Sparse and powerful cortical spikes.
Wolfe, Jason; Houweling, Arthur R; Brecht, Michael
2010-06-01
Activity in cortical networks is heterogeneous, sparse and often precisely timed. The functional significance of sparseness and precise spike timing is debated, but our understanding of the developmental and synaptic mechanisms that shape neuronal discharge patterns has improved. Evidence for highly specialized, selective and abstract cortical response properties is accumulating. Singe-cell stimulation experiments demonstrate a high sensitivity of cortical networks to the action potentials of some, but not all, single neurons. It is unclear how this sensitivity of cortical networks to small perturbations comes about and whether it is a generic property of cortex. The unforeseen sensitivity to cortical spikes puts serious constraints on the nature of neural coding schemes.
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.
Reconstructing cortical current density by exploring sparseness in the transform domain.
Ding, Lei
2009-05-07
In the present study, we have developed a novel electromagnetic source imaging approach to reconstruct extended cortical sources by means of cortical current density (CCD) modeling and a novel EEG imaging algorithm which explores sparseness in cortical source representations through the use of L1-norm in objective functions. The new sparse cortical current density (SCCD) imaging algorithm is unique since it reconstructs cortical sources by attaining sparseness in a transform domain (the variation map of cortical source distributions). While large variations are expected to occur along boundaries (sparseness) between active and inactive cortical regions, cortical sources can be reconstructed and their spatial extents can be estimated by locating these boundaries. We studied the SCCD algorithm using numerous simulations to investigate its capability in reconstructing cortical sources with different extents and in reconstructing multiple cortical sources with different extent contrasts. The SCCD algorithm was compared with two L2-norm solutions, i.e. weighted minimum norm estimate (wMNE) and cortical LORETA. Our simulation data from the comparison study show that the proposed sparse source imaging algorithm is able to accurately and efficiently recover extended cortical sources and is promising to provide high-accuracy estimation of cortical source extents.
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.
Liao, Ke; Zhu, Min; Ding, Lei
2013-08-01
The present study investigated the use of transform sparseness of cortical current density on human brain surface to improve electroencephalography/magnetoencephalography (EEG/MEG) inverse solutions. Transform sparseness was assessed by evaluating compressibility of cortical current densities in transform domains. To do that, a structure compression method from computer graphics was first adopted to compress cortical surface structure, either regular or irregular, into hierarchical multi-resolution meshes. Then, a new face-based wavelet method based on generated multi-resolution meshes was proposed to compress current density functions defined on cortical surfaces. Twelve cortical surface models were built by three EEG/MEG softwares and their structural compressibility was evaluated and compared by the proposed method. Monte Carlo simulations were implemented to evaluate the performance of the proposed wavelet method in compressing various cortical current density distributions as compared to other two available vertex-based wavelet methods. The present results indicate that the face-based wavelet method can achieve higher transform sparseness than vertex-based wavelet methods. Furthermore, basis functions from the face-based wavelet method have lower coherence against typical EEG and MEG measurement systems than vertex-based wavelet methods. Both high transform sparseness and low coherent measurements suggest that the proposed face-based wavelet method can improve the performance of L1-norm regularized EEG/MEG inverse solutions, which was further demonstrated in simulations and experimental setups using MEG data. Thus, this new transform on complicated cortical structure is promising to significantly advance EEG/MEG inverse source imaging technologies.
Sparse imaging of cortical electrical current densities via wavelet transforms
NASA Astrophysics Data System (ADS)
Liao, Ke; Zhu, Min; Ding, Lei; Valette, Sébastien; Zhang, Wenbo; Dickens, Deanna
2012-11-01
While the cerebral cortex in the human brain is of functional importance, functions defined on this structure are difficult to analyze spatially due to its highly convoluted irregular geometry. This study developed a novel L1-norm regularization method using a newly proposed multi-resolution face-based wavelet method to estimate cortical electrical activities in electroencephalography (EEG) and magnetoencephalography (MEG) inverse problems. The proposed wavelets were developed based on multi-resolution models built from irregular cortical surface meshes, which were realized in this study too. The multi-resolution wavelet analysis was used to seek sparse representation of cortical current densities in transformed domains, which was expected due to the compressibility of wavelets, and evaluated using Monte Carlo simulations. The EEG/MEG inverse problems were solved with the use of the novel L1-norm regularization method exploring the sparseness in the wavelet domain. The inverse solutions obtained from the new method using MEG data were evaluated by Monte Carlo simulations too. The present results indicated that cortical current densities could be efficiently compressed using the proposed face-based wavelet method, which exhibited better performance than the vertex-based wavelet method. In both simulations and auditory experimental data analysis, the proposed L1-norm regularization method showed better source detection accuracy and less estimation errors than other two classic methods, i.e. weighted minimum norm (wMNE) and cortical low-resolution electromagnetic tomography (cLORETA). This study suggests that the L1-norm regularization method with the use of face-based wavelets is a promising tool for studying functional activations of the human brain.
Sparse cortical source localization using spatio-temporal atoms.
Korats, Gundars; Ranta, Radu; Le Cam, Steven; Louis-Dorr, Valérie
2015-01-01
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.
Surround suppression and sparse coding in visual and barrel cortices
Sachdev, Robert N. S.; Krause, Matthew R.; Mazer, James A.
2012-01-01
During natural vision the entire retina is stimulated. Likewise, during natural tactile behaviors, spatially extensive regions of the somatosensory surface are co-activated. The large spatial extent of naturalistic stimulation means that surround suppression, a phenomenon whose neural mechanisms remain a matter of debate, must arise during natural behavior. To identify common neural motifs that might instantiate surround suppression across modalities, we review models of surround suppression and compare the evidence supporting the competing ideas that surround suppression has either cortical or sub-cortical origins in visual and barrel cortex. In the visual system there is general agreement lateral inhibitory mechanisms contribute to surround suppression, but little direct experimental evidence that intracortical inhibition plays a major role. Two intracellular recording studies of V1, one using naturalistic stimuli (Haider et al., 2010), the other sinusoidal gratings (Ozeki et al., 2009), sought to identify the causes of reduced activity in V1 with increasing stimulus size, a hallmark of surround suppression. The former attributed this effect to increased inhibition, the latter to largely balanced withdrawal of excitation and inhibition. In rodent primary somatosensory barrel cortex, multi-whisker responses are generally weaker than single whisker responses, suggesting multi-whisker stimulation engages similar surround suppressive mechanisms. The origins of suppression in S1 remain elusive: studies have implicated brainstem lateral/internuclear interactions and both thalamic and cortical inhibition. Although the anatomical organization and instantiation of surround suppression in the visual and somatosensory systems differ, we consider the idea that one common function of surround suppression, in both modalities, is to remove the statistical redundancies associated with natural stimuli by increasing the sparseness or selectivity of sensory responses. PMID:22783169
Sparse cortical current density imaging in motor potentials induced by finger movement
NASA Astrophysics Data System (ADS)
Ding, Lei; Ni, Ying; Sweeney, John; He, Bin
2011-06-01
Predominant components in electro- or magneto-encephalography (EEG/MEG) are scalp projections of synchronized neuronal electrical activity distributed over cortical structures. Reconstruction of cortical sources underlying EEG/MEG can thus be achieved with the use of the cortical current density (CCD) model. We have developed a sparse electromagnetic source imaging method based on the CCD model, named as the variation-based cortical current density (VB-SCCD) algorithm, and have shown that it has much enhanced performance in reconstructing extended cortical sources in simulations (Ding 2009 Phys. Med. Biol. 54 2683-97). The present study aims to evaluate the performance of VB-SCCD, for the first time, using experimental data obtained from six participants. The results indicate that the VB-SCCD algorithm is able to successfully reveal spatially distributed cortical sources behind motor potentials induced by visually cued repetitive finger movements, and their dynamic patterns, with millisecond resolution. These findings of motor sources and cortical systems are supported by the physiological knowledge of motor control and evidence from various neuroimaging studies with similar experiments. Furthermore, our present results indicate the improvement of cortical source resolvability of VB-SCCD, as compared with two other classical algorithms. The proposed solver embedded in VB-SCCD is able to handle large-scale computational problems, which makes the use of high-density CCD models possible and, thus, reduces model misspecifications. The present results suggest that VB-SCCD provides high resolution source reconstruction capability and is a promising tool for studying complicated dynamic systems of brain activity for basic neuroscience and clinical neuropsychiatric research.
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.
Multiple sparse volumetric priors for distributed EEG source reconstruction.
Strobbe, Gregor; van Mierlo, Pieter; De Vos, Maarten; Mijović, Bogdan; Hallez, Hans; Van Huffel, Sabine; López, José David; Vandenberghe, Stefaan
2014-10-15
We revisit the multiple sparse priors (MSP) algorithm implemented in the statistical parametric mapping software (SPM) for distributed EEG source reconstruction (Friston et al., 2008). In the present implementation, multiple cortical patches are introduced as source priors based on a dipole source space restricted to a cortical surface mesh. In this note, we present a technique to construct volumetric cortical regions to introduce as source priors by restricting the dipole source space to a segmented gray matter layer and using a region growing approach. This extension allows to reconstruct brain structures besides the cortical surface and facilitates the use of more realistic volumetric head models including more layers, such as cerebrospinal fluid (CSF), compared to the standard 3-layered scalp-skull-brain head models. We illustrated the technique with ERP data and anatomical MR images in 12 subjects. Based on the segmented gray matter for each of the subjects, cortical regions were created and introduced as source priors for MSP-inversion assuming two types of head models. The standard 3-layered scalp-skull-brain head models and extended 4-layered head models including CSF. We compared these models with the current implementation by assessing the free energy corresponding with each of the reconstructions using Bayesian model selection for group studies. Strong evidence was found in favor of the volumetric MSP approach compared to the MSP approach based on cortical patches for both types of head models. Overall, the strongest evidence was found in favor of the volumetric MSP reconstructions based on the extended head models including CSF. These results were verified by comparing the reconstructed activity. The use of volumetric cortical regions as source priors is a useful complement to the present implementation as it allows to introduce more complex head models and volumetric source priors in future studies.
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.
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
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 patterns.
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.
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.
Multiple peaks of species abundance distributions induced by sparse interactions
NASA Astrophysics Data System (ADS)
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.
Mejias, Jorge F.; Longtin, André
2014-01-01
Recent experimental and theoretical studies have highlighted the importance of cell-to-cell differences in the dynamics and functions of neural networks, such as in different types of neural coding or synchronization. It is still not known, however, how neural heterogeneity can affect cortical computations, or impact the dynamics of typical cortical circuits constituted of sparse excitatory and inhibitory networks. In this work, we analytically and numerically study the dynamics of a typical cortical circuit with a certain level of neural heterogeneity. Our circuit includes realistic features found in real cortical populations, such as network sparseness, excitatory, and inhibitory subpopulations of neurons, and different cell-to-cell heterogeneities for each type of population in the system. We find highly differentiated roles for heterogeneity, depending on the subpopulation in which it is found. In particular, while heterogeneity among excitatory neurons non-linearly increases the mean firing rate and linearizes the f-I curves, heterogeneity among inhibitory neurons may decrease the network activity level and induces divisive gain effects in the f-I curves of the excitatory cells, providing an effective gain control mechanism to influence information flow. In addition, we compute the conditions for stability of the network activity, finding that the synchronization onset is robust to inhibitory heterogeneity, but it shifts to lower input levels for higher excitatory heterogeneity. Finally, we provide an extension of recently reported heterogeneity-induced mechanisms for signal detection under rate coding, and we explore the validity of our findings when multiple sources of heterogeneity are present. These results allow for a detailed characterization of the role of neural heterogeneity in asynchronous cortical networks. PMID:25309409
Probing the sparse tails of redshift distributions with Voronoi tessellations
NASA Astrophysics Data System (ADS)
Granett, B. R.
