This suggests that the brain's solution is particularly well suited for coding natural sound; therefore, it may prove useful in the design of new computational methods for processing speech.
Resistant multiple sparse canonical correlation.
Coleman, Jacob; Replogle, Joseph; Chandler, Gabriel; Hardin, Johanna
2016-04-01
Canonical correlation analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to the preceding pair, meaning that new information is gleaned from each pair. By looking at the magnitude of coefficient values, we can find out which variables can be grouped together, thus better understanding multiple interactions that are otherwise difficult to compute or grasp intuitively. CCA appears to have quite powerful applications to high-throughput data, as we can use it to discover, for example, relationships between gene expression and gene copy number variation. One of the biggest problems of CCA is that the number of variables (often upwards of 10,000) makes biological interpretation of linear combinations nearly impossible. To limit variable output, we have employed a method known as sparse canonical correlation analysis (SCCA), while adding estimation which is resistant to extreme observations or other types of deviant data. In this paper, we have demonstrated the success of resistant estimation in variable selection using SCCA. Additionally, we have used SCCA to find multiple canonical pairs for extended knowledge about the datasets at hand. Again, using resistant estimators provided more accurate estimates than standard estimators in the multiple canonical correlation setting. R code is available and documented at https://github.com/hardin47/rmscca. PMID:26963062
Sparse Bayesian infinite factor models
Bhattacharya, A.; Dunson, D. B.
2011-01-01
We focus on sparse modelling of high-dimensional covariance matrices using Bayesian latent factor models. We propose a multiplicative gamma process shrinkage prior on the factor loadings which allows introduction of infinitely many factors, with the loadings increasingly shrunk towards zero as the column index increases. We use our prior on a parameter-expanded loading matrix to avoid the order dependence typical in factor analysis models and develop an efficient Gibbs sampler that scales well as data dimensionality increases. The gain in efficiency is achieved by the joint conjugacy property of the proposed prior, which allows block updating of the loadings matrix. We propose an adaptive Gibbs sampler for automatically truncating the infinite loading matrix through selection of the number of important factors. Theoretical results are provided on the support of the prior and truncation approximation bounds. A fast algorithm is proposed to produce approximate Bayes estimates. Latent factor regression methods are developed for prediction and variable selection in applications with high-dimensional correlated predictors. Operating characteristics are assessed through simulation studies, and the approach is applied to predict survival times from gene expression data. PMID:23049129
Recent History Functional Linear Models for Sparse Longitudinal Data
Kim, Kion; Şentürk, Damla; Li, Runze
2011-01-01
We consider the recent history functional linear models, relating a longitudinal response to a longitudinal predictor where the predictor process only in a sliding window into the recent past has an effect on the response value at the current time. We propose an estimation procedure for recent history functional linear models that is geared towards sparse longitudinal data, where the observation times across subjects are irregular and total number of measurements per subject is small. The proposed estimation procedure builds upon recent developments in literature for estimation of functional linear models with sparse data and utilizes connections between the recent history functional linear models and varying coefficient models. We establish uniform consistency of the proposed estimators, propose prediction of the response trajectories and derive their asymptotic distribution leading to asymptotic point-wise confidence bands. We include a real data application and simulation studies to demonstrate the efficacy of the proposed methodology. PMID:21691421
Sparse matrix methods research using the CSM testbed software system
NASA Technical Reports Server (NTRS)
Chu, Eleanor; George, J. Alan
1989-01-01
Research is described on sparse matrix techniques for the Computational Structural Mechanics (CSM) Testbed. The primary objective was to compare the performance of state-of-the-art techniques for solving sparse systems with those that are currently available in the CSM Testbed. Thus, one of the first tasks was to become familiar with the structure of the testbed, and to install some or all of the SPARSPAK package in the testbed. A suite of subroutines to extract from the data base the relevant structural and numerical information about the matrix equations was written, and all the demonstration problems distributed with the testbed were successfully solved. These codes were documented, and performance studies comparing the SPARSPAK technology to the methods currently in the testbed were completed. In addition, some preliminary studies were done comparing some recently developed out-of-core techniques with the performance of the testbed processor INV.
Enhancing Scalability of Sparse Direct Methods
Li, Xiaoye S.; Demmel, James; Grigori, Laura; Gu, Ming; Xia,Jianlin; Jardin, Steve; Sovinec, Carl; Lee, Lie-Quan
2007-07-23
TOPS is providing high-performance, scalable sparse direct solvers, which have had significant impacts on the SciDAC applications, including fusion simulation (CEMM), accelerator modeling (COMPASS), as well as many other mission-critical applications in DOE and elsewhere. Our recent developments have been focusing on new techniques to overcome scalability bottleneck of direct methods, in both time and memory. These include parallelizing symbolic analysis phase and developing linear-complexity sparse factorization methods. The new techniques will make sparse direct methods more widely usable in large 3D simulations on highly-parallel petascale computers.
Sparse High Dimensional Models in Economics
Fan, Jianqing; Lv, Jinchi; Qi, Lei
2010-01-01
This paper reviews the literature on sparse high dimensional models and discusses some applications in economics and finance. Recent developments of theory, methods, and implementations in penalized least squares and penalized likelihood methods are highlighted. These variable selection methods are proved to be effective in high dimensional sparse modeling. The limits of dimensionality that regularization methods can handle, the role of penalty functions, and their statistical properties are detailed. Some recent advances in ultra-high dimensional sparse modeling are also briefly discussed. PMID:22022635
Imaging correlography with sparse collecting apertures
NASA Astrophysics Data System (ADS)
Idell, Paul S.; Fienup, J. R.
1987-01-01
This paper investigates the possibility of implementing an imaging correlography system with sparse arrays of intensity detectors. The theory underlying the image formation process for imaging correlography is reviewed, emphasizing the spatial filtering effects that sparse collecting apertures have on the reconstructed imagery. Image recovery with sparse arrays of intensity detectors through the use of computer experiments in which laser speckle measurements are digitally simulated is then demonstrated. It is shown that the quality of imagery reconstructed using this technique is visibly enhanced when appropriate filtering techniques are applied. A performance tradeoff between collecting array redundancy and the number of speckle pattern measurements is briefly discussed.
Feature selection and multi-kernel learning for sparse representation on a manifold.
Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin
2014-03-01
Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. PMID:24333479
Cortical thinning in psychopathy
Ly, Martina; Motzkin, Julian C.; Philippi, Carissa L.; Kirk, Gregory R.; Newman, Joseph P.; Kiehl, Kent A.; Koenigs, Michael
2013-01-01
Objective Psychopathy is a personality disorder associated with severely antisocial behavior and a host of cognitive and affective deficits. The neuropathological basis of the disorder has not been clearly established. Cortical thickness is a sensitive measure of brain structure that has been used to identify neurobiological abnormalities in a number of psychiatric disorders. The purpose of this study is to evaluate cortical thickness and corresponding functional connectivity in criminal psychopaths. Method Using T1 MRI data, we computed cortical thickness maps in a sample of adult male prison inmates selected based on psychopathy diagnosis (n=21 psychopathic inmates, n=31 non-psychopathic inmates). Using rest-fMRI data from a subset of these inmates (n=20 psychopathic inmates, n=20 non-psychopathic inmates), we then computed functional connectivity within networks exhibiting significant thinning among psychopaths. Results Relative to non-psychopaths, psychopaths exhibited significantly thinner cortex in a number of regions, including left insula and dorsal anterior cingulate cortex, bilateral precentral gyrus, bilateral anterior temporal cortex, and right inferior frontal gyrus. These neurostructural differences were not due to differences in age, IQ, or substance abuse. Psychopaths also exhibited a corresponding reduction in functional connectivity between left insula and left dorsal anterior cingulate cortex. Conclusions Psychopathy is associated with a distinct pattern of cortical thinning and reduced functional connectivity. PMID:22581200
Cortical control of facial expression.
Müri, René M
2016-06-01
The present Review deals with the motor control of facial expressions in humans. Facial expressions are a central part of human communication. Emotional face expressions have a crucial role in human nonverbal behavior, allowing a rapid transfer of information between individuals. Facial expressions can be either voluntarily or emotionally controlled. Recent studies in nonhuman primates and humans have revealed that the motor control of facial expressions has a distributed neural representation. At least five cortical regions on the medial and lateral aspects of each hemisphere are involved: the primary motor cortex, the ventral lateral premotor cortex, the supplementary motor area on the medial wall, and the rostral and caudal cingulate cortex. The results of studies in humans and nonhuman primates suggest that the innervation of the face is bilaterally controlled for the upper part and mainly contralaterally controlled for the lower part. Furthermore, the primary motor cortex, the ventral lateral premotor cortex, and the supplementary motor area are essential for the voluntary control of facial expressions. In contrast, the cingulate cortical areas are important for emotional expression, because they receive input from different structures of the limbic system. PMID:26418049
Sparse principal component analysis in cancer research
Hsu, Ying-Lin; Huang, Po-Yu; Chen, Dung-Tsa
2015-01-01
A critical challenging component in analyzing high-dimensional data in cancer research is how to reduce the dimension of data and how to extract relevant features. Sparse principal component analysis (PCA) is a powerful statistical tool that could help reduce data dimension and select important variables simultaneously. In this paper, we review several approaches for sparse PCA, including variance maximization (VM), reconstruction error minimization (REM), singular value decomposition (SVD), and probabilistic modeling (PM) approaches. A simulation study is conducted to compare PCA and the sparse PCAs. An example using a published gene signature in a lung cancer dataset is used to illustrate the potential application of sparse PCAs in cancer research. PMID:26719835
Finding Nonoverlapping Substructures of a Sparse Matrix
Pinar, Ali; Vassilevska, Virginia
2005-08-11
Many applications of scientific computing rely on computations on sparse matrices. The design of efficient implementations of sparse matrix kernels is crucial for the overall efficiency of these applications. Due to the high compute-to-memory ratio and irregular memory access patterns, the performance of sparse matrix kernels is often far away from the peak performance on a modern processor. Alternative data structures have been proposed, which split the original matrix A into A{sub d} and A{sub s}, so that A{sub d} contains all dense blocks of a specified size in the matrix, and A{sub s} contains the remaining entries. This enables the use of dense matrix kernels on the entries of A{sub d} producing better memory performance. In this work, we study the problem of finding a maximum number of nonoverlapping dense blocks in a sparse matrix, which is previously not studied in the sparse matrix community. We show that the maximum nonoverlapping dense blocks problem is NP-complete by using a reduction from the maximum independent set problem on cubic planar graphs. We also propose a 2/3-approximation algorithm that runs in linear time in the number of nonzeros in the matrix. This extended abstract focuses on our results for 2x2 dense blocks. However we show that our results can be generalized to arbitrary sized dense blocks, and many other oriented substructures, which can be exploited to improve the memory performance of sparse matrix operations.
Scenario generation for stochastic optimization problems via the sparse grid method
Chen, Michael; Mehrotra, Sanjay; Papp, David
2015-04-19
We study the use of sparse grids in the scenario generation (or discretization) problem in stochastic programming problems where the uncertainty is modeled using a continuous multivariate distribution. We show that, under a regularity assumption on the random function involved, the sequence of optimal objective function values of the sparse grid approximations converges to the true optimal objective function values as the number of scenarios increases. The rate of convergence is also established. We treat separately the special case when the underlying distribution is an affine transform of a product of univariate distributions, and show how the sparse grid methodmore » can be adapted to the distribution by the use of quadrature formulas tailored to the distribution. We numerically compare the performance of the sparse grid method using different quadrature rules with classic quasi-Monte Carlo (QMC) methods, optimal rank-one lattice rules, and Monte Carlo (MC) scenario generation, using a series of utility maximization problems with up to 160 random variables. The results show that the sparse grid method is very efficient, especially if the integrand is sufficiently smooth. In such problems the sparse grid scenario generation method is found to need several orders of magnitude fewer scenarios than MC and QMC scenario generation to achieve the same accuracy. As a result, it is indicated that the method scales well with the dimension of the distribution--especially when the underlying distribution is an affine transform of a product of univariate distributions, in which case the method appears scalable to thousands of random variables.« less
Ascenzi, Maria-Grazia; Liao, Vivian P; Lee, Brittany M; Billi, Fabrizio; Zhou, Hua; Lindsay, Robert; Cosman, Felicia; Nieves, Jeri; Bilezikian, John P; Dempster, David W
2012-03-01
Although an important index, the level of bone mineral density (BMD) does not completely describe fracture risk. Another bone structural parameter, the orientation of type I collagen, is known to add to risk determination, independently of BMD, ex vivo. We investigated the Haversian system of transiliac crest biopsies from postmenopausal women before and after treatment with parathyroid hormone (PTH). We used the birefringent signal of circularly polarized light and its underlying collagen arrangements by confocal and electron microscopy, in conjunction with the degree of calcification by high-resolution micro-X-ray. We found that PTH treatment increased the Haversian system area by 11.92 ± 5.82 mm² to 12.76 ± 4.50 mm² (p = 0.04); decreased bright birefringence from 0.45 ± 0.02 to 0.40 ± 0.01 (scale zero to one, p = 0.0005); increased the average percent area of osteons with alternating birefringence from 48.15% ± 10.27% to 66.33% ± 7.73% (p = 0.034); and nonsignificantly decreased the average percent area of semihomogeneous birefringent osteons (8.36% ± 10.63% versus 5.41% ± 9.13%, p = 0.40) and of birefringent bright osteons (4.14% ± 8.90% versus 2.08% ± 3.36%, p = 0.10). Further, lamellar thickness significantly increased from 3.78 ± 0.11 µm to 4.47 ± 0.14 µm (p = 0.0002) for bright lamellae, and from 3.32 ± 0.12 µm to 3.70 ± 0.12 µm (p = 0.045) for extinct lamellae. This increased lamellar thickness altered the distribution of birefringence and therefore the distribution of collagen orientation in the tissue. With PTH treatment, a higher percent area of osteons at the initial degree of calcification was observed, relative to the intermediate-low degree of calcification (57.16% ± 3.08% versus 32.90% ± 3.69%, p = 0.04), with percentage of alternating osteons at initial stages of calcification increasing from 19.75 ± 1.22 to 80.13 ± 6.47, p = 0.001. In conclusion, PTH treatment increases heterogeneity of collagen orientation, a starting
Ascenzi, Maria-Grazia; Liao, Vivian P.; Lee, Brittany M.; Billi, Fabrizio; Zhou, Hua; Lindsay, Robert; Cosman, Felicia; Nieves, Jeri; Bilezikian, John P.; Dempster, David W.
2014-01-01
Although an important index, the level of bone mineral density (BMD) does not completely describe fracture risk. Another bone structural parameter, the orientation of type I collagen, is known to add to risk determination, independently of BMD, ex vivo. We investigated the Haversian system of transiliac crest biopsies from postmenopausal women before and after treatment with parathyroid hormone (PTH). We used the birefringence of circularly polarized light and its underlying collagen arrangements by confocal and electron microscopy, in conjunction with the degree of calcification by high-resolution micro-X-ray. We found that PTH treatment increased the Haversian system area by 48.28±38.78%; decreased bright birefringence from 0.45±0.02 to 0.40±0.01 (scale zero to one, p=0.0005); increased the average percent area of osteons with alternating birefringence from 48.15±10.27 to 66.33±7.73 (p=0.034), decreased non-significantly the semi-homogeneous birefringent osteons (8.36±10.63 vs. 5.41±9.13, p=0.40) and birefringent bright (4.14±8.90 vs. 2.08±3.36, p=0.10) osteons. Further, lamellar thickness significantly increased from 3.78±0.11μm to 4.47±0.14μm (p=0.0002) for bright, and from 3.32±0.12μm to 3.70±0.12μm (p=0.045) for extinct, lamellae. This increased lamellar thickness altered the distribution of birefringence and therefore the distribution of collagen orientation in the tissue. With PTH treatment, a higher percent area of osteons at initial degree of calcification was observed, relative to intermediate-low degree of calcification (57.16±3.08 vs. 32.90±3.69, p=0.04), the percentage of alternating osteons at initial stages of calcification increasing from 19.75±1.22 to 80.13±6.47, p=0.001. In conclusion, PTH treatment increases heterogeneity of collagen orientation, a starting point to study the reduction in fracture risk when PTH is used to treat osteoporosis. PMID:22161803
Inversion of magnetotelluric data in a sparse model domain
NASA Astrophysics Data System (ADS)
Nittinger, Christian G.; Becken, Michael
2016-08-01
The inversion of magnetotelluric data into subsurface electrical conductivity poses an ill-posed problem. Smoothing constraints are widely employed to estimate a regularized solution. Here, we present an alternative inversion scheme that estimates a sparse representation of the model in a wavelet basis. The objective of the inversion is to determine the few non-zero wavelet coefficients which are required to fit the data. This approach falls into the class of sparsity constrained inversion schemes and minimizes the combination of the data misfit in a least-squares ℓ2 sense and of a model coefficient norm in an ℓ1 sense (ℓ2-ℓ1 minimization). The ℓ1 coefficient norm renders the solution sparse in a suitable representation such as the multiresolution wavelet basis, but does not impose explicit structural penalties on the model as it is the case for ℓ2 regularization. The presented numerical algorithm solves the mixed ℓ2-ℓ1 norm minimization problem for the nonlinear magnetotelluric inverse problem. We demonstrate the feasibility of our algorithm on synthetic 2-D MT data as well as on a real data example. We found that sparse models can be estimated by inversion and that the spatial distribution of non-vanishing coefficients indicates regions in the model which are resolved.
Inversion of magnetotelluric data in a sparse model domain
NASA Astrophysics Data System (ADS)
Nittinger, Christian G.; Becken, Michael
2016-06-01
The inversion of magnetotelluric data into subsurface electrical conductivity poses an ill-posed problem. Smoothing constraints are widely employed to estimate a regularized solution. Here, we present an alternative inversion scheme that estimates a sparse representation of the model in a wavelet basis. The objective of the inversion is to determine the few non-zero wavelet coefficients which are required to fit the data. This approach falls into the class of sparsity constrained inversion schemes and minimizes the combination of the data misfit in a least squares ℓ2 sense and of a model coefficient norm in a ℓ1 sense (ℓ2-ℓ1 minimization). The ℓ1 coefficient norm renders the solution sparse in a suitable representation such as the multi-resolution wavelet basis, but does not impose explicit structural penalties on the model as it is the case for ℓ2 regularization. The presented numerical algorithm solves the mixed ℓ2-ℓ1 norm minimization problem for the non-linear magnetotelluric inverse problem. We demonstrate the feasibility of our algorithm on synthetic 2-D MT data as well as on a real data example. We found that sparse models can be estimated by inversion and that the spatial distribution of non-vanishing coefficients indicates regions in the model which are resolved.
Increased temporolimbic cortical folding complexity in temporal lobe epilepsy
Voets, N.L.; Bernhardt, B.C.; Kim, H.; Yoon, U.
2011-01-01
Objective: Converging evidence suggests that abnormalities of brain development may play a role in the pathogenesis of temporal lobe epilepsy (TLE). As sulco-gyral patterns are thought to be a footprint of cortical development, we set out to quantitatively map folding complexity across the neocortex in TLE. Additionally, we tested whether there was a relationship between cortical complexity and features of hippocampal maldevelopment, commonly referred to as malrotation. Methods: To quantify folding complexity, we obtained whole-brain surface-based measures of absolute mean cortical curvature from MRI scans acquired in 43 drug-resistant patients with TLE with unilateral hippocampal atrophy, and 40 age- and sex-matched healthy controls. In patients, we correlated changes in cortical curvature with 3-dimensional measures of hippocampal positioning. Results: We found increased folding complexity in the temporolimbic cortices encompassing parahippocampal, temporopolar, insular, and fronto-opercular regions. Increased complexity was observed ipsilateral to the seizure focus in patients with left TLE (LTLE), whereas these changes were bilateral in patients with right TLE (RTLE). In both TLE groups, increased temporolimbic complexity was associated with increased hippocampal malrotation. We found tendencies for increased complexity in bilateral posterior temporal cortices in LTLE and contralateral parahippocampal cortices in RTLE to be predictive of unfavorable seizure outcome after surgery. Conclusion: The anatomic distribution of increased cortical complexity overlapping with limbic seizure networks in TLE and its association with hippocampal maldevelopment further imply that neurodevelopmental factors may play a role in the epileptogenic process of TLE. PMID:21148116
Sparse and redundant representations for inverse problems and recognition
NASA Astrophysics Data System (ADS)
Patel, Vishal M.
Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed
Pothen, A.
1999-02-01
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for minimizing the wavefront and envelope size of sparse matrices and graphs. These algorithms compute orderings for irregular data structures (e.g., unstructured meshes) that reduce the number of cache misses on modern workstation architectures. They have completed the implementation of a parallel solver for sparse, symmetric indefinite systems for distributed memory computers such as the IBM SP-2. The indefiniteness requires one to incorporate block pivoting (2 by 2 blocks) in the algorithm, thus demanding dynamic, parallel data structures. This is the first reported parallel solver for the indefinite problem. Direct methods for solving systems of linear equations employ sophisticated combinatorial and algebraic algorithms that contribute to software complexity, and hence it is natural to consider object-oriented design (OOD) in this context. The authors have continued to create software for solving sparse systems of linear equations by direct methods employing OOD. Fast computation of robust preconditioners is a priority for solving large systems of equations on unstructured grids and in other applications. They have developed new algorithms and software that can compute incomplete factorization preconditioners for high level fill in time proportional to the number of floating point operations and memory accesses.
Crutch, Sebastian J; Lehmann, Manja; Schott, Jonathan M; Rabinovici, Gil D; Rossor, Martin N; Fox, Nick C
2013-01-01
Posterior cortical atrophy (PCA) is a neurodegenerative syndrome that is characterized by a progressive decline in visuospatial, visuoperceptual, literacy and praxic skills. The progressive neurodegeneration affecting parietal, occipital and occipito-temporal cortices which underlies PCA is attributable to Alzheimer's disease (AD) in the majority of patients. However, alternative underlying aetiologies including Dementia with Lewy Bodies (DLB), corticobasal degeneration (CBD) and prion disease have also been identified, and not all PCA patients have atrophy on clinical imaging. This heterogeneity has led to diagnostic and terminological inconsistencies, caused difficulty comparing studies from different centres, and limited the generalizability of clinical trials and investigations of factors driving phenotypic variability. Significant challenges remain in identifying the factors associated with both the selective vulnerability of posterior cortical regions and the young age of onset seen in PCA. Greater awareness of the syndrome and agreement over the correspondence between syndrome-and disease-level classifications are required in order to improve diagnostic accuracy, research study design and clinical management. PMID:22265212
Malformations of cortical development
Pang, Trudy; Atefy, Ramin; Sheen, Volney
2012-01-01
Background Malformations of cortical development (MCD) are increasingly recognized as an important cause of epilepsy and developmental delay. MCD encompass a wide spectrum of disorders with various underlying genetic etiologies and clinical manifestations. High resolution imaging has dramatically improved our recognition of MCD. Review Summary This review will provide a brief overview of the stages of normal cortical development, including neuronal proliferation, neuroblast migration, and neuronal organization. Disruptions at various stages lead to characteristic MCD. Disorders of neurogenesis give rise to microcephaly (small brain) or macrocephaly (large brain). Disorders of early neuroblast migration give rise to periventricular heterotopia (neurons located along the ventricles), whereas abnormalities later in migration lead to lissencephaly (smooth brain) or subcortical band heterotopia (smooth brain with a band of heterotopic neurons under the cortex). Abnormal neuronal migration arrest give rise to over-migration of neurons in cobblestone lissencephaly. Lastly, disorders of neuronal organization cause polymicrogyria (abnormally small gyri and sulci). This review will also discuss the known genetic mutations and potential mechanisms that contribute to these syndromes. Conclusion Identification of various gene mutations has not only given us greater insight into some of the pathophysiologic basis of MCD, but also an understanding of the processes involved in normal cortical development. PMID:18469675
Sparse source configurations for asteroid tomography
NASA Astrophysics Data System (ADS)
Pursiainen, S.; Kaasalainen, M.
2014-04-01
The objective of our recent research has been to develop non-invasive imaging techniques for future planetary research and mining activities involving a challenging in situ environment and tight payload limits [1]. This presentation will deal in particular with an approach in which the internal relative permittivity ∈r or the refractive index n = √ ∈r of an asteroid is to be recovered based on radio signal transmitted by a sparse set [2] of fixed or movable landers. To address important aspects of mission planning, we have analyzed different signal source configurations to find the minimal number of source positions needed for robust localization of anomalies, such as internal voids. Characteristic to this inverse problem are the large relative changes in signal speed caused by the high permittivity of typical asteroid minerals (e.g. basalt), leading to strong refractions and reflections of the signal. Finding an appropriate problemspecific signaling arrangement is an important premission goal for successful in situ measurements. This presentation will include inversion results obtained with laboratory-recorded travel time data y of the form in which n δ denotes a perturbation of a refractive index n = n δ + nbg; gi estimates the total noise due to different error sources; (ybg)i = ∫Ci nbg ds is an entry of noiseless background data ybg; and Ci is a signal path. Also simulated time-evolution data will be covered with respect to potential u satisfying the wave equation ∈rδ2/δt2+ ōδu/δt-∆u = f, where ō is a (latent) conductivity distribution and f is a source term. Special interest will be paid to inversion robustness regarding changes of the prior model and source positioning. Among other things, our analysis suggests that strongly refractive anomalies can be detected with three or four sources independently of their positioning.
Finding nonoverlapping substructures of a sparse matrix
Pinar, Ali; Vassilevska, Virginia
2004-08-09
Many applications of scientific computing rely on computations on sparse matrices, thus the design of efficient implementations of sparse matrix kernels is crucial for the overall efficiency of these applications. Due to the high compute-to-memory ratio and irregular memory access patterns, the performance of sparse matrix kernels is often far away from the peak performance on a modern processor. Alternative data structures have been proposed, which split the original matrix A into A{sub d} and A{sub s}, so that A{sub d} contains all dense blocks of a specified size in the matrix, and A{sub s} contains the remaining entries. This enables the use of dense matrix kernels on the entries of A{sub d} producing better memory performance. In this work, we study the problem of finding a maximum number of non overlapping rectangular dense blocks in a sparse matrix, which has not been studied in the sparse matrix community. We show that the maximum non overlapping dense blocks problem is NP-complete by using a reduction from the maximum independent set problem on cubic planar graphs. We also propose a 2/3-approximation algorithm for 2 times 2 blocks that runs in linear time in the number of nonzeros in the matrix. We discuss alternatives to rectangular blocks such as diagonal blocks and cross blocks and present complexity analysis and approximation algorithms.
Removing sparse noise from hyperspectral images with sparse and low-rank penalties
NASA Astrophysics Data System (ADS)
Tariyal, Snigdha; Aggarwal, Hemant Kumar; Majumdar, Angshul
2016-03-01
In diffraction grating, at times, there are defective pixels on the focal plane array; this results in horizontal lines of corrupted pixels in some channels. Since only a few such pixels exist, the corruption/noise is sparse. Studies on sparse noise removal from hyperspectral noise are parsimonious. To remove such sparse noise, a prior work exploited the interband spectral correlation along with intraband spatial redundancy to yield a sparse representation in transform domains. We improve upon the prior technique. The intraband spatial redundancy is modeled as a sparse set of transform coefficients and the interband spectral correlation is modeled as a rank deficient matrix. The resulting optimization problem is solved using the split Bregman technique. Comparative experimental results show that our proposed approach is better than the previous one.
Cortical Clefts and Cortical Bumps: A Continuous Spectrum
Furruqh, Farha; Thirunavukarasu, Suresh; Vivekandan, Ravichandran
2016-01-01
Cortical ‘clefts’ (schizencephaly) and cortical ‘bumps’ (polymicrogyria) are malformations arising due to defects in postmigrational development of neurons. They are frequently encountered together, with schizencephalic clefts being lined by polymicrogyria. We present the case of an eight-year-old boy who presented with seizures. Imaging revealed closed lip schizencephaly, polymicrogyria and a deep ‘incomplete’ cleft lined by polymicrogyria not communicating with the lateral ventricle. We speculate that hypoperfusion or ischaemic cortical injury during neuronal development may lead to a spectrum of malformations ranging from polymicrogyria to incomplete cortical clefts to schizencephaly.
The biology and dynamics of mammalian cortical granules
2011-01-01
Cortical granules are membrane bound organelles located in the cortex of unfertilized oocytes. Following fertilization, cortical granules undergo exocytosis to release their contents into the perivitelline space. This secretory process, which is calcium dependent and SNARE protein-mediated pathway, is known as the cortical reaction. After exocytosis, the released cortical granule proteins are responsible for blocking polyspermy by modifying the oocytes' extracellular matrices, such as the zona pellucida in mammals. Mammalian cortical granules range in size from 0.2 um to 0.6 um in diameter and different from most other regulatory secretory organelles in that they are not renewed once released. These granules are only synthesized in female germ cells and transform an egg upon sperm entry; therefore, this unique cellular structure has inherent interest for our understanding of the biology of fertilization. Cortical granules are long thought to be static and awaiting in the cortex of unfertilized oocytes to be stimulated undergoing exocytosis upon gamete fusion. Not till recently, the dynamic nature of cortical granules is appreciated and understood. The latest studies of mammalian cortical granules document that this organelle is not only biochemically heterogeneous, but also displays complex distribution during oocyte development. Interestingly, some cortical granules undergo exocytosis prior to fertilization; and a number of granule components function beyond the time of fertilization in regulating embryonic cleavage and preimplantation development, demonstrating their functional significance in fertilization as well as early embryonic development. The following review will present studies that investigate the biology of cortical granules and will also discuss new findings that uncover the dynamic aspect of this organelle in mammals. PMID:22088197
Fast wavelet based sparse approximate inverse preconditioner
Wan, W.L.
1996-12-31
Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.
A unified approach to sparse signal processing
NASA Astrophysics Data System (ADS)
Marvasti, Farokh; Amini, Arash; Haddadi, Farzan; Soltanolkotabi, Mahdi; Khalaj, Babak Hossein; Aldroubi, Akram; Sanei, Saeid; Chambers, Janathon
2012-12-01
A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, component analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing, and rate of innovation. The redundancy introduced by channel coding in finite and real Galois fields is then related to over-sampling with similar reconstruction algorithms. The error locator polynomial (ELP) and iterative methods are shown to work quite effectively for both sampling and coding applications. The methods of Prony, Pisarenko, and MUltiple SIgnal Classification (MUSIC) are next shown to be targeted at analyzing signals with sparse frequency domain representations. Specifically, the relations of the approach of Prony to an annihilating filter in rate of innovation and ELP in coding are emphasized; the Pisarenko and MUSIC methods are further improvements of the Prony method under noisy environments. The iterative methods developed for sampling and coding applications are shown to be powerful tools in spectral estimation. Such narrowband spectral estimation is then related to multi-source location and direction of arrival estimation in array processing. Sparsity in unobservable source signals is also shown to facilitate source separation in sparse component analysis; the algorithms developed in this area such as linear programming and matching pursuit are also widely used in compressed sensing. Finally
Sparse regularization for force identification using dictionaries
NASA Astrophysics Data System (ADS)
Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng
2016-04-01
The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.
Native ultrametricity of sparse random ensembles
NASA Astrophysics Data System (ADS)
Avetisov, V.; Krapivsky, P. L.; Nechaev, S.
2016-01-01
We investigate the eigenvalue density in ensembles of large sparse Bernoulli random matrices. Analyzing in detail the spectral density of ensembles of linear subgraphs, we discuss its ultrametric nature and show that near the spectrum boundary, the tails of the spectral density exhibit a Lifshitz singularity typical for Anderson localization. We pay attention to an intriguing connection of the spectral density to the Dedekind η-function. We conjecture that ultrametricity emerges in rare-event statistics and is inherit to generic complex sparse systems.
Analog system for computing sparse codes
Rozell, Christopher John; Johnson, Don Herrick; Baraniuk, Richard Gordon; Olshausen, Bruno A.; Ortman, Robert Lowell
2010-08-24
A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.
Sparse representation of complex MRI images.
Nandakumar, Hari Prasad; Ji, Jim
2008-01-01
Sparse representation of images acquired from Magnet Resonance Imaging (MRI) has several potential applications. MRI is unique in that the raw images are complex. Complex wavelet transforms (CWT) can be used to produce flexible signal representations when compared to Discrete Wavelet Transform (DWT). In this work, five different schemes using CWT or DWT are tested for sparse representation of MRI images which are in the form of complex values, separate real/imaginary, or separate magnitude/phase. The experimental results on real in-vivo MRI images show that appropriate CWT, e.g., dual-tree CWT (DTCWT), can achieve sparsity better than DWT with similar Mean Square Error. PMID:19162677
Tensor methods for large, sparse unconstrained optimization
Bouaricha, A.
1996-11-01
Tensor methods for unconstrained optimization were first introduced by Schnabel and Chow [SIAM J. Optimization, 1 (1991), pp. 293-315], who describe these methods for small to moderate size problems. This paper extends these methods to large, sparse unconstrained optimization problems. This requires an entirely new way of solving the tensor model that makes the methods suitable for solving large, sparse optimization problems efficiently. We present test results for sets of problems where the Hessian at the minimizer is nonsingular and where it is singular. These results show that tensor methods are significantly more efficient and more reliable than standard methods based on Newton`s method.
Sparse representation in speech signal processing
NASA Astrophysics Data System (ADS)
Lee, Te-Won; Jang, Gil-Jin; Kwon, Oh-Wook
2003-11-01
We review the sparse representation principle for processing speech signals. A transformation for encoding the speech signals is learned such that the resulting coefficients are as independent as possible. We use independent component analysis with an exponential prior to learn a statistical representation for speech signals. This representation leads to extremely sparse priors that can be used for encoding speech signals for a variety of purposes. We review applications of this method for speech feature extraction, automatic speech recognition and speaker identification. Furthermore, this method is also suited for tackling the difficult problem of separating two sounds given only a single microphone.
Cortical basal ganglionic degeneration.
Scarmeas, N; Chin, S S; Marder, K
2001-10-01
In this case study, we describe the symptoms, neuropsychological testing, and brain pathology of a retired mason's assistant with cortical basal ganglionic degeneration (CBGD). CBGD is an extremely rare neurodegenerative disease that is categorized under both Parkinsonian syndromes and frontal lobe dementias. It affects men and women nearly equally, and the age of onset is usually in the sixth decade of life. CBGD is characterized by Parkinson's-like motor symptoms and by deficits of movement and cognition, indicating focal brain pathology. Neuronal cell loss is ultimately responsible for the neurological symptoms. PMID:14602941
A sparse Gaussian process framework for photometric redshift estimation
NASA Astrophysics Data System (ADS)
Almosallam, Ibrahim A.; Lindsay, Sam N.; Jarvis, Matt J.; Roberts, Stephen J.