2017-01-01
We introduce an empirical galaxy photometric redshift algorithm based upon the Voronoi tessellation density estimator in the space of redshift and photometric parameters. Our aim is to use sparse survey datasets to estimate the full shape of the redshift distribution that is defined by the degeneracies in galaxy photometric properties and redshift. We describe the algorithm implementation and provide a proof of concept using the first public data release from the VIMOS Public Extragalactic Redshift Survey (VIPERS PDR-1). We validate the method by comparing against the standard empirical redshift distribution code Trees for Photo-Z (TPZ) on both mock and real data. We find that the Voronoi tessellation algorithm accurately recovers the full shape of the redshift distribution quantified by its second moment and inferred redshift confidence intervals. The analysis allows us to properly account for galaxies in the tails of the distributions that would otherwise be classified as catastrophic outliers. The source code is publicly available at http://bitbucket.org/bengranett/tailz.
Haider, Bilal; Krause, Matthew R.; Duque, Alvaro; Yu, Yuguo; Touryan, Jonathan; Mazer, James A.; McCormick, David A.
2011-01-01
SUMMARY During natural vision, the entire visual field is stimulated by images rich in spatiotemporal structure. Although many visual system studies restrict stimuli to the classical receptive field (CRF), it is known that costimulation of the CRF and the surrounding nonclassical receptive field (nCRF) increases neuronal response sparseness. The cellular and network mechanisms underlying increased response sparseness remain largely unexplored. Here we show that combined CRF + nCRF stimulation increases the sparseness, reliability, and precision of spiking and membrane potential responses in classical regular spiking (RSC) pyramidal neurons of cat primary visual cortex. Conversely, fast-spiking interneurons exhibit increased activity and decreased selectivity during CRF + nCRF stimulation. The increased sparseness and reliability of RSC neuron spiking is associated with increased inhibitory barrages and narrower visually evoked synaptic potentials. Our experimental observations were replicated with a simple computational model, suggesting that network interactions among neuronal subtypes ultimately sharpen recurrent excitation, producing specific and reliable visual responses. PMID:20152117
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.
Sparse Distributed Representation and Hierarchy: Keys to Scalable Machine Intelligence
2016-04-01
Government. Report contains color. 14. ABSTRACT We developed and tested a cortically-inspired model of spatiotemporal pattern learning and recognition...analogs of the canonical cortical processing module known as macrocolumns. Sparsey differs from mainstream neural models, e.g., Deep Learning , in many...ways including: a) it uses single-trial, Hebbian learning rather than incremental, many-trial, gradient- based learning ; and b) it multiplicatively
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.
Sleep regulation of the distribution of cortical firing rates.
Levenstein, Daniel; Watson, Brendon O; Rinzel, John; Buzsáki, György
2017-03-10
Sleep is thought to mediate both mnemonic and homeostatic functions. However, the mechanism by which this brain state can simultaneously implement the 'selective' plasticity needed to consolidate novel memory traces and the 'general' plasticity necessary to maintain a well-functioning neuronal system is unclear. Recent findings show that both of these functions differentially affect neurons based on their intrinsic firing rate, a ubiquitous neuronal heterogeneity. Furthermore, they are both implemented by the NREM slow oscillation, which also distinguishes neurons based on firing rate during sequential activity at the DOWN→UP transition. These findings suggest a mechanism by which spiking activity during the slow oscillation acts to maintain network statistics that promote a skewed distribution of neuronal firing rates, and perturbation of that activity by hippocampal replay acts to integrate new memory traces into the existing cortical network.
The Cortex Transform as an image preprocessor for sparse distributed memory: An initial study
NASA Technical Reports Server (NTRS)
Olshausen, Bruno; Watson, Andrew
1990-01-01
An experiment is described which was designed to evaluate the use of the Cortex Transform as an image processor for Sparse Distributed Memory (SDM). In the experiment, a set of images were injected with Gaussian noise, preprocessed with the Cortex Transform, and then encoded into bit patterns. The various spatial frequency bands of the Cortex Transform were encoded separately so that they could be evaluated based on their ability to properly cluster patterns belonging to the same class. The results of this study indicate that by simply encoding the low pass band of the Cortex Transform, a very suitable input representation for the SDM can be achieved.
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.
Oryspayev, Dossay; Aktulga, Hasan Metin; Sosonkina, Masha; ...
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
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.
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.
Ohman, J C; Krochta, T J; Lovejoy, C O; Mensforth, R P; Latimer, B
1997-09-01
Contiguous high resolution computed tomography images were obtained at a 1.5 mm slice thickness perpendicular to the neck axis from the base of the femoral head to the trochanteric line in a sample of 10 specimens each of Homo sapiens, Pan troglodytes, and Gorilla gorilla, plus five specimens of Pan paniscus. Superior, inferior, anterior, and posterior cortical thicknesses were automatically measured directly from these digital images. Throughout the femoral neck H. sapiens displays thin superior cortical bone and inferior cortical bone that thickens distally. In marked contrast, cortical bone in the femoral neck of African apes is more uniformly thick in all directions, with even greater thickening of the superior cortical bone distally. Because the femoral neck acts as a cantilevered beam, its anchorage at the neck-shaft junction is subjected to the highest bending stresses and is the most biomechanically relevant region to inspect for response to strain. As evinced by A.L. 128-1, A.L. 211-1 and MAK-VP-1/1, Australopithecus afarensis is indistinguishable from H. sapiens, but markedly different from African apes in cortical bone distribution at the femoral neck-shaft junction. Cortical distribution in the African ape indicates much greater variation in loading conditions consistent with their more varied locomotor repertoire. Cortical distribution in hominids is a response to the more stereotypic loading pattern imposed by habitual bipedality, and thin superior cortex in A. afarensis confirms the absence of a significant arboreal component in its locomotor repertoire.
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…
Kada, Hisashi; Teramae, Jun-Nosuke; Tokuda, Isao T.
2016-01-01
Even without external random input, cortical networks in vivo sustain asynchronous irregular firing with low firing rate. In addition to detailed balance between excitatory and inhibitory activities, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., long-tailed distribution of excitatory synapses implying coexistence of many weak and a few extremely strong excitatory synapses, plays an essential role in realizing the self-sustained activity in recurrent networks of biologically plausible spiking neurons. The previous studies, however, have not considered highly non-random features of the synaptic connectivity, namely, bidirectional connections between cortical neurons are more common than expected by chance and strengths of synapses are positively correlated between pre- and postsynaptic neurons. The positive correlation of synaptic connections may destabilize asynchronous activity of networks with the long-tailed synaptic distribution and induce pathological synchronized firing among neurons. It remains unclear how the cortical network avoids such pathological synchronization. Here, we demonstrate that introduction of the correlated connections indeed gives rise to synchronized firings in a cortical network model with the long-tailed distribution. By using a simplified feed-forward network model of spiking neurons, we clarify the underlying mechanism of the synchronization. We then show that the synchronization can be efficiently suppressed by highly heterogeneous distribution, typically a lognormal distribution, of inhibitory-to-excitatory connection strengths in a recurrent network model of cortical neurons. PMID:27803659
Xie, Huiqiao; Yang, Yi; Tang, Xiangyang; Niu, Tianye; Ren, Yi
2015-06-15
Purpose: Optimization-based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi-cycle cardiovascular imaging, we investigate the performance of optimization-based image reconstruction from unevenly sparse projection views in this work. Methods: The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, which are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub-scan I. Another 82 views are sorted out in a similar manner to form sub-scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub-scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization-based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization-based reconstruction is used to evaluate the performance quantitatively. Results: In visual inspection, the optimization-based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom. Conclusion: The optimization-based image reconstruction can save radiation dose up to 12-fold while providing acceptable image quality
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.
NASA Astrophysics Data System (ADS)
Shi, X.; Zhang, G.
2013-12-01
Because of the extensive computational burden, parametric uncertainty analyses are rarely conducted for geological carbon sequestration (GCS) process based multi-phase models. The difficulty of predictive uncertainty analysis for the CO2 plume migration in realistic GCS models is not only due to the spatial distribution of the caprock and reservoir (i.e. heterogeneous model parameters), but also because the GCS optimization estimation problem has multiple local minima due to the complex nonlinear multi-phase (gas and aqueous), and multi-component (water, CO2, salt) transport equations. The geological model built by Doughty and Pruess (2004) for the Frio pilot site (Texas) was selected and assumed to represent the 'true' system, which was composed of seven different facies (geological units) distributed among 10 layers. We chose to calibrate the permeabilities of these facies. Pressure and gas saturation values from this true model were then extracted and used as observations for subsequent model calibration. Random noise was added to the observations to approximate realistic field conditions. Each simulation of the model lasts about 2 hours. In this study, we develop a new approach that improves computational efficiency of Bayesian inference by constructing a surrogate system based on an adaptive sparse-grid stochastic collocation method. This surrogate response surface global optimization algorithm is firstly used to calibrate the model parameters, then prediction uncertainty of the CO2 plume position is quantified due to the propagation from parametric uncertainty in the numerical experiments, which is also compared to the actual plume from the 'true' model. Results prove that the approach is computationally efficient for multi-modal optimization and prediction uncertainty quantification for computationally expensive simulation models. Both our inverse methodology and findings can be broadly applicable to GCS in heterogeneous storage formations.
Uncertainty in flood forecasting: A distributed modeling approach in a sparse data catchment
NASA Astrophysics Data System (ADS)
Mendoza, Pablo A.; McPhee, James; Vargas, Ximena
2012-09-01
Data scarcity has traditionally precluded the application of advanced hydrologic techniques in developing countries. In this paper, we evaluate the performance of a flood forecasting scheme in a sparsely monitored catchment based on distributed hydrologic modeling, discharge assimilation, and numerical weather predictions with explicit validation uncertainty analysis. For the hydrologic component of our framework, we apply TopNet to the Cautin River basin, located in southern Chile, using a fully distributed a priori parameterization based on both literature-suggested values and data gathered during field campaigns. Results obtained from this step indicate that the incremental effort spent in measuring directly a set of model parameters was insufficient to represent adequately the most relevant hydrologic processes related to spatiotemporal runoff patterns. Subsequent uncertainty validation performed over a six month ensemble simulation shows that streamflow uncertainty is better represented during flood events, due to both the increase of state perturbation introduced by rainfall and the flood-oriented calibration strategy adopted here. Results from different assimilation configurations suggest that the upper part of the basin is the major source of uncertainty in hydrologic process representation and hint at the usefulness of interpreting assimilation results in terms of model input and parameterization inadequacy. Furthermore, in this case study the violation of Markovian state properties by the Ensemble Kalman filter did affect the numerical results, showing that an explicit treatment of the time delay between the generation of surface runoff and the arrival at the basin outlet is required in the assimilation scheme. Peak flow forecasting results demonstrate that there is a major problem with the Weather Research and Forecasting model outputs, which systematically overestimate precipitation over the catchment. A final analysis performed for a large flooding
Retrieval of high-fidelity memory arises from distributed cortical networks.
Wais, Peter E; Jahanikia, Sahar; Steiner, Daniel; Stark, Craig E L; Gazzaley, Adam
2017-04-01
Medial temporal lobe (MTL) function is well established as necessary for memory of facts and events. It is likely that lateral cortical regions critically guide cognitive control processes to tune in high-fidelity details that are most relevant for memory retrieval. Here, convergent results from functional and structural MRI show that retrieval of detailed episodic memory arises from lateral cortical-MTL networks, including regions of inferior frontal and angular gyrii. Results also suggest that recognition of items based on low-fidelity, generalized information, rather than memory arising from retrieval of relevant episodic details, is not associated with functional connectivity between MTL and lateral cortical regions. Additionally, individual differences in microstructural properties in white matter pathways, associated with distributed MTL-cortical networks, are positively correlated with better performance on a mnemonic discrimination task.
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.
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
Reinsberger, Claus; Tanaka, Naoaki; Cole, Andrew J; Lee, Jong Woo; Dworetzky, Barbara A; Bromfield, Edward B; Hamiwka, Lorie; Bourgeois, Blaise F; Golby, Alexandra J; Madsen, Joseph R; Stufflebeam, Steven M
2010-10-01
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) have vertically oriented antero-medial equivalent current dipoles (ECDs). 10 patients (Group B) have ECDs that are diversely oriented and widely distributed. Group analysis of MRI data shows widespread cortical thinning in Group B compared with Group A, in the left hemisphere involving the cingulate, supramarginal, occipitotemporal 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.
NASA Astrophysics Data System (ADS)
Hanuš, J.; Ďurech, J.; Brož, M.; Marciniak, A.; Warner, B. D.; Pilcher, F.; Stephens, R.; Behrend, R.; Carry, B.; Čapek, D.; Antonini, P.; Audejean, M.; Augustesen, K.; Barbotin, E.; Baudouin, P.; Bayol, A.; Bernasconi, L.; Borczyk, W.; Bosch, J.-G.; Brochard, E.; Brunetto, L.; Casulli, S.; Cazenave, A.; Charbonnel, S.; Christophe, B.; Colas, F.; Coloma, J.; Conjat, M.; Cooney, W.; Correira, H.; Cotrez, V.; Coupier, A.; Crippa, R.; Cristofanelli, M.; Dalmas, Ch.; Danavaro, C.; Demeautis, C.; Droege, T.; Durkee, R.; Esseiva, N.; Esteban, M.; Fagas, M.; Farroni, G.; Fauvaud, M.; Fauvaud, S.; Del Freo, F.; Garcia, L.; Geier, S.; Godon, C.; Grangeon, K.; Hamanowa, H.; Hamanowa, H.; Heck, N.; Hellmich, S.; Higgins, D.; Hirsch, R.; Husarik, M.; Itkonen, T.; Jade, O.; Kamiński, K.; Kankiewicz, P.; Klotz, A.; Koff, R. A.; Kryszczyńska, A.; Kwiatkowski, T.; Laffont, A.; Leroy, A.; Lecacheux, J.; Leonie, Y.; Leyrat, C.; Manzini, F.; Martin, A.; Masi, G.; Matter, D.; Michałowski, J.; Michałowski, M. J.; Michałowski, T.; Michelet, J.; Michelsen, R.; Morelle, E.; Mottola, S.; Naves, R.; Nomen, J.; Oey, J.; Ogłoza, W.; Oksanen, A.; Oszkiewicz, D.; Pääkkönen, P.; Paiella, M.; Pallares, H.; Paulo, J.; Pavic, M.; Payet, B.; Polińska, M.; Polishook, D.; Poncy, R.; Revaz, Y.; Rinner, C.; Rocca, M.; Roche, A.; Romeuf, D.; Roy, R.; Saguin, H.; Salom, P. A.; Sanchez, S.; Santacana, G.; Santana-Ros, T.; Sareyan, J.-P.; Sobkowiak, K.; Sposetti, S.; Starkey, D.; Stoss, R.; Strajnic, J.; Teng, J.-P.; Trégon, B.; Vagnozzi, A.; Velichko, F. P.; Waelchli, N.; Wagrez, K.; Wücher, H.
2013-03-01
Context. The larger number of models of asteroid shapes and their rotational states derived by the lightcurve inversion give us better insight into both the nature of individual objects and the whole asteroid population. With a larger statistical sample we can study the physical properties of asteroid populations, such as main-belt asteroids or individual asteroid families, in more detail. Shape models can also be used in combination with other types of observational data (IR, adaptive optics images, stellar occultations), e.g., to determine sizes and thermal properties. Aims: We use all available photometric data of asteroids to derive their physical models by the lightcurve inversion method and compare the observed pole latitude distributions of all asteroids with known convex shape models with the simulated pole latitude distributions. Methods: We used classical dense photometric lightcurves from several sources (Uppsala Asteroid Photometric Catalogue, Palomar Transient Factory survey, and from individual observers) and sparse-in-time photometry from the U.S. Naval Observatory in Flagstaff, Catalina Sky Survey, and La Palma surveys (IAU codes 689, 703, 950) in the lightcurve inversion method to determine asteroid convex models and their rotational states. We also extended a simple dynamical model for the spin evolution of asteroids used in our previous paper. Results: We present 119 new asteroid models derived from combined dense and sparse-in-time photometry. We discuss the reliability of asteroid shape models derived only from Catalina Sky Survey data (IAU code 703) and present 20 such models. By using different values for a scaling parameter cYORP (corresponds to the magnitude of the YORP momentum) in the dynamical model for the spin evolution and by comparing synthetic and observed pole-latitude distributions, we were able to constrain the typical values of the cYORP parameter as between 0.05 and 0.6. Table 3 is available in electronic form at http://www.aanda.org
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.
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
Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants.
Meng, Yu; Li, Gang; Lin, Weili; Gilmore, John H; Shen, Dinggang
2014-10-15
Sulcal pits, the locally deepest points in sulci of the highly convoluted and variable cerebral cortex, are found to be spatially consistent across human adult individuals. It is suggested that sulcal pits are genetically controlled and have close relationships with functional areas. To date, the existing imaging studies of sulcal pits are mainly focused on adult brains, yet little is known about the spatial distribution and temporal development of sulcal pits in the first 2 years of life, which is the most dynamic and critical period of postnatal brain development. Studying sulcal pits during this period would greatly enrich our limited understandings of the origins and developmental trajectories of sulcal pits, and would also provide important insights into many neurodevelopmental disorders associated with abnormal cortical foldings. In this paper, by using surface-based morphometry, for the first time, we systemically investigated the spatial distribution and temporal development of sulcal pits in major cortical sulci from 73 healthy infants, each with three longitudinal 3T MR scans at term birth, 1 year, and 2 years of age. Our results suggest that the spatially consistent distributions of sulcal pits in major sulci across individuals have already existed at term birth and this spatial distribution pattern keeps relatively stable in the first 2 years of life, despite that the cerebral cortex expands dramatically and the sulcal depth increases considerably during this period. Specially, the depth of sulcal pits increases regionally heterogeneously, with more rapid growth in the high-order association cortex, including the prefrontal and temporal cortices, than the sensorimotor cortex in the first 2 years of life. Meanwhile, our results also suggest that there exist hemispheric asymmetries of the spatial distributions of sulcal pits in several cortical regions, such as the central, superior temporal and postcentral sulci, consistently from birth to 2 years of age
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.
Hernández, Adrián; Nácher, Verónica; Luna, Rogelio; Alvarez, Manuel; Zainos, Antonio; Cordero, Silvia; Camarillo, Liliana; Vázquez, Yuriria; Lemus, Luis; Romo, Ranulfo
2008-01-01
We report a procedure for recording the simultaneous activity of single neurons distributed across five cortical areas in behaving monkeys. The procedure consists of a commercially available microdrive adapted to a commercially available neural data collection system. The critical advantage of this procedure is that, in each cortical area, a configuration of seven microelectrodes spaced 250–500 μm can be inserted transdurally and each can be moved independently in the z axis. For each microelectrode, the data collection system can record the activity of up to five neurons together with the local field potential (LFP). With this procedure, we normally monitor the simultaneous activity of 70–100 neurons while trained monkeys discriminate the difference in frequency between two vibrotactile stimuli. Approximately 20–60 of these neurons have response properties previously reported in this task. The neuronal recordings show good signal-to-noise ratio, are remarkably stable along a 1-day session, and allow testing several protocols. Microelectrodes are removed from the brain after a 1-day recording session, but are reinserted again the next day by using the same or different x-y microelectrode array configurations. The fact that microelectrodes can be moved in the z axis during the recording session and that the x-y configuration can be changed from day to day maximizes the probability of studying simultaneous interactions, both local and across distant cortical areas, between neurons associated with the different components of this task. PMID:18946031
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
Sowell, Elizabeth R; Thompson, Paul M; Rex, David; Kornsand, David; Tessner, Kevin D; Jernigan, Terry L; Toga, Arthur W
2002-01-01
Previous in vivo morphometric studies of human brain maturation between childhood and young adulthood have revealed a spatial and temporal pattern of progressive brain changes that is consistent with the post mortem cytoarchitectonic and cognitive developmental literatures. In this study, we mapped age differences in structural asymmetries at the cortical surface in groups of normally developing children (7-11 years), adolescents (12-16 years) and young adults (23-30 years) using novel surface-based mesh modeling image analytic methods. We also assessed relationships between cortical surface sulcal asymmetry and the local density of the underlying cortical gray matter. Results from this study reveal that perisylvian sulcal asymmetries are much more prominent in the adults than in the children studied. The superior posterior extent of the Sylvian fissure in the right hemisphere is approximately 7 mm more superior in the average adult than in the average child studied, whereas little difference is observed during this age range in the location of this anatomical structure in the left hemisphere. Age-related differences in Sylvian fissure asymmetry were significant (P = 0.0129, permutation test), showing increased asymmetry with increasing age. We also show age-related increases in local gray matter proportion bilaterally in the temporo-parietal cortices that are anatomically and temporally related to the sulcal asymmetries. Results from this cross-sectional study imply that asymmetries in the Sylvian fissure are dynamically changing into young adulthood and show that variability in brain tissue density is related to asymmetry in this region. These morphological differences may be related to changing cognitive abilities and are relevant in interpreting results from studies of abnormal brain development where perisylvian brain regions are implicated.
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
Bazzo, João Paulo; Pipa, Daniel Rodrigues; da Silva, Erlon Vagner; Martelli, Cicero; Cardozo da Silva, Jean Carlos
2016-09-07
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.
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.