2016-01-01
Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshift estimation is presented. Synthetic data set simulating the Euclid survey and real data from SDSS DR12 are used to train and test the proposed models. We show that approaches which include careful data preparation and model design offer a significant improvement in comparison with several competing machine learning algorithms. Standard implementations of most regression algorithms use the minimization of the sum of squared errors as the objective function. For redshift inference, this induces a bias in the posterior mean of the output distribution, which can be problematic. In this paper, we directly minimize the target metric Δz = (zs - zp)/(1 + zs) and address the bias problem via a distribution-based weighting scheme, incorporated as part of the optimization objective. The results are compared with other machine learning algorithms in the field such as artificial neural networks (ANN), Gaussian processes (GPs) and sparse GPs. The proposed framework reaches a mean absolute Δz = 0.0026(1 + zs), over the redshift range of 0 ≤ zs ≤ 2 on the simulated data, and Δz = 0.0178(1 + zs) over the entire redshift range on the SDSS DR12 survey, outperforming the standard ANNz used in the literature. We also investigate how the relative size of the training sample affects the photometric redshift accuracy. We find that a training sample of >30 per cent of total sample size, provides little additional constraint on the photometric redshifts, and note that our GP formalism strongly outperforms ANNz in the sparse data regime for the simulated data set.
Infrared image recognition based on structure sparse and atomic sparse parallel
NASA Astrophysics Data System (ADS)
Wu, Yalu; Li, Ruilong; Xu, Yi; Wang, Liping
2015-12-01
Use the redundancy of the super complete dictionary can capture the structural features of the image effectively, can achieving the effective representation of the image. However, the commonly used atomic sparse representation without regard the structure of the dictionary and the unrelated non-zero-term in the process of the computation, though structure sparse consider the structure feature of dictionary, the majority coefficients of the blocks maybe are non-zero, it may affect the identification efficiency. For the disadvantages of these two sparse expressions, a weighted parallel atomic sparse and sparse structure is proposed, and the recognition efficiency is improved by the adaptive computation of the optimal weights. The atomic sparse expression and structure sparse expression are respectively, and the optimal weights are calculated by the adaptive method. Methods are as follows: training by using the less part of the identification sample, the recognition rate is calculated by the increase of the certain step size and t the constraint between weight. The recognition rate as the Z axis, two weight values respectively as X, Y axis, the resulting points can be connected in a straight line in the 3 dimensional coordinate system, by solving the highest recognition rate, the optimal weights can be obtained. Through simulation experiments can be known, the optimal weights based on adaptive method are better in the recognition rate, weights obtained by adaptive computation of a few samples, suitable for parallel recognition calculation, can effectively improve the recognition rate of infrared images.
Six Principles of Visual Cortical Dynamics
Roland, Per E.
2010-01-01
A fundamental goal in vision science is to determine how many neurons in how many areas are required to compute a coherent interpretation of the visual scene. Here I propose six principles of cortical dynamics of visual processing in the first 150 ms following the appearance of a visual stimulus. Fast synaptic communication between neurons depends on the driving neurons and the biophysical history and driving forces of the target neurons. Under these constraints, the retina communicates changes in the field of view driving large populations of neurons in visual areas into a dynamic sequence of feed-forward communication and integration of the inward current of the change signal into the dendrites of higher order area neurons (30–70 ms). Simultaneously an even larger number of neurons within each area receiving feed-forward input are pre-excited to sub-threshold levels. The higher order area neurons communicate the results of their computations as feedback adding inward current to the excited and pre-excited neurons in lower areas. This feedback reconciles computational differences between higher and lower areas (75–120 ms). This brings the lower area neurons into a new dynamic regime characterized by reduced driving forces and sparse firing reflecting the visual areas interpretation of the current scene (140 ms). The population membrane potentials and net-inward/outward currents and firing are well behaved at the mesoscopic scale, such that the decoding in retinotopic cortical space shows the visual areas’ interpretation of the current scene. These dynamics have plausible biophysical explanations. The principles are theoretical, predictive, supported by recent experiments and easily lend themselves to experimental tests or computational modeling. PMID:20661451
Shadlen, Michael N.; Jazayeri, Mehrdad; Nobre, Anna C.; Buonomano, Dean V.
2015-01-01
Time is central to cognition. However, the neural basis for time-dependent cognition remains poorly understood. We explore how the temporal features of neural activity in cortical circuits and their capacity for plasticity can contribute to time-dependent cognition over short time scales. This neural activity is linked to cognition that operates in the present or anticipates events or stimuli in the near future. We focus on deliberation and planning in the context of decision making as a cognitive process that integrates information across time. We progress to consider how temporal expectations of the future modulate perception. We propose that understanding the neural basis for how the brain tells time and operates in time will be necessary to develop general models of cognition. SIGNIFICANCE STATEMENT Time is central to cognition. However, the neural basis for time-dependent cognition remains poorly understood. We explore how the temporal features of neural activity in cortical circuits and their capacity for plasticity can contribute to time-dependent cognition over short time scales. We propose that understanding the neural basis for how the brain tells time and operates in time will be necessary to develop general models of cognition. PMID:26468192
Facial expression recognition with facial parts based sparse representation classifier
NASA Astrophysics Data System (ADS)
Zhi, Ruicong; Ruan, Qiuqi
2009-10-01
Facial expressions play important role in human communication. The understanding of facial expression is a basic requirement in the development of next generation human computer interaction systems. Researches show that the intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse representation to select face features and classify facial expressions. The sparse solution is obtained by solving l1 -norm minimization problem with constraint of linear combination equation. Experimental results show that sparse representation is efficient for facial expression recognition and sparse representation classifier obtain much higher recognition accuracies than other compared methods.
Second SIAM conference on sparse matrices: Abstracts. Final technical report
1996-12-31
This report contains abstracts on the following topics: invited and long presentations (IP1 & LP1); sparse matrix reordering & graph theory I; sparse matrix tools & environments I; eigenvalue computations I; iterative methods & acceleration techniques I; applications I; parallel algorithms I; sparse matrix reordering & graphy theory II; sparse matrix tool & environments II; least squares & optimization I; iterative methods & acceleration techniques II; applications II; eigenvalue computations II; least squares & optimization II; parallel algorithms II; sparse direct methods; iterative methods & acceleration techniques III; eigenvalue computations III; and sparse matrix reordering & graph theory III.
Data traffic reduction schemes for sparse Cholesky factorizations
NASA Technical Reports Server (NTRS)
Naik, Vijay K.; Patrick, Merrell L.
1988-01-01
Load distribution schemes are presented which minimize the total data traffic in the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems with local and shared memory. The total data traffic in factoring an n x n sparse, symmetric, positive definite matrix representing an n-vertex regular 2-D grid graph using n (sup alpha), alpha is equal to or less than 1, processors are shown to be O(n(sup 1 + alpha/2)). It is O(n(sup 3/2)), when n (sup alpha), alpha is equal to or greater than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal. The schemes allow efficient use of up to O(n) processors before the total data traffic reaches the maximum value of O(n(sup 3/2)). The partitioning employed within the scheme, allows a better utilization of the data accessed from shared memory than those of previously published methods.
Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing
NASA Astrophysics Data System (ADS)
Fang, Jun; Zhang, Lizao; Li, Hongbin
2016-06-01
We consider the problem of recovering two-dimensional (2-D) block-sparse signals with \\emph{unknown} cluster patterns. Two-dimensional block-sparse patterns arise naturally in many practical applications such as foreground detection and inverse synthetic aperture radar imaging. To exploit the block-sparse structure, we introduce a 2-D pattern-coupled hierarchical Gaussian prior model to characterize the statistical pattern dependencies among neighboring coefficients. Unlike the conventional hierarchical Gaussian prior model where each coefficient is associated independently with a unique hyperparameter, the pattern-coupled prior for each coefficient not only involves its own hyperparameter, but also its immediate neighboring hyperparameters. Thus the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage 2-D structured-sparse solutions. An expectation-maximization (EM) strategy is employed to obtain the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. In addition, the generalized approximate message passing (GAMP) algorithm is embedded into the EM framework to efficiently compute an approximation of the posterior distribution of hidden variables, which results in a significant reduction in computational complexity. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.
A Comparative Study of Sparse Associative Memories
NASA Astrophysics Data System (ADS)
Gripon, Vincent; Heusel, Judith; Löwe, Matthias; Vermet, Franck
2016-05-01
We study various models of associative memories with sparse information, i.e. a pattern to be stored is a random string of 0s and 1s with about log N 1s, only. We compare different synaptic weights, architectures and retrieval mechanisms to shed light on the influence of the various parameters on the storage capacity.
Self-Control in Sparsely Coded Networks
NASA Astrophysics Data System (ADS)
Dominguez, D. R. C.; Bollé, D.
1998-03-01
A complete self-control mechanism is proposed in the dynamics of neural networks through the introduction of a time-dependent threshold, determined in function of both the noise and the pattern activity in the network. Especially for sparsely coded models this mechanism is shown to considerably improve the storage capacity, the basins of attraction, and the mutual information content.
Structured Sparse Method for Hyperspectral Unmixing
NASA Astrophysics Data System (ADS)
Zhu, Feiyun; Wang, Ying; Xiang, Shiming; Fan, Bin; Pan, Chunhong
2014-02-01
Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.
Sparse matrix orderings for factorized inverse preconditioners
Benzi, M.; Tuama, M.
1998-09-01
The effect of reorderings on the performance of factorized sparse approximate inverse preconditioners is considered. It is shown that certain reorderings can be very beneficial both in the preconditioner construction phase and in terms of the rate of convergence of the preconditioned iteration.
A Comparative Study of Sparse Associative Memories
NASA Astrophysics Data System (ADS)
Gripon, Vincent; Heusel, Judith; Löwe, Matthias; Vermet, Franck
2016-07-01
We study various models of associative memories with sparse information, i.e. a pattern to be stored is a random string of 0s and 1s with about log N 1s, only. We compare different synaptic weights, architectures and retrieval mechanisms to shed light on the influence of the various parameters on the storage capacity.
Multilevel sparse functional principal component analysis.
Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S
2014-01-29
We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions. PMID:24872597
Multilevel sparse functional principal component analysis
Di, Chongzhi; Crainiceanu, Ciprian M.; Jank, Wolfgang S.
2014-01-01
We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions. PMID:24872597
Spatial Learning and Action Planning in a Prefrontal Cortical Network Model
Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo
2011-01-01
The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569
Automatic anatomy recognition of sparse objects
NASA Astrophysics Data System (ADS)
Zhao, Liming; Udupa, Jayaram K.; Odhner, Dewey; Wang, Huiqian; Tong, Yubing; Torigian, Drew A.
2015-03-01
A general body-wide automatic anatomy recognition (AAR) methodology was proposed in our previous work based on hierarchical fuzzy models of multitudes of objects which was not tied to any specific organ system, body region, or image modality. That work revealed the challenges encountered in modeling, recognizing, and delineating sparse objects throughout the body (compared to their non-sparse counterparts) if the models are based on the object's exact geometric representations. The challenges stem mainly from the variation in sparse objects in their shape, topology, geographic layout, and relationship to other objects. That led to the idea of modeling sparse objects not from the precise geometric representations of their samples but by using a properly designed optimal super form. This paper presents the underlying improved methodology which includes 5 steps: (a) Collecting image data from a specific population group G and body region Β and delineating in these images the objects in Β to be modeled; (b) Building a super form, S-form, for each object O in Β; (c) Refining the S-form of O to construct an optimal (minimal) super form, S*-form, which constitutes the (fuzzy) model of O; (d) Recognizing objects in Β using the S*-form; (e) Defining confounding and background objects in each S*-form for each object and performing optimal delineation. Our evaluations based on 50 3D computed tomography (CT) image sets in the thorax on four sparse objects indicate that substantially improved performance (FPVF~2%, FNVF~10%, and success where the previous approach failed) can be achieved using the new approach.
Cortico-cortical communication dynamics
Roland, Per E.; Hilgetag, Claus C.; Deco, Gustavo
2014-01-01
In principle, cortico-cortical communication dynamics is simple: neurons in one cortical area communicate by sending action potentials that release glutamate and excite their target neurons in other cortical areas. In practice, knowledge about cortico-cortical communication dynamics is minute. One reason is that no current technique can capture the fast spatio-temporal cortico-cortical evolution of action potential transmission and membrane conductances with sufficient spatial resolution. A combination of optogenetics and monosynaptic tracing with virus can reveal the spatio-temporal cortico-cortical dynamics of specific neurons and their targets, but does not reveal how the dynamics evolves under natural conditions. Spontaneous ongoing action potentials also spread across cortical areas and are difficult to separate from structured evoked and intrinsic brain activity such as thinking. At a certain state of evolution, the dynamics may engage larger populations of neurons to drive the brain to decisions, percepts and behaviors. For example, successfully evolving dynamics to sensory transients can appear at the mesoscopic scale revealing how the transient is perceived. As a consequence of these methodological and conceptual difficulties, studies in this field comprise a wide range of computational models, large-scale measurements (e.g., by MEG, EEG), and a combination of invasive measurements in animal experiments. Further obstacles and challenges of studying cortico-cortical communication dynamics are outlined in this critical review. PMID:24847217
Analysing Local Sparseness in the Macaque Brain Network
Singh, Raghavendra; Nagar, Seema; Nanavati, Amit A.
2015-01-01
Understanding the network structure of long distance pathways in the brain is a necessary step towards developing an insight into the brain’s function, organization and evolution. Dense global subnetworks of these pathways have often been studied, primarily due to their functional implications. Instead we study sparse local subnetworks of the pathways to establish the role of a brain area in enabling shortest path communication between its non-adjacent topological neighbours. We propose a novel metric to measure the topological communication load on a vertex due to its immediate neighbourhood, and show that in terms of distribution of this local communication load, a network of Macaque long distance pathways is substantially different from other real world networks and random graph models. Macaque network contains the entire range of local subnetworks, from star-like networks to clique-like networks, while other networks tend to contain a relatively small range of subnetworks. Further, sparse local subnetworks in the Macaque network are not only found across topographical super-areas, e.g., lobes, but also within a super-area, arguing that there is conservation of even relatively short-distance pathways. To establish the communication role of a vertex we borrow the concept of brokerage from social science, and present the different types of brokerage roles that brain areas play, highlighting that not only the thalamus, but also cingulate gyrus and insula often act as “relays” for areas in the neocortex. These and other analysis of communication load and roles of the sparse subnetworks of the Macaque brain provide new insights into the organisation of its pathways. PMID:26437077
A Comparison of Methods for Ocean Reconstruction from Sparse Observations
NASA Astrophysics Data System (ADS)
Streletz, G. J.; Kronenberger, M.; Weber, C.; Gebbie, G.; Hagen, H.; Garth, C.; Hamann, B.; Kreylos, O.; Kellogg, L. H.; Spero, H. J.
2014-12-01
We present a comparison of two methods for developing reconstructions of oceanic scalar property fields from sparse scattered observations. Observed data from deep sea core samples provide valuable information regarding the properties of oceans in the past. However, because the locations of sample sites are distributed on the ocean floor in a sparse and irregular manner, developing a global ocean reconstruction is a difficult task. Our methods include a flow-based and a moving least squares -based approximation method. The flow-based method augments the process of interpolating or approximating scattered scalar data by incorporating known flow information. The scheme exploits this additional knowledge to define a non-Euclidean distance measure between points in the spatial domain. This distance measure is used to create a reconstruction of the desired scalar field on the spatial domain. The resulting reconstruction thus incorporates information from both the scattered samples and the known flow field. The second method does not assume a known flow field, but rather works solely with the observed scattered samples. It is based on a modification of the moving least squares approach, a weighted least squares approximation method that blends local approximations into a global result. The modifications target the selection of data used for these local approximations and the construction of the weighting function. The definition of distance used in the weighting function is crucial for this method, so we use a machine learning approach to determine a set of near-optimal parameters for the weighting. We have implemented both of the reconstruction methods and have tested them using several sparse oceanographic datasets. Based upon these studies, we discuss the advantages and disadvantages of each method and suggest possible ways to combine aspects of both methods in order to achieve an overall high-quality reconstruction.
Analysing Local Sparseness in the Macaque Brain Network.
Singh, Raghavendra; Nagar, Seema; Nanavati, Amit A
2015-01-01
Understanding the network structure of long distance pathways in the brain is a necessary step towards developing an insight into the brain's function, organization and evolution. Dense global subnetworks of these pathways have often been studied, primarily due to their functional implications. Instead we study sparse local subnetworks of the pathways to establish the role of a brain area in enabling shortest path communication between its non-adjacent topological neighbours. We propose a novel metric to measure the topological communication load on a vertex due to its immediate neighbourhood, and show that in terms of distribution of this local communication load, a network of Macaque long distance pathways is substantially different from other real world networks and random graph models. Macaque network contains the entire range of local subnetworks, from star-like networks to clique-like networks, while other networks tend to contain a relatively small range of subnetworks. Further, sparse local subnetworks in the Macaque network are not only found across topographical super-areas, e.g., lobes, but also within a super-area, arguing that there is conservation of even relatively short-distance pathways. To establish the communication role of a vertex we borrow the concept of brokerage from social science, and present the different types of brokerage roles that brain areas play, highlighting that not only the thalamus, but also cingulate gyrus and insula often act as "relays" for areas in the neocortex. These and other analysis of communication load and roles of the sparse subnetworks of the Macaque brain provide new insights into the organisation of its pathways. PMID:26437077
Rohrer, Brandon Robinson; Rothganger, Fredrick H.; Verzi, Stephen J.; Xavier, Patrick Gordon
2010-09-01
The neocortex is perhaps the highest region of the human brain, where audio and visual perception takes place along with many important cognitive functions. An important research goal is to describe the mechanisms implemented by the neocortex. There is an apparent regularity in the structure of the neocortex [Brodmann 1909, Mountcastle 1957] which may help simplify this task. The work reported here addresses the problem of how to describe the putative repeated units ('cortical circuits') in a manner that is easily understood and manipulated, with the long-term goal of developing a mathematical and algorithmic description of their function. The approach is to reduce each algorithm to an enhanced perceptron-like structure and describe its computation using difference equations. We organize this algorithmic processing into larger structures based on physiological observations, and implement key modeling concepts in software which runs on parallel computing hardware.
NASA Astrophysics Data System (ADS)
Khaninezhad, M. R. M.; Jafarpour, B.
2014-12-01
Inference of spatially distributed reservoir and aquifer properties from scattered and spatially limited data poses a poorly constrained nonlinear inverse problem that can have many solutions. In particular, the uncertainty in the geologic continuity model can remarkably degrade the quality of fluid displacement predictions, hence, the efficiency of resource development plans. For model calibration, instead of estimating aquifer properties for each grid cell in the model, the sparse representation of the aquifer properties is estimated from nonlinear production data. The resulting calibration problem can be solved using recent developments in sparse signal processing, widely known as compressed sensing. This novel formulation leads to a sparse data inversion technique that effectively searches for relevant geologic patterns that can explain the available spatiotemporal data. We recently introduced a new model calibration framework by using sparse geologic dictionaries that are constructed from uncertain prior geologic models. Here, we first demonstrate the effectiveness of the proposed sparse geologic dictionaries for flexible and robust model calibration under prior geologic uncertainty. We illustrate the effectiveness of the proposed approach in using limited nonlinear production data to identify a consistent geologic scenario from a number of candidate scenarios, which is usually a challenging problem in geostatistical reservoir characterization. We then evaluate the feasibility of adopting this framework for field application. In particular, we present subsurface field model calibration applications in which sparse geologic dictionaries are learned from uncertain prior information on large-scale reservoir property descriptions. We consider two large-scale field case studies, the Brugges and the Norne field examples. We discuss the construction of geologic dictionaries for large-scale problems and present reduced-order methods to speed up the computational
Venugopal, S.; Naik, V.K.
1991-10-01
A block based, automatic partitioning and scheduling methodology is presented for sparse matrix factorization on distributed memory systems. Using experimental results, this technique is analyzed for communication and load imbalance overhead. To study the performance effects, these overheads were compared with those obtained from a straightforward 'wrap mapped' column assignment scheme. All experimental results were obtained using test sparse matrices from the Harwell-Boeing data set. The results show that there is a communication and load balance tradeoff. The block based method results in lower communication cost whereas the wrap mapped scheme gives better load balance.
Effects of partitioning and scheduling sparse matrix factorization on communication and load balance
NASA Technical Reports Server (NTRS)
Venugopal, Sesh; Naik, Vijay K.
1991-01-01
A block based, automatic partitioning and scheduling methodology is presented for sparse matrix factorization on distributed memory systems. Using experimental results, this technique is analyzed for communication and load imbalance overhead. To study the performance effects, these overheads were compared with those obtained from a straightforward 'wrap mapped' column assignment scheme. All experimental results were obtained using test sparse matrices from the Harwell-Boeing data set. The results show that there is a communication and load balance tradeoff. The block based method results in lower communication cost whereas the wrap mapped scheme gives better load balance.
Viewing a forelimb induces widespread cortical activations.
Raos, Vassilis; Kilintari, Marina; Savaki, Helen E
2014-04-01
Given that prerequisite of activating the mirror neuron system is the preshaping of the hand and its interaction with the object during observation of a reaching-to-grasp-an-object action, the effects of viewing the object, the reaching forelimb and the static hand may obscure the effects of observing the grasping action per se. To disentangle these effects, we employed the (14)C-deoxyglucose quantitative autoradiographic method to map the functional activity in the entire cortex of monkeys (Macaca mulatta) which observed the experimenter performing non-goal-directed (purposeless) forelimb movements towards an object that was previously presented but no longer visible. Thus, our monkeys were exposed to the view of an object, a moving arm and a static hand with extended wrist and fingers. The distribution of metabolic activity was analyzed in 20μm thick brain sections, and two dimensional maps were reconstructed in the occipital operculum, the temporal, the lateral and medial parietal, the lateral and medial frontal, the lateral prefrontal and orbitofrontal cortices, including the cortex within the lunate, superior temporal, lateral, parietoccipital, intraparietal, central, arcuate and principal sulci. Increased metabolic activity, as compared to fixation-control monkeys, was measured in the forelimb representation of the primary motor and somatosensory cortices, the premotor cortices F2 and F5, cingulate motor areas, the secondary somatosensory cortex SII, the posterior intraparietal area 5 and areas TPOc and FST, in the hemisphere contralateral to the moving arm. Moreover, bilateral activations were elicited in areas pre-SMA, 8m, SSA and the somatorecipient area VS, the retroinsula, the auditory belt area CM, motion areas MT, MST, LOP/CIP, area 31, visual areas TEO, V6, V6Av and the parafoveal and peripheral visual representations of areas V1 and V2, respectively. Few parietal, auditory and visual areas were bilaterally depressed. In brief, a surprisingly wide
A sparse digital signal model for ultrasonic nondestructive evaluation of layered materials.
Bochud, N; Gomez, A M; Rus, G; Peinado, A M
2015-09-01
Signal modeling has been proven to be an useful tool to characterize damaged materials under ultrasonic nondestructive evaluation (NDE). In this paper, we introduce a novel digital signal model for ultrasonic NDE of multilayered materials. This model borrows concepts from lattice filter theory, and bridges them to the physics involved in the wave-material interactions. In particular, the proposed theoretical framework shows that any multilayered material can be characterized by a transfer function with sparse coefficients. The filter coefficients are linked to the physical properties of the material and are analytically obtained from them, whereas a sparse distribution naturally arises and does not rely on heuristic approaches. The developed model is first validated with experimental measurements obtained from multilayered media consisting of homogeneous solids. Then, the sparse structure of the obtained digital filter is exploited through a model-based inverse problem for damage identification in a carbon fiber-reinforced polymer (CFRP) plate. PMID:26092090
Solving large sparse eigenvalue problems on supercomputers
NASA Technical Reports Server (NTRS)
Philippe, Bernard; Saad, Youcef
1988-01-01
An important problem in scientific computing consists in finding a few eigenvalues and corresponding eigenvectors of a very large and sparse matrix. The most popular methods to solve these problems are based on projection techniques on appropriate subspaces. The main attraction of these methods is that they only require the use of the matrix in the form of matrix by vector multiplications. The implementations on supercomputers of two such methods for symmetric matrices, namely Lanczos' method and Davidson's method are compared. Since one of the most important operations in these two methods is the multiplication of vectors by the sparse matrix, methods of performing this operation efficiently are discussed. The advantages and the disadvantages of each method are compared and implementation aspects are discussed. Numerical experiments on a one processor CRAY 2 and CRAY X-MP are reported. Possible parallel implementations are also discussed.
Sparse decomposition learning based dynamic MRI reconstruction
NASA Astrophysics Data System (ADS)
Zhu, Peifei; Zhang, Qieshi; Kamata, Sei-ichiro
2015-02-01
Dynamic MRI is widely used for many clinical exams but slow data acquisition becomes a serious problem. The application of Compressed Sensing (CS) demonstrated great potential to increase imaging speed. However, the performance of CS is largely depending on the sparsity of image sequence in the transform domain, where there are still a lot to be improved. In this work, the sparsity is exploited by proposed Sparse Decomposition Learning (SDL) algorithm, which is a combination of low-rank plus sparsity and Blind Compressed Sensing (BCS). With this decomposition, only sparsity component is modeled as a sparse linear combination of temporal basis functions. This enables coefficients to be sparser and remain more details of dynamic components comparing learning the whole images. A reconstruction is performed on the undersampled data where joint multicoil data consistency is enforced by combing Parallel Imaging (PI). The experimental results show the proposed methods decrease about 15~20% of Mean Square Error (MSE) compared to other existing methods.
Sparse brain network using penalized linear regression
NASA Astrophysics Data System (ADS)
Lee, Hyekyoung; Lee, Dong Soo; Kang, Hyejin; Kim, Boong-Nyun; Chung, Moo K.
2011-03-01
Sparse partial correlation is a useful connectivity measure for brain networks when it is difficult to compute the exact partial correlation in the small-n large-p setting. In this paper, we formulate the problem of estimating partial correlation as a sparse linear regression with a l1-norm penalty. The method is applied to brain network consisting of parcellated regions of interest (ROIs), which are obtained from FDG-PET images of the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. To validate the results, we check their reproducibilities of the obtained brain networks by the leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.
Color demosaicking via robust adaptive sparse representation
NASA Astrophysics Data System (ADS)
Huang, Lili; Xiao, Liang; Chen, Qinghua; Wang, Kai
2015-09-01
A single sensor camera can capture scenes by means of a color filter array. Each pixel samples only one of the three primary colors. We use a color demosaicking (CDM) technique to produce full color images and propose a robust adaptive sparse representation model for high quality CDM. The data fidelity term is characterized by l1 norm to suppress the heavy-tailed visual artifacts with an adaptively learned dictionary, while the regularization term is encouraged to seek sparsity by forcing sparse coding close to its nonlocal means to reduce coding errors. Based on the classical quadratic penalty function technique in optimization and an operator splitting method in convex analysis, we further present an effective iterative algorithm to solve the variational problem. The efficiency of the proposed method is demonstrated by experimental results with simulated and real camera data.
Sparse representation for color image restoration.
Mairal, Julien; Elad, Michael; Sapiro, Guillermo
2008-01-01
Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper. PMID:18229804
Blind deconvolution subject to sparse representation for fluorescence microscopy
NASA Astrophysics Data System (ADS)
Wang, Yu; Dai, Qionghai; Cai, Qiang; Guo, Peiyuan; Liu, Zaiwen
2013-01-01
Blind deconvolution is an effective fluorescence microscopic image processing technique to improve the quality of degraded digital images resulting from photon counting noise and out-of-focus blur. For solving the severely ill-posed problem of deconvolution, in this paper we propose an alternate minimized blind deconvolution method which considers sparse representation as constraint condition to confine the solution space of traditional Richardson-Lucy method and Gaussian model as initial value of point spread function (PSF). We assume that Poisson noise is dominating during the course of imaging. The maximum-likelihood estimation on a fluorescence image and corresponding point spread function (PSF) is developed. By solving the Euler-Lagrange equation of the total cost function, including the data term obtained by the hypothetical Poisson noise distribution model and the regularized term corresponding to the sparse representation constraint, and using gradient descent method we can get the iterative equations of the original fluorescence image and PSF respectively. Compared with the related blind deconvolution methods, our model shows superior performance in terms of both objective criteria and subjective human vision via processing simulated and real fluorescence microscopic degraded images.
Modeling human performance with low light sparse color imagers
NASA Astrophysics Data System (ADS)
Haefner, David P.; Reynolds, Joseph P.; Cha, Jae; Hodgkin, Van
2011-05-01
Reflective band sensors are often signal to noise limited in low light conditions. Any additional filtering to obtain spectral information further reduces the signal to noise, greatly affecting range performance. Modern sensors, such as the sparse color filter CCD, circumvent this additional degradation through reducing the number of pixels affected by filters and distributing the color information. As color sensors become more prevalent in the warfighter arsenal, the performance of the sensor-soldier system must be quantified. While field performance testing ultimately validates the success of a sensor, accurately modeling sensor performance greatly reduces the development time and cost, allowing the best technology to reach the soldier the fastest. Modeling of sensors requires accounting for how the signal is affected through the modulation transfer function (MTF) and noise of the system. For the modeling of these new sensors, the MTF and noise for each color band must be characterized, and the appropriate sampling and blur must be applied. We show how sparse array color filter sensors may be modeled and how a soldier's performance with such a sensor may be predicted. This general approach to modeling color sensors can be extended to incorporate all types of low light color sensors.
Synthesizing spatiotemporally sparse smartphone sensor data for bridge modal identification
NASA Astrophysics Data System (ADS)
Ozer, Ekin; Feng, Maria Q.
2016-08-01
Smartphones as vibration measurement instruments form a large-scale, citizen-induced, and mobile wireless sensor network (WSN) for system identification and structural health monitoring (SHM) applications. Crowdsourcing-based SHM is possible with a decentralized system granting citizens with operational responsibility and control. Yet, citizen initiatives introduce device mobility, drastically changing SHM results due to uncertainties in the time and the space domains. This paper proposes a modal identification strategy that fuses spatiotemporally sparse SHM data collected by smartphone-based WSNs. Multichannel data sampled with the time and the space independence is used to compose the modal identification parameters such as frequencies and mode shapes. Structural response time history can be gathered by smartphone accelerometers and converted into Fourier spectra by the processor units. Timestamp, data length, energy to power conversion address temporal variation, whereas spatial uncertainties are reduced by geolocation services or determining node identity via QR code labels. Then, parameters collected from each distributed network component can be extended to global behavior to deduce modal parameters without the need of a centralized and synchronous data acquisition system. The proposed method is tested on a pedestrian bridge and compared with a conventional reference monitoring system. The results show that the spatiotemporally sparse mobile WSN data can be used to infer modal parameters despite non-overlapping sensor operation schedule.
Music emotion detection using hierarchical sparse kernel machines.
Chin, Yu-Hao; Lin, Chang-Hong; Siahaan, Ernestasia; Wang, Jia-Ching
2014-01-01
For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion. PMID:24729748
Fault feature extraction of rolling element bearings using sparse representation
NASA Astrophysics Data System (ADS)
He, Guolin; Ding, Kang; Lin, Huibin
2016-03-01
Influenced by factors such as speed fluctuation, rolling element sliding and periodical variation of load distribution and impact force on the measuring direction of sensor, the impulse response signals caused by defective rolling bearing are non-stationary, and the amplitudes of the impulse may even drop to zero when the fault is out of load zone. The non-stationary characteristic and impulse missing phenomenon reduce the effectiveness of the commonly used demodulation method on rolling element bearing fault diagnosis. Based on sparse representation theories, a new approach for fault diagnosis of rolling element bearing is proposed. The over-complete dictionary is constructed by the unit impulse response function of damped second-order system, whose natural frequencies and relative damping ratios are directly identified from the fault signal by correlation filtering method. It leads to a high similarity between atoms and defect induced impulse, and also a sharply reduction of the redundancy of the dictionary. To improve the matching accuracy and calculation speed of sparse coefficient solving, the fault signal is divided into segments and the matching pursuit algorithm is carried out by segments. After splicing together all the reconstructed signals, the fault feature is extracted successfully. The simulation and experimental results show that the proposed method is effective for the fault diagnosis of rolling element bearing in large rolling element sliding and low signal to noise ratio circumstances.
Threshold partitioning of sparse matrices and applications to Markov chains
Choi, Hwajeong; Szyld, D.B.
1996-12-31
It is well known that the order of the variables and equations of a large, sparse linear system influences the performance of classical iterative methods. In particular if, after a symmetric permutation, the blocks in the diagonal have more nonzeros, classical block methods have a faster asymptotic rate of convergence. In this paper, different ordering and partitioning algorithms for sparse matrices are presented. They are modifications of PABLO. In the new algorithms, in addition to the location of the nonzeros, the values of the entries are taken into account. The matrix resulting after the symmetric permutation has dense blocks along the diagonal, and small entries in the off-diagonal blocks. Parameters can be easily adjusted to obtain, for example, denser blocks, or blocks with elements of larger magnitude. In particular, when the matrices represent Markov chains, the permuted matrices are well suited for block iterative methods that find the corresponding probability distribution. Applications to three types of methods are explored: (1) Classical block methods, such as Block Gauss Seidel. (2) Preconditioned GMRES, where a block diagonal preconditioner is used. (3) Iterative aggregation method (also called aggregation/disaggregation) where the partition obtained from the ordering algorithm with certain parameters is used as an aggregation scheme. In all three cases, experiments are presented which illustrate the performance of the methods with the new orderings. The complexity of the new algorithms is linear in the number of nonzeros and the order of the matrix, and thus adding little computational effort to the overall solution.
Computational modeling of epidural cortical stimulation
NASA Astrophysics Data System (ADS)
Wongsarnpigoon, Amorn; Grill, Warren M.
2008-12-01
Epidural cortical stimulation (ECS) is a developing therapy to treat neurological disorders. However, it is not clear how the cortical anatomy or the polarity and position of the electrode affects current flow and neural activation in the cortex. We developed a 3D computational model simulating ECS over the precentral gyrus. With the electrode placed directly above the gyrus, about half of the stimulus current flowed through the crown of the gyrus while current density was low along the banks deep in the sulci. Beneath the electrode, neurons oriented perpendicular to the cortical surface were depolarized by anodic stimulation, and neurons oriented parallel to the boundary were depolarized by cathodic stimulation. Activation was localized to the crown of the gyrus, and neurons on the banks deep in the sulci were not polarized. During regulated voltage stimulation, the magnitude of the activating function was inversely proportional to the thickness of the CSF and dura. During regulated current stimulation, the activating function was not sensitive to the thickness of the dura but was slightly more sensitive than during regulated voltage stimulation to the thickness of the CSF. Varying the width of the gyrus and the position of the electrode altered the distribution of the activating function due to changes in the orientation of the neurons beneath the electrode. Bipolar stimulation, although often used in clinical practice, reduced spatial selectivity as well as selectivity for neuron orientation.
Dictionary Learning Algorithms for Sparse Representation
Kreutz-Delgado, Kenneth; Murray, Joseph F.; Rao, Bhaskar D.; Engan, Kjersti; Lee, Te-Won; Sejnowski, Terrence J.
2010-01-01
Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial “25 words or less”), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an over-complete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error). PMID:12590811
Inpainting with sparse linear combinations of exemplars
Wohlberg, Brendt
2008-01-01
We introduce a new exemplar-based inpainting algorithm based on representing the region to be inpainted as a sparse linear combination of blocks extracted from similar parts of the image being inpainted. This method is conceptually simple, being computed by functional minimization, and avoids the complexity of correctly ordering the filling in of missing regions of other exemplar-based methods. Initial performance comparisons on small inpainting regions indicate that this method provides similar or better performance than other recent methods.