Effect of Two-Body Motion on Radar Beam Quality for Various Distributed Sparse Array Configurations
1988-12-01
help of many. I would like to thank my thesis advisor, Dr. W. E. Wiesel , for his patience and encouragement throughout the duration of this project. I...corltirue =sm re,1 tururn 7 4 Riblioaraphy 1. Brookner, Eli "Phased-Array Radars," S American. 2J: 94-102 (February 1985). 2. Canan, James W "USAF in the...Warren L. and Thiele, Gary A. Antenna -1 Theory a-nd Design. New York: John Wiley and Sons, 1981. 10. Wiesel , William E Jr. Class handout distributed
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
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.
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.
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.
Krieg, Daniel; Triesch, Jochen
2014-01-01
Long-term synaptic plasticity is fundamental to learning and network function. It has been studied under various induction protocols and depends on firing rates, membrane voltage, and precise timing of action potentials. These protocols show different facets of a common underlying mechanism but they are mostly modeled as distinct phenomena. Here, we show that all of these different dependencies can be explained from a single computational principle. The objective is a sparse distribution of excitatory synaptic strength, which may help to reduce metabolic costs associated with synaptic transmission. Based on this objective we derive a stochastic gradient ascent learning rule which is of differential-Hebbian type. It is formulated in biophysical quantities and can be related to current mechanistic theories of synaptic plasticity. The learning rule accounts for experimental findings from all major induction protocols and explains a classic phenomenon of metaplasticity. Furthermore, our model predicts the existence of metaplasticity for spike-timing-dependent plasticity Thus, we provide a theory of long-term synaptic plasticity that unifies different induction protocols and provides a connection between functional and mechanistic levels of description. PMID:24624080
Child, Nicholas D; Benarroch, Eduardo E
2014-03-18
Neurons contain different functional somatodendritic and axonal domains, each with a characteristic distribution of voltage-gated ion channels, synaptic inputs, and function. The dendritic tree of a cortical pyramidal neuron has 2 distinct domains, the basal and the apical dendrites, both containing dendritic spines; the different domains of the axon are the axonal initial segment (AIS), axon proper (which in myelinated axons includes the node of Ranvier, paranodes, juxtaparanodes, and internodes), and the axon terminals. In the cerebral cortex, the dendritic spines of the pyramidal neurons receive most of the excitatory synapses; distinct populations of γ-aminobutyric acid (GABA)ergic interneurons target specific cellular domains and thus exert different influences on pyramidal neurons. The multiple synaptic inputs reaching the somatodendritic region and generating excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs) sum and elicit changes in membrane potential at the AIS, the site of initiation of the action potential.
Inferring learning rules from distribution of firing rates in cortical neurons
Lim, Sukbin; McKee, Jillian L.; Woloszyn, Luke; Amit, Yali; Freedman, David J.; Sheinberg, David L.; Brunel, Nicolas
2015-01-01
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 inferring the dependence of the ‘learning rule’ on post-synaptic firing rate, and show that the inferred learning rule exhibits depression for low post-synaptic rates and potentiation for high rates. The threshold separating depression from potentiation is strongly correlated with both mean and standard deviation 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
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.
Distribution of cortical granules and meiotic maturation of canine oocytes in bi-phasic systems.
Apparicio, Maricy; Mostachio, Giuliano Q; Motheo, Tathiana F; Alves, Aracelle E; Padilha, Luciana; Pires-Butler, Eliandra A; Savi, Paula A P; Uscategui, Ricardo A R; Luvoni, Gaia C; Vicente, Wilter R R
2015-09-01
The aim of this study was to evaluate the influence of different bi-phasic systems with gonadotrophins and steroids on in vitro maturation rates of oocytes obtained from bitches at different reproductive stages (follicular, luteal, anoestrous). In System A (control) oocytes were matured for 72h in base medium (BM) with 10IUmL(-1) human chorionic gonadotrophin (hCG), 1μgmL(-1) progesterone (P4) and 1μgmL(-1) oestradiol (E2); in bi-phasic System B oocytes were matured for 48h in BM with hCG and for 24h in BM with P4; in bi-phasic System C oocytes were matured for 48h in BM with hCG, P4 and E2, and for 24h in BM with P4; in System D, oocytes were cultured in BM without hormonal supplementation. Data were analysed by ANOVA. There was a positive effect of the bi-phasic systems on germinal vesicle breakdown, metaphase I and metaphase II rates, irrespective of reproductive status (P<0.05). Bi-phasic systems were also beneficial for cortical granule distribution (an indication of cytoplasmic maturation) and its relationship to nuclear status: 74.5% of the oocytes cultured in System B and 85.4% of those cultured in System C presented both nuclear and cytoplasmic maturation (P<0.001). The stage of the oestrous cycle did not influence maturation rates.
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
Shen, Qin; Zhong, Weimin; Jan, Yuh Nung; Temple, Sally
2002-10-01
Stem cells and neuroblasts derived from mouse embryos undergo repeated asymmetric cell divisions, generating neural lineage trees similar to those of invertebrates. In Drosophila, unequal distribution of Numb protein during mitosis produces asymmetric cell divisions and consequently diverse neural cell fates. We investigated whether a mouse homologue m-numb had a similar role during mouse cortical development. Progenitor cells isolated from the embryonic mouse cortex were followed as they underwent their next cell division in vitro. Numb distribution was predominantly asymmetric during asymmetric cell divisions yielding a beta-tubulin III(-) progenitor and a beta-tubulin III(+) neuronal cell (P/N divisions) and predominantly symmetric during divisions producing two neurons (N/N divisions). Cells from the numb knockout mouse underwent significantly fewer asymmetric P/N divisions compared to wild type, indicating a causal role for Numb. When progenitor cells derived from early (E10) cortex undergo P/N divisions, both daughters express the progenitor marker Nestin, indicating their immature state, and Numb segregates into the P or N daughter with similar frequency. In contrast, when progenitor cells derived from later E13 cortex (during active neurogenesis in vivo) undergo P/N divisions they produce a Nestin(+) progenitor and a Nestin(-) neuronal daughter, and Numb segregates preferentially into the neuronal daughter. Thus during mouse cortical neurogenesis, as in Drosophila neurogenesis, asymmetric segregation of Numb could inhibit Notch activity in one daughter to induce neuronal differentiation. At terminal divisions generating two neurons, Numb was symmetrically distributed in approximately 80% of pairs and asymmetrically in 20%. We found a significant association between Numb distribution and morphology: most sisters of neuron pairs with symmetric Numb were similar and most with asymmetric Numb were different. Developing cortical neurons with Numb had longer
Apparicio, M; Alves, A E; Pires-Butler, E A; Ribeiro, A P C; Covizzi, G J; Vicente, W R R
2011-10-01
The aim of this study was to evaluate the effects of hCG, progesterone and oestradiol supplementation on nuclear and cytoplasmic maturation of canine oocytes cultured for 24, 48, 72 and 96 h. Oocytes obtained from 18 healthy bitches were divided into three groups according to their reproductive status (follicular, luteal and anoestrus stages) and cultured in TCM 199 + 25 UI/ml of hCG + 1 μg/ml of progesterone + 1 μg/ml of 17-β oestradiol or without hormonal supplementation (control) for different periods. Then, they were stained with FITC-LCA-Hoescht for chromatin configuration and cortical granules distribution and evaluated under an epifluorescence microscope. Culture time and the influence of different stages of the oestrous cycle were also evaluated. The present study demonstrated that there was no significant difference among the reproductive stages. With regards to culture medium, only oocytes from the supplemented medium were able to complete meiosis; however, significant difference was only noticed in the percentage of MI stage oocytes (p < 0.05) in the follicular and luteal group at 72 h of culture. Most oocytes in germinal vesicle, germinal vesicle breakdown and metaphase I stage had cortical granules distributed throughout the cytoplasm (immature pattern), irrespective of the culture period (p < 0.05). Cortical granules distributed immediately beneath the plasma membrane (mature) was only observed in metaphase II stage oocytes, but not all of them presented matured cytoplasm. Our results reveal that cortical granules distribution in canine oocytes matured in vitro did not progressed in correspondence with nuclear stage changes and are in accordance with those from other species.
Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.; Makedonska, Nataliia; Karra, Satish
2016-08-01
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 so 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.
Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.; ...
2016-08-01
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
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.
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
Ota, Satoru; Tsuchiya, Kuniaki; Anno, Midori; Niizato, Kazuhiro; Akiyama, Haruhiko
2008-02-01
Late cortical cerebellar atrophy (LCCA) is a neurodegenerative disease which presents with slowly progressive cerebellar ataxia as a prominent symptom and is characterized neuropathologically by a limited main lesion to the cerebellar cortex and inferior olivary nucleus. To elucidate the features of lesions in the cerebellar cortex and inferior olivary nucleus, four autopsy cases suffering from idiopathic LCCA without other cortical cerebellar atrophies, such as alcoholic cerebellar degeneration, phenytoin intoxication, or hereditary cerebellar atrophy including spinocerebellar ataxia type 6, were examined. All affected patients had identical distinct features of cerebellar cortical lesions. In all four cases, the most obvious pathological finding throughout the cerebellum was loss of Purkinje cells, but the rarefaction of granular cell layers was observed only where loss of Purkinje cells was very severe, and thinning of the molecular layer was seen only where the rarefaction of granular cell layers was moderate to severe. Two patients presented with vermis dominant cerebellar cortical lesions, but the other two patients showed hemispheric dominant pathological changes. Neuronal loss of the inferior olivary nucleus was observed in the three autopsy cases. Two of the three cases had a prominent lesion in the dorsal part of the inferior olive and the cerebellar cortical lesion disclosed the vermis dominance, but the other patient, showing prominent neuronal loss in the ventral olivary nucleus, had a cerebellar hemisphere dominant lesion. The patient without neuronal loss in the inferior olivary nucleus had suffered from a shorter period of disease than the others and the rarefaction of granular cell layers and narrowing of the molecular layer of the cerebellar cortex were mild. Therefore, it is obvious that there are two types of cerebellar cortex lesions in idiopathic LCCA; one is vermis dominant and the other is cerebellar hemispheric dominant. The lesion of the
Velilla, E; Izquierdo, D; Rodríguez-González, E; López-Béjar, M; Vidal, F; Paramio, M T
2004-08-01
The aim of this study was evaluate cortical granule (CG) distribution during in vitro maturation (IVM) and fertilisation of prepubertal goat oocytes compared to CG distribution of ovulated and in vitro fertilised oocytes from adult goats. Oocytes from prepubertal goats were recovered from a slaughterhouse and were matured in M199 with hormones and serum for 27 hr. Ovulated oocytes were collected from gonadotrophin treated Murciana goats. Frozen-thawed spermatozoa were selected by centrifugation in percoll gradient and were capacitated in DMH with 20% steer serum for 1 hr. Ovulated and IVM-oocytes were inseminated in DMH medium with steer serum and calcium lactate for 20 hr. Oocytes and presumptive zygotes were stained with FITC-LCA (Lens culinaris agglutinin labelled with fluorescein isothiocyanate) and observed under a confocal laser scanning microscope. Ultrastructure morphology of oocytes and presumptive zygotes were analysed by transmission electron microscopy (TEM). Prepubertal goat oocytes at germinal vesicle stage show a homogeneous CG distribution in the cytoplasm. IVM-oocytes at Metaphase II (MII) and ovulated oocytes presented CGs located in the cortex with the formation of a monolayer beneath to the plasma membrane. At 20 hr postinsemination (hpi), zygotes from IVM-oocytes showed a complete CG exocytosis whereas zygotes from ovulated oocytes presented aggregates of CGs located at the cortical region. Images by TEM detected that CGs were more electrodense and compacts in oocytes from prepubertal than from adult goats.
Lou, Di-Dong; Guan, Zhi-Zhong; Liu, Yan-Jie; Liu, Yan-Fei; Zhang, Kai-Lin; Pan, Ji-Gang; Pei, Jin-Jing
2013-03-01
The present study was designed to evaluate the effects of chronic fluorosis on the dynamics (including fusion and fission proteins), fragmentation, and distribution of mitochondria in the cortical neurons of the rat brain in an attempt to elucidate molecular mechanisms underlying the brain damage associated with excess accumulation of fluoride. Sixty Sprague-Dawley rats were divided randomly into three groups of 20 each, that is, the untreated control group (drinking water naturally containing <0.5 mg fluoride/l, NaF), the low-fluoride group (whose drinking water was supplemented with 10 mg fluoride/l) and the high-fluoride group (50 mg fluoride/l). After 6 months of exposure, the expression of mitofusin-1 (Mfn1), fission-1 (Fis1), and dynamin-related protein-1 (Drp1) at both the protein and mRNA levels were detected by Western blotting, immunohistochemistry, and real-time PCR, respectively. Moreover, mitochondrial morphology and distribution in neurons were observed by transmission electron or fluorescence microscopy. In the cortices of the brains of rats with chronic fluorosis, the level of Mfn1 protein was clearly reduced, whereas the levels of Fis1 and Drp1 were elevated. The alternations of expression of the mRNAs encoding all three of these proteins were almost the same as the corresponding changes at the protein levels. The mitochondria were fragmented and the redistributed away from the axons of the cortical neurons. These findings indicate that chronic fluorosis induces abnormal mitochondrial dynamics, which might in turn result in a high level of oxidative stress.
Holper, Lisa K. B.; Aleksandrowicz, Alekandra; Müller, Mario; Ajdacic-Gross, Vladeta; Haker, Helene; Fallgatter, Andreas J.; Hagenmuller, Florence; Kawohl, Wolfram; Rössler, Wulf
2016-01-01
A psychosis phenotype can be observed below the threshold of clinical detection. The study aimed to investigate whether subclinical psychotic symptoms are associated with deficits in controlling emotional interference, and whether cortical brain and cardiac correlates of these deficits can be detected using functional near-infrared spectroscopy (fNIRS). A data set derived from a community sample was obtained from the Zurich Program for Sustainable Development of Mental Health Services. 174 subjects (mean age 29.67 ± 6.41, 91 females) were assigned to four groups ranging from low to high levels of subclinical psychotic symptoms (derived from the Symptom Checklist-90-R). Emotional interference was assessed using the emotional Stroop task comprising neutral, positive, and negative conditions. Statistical distributional methods based on delta plots [behavioral response time (RT) data] and quantile analysis (fNIRS data) were applied to evaluate the emotional interference effects. Results showed that both interference effects and disorder-specific (i.e., group-specific) effects could be detected, based on behavioral RTs, cortical hemodynamic signals (brain correlates), and heart rate variability (cardiac correlates). Subjects with high compared to low subclinical psychotic symptoms revealed significantly reduced amplitudes in dorsolateral prefrontal cortices (interference effect, p < 0.001) and middle temporal gyrus (disorder-specific group effect, p < 0.001), supported by behavioral and heart rate results. The present findings indicate that distributional analyses methods can support the detection of emotional interference effects in the emotional Stroop. The results suggested that subjects with high subclinical psychosis exhibit enhanced emotional interference effects. Based on these observations, subclinical psychosis may therefore prove to represent a valid extension of the clinical psychosis phenotype. PMID:27660608
Cortical commands in active touch.
Brecht, Michael
2006-01-01
The neocortex is an enormous network of extensively interconnected neurons. It has become clear that the computations performed by individual cortical neurons will critically depend on the quantitative composition of cortical activity. Here we discuss quantitative aspects of cortical activity and modes of cortical processing in the context of rodent active touch. Through in vivo whole-cell recordings one observes widespread subthreshold and very sparse evoked action potential (AP) activity in the somatosensory cortex both for passive whisker deflection in anaesthetized animals and during active whisker movements in awake animals. Neurons of the somatosensory cortex become either suppressed during whisking or activated by an efference copy of whisker movement signal that depolarize cells at certain phases of the whisking cycle. To probe the read out of cortical motor commands we applied intracellular stimulation in rat whisker motor cortex. We find that APs in individual cortical neurons can evoke long sequences of small whisker movements. The capacity of an individual neuron to evoke movements is most astonishing given the large number of neurons in whisker motor cortex. Thus, few cortical APs may suffice to control motor behaviour and such APs can be translated into action with the utmost precision. We conclude that there is very widespread subthreshold cortical activity and very sparse, highly specific cortical AP activity.
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.
Matsutani, S
2010-08-11
The olfactory bulb receives a large number of centrifugal fibers whose functions remain unclear. To gain insight into the function of the bulbar centrifugal system, the morphology of individual centrifugal axons from olfactory cortical areas was examined in detail. An anterograde tracer, Phaseolus vulgaris leucoagglutinin, was injected into rat olfactory cortical areas, including the pars lateralis of the anterior olfactory nucleus (lAON) and the anterior part of the piriform cortex (aPC). Reconstruction from serial sections revealed that the extrabulbar segments of centrifugal axons from the lAON and those from the aPC had distinct trajectories: the former tended to innervate the pars externa of the AON before entering the olfactory bulb, while the latter had extrabulbar collaterals that extended to a variety of targets. In contrast to the extrabulbar segments, no clear differences were found between the intrabulbar segments of axons from the lAON and from the aPC. The intrabulbar segments of centrifugal axons were mainly found in the granule cell layer but a few axons extended into the external plexiform and glomerular layer. Approximately 40% of centrifugal axons innervated both the medial and lateral aspects of the olfactory bulb. The number of boutons found on single intrabulbar segments was typically less than 1000. Boutons tended to aggregate and form complex terminal tufts with short axonal branches. Terminal tufts, no more than 10 in single axons from ipsilateral cortical areas, were localized to the granule cell layer with varying intervals; some tufts formed patchy clusters and others were scattered over areas that extended for a few millimeters. The patchy, widespread distribution of terminals suggests that the centrifugal axons are able to couple the activity of specific subsets of bulbar neurons even when the subsets are spatially separated.
Torres-Fernández, Orlando; Yepes, Gloria E; Gómez, Javier E; Pimienta, Hernán J
2005-10-01
Rabies has been an enigmatic disease of the nervous system because microscopic findings in the brain tissue are not paralleled by the severity of the clinical illness. The calcium binding protein calbindin (CB) is a neuronal marker of great interest in neuroanatomy and neuropathology. CB-ir neurons in the striatum and cerebral cortex are gabaergic cells. In the present work CB-immunoreactivity was evaluated in brains of normal and rabies-infected mice. Rabies infection caused loss of CB-immunostaining in the cortical supragranular layers as well as in the striatum. Loss of CB in the brains of mice infected with rabies virus can produce impairment in Ca++ homeostasis and in the gabaergic neurotransmission.
van Ackeren, Markus J; Schneider, Till R; Müsch, Kathrin; Rueschemeyer, Shirley-Ann
2014-10-22
Research from the previous decade suggests that word meaning is partially stored in distributed modality-specific cortical networks. However, little is known about the mechanisms by which semantic content from multiple modalities is integrated into a coherent multisensory representation. Therefore we aimed to characterize differences between integration of lexical-semantic information from a single modality compared with two sensory modalities. We used magnetoencephalography in humans to investigate changes in oscillatory neuronal activity while participants verified two features for a given target word (e.g., "bus"). Feature pairs consisted of either two features from the same modality (visual: "red," "big") or different modalities (auditory and visual: "red," "loud"). The results suggest that integrating modality-specific features of the target word is associated with enhanced high-frequency power (80-120 Hz), while integrating features from different modalities is associated with a sustained increase in low-frequency power (2-8 Hz). Source reconstruction revealed a peak in the anterior temporal lobe for low-frequency and high-frequency effects. These results suggest that integrating lexical-semantic knowledge at different cortical scales is reflected in frequency-specific oscillatory neuronal activity in unisensory and multisensory association networks.
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.
Ozawa, Shota; Ueda, Shuko; Imamura, Hiromi; Mori, Kiyoshi; Asanuma, Katsuhiko; Yanagita, Motoko; Nakagawa, Takahiko
2015-12-18
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.
Nieto-Diego, Javier; Malmierca, Manuel S
2016-03-01
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.
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.
NASA Astrophysics Data System (ADS)
Kumar, S. S.; Roy, D. P.; Boschetti, L.; Kremens, R.
2011-10-01
Instantaneous estimates of the power released by fire (fire radiative power, FRP) are available with satellite active fire detection products. The temporal integral of FRP provides an estimate of the fire radiative energy (FRE) that is related linearly to the amount of biomass burned needed by the atmospheric emissions modeling community. The FRE, however, is sensitive to satellite temporal and spatial FRP undersampling due to infrequent satellite overpasses, cloud and smoke obscuration, and failure to detect cool and/or small fires. Satellite FRPs derived over individual burned areas and fires have been observed to exhibit power law distributions. This property is exploited to develop a new way to derive FRE, as the product of the fire duration and the expected FRP value derived from the FRP power law probability distribution function. The method is demonstrated and validated by the use of FRP data measured with a dual-band radiometer over prescribed fires in the United States and by the use of FRP data retrieved from moderate resolution imaging spectroradiometer (MODIS) active-fire detections over Brazilian deforestation and Australian savanna fires. The biomass burned derived using the conventional FRP temporal integration and power law FRE estimation methods is compared with biomass burned measurements (prescribed fires) and available fuel load information reported in the literature (Australian and Brazilian fires). The results indicate that the FRE power law derivation method may provide more reliable burned biomass estimates under sparse satellite FRP sampling conditions and correct for satellite active-fire detection omission errors if the FRP power law distribution parameters and the fire duration are known.
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
Vectorized Sparse Elimination.
1984-03-01
Grids," Proc. 6th Symposium on Reservoir Simulation , New Orleans, Feb. 1-2, 1982, pp. 489-506. [51 Arya, S., and D. A. Calahan, "Optimal Scheduling of...of Computer Architecture on Direct Sparse Matrix Routines in Petroleum Reservoir Simulation ," Sparse Matrix Symposium, Fairfield Glade, TE, October
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.
Evolving sparse stellar populations
NASA Astrophysics Data System (ADS)
Bruzual, Gustavo; Gladis Magris, C.; Hernández-Pérez, Fabiola
2017-03-01
We examine the role that stochastic fluctuations in the IMF and in the number of interacting binaries have on the spectro-photometric properties of sparse stellar populations as a function of age and metallicity.
Chokron, S
2014-02-01
Cortical blindness refers to a visual loss induced by a bilateral occipital lesion. The very strong cooperation between psychophysics, cognitive psychology, neurophysiology and neuropsychology these latter twenty years as well as recent progress in cerebral imagery have led to a better understanding of neurovisual deficits, such as cortical blindness. It thus becomes possible now to propose an earlier diagnosis of cortical blindness as well as new perspectives for rehabilitation in children as well as in adults. On the other hand, studying complex neurovisual deficits, such as cortical blindness is a way to infer normal functioning of the visual system.