Automatic target recognition via sparse representations
NASA Astrophysics Data System (ADS)
Estabridis, Katia
2010-04-01
Automatic target recognition (ATR) based on the emerging technology of Compressed Sensing (CS) can considerably improve accuracy, speed and cost associated with these types of systems. An image based ATR algorithm has been built upon this new theory, which can perform target detection and recognition in a low dimensional space. Compressed dictionaries (A) are formed to include rotational information for a scale of interest. The algorithm seeks to identify y(test sample) as a linear combination of the dictionary elements : y=Ax, where A ∈ Rnxm(n<
Robust Fringe Projection Profilometry via Sparse Representation.
Budianto; Lun, Daniel P K
2016-04-01
In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment. PMID:26890867
Modified sparse regularization for electrical impedance tomography.
Fan, Wenru; Wang, Huaxiang; Xue, Qian; Cui, Ziqiang; Sun, Benyuan; Wang, Qi
2016-03-01
Electrical impedance tomography (EIT) aims to estimate the electrical properties at the interior of an object from current-voltage measurements on its boundary. It has been widely investigated due to its advantages of low cost, non-radiation, non-invasiveness, and high speed. Image reconstruction of EIT is a nonlinear and ill-posed inverse problem. Therefore, regularization techniques like Tikhonov regularization are used to solve the inverse problem. A sparse regularization based on L1 norm exhibits superiority in preserving boundary information at sharp changes or discontinuous areas in the image. However, the limitation of sparse regularization lies in the time consumption for solving the problem. In order to further improve the calculation speed of sparse regularization, a modified method based on separable approximation algorithm is proposed by using adaptive step-size and preconditioning technique. Both simulation and experimental results show the effectiveness of the proposed method in improving the image quality and real-time performance in the presence of different noise intensities and conductivity contrasts. PMID:27036798
Aerial Scene Recognition using Efficient Sparse Representation
Cheriyadat, Anil M
2012-01-01
Advanced scene recognition systems for processing large volumes of high-resolution aerial image data are in great demand today. However, automated scene recognition remains a challenging problem. Efficient encoding and representation of spatial and structural patterns in the imagery are key in developing automated scene recognition algorithms. We describe an image representation approach that uses simple and computationally efficient sparse code computation to generate accurate features capable of producing excellent classification performance using linear SVM kernels. Our method exploits unlabeled low-level image feature measurements to learn a set of basis vectors. We project the low-level features onto the basis vectors and use simple soft threshold activation function to derive the sparse features. The proposed technique generates sparse features at a significantly lower computational cost than other methods~\\cite{Yang10, newsam11}, yet it produces comparable or better classification accuracy. We apply our technique to high-resolution aerial image datasets to quantify the aerial scene classification performance. We demonstrate that the dense feature extraction and representation methods are highly effective for automatic large-facility detection on wide area high-resolution aerial imagery.
Learning joint intensity-depth sparse representations.
Tosic, Ivana; Drewes, Sarah
2014-05-01
This paper presents a method for learning overcomplete dictionaries of atoms composed of two modalities that describe a 3D scene: 1) image intensity and 2) scene depth. We propose a novel joint basis pursuit (JBP) algorithm that finds related sparse features in two modalities using conic programming and we integrate it into a two-step dictionary learning algorithm. The JBP differs from related convex algorithms because it finds joint sparsity models with different atoms and different coefficient values for intensity and depth. This is crucial for recovering generative models where the same sparse underlying causes (3D features) give rise to different signals (intensity and depth). We give a bound for recovery error of sparse coefficients obtained by JBP, and show numerically that JBP is superior to the group lasso algorithm. When applied to the Middlebury depth-intensity database, our learning algorithm converges to a set of related features, such as pairs of depth and intensity edges or image textures and depth slants. Finally, we show that JBP outperforms state of the art methods on depth inpainting for time-of-flight and Microsoft Kinect 3D data. PMID:24723574
Sparseness- and continuity-constrained seismic imaging
NASA Astrophysics Data System (ADS)
Herrmann, Felix J.
2005-04-01
Non-linear solution strategies to the least-squares seismic inverse-scattering problem with sparseness and continuity constraints are proposed. Our approach is designed to (i) deal with substantial amounts of additive noise (SNR < 0 dB); (ii) use the sparseness and locality (both in position and angle) of directional basis functions (such as curvelets and contourlets) on the model: the reflectivity; and (iii) exploit the near invariance of these basis functions under the normal operator, i.e., the scattering-followed-by-imaging operator. Signal-to-noise ratio and the continuity along the imaged reflectors are significantly enhanced by formulating the solution of the seismic inverse problem in terms of an optimization problem. During the optimization, sparseness on the basis and continuity along the reflectors are imposed by jointly minimizing the l1- and anisotropic diffusion/total-variation norms on the coefficients and reflectivity, respectively. [Joint work with Peyman P. Moghaddam was carried out as part of the SINBAD project, with financial support secured through ITF (the Industry Technology Facilitator) from the following organizations: BG Group, BP, ExxonMobil, and SHELL. Additional funding came from the NSERC Discovery Grants 22R81254.
SAR Image despeckling via sparse representation
NASA Astrophysics Data System (ADS)
Wang, Zhongmei; Yang, Xiaomei; Zheng, Liang
2014-11-01
SAR image despeckling is an active research area in image processing due to its importance in improving the quality of image for object detection and classification.In this paper, a new approach is proposed for multiplicative noise in SAR image removal based on nonlocal sparse representation by dictionary learning and collaborative filtering. First, a image is divided into many patches, and then a cluster is formed by clustering log-similar image patches using Fuzzy C-means (FCM). For each cluster, an over-complete dictionary is computed using the K-SVD method that iteratively updates the dictionary and the sparse coefficients. The patches belonging to the same cluster are then reconstructed by a sparse combination of the corresponding dictionary atoms. The reconstructed patches are finally collaboratively aggregated to build the denoised image. The experimental results show that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both objective evaluation index (PSNR and ENL) and subjective visual perception.
Imaging black holes with sparse modeling
NASA Astrophysics Data System (ADS)
Honma, Mareki; Akiyama, Kazunori; Tazaki, Fumie; Kuramochi, Kazuki; Ikeda, Shiro; Hada, Kazuhiro; Uemura, Makoto
2016-03-01
We introduce a new imaging method for radio interferometry based on sparse- modeling. The direct observables in radio interferometry are visibilities, which are Fourier transformation of an astronomical image on the sky-plane, and incomplete sampling of visibilities in the spatial frequency domain results in an under-determined problem, which has been usually solved with 0 filling to un-sampled grids. In this paper we propose to directly solve this under-determined problem using sparse modeling without 0 filling, which realizes super resolution, i.e., resolution higher than the standard refraction limit. We show simulation results of sparse modeling for the Event Horizon Telescope (EHT) observations of super-massive black holes and demonstrate that our approach has significant merit in observations of black hole shadows expected to be realized in near future. We also present some results with the method applied to real data, and also discuss more advanced techniques for practical observations such as imaging with closure phase as well as treating the effect of interstellar scattering effect.
Cortical subnetwork dynamics during human language tasks.
Collard, Maxwell J; Fifer, Matthew S; Benz, Heather L; McMullen, David P; Wang, Yujing; Milsap, Griffin W; Korzeniewska, Anna; Crone, Nathan E
2016-07-15
results demonstrate that subnetwork decomposition of event-related cortical interactions is a powerful paradigm for interpreting the rich dynamics of large-scale, distributed cortical networks during human cognitive tasks. PMID:27046113
New shape models of asteroids reconstructed from sparse-in-time photometry
NASA Astrophysics Data System (ADS)
Durech, Josef; Hanus, Josef; Vanco, Radim; Oszkiewicz, Dagmara Anna
2015-08-01
Asteroid physical parameters - the shape, the sidereal rotation period, and the spin axis orientation - can be reconstructed from the disk-integrated photometry either dense (classical lightcurves) or sparse in time by the lightcurve inversion method. We will review our recent progress in asteroid shape reconstruction from sparse photometry. The problem of finding a unique solution of the inverse problem is time consuming because the sidereal rotation period has to be found by scanning a wide interval of possible periods. This can be efficiently solved by splitting the period parameter space into small parts that are sent to computers of volunteers and processed in parallel. We will show how this approach of distributed computing works with currently available sparse photometry processed in the framework of project Asteroids@home. In particular, we will show the results based on the Lowell Photometric Database. The method produce reliable asteroid models with very low rate of false solutions and the pipelines and codes can be directly used also to other sources of sparse photometry - Gaia data, for example. We will present the distribution of spin axis of hundreds of asteroids, discuss the dependence of the spin obliquity on the size of an asteroid,and show examples of spin-axis distribution in asteroid families that confirm the Yarkovsky/YORP evolution scenario.
Efficient visual tracking via low-complexity sparse representation
NASA Astrophysics Data System (ADS)
Lu, Weizhi; Zhang, Jinglin; Kpalma, Kidiyo; Ronsin, Joseph
2015-12-01
Thanks to its good performance on object recognition, sparse representation has recently been widely studied in the area of visual object tracking. Up to now, little attention has been paid to the complexity of sparse representation, while most works are focused on the performance improvement. By reducing the computation load related to sparse representation hundreds of times, this paper proposes by far the most computationally efficient tracking approach based on sparse representation. The proposal simply consists of two stages of sparse representation, one is for object detection and the other for object validation. Experimentally, it achieves better performance than some state-of-the-art methods in both accuracy and speed.
Human Adult Cortical Reorganization and Consequent Visual Distortion
Dilks, Daniel D.; Serences, John T.; Rosenau, Benjamin J.; Yantis, Steven; McCloskey, Michael
2009-01-01
Neural and behavioral evidence for cortical reorganization in the adult somatosensory system after loss of sensory input (e.g., amputation) has been well documented. In contrast, evidence for reorganization in the adult visual system is far less clear: neural evidence is the subject of controversy, behavioral evidence is sparse, and studies combining neural and behavioral evidence have not previously been reported. Here, we report converging behavioral and neuroimaging evidence from a stroke patient (B.L.) in support of cortical reorganization in the adult human visual system. B.L.’s stroke spared the primary visual cortex (V1), but destroyed fibers that normally provide input to V1 from the upper left visual field (LVF). As a consequence, B.L. is blind in the upper LVF, and exhibits distorted perception in the lower LVF: stimuli appear vertically elongated, toward and into the blind upper LVF. For example, a square presented in the lower LVF is perceived as a rectangle extending upward. We hypothesized that the perceptual distortion was a consequence of cortical reorganization in V1. Extensive behavioral testing supported our hypothesis, and functional magnetic resonance imaging (fMRI) confirmed V1 reorganization. Together, the behavioral and fMRI data show that loss of input to V1 after a stroke leads to cortical reorganization in the adult human visual system, and provide the first evidence that reorganization of the adult visual system affects visual perception. These findings contribute to our understanding of the human adult brain’s capacity to change and has implications for topics ranging from learning to recovery from brain damage. PMID:17804619
Cable energy function of cortical axons
Ju, Huiwen; Hines, Michael L.; Yu, Yuguo
2016-01-01
Accurate estimation of action potential (AP)-related metabolic cost is essential for understanding energetic constraints on brain connections and signaling processes. Most previous energy estimates of the AP were obtained using the Na+-counting method, which seriously limits accurate assessment of metabolic cost of ionic currents that underlie AP conduction along the axon. Here, we first derive a full cable energy function for cortical axons based on classic Hodgkin-Huxley (HH) neuronal equations and then apply the cable energy function to precisely estimate the energy consumption of AP conduction along axons with different geometric shapes. Our analytical approach predicts an inhomogeneous distribution of metabolic cost along an axon with either uniformly or nonuniformly distributed ion channels. The results show that the Na+-counting method severely underestimates energy cost in the cable model by 20–70%. AP propagation along axons that differ in length may require over 15% more energy per unit of axon area than that required by a point model. However, actual energy cost can vary greatly depending on axonal branching complexity, ion channel density distributions, and AP conduction states. We also infer that the metabolic rate (i.e. energy consumption rate) of cortical axonal branches as a function of spatial volume exhibits a 3/4 power law relationship. PMID:27439954
Cable energy function of cortical axons.
Ju, Huiwen; Hines, Michael L; Yu, Yuguo
2016-01-01
Accurate estimation of action potential (AP)-related metabolic cost is essential for understanding energetic constraints on brain connections and signaling processes. Most previous energy estimates of the AP were obtained using the Na(+)-counting method, which seriously limits accurate assessment of metabolic cost of ionic currents that underlie AP conduction along the axon. Here, we first derive a full cable energy function for cortical axons based on classic Hodgkin-Huxley (HH) neuronal equations and then apply the cable energy function to precisely estimate the energy consumption of AP conduction along axons with different geometric shapes. Our analytical approach predicts an inhomogeneous distribution of metabolic cost along an axon with either uniformly or nonuniformly distributed ion channels. The results show that the Na(+)-counting method severely underestimates energy cost in the cable model by 20-70%. AP propagation along axons that differ in length may require over 15% more energy per unit of axon area than that required by a point model. However, actual energy cost can vary greatly depending on axonal branching complexity, ion channel density distributions, and AP conduction states. We also infer that the metabolic rate (i.e. energy consumption rate) of cortical axonal branches as a function of spatial volume exhibits a 3/4 power law relationship. PMID:27439954
Hansen, Kelly; Azimi-Zonooz, Aryan; Chen, Kevin; Riddle, Art; Gong, Xi; Sharifnia, Elica; Hagen, Matthew; Ahmad, Tahir; Leigland, Lindsey A.; Back, Stephen A.
2013-01-01
Children who survive preterm birth exhibit persistent unexplained disturbances in cerebral cortical growth with associated cognitive and learning disabilities. The mechanisms underlying these deficits remain elusive. We used ex vivo diffusion magnetic resonance imaging to demonstrate in a preterm large-animal model that cerebral ischemia impairs cortical growth and the normal maturational decline in cortical fractional anisotropy (FA). Analysis of pyramidal neurons revealed that cortical deficits were associated with impaired expansion of the dendritic arbor and reduced synaptic density. Together, these findings suggest a link between abnormal cortical FA and disturbances of neuronal morphological development. To experimentally investigate this possibility, we measured the orientation distribution of dendritic branches and observed that it corresponds with the theoretically predicted pattern of increased anisotropy within cases that exhibited elevated cortical FA after ischemia. We conclude that cortical growth impairments are associated with diffuse disturbances in the dendritic arbor and synapse formation of cortical neurons, which may underlie the cognitive and learning disabilities in survivors of preterm birth. Further, measurement of cortical FA may be useful for noninvasively detecting neurological disorders affecting cortical development. PMID:23325800
Cortical thickness reduction in individuals at ultra-high-risk for psychosis.
Jung, Wi Hoon; Kim, June Sic; Jang, Joon Hwan; Choi, Jung-Seok; Jung, Myung Hun; Park, Ji-Young; Han, Ji Yeon; Choi, Chi-Hoon; Kang, Do-Hyung; Chung, Chun Kee; Kwon, Jun Soo
2011-07-01
Although schizophrenia is characterized by gray matter (GM) abnormalities, particularly in the prefrontal and temporal cortices, it is unclear whether cerebral cortical GM is abnormal in individuals at ultra-high-risk (UHR) for psychosis. We addressed this issue by studying cortical thickness in this group with magnetic resonance imaging (MRI). We measured cortical thickness of 29 individuals with no family history of psychosis at UHR, 31 patients with schizophrenia, and 29 healthy matched control subjects using automated surface-based analysis of structural MRI data. Hemispheric mean and regional cortical thickness were significantly different according to the stage of the disease. Significant cortical differences across these 3 groups were found in the distributed area of cerebral cortices. UHR group showed significant cortical thinning in the prefrontal cortex, anterior cingulate cortex, inferior parietal cortex, parahippocampal cortex, and superior temporal gyrus compared with healthy control subjects. Significant cortical thinning in schizophrenia group relative to UHR group was found in all the regions described above in addition with posterior cingulate cortex, insular cortex, and precentral cortex. These changes were more pronounced in the schizophrenia group compared with the control subjects. These findings suggest that UHR is associated with cortical thinning in regions that correspond to the structural abnormalities found in schizophrenia. These structural abnormalities might reflect functional decline at the prodromal stage of schizophrenia, and there may be progressive thinning of GM cortex over time. PMID:20026559
Bielza, Concha; Benavides-Piccione, Ruth; López-Cruz, Pedro; Larrañaga, Pedro; DeFelipe, Javier
2014-01-01
Unraveling pyramidal cell structure is crucial to understanding cortical circuit computations. Although it is well known that pyramidal cell branching structure differs in the various cortical areas, the principles that determine the geometric shapes of these cells are not fully understood. Here we analyzed and modeled with a von Mises distribution the branching angles in 3D reconstructed basal dendritic arbors of hundreds of intracellularly injected cortical pyramidal cells in seven different cortical regions of the frontal, parietal, and occipital cortex of the mouse. We found that, despite the differences in the structure of the pyramidal cells in these distinct functional and cytoarchitectonic cortical areas, there are common design principles that govern the geometry of dendritic branching angles of pyramidal cells in all cortical areas. PMID:25081193
Bielza, Concha; Benavides-Piccione, Ruth; López-Cruz, Pedro; Larrañaga, Pedro; DeFelipe, Javier
2014-01-01
Unraveling pyramidal cell structure is crucial to understanding cortical circuit computations. Although it is well known that pyramidal cell branching structure differs in the various cortical areas, the principles that determine the geometric shapes of these cells are not fully understood. Here we analyzed and modeled with a von Mises distribution the branching angles in 3D reconstructed basal dendritic arbors of hundreds of intracellularly injected cortical pyramidal cells in seven different cortical regions of the frontal, parietal, and occipital cortex of the mouse. We found that, despite the differences in the structure of the pyramidal cells in these distinct functional and cytoarchitectonic cortical areas, there are common design principles that govern the geometry of dendritic branching angles of pyramidal cells in all cortical areas. PMID:25081193
[Cortical control of saccades].
Pierrot-Deseilligny, C
1989-01-01
Among saccades triggered by the cerebral cortex, visually guided saccades are the best known and their cortical control is reviewed here. Only two immediately supra-reticular structures are able to trigger saccades (whatever their type): the frontal eye fields (FEF) and the superior colliculus (SC). These structures control two parallel excitatory pathways, which can replace each other in the event of lesion. Experimental findings have suggested that the colliculo-reticular pathway would, in the normal state, play the main role in the triggering of reflexive visually guided saccades. Furthermore experimental and clinical data suggest that the SC would receive an excitatory afference from the posterior part of the intraparietal sulcus, which could be involved in the triggering of these saccades. The parietal lobe could influence the SC by increasing the pre-excitation due to the onset of the visual target. There are also inhibitory pathways which prevent saccades, in particular during fixation. Two groups of tonic neurons inhibit the excitatory pathways. These are the omnipause neurons and the neurons of the substantia nigra (pars reticulata), which project upon the premotor reticular formations and the SC respectively. The pathways projecting upon these 2 types of neurons are multiple and still little known. Nevertheless, some arguments suggest that the frontal lobe partly controls inhibition. These arguments are based on a somewhat disinhibited triggering of reflexive visually guided saccades in focal or degenerative (progressive supranuclear palsy) frontal lesions. The prefrontal cortex could be involved in inhibition control, and it could act functionally above the FEF. PMID:2682934
SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos
NASA Astrophysics Data System (ADS)
Ahlfeld, R.; Belkouchi, B.; Montomoli, F.
2016-09-01
A new arbitrary Polynomial Chaos (aPC) method is presented for moderately high-dimensional problems characterised by limited input data availability. The proposed methodology improves the algorithm of aPC and extends the method, that was previously only introduced as tensor product expansion, to moderately high-dimensional stochastic problems. The fundamental idea of aPC is to use the statistical moments of the input random variables to develop the polynomial chaos expansion. This approach provides the possibility to propagate continuous or discrete probability density functions and also histograms (data sets) as long as their moments exist, are finite and the determinant of the moment matrix is strictly positive. For cases with limited data availability, this approach avoids bias and fitting errors caused by wrong assumptions. In this work, an alternative way to calculate the aPC is suggested, which provides the optimal polynomials, Gaussian quadrature collocation points and weights from the moments using only a handful of matrix operations on the Hankel matrix of moments. It can therefore be implemented without requiring prior knowledge about statistical data analysis or a detailed understanding of the mathematics of polynomial chaos expansions. The extension to more input variables suggested in this work, is an anisotropic and adaptive version of Smolyak's algorithm that is solely based on the moments of the input probability distributions. It is referred to as SAMBA (PC), which is short for Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos. It is illustrated that for moderately high-dimensional problems (up to 20 different input variables or histograms) SAMBA can significantly simplify the calculation of sparse Gaussian quadrature rules. SAMBA's efficiency for multivariate functions with regard to data availability is further demonstrated by analysing higher order convergence and accuracy for a set of nonlinear test functions with 2, 5 and 10
NASA Astrophysics Data System (ADS)
Li, Lianlin; Jafarpour, Behnam
2010-09-01
We present a Bayesian framework for reconstructing hydraulic properties of rock formations from nonlinear dynamic flow data by imposing sparsity on the distribution of the parameters in a sparse transform basis through Laplace prior distribution. Sparse representation of the subsurface flow properties in a compression transform basis (where a compact representation is often possible) lends itself to a natural regularization approach, i.e. sparsity regularization, which has recently been exploited in solving ill-posed subsurface flow inverse problems. The Bayesian estimation approach presented here allows for a probabilistic treatment of the sparse reconstruction problem and has its roots in machine learning and the recently introduced relevance vector machine algorithm for linear inverse problems. We formulate the Bayesian sparse reconstruction algorithm and apply it to nonlinear subsurface inverse problems where solution sparsity in a discrete cosine transform is assumed. The probabilistic description of solution sparsity, as opposed to deterministic regularization, allows for quantification of the estimation uncertainty and avoids the need for specifying a regularization parameter. Several numerical experiments from multiphase subsurface flow application are presented to illustrate the performance of the proposed method and compare it with the regular Bayesian estimation approach that does not impose solution sparsity. While the examples are derived from subsurface flow modeling, the proposed framework can be applied to nonlinear inverse problems in other imaging applications including geophysical and medical imaging and electromagnetic inverse problem.
Parallel preconditioning techniques for sparse CG solvers
Basermann, A.; Reichel, B.; Schelthoff, C.
1996-12-31
Conjugate gradient (CG) methods to solve sparse systems of linear equations play an important role in numerical methods for solving discretized partial differential equations. The large size and the condition of many technical or physical applications in this area result in the need for efficient parallelization and preconditioning techniques of the CG method. In particular for very ill-conditioned matrices, sophisticated preconditioner are necessary to obtain both acceptable convergence and accuracy of CG. Here, we investigate variants of polynomial and incomplete Cholesky preconditioners that markedly reduce the iterations of the simply diagonally scaled CG and are shown to be well suited for massively parallel machines.
Guided wavefield reconstruction from sparse measurements
NASA Astrophysics Data System (ADS)
Mesnil, Olivier; Ruzzene, Massimo
2016-02-01
Guided wave measurements are at the basis of several Non-Destructive Evaluation (NDE) techniques. Although sparse measurements of guided wave obtained using piezoelectric sensors can efficiently detect and locate defects, extensive informa-tion on the shape and subsurface location of defects can be extracted from full-field measurements acquired by Laser Doppler Vibrometers (LDV). Wavefield acquisition from LDVs is generally a slow operation due to the fact that the wave propagation to record must be repeated for each point measurement and the initial conditions must be reached between each measurement. In this research, a Sparse Wavefield Reconstruction (SWR) process using Compressed Sensing is developed. The goal of this technique is to reduce the number of point measurements needed to apply NDE techniques by at least one order of magnitude by extrapolating the knowledge of a few randomly chosen measured pixels over an over-sampled grid. To achieve this, the Lamb wave propagation equation is used to formulate a basis of shape functions in which the wavefield has a sparse representation, in order to comply with the Compressed Sensing requirements and use l1-minimization solvers. The main assumption of this reconstruction process is that every material point of the studied area is a potential source. The Compressed Sensing matrix is defined as being the contribution that would have been received at a measurement location from each possible source, using the dispersion relations of the specimen computed using a Semi-Analytical Finite Element technique. The measurements are then processed through an l1-minimizer to find a minimum corresponding to the set of active sources and their corresponding excitation functions. This minimum represents the best combination of the parameters of the model matching the sparse measurements. Wavefields are then reconstructed using the propagation equation. The set of active sources found by minimization contains all the wave
Sparse Multivariate Regression With Covariance Estimation
Rothman, Adam J.; Levina, Elizaveta; Zhu, Ji
2014-01-01
We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. This method, which we call multivariate regression with covariance estimation (MRCE), involves penalized likelihood with simultaneous estimation of the regression coefficients and the covariance structure. An efficient optimization algorithm and a fast approximation are developed for computing MRCE. Using simulation studies, we show that the proposed method outperforms relevant competitors when the responses are highly correlated. We also apply the new method to a finance example on predicting asset returns. An R-package containing this dataset and code for computing MRCE and its approximation are available online. PMID:24963268
Sparse dynamics for partial differential equations
Schaeffer, Hayden; Caflisch, Russel; Hauck, Cory D.; Osher, Stanley
2013-01-01
We investigate the approximate dynamics of several differential equations when the solutions are restricted to a sparse subset of a given basis. The restriction is enforced at every time step by simply applying soft thresholding to the coefficients of the basis approximation. By reducing or compressing the information needed to represent the solution at every step, only the essential dynamics are represented. In many cases, there are natural bases derived from the differential equations, which promote sparsity. We find that our method successfully reduces the dynamics of convection equations, diffusion equations, weak shocks, and vorticity equations with high-frequency source terms. PMID:23533273
Effective dimension reduction for sparse functional data
YAO, F.; LEI, E.; WU, Y.
2015-01-01
Summary We propose a method of effective dimension reduction for functional data, emphasizing the sparse design where one observes only a few noisy and irregular measurements for some or all of the subjects. The proposed method borrows strength across the entire sample and provides a way to characterize the effective dimension reduction space, via functional cumulative slicing. Our theoretical study reveals a bias-variance trade-off associated with the regularizing truncation and decaying structures of the predictor process and the effective dimension reduction space. A simulation study and an application illustrate the superior finite-sample performance of the method. PMID:26566293
Mishchenko, Yuriy
2016-10-01
We investigate the properties of recently proposed "shotgun" sampling approach for the common inputs problem in the functional estimation of neuronal connectivity. We study the asymptotic correctness, the speed of convergence, and the data size requirements of such an approach. We show that the shotgun approach can be expected to allow the inference of complete connectivity matrix in large neuronal populations under some rather general conditions. However, we find that the posterior error of the shotgun connectivity estimator grows quickly with the size of unobserved neuronal populations, the square of average connectivity strength, and the square of observation sparseness. This implies that the shotgun connectivity estimation will require significantly larger amounts of neuronal activity data whenever the number of neurons in observed neuronal populations remains small. We present a numerical approach for solving the shotgun estimation problem in general settings and use it to demonstrate the shotgun connectivity inference in the examples of simulated synfire and weakly coupled cortical neuronal networks. PMID:27515518
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels
Vuduc, R; Demmel, J W; Yelick, K A
2005-07-19
The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives that provide automatically tuned computational kernels on sparse matrices, for use by solver libraries and applications. These kernels include sparse matrix-vector multiply and sparse triangular solve, among others. The primary aim of this interface is to hide the complex decision-making process needed to tune the performance of a kernel implementation for a particular user's sparse matrix and machine, while also exposing the steps and potentially non-trivial costs of tuning at run-time. This paper provides an overview of OSKI, which is based on our research on automatically tuned sparse kernels for modern cache-based superscalar machines.
Inferring sparse networks for noisy transient processes.
Tran, Hoang M; Bukkapatnam, Satish T S
2016-01-01
Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the l1-min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of l1-min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues. PMID:26916813
A density functional for sparse matter
NASA Astrophysics Data System (ADS)
Langreth, D. C.; Lundqvist, B. I.; Chakarova-Käck, S. D.; Cooper, V. R.; Dion, M.; Hyldgaard, P.; Kelkkanen, A.; Kleis, J.; Kong, Lingzhu; Li, Shen; Moses, P. G.; Murray, E.; Puzder, A.; Rydberg, H.; Schröder, E.; Thonhauser, T.
2009-02-01
Sparse matter is abundant and has both strong local bonds and weak nonbonding forces, in particular nonlocal van der Waals (vdW) forces between atoms separated by empty space. It encompasses a broad spectrum of systems, like soft matter, adsorption systems and biostructures. Density-functional theory (DFT), long since proven successful for dense matter, seems now to have come to a point, where useful extensions to sparse matter are available. In particular, a functional form, vdW-DF (Dion et al 2004 Phys. Rev. Lett. 92 246401; Thonhauser et al 2007 Phys. Rev. B 76 125112), has been proposed for the nonlocal correlations between electrons and applied to various relevant molecules and materials, including to those layered systems like graphite, boron nitride and molybdenum sulfide, to dimers of benzene, polycyclic aromatic hydrocarbons (PAHs), doped benzene, cytosine and DNA base pairs, to nonbonding forces in molecules, to adsorbed molecules, like benzene, naphthalene, phenol and adenine on graphite, alumina and metals, to polymer and carbon nanotube (CNT) crystals, and hydrogen storage in graphite and metal-organic frameworks (MOFs), and to the structure of DNA and of DNA with intercalators. Comparison with results from wavefunction calculations for the smaller systems and with experimental data for the extended ones show the vdW-DF path to be promising. This could have great ramifications.
Inferring sparse networks for noisy transient processes
NASA Astrophysics Data System (ADS)
Tran, Hoang M.; Bukkapatnam, Satish T. S.
2016-02-01
Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the -min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of -min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues.
Inferring sparse networks for noisy transient processes
Tran, Hoang M.; Bukkapatnam, Satish T.S.
2016-01-01
Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the -min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of -min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues. PMID:26916813
Topological sparse learning of dynamic form patterns.
Guthier, T; Willert, V; Eggert, J
2015-01-01
Motion is a crucial source of information for a variety of tasks in social interactions. The process of how humans recognize complex articulated movements such as gestures or face expressions remains largely unclear. There is an ongoing discussion if and how explicit low-level motion information, such as optical flow, is involved in the recognition process. Motivated by this discussion, we introduce a computational model that classifies the spatial configuration of gradient and optical flow patterns. The patterns are learned with an unsupervised learning algorithm based on translation-invariant nonnegative sparse coding called VNMF that extracts prototypical optical flow patterns shaped, for example, as moving heads or limb parts. A key element of the proposed system is a lateral inhibition term that suppresses activations of competing patterns in the learning process, leading to a low number of dominant and topological sparse activations. We analyze the classification performance of the gradient and optical flow patterns on three real-world human action recognition and one face expression recognition data set. The results indicate that the recognition of human actions can be achieved by gradient patterns alone, but adding optical flow patterns increases the classification performance. The combined patterns outperform other biological-inspired models and are competitive with current computer vision approaches. PMID:25248088
Towards robust topology of sparsely sampled data.
Correa, Carlos D; Lindstrom, Peter
2011-12-01
Sparse, irregular sampling is becoming a necessity for reconstructing large and high-dimensional signals. However, the analysis of this type of data remains a challenge. One issue is the robust selection of neighborhoods--a crucial part of analytic tools such as topological decomposition, clustering and gradient estimation. When extracting the topology of sparsely sampled data, common neighborhood strategies such as k-nearest neighbors may lead to inaccurate results, either due to missing neighborhood connections, which introduce false extrema, or due to spurious connections, which conceal true extrema. Other neighborhoods, such as the Delaunay triangulation, are costly to compute and store even in relatively low dimensions. In this paper, we address these issues. We present two new types of neighborhood graphs: a variation on and a generalization of empty region graphs, which considerably improve the robustness of neighborhood-based analysis tools, such as topological decomposition. Our findings suggest that these neighborhood graphs lead to more accurate topological representations of low- and high- dimensional data sets at relatively low cost, both in terms of storage and computation time. We describe the implications of our work in the analysis and visualization of scalar functions, and provide general strategies for computing and applying our neighborhood graphs towards robust data analysis. PMID:22034302
Reliable positioning in a sparse GPS network, eastern Ontario
NASA Astrophysics Data System (ADS)
Samadi Alinia, H.; Tiampo, K.; Atkinson, G. M.
2013-12-01
Canada hosts two regions that are prone to large earthquakes: western British Columbia, and the St. Lawrence River region in eastern Canada. Although eastern Ontario is not as seismically active as other areas of eastern Canada, such as the Charlevoix/Ottawa Valley seismic zone, it experiences ongoing moderate seismicity. In historic times, potentially damaging events have occurred in New York State (Attica, 1929, M=5.7; Plattsburg, 2002, M=5.0), north-central Ontario (Temiskaming, 1935, M=6.2; North Bay, 2000, M=5.0), eastern Ontario (Cornwall, 1944, M=5.8), Georgian Bay (2005, MN=4.3), and western Quebec (Val-Des-Bois,2010, M=5.0, MN=5.8). In eastern Canada, the analysis of detailed, high-precision measurements of surface deformation is a key component in our efforts to better characterize the associated seismic hazard. The data from precise, continuous GPS stations is necessary to adequately characterize surface velocities from which patterns and rates of stress accumulation on faults can be estimated (Mazzotti and Adams, 2005; Mazzotti et al., 2005). Monitoring of these displacements requires employing high accuracy GPS positioning techniques. Detailed strain measurements can determine whether the regional strain everywhere is commensurate with a large event occurring every few hundred years anywhere within this general area or whether large earthquakes are limited to specific areas (Adams and Halchuck, 2003; Mazzotti and Adams, 2005). In many parts of southeastern Ontario and western Québec, GPS stations are distributed quite sparsely, with spacings of approximately 100 km or more. The challenge is to provide accurate solutions for these sparse networks with an approach that is capable of achieving high-accuracy positioning. Here, various reduction techniques are applied to a sparse network installed with the Southern Ontario Seismic Network in eastern Ontario. Recent developments include the implementation of precise point positioning processing on acquired
NASA Astrophysics Data System (ADS)
Song, Sutao; Chen, Gongxiang; Zhan, Yu; Zhang, Jiacai; Yao, Li
2014-03-01
Recently, sparse algorithms, such as Sparse Multinomial Logistic Regression (SMLR), have been successfully applied in decoding visual information from functional magnetic resonance imaging (fMRI) data, where the contrast of visual stimuli was predicted by a classifier. The contrast classifier combined brain activities of voxels with sparse weights. For sparse algorithms, the goal is to learn a classifier whose weights distributed as sparse as possible by introducing some prior belief about the weights. There are two ways to introduce a sparse prior constraints for weights: the Automatic Relevance Determination (ARD-SMLR) and Laplace prior (LAP-SMLR). In this paper, we presented comparison results between the ARD-SMLR and LAP-SMLR models in computational time, classification accuracy and voxel selection. Results showed that, for fMRI data, no significant difference was found in classification accuracy between these two methods when voxels in V1 were chosen as input features (totally 1017 voxels). As for computation time, LAP-SMLR was superior to ARD-SMLR; the survived voxels for ARD-SMLR was less than LAP-SMLR. Using simulation data, we confirmed the classification performance for the two SMLR models was sensitive to the sparsity of the initial features, when the ratio of relevant features to the initial features was larger than 0.01, ARD-SMLR outperformed LAP-SMLR; otherwise, LAP-SMLR outperformed LAP-SMLR. Simulation data showed ARD-SMLR was more efficient in selecting relevant features.
Feature Selection and Pedestrian Detection Based on Sparse Representation.
Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei
2015-01-01
Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony. PMID:26295480
Genetic apertures: an improved sparse aperture design framework.
Salvaggio, Philip S; Schott, John R; McKeown, Donald M
2016-04-20
The majority of optical sparse aperture imaging research in the remote sensing field has been confined to a small set of aperture layouts. While these layouts possess some desirable properties for imaging, they may not be ideal for all applications. This work introduces an optimization framework for sparse aperture layouts based on genetic algorithms as well as a small set of fitness functions for incoherent sparse aperture image quality. The optimization results demonstrate the merits of existing designs and the opportunity for creating new sparse aperture layouts. PMID:27140086
Feature Selection and Pedestrian Detection Based on Sparse Representation
Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei
2015-01-01
Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony. PMID:26295480
Nonlinear spike-and-slab sparse coding for interpretable image encoding.
Shelton, Jacquelyn A; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg
2015-01-01
Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process. PMID:25954947
Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding
Shelton, Jacquelyn A.; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg
2015-01-01
Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process. PMID:25954947
Broadcasting of cortical activity to the olfactory bulb.
Boyd, Alison M; Kato, Hiroyuki K; Komiyama, Takaki; Isaacson, Jeffry S
2015-02-24
Odor representations are initially formed in the olfactory bulb, which contains a topographic glomerular map of odor molecular features. The bulb transmits sensory information directly to piriform cortex, where it is encoded by distributed ensembles of pyramidal cells without spatial order. Intriguingly, piriform cortex pyramidal cells project back to the bulb, but the information contained in this feedback projection is unknown. Here, we use imaging in awake mice to directly monitor activity in the presynaptic boutons of cortical feedback fibers. We show that the cortex provides the bulb with a rich array of information for any individual odor and that cortical feedback is dependent on brain state. In contrast to the stereotyped, spatial arrangement of olfactory bulb glomeruli, cortical inputs tuned to different odors commingle and indiscriminately target individual glomerular channels. Thus, the cortex modulates early odor representations by broadcasting sensory information diffusely onto spatially ordered bulbar circuits. PMID:25704808
Altered cortical anatomical networks in temporal lobe epilepsy
NASA Astrophysics Data System (ADS)
Lv, Bin; He, Huiguang; Lu, Jingjing; Li, Wenjing; Dai, Dai; Li, Meng; Jin, Zhengyu
2011-03-01
Temporal lobe epilepsy (TLE) is one of the most common epilepsy syndromes with focal seizures generated in the left or right temporal lobes. With the magnetic resonance imaging (MRI), many evidences have demonstrated that the abnormalities in hippocampal volume and the distributed atrophies in cortical cortex. However, few studies have investigated if TLE patients have the alternation in the structural networks. In the present study, we used the cortical thickness to establish the morphological connectivity networks, and investigated the network properties using the graph theoretical methods. We found that all the morphological networks exhibited the small-world efficiency in left TLE, right TLE and normal groups. And the betweenness centrality analysis revealed that there were statistical inter-group differences in the right uncus region. Since the right uncus located at the right temporal lobe, these preliminary evidences may suggest that there are topological alternations of the cortical anatomical networks in TLE, especially for the right TLE.
Imprinting and recalling cortical ensembles.
Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S; Yuste, Rafael
2016-08-12
Neuronal ensembles are coactive groups of neurons that may represent building blocks of cortical circuits. These ensembles could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from ensembles in the visual cortex of awake mice builds neuronal ensembles that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted ensembles can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. PMID:27516599
Selective Thalamic Innervation of Rat Frontal Cortical Neurons.
Shigematsu, Naoki; Ueta, Yoshifumi; Mohamed, Alsayed A; Hatada, Sayuri; Fukuda, Takaichi; Kubota, Yoshiyuki; Kawaguchi, Yasuo
2016-06-01
Most glutamatergic inputs in the neocortex originate from the thalamus or neocortical pyramidal cells. To test whether thalamocortical afferents selectively innervate specific cortical cell subtypes and surface domains, we investigated the distribution patterns of thalamocortical and corticocortical excitatory synaptic inputs in identified postsynaptic cortical cell subtypes using intracellular and immunohistochemical staining combined with confocal laser scanning and electron microscopic observations in 2 thalamorecipient sublayers, lower layer 2/3 (L2/3b) and lower layer 5 (L5b) of rat frontal cortex. The dendrites of GABAergic parvalbumin (PV) cells preferentially received corticocortical inputs in both sublayers. The somata of L2/3b PV cells received thalamic inputs in similar proportions to the basal dendritic spines of L2/3b pyramidal cells, whereas L5b PV somata were mostly innervated by cortical inputs. The basal dendrites of L2/3b pyramidal and L5b corticopontine pyramidal cells received cortical and thalamic glutamatergic inputs in proportion to their local abundance, whereas crossed-corticostriatal pyramidal cells in L5b exhibited a preference for thalamic inputs, particularly in their distal dendrites. Our data demonstrate an exquisite selectivity among thalamocortical afferents in which synaptic connectivity is dependent on the postsynaptic neuron subtype, cortical sublayer, and cell surface domain. PMID:26045568
Critical Fluctuations in Cortical Models Near Instability
Aburn, Matthew J.; Holmes, C. A.; Roberts, James A.; Boonstra, Tjeerd W.; Breakspear, Michael
2012-01-01
Computational studies often proceed from the premise that cortical dynamics operate in a linearly stable domain, where fluctuations dissipate quickly and show only short memory. Studies of human electroencephalography (EEG), however, have shown significant autocorrelation at time lags on the scale of minutes, indicating the need to consider regimes where non-linearities influence the dynamics. Statistical properties such as increased autocorrelation length, increased variance, power law scaling, and bistable switching have been suggested as generic indicators of the approach to bifurcation in non-linear dynamical systems. We study temporal fluctuations in a widely-employed computational model (the Jansen–Rit model) of cortical activity, examining the statistical signatures that accompany bifurcations. Approaching supercritical Hopf bifurcations through tuning of the background excitatory input, we find a dramatic increase in the autocorrelation length that depends sensitively on the direction in phase space of the input fluctuations and hence on which neuronal subpopulation is stochastically perturbed. Similar dependence on the input direction is found in the distribution of fluctuation size and duration, which show power law scaling that extends over four orders of magnitude at the Hopf bifurcation. We conjecture that the alignment in phase space between the input noise vector and the center manifold of the Hopf bifurcation is directly linked to these changes. These results are consistent with the possibility of statistical indicators of linear instability being detectable in real EEG time series. However, even in a simple cortical model, we find that these indicators may not necessarily be visible even when bifurcations are present because their expression can depend sensitively on the neuronal pathway of incoming fluctuations. PMID:22952464
NASA Astrophysics Data System (ADS)
Zhang, Baoqing; Mu, Zhichun; Li, Chen; Zeng, Hui
2014-06-01
The Gabor wavelets have been experimentally verified to be a good approximation to the response of cortical neurons. A new feature extraction approach is investigated for ear recognition by using scale information of Gabor wavelets. The proposed Gabor scale feature conforms to human visual perception of objects from far to near. It can not only avoid too much redundancy in Gabor features but also tends to extract more precise structural information that is robust to image variations. Then, Gabor scale feature-based non-negative sparse representation classification (G-NSRC) is proposed for ear recognition under occlusion. Compared with SRC in which the sparse coding coefficients can be negative, the non-negativity of G-NSRC conforms to the intuitive notion of combing parts to form a whole and therefore is more consistent with the biological modeling of visual data. Additionally, the use of Gabor scale features increases the discriminative power of G-NSRC. Finally, the proposed classification paradigm is applied to occluded ear recognition. Experimental results demonstrate the effectiveness of our proposed algorithm. Especially when the ear is occluded, the proposed algorithm exhibits great robustness and achieves state-of-the-art recognition performance.
Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data
Lin, Youzuo; Huang, Lianjie
2015-01-26
Accurate velocity models of compressional- and shear-waves are essential for geothermal reservoir characterization and microseismic imaging. Elastic-waveform inversion of multi-component seismic data can provide high-resolution inversion results of subsurface geophysical properties. However, the method requires seismic data acquired using dense source and receiver arrays. In practice, seismic sources and/or geophones are often sparsely distributed on the surface and/or in a borehole, such as 3D vertical seismic profiling (VSP) surveys. We develop a novel elastic-waveform inversion method with compressive sensing for inversion of sparse seismic data. We employ an alternating-minimization algorithm to solve the optimization problem of our new waveform inversion method. We validate our new method using synthetic VSP data for a geophysical model built using geologic features found at the Raft River enhanced-geothermal-system (EGS) field. We apply our method to synthetic VSP data with a sparse source array and compare the results with those obtained with a dense source array. Our numerical results demonstrate that the velocity mode ls produced with our new method using a sparse source array are almost as accurate as those obtained using a dense source array.
Perks, Krista Eva; Gentner, Timothy Q.
2015-01-01
Natural acoustic communication signals, such as speech, are typically high-dimensional with a wide range of co-varying spectro-temporal features at multiple timescales. The synaptic and network mechanisms for encoding these complex signals are largely unknown. We are investigating these mechanisms in high-level sensory regions of the songbird auditory forebrain, where single neurons show sparse, object-selective spiking responses to conspecific songs. Using whole-cell in-vivo patch clamp techniques in the caudal mesopallium and the caudal nidopallium of starlings, we examine song-driven subthreshold and spiking activity. We find that both the subthreshold and the spiking activity are reliable (i.e., the same song drives a similar response each time it is presented) and specific (i.e. responses to different songs are distinct). Surprisingly, however, the reliability and specificity of the sub-threshold response was uniformly high regardless of when the cell spiked, even for song stimuli that drove no spikes. We conclude that despite a selective and sparse spiking response, high-level auditory cortical neurons are under continuous, non-selective, stimulus-specific synaptic control. To investigate the role of local network inhibition in this synaptic control, we then recorded extracellularly while pharmacologically blocking local GABA-ergic transmission. This manipulation modulated the strength and the reliability of stimulus-driven spiking, consistent with a role for local inhibition in regulating the reliability of network activity and the stimulus specificity of the subthreshold response in single cells. We discuss these results in the context of underlying computations that could generate sparse, stimulus-selective spiking responses, and models for hierarchical pooling. PMID:25728189
Perks, Krista E; Gentner, Timothy Q
2015-03-01
Natural acoustic communication signals, such as speech, are typically high-dimensional with a wide range of co-varying spectro-temporal features at multiple timescales. The synaptic and network mechanisms for encoding these complex signals are largely unknown. We are investigating these mechanisms in high-level sensory regions of the songbird auditory forebrain, where single neurons show sparse, object-selective spiking responses to conspecific songs. Using whole-cell in vivo patch clamp techniques in the caudal mesopallium and the caudal nidopallium of starlings, we examine song-driven subthreshold and spiking activity. We find that both the subthreshold and the spiking activity are reliable (i.e. the same song drives a similar response each time it is presented) and specific (i.e. responses to different songs are distinct). Surprisingly, however, the reliability and specificity of the subthreshold response was uniformly high regardless of when the cell spiked, even for song stimuli that drove no spikes. We conclude that despite a selective and sparse spiking response, high-level auditory cortical neurons are under continuous, non-selective, stimulus-specific synaptic control. To investigate the role of local network inhibition in this synaptic control, we then recorded extracellularly while pharmacologically blocking local GABAergic transmission. This manipulation modulated the strength and the reliability of stimulus-driven spiking, consistent with a role for local inhibition in regulating the reliability of network activity and the stimulus specificity of the subthreshold response in single cells. We discuss these results in the context of underlying computations that could generate sparse, stimulus-selective spiking responses, and models for hierarchical pooling. PMID:25728189
Evolutionary induction of sparse neural trees
Zhang; Ohm; Muhlenbein
1997-01-01
This paper is concerned with the automatic induction of parsimonious neural networks. In contrast to other program induction situations, network induction entails parametric learning as well as structural adaptation. We present a novel representation scheme called neural trees that allows efficient learning of both network architectures and parameters by genetic search. A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle. The method is successfully applied to the induction of higher order neural trees while still keeping the resulting structures sparse to ensure good generalization performance. Empirical results are provided on two chaotic time series prediction problems of practical interest. PMID:10021759
Eigensolver for a Sparse, Large Hermitian Matrix
NASA Technical Reports Server (NTRS)
Tisdale, E. Robert; Oyafuso, Fabiano; Klimeck, Gerhard; Brown, R. Chris
2003-01-01
A parallel-processing computer program finds a few eigenvalues in a sparse Hermitian matrix that contains as many as 100 million diagonal elements. This program finds the eigenvalues faster, using less memory, than do other, comparable eigensolver programs. This program implements a Lanczos algorithm in the American National Standards Institute/ International Organization for Standardization (ANSI/ISO) C computing language, using the Message Passing Interface (MPI) standard to complement an eigensolver in PARPACK. [PARPACK (Parallel Arnoldi Package) is an extension, to parallel-processing computer architectures, of ARPACK (Arnoldi Package), which is a collection of Fortran 77 subroutines that solve large-scale eigenvalue problems.] The eigensolver runs on Beowulf clusters of computers at the Jet Propulsion Laboratory (JPL).
Multiplication method for sparse interferometric fringes.
Liu, Cong; Zhang, Xingyi; Zhou, Youhe
2016-04-01
Fringe analysis in the interferometry has been of long-standing interest to the academic community. However, the process of sparse fringe is always a headache in the measurement, especially when the specimen is very small. Through theoretical derivation and experimental measurements, our work demonstrates a new method for fringe multiplication. Theoretically, arbitrary integral-multiple fringe multiplication can be acquired by using the interferogram phase as the parameter. We simulate digital images accordingly and find that not only the skeleton lines of the multiplied fringe are very convenient to extract, but also the main frequency of which can be easily separated from the DC component. Meanwhile, the experimental results have a good agreement with the theoretic ones in a validation using the classical photoelasticity. PMID:27137055
Predicting structure in nonsymmetric sparse matrix factorizations
Gilbert, J.R.; Ng, E.
1991-12-31
Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.
Predicting structure in nonsymmetric sparse matrix factorizations
Gilbert, J.R. ); Ng, E. )
1991-01-01
Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.
Functional fixedness in a technologically sparse culture.
German, Tim P; Barrett, H Clark
2005-01-01
Problem solving can be inefficient when the solution requires subjects to generate an atypical function for an object and the object's typical function has been primed. Subjects become "fixed" on the design function of the object, and problem solving suffers relative to control conditions in which the object's function is not demonstrated. In the current study, such functional fixedness was demonstrated in a sample of adolescents (mean age of 16 years) among the Shuar of Ecuadorian Amazonia, whose technologically sparse culture provides limited access to large numbers of artifacts with highly specialized functions. This result suggests that design function may universally be the core property of artifact concepts in human semantic memory. PMID:15660843
Predicting structure in nonsymmetric sparse matrix factorizations
Gilbert, J.R. ); Ng, E.G. )
1992-10-01
Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.
Bayesian learning of sparse multiscale image representations.
Hughes, James Michael; Rockmore, Daniel N; Wang, Yang
2013-12-01
Multiscale representations of images have become a standard tool in image analysis. Such representations offer a number of advantages over fixed-scale methods, including the potential for improved performance in denoising, compression, and the ability to represent distinct but complementary information that exists at various scales. A variety of multiresolution transforms exist, including both orthogonal decompositions such as wavelets as well as nonorthogonal, overcomplete representations. Recently, techniques for finding adaptive, sparse representations have yielded state-of-the-art results when applied to traditional image processing problems. Attempts at developing multiscale versions of these so-called dictionary learning models have yielded modest but encouraging results. However, none of these techniques has sought to combine a rigorous statistical formulation of the multiscale dictionary learning problem and the ability to share atoms across scales. We present a model for multiscale dictionary learning that overcomes some of the drawbacks of previous approaches by first decomposing an input into a pyramid of distinct frequency bands using a recursive filtering scheme, after which we perform dictionary learning and sparse coding on the individual levels of the resulting pyramid. The associated image model allows us to use a single set of adapted dictionary atoms that is shared--and learned--across all scales in the model. The underlying statistical model of our proposed method is fully Bayesian and allows for efficient inference of parameters, including the level of additive noise for denoising applications. We apply the proposed model to several common image processing problems including non-Gaussian and nonstationary denoising of real-world color images. PMID:24002002
Communication Structure of Cortical Networks
da Fontoura Costa, Luciano; Batista, João Luiz B.; Ascoli, Giorgio A.
2011-01-01
Large-scale cortical networks exhibit characteristic topological properties that shape communication between brain regions and global cortical dynamics. Analysis of complex networks allows the description of connectedness, distance, clustering, and centrality that reveal different aspects of how the network's nodes communicate. Here, we focus on a novel analysis of complex walks in a series of mammalian cortical networks that model potential dynamics of information flow between individual brain regions. We introduce two new measures called absorption and driftness. Absorption is the average length of random walks between any two nodes, and takes into account all paths that may diffuse activity throughout the network. Driftness is the ratio between absorption and the corresponding shortest path length. For a given node of the network, we also define four related measurements, namely in- and out-absorption as well as in- and out-driftness, as the averages of the corresponding measures from all nodes to that node, and from that node to all nodes, respectively. We find that the cat thalamo-cortical system incorporates features of two classic network topologies, Erdös–Rényi graphs with respect to in-absorption and in-driftness, and configuration models with respect to out-absorption and out-driftness. Moreover, taken together these four measures separate the network nodes based on broad functional roles (visual, auditory, somatomotor, and frontolimbic). PMID:21427794
Biomechanics of Single Cortical Neurons
Bernick, Kristin B.; Prevost, Thibault P.; Suresh, Subra; Socrate, Simona
2011-01-01
This study presents experimental results and computational analysis of the large strain dynamic behavior of single neurons in vitro with the objective of formulating a novel quantitative framework for the biomechanics of cortical neurons. Relying on the atomic force microscopy (AFM) technique, novel testing protocols are developed to enable the characterization of neural soma deformability over a range of indentation rates spanning three orders of magnitude – 10, 1, and 0.1 μm/s. Modified spherical AFM probes were utilized to compress the cell bodies of neonatal rat cortical neurons in load, unload, reload and relaxation conditions. The cell response showed marked hysteretic features, strong non-linearities, and substantial time/rate dependencies. The rheological data were complemented with geometrical measurements of cell body morphology, i.e. cross-diameter and height estimates. A constitutive model, validated by the present experiments, is proposed to quantify the mechanical behavior of cortical neurons. The model aimed to correlate empirical findings with measurable degrees of (hyper-) elastic resilience and viscosity at the cell level. The proposed formulation, predicated upon previous constitutive model developments undertaken at the cortical tissue level, was implemented into a three-dimensional finite element framework. The simulated cell response was calibrated to the experimental measurements under the selected test conditions, providing a novel single cell model that could form the basis for further refinements. PMID:20971217
Sparse and shrunken estimates of MRI networks in the brain and their influence on network properties
NASA Astrophysics Data System (ADS)
Romero-Garcia, Rafael; Clemmensen, Line H.
2014-03-01
Estimation of morphometric relationships between cortical regions is a widely used approach to identify and characterize structural connectivity. The elevated number of regions that can be considered in a whole-brain correlation analysis might lead to overfitted models. However, the overfitting can be avoided by using regularization methods. We found that, as expected, non-regularized correlations had low variability when a scarce number of variables were considered. However, a slight increase of variables led to an increase of variance of several magnitude orders. On the other hand, the regularized approaches showed more stable results with a relative low variance at the expense of a little bias. Interestingly, topological properties as local and global efficiency estimated in networks constructed from traditional non-regularized correlations also showed higher variability when compared to those from regularized networks. Our findings suggest that a population-based connectivity study can achieve a more robust description of cortical topology through regularization of the correlation estimates. Four regularization methods were examined: Two with shrinkage (Ridge and Schäfer's shrinkage), one with sparsity (Lasso) and one with both shrinkage and sparsity (Elastic net). Furthermore, the different regularizations resulted in different correlation estimates as well as network properties. The shrunken estimates resulted in lower variance of the estimates than the sparse estimates.
Zhang, Di; He, Jiazhong; Zhao, Yun; Du, Minghui
2015-03-01
In magnetic resonance (MR) imaging, image spatial resolution is determined by various instrumental limitations and physical considerations. This paper presents a new algorithm for producing a high-resolution version of a low-resolution MR image. The proposed method consists of two consecutive steps: (1) reconstructs a high-resolution MR image from a given low-resolution observation via solving a joint sparse representation and nonlocal similarity L1-norm minimization problem; and (2) applies a sparse derivative prior based post-processing to suppress blurring effects. Extensive experiments on simulated brain MR images and two real clinical MR image datasets validate that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both quantitative measures and visual perception. PMID:25638262
Aghagolzadeh, Mehdi; Zhang, Fei; Oweiss, Karim
2010-01-01
Brain Machine Interface (BMI) systems demand real-time spike sorting to instantaneously decode the spike trains of simultaneously recorded cortical neurons. Real-time spike sorting, however, requires extensive computational power that is not feasible to implement in implantable BMI architectures, thereby requiring transmission of high-bandwidth raw neural data to an external computer. In this work, we describe a miniaturized, low power, programmable hardware module capable of performing this task within the resource constraints of an implantable chip. The module computes a sparse representation of the spike waveforms followed by "smart" thresholding. This cascade restricts the sparse representation to a subset of projections that preserve the discriminative features of neuron-specific spike waveforms. In addition, it further reduces telemetry bandwidth making it feasible to wirelessly transmit only the important biological information to the outside world, thereby improving the efficiency, practicality and viability of BMI systems in clinical applications. PMID:21096383
Nam, Kie Woo; Castellanos, Nazareth; Simmons, Andrew; Froudist-Walsh, Seán; Allin, Matthew P; Walshe, Muriel; Murray, Robin M; Evans, Alan; Muehlboeck, J-Sebastian; Nosarti, Chiara
2015-07-15
Very preterm birth (gestational age <33 weeks) is associated with alterations in cortical thickness and with neuropsychological/behavioural impairments. Here we studied cortical thickness in very preterm born individuals and controls in mid-adolescence (mean age 15 years) and beginning of adulthood (mean age 20 years), as well as longitudinal changes between the two time points. Using univariate approaches, we showed both increases and decreases in cortical thickness in very preterm born individuals compared to controls. Specifically (1) very preterm born adolescents displayed extensive areas of greater cortical thickness, especially in occipitotemporal and prefrontal cortices, differences which decreased substantially by early adulthood; (2) at both time points, very preterm-born participants showed smaller cortical thickness, especially in parahippocampal and insular regions. We then employed a multivariate approach (support vector machine) to study spatially discriminating features between the two groups, which achieved a mean accuracy of 86.5%. The spatially distributed regions in which cortical thickness best discriminated between the groups (top 5%) included temporal, occipitotemporal, parietal and prefrontal cortices. Within these spatially distributed regions (top 1%), longitudinal changes in cortical thickness in left temporal pole, right occipitotemporal gyrus and left superior parietal lobe were significantly associated with scores on language-based tests of executive function. These results describe alterations in cortical thickness development in preterm-born individuals in their second decade of life, with implications for high-order cognitive processing. PMID:25871628
Nam, Kie Woo; Castellanos, Nazareth; Simmons, Andrew; Froudist-Walsh, Seán; Allin, Matthew P.; Walshe, Muriel; Murray, Robin M.; Evans, Alan; Muehlboeck, J-Sebastian; Nosarti, Chiara
2015-01-01
Very preterm birth (gestational age < 33 weeks) is associated with alterations in cortical thickness and with neuropsychological/behavioural impairments. Here we studied cortical thickness in very preterm born individuals and controls in mid-adolescence (mean age 15 years) and beginning of adulthood (mean age 20 years), as well as longitudinal changes between the two time points. Using univariate approaches, we showed both increases and decreases in cortical thickness in very preterm born individuals compared to controls. Specifically (1) very preterm born adolescents displayed extensive areas of greater cortical thickness, especially in occipitotemporal and prefrontal cortices, differences which decreased substantially by early adulthood; (2) at both time points, very preterm-born participants showed smaller cortical thickness, especially in parahippocampal and insular regions. We then employed a multivariate approach (support vector machine) to study spatially discriminating features between the two groups, which achieved a mean accuracy of 86.5%. The spatially distributed regions in which cortical thickness best discriminated between the groups (top 5%) included temporal, occipitotemporal, parietal and prefrontal cortices. Within these spatially distributed regions (top 1%), longitudinal changes in cortical thickness in left temporal pole, right occipitotemporal gyrus and left superior parietal lobe were significantly associated with scores on language-based tests of executive function. These results describe alterations in cortical thickness development in preterm-born individuals in their second decade of life, with implications for high-order cognitive processing. PMID:25871628
Group-based sparse representation for image restoration.
Zhang, Jian; Zhao, Debin; Gao, Wen
2014-08-01
Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational complexity in dictionary learning. Second, each patch is considered independently in dictionary learning and sparse coding, which ignores the relationship among patches, resulting in inaccurate sparse coding coefficients. In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR). The proposed GSR is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. In addition, an effective self-adaptive dictionary learning method for each group with low complexity is designed, rather than dictionary learning from natural images. To make GSR tractable and robust, a split Bregman-based technique is developed to solve the proposed GSR-driven ℓ0 minimization problem for image restoration efficiently. Extensive experiments on image inpainting, image deblurring and image compressive sensing recovery manifest that the proposed GSR modeling outperforms many current state-of-the-art schemes in both peak signal-to-noise ratio and visual perception. PMID:24835225
Sparse source configurations in radio tomography of asteroids
NASA Astrophysics Data System (ADS)
Pursiainen, S.; Kaasalainen, M.
2014-07-01
Our research targets at progress in non-invasive imaging of asteroids to support future planetary research and extra-terrestrial mining activities. This presentation concerns principally radio tomography in which the permittivity distribution inside an asteroid is to be recovered based on the radio frequency signal transmitted from the asteroid's surface and gathered by an orbiter. The focus will be on a sparse distribution (Pursiainen and Kaasalainen, 2013) of signal sources that can be necessary in the challenging in situ environment and within tight payload limits. The general goal in our recent research has been to approximate the minimal number of source positions needed for robust localization of anomalies caused, for example, by an internal void. Characteristic to the localization problem are the large relative changes in signal speed caused by the high permittivity of typical asteroid minerals (e.g. basalt), meaning that a signal path can include strong refractions and reflections. This presentation introduces results of a laboratory experiment in which real travel time data was inverted using a hierarchical Bayesian approach combined with the iterative alternating sequential (IAS) posterior exploration algorithm. Special interest was paid to robustness of the inverse results regarding changes of the prior model and source positioning. According to our results, strongly refractive anomalies can be detected with three or four sources independently of their positioning.
Cervigram image segmentation based on reconstructive sparse representations
NASA Astrophysics Data System (ADS)
Zhang, Shaoting; Huang, Junzhou; Wang, Wei; Huang, Xiaolei; Metaxas, Dimitris
2010-03-01
We proposed an approach based on reconstructive sparse representations to segment tissues in optical images of the uterine cervix. Because of large variations in image appearance caused by the changing of the illumination and specular reflection, the color and texture features in optical images often overlap with each other and are not linearly separable. By leveraging sparse representations the data can be transformed to higher dimensions with sparse constraints and become more separated. K-SVD algorithm is employed to find sparse representations and corresponding dictionaries. The data can be reconstructed from its sparse representations and positive and/or negative dictionaries. Classification can be achieved based on comparing the reconstructive errors. In the experiments we applied our method to automatically segment the biomarker AcetoWhite (AW) regions in an archive of 60,000 images of the uterine cervix. Compared with other general methods, our approach showed lower space and time complexity and higher sensitivity.
Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences.
Harandi, Mehrtash T; Hartley, Richard; Lovell, Brian; Sanderson, Conrad
2016-06-01
This paper introduces sparse coding and dictionary learning for symmetric positive definite (SPD) matrices, which are often used in machine learning, computer vision, and related areas. Unlike traditional sparse coding schemes that work in vector spaces, in this paper, we discuss how SPD matrices can be described by sparse combination of dictionary atoms, where the atoms are also SPD matrices. We propose to seek sparse coding by embedding the space of SPD matrices into the Hilbert spaces through two types of the Bregman matrix divergences. This not only leads to an efficient way of performing sparse coding but also an online and iterative scheme for dictionary learning. We apply the proposed methods to several computer vision tasks where images are represented by region covariance matrices. Our proposed algorithms outperform state-of-the-art methods on a wide range of classification tasks, including face recognition, action recognition, material classification, and texture categorization. PMID:25643414
Actin kinetics shapes cortical network structure and mechanics
Fritzsche, Marco; Erlenkämper, Christoph; Moeendarbary, Emad; Charras, Guillaume; Kruse, Karsten
2016-01-01
The actin cortex of animal cells is the main determinant of cellular mechanics. The continuous turnover of cortical actin filaments enables cells to quickly respond to stimuli. Recent work has shown that most of the cortical actin is generated by only two actin nucleators, the Arp2/3 complex and the formin Diaph1. However, our understanding of their interplay, their kinetics, and the length distribution of the filaments that they nucleate within living cells is poor. Such knowledge is necessary for a thorough comprehension of cellular processes and cell mechanics from basic polymer physics principles. We determined cortical assembly rates in living cells by using single-molecule fluorescence imaging in combination with stochastic simulations. We find that formin-nucleated filaments are, on average, 10 times longer than Arp2/3-nucleated filaments. Although formin-generated filaments represent less than 10% of all actin filaments, mechanical measurements indicate that they are important determinants of cortical elasticity. Tuning the activity of actin nucleators to alter filament length distribution may thus be a mechanism allowing cells to adjust their macroscopic mechanical properties to their physiological needs. PMID:27152338
Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis.
Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang
2016-06-01
Recently, neuroimaging-based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis has attracted researchers in the field, due to the increasing prevalence of the diseases. Unfortunately, the unfavorable high-dimensional nature of neuroimaging data, but a limited small number of samples available, makes it challenging to build a robust computer-aided diagnosis system. Machine learning techniques have been considered as a useful tool in this respect and, among various methods, sparse regression has shown its validity in the literature. However, to our best knowledge, the existing sparse regression methods mostly try to select features based on the optimal regression coefficients in one step. We argue that since the training feature vectors are composed of both informative and uninformative or less informative features, the resulting optimal regression coefficients are inevidently affected by the uninformative or less informative features. To this end, we first propose a novel deep architecture to recursively discard uninformative features by performing sparse multi-task learning in a hierarchical fashion. We further hypothesize that the optimal regression coefficients reflect the relative importance of features in representing the target response variables. In this regard, we use the optimal regression coefficients learned in one hierarchy as feature weighting factors in the following hierarchy, and formulate a weighted sparse multi-task learning method. Lastly, we also take into account the distributional characteristics of samples per class and use clustering-induced subclass label vectors as target response values in our sparse regression model. In our experiments on the ADNI cohort, we performed both binary and multi-class classification tasks in AD/MCI diagnosis and showed the superiority of the proposed method by comparing with the state-of-the-art methods. PMID:25993900
Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis
Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang
2016-01-01
Recently, neuroimaging-based Alzheimer’s disease (AD) or mild cognitive impairment (MCI) diagnosis has attracted researchers in the field, due to the increasing prevalence of the diseases. Unfortunately, the unfavorable high-dimensional nature of neuroimaging data, but a limited small number of samples available, makes it challenging to build a robust computer-aided diagnosis system. Machine learning techniques have been considered as a useful tool in this respect and, among various methods, sparse regression has shown its validity in the literature. However, to our best knowledge, the existing sparse regression methods mostly try to select features based on the optimal regression coefficients in one step. We argue that since the training feature vectors are composed of both informative and uninformative or less informative features, the resulting optimal regression coefficients are inevidently affected by the uninformative or less informative features. To this end, we first propose a novel deep architecture to recursively discard uninformative features by performing sparse multi-task learning in a hierarchical fashion. We further hypothesize that the optimal regression coefficients reflect the relative importance of features in representing the target response variables. In this regard, we use the optimal regression co-efficients learned in one hierarchy as feature weighting factors in the following hierarchy, and formulate a weighted sparse multi-task learning method. Lastly, we also take into account the distributional characteristics of samples per class and use clustering-induced subclass label vectors as target response values in our sparse regression model. In our experiments on the ADNI cohort, we performed both binary and multi-class classification tasks in AD/MCI diagnosis and showed the superiority of the proposed method by comparing with the state-of-the-art methods. PMID:25993900
ERIC Educational Resources Information Center
Sadler, Peter G.
The Institute for the Study of Sparsely Populated Areas is a multidisciplinary research unit which acts to coordinate, further, and initiate studies of the economic and social conditions of sparsely populated areas. Short summaries of the eight studies completed in the session of 1977-78 indicate work in such areas as the study of political life…
Development of cortical orientation selectivity in the absence of visual experience with contour.
Ohshiro, Tomokazu; Hussain, Shaista; Weliky, Michael
2011-10-01
Visual cortical neurons are selective for the orientation of lines, and the full development of this selectivity requires natural visual experience after eye opening. Here we examined whether this selectivity develops without seeing lines and contours. Juvenile ferrets were reared in a dark room and visually trained by being shown a movie of flickering, sparse spots. We found that despite the lack of contour visual experience, the cortical neurons of these ferrets developed strong orientation selectivity and exhibited simple-cell receptive fields. This finding suggests that overt contour visual experience is unnecessary for the maturation of orientation selectivity and is inconsistent with the computational models that crucially require the visual inputs of lines and contours for the development of orientation selectivity. We propose that a correlation-based model supplemented with a constraint on synaptic strength dynamics is able to account for our experimental result. PMID:21753023
Auditory experience-dependent cortical circuit shaping for memory formation in bird song learning
Yanagihara, Shin; Yazaki-Sugiyama, Yoko
2016-01-01
As in human speech acquisition, songbird vocal learning depends on early auditory experience. During development, juvenile songbirds listen to and form auditory memories of adult tutor songs, which they use to shape their own vocalizations in later sensorimotor learning. The higher-level auditory cortex, called the caudomedial nidopallium (NCM), is a potential storage site for tutor song memory, but no direct electrophysiological evidence of tutor song memory has been found. Here, we identify the neuronal substrate for tutor song memory by recording single-neuron activity in the NCM of behaving juvenile zebra finches. After tutor song experience, a small subset of NCM neurons exhibit highly selective auditory responses to the tutor song. Moreover, blockade of GABAergic inhibition, and sleep decrease their selectivity. Taken together, these results suggest that experience-dependent recruitment of GABA-mediated inhibition shapes auditory cortical circuits, leading to sparse representation of tutor song memory in auditory cortical neurons. PMID:27327620
Auditory experience-dependent cortical circuit shaping for memory formation in bird song learning.