Xiao, Jianbo
2015-01-01
Segmenting visual scenes into distinct objects and surfaces is a fundamental visual function. To better understand the underlying neural mechanism, we investigated how neurons in the middle temporal cortex (MT) of macaque monkeys represent overlapping random-dot stimuli moving transparently in slightly different directions. It has been shown that the neuronal response elicited by two stimuli approximately follows the average of the responses elicited by the constituent stimulus components presented alone. In this scheme of response pooling, the ability to segment two simultaneously presented motion directions is limited by the width of the tuning curve to motion in a single direction. We found that, although the population-averaged neuronal tuning showed response averaging, subgroups of neurons showed distinct patterns of response tuning and were capable of representing component directions that were separated by a small angle—less than the tuning width to unidirectional stimuli. One group of neurons preferentially represented the component direction at a specific side of the bidirectional stimuli, weighting one stimulus component more strongly than the other. Another group of neurons pooled the component responses nonlinearly and showed two separate peaks in their tuning curves even when the average of the component responses was unimodal. We also show for the first time that the direction tuning of MT neurons evolved from initially representing the vector-averaged direction of slightly different stimuli to gradually representing the component directions. Our results reveal important neural processes underlying image segmentation and suggest that information about slightly different stimulus components is computed dynamically and distributed across neurons. SIGNIFICANCE STATEMENT Natural scenes often contain multiple entities. The ability to segment visual scenes into distinct objects and surfaces is fundamental to sensory processing and is crucial for
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.
Ribeiro, Pedro F. M.; Ventura-Antunes, Lissa; Gabi, Mariana; Mota, Bruno; Grinberg, Lea T.; Farfel, José M.; Ferretti-Rebustini, Renata E. L.; Leite, Renata E. P.; Filho, Wilson J.; Herculano-Houzel, Suzana
2013-01-01
The human prefrontal cortex has been considered different in several aspects and relatively enlarged compared to the rest of the cortical areas. Here we determine whether the white and gray matter of the prefrontal portion of the human cerebral cortex have similar or different cellular compositions relative to the rest of the cortical regions by applying the Isotropic Fractionator to analyze the distribution of neurons along the entire anteroposterior axis of the cortex, and its relationship with the degree of gyrification, number of neurons under the cortical surface, and other parameters. The prefrontal region shares with the remainder of the cerebral cortex (except for occipital cortex) the same relationship between cortical volume and number of neurons. In contrast, both occipital and prefrontal areas vary from other cortical areas in their connectivity through the white matter, with a systematic reduction of cortical connectivity through the white matter and an increase of the mean axon caliber along the anteroposterior axis. These two parameters explain local differences in the distribution of neurons underneath the cortical surface. We also show that local variations in cortical folding are neither a function of local numbers of neurons nor of cortical thickness, but correlate with properties of the white matter, and are best explained by the folding of the white matter surface. Our results suggest that the human cerebral cortex is divided in two zones (occipital and non-occipital) that differ in how neurons are distributed across their gray matter volume and in three zones (prefrontal, occipital, and non-occipital) that differ in how neurons are connected through the white matter. Thus, the human prefrontal cortex has the largest fraction of neuronal connectivity through the white matter and the smallest average axonal caliber in the white matter within the cortex, although its neuronal composition fits the pattern found for other, non-occipital areas. PMID
Multiple Sparse Representations Classification
Plenge, Esben; Klein, Stefan S.; Niessen, Wiro J.; Meijering, Erik
2015-01-01
Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and
Dimidschstein, Jordane; Passante, Lara; Dufour, Audrey; van den Ameele, Jelle; Tiberi, Luca; Hrechdakian, Tatyana; Adams, Ralf; Klein, Rüdiger; Lie, Dieter Chichung; Jossin, Yves; Vanderhaeghen, Pierre
2013-09-18
Neurons of the cerebral cortex are organized in layers and columns. Unlike laminar patterning, the mechanisms underlying columnar organization remain largely unexplored. Here, we show that ephrin-B1 plays a key role in this process through the control of nonradial steps of migration of pyramidal neurons. In vivo gain of function of ephrin-B1 resulted in a reduction of tangential motility of pyramidal neurons, leading to abnormal neuronal clustering. Conversely, following genetic disruption of ephrin-B1, cortical neurons displayed a wider lateral dispersion, resulting in enlarged ontogenic columns. Dynamic analyses revealed that ephrin-B1 controls the lateral spread of pyramidal neurons by limiting neurite extension and tangential migration during the multipolar phase. Furthermore, we identified P-Rex1, a guanine-exchange factor for Rac3, as a downstream ephrin-B1 effector required to control migration during the multipolar phase. Our results demonstrate that ephrin-B1 inhibits nonradial migration of pyramidal neurons, thereby controlling the pattern of cortical columns.
Protein family classification using sparse Markov transducers.
Eskin, E; Grundy, W N; Singer, Y
2000-01-01
In this paper we present a method for classifying proteins into families using sparse Markov transducers (SMTs). Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Because substitutions of amino acids are common in protein families, incorporating wildcards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. We also present efficient data structures to improve the memory usage of the models. We evaluate SMTs by building protein family classifiers using the Pfam database and compare our results to previously published results.
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
Sparse inpainting and isotropy
NASA Astrophysics Data System (ADS)
Feeney, Stephen M.; Marinucci, Domenico; McEwen, Jason D.; Peiris, Hiranya V.; Wandelt, Benjamin D.; Cammarota, Valentina
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.
Yin, Junming; Chen, Xi; Xing, Eric P.
2016-01-01
We consider the problem of sparse variable selection in nonparametric additive models, with the prior knowledge of the structure among the covariates to encourage those variables within a group to be selected jointly. Previous works either study the group sparsity in the parametric setting (e.g., group lasso), or address the problem in the nonparametric setting without exploiting the structural information (e.g., sparse additive models). In this paper, we present a new method, called group sparse additive models (GroupSpAM), which can handle group sparsity in additive models. We generalize the ℓ1/ℓ2 norm to Hilbert spaces as the sparsity-inducing penalty in GroupSpAM. Moreover, we derive a novel thresholding condition for identifying the functional sparsity at the group level, and propose an efficient block coordinate descent algorithm for constructing the estimate. We demonstrate by simulation that GroupSpAM substantially outperforms the competing methods in terms of support recovery and prediction accuracy in additive models, and also conduct a comparative experiment on a real breast cancer dataset.
Cortical rewiring and information storage
NASA Astrophysics Data System (ADS)
Chklovskii, D. B.; Mel, B. W.; Svoboda, K.
2004-10-01
Current thinking about long-term memory in the cortex is focused on changes in the strengths of connections between neurons. But ongoing structural plasticity in the adult brain, including synapse formation/elimination and remodelling of axons and dendrites, suggests that memory could also depend on learning-induced changes in the cortical `wiring diagram'. Given that the cortex is sparsely connected, wiring plasticity could provide a substantial boost in storage capacity, although at a cost of more elaborate biological machinery and slower learning.
Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways
Farkhooi, Farzad; Froese, Anja; Muller, Eilif; Menzel, Randolf; Nawrot, Martin P.
2013-01-01
Most neurons in peripheral sensory pathways initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. It is unclear how this phenomenon affects stimulus coding in the later stages of sensory processing. Here, we show that a temporally sparse and reliable stimulus representation develops naturally in sequential stages of a sensory network with adapting neurons. As a modeling framework we employ a mean-field approach together with an adaptive population density treatment, accompanied by numerical simulations of spiking neural networks. We find that cellular adaptation plays a critical role in the dynamic reduction of the trial-by-trial variability of cortical spike responses by transiently suppressing self-generated fast fluctuations in the cortical balanced network. This provides an explanation for a widespread cortical phenomenon by a simple mechanism. We further show that in the insect olfactory system cellular adaptation is sufficient to explain the emergence of the temporally sparse and reliable stimulus representation in the mushroom body. Our results reveal a generic, biophysically plausible mechanism that can explain the emergence of a temporally sparse and reliable stimulus representation within a sequential processing architecture. PMID:24098101
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
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.
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
Flexible sparse regularization
NASA Astrophysics Data System (ADS)
Lorenz, Dirk A.; Resmerita, Elena
2017-01-01
The seminal paper of Daubechies, Defrise, DeMol made clear that {{\\ell }}p spaces with p\\in [1,2) and p-powers of the corresponding norms are appropriate settings for dealing with reconstruction of sparse solutions of ill-posed problems by regularization. It seems that the case p = 1 provides the best results in most of the situations compared to the cases p\\in (1,2). An extensive literature gives great credit also to using {{\\ell }}p spaces with p\\in (0,1) together with the corresponding quasi-norms, although one has to tackle challenging numerical problems raised by the non-convexity of the quasi-norms. In any of these settings, either superlinear, linear or sublinear, the question of how to choose the exponent p has been not only a numerical issue, but also a philosophical one. In this work we introduce a more flexible way of sparse regularization by varying exponents. We introduce the corresponding functional analytic framework, that leaves the setting of normed spaces but works with so-called F-norms. One curious result is that there are F-norms which generate the ℓ 1 space, but they are strictly convex, while the ℓ 1-norm is just convex.
Fujita, Satoshi; Mizoguchi, Naoko; Aoki, Ryuhei; Cui, Yilong; Koshikawa, Noriaki; Kobayashi, Masayuki
2016-04-01
Cortical spreading depression (SD) is a self-propagating wave of depolarization accompanied by a substantial disturbance of the ionic distribution between the intra- and extracellular compartments. Glial cells, including astrocytes, play critical roles in maintenance of the extracellular environment, including ionic distribution. Therefore, SD propagation in the cerebral cortex may depend on the density of astrocytes. The present study aimed to examine the profile of SD propagation in the insular cortex (IC), which is located between the neocortex and paleocortex and is where the density of astrocytes gradually changes. The velocity of SD propagation in the neocortex, including the somatosensory, motor, and granular insular cortices (5.7 mm/min), was higher than that (2.8 mm/min) in the paleocortex (agranular insular and piriform cortices). Around thick vessels, including the middle cerebral artery, SD propagation was frequently delayed and sometimes disappeared. Immunohistological analysis of glial fibrillary acidic protein (GFAP) demonstrated the sparse distribution of astrocytes in the somatosensory cortex and the IC dorsal to the rhinal fissure, whereas the ventral IC showed a higher density of astrocytes. These results suggest that cortical cytoarchitectonic features, which possibly involve the distribution of astrocytes, are crucial for regulating the velocity of SD propagation in the cerebral cortex.
Blind spectral unmixing based on sparse nonnegative matrix factorization.