Yanagihara, Shin; Yazaki-Sugiyama, Yoko
2016-01-01
As in human speech acquisition, songbird vocal learning depends on early auditory experience. During development, juvenile songbirds listen to and form auditory memories of adult tutor songs, which they use to shape their own vocalizations in later sensorimotor learning. The higher-level auditory cortex, called the caudomedial nidopallium (NCM), is a potential storage site for tutor song memory, but no direct electrophysiological evidence of tutor song memory has been found. Here, we identify the neuronal substrate for tutor song memory by recording single-neuron activity in the NCM of behaving juvenile zebra finches. After tutor song experience, a small subset of NCM neurons exhibit highly selective auditory responses to the tutor song. Moreover, blockade of GABAergic inhibition, and sleep decrease their selectivity. Taken together, these results suggest that experience-dependent recruitment of GABA-mediated inhibition shapes auditory cortical circuits, leading to sparse representation of tutor song memory in auditory cortical neurons. PMID:27327620
Development of cortical orientation selectivity in the absence of visual experience with contour
Hussain, Shaista; Weliky, Michael
2011-01-01
Visual cortical neurons are selective for the orientation of lines, and the full development of this selectivity requires natural visual experience after eye opening. Here we examined whether this selectivity develops without seeing lines and contours. Juvenile ferrets were reared in a dark room and visually trained by being shown a movie of flickering, sparse spots. We found that despite the lack of contour visual experience, the cortical neurons of these ferrets developed strong orientation selectivity and exhibited simple-cell receptive fields. This finding suggests that overt contour visual experience is unnecessary for the maturation of orientation selectivity and is inconsistent with the computational models that crucially require the visual inputs of lines and contours for the development of orientation selectivity. We propose that a correlation-based model supplemented with a constraint on synaptic strength dynamics is able to account for our experimental result. PMID:21753023
The Influence of Synaptic Weight Distribution on Neuronal Population Dynamics
Buice, Michael; Koch, Christof; Mihalas, Stefan
2013-01-01
The manner in which different distributions of synaptic weights onto cortical neurons shape their spiking activity remains open. To characterize a homogeneous neuronal population, we use the master equation for generalized leaky integrate-and-fire neurons with shot-noise synapses. We develop fast semi-analytic numerical methods to solve this equation for either current or conductance synapses, with and without synaptic depression. We show that its solutions match simulations of equivalent neuronal networks better than those of the Fokker-Planck equation and we compute bounds on the network response to non-instantaneous synapses. We apply these methods to study different synaptic weight distributions in feed-forward networks. We characterize the synaptic amplitude distributions using a set of measures, called tail weight numbers, designed to quantify the preponderance of very strong synapses. Even if synaptic amplitude distributions are equated for both the total current and average synaptic weight, distributions with sparse but strong synapses produce higher responses for small inputs, leading to a larger operating range. Furthermore, despite their small number, such synapses enable the network to respond faster and with more stability in the face of external fluctuations. PMID:24204219
Partially sparse imaging of stationary indoor scenes
NASA Astrophysics Data System (ADS)
Ahmad, Fauzia; Amin, Moeness G.; Dogaru, Traian
2014-12-01
In this paper, we exploit the notion of partial sparsity for scene reconstruction associated with through-the-wall radar imaging of stationary targets under reduced data volume. Partial sparsity implies that the scene being imaged consists of a sparse part and a dense part, with the support of the latter assumed to be known. For the problem at hand, sparsity is represented by a few stationary indoor targets, whereas the high scene density is defined by exterior and interior walls. Prior knowledge of wall positions and extent may be available either through building blueprints or from prior surveillance operations. The contributions of the exterior and interior walls are removed from the data through the use of projection matrices, which are determined from wall- and corner-specific dictionaries. The projected data, with enhanced sparsity, is then processed using l 1 norm reconstruction techniques. Numerical electromagnetic data is used to demonstrate the effectiveness of the proposed approach for imaging stationary indoor scenes using a reduced set of measurements.
Sparse coding for layered neural networks
NASA Astrophysics Data System (ADS)
Katayama, Katsuki; Sakata, Yasuo; Horiguchi, Tsuyoshi
2002-07-01
We investigate storage capacity of two types of fully connected layered neural networks with sparse coding when binary patterns are embedded into the networks by a Hebbian learning rule. One of them is a layered network, in which a transfer function of even layers is different from that of odd layers. The other is a layered network with intra-layer connections, in which the transfer function of inter-layer is different from that of intra-layer, and inter-layered neurons and intra-layered neurons are updated alternately. We derive recursion relations for order parameters by means of the signal-to-noise ratio method, and then apply the self-control threshold method proposed by Dominguez and Bollé to both layered networks with monotonic transfer functions. We find that a critical value αC of storage capacity is about 0.11|a ln a| -1 ( a≪1) for both layered networks, where a is a neuronal activity. It turns out that the basin of attraction is larger for both layered networks when the self-control threshold method is applied.
Anisotropic interpolation of sparse generalized image samples.
Bourquard, Aurélien; Unser, Michael
2013-02-01
Practical image-acquisition systems are often modeled as a continuous-domain prefilter followed by an ideal sampler, where generalized samples are obtained after convolution with the impulse response of the device. In this paper, our goal is to interpolate images from a given subset of such samples. We express our solution in the continuous domain, considering consistent resampling as a data-fidelity constraint. To make the problem well posed and ensure edge-preserving solutions, we develop an efficient anisotropic regularization approach that is based on an improved version of the edge-enhancing anisotropic diffusion equation. Following variational principles, our reconstruction algorithm minimizes successive quadratic cost functionals. To ensure fast convergence, we solve the corresponding sequence of linear problems by using multigrid iterations that are specifically tailored to their sparse structure. We conduct illustrative experiments and discuss the potential of our approach both in terms of algorithmic design and reconstruction quality. In particular, we present results that use as little as 2% of the image samples. PMID:22968212
Approximation and compression with sparse orthonormal transforms.
Sezer, Osman Gokhan; Guleryuz, Onur G; Altunbasak, Yucel
2015-08-01
We propose a new transform design method that targets the generation of compression-optimized transforms for next-generation multimedia applications. The fundamental idea behind transform compression is to exploit regularity within signals such that redundancy is minimized subject to a fidelity cost. Multimedia signals, in particular images and video, are well known to contain a diverse set of localized structures, leading to many different types of regularity and to nonstationary signal statistics. The proposed method designs sparse orthonormal transforms (SOTs) that automatically exploit regularity over different signal structures and provides an adaptation method that determines the best representation over localized regions. Unlike earlier work that is motivated by linear approximation constructs and model-based designs that are limited to specific types of signal regularity, our work uses general nonlinear approximation ideas and a data-driven setup to significantly broaden its reach. We show that our SOT designs provide a safe and principled extension of the Karhunen-Loeve transform (KLT) by reducing to the KLT on Gaussian processes and by automatically exploiting non-Gaussian statistics to significantly improve over the KLT on more general processes. We provide an algebraic optimization framework that generates optimized designs for any desired transform structure (multiresolution, block, lapped, and so on) with significantly better n -term approximation performance. For each structure, we propose a new prototype codec and test over a database of images. Simulation results show consistent increase in compression and approximation performance compared with conventional methods. PMID:25823033
Transformer fault diagnosis using continuous sparse autoencoder.
Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou
2016-01-01
This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis. PMID:27119052
Partitioning sparse matrices with eigenvectors of graphs
NASA Technical Reports Server (NTRS)
Pothen, Alex; Simon, Horst D.; Liou, Kang-Pu
1990-01-01
The problem of computing a small vertex separator in a graph arises in the context of computing a good ordering for the parallel factorization of sparse, symmetric matrices. An algebraic approach for computing vertex separators is considered in this paper. It is shown that lower bounds on separator sizes can be obtained in terms of the eigenvalues of the Laplacian matrix associated with a graph. The Laplacian eigenvectors of grid graphs can be computed from Kronecker products involving the eigenvectors of path graphs, and these eigenvectors can be used to compute good separators in grid graphs. A heuristic algorithm is designed to compute a vertex separator in a general graph by first computing an edge separator in the graph from an eigenvector of the Laplacian matrix, and then using a maximum matching in a subgraph to compute the vertex separator. Results on the quality of the separators computed by the spectral algorithm are presented, and these are compared with separators obtained from other algorithms for computing separators. Finally, the time required to compute the Laplacian eigenvector is reported, and the accuracy with which the eigenvector must be computed to obtain good separators is considered. The spectral algorithm has the advantage that it can be implemented on a medium-size multiprocessor in a straightforward manner.
Optimal parallel solution of sparse triangular systems
NASA Technical Reports Server (NTRS)
Alvarado, Fernando L.; Schreiber, Robert
1990-01-01
A method for the parallel solution of triangular sets of equations is described that is appropriate when there are many right-handed sides. By preprocessing, the method can reduce the number of parallel steps required to solve Lx = b compared to parallel forward or backsolve. Applications are to iterative solvers with triangular preconditioners, to structural analysis, or to power systems applications, where there may be many right-handed sides (not all available a priori). The inverse of L is represented as a product of sparse triangular factors. The problem is to find a factored representation of this inverse of L with the smallest number of factors (or partitions), subject to the requirement that no new nonzero elements be created in the formation of these inverse factors. A method from an earlier reference is shown to solve this problem. This method is improved upon by constructing a permutation of the rows and columns of L that preserves triangularity and allow for the best possible such partition. A number of practical examples and algorithmic details are presented. The parallelism attainable is illustrated by means of elimination trees and clique trees.
Sparse alignment for robust tensor learning.
Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming
2014-10-01
Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods. PMID:25291733
Index statistical properties of sparse random graphs
NASA Astrophysics Data System (ADS)
Metz, F. L.; Stariolo, Daniel A.
2015-10-01
Using the replica method, we develop an analytical approach to compute the characteristic function for the probability PN(K ,λ ) that a large N ×N adjacency matrix of sparse random graphs has K eigenvalues below a threshold λ . The method allows to determine, in principle, all moments of PN(K ,λ ) , from which the typical sample-to-sample fluctuations can be fully characterized. For random graph models with localized eigenvectors, we show that the index variance scales linearly with N ≫1 for |λ |>0 , with a model-dependent prefactor that can be exactly calculated. Explicit results are discussed for Erdös-Rényi and regular random graphs, both exhibiting a prefactor with a nonmonotonic behavior as a function of λ . These results contrast with rotationally invariant random matrices, where the index variance scales only as lnN , with an universal prefactor that is independent of λ . Numerical diagonalization results confirm the exactness of our approach and, in addition, strongly support the Gaussian nature of the index fluctuations.
Cortical Visual Impairment: New Directions
Good, William V.
2009-01-01
Cortical visual impairment is the leading cause of bilateral low vision in children in the U.S., yet very little research is being done to find new diagnostic measures and treatments. Dr. Velma Dobson's pioneering work on visual assessments of developmentally delayed children stands out as highly significant in this field. Future research will assess new diagnostic measures, including advanced imaging techniques. In addition, research will evaluate methods to prevent, treat, and rehabilitate infants and children afflicted with this condition. PMID:19417710
Tan, Mingzhen; Qiu, Anqi
2016-09-01
Brain surface registration is an important tool for characterizing cortical anatomical variations and understanding their roles in normal cortical development and psychiatric diseases. However, surface registration remains challenging due to complicated cortical anatomy and its large differences across individuals. In this paper, we propose a fast coarse-to-fine algorithm for surface registration by adapting the large diffeomorphic deformation metric mapping (LDDMM) framework for surface mapping and show improvements in speed and accuracy via a multiresolution analysis of surface meshes and the construction of multiresolution diffeomorphic transformations. The proposed method constructs a family of multiresolution meshes that are used as natural sparse priors of the cortical morphology. At varying resolutions, these meshes act as anchor points where the parameterization of multiresolution deformation vector fields can be supported, allowing the construction of a bundle of multiresolution deformation fields, each originating from a different resolution. Using a coarse-to-fine approach, we show a potential reduction in computation cost along with improvements in sulcal alignment when compared with LDDMM surface mapping. PMID:27254865
Cortical Control of Affective Networks
Kumar, Sunil; Black, Sherilynn J.; Hultman, Rainbo; Szabo, Steven T.; DeMaio, Kristine D.; Du, Jeanette; Katz, Brittany M.; Feng, Guoping; Covington, Herbert E.; Dzirasa, Kafui
2013-01-01
Transcranial magnetic stimulation and deep brain stimulation have emerged as therapeutic modalities for treatment refractory depression; however, little remains known regarding the circuitry that mediates the therapeutic effect of these approaches. Here we show that direct optogenetic stimulation of prefrontal cortex (PFC) descending projection neurons in mice engineered to express Chr2 in layer V pyramidal neurons (Thy1–Chr2 mice) models an antidepressant-like effect in mice subjected to a forced-swim test. Furthermore, we show that this PFC stimulation induces a long-lasting suppression of anxiety-like behavior (but not conditioned social avoidance) in socially stressed Thy1–Chr2 mice: an effect that is observed >10 d after the last stimulation. Finally, we use optogenetic stimulation and multicircuit recording techniques concurrently in Thy1–Chr2 mice to demonstrate that activation of cortical projection neurons entrains neural oscillatory activity and drives synchrony across limbic brain areas that regulate affect. Importantly, these neural oscillatory changes directly correlate with the temporally precise activation and suppression of limbic unit activity. Together, our findings show that the direct activation of cortical projection systems is sufficient to modulate activity across networks underlying affective regulation. They also suggest that optogenetic stimulation of cortical projection systems may serve as a viable therapeutic strategy for treating affective disorders. PMID:23325249
Optimized design and analysis of sparse-sampling FMRI experiments.
Perrachione, Tyler K; Ghosh, Satrajit S
2013-01-01
Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional time series. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR) delay (an acquisition parameter), stimulation rate (an experimental design parameter), and model basis function (an analysis parameter) act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest the employment of three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1) Sparse analyses should utilize a physiologically informed model that incorporates hemodynamic response convolution to reduce model error. (2) The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3) TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to increase
Detecting a Cortical Fingerprint of Parkinson's Disease for Closed-Loop Neuromodulation
Kern, Kevin; Naros, Georgios; Braun, Christoph; Weiss, Daniel; Gharabaghi, Alireza
2016-01-01
Recent evidence suggests that deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) mediates its clinical effects by modulating cortical oscillatory activity, presumably via a direct cortico-subthalamic connection. This observation might pave the way for novel closed-loop approaches comprising a cortical sensor. Enhanced beta oscillations (13-35 Hz) have been linked to the pathophysiology of PD and may serve as such a candidate marker to localize a cortical area reliably modulated by DBS. However, beta-oscillations are widely distributed over the cortical surface, necessitating an additional signal source for spotting the cortical area linked to the pathologically synchronized cortico-subcortical motor network. In this context, both cortico-subthalamic coherence and cortico-muscular coherence (CMC) have been studied in PD patients. Whereas, the former requires invasive recordings, the latter allows for non-invasive detection, but displays a rather distributed cortical synchronization pattern in motor tasks. This distributed cortical representation may conflict with the goal of detecting a cortical localization with robust biomarker properties which is detectable on a single subject basis. We propose that this limitation could be overcome when recording CMC at rest. We hypothesized that—unlike healthy subjects—PD would show CMC at rest owing to the enhanced beta oscillations observed in PD. By performing source space analysis of beta CMC recorded during resting-state magnetoencephalography, we provide preliminary evidence in one patient for a cortical hot spot that is modulated most strongly by subthalamic DBS. Such a spot would provide a prominent target region either for direct neuromodulation or for placing a potential sensor in closed-loop DBS approaches, a proposal that requires investigation in a larger cohort of PD patients. PMID:27065781
Detecting a Cortical Fingerprint of Parkinson's Disease for Closed-Loop Neuromodulation.
Kern, Kevin; Naros, Georgios; Braun, Christoph; Weiss, Daniel; Gharabaghi, Alireza
2016-01-01
Recent evidence suggests that deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) mediates its clinical effects by modulating cortical oscillatory activity, presumably via a direct cortico-subthalamic connection. This observation might pave the way for novel closed-loop approaches comprising a cortical sensor. Enhanced beta oscillations (13-35 Hz) have been linked to the pathophysiology of PD and may serve as such a candidate marker to localize a cortical area reliably modulated by DBS. However, beta-oscillations are widely distributed over the cortical surface, necessitating an additional signal source for spotting the cortical area linked to the pathologically synchronized cortico-subcortical motor network. In this context, both cortico-subthalamic coherence and cortico-muscular coherence (CMC) have been studied in PD patients. Whereas, the former requires invasive recordings, the latter allows for non-invasive detection, but displays a rather distributed cortical synchronization pattern in motor tasks. This distributed cortical representation may conflict with the goal of detecting a cortical localization with robust biomarker properties which is detectable on a single subject basis. We propose that this limitation could be overcome when recording CMC at rest. We hypothesized that-unlike healthy subjects-PD would show CMC at rest owing to the enhanced beta oscillations observed in PD. By performing source space analysis of beta CMC recorded during resting-state magnetoencephalography, we provide preliminary evidence in one patient for a cortical hot spot that is modulated most strongly by subthalamic DBS. Such a spot would provide a prominent target region either for direct neuromodulation or for placing a potential sensor in closed-loop DBS approaches, a proposal that requires investigation in a larger cohort of PD patients. PMID:27065781
Iterative solution of general sparse linear systems on clusters of workstations
Lo, Gen-Ching; Saad, Y.
1996-12-31
Solving sparse irregularly structured linear systems on parallel platforms poses several challenges. First, sparsity makes it difficult to exploit data locality, whether in a distributed or shared memory environment. A second, perhaps more serious challenge, is to find efficient ways to precondition the system. Preconditioning techniques which have a large degree of parallelism, such as multicolor SSOR, often have a slower rate of convergence than their sequential counterparts. Finally, a number of other computational kernels such as inner products could ruin any gains gained from parallel speed-ups, and this is especially true on workstation clusters where start-up times may be high. In this paper we discuss these issues and report on our experience with PSPARSLIB, an on-going project for building a library of parallel iterative sparse matrix solvers.
Unified inference for sparse and dense longitudinal models.
Kim, Seonjin; Zhao, Zhibiao
2013-03-01
In longitudinal data analysis, statistical inference for sparse data and dense data could be substantially different. For kernel smoothing estimate of the mean function, the convergence rates and limiting variance functions are different under the two scenarios. The latter phenomenon poses challenges for statistical inference as a subjective choice between the sparse and dense cases may lead to wrong conclusions. We develop self-normalization based methods that can adapt to the sparse and dense cases in a unified framework. Simulations show that the proposed methods outperform some existing methods. PMID:24966413
SPARSKIT: A basic tool kit for sparse matrix computations
NASA Technical Reports Server (NTRS)
Saad, Youcef
1990-01-01
Presented here are the main features of a tool package for manipulating and working with sparse matrices. One of the goals of the package is to provide basic tools to facilitate the exchange of software and data between researchers in sparse matrix computations. The starting point is the Harwell/Boeing collection of matrices for which the authors provide a number of tools. Among other things, the package provides programs for converting data structures, printing simple statistics on a matrix, plotting a matrix profile, and performing linear algebra operations with sparse matrices.
Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays
NASA Technical Reports Server (NTRS)
Kwon, Daniel W.; Miller, David W.; Sedwick, Raymond J.
2004-01-01
Traditional methods of actuating spacecraft in sparse aperture arrays use propellant as a reaction mass. For formation flying systems, propellant becomes a critical consumable which can be quickly exhausted while maintaining relative orientation. Additional problems posed by propellant include optical contamination, plume impingement, thermal emission, and vibration excitation. For these missions where control of relative degrees of freedom is important, we consider using a system of electromagnets, in concert with reaction wheels, to replace the consumables. Electromagnetic Formation Flight sparse apertures, powered by solar energy, are designed differently from traditional propulsion systems, which are based on V. This paper investigates the design of sparse apertures both inside and outside the Earth's gravity field.
A new scheduling algorithm for parallel sparse LU factorization with static pivoting
Grigori, Laura; Li, Xiaoye S.
2002-08-20
In this paper we present a static scheduling algorithm for parallel sparse LU factorization with static pivoting. The algorithm is divided into mapping and scheduling phases, using the symmetric pruned graphs of L' and U to represent dependencies. The scheduling algorithm is designed for driving the parallel execution of the factorization on a distributed-memory architecture. Experimental results and comparisons with SuperLU{_}DIST are reported after applying this algorithm on real world application matrices on an IBM SP RS/6000 distributed memory machine.
Design and evaluation of sparse quantization index modulation watermarking schemes
NASA Astrophysics Data System (ADS)
Cornelis, Bruno; Barbarien, Joeri; Dooms, Ann; Munteanu, Adrian; Cornelis, Jan; Schelkens, Peter
2008-08-01
In the past decade the use of digital data has increased significantly. The advantages of digital data are, amongst others, easy editing, fast, cheap and cross-platform distribution and compact storage. The most crucial disadvantages are the unauthorized copying and copyright issues, by which authors and license holders can suffer considerable financial losses. Many inexpensive methods are readily available for editing digital data and, unlike analog information, the reproduction in the digital case is simple and robust. Hence, there is great interest in developing technology that helps to protect the integrity of a digital work and the copyrights of its owners. Watermarking, which is the embedding of a signal (known as the watermark) into the original digital data, is one method that has been proposed for the protection of digital media elements such as audio, video and images. In this article, we examine watermarking schemes for still images, based on selective quantization of the coefficients of a wavelet transformed image, i.e. sparse quantization-index modulation (QIM) watermarking. Different grouping schemes for the wavelet coefficients are evaluated and experimentally verified for robustness against several attacks. Wavelet tree-based grouping schemes yield a slightly improved performance over block-based grouping schemes. Additionally, the impact of the deployment of error correction codes on the most promising configurations is examined. The utilization of BCH-codes (Bose, Ray-Chaudhuri, Hocquenghem) results in an improved robustness as long as the capacity of the error codes is not exceeded (cliff-effect).
Mixed noise removal by weighted encoding with sparse nonlocal regularization.
Jiang, Jielin; Zhang, Lei; Yang, Jian
2014-06-01
Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of IN pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet effective method, namely weighted encoding with sparse nonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality. PMID:24760906
Sparse Contextual Activation for Efficient Visual Re-Ranking.
Bai, Song; Bai, Xiang
2016-03-01
In this paper, we propose an extremely efficient algorithm for visual re-ranking. By considering the original pairwise distance in the contextual space, we develop a feature vector called sparse contextual activation (SCA) that encodes the local distribution of an image. Hence, re-ranking task can be simply accomplished by vector comparison under the generalized Jaccard metric, which has its theoretical meaning in the fuzzy set theory. In order to improve the time efficiency of re-ranking procedure, inverted index is successfully introduced to speed up the computation of generalized Jaccard metric. As a result, the average time cost of re-ranking for a certain query can be controlled within 1 ms. Furthermore, inspired by query expansion, we also develop an additional method called local consistency enhancement on the proposed SCA to improve the retrieval performance in an unsupervised manner. On the other hand, the retrieval performance using a single feature may not be satisfactory enough, which inspires us to fuse multiple complementary features for accurate retrieval. Based on SCA, a robust feature fusion algorithm is exploited that also preserves the characteristic of high time efficiency. We assess our proposed method in various visual re-ranking tasks. Experimental results on Princeton shape benchmark (3D object), WM-SRHEC07 (3D competition), YAEL data set B (face), MPEG-7 data set (shape), and Ukbench data set (image) manifest the effectiveness and efficiency of SCA. PMID:26742133
A Bayesian semiparametric model for bivariate sparse longitudinal data.
Das, Kiranmoy; Li, Runze; Sengupta, Subhajit; Wu, Rongling
2013-09-30
Mixed-effects models have recently become popular for analyzing sparse longitudinal data that arise naturally in biological, agricultural and biomedical studies. Traditional approaches assume independent residuals over time and explain the longitudinal dependence by random effects. However, when bivariate or multivariate traits are measured longitudinally, this fundamental assumption is likely to be violated because of intertrait dependence over time. We provide a more general framework where the dependence of the observations from the same subject over time is not assumed to be explained completely by the random effects of the model. We propose a novel, mixed model-based approach and estimate the error-covariance structure nonparametrically under a generalized linear model framework. We use penalized splines to model the general effect of time, and we consider a Dirichlet process mixture of normal prior for the random-effects distribution. We analyze blood pressure data from the Framingham Heart Study where body mass index, gender and time are treated as covariates. We compare our method with traditional methods including parametric modeling of the random effects and independent residual errors over time. We conduct extensive simulation studies to investigate the practical usefulness of the proposed method. The current approach is very helpful in analyzing bivariate irregular longitudinal traits. PMID:23553747
Method and apparatus for distinguishing actual sparse events from sparse event false alarms
Spalding, Richard E.; Grotbeck, Carter L.
2000-01-01
Remote sensing method and apparatus wherein sparse optical events are distinguished from false events. "Ghost" images of actual optical phenomena are generated using an optical beam splitter and optics configured to direct split beams to a single sensor or segmented sensor. True optical signals are distinguished from false signals or noise based on whether the ghost image is presence or absent. The invention obviates the need for dual sensor systems to effect a false target detection capability, thus significantly reducing system complexity and cost.
Lund, R.D.; Harvey, A.R.
1981-01-01
We have examined the maturation of tectal tissue transplanted from fetal rats to the midbrain of newborns and have characterized the distribution of host retinal and cortical afferents within the transplants. The transplants develop characteristic internal order and connections which distinguish them from either embryonic cortex or retina placed in the same region. Host retinal afferents project to clearly circumscribed regions, where they synapse mainly on small dendrites or dendritic spines, and only rarely on vesicle-containing profiles. The retinorecipient areas contain few stained axons in neurofibrillar preparations and are almost always located at the surface of the transplant. There is very little overlap in the input from the two eyes into a single transplant even though the projections from each eye may lie adjacent to one another. Cortical afferents spread more broadly in the transplants, but are largely absent from areas of optic termination and from other more deeply located regions with sparse fiber staining properties. The observations suggest that when placed close to its normal location, tectal tissue can develop a number of features characteristic of normal superior colliculus. Appreciation of the internal order of the transplants makes it possible to investigate the cortical and retinal afferent pathways using physiological techniques.
Cortical network architecture for context processing in primate brain
Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka
2015-01-01
Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition. DOI: http://dx.doi.org/10.7554/eLife.06121.001 PMID:26416139
Finding One Community in a Sparse Graph
NASA Astrophysics Data System (ADS)
Montanari, Andrea
2015-10-01
We consider a random sparse graph with bounded average degree, in which a subset of vertices has higher connectivity than the background. In particular, the average degree inside this subset of vertices is larger than outside (but still bounded). Given a realization of such graph, we aim at identifying the hidden subset of vertices. This can be regarded as a model for the problem of finding a tightly knitted community in a social network, or a cluster in a relational dataset. In this paper we present two sets of contributions: ( i) We use the cavity method from spin glass theory to derive an exact phase diagram for the reconstruction problem. In particular, as the difference in edge probability increases, the problem undergoes two phase transitions, a static phase transition and a dynamic one. ( ii) We establish rigorous bounds on the dynamic phase transition and prove that, above a certain threshold, a local algorithm (belief propagation) correctly identify most of the hidden set. Below the same threshold no local algorithm can achieve this goal. However, in this regime the subset can be identified by exhaustive search. For small hidden sets and large average degree, the phase transition for local algorithms takes an intriguingly simple form. Local algorithms succeed with high probability for deg _in - deg _out > √{deg _out/e} and fail for deg _in - deg _out < √{deg _out/e} (with deg _in, deg _out the average degrees inside and outside the community). We argue that spectral algorithms are also ineffective in the latter regime. It is an open problem whether any polynomial time algorithms might succeed for deg _in - deg _out < √{deg _out/e}.
Detecting novel genes with sparse arrays
Haiminen, Niina; Smit, Bart; Rautio, Jari; Vitikainen, Marika; Wiebe, Marilyn; Martinez, Diego; Chee, Christine; Kunkel, Joe; Sanchez, Charles; Nelson, Mary Anne; Pakula, Tiina; Saloheimo, Markku; Penttilä, Merja; Kivioja, Teemu
2014-01-01
Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently find genes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these methods are expensive and thus are not easily applicable, e.g., to finding genes that are expressed only in very specific conditions. We demonstrate a method for finding novel genes with sparse arrays, applying it on the 33.9 Mb genome of the filamentous fungus Trichoderma reesei. Our computational method does not require normalisations between arrays and it takes into account the multiple-testing problem typical for analysis of microarray data. In contrast to tiling arrays, that use overlapping probes, only one 25mer microarray oligonucleotide probe was used for every 100 b. Thus, only relatively little space on a microarray slide was required to cover the intergenic regions of a genome. The analysis was done as a by-product of a conventional microarray experiment with no additional costs. We found at least 23 good candidates for novel transcripts that could code for proteins and all of which were expressed at high levels. Candidate genes were found to neighbour ire1 and cre1 and many other regulatory genes. Our simple, low-cost method can easily be applied to finding novel species-specific genes without prior knowledge of their sequence properties. PMID:20691772
Sprecher, Christoph M; Schmidutz, Florian; Helfen, Tobias; Richards, R Geoff; Blauth, Michael; Milz, Stefan
2015-12-01
Osteoporosis is a systemic disorder predominantly affecting postmenopausal women but also men at an advanced age. Both genders may suffer from low-energy fractures of, for example, the proximal humerus when reduction of the bone stock or/and quality has occurred.The aim of the current study was to compare the amount of bone in typical fracture zones of the proximal humerus in osteoporotic and non-osteoporotic individuals.The amount of bone in the proximal humerus was determined histomorphometrically in frontal plane sections. The donor bones were allocated to normal and osteoporotic groups using the T-score from distal radius DXA measurements of the same extremities. The T-score evaluation was done according to WHO criteria. Regional thickness of the subchondral plate and the metaphyseal cortical bone were measured using interactive image analysis.At all measured locations the amount of cancellous bone was significantly lower in individuals from the osteoporotic group compared to the non-osteoporotic one. The osteoporotic group showed more significant differences between regions of the same bone than the non-osteoporotic group. In both groups the subchondral cancellous bone and the subchondral plate were least affected by bone loss. In contrast, the medial metaphyseal region in the osteoporotic group exhibited higher bone loss in comparison to the lateral side.This observation may explain prevailing fracture patterns, which frequently involve compression fractures and certainly has an influence on the stability of implants placed in this medial region. It should be considered when planning the anchoring of osteosynthesis materials in osteoporotic patients with fractures of the proximal humerus. PMID:26705200
Ensemble polarimetric SAR image classification based on contextual sparse representation
NASA Astrophysics Data System (ADS)
Zhang, Lamei; Wang, Xiao; Zou, Bin; Qiao, Zhijun
2016-05-01
Polarimetric SAR image interpretation has become one of the most interesting topics, in which the construction of the reasonable and effective technique of image classification is of key importance. Sparse representation represents the data using the most succinct sparse atoms of the over-complete dictionary and the advantages of sparse representation also have been confirmed in the field of PolSAR classification. However, it is not perfect, like the ordinary classifier, at different aspects. So ensemble learning is introduced to improve the issue, which makes a plurality of different learners training and obtained the integrated results by combining the individual learner to get more accurate and ideal learning results. Therefore, this paper presents a polarimetric SAR image classification method based on the ensemble learning of sparse representation to achieve the optimal classification.
Image inpainting based on sparse representations with a perceptual metric
NASA Astrophysics Data System (ADS)
Ogawa, Takahiro; Haseyama, Miki
2013-12-01
This paper presents an image inpainting method based on sparse representations optimized with respect to a perceptual metric. In the proposed method, the structural similarity (SSIM) index is utilized as a criterion to optimize the representation performance of image data. Specifically, the proposed method enables the formulation of two important procedures in the sparse representation problem, 'estimation of sparse representation coefficients' and 'update of the dictionary', based on the SSIM index. Then, using the generated dictionary, approximation of target patches including missing areas via the SSIM-based sparse representation becomes feasible. Consequently, image inpainting for which procedures are totally derived from the SSIM index is realized. Experimental results show that the proposed method enables successful inpainting of missing areas.
Multiple kernel learning for sparse representation-based classification.
Shrivastava, Ashish; Patel, Vishal M; Chellappa, Rama
2014-07-01
In this paper, we propose a multiple kernel learning (MKL) algorithm that is based on the sparse representation-based classification (SRC) method. Taking advantage of the nonlinear kernel SRC in efficiently representing the nonlinearities in the high-dimensional feature space, we propose an MKL method based on the kernel alignment criteria. Our method uses a two step training method to learn the kernel weights and sparse codes. At each iteration, the sparse codes are updated first while fixing the kernel mixing coefficients, and then the kernel mixing coefficients are updated while fixing the sparse codes. These two steps are repeated until a stopping criteria is met. The effectiveness of the proposed method is demonstrated using several publicly available image classification databases and it is shown that this method can perform significantly better than many competitive image classification algorithms. PMID:24835226
Hall, Amee J; Brown, Trecia A; Grahn, Jessica A; Gati, Joseph S; Nixon, Pam L; Hughes, Sarah M; Menon, Ravi S; Lomber, Stephen G
2014-03-15
When conducting auditory investigations using functional magnetic resonance imaging (fMRI), there are inherent potential confounds that need to be considered. Traditional continuous fMRI acquisition methods produce sounds >90 dB which compete with stimuli or produce neural activation masking evoked activity. Sparse scanning methods insert a period of reduced MRI-related noise, between image acquisitions, in which a stimulus can be presented without competition. In this study, we compared sparse and continuous scanning methods to identify the optimal approach to investigate acoustically evoked cortical, thalamic and midbrain activity in the cat. Using a 7 T magnet, we presented broadband noise, 10 kHz tones, or 0.5 kHz tones in a block design, interleaved with blocks in which no stimulus was presented. Continuous scanning resulted in larger clusters of activation and more peak voxels within the auditory cortex. However, no significant activation was observed within the thalamus. Also, there was no significant difference found, between continuous or sparse scanning, in activations of midbrain structures. Higher magnitude activations were identified in auditory cortex compared to the midbrain using both continuous and sparse scanning. These results indicate that continuous scanning is the preferred method for investigations of auditory cortex in the cat using fMRI. Also, choice of method for future investigations of midbrain activity should be driven by other experimental factors, such as stimulus intensity and task performance during scanning. PMID:24389047
Testing of Error-Correcting Sparse Permutation Channel Codes
NASA Technical Reports Server (NTRS)
Shcheglov, Kirill, V.; Orlov, Sergei S.
2008-01-01
A computer program performs Monte Carlo direct numerical simulations for testing sparse permutation channel codes, which offer strong error-correction capabilities at high code rates and are considered especially suitable for storage of digital data in holographic and volume memories. A word in a code of this type is characterized by, among other things, a sparseness parameter (M) and a fixed number (K) of 1 or "on" bits in a channel block length of N.
Out-of-Core Solutions of Complex Sparse Linear Equations
NASA Technical Reports Server (NTRS)
Yip, E. L.
1982-01-01
ETCLIB is library of subroutines for obtaining out-of-core solutions of complex sparse linear equations. Routines apply to dense and sparse matrices too large to be stored in core. Useful for solving any set of linear equations, but particularly useful in cases where coefficient matrix has no special properties that guarantee convergence with any of interative processes. The only assumption made is that coefficient matrix is not singular.
Sparse source approaches to radio tomography of asteroid
NASA Astrophysics Data System (ADS)
Pursiainen, Sampsa
Currently, there is a growing interest towards non-invasive imaging of asteroids to support future planetary research and extra-terrestrial mining activities. This presentation concentrates on our recent developments regarding radio tomography in which the signal velocity (refractive index) distribution inside an asteroid is to be recovered from radio frequency data gathered by an orbiter. The tomography approach in question is closely related to that of the CONSERT experiment aiming at recovery of a comet nucleus structure as a part of the ROSETTA mission. The focus will be on a sparse distribution of signal sources that can be necessary in the challenging in situ environment and within tight payload limits. The general goal in our recent research has been to approximate the minimal number of source positions needed for robust localization of anomalies caused, for example, by an internal void. Characteristic to the localization problem are the large relative changes in signal speed caused by the high refractive index of typical asteroid minerals (e.g. basalt), meaning that a signal path can include strong refractions and reflections. The inversion strategy applied combines a hierarchical Bayesian inverse model and the iterative alternating sequential (IAS) posterior exploration algorithm. Methods relying on ray tracing and finite-difference time-domain (FDTD) forward simulation have been utilized in forward (data) simulation. Both simulated and real experimental data have been utilized. Special interest has been paid to robustness of the inverse results regarding changes of the prior model and source positioning. The results have been encouraging: strongly refractive anomalies can be detected already with two sources independently of their positioning, and the robustness has been observed to increase rapidly along with the number of sources.
Inhibitory Circuits in Cortical Layer 5
Naka, Alexander; Adesnik, Hillel
2016-01-01
Inhibitory neurons play a fundamental role in cortical computation and behavior. Recent technological advances, such as two photon imaging, targeted in vivo recording, and molecular profiling, have improved our understanding of the function and diversity of cortical interneurons, but for technical reasons most work has been directed towards inhibitory neurons in the superficial cortical layers. Here we review current knowledge specifically on layer 5 (L5) inhibitory microcircuits, which play a critical role in controlling cortical output. We focus on recent work from the well-studied rodent barrel cortex, but also draw on evidence from studies in primary visual cortex and other cortical areas. The diversity of both deep inhibitory neurons and their pyramidal cell targets make this a challenging but essential area of study in cortical computation and sensory processing. PMID:27199675
Circadian regulation of human cortical excitability.
Ly, Julien Q M; Gaggioni, Giulia; Chellappa, Sarah L; Papachilleos, Soterios; Brzozowski, Alexandre; Borsu, Chloé; Rosanova, Mario; Sarasso, Simone; Middleton, Benita; Luxen, André; Archer, Simon N; Phillips, Christophe; Dijk, Derk-Jan; Maquet, Pierre; Massimini, Marcello; Vandewalle, Gilles
2016-01-01
Prolonged wakefulness alters cortical excitability, which is essential for proper brain function and cognition. However, besides prior wakefulness, brain function and cognition are also affected by circadian rhythmicity. Whether the regulation of cognition involves a circadian impact on cortical excitability is unknown. Here, we assessed cortical excitability from scalp electroencephalography (EEG) responses to transcranial magnetic stimulation in 22 participants during 29 h of wakefulness under constant conditions. Data reveal robust circadian dynamics of cortical excitability that are strongest in those individuals with highest endocrine markers of circadian amplitude. In addition, the time course of cortical excitability correlates with changes in EEG synchronization and cognitive performance. These results demonstrate that the crucial factor for cortical excitability, and basic brain function in general, is the balance between circadian rhythmicity and sleep need, rather than sleep homoeostasis alone. These findings have implications for clinical applications such as non-invasive brain stimulation in neurorehabilitation. PMID:27339884
Circadian regulation of human cortical excitability
Ly, Julien Q. M.; Gaggioni, Giulia; Chellappa, Sarah L.; Papachilleos, Soterios; Brzozowski, Alexandre; Borsu, Chloé; Rosanova, Mario; Sarasso, Simone; Middleton, Benita; Luxen, André; Archer, Simon N.; Phillips, Christophe; Dijk, Derk-Jan; Maquet, Pierre; Massimini, Marcello; Vandewalle, Gilles
2016-01-01
Prolonged wakefulness alters cortical excitability, which is essential for proper brain function and cognition. However, besides prior wakefulness, brain function and cognition are also affected by circadian rhythmicity. Whether the regulation of cognition involves a circadian impact on cortical excitability is unknown. Here, we assessed cortical excitability from scalp electroencephalography (EEG) responses to transcranial magnetic stimulation in 22 participants during 29 h of wakefulness under constant conditions. Data reveal robust circadian dynamics of cortical excitability that are strongest in those individuals with highest endocrine markers of circadian amplitude. In addition, the time course of cortical excitability correlates with changes in EEG synchronization and cognitive performance. These results demonstrate that the crucial factor for cortical excitability, and basic brain function in general, is the balance between circadian rhythmicity and sleep need, rather than sleep homoeostasis alone. These findings have implications for clinical applications such as non-invasive brain stimulation in neurorehabilitation. PMID:27339884
Hamilton-Jacobi skeleton on cortical surfaces.
Shi, Y; Thompson, P M; Dinov, I; Toga, A W
2008-05-01
In this paper, we propose a new method to construct graphical representations of cortical folding patterns by computing skeletons on triangulated cortical surfaces. In our approach, a cortical surface is first partitioned into sulcal and gyral regions via the solution of a variational problem using graph cuts, which can guarantee global optimality. After that, we extend the method of Hamilton-Jacobi skeleton [1] to subsets of triangulated surfaces, together with a geometrically intuitive pruning process that can trade off between skeleton complexity and the completeness of representing folding patterns. Compared with previous work that uses skeletons of 3-D volumes to represent sulcal patterns, the skeletons on cortical surfaces can be easily decomposed into branches and provide a simpler way to construct graphical representations of cortical morphometry. In our experiments, we demonstrate our method on two different cortical surface models, its ability of capturing major sulcal patterns and its application to compute skeletons of gyral regions. PMID:18450539
Inhibitory Circuits in Cortical Layer 5.
Naka, Alexander; Adesnik, Hillel
2016-01-01
Inhibitory neurons play a fundamental role in cortical computation and behavior. Recent technological advances, such as two photon imaging, targeted in vivo recording, and molecular profiling, have improved our understanding of the function and diversity of cortical interneurons, but for technical reasons most work has been directed towards inhibitory neurons in the superficial cortical layers. Here we review current knowledge specifically on layer 5 (L5) inhibitory microcircuits, which play a critical role in controlling cortical output. We focus on recent work from the well-studied rodent barrel cortex, but also draw on evidence from studies in primary visual cortex and other cortical areas. The diversity of both deep inhibitory neurons and their pyramidal cell targets make this a challenging but essential area of study in cortical computation and sensory processing. PMID:27199675
Menary, Kyle; Collins, Paul F.; Porter, James N.; Muetzel, Ryan; Olson, Elizabeth A.; Kumar, Vipin; Steinbach, Michael; Lim, Kelvin O.; Luciana, Monica
2013-01-01
Neuroimaging research indicates that human intellectual ability is related to brain structure including the thickness of the cerebral cortex. Most studies indicate that general intelligence is positively associated with cortical thickness in areas of association cortex distributed throughout both brain hemispheres. In this study, we performed a cortical thickness mapping analysis on data from 182 healthy typically developing males and females ages 9 to 24 years to identify correlates of general intelligence (g) scores. To determine if these correlates also mediate associations of specific cognitive abilities with cortical thickness, we regressed specific cognitive test scores on g scores and analyzed the residuals with respect to cortical thickness. The effect of age on the association between cortical thickness and intelligence was examined. We found a widely distributed pattern of positive associations between cortical thickness and g scores, as derived from the first unrotated principal factor of a factor analysis of Wechsler Abbreviated Scale of Intelligence (WASI) subtest scores. After WASI specific cognitive subtest scores were regressed on g factor scores, the residual score variances did not correlate significantly with cortical thickness in the full sample with age covaried. When participants were grouped at the age median, significant positive associations of cortical thickness were obtained in the older group for g-residualized scores on Block Design (a measure of visual-motor integrative processing) while significant negative associations of cortical thickness were observed in the younger group for g-residualized Vocabulary scores. These results regarding correlates of general intelligence are concordant with the existing literature, while the findings from younger versus older subgroups have implications for future research on brain structural correlates of specific cognitive abilities, as well as the cognitive domain specificity of behavioral
Visual Tracking Based on Extreme Learning Machine and Sparse Representation
Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen
2015-01-01
The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. PMID:26506359
Vector sparse representation of color image using quaternion matrix analysis.
Xu, Yi; Yu, Licheng; Xu, Hongteng; Zhang, Hao; Nguyen, Truong
2015-04-01
Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain. PMID:25643407
A sparse embedding and least variance encoding approach to hashing.
Zhu, Xiaofeng; Zhang, Lei; Huang, Zi
2014-09-01
Hashing is becoming increasingly important in large-scale image retrieval for fast approximate similarity search and efficient data storage. Many popular hashing methods aim to preserve the kNN graph of high dimensional data points in the low dimensional manifold space, which is, however, difficult to achieve when the number of samples is big. In this paper, we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary. To this end, we partition the sample space into clusters via a linear spectral clustering method, and then represent each sample as a sparse vector of normalized probabilities that it falls into its several closest clusters. This actually embeds each sample sparsely in the sample space. The sparse embedding vector is employed as the feature of each sample for hashing. We then propose a least variance encoding model, which learns a dictionary to encode the sparse embedding feature, and consequently binarize the coding coefficients as the hash codes. The dictionary and the binarization threshold are jointly optimized in our model. Experimental results on benchmark data sets demonstrated the effectiveness of the proposed approach in comparison with state-of-the-art methods. PMID:24968174
Visual tracking based on extreme learning machine and sparse representation.
Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen
2015-01-01
The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. PMID:26506359
Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery.
Feng, Yunlong; Lv, Shao-Gao; Hang, Hanyuan; Suykens, Johan A K
2016-03-01
Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic net regularization (Zou & Hastie, 2005 ). The kernel in KENReg is not required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens ( 2014 ) showed that KENReg has some nice properties including stability, sparseness, and generalization. In this letter, we continue our study on KENReg by conducting a refined learning theory analysis. This letter makes the following three main contributions. First, we present refined error analysis on the generalization performance of KENReg. The main difficulty of analyzing the generalization error of KENReg lies in characterizing the population version of its empirical target function. We overcome this by introducing a weighted Banach space associated with the elastic net regularization. We are then able to conduct elaborated learning theory analysis and obtain fast convergence rates under proper complexity and regularity assumptions. Second, we study the sparse recovery problem in KENReg with fixed design and show that the kernelization may improve the sparse recovery ability compared to the classical elastic net regularization. Finally, we discuss the interplay among different properties of KENReg that include sparseness, stability, and generalization. We show that the stability of KENReg leads to generalization, and its sparseness confidence can be derived from generalization. Moreover, KENReg is stable and can be simultaneously sparse, which makes it attractive theoretically and practically. PMID:26735744
Cortical Tremor (CT) with coincident orthostatic movements.
Termsarasab, Pichet; Frucht, Steven J
2015-01-01
Cortical tremor (CT) is a form of cortical reflex myoclonus that can mimic essential tremor (ET). Clinical features that are helpful in distinguishing CT from ET are the irregular and jerky appearance of the movements. We report two patients with CT with coexisting orthostatic movements, either orthostatic tremor (OT) or myoclonus, who experienced functional improvement in both cortical myoclonus and orthostatic movements when treated with levetiracetam. PMID:26788343
Communication and wiring in the cortical connectome
Budd, Julian M. L.; Kisvárday, Zoltán F.
2012-01-01
In cerebral cortex, the huge mass of axonal wiring that carries information between near and distant neurons is thought to provide the neural substrate for cognitive and perceptual function. The goal of mapping the connectivity of cortical axons at different spatial scales, the cortical connectome, is to trace the paths of information flow in cerebral cortex. To appreciate the relationship between the connectome and cortical function, we need to discover the nature and purpose of the wiring principles underlying cortical connectivity. A popular explanation has been that axonal length is strictly minimized both within and between cortical regions. In contrast, we have hypothesized the existence of a multi-scale principle of cortical wiring where to optimize communication there is a trade-off between spatial (construction) and temporal (routing) costs. Here, using recent evidence concerning cortical spatial networks we critically evaluate this hypothesis at neuron, local circuit, and pathway scales. We report three main conclusions. First, the axonal and dendritic arbor morphology of single neocortical neurons may be governed by a similar wiring principle, one that balances the conservation of cellular material and conduction delay. Second, the same principle may be observed for fiber tracts connecting cortical regions. Third, the absence of sufficient local circuit data currently prohibits any meaningful assessment of the hypothesis at this scale of cortical organization. To avoid neglecting neuron and microcircuit levels of cortical organization, the connectome framework should incorporate more morphological description. In addition, structural analyses of temporal cost for cortical circuits should take account of both axonal conduction and neuronal integration delays, which appear mostly of the same order of magnitude. We conclude the hypothesized trade-off between spatial and temporal costs may potentially offer a powerful explanation for cortical wiring patterns
Photophobia and cortical visual impairment.
Jan, J E; Groenveld, M; Anderson, D P
1993-06-01
Photophobia, or intolerance of light, is not completely understood as a symptom. It has been divided into ocular and central types. This study shows that persistent, usually mild, photophobia occurs in about one-third of children with cortical visual impairment (CVI). When the CVI is congenital the photophobia is present from birth, and when it is acquired the sensitivity to light appears immediately after the brain insult. The intensity of photophobia tends to diminish with time and occasionally it may even disappear. The pathophysiology is unclear, as in all other neurological disorders associated with photophobia. PMID:8504889
Cortical auditory disorders: clinical and psychoacoustic features.
Mendez, M F; Geehan, G R
1988-01-01
The symptoms of two patients with bilateral cortical auditory lesions evolved from cortical deafness to other auditory syndromes: generalised auditory agnosia, amusia and/or pure word deafness, and a residual impairment of temporal sequencing. On investigation, both had dysacusis, absent middle latency evoked responses, acoustic errors in sound recognition and matching, inconsistent auditory behaviours, and similarly disturbed psychoacoustic discrimination tasks. These findings indicate that the different clinical syndromes caused by cortical auditory lesions form a spectrum of related auditory processing disorders. Differences between syndromes may depend on the degree of involvement of a primary cortical processing system, the more diffuse accessory system, and possibly the efferent auditory system. Images PMID:2450968
Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery
NASA Astrophysics Data System (ADS)
Zhong, Yanfei; Wang, Xinyu; Zhao, Lin; Feng, Ruyi; Zhang, Liangpei; Xu, Yanyan
2016-09-01
Recently, many blind source separation (BSS)-based techniques have been applied to hyperspectral unmixing. In this paper, a new blind spectral unmixing method based on sparse component analysis (BSUSCA) is proposed to solve the problem of highly mixed data. The BSUSCA algorithm consists of an alternative scheme based on two-block alternating optimization, by which we can simultaneously obtain the endmember signatures and their corresponding fractional abundances. According to the spatial distribution of the endmembers, the sparse properties of the fractional abundances are considered in the proposed algorithm. A sparse component analysis (SCA)-based mixing matrix estimation method is applied to update the endmember signatures, and the abundance estimation problem is solved by the alternating direction method of multipliers (ADMM). SCA is utilized for the unmixing due to its various advantages, including the unique solution and robust modeling assumption. The robustness of the proposed algorithm is verified through simulated experimental study. The experimental results using both simulated data and real hyperspectral remote sensing images confirm the high efficiency and precision of the proposed algorithm.
NASA Astrophysics Data System (ADS)
Shao, Xiaopeng; Li, Huijuan; Wu, Tengfei; Dai, Weijia; Bi, Xiangli
2015-05-01
The incident light will be scattered away due to the inhomogeneity of the refractive index in many materials which will greatly reduce the imaging depth and degrade the imaging quality. Many exciting methods have been presented in recent years for solving this problem and realizing imaging through a highly scattering medium, such as the wavefront modulation technique and reconstruction technique. The imaging method based on compressed sensing (CS) theory can decrease the computational complexity because it doesn't require the whole speckle pattern to realize reconstruction. One of the key premises of this method is that the object is sparse or can be sparse representation. However, choosing a proper projection matrix is very important to the imaging quality. In this paper, we analyzed that the transmission matrix (TM) of a scattering medium obeys circular Gaussian distribution, which makes it possible that a scattering medium can be used as the measurement matrix in the CS theory. In order to verify the performance of this method, a whole optical system is simulated. Various projection matrices are introduced to make the object sparse, including the fast Fourier transform (FFT) basis, the discrete cosine transform (DCT) basis and the discrete wavelet transform (DWT) basis, the imaging performances of each of which are compared comprehensively. Simulation results show that for most targets, applying the discrete wavelet transform basis will obtain an image in good quality. This work can be applied to biomedical imaging and used to develop real-time imaging through highly scattering media.
A novel imaging technique for structural health monitoring using sparse and compact arrays
NASA Astrophysics Data System (ADS)
Masson, Patrice; Quaegebeur, Nicolas; Demers, Dominique L.
2011-04-01
In the present paper, a technique called Excitelet is presented for the imaging of damages in thin-walled structures using the correlation of the measured signals with dispersed versions of the excitation signal. Piezoceramic (PZT) actuators are used to generate burst Lamb waves which interact with defects in metallic structures and the measurement is taken using sparse and compact array configurations of PZT sensors. The sparse sensing configuration consists of individual circular PZT elements distributed over the plate while the compact array configuration consists of a linear arrangement of sensors micro-machined on a single piece of bulk PZT wafer. This approach is presented as an extension of the classical imaging techniques and takes advantage of the chirplet-based matching pursuit algorithm. The approach is investigated experimentally on a 1.54 mm thick aluminum plate and comparison with existing Embedded Ultrasonic Structural Radar (EUSR) algorithm is performed for A0 and S0 modes for two frequency ranges of interest (centered at 150 kHz and 550 kHz). Damages are simulated using stacked magnets at different locations on the plate. Significant improvement of imaging quality is demonstrated with respect to existing imaging techniques based on group velocity and Time-of-Flight (ToF), for both sparse and compact PZT array configurations. Multimodal imaging strategies are presented to improve the imaging results. Moreover, it is shown that the proposed imaging technique provide accurate results in the case of dispersive propagation, while existing imaging techniques are no longer applicable.
NASA Astrophysics Data System (ADS)
Serra, Michael; Guaraldi, Mary; Shea, Thomas B.
2010-06-01
Cortical neuronal activity depends on a balance between excitatory and inhibitory influences. Culturing of neurons on multi-electrode arrays (MEAs) has provided insight into the development and maintenance of neuronal networks. Herein, we seeded MEAs with murine embryonic cortical/hippocampal neurons at different densities (<150 or >1000 cells mm-2) and monitored resultant spontaneous signaling. Sparsely seeded cultures displayed a large number of bipolar, rapid, high-amplitude individual signals with no apparent temporal regularity. By contrast, densely seeded cultures instead displayed clusters of signals at regular intervals. These patterns were observed even within thinner and thicker areas of the same culture. GABAergic neurons (25% of total neurons in our cultures) mediated the differential signal patterns observed above, since addition of the inhibitory antagonist bicuculline to dense cultures and hippocampal slice cultures induced the signal pattern characteristic of sparse cultures. Sparsely seeded cultures likely lacked sufficient inhibitory neurons to modulate excitatory activity. Differential seeding of MEAs can provide a unique model for analyses of pertubation in the interaction between excitatory and inhibitory function during aging and neuropathological conditions where dysregulation of GABAergic neurons is a significant component.
Suh, Jun-Won; Hansen, John H L
2012-02-01
In this study, the problem of sparse enrollment data for in-set versus out-of-set speaker recognition is addressed. The challenge here is that both the training speaker data (5 s) and test material (2~6 s) is of limited test duration. The limited enrollment data result in a sparse acoustic model space for the desired speaker model. The focus of this study is on filling these acoustic holes by harvesting neighbor speaker information to leverage overall system performance. Acoustically similar speakers are selected from a separate available corpus via three different methods for speaker similarity measurement. The selected data from these similar acoustic speakers are exploited to fill the lack of phone coverage caused by the original sparse enrollment data. The proposed speaker modeling process mimics the naturally distributed acoustic space for conversational speech. The Gaussian mixture model (GMM) tagging process allows simulated natural conversation speech to be included for in-set speaker modeling, which maintains the original system requirement of text independent speaker recognition. A human listener evaluation is also performed to compare machine versus human speaker recognition performance, with machine performance of 95% compared to 72.2% accuracy for human in-set/out-of-set performance. Results show that for extreme sparse train/reference audio streams, human speaker recognition is not nearly as reliable as machine based speaker recognition. The proposed acoustic hole filling solution (MRNC) produces an averaging 7.42% relative improvement over a GMM-Cohort UBM baseline and a 19% relative improvement over the Eigenvoice baseline using the FISHER corpus. PMID:22352521
Harquel, Sylvain; Bacle, Thibault; Beynel, Lysianne; Marendaz, Christian; Chauvin, Alan; David, Olivier
2016-07-15
Brain dynamics at rest depend on the large-scale interactions between oscillating cortical microcircuits arranged into macrocolumns. Cytoarchitectonic studies have shown that the structure of those microcircuits differs between cortical regions, but very little is known about interregional differences of their intrinsic dynamics at a macro-scale in human. We developed here a new method aiming at mapping the dynamical properties of cortical microcircuits non-invasively using the coupling between robotized transcranial magnetic stimulation and electroencephalography. We recorded the responses evoked by the stimulation of 18 cortical targets largely covering the accessible neocortex in 22 healthy volunteers. Specific data processing methods were developed to map the local source activity of each cortical target, which showed inter-regional differences with very good interhemispheric reproducibility. Functional signatures of cortical microcircuits were further studied using spatio-temporal decomposition of local source activities in order to highlight principal brain modes. The identified brain modes revealed that cortical areas with similar intrinsic dynamical properties could be distributed either locally or not, with a spatial signature that was somewhat reminiscent of resting state networks. Our results provide the proof of concept of "functional cytoarchitectonics", that would guide the parcellation of the human cortex using not only its cytoarchitecture but also its intrinsic responses to local perturbations. This opens new avenues for brain modelling and physiopathology readouts. PMID:27153976
Gyrification from constrained cortical expansion
Tallinen, Tuomas; Chung, Jun Young; Biggins, John S.; Mahadevan, L.
2014-01-01
The exterior of the mammalian brain—the cerebral cortex—has a conserved layered structure whose thickness varies little across species. However, selection pressures over evolutionary time scales have led to cortices that have a large surface area to volume ratio in some organisms, with the result that the brain is strongly convoluted into sulci and gyri. Here we show that the gyrification can arise as a nonlinear consequence of a simple mechanical instability driven by tangential expansion of the gray matter constrained by the white matter. A physical mimic of the process using a layered swelling gel captures the essence of the mechanism, and numerical simulations of the brain treated as a soft solid lead to the formation of cusped sulci and smooth gyri similar to those in the brain. The resulting gyrification patterns are a function of relative cortical expansion and relative thickness (compared with brain size), and are consistent with observations of a wide range of brains, ranging from smooth to highly convoluted. Furthermore, this dependence on two simple geometric parameters that characterize the brain also allows us to qualitatively explain how variations in these parameters lead to anatomical anomalies in such situations as polymicrogyria, pachygyria, and lissencephalia. PMID:25136099
Sleep and olfactory cortical plasticity
Barnes, Dylan C.; Wilson, Donald A.
2014-01-01
In many systems, sleep plays a vital role in memory consolidation and synaptic homeostasis. These processes together help store information of biological significance and reset synaptic circuits to facilitate acquisition of information in the future. In this review, we describe recent evidence of sleep-dependent changes in olfactory system structure and function which contribute to odor memory and perception. During slow-wave sleep, the piriform cortex becomes hypo-responsive to odor stimulation and instead displays sharp-wave activity similar to that observed within the hippocampal formation. Furthermore, the functional connectivity between the piriform cortex and other cortical and limbic regions is enhanced during slow-wave sleep compared to waking. This combination of conditions may allow odor memory consolidation to occur during a state of reduced external interference and facilitate association of odor memories with stored hedonic and contextual cues. Evidence consistent with sleep-dependent odor replay within olfactory cortical circuits is presented. These data suggest that both the strength and precision of odor memories is sleep-dependent. The work further emphasizes the critical role of synaptic plasticity and memory in not only odor memory but also basic odor perception. The work also suggests a possible link between sleep disturbances that are frequently co-morbid with a wide range of pathologies including Alzheimer’s disease, schizophrenia and depression and the known olfactory impairments associated with those disorders. PMID:24795585
A research of 3D gravity inversion based on the recovery of sparse underdetermined linear equations
NASA Astrophysics Data System (ADS)
Zhaohai, M.
2014-12-01
Because of the properties of gravity data, it is made difficult to solve the problem of multiple solutions. There are two main types of 3D gravity inversion methods：One of two methods is based on the improvement of the instability of the sensitive matrix, solving the problem of multiple solutions and instability in 3D gravity inversion. Another is to join weight function into the 3D gravity inversion iteration. Through constant iteration, it can renewal density values and weight function to achieve the purpose to solve the multiple solutions and instability of the 3D gravity data inversion. Thanks to the sparse nature of the solutions of 3D gravity data inversions, we can transform it into a sparse equation. Then, through solving the sparse equations, we can get perfect 3D gravity inversion results. The main principle is based on zero norm of sparse matrix solution of the equation. Zero norm is mainly to solve the nonzero solution of the sparse matrix. However, the method of this article adopted is same as the principle of zero norm. But the method is the opposite of zero norm to obtain zero value solution. Through the form of a Gaussian fitting solution of the zero norm, we can find the solution by using regularization principle. Moreover, this method has been proved that it had a certain resistance to random noise in the mathematics, and it was more suitable than zero norm for the solution of the geophysical data. 3D gravity which is adopted in this article can well identify abnormal body density distribution characteristics, and it can also recognize the space position of abnormal distribution very well. We can take advantage of the density of the upper and lower limit penalty function to make each rectangular residual density within a reasonable range. Finally, this 3D gravity inversion is applied to a variety of combination model test, such as a single straight three-dimensional model, the adjacent straight three-dimensional model and Y three
Garcia-Marin, Virginia; Ahmed, Tunazzina H.; Afzal, Yasmeen C.; Hawken, Michael J.
2014-01-01
The majority of thalamic terminals in V1 arise from lateral geniculate nucleus (LGN) afferents. Thalamic afferent terminals are preferentially labeled by an isoform of the vesicular glutamate transporter, VGluT2. The goal of our study was to determine the distribution of VGluT2-ir puncta in macaque and human visual cortex. First, we investigated the distribution of VGluT2-ir puncta in all layers of macaque monkey primary visual cortex (V1), and found a very close correspondence between the known distribution of LGN afferents from previous studies and the distribution of VGluT2-immunoreactive (-ir) puncta. There was also a close correspondence between cytochrome oxidase density and VGluT2-ir puncta distribution. After validating the correspondence in macaque, we made a comparative study in human V1. In many aspects, the distribution of VGluT2-ir puncta in human was qualitatively similar to that of the macaque: high densities in layer 4C, patches of VGluT2-ir puncta in the supragranular layer (2/3), lower but clear distribution in layers 1 and 6, and very few puncta in layers 5 and 4B. However, there were also important differences between macaques and humans. In layer 4A of human, there was a sparse distribution of VGluT2-ir puncta, whereas in macaque, there was a dense distribution with the characteristic honeycomb organization. The results suggest important changes in the pattern of cortical VGluT2 immunostaining that may be related to evolutionary differences in the cortical organization of LGN afferents between Old World monkeys and humans. PMID:22684983
Cortical idiosyncrasies predict the perception of object size.
Moutsiana, Christina; de Haas, Benjamin; Papageorgiou, Andriani; van Dijk, Jelle A; Balraj, Annika; Greenwood, John A; Schwarzkopf, D Samuel
2016-01-01
Perception is subjective. Even basic judgments, like those of visual object size, vary substantially between observers and also across the visual field within the same observer. The way in which the visual system determines the size of objects remains unclear, however. We hypothesize that object size is inferred from neuronal population activity in V1 and predict that idiosyncrasies in cortical functional architecture should therefore explain individual differences in size judgments. Here we show results from novel behavioural methods and functional magnetic resonance imaging (fMRI) demonstrating that biases in size perception are correlated with the spatial tuning of neuronal populations in healthy volunteers. To explain this relationship, we formulate a population read-out model that directly links the spatial distribution of V1 representations to our perceptual experience of visual size. Taken together, our results suggest that the individual perception of simple stimuli is warped by idiosyncrasies in visual cortical organization. PMID:27357864
Cortical idiosyncrasies predict the perception of object size
Moutsiana, Christina; de Haas, Benjamin; Papageorgiou, Andriani; van Dijk, Jelle A.; Balraj, Annika; Greenwood, John A.; Schwarzkopf, D. Samuel
2016-01-01
Perception is subjective. Even basic judgments, like those of visual object size, vary substantially between observers and also across the visual field within the same observer. The way in which the visual system determines the size of objects remains unclear, however. We hypothesize that object size is inferred from neuronal population activity in V1 and predict that idiosyncrasies in cortical functional architecture should therefore explain individual differences in size judgments. Here we show results from novel behavioural methods and functional magnetic resonance imaging (fMRI) demonstrating that biases in size perception are correlated with the spatial tuning of neuronal populations in healthy volunteers. To explain this relationship, we formulate a population read-out model that directly links the spatial distribution of V1 representations to our perceptual experience of visual size. Taken together, our results suggest that the individual perception of simple stimuli is warped by idiosyncrasies in visual cortical organization. PMID:27357864
NASA Astrophysics Data System (ADS)
Magyar, Andrew
The recent discovery of cells that respond to purely conceptual features of the environment (particular people, landmarks, objects, etc) in the human medial temporal lobe (MTL), has raised many questions about the nature of the neural code in humans. The goal of this dissertation is to develop a novel statistical method based upon maximum likelihood regression which will then be applied to these experiments in order to produce a quantitative description of the coding properties of the human MTL. In general, the method is applicable to any experiments in which a sequence of stimuli are presented to an organism while the binary responses of a large number of cells are recorded in parallel. The central concept underlying the approach is the total probability that a neuron responds to a random stimulus, called the neuronal sparsity. The model then estimates the distribution of response probabilities across the population of cells. Applying the method to single-unit recordings from the human medial temporal lobe, estimates of the sparsity distributions are acquired in four regions: the hippocampus, the entorhinal cortex, the amygdala, and the parahippocampal cortex. The resulting distributions are found to be sparse (large fraction of cells with a low response probability) and highly non-uniform, with a large proportion of ultra-sparse neurons that possess a very low response probability, and a smaller population of cells which respond much more frequently. Rammifications of the results are discussed in relation to the sparse coding hypothesis, and comparisons are made between the statistics of the human medial temporal lobe cells and place cells observed in the rodent hippocampus.
Measuring and comparing brain cortical surface area and other areal quantities
Winkler, Anderson M.; Sabuncu, Mert R.; Yeo, B.T. Thomas; Fischl, Bruce; Greve, Douglas N.; Kochunov, Peter; Nichols, Thomas E.; Blangero, John; Glahn, David C.
2012-01-01
Structural analysis of MRI data on the cortical surface usually focuses on cortical thickness. Cortical surface area, when considered, has been measured only over gross regions or approached indirectly via comparisons with a standard brain. Here we demonstrate that direct measurement and comparison of the surface area of the cerebral cortex at a fine scale is possible using mass conservative interpolation methods. We present a framework for analyses of the cortical surface area, as well as for any other measurement distributed across the cortex that is areal by nature. The method consists of the construction of a mesh representation of the ortex, registration to a common coordinate system and, crucially, interpolation using a pycnophylactic method. Statistical analysis of surface area is done with power-transformed data to address lognormality, and inference is done with permutation methods. We introduce the concept of facewise analysis, discuss its interpretation and potential applications. PMID:22446492
X-ray computed tomography using curvelet sparse regularization
Wieczorek, Matthias Vogel, Jakob; Lasser, Tobias; Frikel, Jürgen; Demaret, Laurent; Eggl, Elena; Pfeiffer, Franz; Kopp, Felix; Noël, Peter B.
2015-04-15
Purpose: Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. Methods: In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Results: Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method’s strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. Conclusions: The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.
Sparse approximation problem: how rapid simulated annealing succeeds and fails
NASA Astrophysics Data System (ADS)
Obuchi, Tomoyuki; Kabashima, Yoshiyuki
2016-03-01
Information processing techniques based on sparseness have been actively studied in several disciplines. Among them, a mathematical framework to approximately express a given dataset by a combination of a small number of basis vectors of an overcomplete basis is termed the sparse approximation. In this paper, we apply simulated annealing, a metaheuristic algorithm for general optimization problems, to sparse approximation in the situation where the given data have a planted sparse representation and noise is present. The result in the noiseless case shows that our simulated annealing works well in a reasonable parameter region: the planted solution is found fairly rapidly. This is true even in the case where a common relaxation of the sparse approximation problem, the G-relaxation, is ineffective. On the other hand, when the dimensionality of the data is close to the number of non-zero components, another metastable state emerges, and our algorithm fails to find the planted solution. This phenomenon is associated with a first-order phase transition. In the case of very strong noise, it is no longer meaningful to search for the planted solution. In this situation, our algorithm determines a solution with close-to-minimum distortion fairly quickly.
Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking
Qu, Shiru
2016-01-01
Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.
Image fusion via nonlocal sparse K-SVD dictionary learning.
Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang
2016-03-01
Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation. PMID:26974648
Cortical cartography and Caret software.
Van Essen, David C
2012-08-15
Caret software is widely used for analyzing and visualizing many types of fMRI data, often in conjunction with experimental data from other modalities. This article places Caret's development in a historical context that spans three decades of brain mapping--from the early days of manually generated flat maps to the nascent field of human connectomics. It also highlights some of Caret's distinctive capabilities. This includes the ease of visualizing data on surfaces and/or volumes and on atlases as well as individual subjects. Caret can display many types of experimental data using various combinations of overlays (e.g., fMRI activation maps, cortical parcellations, areal boundaries), and it has other features that facilitate the analysis and visualization of complex neuroimaging datasets. PMID:22062192
Gyrification from constrained cortical expansion
NASA Astrophysics Data System (ADS)
Tallinen, Tuomas
The convolutions of the human brain are a symbol of its functional complexity. But how does the outer surface of the brain, the layered cortex of neuronal gray matter get its folds? In this talk, we ask to which extent folding of the brain can be explained as a purely mechanical consequence of unpatterned growth of the cortical layer relative to the sublayers. Modeling the growing brain as a soft layered solid leads to elastic instabilities and the formation of cusped sulci and smooth gyri consistent with observations across species in both normal and pathological situations. Furthermore, we apply initial geometries obtained from fetal brain MRI to address the question of how the brain geometry and folding patterns may be coupled via mechanics.
Cortical Cartography and Caret Software
Van Essen, David C.
2011-01-01
Caret software is widely used for analyzing and visualizing many types of fMRI data, often in conjunction with experimental data from other modalities. This article places Caret’s development in a historical context that spans three decades of brain mapping – from the early days of manually generated flat maps to the nascent field of human connectomics. It also highlights some of Caret’s distinctive capabilities. This includes the ease of visualizing data on surfaces and/or volumes and on atlases as well as individual subjects. Caret can display many types of experimental data using various combinations of overlays (e.g., fMRI activation maps, cortical parcellations, areal boundaries), and it has other features that facilitate the analysis and visualization of complex neuroimaging datasets. PMID:22062192
Cortical spreading depression: An enigma
NASA Astrophysics Data System (ADS)
Miura, R. M.; Huang, H.; Wylie, J. J.