Yang, Zuyuan; Zhou, Guoxu; Xie, Shengli; Ding, Shuxue; Yang, Jun-Mei; Zhang, Jun
2011-04-01
Nonnegative matrix factorization (NMF) is a widely used method for blind spectral unmixing (SU), which aims at obtaining the endmembers and corresponding fractional abundances, knowing only the collected mixing spectral data. It is noted that the abundance may be sparse (i.e., the endmembers may be with sparse distributions) and sparse NMF tends to lead to a unique result, so it is intuitive and meaningful to constrain NMF with sparseness for solving SU. However, due to the abundance sum-to-one constraint in SU, the traditional sparseness measured by L0/L1-norm is not an effective constraint any more. A novel measure (termed as S-measure) of sparseness using higher order norms of the signal vector is proposed in this paper. It features the physical significance. By using the S-measure constraint (SMC), a gradient-based sparse NMF algorithm (termed as NMF-SMC) is proposed for solving the SU problem, where the learning rate is adaptively selected, and the endmembers and abundances are simultaneously estimated. In the proposed NMF-SMC, there is no pure index assumption and no need to know the exact sparseness degree of the abundance in prior. Yet, it does not require the preprocessing of dimension reduction in which some useful information may be lost. Experiments based on synthetic mixtures and real-world images collected by AVIRIS and HYDICE sensors are performed to evaluate the validity of the proposed method.
Hardware Acceleration of Sparse Cognitive Algorithms
2016-05-01
15. SUBJECT TERMS Cortical Algorithms; Machine Learning ; Hardware; VLSI; ASIC 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT: SAR...Approved for public release; distribution unlimited. Table of Contents Section Page List of Figures... integrated circuit (ASIC) in which certain parameters can be changed at compile time and/or run time. However, it can only run one version of the
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.
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.
Neonatal atlas construction using sparse representation.
Shi, Feng; Wang, Li; Wu, Guorong; Li, Gang; Gilmore, John H; Lin, Weili; Shen, Dinggang
2014-09-01
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.
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.
Sparse Event Modeling with Hierarchical Bayesian Kernel Methods
2016-01-05
events (and subsequently, their likelihood of occurrence) based on historical evidence of the counts of previous event occurrences. The novel Bayesian...Aug-2014 22-May-2015 Approved for Public Release; Distribution Unlimited Final Report: Sparse Event Modeling with Hierarchical Bayesian Kernel Methods...Sparse Event Modeling with Hierarchical Bayesian Kernel Methods Report Title The research objective of this proposal was to develop a predictive Bayesian
Hallam, Glyn P; Whitney, Carin; Hymers, Mark; Gouws, Andre D; Jefferies, Elizabeth
2016-12-01
Semantic memory comprises our knowledge of the meanings of words and objects but only some of this knowledge is relevant at any given time. Thus, semantic control processes are needed to focus retrieval on relevant information. Research on the neural basis of semantic control has strongly implicated left inferior frontal gyrus (LIFG) but recent work suggests that a wider network supports semantic control, including left posterior middle temporal gyrus (pMTG), right inferior frontal gyrus (RIFG) and pre-supplementary motor area (pre-SMA). In the current study, we used repetitive transcranial magnetic stimulation (1Hz offline TMS) over LIFG, immediately followed by fMRI, to examine modulation of the semantic network. We compared the effect of stimulation on judgements about strongly-associated words (dog-bone) and weaker associations (dog-beach), since previous studies have found that dominant links can be recovered largely automatically with little engagement of LIFG, while more distant connections require greater control. Even though behavioural performance was maintained in response to TMS, LIFG stimulation increased the effect of semantic control demands in pMTG and pre-SMA, relative to stimulation of a control site (occipital pole). These changes were accompanied by reduced recruitment of both the stimulated region (LIFG) and its right hemisphere homologue (RIFG), particularly for strong associations with low control requirements. Thus repetitive TMS to LIFG modulated the contribution of distributed regions to semantic judgements in two distinct ways.
... Frequently Asked Questions Español Condiciones Chinese Conditions Cortical Visual Impairment En Español Read in Chinese What is cortical visual impairment? Cortical visual impairment (CVI) is a decreased ...
Shalini, Suku-Maran; Ho, Christabel Fung-Yih; Ng, Yee-Kong; Tong, Jie-Xin; Ong, Eng-Shi; Herr, Deron R; Dawe, Gavin S; Ong, Wei-Yi
2017-02-08
Docosahexaenoic acid (DHA) is enriched in membrane phospholipids of the central nervous system (CNS) and has a role in aging and neuropsychiatric disorders. DHA is metabolized by the enzyme Alox15 to 17S-hydroxy-DHA, which is then converted to 7S-hydroperoxy,17S-hydroxy-DHA by a 5-lipoxygenase, and thence via epoxy intermediates to the anti-inflammatory molecule, resolvin D1 (RvD1 or 7S,8R,17S-trihydroxy-docosa-Z,9E,11E,13Z,15E,19Z-hexaenoic acid). In this study, we investigated the distribution and function of Alox15 in the CNS. RT-PCR of the CNS showed that the prefrontal cortex exhibits the highest Alox15 mRNA expression level, followed by the parietal association cortex and secondary auditory cortex, olfactory bulb, motor and somatosensory cortices, and the hippocampus. Western blot analysis was consistent with RT-PCR data, in that the prefrontal cortex, cerebral cortex, hippocampus, and olfactory bulb had high Alox15 protein expression. Immunohistochemistry showed moderate staining in the olfactory bulb, cerebral cortex, septum, striatum, cerebellar cortex, cochlear nuclei, spinal trigeminal nucleus, and dorsal horn of the spinal cord. Immuno-electron microscopy showed localization of Alox15 in dendrites, in the prefrontal cortex. Liquid chromatography mass spectrometry analysis showed significant decrease in resolvin D1 levels in the prefrontal cortex after inhibition or antisense knockdown of Alox15. Alox15 inhibition or antisense knockdown in the prefrontal cortex also blocked long-term potentiation of the hippocampo-prefrontal cortex pathway and increased errors in alternation, in the T-maze test. They indicate that Alox15 processing of DHA contributes to production of resolvin D1 and LTP at hippocampo-prefrontal cortical synapses and associated spatial working memory performance. Together, results provide evidence for a key role of anti-inflammatory molecules generated by Alox15 and DHA, such as resolvin D1, in memory. They suggest that neuroinflammatory
Large-scale cortical networks and cognition.
Bressler, S L
1995-03-01
The well-known parcellation of the mammalian cerebral cortex into a large number of functionally distinct cytoarchitectonic areas presents a problem for understanding the complex cortical integrative functions that underlie cognition. How do cortical areas having unique individual functional properties cooperate to accomplish these complex operations? Do neurons distributed throughout the cerebral cortex act together in large-scale functional assemblages? This review examines the substantial body of evidence supporting the view that complex integrative functions are carried out by large-scale networks of cortical areas. Pathway tracing studies in non-human primates have revealed widely distributed networks of interconnected cortical areas, providing an anatomical substrate for large-scale parallel processing of information in the cerebral cortex. Functional coactivation of multiple cortical areas has been demonstrated by neurophysiological studies in non-human primates and several different cognitive functions have been shown to depend on multiple distributed areas by human neuropsychological studies. Electrophysiological studies on interareal synchronization have provided evidence that active neurons in different cortical areas may become not only coactive, but also functionally interdependent. The computational advantages of synchronization between cortical areas in large-scale networks have been elucidated by studies using artificial neural network models. Recent observations of time-varying multi-areal cortical synchronization suggest that the functional topology of a large-scale cortical network is dynamically reorganized during visuomotor behavior.
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.
Sparse Methods for Biomedical Data.
Ye, Jieping; Liu, Jun
2012-06-01
Following recent technological revolutions, the investigation of massive biomedical data with growing scale, diversity, and complexity has taken a center stage in modern data analysis. Although complex, the underlying representations of many biomedical data are often sparse. For example, for a certain disease such as leukemia, even though humans have tens of thousands of genes, only a few genes are relevant to the disease; a gene network is sparse since a regulatory pathway involves only a small number of genes; many biomedical signals are sparse or compressible in the sense that they have concise representations when expressed in a proper basis. Therefore, finding sparse representations is fundamentally important for scientific discovery. Sparse methods based on the [Formula: see text] norm have attracted a great amount of research efforts in the past decade due to its sparsity-inducing property, convenient convexity, and strong theoretical guarantees. They have achieved great success in various applications such as biomarker selection, biological network construction, and magnetic resonance imaging. In this paper, we review state-of-the-art sparse methods and their applications to biomedical data.
Sparse Methods for Biomedical Data
Ye, Jieping; Liu, Jun
2013-01-01
Following recent technological revolutions, the investigation of massive biomedical data with growing scale, diversity, and complexity has taken a center stage in modern data analysis. Although complex, the underlying representations of many biomedical data are often sparse. For example, for a certain disease such as leukemia, even though humans have tens of thousands of genes, only a few genes are relevant to the disease; a gene network is sparse since a regulatory pathway involves only a small number of genes; many biomedical signals are sparse or compressible in the sense that they have concise representations when expressed in a proper basis. Therefore, finding sparse representations is fundamentally important for scientific discovery. Sparse methods based on the ℓ1 norm have attracted a great amount of research efforts in the past decade due to its sparsity-inducing property, convenient convexity, and strong theoretical guarantees. They have achieved great success in various applications such as biomarker selection, biological network construction, and magnetic resonance imaging. In this paper, we review state-of-the-art sparse methods and their applications to biomedical data. PMID:24076585
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.
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
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.
All scale-free networks are sparse.
Del Genio, Charo I; Gross, Thilo; Bassler, Kevin E
2011-10-21
We study the realizability of scale-free networks with a given degree sequence, showing that the fraction of realizable sequences undergoes two first-order transitions at the values 0 and 2 of the power-law exponent. We substantiate this finding by analytical reasoning and by a numerical method, proposed here, based on extreme value arguments, which can be applied to any given degree distribution. Our results reveal a fundamental reason why large scale-free networks without constraints on minimum and maximum degree must be sparse.
Communication requirements of sparse Cholesky factorization with nested dissection ordering
NASA Technical Reports Server (NTRS)
Naik, Vijay K.; Patrick, Merrell L.
1989-01-01
Load distribution schemes for minimizing the communication requirements of the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems are presented. The total data traffic in factoring an n x n sparse symmetric positive definite matrix representing an n-vertex regular two-dimensional grid graph using n exp alpha, alpha not greater than 1, processors are shown to be O(n exp 1 + alpha/2). It is O(n), when n exp alpha, alpha not smaller than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal.
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.
Prediction of cortical responses to simultaneous electrical stimulation of the retina
NASA Astrophysics Data System (ADS)
Halupka, Kerry J.; Shivdasani, Mohit N.; Cloherty, Shaun L.; Grayden, David B.; Wong, Yan T.; Burkitt, Anthony N.; Meffin, Hamish
2017-02-01
Objective. Simultaneous electrical stimulation of multiple electrodes has shown promise in diversifying the responses that can be elicited by retinal prostheses compared to interleaved single electrode stimulation. However, the effects of interactions between electrodes are not well understood and clinical trials with simultaneous stimulation have produced inconsistent results. We investigated the effects of multiple electrode stimulation of the retina by developing a model of cortical responses to retinal stimulation. Approach. Electrical stimuli consisting of temporally sparse, biphasic current pulses, with amplitudes sampled from a bi-dimensional Gaussian distribution, were simultaneously delivered to the retina across a 42-channel electrode array implanted in the suprachoroidal space of anesthetized cats. Visual cortex activity was recorded using penetrating microelectrode arrays. These data were used to identify a linear-nonlinear model of cortical responses to retinal stimulation. The ability of the model to generalize was tested by predicting responses to non-white patterned stimuli. Main results. The model accurately predicted two cortical activity measures: multi-unit neural responses and evoked potential responses to white noise stimuli. The model also provides information about electrical receptive fields, including the relative effects of each stimulating electrode on every recording site. Significance. We have demonstrated a simple model that accurately describes cortical responses to simultaneous stimulation of a suprachoroidal retinal prosthesis. Overall, our results demonstrate that cortical responses to simultaneous multi-electrode stimulation of the retina are repeatable and predictable, and that interactions between electrodes during simultaneous stimulation are predominantly linear. The model shows promise for determining optimal stimulation paradigms for exploiting interactions between electrodes to shape neural activity, thereby improving
Protein family classification using sparse markov transducers.