2007-08-01
The brain is a complex organ with active components composed largely of neurons, glial cells, and blood vessels. There exists an enormous experimental and theoretical literature on the mechanisms involved in the functioning of the brain, but we still do not have a good understanding of how it works on a gross mechanistic level. In general, the brain maintains a homeostatic state with relatively small ion concentration changes, the major ions being sodium, potassium, and chloride. Calcium ions are present in smaller quantities but still play an important role in many phenomena. Cortical spreading depression (CSD for short) was discovered over 60 years ago by A.A.P. Leão, a Brazilian physiologist doing his doctoral research on epilepsy at Harvard University, “Spreading depression of activity in the cerebral cortex," J. Neurophysiol., 7 (1944), pp. 359-390. Cortical spreading depression is characterized by massive changes in ionic concentrations and slow nonlinear chemical waves, with speeds on the order of mm/min, in the cortex of different brain structures in various experimental animals. In humans, CSD is associated with migraine with aura, where a light scintillation in the visual field propagates, then disappears, and is followed by a sustained headache. To date, CSD remains an enigma, and further detailed experimental and theoretical investigations are needed to develop a comprehensive picture of the diverse mechanisms involved in producing CSD. A number of mechanisms have been hypothesized to be important for CSD wave propagation. In this paper, we briefly describe several characteristics of CSD wave propagation, and examine some of the mechanisms that are believed to be important, including ion diffusion, membrane ionic currents, osmotic effects, spatial buffering, neurotransmitter substances, gap junctions, metabolic pumps, and synaptic connections. Continuum models of CSD, consisting of coupled nonlinear diffusion equations for the ion concentrations, and
Unsupervised fetal cortical surface parcellation
NASA Astrophysics Data System (ADS)
Dahdouh, Sonia; Limperopoulos, Catherine
2016-03-01
At the core of many neuro-imaging studies, atlas-based brain parcellations are used for example to study normal brain evolution across the lifespan. These atlases rely on the assumption that the same anatomical features are present on all subjects to be studied and that these features are stable enough to allow meaningful comparisons between different brain surfaces and structures These methods, however, often fail when applied to fetal MRI data, due to the lack of consistent anatomical features present across gestation. This paper presents a novel surface-based fetal cortical parcellation framework which attempts to circumvent the lack of consistent anatomical features by proposing a brain parcellation scheme that is based solely on learned geometrical features. A mesh signature incorporating both extrinsic and intrinsic geometrical features is proposed and used in a clustering scheme to define a parcellation of the fetal brain. This parcellation is then learned using a Random Forest (RF) based learning approach and then further refined in an alpha-expansion graph-cut scheme. Based on the votes obtained by the RF inference procedure, a probability map is computed and used as a data term in the graph-cut procedure. The smoothness term is defined by learning a transition matrix based on the dihedral angles of the faces. Qualitative and quantitative results on a cohort of both healthy and high-risk fetuses are presented. Both visual and quantitative assessments show good results demonstrating a reliable method for fetal brain data and the possibility of obtaining a parcellation of the fetal cortical surfaces using only geometrical features.
Human interaction recognition through two-phase sparse coding
NASA Astrophysics Data System (ADS)
Zhang, B.; Conci, N.; De Natale, Francesco G. B.
2014-03-01
In this paper, we propose a novel method to recognize two-person interactions through a two-phase sparse coding approach. In the first phase, we adopt the non-negative sparse coding on the spatio-temporal interest points (STIPs) extracted from videos, and then construct the feature vector for each video by sum-pooling and l2-normalization. At the second stage, we apply the label-consistent KSVD (LC-KSVD) algorithm on the video feature vectors to train a new dictionary. The algorithm has been validated on the TV human interaction dataset, and the experimental results show that the classification performance is considerably improved compared with the standard bag-of-words approach and the single layer non-negative sparse coding.
Analyzing Sparse Dictionaries for Online Learning With Kernels
NASA Astrophysics Data System (ADS)
Honeine, Paul
2015-12-01
Many signal processing and machine learning methods share essentially the same linear-in-the-parameter model, with as many parameters as available samples as in kernel-based machines. Sparse approximation is essential in many disciplines, with new challenges emerging in online learning with kernels. To this end, several sparsity measures have been proposed in the literature to quantify sparse dictionaries and constructing relevant ones, the most prolific ones being the distance, the approximation, the coherence and the Babel measures. In this paper, we analyze sparse dictionaries based on these measures. By conducting an eigenvalue analysis, we show that these sparsity measures share many properties, including the linear independence condition and inducing a well-posed optimization problem. Furthermore, we prove that there exists a quasi-isometry between the parameter (i.e., dual) space and the dictionary's induced feature space.
Sparse Partial Equilibrium Tables in Chemically Resolved Reactive Flow
Vitello, P; Fried, L E; Pudliner, B; McAbee, T
2003-07-14
The detonation of an energetic material is the result of a complex interaction between kinetic chemical reactions and hydrodynamics. Unfortunately, little is known concerning the detailed chemical kinetics of detonations in energetic materials. CHEETAH uses rate laws to treat species with the slowest chemical reactions, while assuming other chemical species are in equilibrium. CHEETAH supports a wide range of elements and condensed detonation products and can also be applied to gas detonations. A sparse hash table of equation of state values, called the ''cache'' is used in CHEETAH to enhance the efficiency of kinetic reaction calculations. For large-scale parallel hydrodynamic calculations, CHEETAH uses MPI communication to updates to the cache. We present here details of the sparse caching model used in the CHEETAH. To demonstrate the efficiency of modeling using a sparse cache model we consider detonations in energetic materials.
Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model
NASA Astrophysics Data System (ADS)
Meng, Jia; Zhang, Jianqiu(Michelle); Qi, Yuan(Alan); Chen, Yidong; Huang, Yufei
2010-12-01
The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ([InlineEquation not available: see fulltext.]) status and Estrogen Receptor negative ([InlineEquation not available: see fulltext.]) status, respectively.
PSPIKE: A Parallel Hybrid Sparse Linear System Solver
NASA Astrophysics Data System (ADS)
Manguoglu, Murat; Sameh, Ahmed H.; Schenk, Olaf
The availability of large-scale computing platforms comprised of tens of thousands of multicore processors motivates the need for the next generation of highly scalable sparse linear system solvers. These solvers must optimize parallel performance, processor (serial) performance, as well as memory requirements, while being robust across broad classes of applications and systems. In this paper, we present a new parallel solver that combines the desirable characteristics of direct methods (robustness) and effective iterative solvers (low computational cost), while alleviating their drawbacks (memory requirements, lack of robustness). Our proposed hybrid solver is based on the general sparse solver PARDISO, and the “Spike” family of hybrid solvers. The resulting algorithm, called PSPIKE, is as robust as direct solvers, more reliable than classical preconditioned Krylov subspace methods, and much more scalable than direct sparse solvers. We support our performance and parallel scalability claims using detailed experimental studies and comparison with direct solvers, as well as classical preconditioned Krylov methods.
Automatic landslide and mudflow detection method via multichannel sparse representation
NASA Astrophysics Data System (ADS)
Chao, Chen; Zhou, Jianjun; Hao, Zhuo; Sun, Bo; He, Jun; Ge, Fengxiang
2015-10-01
Landslide and mudflow detection is an important application of aerial images and high resolution remote sensing images, which is crucial for national security and disaster relief. Since the high resolution images are often large in size, it's necessary to develop an efficient algorithm for landslide and mudflow detection. Based on the theory of sparse representation and, we propose a novel automatic landslide and mudflow detection method in this paper, which combines multi-channel sparse representation and eight neighbor judgment methods. The whole process of the detection is totally automatic. We make the experiment on a high resolution image of ZhouQu district of Gansu province in China on August, 2010 and get a promising result which proved the effective of using sparse representation on landslide and mudflow detection.
A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.
Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi
2015-12-01
Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features. PMID:26340790
Sparse representation based face recognition using weighted regions
NASA Astrophysics Data System (ADS)
Bilgazyev, Emil; Yeniaras, E.; Uyanik, I.; Unan, Mahmut; Leiss, E. L.
2013-12-01
Face recognition is a challenging research topic, especially when the training (gallery) and recognition (probe) images are acquired using different cameras under varying conditions. Even a small noise or occlusion in the images can compromise the accuracy of recognition. Lately, sparse encoding based classification algorithms gave promising results for such uncontrollable scenarios. In this paper, we introduce a novel methodology by modeling the sparse encoding with weighted patches to increase the robustness of face recognition even further. In the training phase, we define a mask (i.e., weight matrix) using a sparse representation selecting the facial regions, and in the recognition phase, we perform comparison on selected facial regions. The algorithm was evaluated both quantitatively and qualitatively using two comprehensive surveillance facial image databases, i.e., SCfaceandMFPV, with the results clearly superior to common state-of-the-art methodologies in different scenarios.
SAR target classification based on multiscale sparse representation
NASA Astrophysics Data System (ADS)
Ruan, Huaiyu; Zhang, Rong; Li, Jingge; Zhan, Yibing
2016-03-01
We propose a novel multiscale sparse representation approach for SAR target classification. It firstly extracts the dense SIFT descriptors on multiple scales, then trains a global multiscale dictionary by sparse coding algorithm. After obtaining the sparse representation, the method applies spatial pyramid matching (SPM) and max pooling to summarize the features for each image. The proposed method can provide more information and descriptive ability than single-scale ones. Moreover, it costs less extra computation than existing multiscale methods which compute a dictionary for each scale. The MSTAR database and ship database collected from TerraSAR-X images are used in classification setup. Results show that the best overall classification rate of the proposed approach can achieve 98.83% on the MSTAR database and 92.67% on the TerraSAR-X ship database.
A note on rank reduction in sparse multivariate regression
Chen, Kun; Chan, Kung-Sik
2016-01-01
A reduced-rank regression with sparse singular value decomposition (RSSVD) approach was proposed by Chen et al. for conducting variable selection in a reduced-rank model. To jointly model the multivariate response, the method efficiently constructs a prespecified number of latent variables as some sparse linear combinations of the predictors. Here, we generalize the method to also perform rank reduction, and enable its usage in reduced-rank vector autoregressive (VAR) modeling to perform automatic rank determination and order selection. We show that in the context of stationary time-series data, the generalized approach correctly identifies both the model rank and the sparse dependence structure between the multivariate response and the predictors, with probability one asymptotically. We demonstrate the efficacy of the proposed method by simulations and analyzing a macro-economical multivariate time series using a reduced-rank VAR model. PMID:26997938
Sparse-based multispectral image encryption via ptychography
NASA Astrophysics Data System (ADS)
Rawat, Nitin; Shi, Yishi; Kim, Byoungho; Lee, Byung-Geun
2015-12-01
Recently, we proposed a model of securing a ptychography-based monochromatic image encryption system via the classical Photon-counting imaging (PCI) technique. In this study, we examine a single-channel multispectral sparse-based photon-counting ptychography imaging (SMPI)-based cryptosystem. A ptychography-based cryptosystem creates a complex object wave field, which can be reconstructed by a series of diffraction intensity patterns through an aperture movement. The PCI sensor records only a few complex Bayer patterned samples that have been utilized in the decryption process. Sparse sensing and nonlinear properties of the classical PCI system, together with the scanning probes, enlarge the key space, and such a combination therefore enhances the system's security. We demonstrate that the sparse samples have adequate information for image decryption, as well as information authentication by means of optical correlation.
Sparse Partial Equilibrium Tables in Chemically Resolved Reactive Flow
NASA Astrophysics Data System (ADS)
Vitello, Peter; Fried, Laurence E.; Pudliner, Brian; McAbee, Tom
2004-07-01
The detonation of an energetic material is the result of a complex interaction between kinetic chemical reactions and hydrodynamics. Unfortunately, little is known concerning the detailed chemical kinetics of detonations in energetic materials. CHEETAH uses rate laws to treat species with the slowest chemical reactions, while assuming other chemical species are in equilibrium. CHEETAH supports a wide range of elements and condensed detonation products and can also be applied to gas detonations. A sparse hash table of equation of state values is used in CHEETAH to enhance the efficiency of kinetic reaction calculations. For large-scale parallel hydrodynamic calculations, CHEETAH uses parallel communication to updates to the cache. We present here details of the sparse caching model used in the CHEETAH coupled to an ALE hydrocode. To demonstrate the efficiency of modeling using a sparse cache model we consider detonations in energetic materials.
Joint sparse representation for robust multimodal biometrics recognition.
Shekhar, Sumit; Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama
2014-01-01
Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods. PMID:24231870
Source term identification in atmospheric modelling via sparse optimization
NASA Astrophysics Data System (ADS)
Adam, Lukas; Branda, Martin; Hamburger, Thomas
2015-04-01
Inverse modelling plays an important role in identifying the amount of harmful substances released into atmosphere during major incidents such as power plant accidents or volcano eruptions. Another possible application of inverse modelling lies in the monitoring the CO2 emission limits where only observations at certain places are available and the task is to estimate the total releases at given locations. This gives rise to minimizing the discrepancy between the observations and the model predictions. There are two standard ways of solving such problems. In the first one, this discrepancy is regularized by adding additional terms. Such terms may include Tikhonov regularization, distance from a priori information or a smoothing term. The resulting, usually quadratic, problem is then solved via standard optimization solvers. The second approach assumes that the error term has a (normal) distribution and makes use of Bayesian modelling to identify the source term. Instead of following the above-mentioned approaches, we utilize techniques from the field of compressive sensing. Such techniques look for a sparsest solution (solution with the smallest number of nonzeros) of a linear system, where a maximal allowed error term may be added to this system. Even though this field is a developed one with many possible solution techniques, most of them do not consider even the simplest constraints which are naturally present in atmospheric modelling. One of such examples is the nonnegativity of release amounts. We believe that the concept of a sparse solution is natural in both problems of identification of the source location and of the time process of the source release. In the first case, it is usually assumed that there are only few release points and the task is to find them. In the second case, the time window is usually much longer than the duration of the actual release. In both cases, the optimal solution should contain a large amount of zeros, giving rise to the
Content addressable systolic array for sparse matrix computation
Wing, O.
1983-01-01
A systolic array is proposed which is specifically designed to solve a system of sparse linear equations. The array consists of a number of processing elements connected in a ring. Each processing element has its own content addressable memory where the nonzero elements of the sparse matrix are stored. Matrix elements to which elementary operations are applied are extracted from the memory by content addressing. The system of equations is solved in a systolic fashion and the solution is obtained in nz+5n-2 steps where nz is the number of nonzero elements along and below the diagonal and n is the number of equations. 13 references.
Reconstruction Techniques for Sparse Multistatic Linear Array Microwave Imaging
Sheen, David M.; Hall, Thomas E.
2014-06-09
Sequentially-switched linear arrays are an enabling technology for a number of near-field microwave imaging applications. Electronically sequencing along the array axis followed by mechanical scanning along an orthogonal axis allows dense sampling of a two-dimensional aperture in near real-time. In this paper, a sparse multi-static array technique will be described along with associated Fourier-Transform-based and back-projection-based image reconstruction algorithms. Simulated and measured imaging results are presented that show the effectiveness of the sparse array technique along with the merits and weaknesses of each image reconstruction approach.
Sparse Matrix for ECG Identification with Two-Lead Features
Tseng, Kuo-Kun; Luo, Jiao; Wang, Wenmin; Haiting, Dong
2015-01-01
Electrocardiograph (ECG) human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods. PMID:25961074
D Super-Resolution Approach for Sparse Laser Scanner Data
NASA Astrophysics Data System (ADS)
Hosseinyalamdary, S.; Yilmaz, A.
2015-08-01
Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.
Three-dimensional sparse-aperture moving-target imaging
NASA Astrophysics Data System (ADS)
Ferrara, Matthew; Jackson, Julie; Stuff, Mark
2008-04-01
If a target's motion can be determined, the problem of reconstructing a 3D target image becomes a sparse-aperture imaging problem. That is, the data lies on a random trajectory in k-space, which constitutes a sparse data collection that yields very low-resolution images if backprojection or other standard imaging techniques are used. This paper investigates two moving-target imaging algorithms: the first is a greedy algorithm based on the CLEAN technique, and the second is a version of Basis Pursuit Denoising. The two imaging algorithms are compared for a realistic moving-target motion history applied to a Xpatch-generated backhoe data set.
Learning control for robotic manipulators with sparse data
NASA Technical Reports Server (NTRS)
Morita, Atsushi; Dubowsky, Steven; Hootsmans, Norbert A. M.
1987-01-01
Learning control algorithms have been proposed for error compensation in repetitive robotic manipulator tasks. It is shown that the performance of such control algorithms can be seriously degraded when the feedback data they use is relatively sparse in time, such as might be provided by vision systems. It is also shown that learning control algorithms can be modified to compensate for the effects of sparse data and thereby yield performance which approaches that of systems without limitations on the sensory information available for control.
A LONE code for the sparse control of quantum systems
NASA Astrophysics Data System (ADS)
Ciaramella, G.; Borzì, A.
2016-03-01
In many applications with quantum spin systems, control functions with a sparse and pulse-shaped structure are often required. These controls can be obtained by solving quantum optimal control problems with L1-penalized cost functionals. In this paper, the MATLAB package LONE is presented aimed to solving L1-penalized optimal control problems governed by unitary-operator quantum spin models. This package implements a new strategy that includes a globalized semi-smooth Krylov-Newton scheme and a continuation procedure. Results of numerical experiments demonstrate the ability of the LONE code in computing accurate sparse optimal control solutions.
NASA Astrophysics Data System (ADS)
Liu, Xing; Liu, Fa Ming; Wang, Xiao Xia; Li, Xiao Dong; Fan, Yu Yan; Cai, Shi Xiang; Ao, Tian Qi
2015-09-01
At present, prediction of streamflow simulation in data-sparse basins of the South East Asia is a challenging task due to the absence of reliable ground-based rainfall information, while satellite-based rainfall estimates are immensely useful to improve our understanding of spatio-temporal variation of rainfall, particularly for data-sparse basins. In this study the TRMM 3B42 V7 and its bias-corrected data were, respectively, used to drive a physically based distributed hydrological model BTOPMC to perform daily streamflow simulations in Nam Khan River and Nam Like River basins during the years from 2000 to 2004 so as to investigate the potential use of the TRMM in complementing rain gauge data in hydrological modelling of data-sparse basins. The results show that although larger difference exists in the high streamflow process and the low streamflow process, the daily simulations fed with TRMM precipitation data could basically reflect the daily streamflow processes at the four stations and determine the time to peak. Furthermore, the calibrated parameters in the Nam Khan River basin are more suitable than that in the Nam Like River basin. By comparing the two precipitation data, it indicates that the integration of TRMM precipitation data and rain gauge data have a promising prospect on the hydrological process simulation in data-sparse basin.
Spatiotemporal SERT expression in cortical map development.
Chen, Xiaoning; Petit, Emilie I; Dobrenis, Kostantin; Sze, Ji Ying
2016-09-01
The cerebral cortex is organized into morphologically distinct areas that provide biological frameworks underlying perception, cognition, and behavior. Profiling mouse and human cortical transcriptomes have revealed temporal-specific differential gene expression modules in distinct neocortical areas during cortical map establishment. However, the biological roles of spatiotemporal gene expression in cortical patterning and how cortical topographic gene expression is regulated are largely unknown. Here, we characterize temporal- and spatial-defined expression of serotonin (5-HT) transporter (SERT) in glutamatergic neurons during sensory map development in mice. SERT is transiently expressed in glutamatergic thalamic neurons projecting to sensory cortices and in pyramidal neurons in the prefrontal cortex (PFC) and hippocampus (HPC) during the period that lays down the basic functional neural circuits. We previously identified that knockout of SERT in the thalamic neurons blocks 5-HT uptake by their thalamocortical axons, resulting in excessive 5-HT signaling that impairs sensory map architecture. In contrast, here we show that selective SERT knockout in the PFC and HPC neurons does not perturb sensory map patterning. These data suggest that transient SERT expression in specific glutamatergic neurons provides area-specific instructions for cortical map patterning. Hence, genetic and pharmacological manipulations of this SERT function could illuminate the fundamental genetic programming of cortex-specific maps and biological roles of temporal-specific cortical topographic gene expression in normal development and mental disorders. PMID:27282696
Cortical feedback control of olfactory bulb circuits.
Boyd, Alison M; Sturgill, James F; Poo, Cindy; Isaacson, Jeffry S
2012-12-20
Olfactory cortex pyramidal cells integrate sensory input from olfactory bulb mitral and tufted (M/T) cells and project axons back to the bulb. However, the impact of cortical feedback projections on olfactory bulb circuits is unclear. Here, we selectively express channelrhodopsin-2 in olfactory cortex pyramidal cells and show that cortical feedback projections excite diverse populations of bulb interneurons. Activation of cortical fibers directly excites GABAergic granule cells, which in turn inhibit M/T cells. However, we show that cortical inputs preferentially target short axon cells that drive feedforward inhibition of granule cells. In vivo, activation of olfactory cortex that only weakly affects spontaneous M/T cell firing strongly gates odor-evoked M/T cell responses: cortical activity suppresses odor-evoked excitation and enhances odor-evoked inhibition. Together, these results indicate that although cortical projections have diverse actions on olfactory bulb microcircuits, the net effect of cortical feedback on M/T cells is an amplification of odor-evoked inhibition. PMID:23259951
Zhang, Jie; Chen, Liang; Gu, Yu-dong
2015-10-01
Brachial plexus injuries induce corresponding cortical representations to be occupied by adjacent cortices. The purpose of this study was to clarify if contralateral homologous motor regions of adjacent cortices influence occupation of deafferented motor cortex. 36 rats were divided into 3 groups of 12 each. In group 1, total brachial plexus root avulsion (tBPRA) was made on the left side. In group 2, rats underwent left tBPRA combined with corpus callosum transection (CCX). In group 3, only CCX was performed. 6 rats in each group were used for intracortical microstimulation (ICMS) to map representations of motor cortex in the right hemisphere at 7 days and the other 6 rats, at 3 months. 18 more rats without any operation underwent ICMS, with 6 each taken to serve as normal control for motor cortical representations' changes caused by different surgery. Results showed that in groups 1 and 2, sites for motor cortical representations of vibrissae, of neck and of the hindlimb was statistically more than that of control, respectively, and statistically more sites were found at 3 months than at 7 days, respectively. At the two time points, sites for vibrissa cortices and that for the hindlimb were statistically more in group 2 than in group 1, respectively. CCX alone did not induce change of site number for motor cortical representations. We conclude that after tBPRA, contralateral homologous motor cortices may, to some extent, prevent neighboring cortices from encroachment on motor cortical representations of the brachial plexus. PMID:26314511
Cerebral perfusion and cortical thickness indicate cortical involvement in mild Parkinson's disease.
Madhyastha, Tara M; Askren, Mary K; Boord, Peter; Zhang, Jing; Leverenz, James B; Grabowski, Thomas J
2015-12-01
Cortical dysfunction in Parkinson's disease (PD) may be caused by disruption to ascending systems or by intrinsic cortical neuropathology. We introduce and conduct a joint analysis of metabolism and atrophy capable of identifying whether metabolic disruption occurs in mild PD without cortical atrophy, to determine the extent and spatial pattern of cortical involvement in mild PD. The design was observational, studying 23 cognitively normal participants with mild PD (mean Hoehn & Yahr stage 2) and 21 healthy controls. Cortical thickness (obtained from analysis of structural magnetic resonance imaging [MRI] with FreeSurfer) and cerebral perfusion measures (obtained from arterial spin labeling [ASL]) analyzed independently and then together in a joint multiple factorial analysis to identify spatial patterns of perfusion and cortical thickness. We identify a pattern of changes in perfusion and cortical thickness characterized by symmetric parietal cortical thinning and reduced precuneus perfusion, with relative preservation of thickness and perfusion in the anterior cingulate cortex (ACC), right prefrontal gyrus, and medial frontal gyrus. The expression of this pattern is correlated with motor system symptoms and speed of processing. A spatial pattern of joint parietal cortical thinning and disproportionate reduction in perfusion occurs in our nondemented PD sample. We found no PD-related components of reduced perfusion without cortical thinning. This suggests that PD affects the cortex itself, even when symptoms are relatively mild. PMID:25759166
NASA Astrophysics Data System (ADS)
Hurdal, Monica K.; Striegel, Deborah A.
2011-11-01
Modeling and understanding cortical folding pattern formation is important for quantifying cortical development. We present a biomathematical model for cortical folding pattern formation in the human brain and apply this model to study diseases involving cortical pattern malformations associated with neural migration disorders. Polymicrogyria is a cortical malformation disease resulting in an excessive number of small gyri. Our mathematical model uses a Turing reaction-diffusion system to model cortical folding. The lateral ventricle (LV) and ventricular zone (VZ) of the brain are critical components in the formation of cortical patterning. In early cortical development the shape of the LV can be modeled with a prolate spheroid and the VZ with a prolate spheroid surface. We use our model to study how global cortex characteristics, such as size and shape of the LV, affect cortical pattern formation. We demonstrate increasing domain scale can increase the number of gyri and sulci formed. Changes in LV shape can account for sulcus directionality. By incorporating LV size and shape, our model is able to elucidate which parameters can lead to excessive cortical folding.
Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits
2005-01-01
How different is local cortical circuitry from a random network? To answer this question, we probed synaptic connections with several hundred simultaneous quadruple whole-cell recordings from layer 5 pyramidal neurons in the rat visual cortex. Analysis of this dataset revealed several nonrandom features in synaptic connectivity. We confirmed previous reports that bidirectional connections are more common than expected in a random network. We found that several highly clustered three-neuron connectivity patterns are overrepresented, suggesting that connections tend to cluster together. We also analyzed synaptic connection strength as defined by the peak excitatory postsynaptic potential amplitude. We found that the distribution of synaptic connection strength differs significantly from the Poisson distribution and can be fitted by a lognormal distribution. Such a distribution has a heavier tail and implies that synaptic weight is concentrated among few synaptic connections. In addition, the strengths of synaptic connections sharing pre- or postsynaptic neurons are correlated, implying that strong connections are even more clustered than the weak ones. Therefore, the local cortical network structure can be viewed as a skeleton of stronger connections in a sea of weaker ones. Such a skeleton is likely to play an important role in network dynamics and should be investigated further. PMID:15737062
Adsorption in sparse networks. 2: Silica aerogels
Scherer, G.W.; Calas, S.; Sempere, R.
1998-06-15
The model developed in Part 1 is applied to nitrogen adsorption isotherms obtained for a series of silica aerogels whose densities are varied by partial sintering. The isotherms are adequately described by a cubic network model, with all of the pores falling in the mesopore range; the adsorption and desorption branches are fit by the same pore size distribution. For the least dense gels, a substantial portion of the pore volume is not detected by condensation. The model attributes this effect to the shape of the adsorbate/adsorptive interface, which can adopt zero curvature even in mesopores, because of the shape of the network.
Cortical astrocytes rewire somatosensory cortical circuits for peripheral neuropathic pain
Hayashi, Hideaki; Ishikawa, Tatsuya; Shibata, Keisuke; Inada, Hiroyuki; Roh, Seung Eon; Kim, Sang Jeong; Moorhouse, Andrew J.
2016-01-01
Long-term treatments to ameliorate peripheral neuropathic pain that includes mechanical allodynia are limited. While glial activation and altered nociceptive transmission within the spinal cord are associated with the pathogenesis of mechanical allodynia, changes in cortical circuits also accompany peripheral nerve injury and may represent additional therapeutic targets. Dendritic spine plasticity in the S1 cortex appears within days following nerve injury; however, the underlying cellular mechanisms of this plasticity and whether it has a causal relationship to allodynia remain unsolved. Furthermore, it is not known whether glial activation occurs within the S1 cortex following injury or whether it contributes to this S1 synaptic plasticity. Using in vivo 2-photon imaging with genetic and pharmacological manipulations of murine models, we have shown that sciatic nerve ligation induces a re-emergence of immature metabotropic glutamate receptor 5 (mGluR5) signaling in S1 astroglia, which elicits spontaneous somatic Ca2+ transients, synaptogenic thrombospondin 1 (TSP-1) release, and synapse formation. This S1 astrocyte reactivation was evident only during the first week after injury and correlated with the temporal changes in S1 extracellular glutamate levels and dendritic spine turnover. Blocking the astrocytic mGluR5-signaling pathway suppressed mechanical allodynia, while activating this pathway in the absence of any peripheral injury induced long-lasting (>1 month) allodynia. We conclude that reawakened astrocytes are a key trigger for S1 circuit rewiring and that this contributes to neuropathic mechanical allodynia. PMID:27064281
Cortical astrocytes rewire somatosensory cortical circuits for peripheral neuropathic pain.
Kim, Sun Kwang; Hayashi, Hideaki; Ishikawa, Tatsuya; Shibata, Keisuke; Shigetomi, Eiji; Shinozaki, Youichi; Inada, Hiroyuki; Roh, Seung Eon; Kim, Sang Jeong; Lee, Gihyun; Bae, Hyunsu; Moorhouse, Andrew J; Mikoshiba, Katsuhiko; Fukazawa, Yugo; Koizumi, Schuichi; Nabekura, Junichi
2016-05-01
Long-term treatments to ameliorate peripheral neuropathic pain that includes mechanical allodynia are limited. While glial activation and altered nociceptive transmission within the spinal cord are associated with the pathogenesis of mechanical allodynia, changes in cortical circuits also accompany peripheral nerve injury and may represent additional therapeutic targets. Dendritic spine plasticity in the S1 cortex appears within days following nerve injury; however, the underlying cellular mechanisms of this plasticity and whether it has a causal relationship to allodynia remain unsolved. Furthermore, it is not known whether glial activation occurs within the S1 cortex following injury or whether it contributes to this S1 synaptic plasticity. Using in vivo 2-photon imaging with genetic and pharmacological manipulations of murine models, we have shown that sciatic nerve ligation induces a re-emergence of immature metabotropic glutamate receptor 5 (mGluR5) signaling in S1 astroglia, which elicits spontaneous somatic Ca2+ transients, synaptogenic thrombospondin 1 (TSP-1) release, and synapse formation. This S1 astrocyte reactivation was evident only during the first week after injury and correlated with the temporal changes in S1 extracellular glutamate levels and dendritic spine turnover. Blocking the astrocytic mGluR5-signaling pathway suppressed mechanical allodynia, while activating this pathway in the absence of any peripheral injury induced long-lasting (>1 month) allodynia. We conclude that reawakened astrocytes are a key trigger for S1 circuit rewiring and that this contributes to neuropathic mechanical allodynia. PMID:27064281
Cortical encoding of pitch: Recent results and open questions
Walker, Kerry M.M.; Bizley, Jennifer K.; King, Andrew J.; Schnupp, Jan W.H.
2011-01-01
It is widely appreciated that the key predictor of the pitch of a sound is its periodicity. Neural structures which support pitch perception must therefore be able to reflect the repetition rate of a sound, but this alone is not sufficient. Since pitch is a psychoacoustic property, a putative cortical code for pitch must also be able to account for the relationship between the amount to which a sound is periodic (i.e. its temporal regularity) and the perceived pitch salience, as well as limits in our ability to detect pitch changes or to discriminate rising from falling pitch. Pitch codes must also be robust in the presence of nuisance variables such as loudness or timbre. Here, we review a large body of work on the cortical basis of pitch perception, which illustrates that the distribution of cortical processes that give rise to pitch perception is likely to depend on both the acoustical features and functional relevance of a sound. While previous studies have greatly advanced our understanding, we highlight several open questions regarding the neural basis of pitch perception. These questions can begin to be addressed through a cooperation of investigative efforts across species and experimental techniques, and, critically, by examining the responses of single neurons in behaving animals. PMID:20457240
Cortical and cap sedimentation in gravitropic Equisetum roots
NASA Technical Reports Server (NTRS)
Ridge, R. W.; Sack, F. D.
1992-01-01
Although the rootcap is required for gravitropic sensing, various classical and contemporary data raise the question of whether additional sensing occurs away from the cap in roots. Roots of Equisetum hyemale L. (horsetail) were examined by light and electron microscopy to determine which cell components were distributed with respect to gravity both in and away from the rootcap. Adventitious roots from stem cuttings were gravitropic in a vertical orientation or if reoriented to the horizontal. Obvious amyloplast sedimentation was found in vertical and in reoriented roots 1) in cells in the center of the rootcap and 2) in young, elongating cortical cells located in two to three layers outside the endodermis. These cortical amyloplasts were smaller than cap amyloplasts and, unlike central cap amyloplasts, were occasionally found in the top of the cell. The nucleus was also sedimented on top of the amyloplasts in both cell types, both in vertical and in reoriented roots. Sedimentation of both organelles ceased as cortical cells elongated further or as cap cells became peripheral in location. In both cell types with sedimentation, endoplasmic reticulum was located in the cell periphery, but showed no obvious enrichment near the lower part of the cell in vertical roots. This is the first modern report of sedimentation away from the cap in roots, and it provides structural evidence that gravitropic sensing may not be confined to the cap in all roots.
Active Chemical Thermodynamics promoted by activity of cortical actin
NASA Astrophysics Data System (ADS)
Bhattacharya, Bhaswati; Chaudhuri, Abhishek; Gowrishankar, Kripa; Rao, Madan
2011-03-01
The spatial distribution and dynamics of formation and breakup of the nanoclusters of cell surface proteins is controlled by the active remodeling dynamics of the underlying cortical actin. To explain these observations, we have proposed a novel mechanism of nanoclustering, involving the transient binding to and advection along constitutively occuring ``asters'' of cortical actin. We study the consequences of such active actin-based clustering, in the context of chemical reactions involving conformational changes of cell surface proteins. We find that the active remodeling of cortical actin, can give rise to a dramatic increase in efficiency and extent of conformational spread, even at low levels of expression at the cell surface. We define a activity temperature (τa) arising due to actin activities which can be used to describe chemical thermodynamics of the system. We plot TTT (time-temparature-transformation) curves and compute the Arrhenius factors which depend on τa . With this, the active asters can be treated as enzymes whose enzymatic reaction rate can be related to the activity.
Anatomic variations of anterior cerebral artery cortical branches.
Stefani, M A; Schneider, F L; Marrone, A C; Severino, A G; Jackowski, A P; Wallace, M C
2000-01-01
The anterior cerebral artery (ACA) is a major vessel responsible for the blood supply to the interhemispheric region. The ACA segment after the anterior communicating artery (AComA) origin is called the distal ACA and has central and cortical branches. The cortical branches are distributed in the different regions of the orbital and medial part of the brain. The objects of this study are the anatomical variations found in the distal ACA. In 76 hemispheres the ACA distal branches were injected with latex and dissected under microscope magnification. Vessel diameters and distances between vessel origins and anterior communicating artery were recorded and analyzed. Microsurgical dissection was carried out to demonstrate anatomic variations of these vessels. Average diameter of ACA at origin was 2.61 +/- 0.34 mm and average diameter of cortical branches diameter ranged from 0.79 +/- 0.27 mm to 1.84 +/- 0.3 mm. Distances between vessel origin and AComA ranged from 7.68 +/- 3.91 mm (orbitofrontal) to 112.6 +/- 11.63 mm (inferior internal parietal). This study found anatomical variations: a single (azygos) ACA was present in one case and three in three cases. Crossing branches of the distal ACA to the contralateral hemisphere were present in 26% of the cases. In some cases a single ACA may supply the posterior hemispheric region through crossing branches. This calls attention to potential bilateral brain infarcts due to a single unilateral ACA occlusion. PMID:10873213
Insights into cortical mechanisms of behavior from microstimulation experiments
Histed, Mark H.; Ni, Amy M.; Maunsell, John H.R.
2012-01-01
Even the simplest behaviors depend on a large number of neurons that are distributed across many brain regions. Because electrical microstimulation can change the activity of localized subsets of neurons, it has provided valuable evidence that specific neurons contribute to particular behaviors. Here we review what has been learned about cortical function from behavioral studies using microstimulation in animals and humans. Experiments that examine how microstimulation affects the perception of stimuli have shown that the effects of microstimulation are usually highly specific and can be related to the stimuli preferred by neurons at the stimulated site. Experiments that ask subjects to detect cortical microstimulation in the absence of other stimuli have provided further insights. Although subjects typically can detect microstimulation of primary sensory or motor cortex, they are generally unable to detect stimulation of most of cortex without extensive practice. With practice, however, stimulation of any part of cortex can become detected. These training effects suggest that some patterns of cortical activity cannot be readily accessed to guide behavior, but that the adult brain retains enough plasticity to learn to process novel patterns of neuronal activity arising anywhere in cortex. PMID:22307059
Prostaglandin E2 Prevents Disuse-Induced Cortical Bone Loss
NASA Technical Reports Server (NTRS)
Jee, Webster S. S.; Akamine, T.; Ke, Hua Zhu; Li, Xiao Jian; Tang, L. Y.; Zeng, Q. Q.
1992-01-01
The object of this study was to determine whether prostaglandin E2 (PGE2) can prevent disuse (underloaded)-induced cortical bone loss as well as add extra bone to underloaded bones. Thirteen-month-old retired female Sprague-Dawley breeders served as controls or were subjected to simultaneous right hindlimb immobilization by bandaging and daily subcutaneous doses of 0, 1, 3, or 6 mg PGE2/kg/d for two and six weeks. Histomorphometric analyses were performed on double-fluorescent labeled undecalcified tibial shaft sections (proximal to the tibiofibular junction). Disuse-induced cortical bone loss occurred by enlarging the marrow cavity and increasing intracortical porosity. PGE2 treatment of disuse shafts further increased intracortical porosity above that in disuse alone controls. This bone loss was counteracted by enhancement of periosteal and corticoendosteal bone formation. Stimulation of periosteal and corticoendosteal bone formation slightly enlarged the total tissue (cross-sectional) area and inhibited marrow cavity enlargement. These PGE2-induced activities netted the same percentage of cortical bone with a different distribution than the beginning and age related controls. These findings indicate the PGE2-induced increase in bone formation compensated for the disuse and PGE2-induced bone loss, and thus prevented immobilization induced bone loss.
Global Neuromagnetic Cortical Fields Have Non-Zero Velocity.
Alexander, David M; Nikolaev, Andrey R; Jurica, Peter; Zvyagintsev, Mikhail; Mathiak, Klaus; van Leeuwen, Cees
2016-01-01
Globally coherent patterns of phase can be obscured by analysis techniques that aggregate brain activity measures across-trials, whether prior to source localization or for estimating inter-areal coherence. We analyzed, at single-trial level, whole head MEG recorded during an observer-triggered apparent motion task. Episodes of globally coherent activity occurred in the delta, theta, alpha and beta bands of the signal in the form of large-scale waves, which propagated with a variety of velocities. Their mean speed at each frequency band was proportional to temporal frequency, giving a range of 0.06 to 4.0 m/s, from delta to beta. The wave peaks moved over the entire measurement array, during both ongoing activity and task-relevant intervals; direction of motion was more predictable during the latter. A large proportion of the cortical signal, measurable at the scalp, exists as large-scale coherent motion. We argue that the distribution of observable phase velocities in MEG is dominated by spatial filtering considerations in combination with group velocity of cortical activity. Traveling waves may index processes involved in global coordination of cortical activity. PMID:26953886
Characterizing Thalamo-Cortical Disturbances in Schizophrenia and Bipolar Illness
Anticevic, Alan; Cole, Michael W.; Repovs, Grega; Murray, John D.; Brumbaugh, Margaret S.; Winkler, Anderson M.; Savic, Aleksandar; Krystal, John H.; Pearlson, Godfrey D.; Glahn, David C.
2014-01-01
Schizophrenia is a devastating neuropsychiatric syndrome associated with distributed brain dysconnectivity that may involve large-scale thalamo-cortical systems. Incomplete characterization of thalamic connectivity in schizophrenia limits our understanding of its relationship to symptoms and to diagnoses with shared clinical presentation, such as bipolar illness, which may exist on a spectrum. Using resting-state functional magnetic resonance imaging, we characterized thalamic connectivity in 90 schizophrenia patients versus 90 matched controls via: (1) Subject-specific anatomically defined thalamic seeds; (2) anatomical and data-driven clustering to assay within-thalamus dysconnectivity; and (3) machine learning to classify diagnostic membership via thalamic connectivity for schizophrenia and for 47 bipolar patients and 47 matched controls. Schizophrenia analyses revealed functionally related disturbances: Thalamic over-connectivity with bilateral sensory–motor cortices, which predicted symptoms, but thalamic under-connectivity with prefrontal–striatal–cerebellar regions relative to controls, possibly reflective of sensory gating and top-down control disturbances. Clustering revealed that this dysconnectivity was prominent for thalamic nuclei densely connected with the prefrontal cortex. Classification and cross-diagnostic results suggest that thalamic dysconnectivity may be a neural marker for disturbances across diagnoses. Present findings, using one of the largest schizophrenia and bipolar neuroimaging samples to date, inform basic understanding of large-scale thalamo-cortical systems and provide vital clues about the complex nature of its disturbances in severe mental illness. PMID:23825317
Global Neuromagnetic Cortical Fields Have Non-Zero Velocity
Alexander, David M.; Nikolaev, Andrey R.; Jurica, Peter; Zvyagintsev, Mikhail; Mathiak, Klaus; van Leeuwen, Cees
2016-01-01
Globally coherent patterns of phase can be obscured by analysis techniques that aggregate brain activity measures across-trials, whether prior to source localization or for estimating inter-areal coherence. We analyzed, at single-trial level, whole head MEG recorded during an observer-triggered apparent motion task. Episodes of globally coherent activity occurred in the delta, theta, alpha and beta bands of the signal in the form of large-scale waves, which propagated with a variety of velocities. Their mean speed at each frequency band was proportional to temporal frequency, giving a range of 0.06 to 4.0 m/s, from delta to beta. The wave peaks moved over the entire measurement array, during both ongoing activity and task-relevant intervals; direction of motion was more predictable during the latter. A large proportion of the cortical signal, measurable at the scalp, exists as large-scale coherent motion. We argue that the distribution of observable phase velocities in MEG is dominated by spatial filtering considerations in combination with group velocity of cortical activity. Traveling waves may index processes involved in global coordination of cortical activity. PMID:26953886
Linking cortical network synchrony and excitability
Meisel, Christian
2016-01-01
ABSTRACT Theoretical approaches based on dynamical systems theory can provide useful frameworks to guide experiments and analysis techniques when investigating cortical network activity. The notion of phase transitions between qualitatively different kinds of network dynamics has been such a framework inspiring novel approaches to neurophysiological data analysis over the recent years. One particular intriguing hypothesis has been that cortical networks reside in the vicinity of a phase transition. Although the final verdict on this hypothesis is still out, trying to understand cortex dynamics from this viewpoint has recently led to interesting insights on cortical network function with relevance for clinical practice. PMID:27065159
Sparse methods for Quantitative Susceptibility Mapping
NASA Astrophysics Data System (ADS)
Bilgic, Berkin; Chatnuntawech, Itthi; Langkammer, Christian; Setsompop, Kawin
2015-09-01
Quantitative Susceptibility Mapping (QSM) aims to estimate the tissue susceptibility distribution that gives rise to subtle changes in the main magnetic field, which are captured by the image phase in a gradient echo (GRE) experiment. The underlying susceptibility distribution is related to the acquired tissue phase through an ill-posed linear system. To facilitate its inversion, spatial regularization that imposes sparsity or smoothness assumptions can be employed. This paper focuses on efficient algorithms for regularized QSM reconstruction. Fast solvers that enforce sparsity under Total Variation (TV) and Total Generalized Variation (TGV) constraints are developed using Alternating Direction Method of Multipliers (ADMM). Through variable splitting that permits closed-form iterations, the computation efficiency of these solvers are dramatically improved. An alternative approach to improve the conditioning of the ill-posed inversion is to acquire multiple GRE volumes at different head orientations relative to the main magnetic field. The phase information from such multi-orientation acquisition can be combined to yield exquisite susceptibility maps and obviate the need for regularized reconstruction, albeit at the cost of increased data acquisition time.
McKenzie, Sam; Keene, Christopher S; Farovik, Anja; Bladon, John; Place, Ryan; Komorowski, Robert; Eichenbaum, Howard
2016-10-01
Here we consider the value of neural population analysis as an approach to understanding how information is represented in the hippocampus and cortical areas and how these areas might interact as a brain system to support memory. We argue that models based on sparse coding of different individual features by single neurons in these areas (e.g., place cells, grid cells) are inadequate to capture the complexity of experience represented within this system. By contrast, population analyses of neurons with denser coding and mixed selectivity reveal new and important insights into the organization of memories. Furthermore, comparisons of the organization of information in interconnected areas suggest a model of hippocampal-cortical interactions that mediates the fundamental features of memory. PMID:26748022
NASA Astrophysics Data System (ADS)
Zhang, G.; Lu, D.; Ye, M.; Gunzburger, M.
2011-12-01
Markov Chain Monte Carlo (MCMC) methods have been widely used in many fields of uncertainty analysis to estimate the posterior distributions of parameters and credible intervals of predictions in the Bayesian framework. However, in practice, MCMC may be computationally unaffordable due to slow convergence and the excessive number of forward model executions required, especially when the forward model is expensive to compute. Both disadvantages arise from the curse of dimensionality, i.e., the posterior distribution is usually a multivariate function of parameters. Recently, sparse grid method has been demonstrated to be an effective technique for coping with high-dimensional interpolation or integration problems. Thus, in order to accelerate the forward model and avoid the slow convergence of MCMC, we propose a new method for uncertainty analysis based on sparse grid interpolation and quasi-Monte Carlo sampling. First, we construct a polynomial approximation of the forward model in the parameter space by using the sparse grid interpolation. This approximation then defines an accurate surrogate posterior distribution that can be evaluated repeatedly at minimal computational cost. Second, instead of using MCMC, a quasi-Monte Carlo method is applied to draw samples in the parameter space. Then, the desired probability density function of each prediction is approximated by accumulating the posterior density values of all the samples according to the prediction values. Our method has the following advantages: (1) the polynomial approximation of the forward model on the sparse grid provides a very efficient evaluation of the surrogate posterior distribution; (2) the quasi-Monte Carlo method retains the same accuracy in approximating the PDF of predictions but avoids all disadvantages of MCMC. The proposed method is applied to a controlled numerical experiment of groundwater flow modeling. The results show that our method attains the same accuracy much more efficiently
LAYER I NEOCORTICAL ECTOPIA: CELLULAR ORGANIZATION AND LOCAL CORTICAL CIRCUITRY
Gabel, Lisa Ann
2011-01-01
Focal cortical dysplasia (FCD) are associated with neurological disorders and cognitive impairments in humans. Molecular layer ectopia, clusters of misplaced cells in layer I of the neocortex, have been identified in patients with developmental dyslexia and psychomotor retardation. Mouse models of this developmental disorder display behavioral impairments and increased seizure susceptibility. Although there is a correlation between cortical malformations and neurological dysfunction, little is known about the morphological and physiological properties of cells within cortical malformations. In the present study we used electrophysiological and immunocytochemical analyses to examine the distribution of neuronal and non-neuronal cell types within and surrounding layer I neocortical ectopia in NXSMD/EiJ mice. We show that cells within ectopia have membrane properties of both pyramidal and a variety of nonpyramidal cell types, including fast-spiking cells. Immunocytochemical analysis for different interneuronal subtypes demonstrates that ectopia contain nonpyramidal cells immunoreactive for calbindin-D28K (CALB), parvalbumin (PARV), and calretinin (CR). Ectopia also contain astrocytes, positive for glial fibrillary acidic protein (GFAP) and oligodendrocyte precursor cells positive for NG2 proteoglycan (NG2). Lastly, we provide electrophysiological and morphological evidence to demonstrate that cells within ectopia receive input from cells within layers I, upper and deeper II/III, and V and provide outputs to cells within deep layer II/III and layer V, but not layers I and upper II/III. These results indicate that ectopia contain cells of different lineages with diverse morphological and physiological properties, and appear to cause disruptions in local cortical circuitry. PMID:21256119
Brain Plasticity in Blind Subjects Centralizes Beyond the Modal Cortices
Ortiz-Terán, Laura; Ortiz, Tomás; Perez, David L.; Aragón, Jose Ignacio; Diez, Ibai; Pascual-Leone, Alvaro; Sepulcre, Jorge
2016-01-01
It is well established that the human brain reorganizes following sensory deprivations. In blind individuals, visual processing regions including the lateral occipital cortex (LOC) are activated by auditory and tactile stimuli as demonstrated by neurophysiological and neuroimaging investigations. The mechanisms for such plasticity remain unclear, but shifts in connectivity across existing neural networks appear to play a critical role. The majority of research efforts to date have focused on neuroplastic changes within visual unimodal regions, however we hypothesized that neuroplastic alterations may also occur in brain networks beyond the visual cortices including involvement of multimodal integration regions and heteromodal cortices. In this study, two recently developed graph-theory based functional connectivity analyses, interconnector analyses and local and distant connectivity, were applied to investigate functional reorganization in regional and distributed neural-systems in late-onset blind (LB) and congenitally blind (CB) cohorts each compared to their own group of sighted controls. While functional network alterations as measured by the degree of differential links (DDL) occurred in sensory cortices, neuroplastic changes were most prominent within multimodal and association cortices. Subjects with LB showed enhanced multimodal integration connections in the parieto-opercular, temporoparietal junction (TPJ) and ventral premotor (vPM) regions, while CB individuals exhibited increased superior parietal cortex (SPC) connections. This study reveals the critical role of recipient multi-sensory integration areas in network reorganization and cross-modal plasticity in blind individuals. These findings suggest that aspects of cross-modal neuroplasticity and adaptive sensory-motor and auditory functions may potentially occur through reorganization in multimodal integration regions. PMID:27458350
Brain Plasticity in Blind Subjects Centralizes Beyond the Modal Cortices.
Ortiz-Terán, Laura; Ortiz, Tomás; Perez, David L; Aragón, Jose Ignacio; Diez, Ibai; Pascual-Leone, Alvaro; Sepulcre, Jorge
2016-01-01
It is well established that the human brain reorganizes following sensory deprivations. In blind individuals, visual processing regions including the lateral occipital cortex (LOC) are activated by auditory and tactile stimuli as demonstrated by neurophysiological and neuroimaging investigations. The mechanisms for such plasticity remain unclear, but shifts in connectivity across existing neural networks appear to play a critical role. The majority of research efforts to date have focused on neuroplastic changes within visual unimodal regions, however we hypothesized that neuroplastic alterations may also occur in brain networks beyond the visual cortices including involvement of multimodal integration regions and heteromodal cortices. In this study, two recently developed graph-theory based functional connectivity analyses, interconnector analyses and local and distant connectivity, were applied to investigate functional reorganization in regional and distributed neural-systems in late-onset blind (LB) and congenitally blind (CB) cohorts each compared to their own group of sighted controls. While functional network alterations as measured by the degree of differential links (DDL) occurred in sensory cortices, neuroplastic changes were most prominent within multimodal and association cortices. Subjects with LB showed enhanced multimodal integration connections in the parieto-opercular, temporoparietal junction (TPJ) and ventral premotor (vPM) regions, while CB individuals exhibited increased superior parietal cortex (SPC) connections. This study reveals the critical role of recipient multi-sensory integration areas in network reorganization and cross-modal plasticity in blind individuals. These findings suggest that aspects of cross-modal neuroplasticity and adaptive sensory-motor and auditory functions may potentially occur through reorganization in multimodal integration regions. PMID:27458350
Rich-Club Organization in Effective Connectivity among Cortical Neurons.
Nigam, Sunny; Shimono, Masanori; Ito, Shinya; Yeh, Fang-Chin; Timme, Nicholas; Myroshnychenko, Maxym; Lapish, Christopher C; Tosi, Zachary; Hottowy, Pawel; Smith, Wesley C; Masmanidis, Sotiris C; Litke, Alan M; Sporns, Olaf; Beggs, John M
2016-01-20
The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a "rich club." We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. Significance statement: Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several
Rich-Club Organization in Effective Connectivity among Cortical Neurons
Shimono, Masanori; Ito, Shinya; Yeh, Fang-Chin; Timme, Nicholas; Myroshnychenko, Maxym; Lapish, Christopher C.; Tosi, Zachary; Hottowy, Pawel; Smith, Wesley C.; Masmanidis, Sotiris C.; Litke, Alan M.; Sporns, Olaf; Beggs, John M.
2016-01-01
The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a “rich club.” We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. SIGNIFICANCE STATEMENT Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several
Griveau, Amélie; Borello, Ugo; Causeret, Frédéric; Tissir, Fadel; Boggetto, Nicole; Karaz, Sonia; Pierani, Alessandra
2010-01-01
Patterning of the cortical neuroepithelium occurs at early stages of embryonic development in response to secreted molecules from signaling centers. These signals have been shown to establish the graded expression of transcription factors in progenitors within the ventricular zone and to control the size and positioning of cortical areas. Cajal-Retzius (CR) cells are among the earliest generated cortical neurons and migrate from the borders of the developing pallium to cover the cortical primordium by E11.5. We show that molecularly distinct CR subtypes distribute in specific combinations in pallial territories at the time of cortical regionalization. By means of genetic ablation experiments in mice, we report that loss of septum Dbx1-derived CR cells in the rostromedial pallium between E10.5 and E11.5 results in the redistribution of CR subtypes. This leads to changes in the expression of transcription factors within the neuroepithelium and in the proliferation properties of medial and dorsal cortical progenitors. Early regionalization defects correlate with shifts in the positioning of cortical areas at postnatal stages in the absence of alterations of gene expression at signaling centers. We show that septum-derived CR neurons express a highly specific repertoire of signaling factors. Our results strongly suggest that these cells, migrating over long distances and positioned in the postmitotic compartment, signal to ventricular zone progenitors and, thus, function as modulators of early cortical patterning. PMID:20668538
Griveau, Amélie; Tissir, Fadel; Boggetto, Nicole; Karaz, Sonia; Pierani, Alessandra
2010-01-01
Patterning of the cortical neuroepithelium occurs at early stages of embryonic development in response to secreted molecules from signaling centers. These signals have been shown to establish the graded expression of transcription factors in progenitors within the ventricular zone and to control the size and positioning of cortical areas. Cajal-Retzius (CR) cells are among the earliest generated cortical neurons and migrate from the borders of the developing pallium to cover the cortical primordium by E11.5. We show that molecularly distinct CR subtypes distribute in specific combinations in pallial territories at the time of cortical regionalization. By means of genetic ablation experiments in mice, we report that loss of septum Dbx1-derived CR cells in the rostromedial pallium between E10.5 and E11.5 results in the redistribution of CR subtypes. This leads to changes in the expression of transcription factors within the neuroepithelium and in the proliferation properties of medial and dorsal cortical progenitors. Early regionalization defects correlate with shifts in the positioning of cortical areas at postnatal stages in the absence of alterations of gene expression at signaling centers. We show that septum-derived CR neurons express a highly specific repertoire of signaling factors. Our results strongly suggest that these cells, migrating over long distances and positioned in the postmitotic compartment, signal to ventricular zone progenitors and, thus, function as modulators of early cortical patterning. PMID:20668538
Meta-cognition is associated with cortical thickness in youth at clinical high risk of psychosis.
Buchy, Lisa; Stowkowy, Jacque; MacMaster, Frank P; Nyman, Karissa; Addington, Jean
2015-09-30
Meta-cognition is compromised in people with schizophrenia and people at clinical high risk (CHR) of psychosis. In the current work in a CHR sample, we hypothesized that meta-cognitive functions would correlate with cortical thickness in five brain regions implicated in the pathogenesis of psychosis: inferior and middle frontal cortices, anterior cingulate cortex, superior temporal cortex and insula. Secondly, we hypothesized that similar neural systems would underlie different meta-cognitive functions. Narratives were gathered for 29 youth at CHR of psychosis using a semi-structured interview. Four meta-cognitive functions within the narratives were measured with the Meta-cognition Assessment Scale and regressed on cortical thickness from our a priori regions of interest using FreeSurfer. Mapping statistics from our a priori regions of interest revealed that meta-cognition functions were associated with cortical thickness in inferior and middle frontal gyri, superior temporal cortex and insula. The distribution of cortical thickness was partially similar across the four MAS items. Results confirm our hypothesis that cortical thickness is significantly associated with meta-cognition in brain regions that consistently show gray matter reductions across the schizophrenia spectrum. Evidence for thickness covariation in a variety of regions suggests partial dependence in the neural architecture underlying various meta-cognitive functions in CHR. PMID:26210694
Sparse covariance estimation in heterogeneous samples*
Rodríguez, Abel; Lenkoski, Alex; Dobra, Adrian
2015-01-01
Standard Gaussian graphical models implicitly assume that the conditional independence among variables is common to all observations in the sample. However, in practice, observations are usually collected from heterogeneous populations where such an assumption is not satisfied, leading in turn to nonlinear relationships among variables. To address such situations we explore mixtures of Gaussian graphical models; in particular, we consider both infinite mixtures and infinite hidden Markov models where the emission distributions correspond to Gaussian graphical models. Such models allow us to divide a heterogeneous population into homogenous groups, with each cluster having its own conditional independence structure. As an illustration, we study the trends in foreign exchange rate fluctuations in the pre-Euro era. PMID:26925189
Kroos, Julia M.; Diez, Ibai; Cortes, Jesus M.; Stramaglia, Sebastiano; Gerardo-Giorda, Luca
2016-01-01
Cortical spreading depression (CSD), a depolarization wave which originates in the visual cortex and travels toward the frontal lobe, has been suggested to be one neural correlate of aura migraine. To the date, little is known about the mechanisms which can trigger or stop aura migraine. Here, to shed some light on this problem and, under the hypothesis that CSD might mediate aura migraine, we aim to study different aspects favoring or disfavoring the propagation of CSD. In particular, by using a computational neuronal model distributed throughout a realistic cortical mesh, we study the role that the geometry has in shaping CSD. Our results are two-fold: first, we found significant differences in the propagation traveling patterns of CSD, both intra and inter-hemispherically, revealing important asymmetries in the propagation profile. Second, we developed methods able to identify brain regions featuring a peculiar behavior during CSD propagation. Our study reveals dynamical aspects of CSD, which, if applied to subject-specific cortical geometry, might shed some light on how to differentiate between healthy subjects and those suffering migraine. PMID:26869913
Kroos, Julia M; Diez, Ibai; Cortes, Jesus M; Stramaglia, Sebastiano; Gerardo-Giorda, Luca
2016-01-01
Cortical spreading depression (CSD), a depolarization wave which originates in the visual cortex and travels toward the frontal lobe, has been suggested to be one neural correlate of aura migraine. To the date, little is known about the mechanisms which can trigger or stop aura migraine. Here, to shed some light on this problem and, under the hypothesis that CSD might mediate aura migraine, we aim to study different aspects favoring or disfavoring the propagation of CSD. In particular, by using a computational neuronal model distributed throughout a realistic cortical mesh, we study the role that the geometry has in shaping CSD. Our results are two-fold: first, we found significant differences in the propagation traveling patterns of CSD, both intra and inter-hemispherically, revealing important asymmetries in the propagation profile. Second, we developed methods able to identify brain regions featuring a peculiar behavior during CSD propagation. Our study reveals dynamical aspects of CSD, which, if applied to subject-specific cortical geometry, might shed some light on how to differentiate between healthy subjects and those suffering migraine. PMID:26869913
Inference of the sparse kinetic Ising model using the decimation method.
Decelle, Aurélien; Zhang, Pan
2015-05-01
In this paper we study the inference of the kinetic Ising model on sparse graphs by the decimation method. The decimation method, which was first proposed in Decelle and Ricci-Tersenghi [Phys. Rev. Lett. 112, 070603 (2014)] for the static inverse Ising problem, tries to recover the topology of the inferred system by setting the weakest couplings to zero iteratively. During the decimation process the likelihood function is maximized over the remaining couplings. Unlike the ℓ(1)-optimization-based methods, the decimation method does not use the Laplace distribution as a heuristic choice of prior to select a sparse solution. In our case, the whole process can be done auto-matically without fixing any parameters by hand. We show that in the dynamical inference problem, where the task is to reconstruct the couplings of an Ising model given the data, the decimation process can be applied naturally into a maximum-likelihood optimization algorithm, as opposed to the static case where pseudolikelihood method needs to be adopted. We also use extensive numerical studies to validate the accuracy of our methods in dynamical inference problems. Our results illustrate that, on various topologies and with different distribution of couplings, the decimation method outperforms the widely used ℓ(1)-optimization-based methods. PMID:26066148
Sparse-flowering orchardgrass is stable across temperate North America
Technology Transfer Automated Retrieval System (TEKTRAN)
Three sparse-flowering orchardgrass populations were developed by selective breeding as a mechanism to reduce stem production during the early spring season in management-intensive grazing systems. These populations and three check cultivars were evaluated under frequent- and infrequent-harvest syst...
Voxel selection in FMRI data analysis based on sparse representation.
Li, Yuanqing; Namburi, Praneeth; Yu, Zhuliang; Guan, Cuntai; Feng, Jianfeng; Gu, Zhenghui
2009-10-01
Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method. PMID:19567340
Global and Local Sparse Subspace Optimization for Motion Segmentation
NASA Astrophysics Data System (ADS)
Yang, M. Ying; Feng, S.; Ackermann, H.; Rosenhahn, B.
2015-08-01
In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result, we propose an error estimation to encourage the projected data that span a same local subspace to be clustered together. In the end, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and sparse neighbors optimization. We test our method extensively and compare it with state-of-the-art methods on the Hopkins 155 dataset. The results show that our method is comparable with the other motion segmentation methods, and in many cases exceed them in terms of precision and computation time.
Multiple kernel sparse representations for supervised and unsupervised learning.
Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas
2014-07-01
In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods. PMID:24833593
Automatic stellar spectral classification via sparse representations and dictionary learning
NASA Astrophysics Data System (ADS)
Díaz-Hernández, R.; Peregrina-Barreto, H.; Altamirano-Robles, L.; González-Bernal, J. A.; Ortiz-Esquivel, A. E.
2014-11-01
Stellar classification is an important topic in astronomical tasks such as the study of stellar populations. However, stellar classification of a region of the sky is a time-consuming process due to the large amount of objects present in an image. Therefore, automatic techniques to speed up the process are required. In this work, we study the application of a sparse representation and a dictionary learning for automatic spectral stellar classification. Our dataset consist of 529 calibrated stellar spectra of classes B to K, belonging to the Pulkovo Spectrophotometric catalog, in the 3400-5500Å range. These stellar spectra are used for both training and testing of the proposed methodology. The sparse technique is applied by using the greedy algorithm OMP (Orthogonal Matching Pursuit) for finding an approximated solution, and the K-SVD (K-Singular Value Decomposition) for the dictionary learning step. Thus, sparse classification is based on the recognition of the common characteristics of a particular stellar type through the construction of a trained basis. In this work, we propose a classification criterion that evaluates the results of the sparse representation techniques and determines the final classification of the spectra. This methodology demonstrates its ability to achieve levels of classification comparable with automatic methodologies previously reported such as the Maximum Correlation Coefficient (MCC) and Artificial Neural Networks (ANN).
SAR imaging via iterative adaptive approach and sparse Bayesian learning
NASA Astrophysics Data System (ADS)
Xue, Ming; Santiago, Enrique; Sedehi, Matteo; Tan, Xing; Li, Jian
2009-05-01
We consider sidelobe reduction and resolution enhancement in synthetic aperture radar (SAR) imaging via an iterative adaptive approach (IAA) and a sparse Bayesian learning (SBL) method. The nonparametric weighted least squares based IAA algorithm is a robust and user parameter-free adaptive approach originally proposed for array processing. We show that it can be used to form enhanced SAR images as well. SBL has been used as a sparse signal recovery algorithm for compressed sensing. It has been shown in the literature that SBL is easy to use and can recover sparse signals more accurately than the l 1 based optimization approaches, which require delicate choice of the user parameter. We consider using a modified expectation maximization (EM) based SBL algorithm, referred to as SBL-1, which is based on a three-stage hierarchical Bayesian model. SBL-1 is not only more accurate than benchmark SBL algorithms, but also converges faster. SBL-1 is used to further enhance the resolution of the SAR images formed by IAA. Both IAA and SBL-1 are shown to be effective, requiring only a limited number of iterations, and have no need for polar-to-Cartesian interpolation of the SAR collected data. This paper characterizes the achievable performance of these two approaches by processing the complex backscatter data from both a sparse case study and a backhoe vehicle in free space with different aperture sizes.
HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION
Mukherjee, Rajarshi; Pillai, Natesh S.; Lin, Xihong
2015-01-01
In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing problem when the design matrix is sparse. We observe a new phenomenon in the behavior of detection boundary which does not occur in the case of Gaussian linear regression. We derive the detection boundary as a function of two components: a design matrix sparsity index and signal strength, each of which is a function of the sparsity of the alternative. For any alternative, if the design matrix sparsity index is too high, any test is asymptotically powerless irrespective of the magnitude of signal strength. For binary design matrices with the sparsity index that is not too high, our results are parallel to those in the Gaussian case. In this context, we derive detection boundaries for both dense and sparse regimes. For the dense regime, we show that the generalized likelihood ratio is rate optimal; for the sparse regime, we propose an extended Higher Criticism Test and show it is rate optimal and sharp. We illustrate the finite sample properties of the theoretical results using simulation studies. PMID:26246645
Tensor methods for large sparse systems of nonlinear equations
Bouaricha, A.; Schnabel, R.B.
1996-12-31
This paper introduces censor methods for solving, large sparse systems of nonlinear equations. Tensor methods for nonlinear equations were developed in the context of solving small to medium- sized dense problems. They base each iteration on a quadratic model of the nonlinear equations. where the second-order term is selected so that the model requires no more derivative or function information per iteration than standard linear model-based methods, and hardly more storage or arithmetic operations per iteration. Computational experiments on small to medium-sized problems have shown censor methods to be considerably more efficient than standard Newton-based methods, with a particularly large advantage on singular problems. This paper considers the extension of this approach to solve large sparse problems. The key issue that must be considered is how to make efficient use of sparsity in forming and solving the censor model problem at each iteration. Accomplishing this turns out to require an entirely new way of solving the tensor model that successfully exploits the sparsity of the Jacobian, whether the Jacobian is nonsingular or singular. We develop such an approach and, based upon it, an efficient tensor method for solving large sparse systems of nonlinear equations. Test results indicate that this tensor method is significantly more efficient and robust than an efficient sparse Newton-based method. in terms of iterations, function evaluations. and execution time.
Sparsely sampling the sky: a Bayesian experimental design approach
NASA Astrophysics Data System (ADS)
Paykari, P.; Jaffe, A. H.
2013-08-01
The next generation of galaxy surveys will observe millions of galaxies over large volumes of the Universe. These surveys are expensive both in time and cost, raising questions regarding the optimal investment of this time and money. In this work, we investigate criteria for selecting amongst observing strategies for constraining the galaxy power spectrum and a set of cosmological parameters. Depending on the parameters of interest, it may be more efficient to observe a larger, but sparsely sampled, area of sky instead of a smaller contiguous area. In this work, by making use of the principles of Bayesian experimental design, we will investigate the advantages and disadvantages of the sparse sampling of the sky and discuss the circumstances in which a sparse survey is indeed the most efficient strategy. For the Dark Energy Survey (DES), we find that by sparsely observing the same area in a smaller amount of time, we only increase the errors on the parameters by a maximum of 0.45 per cent. Conversely, investing the same amount of time as the original DES to observe a sparser but larger area of sky, we can in fact constrain the parameters with errors reduced by 28 per cent.
Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering
Sicat, Ronell; Krüger, Jens; Möller, Torsten; Hadwiger, Markus
2015-01-01
This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs. PMID:26146475
Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules
Sacramento, João; Wichert, Andreas; van Rossum, Mark C. W.
2015-01-01
It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L 1-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum. PMID:26046817
Robust visual tracking of infrared object via sparse representation model
NASA Astrophysics Data System (ADS)
Ma, Junkai; Liu, Haibo; Chang, Zheng; Hui, Bin
2014-11-01
In this paper, we propose a robust tracking method for infrared object. We introduce the appearance model and the sparse representation in the framework of particle filter to achieve this goal. Representing every candidate image patch as a linear combination of bases in the subspace which is spanned by the target templates is the mechanism behind this method. The natural property, that if the candidate image patch is the target so the coefficient vector must be sparse, can ensure our algorithm successfully. Firstly, the target must be indicated manually in the first frame of the video, then construct the dictionary using the appearance model of the target templates. Secondly, the candidate image patches are selected in following frames and the sparse coefficient vectors of them are calculated via l1-norm minimization algorithm. According to the sparse coefficient vectors the right candidates is determined as the target. Finally, the target templates update dynamically to cope with appearance change in the tracking process. This paper also addresses the problem of scale changing and the rotation of the target occurring in tracking. Theoretic analysis and experimental results show that the proposed algorithm is effective and robust.
Beam hardening correction for sparse-view CT reconstruction
NASA Astrophysics Data System (ADS)
Liu, Wenlei; Rong, Junyan; Gao, Peng; Liao, Qimei; Lu, HongBing
2015-03-01
Beam hardening, which is caused by spectrum polychromatism of the X-ray beam, may result in various artifacts in the reconstructed image and degrade image quality. The artifacts would be further aggravated for the sparse-view reconstruction due to insufficient sampling data. Considering the advantages of the total-variation (TV) minimization in CT reconstruction with sparse-view data, in this paper, we propose a beam hardening correction method for sparse-view CT reconstruction based on Brabant's modeling. In this correction model for beam hardening, the attenuation coefficient of each voxel at the effective energy is modeled and estimated linearly, and can be applied in an iterative framework, such as simultaneous algebraic reconstruction technique (SART). By integrating the correction model into the forward projector of the algebraic reconstruction technique (ART), the TV minimization can recover images when only a limited number of projections are available. The proposed method does not need prior information about the beam spectrum. Preliminary validation using Monte Carlo simulations indicates that the proposed method can provide better reconstructed images from sparse-view projection data, with effective suppression of artifacts caused by beam hardening. With appropriate modeling of other degrading effects such as photon scattering, the proposed framework may provide a new way for low-dose CT imaging.
Inference algorithms and learning theory for Bayesian sparse factor analysis
NASA Astrophysics Data System (ADS)
Rattray, Magnus; Stegle, Oliver; Sharp, Kevin; Winn, John
2009-12-01
Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.