Eskin, Eleazar; Noble, William Stafford; Singer, Yoram
2003-01-01
We present a method for classifying proteins into families based on short subsequences of amino acids using a new probabilistic model called sparse Markov transducers (SMT). We classify a protein by estimating probability distributions over subsequences of amino acids from the protein. Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Since substitutions of amino acids are common in protein families, incorporating wild-cards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. As protein databases become larger, data driven learning algorithms for probabilistic models such as SMTs will require vast amounts of memory. We therefore describe and use efficient data structures to improve the memory usage of SMTs. We evaluate SMTs by building protein family classifiers using the Pfam and SCOP databases and compare our results to previously published results and state-of-the-art protein homology detection methods. SMTs outperform previous probabilistic suffix tree methods and under certain conditions perform comparably to state-of-the-art protein homology methods.
Learn Sparse Dictionaries for Edit Propagation.
Xiaowu Chen; Jianwei Li; Dongqing Zou; Qinping Zhao
2016-04-01
With the increasing availability of high-resolution images, videos, and 3D models, the demand for scalable large data processing techniques increases. We introduce a method of sparse dictionary learning for edit propagation of large input data. Previous approaches for edit propagation typically employ a global optimization over the whole set of pixels (or vertexes), incurring a prohibitively high memory and time-consumption for large input data. Rather than propagating an edit pixel by pixel, we follow the principle of sparse representation to obtain a representative and compact dictionary and perform edit propagation on the dictionary instead. The sparse dictionary provides an intrinsic basis for input data, and the coding coefficients capture the linear relationship between all pixels and the dictionary atoms. The learned dictionary is then optimized by a novel scheme, which maximizes the Kullback-Leibler divergence between each atom pair to remove redundant atoms. To enable local edit propagation for images or videos with similar appearance, a dictionary learning strategy is proposed by considering range constraint to better account for the global distribution of pixels in their feature space. We show several applications of the sparsity-based edit propagation, including video recoloring, theme editing, and seamless cloning, operating on both color and texture features. Our approach can also be applied to computer graphics tasks, such as 3D surface deformation. We demonstrate that with an atom-to-pixel ratio in the order of 0.01% signifying a significant reduction on memory consumption, our method still maintains a high degree of visual fidelity.
Sparse codes of harmonic natural sounds and their modulatory interactions.
Terashima, Hiroki; Hosoya, Haruo
2009-01-01
Sparse coding and its related theories have been successful to explain various response properties of early stages of sensory information processing such as primary visual cortex and peripheral auditory system, which suggests that the emergence of such properties results from adaptation of the nerve system to natural stimuli. The present study continues this line of research in a higher stage of auditory processing, focusing on harmonic structures that are often found in behaviourally important natural sound like animal vocalization. It has been physiologically shown that monkey primary auditory cortices (A1) have neurons with response properties capturing such harmonic structures: their response and modulation peaks are often found at frequencies that are harmonically related to each other. We hypothesize that such relations emerge from sparse coding of harmonic natural sounds. Our simulation shows that similar harmonic relations emerge from frequency-domain sparse codes of harmonic sounds, namely, piano performance and human speech. Moreover, the modulatory behaviours can be explained by competitive interactions of model neurons that capture partially common harmonic structures.
Park, Soon-Yong; Heo, Min-Suk; Chien, Sung-Il
2016-01-01
It is important to investigate the irregularities in aging-associated changes in bone, between men and women for bone strength and osteoporosis. The purpose of this study was to characterize the changes and associations of mandibular cortical and trabecular bone measures of men and women based on age and to the evaluation of cortical shape categories, in a large Korean population. Panoramic radiographs of 1047 subjects (603 women and 444 men) aged between 15 to 90 years were used. Mandibular cortical width (MCW), mandibular cortical index (MCI), and fractal dimensions (FD) of the molar, premolar, and anterior regions of the mandibular trabecular bone were measured. Study subjects were grouped into six 10-years age groups. A local linear regression smoothing with bootstrap resampling for robust fitting of data was used to estimate the relationship between radiographic mandibular variables and age groups as well as genders. The mean age of women (49.56 ± 19.5 years) was significantly higher than that of men (45.57 ± 19.6 years). The MCW of men and women (3.17mm and 2.91mm, respectively, p < 0.0001) was strongly associated with age and MCI. Indeed, trabecular measures also correlated with age in men (r > −0.140, p = 0.003), though not as strongly as in women (r > −0.210, p < 0.0001). In men aged over 55 years, only MCW was significantly associated (r = −0.412, p < 0.0001). Furthermore, by comparison of mandibular variables from different age groups and MCI categories, the results suggest that MCW was detected to be strongly associated in both men and women for the detection of bone strength and osteoporosis. The FD measures revealed relatively higher association with age among women than men, but not as strong as MCW. PMID:28002443
Sparse Regression by Projection and Sparse Discriminant Analysis.
Qi, Xin; Luo, Ruiyan; Carroll, Raymond J; Zhao, Hongyu
2015-04-01
Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared to the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplemental materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.
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.
1982-10-27
sparse matrices as well as other areas. Contents 1. operations on Sparse Matrices .. . . . . . . . . . . . . . . . . . . . . . . . I 1.1 Multi...22 2.1.1 Nonsymmetric systems ............................................. 22 2.1.1.1 General sparse matrices ...46 2.1.2.1 General sparse matrices ......................................... 46 2.1.2.2 Band or profile forms
Sparse estimation of a covariance matrix.
Bien, Jacob; Tibshirani, Robert J
2011-12-01
We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even when the dimension of the vectors is smaller than the sample size since the number of parameters grows quadratically in the number of variables, and it produces an estimate which is sparse. In contrast to sparse inverse covariance estimation, our method's close relative, the sparsity attained here is in the covariance matrix itself rather than in the inverse matrix. Zeros in the covariance matrix correspond to marginal independencies; thus, our method performs model selection while providing a positive definite estimate of the covariance. The proposed penalized maximum likelihood problem is not convex, so we use a majorize-minimize approach in which we iteratively solve convex approximations to the original nonconvex problem. We discuss tuning parameter selection and demonstrate on a flow-cytometry dataset how our method produces an interpretable graphical display of the relationship between variables. We perform simulations that suggest that simple elementwise thresholding of the empirical covariance matrix is competitive with our method for identifying the sparsity structure. Additionally, we show how our method can be used to solve a previously studied special case in which a desired sparsity pattern is prespecified.
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.
Structured sparse models for classification
NASA Astrophysics Data System (ADS)
Castrodad, Alexey
The main focus of this thesis is the modeling and classification of high dimensional data using structured sparsity. Sparse models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and its use has led to state-of-the-art results in many signal and image processing tasks. The success of sparse modeling is highly due to its ability to efficiently use the redundancy of the data and find its underlying structure. On a classification setting, we capitalize on this advantage to properly model and separate the structure of the classes. We design and validate modeling solutions to challenging problems arising in computer vision and remote sensing. We propose both supervised and unsupervised schemes for the modeling of human actions from motion imagery under a wide variety of acquisition condi- tions. In the supervised case, the main goal is to classify the human actions in the video given a predefined set of actions to learn from. In the unsupervised case, the main goal is to an- alyze the spatio-temporal dynamics of the individuals in the scene without having any prior information on the actions themselves. We also propose a model for remotely sensed hysper- spectral imagery, where the main goal is to perform automatic spectral source separation and mapping at the subpixel level. Finally, we present a sparse model for sensor fusion to exploit the common structure and enforce collaboration of hyperspectral with LiDAR data for better mapping capabilities. In all these scenarios, we demonstrate that these data can be expressed as a combination of atoms from a class-structured dictionary. These data representation becomes essentially a "mixture of classes," and by directly exploiting the sparse codes, one can attain highly accurate classification performance with relatively unsophisticated classifiers.
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.
Double shrinking sparse dimension reduction.
Zhou, Tianyi; Tao, Dacheng
2013-01-01
Learning tasks such as classification and clustering usually perform better and cost less (time and space) on compressed representations than on the original data. Previous works mainly compress data via dimension reduction. In this paper, we propose "double shrinking" to compress image data on both dimensionality and cardinality via building either sparse low-dimensional representations or a sparse projection matrix for dimension reduction. We formulate a double shrinking model (DSM) as an l(1) regularized variance maximization with constraint ||x||(2)=1, and develop a double shrinking algorithm (DSA) to optimize DSM. DSA is a path-following algorithm that can build the whole solution path of locally optimal solutions of different sparse levels. Each solution on the path is a "warm start" for searching the next sparser one. In each iteration of DSA, the direction, the step size, and the Lagrangian multiplier are deduced from the Karush-Kuhn-Tucker conditions. The magnitudes of trivial variables are shrunk and the importances of critical variables are simultaneously augmented along the selected direction with the determined step length. Double shrinking can be applied to manifold learning and feature selections for better interpretation of features, and can be combined with classification and clustering to boost their performance. The experimental results suggest that double shrinking produces efficient and effective data compression.
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.
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.
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
ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES.
Fan, Jianqing; Rigollet, Philippe; Wang, Weichen
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 ℓ r 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.
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
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
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
Classification of sparse high-dimensional vectors.
Ingster, Yuri I; Pouet, Christophe; Tsybakov, Alexandre B
2009-11-13
We study the problem of classification of d-dimensional vectors into two classes (one of which is 'pure noise') based on a training sample of size m. The main specific feature is that the dimension d can be very large. We suppose that the difference between the distribution of the population and that of the noise is only in a shift, which is a sparse vector. For Gaussian noise, fixed sample size m, and dimension d that tends to infinity, we obtain the sharp classification boundary, i.e. the necessary and sufficient conditions for the possibility of successful classification. We propose classifiers attaining this boundary. We also give extensions of the result to the case where the sample size m depends on d and satisfies the condition (log m)/log d --> gamma, 0
Estimation of Sparse Directed Acyclic Graphs for Multivariate Counts Data
Han, Sung Won; Zhong, Hua
2016-01-01
Summary The next-generation sequencing data, called high throughput sequencing data, are recorded as count data, which is generally far from normal distribution. Under the assumption that the count data follow the Poisson log-normal distribution, this paper provides an L1-penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs) for multivariate counts data. In searching for the solution, we use iterative optimization procedures to estimate the adjacency matrix and the variance matrix of the latent variables. The simulation result shows that our proposed method outperforms the approach which assumes multivariate normal distributions, and the log-transformation approach. It also shows that the proposed method outperforms the rank-based PC method under sparse network or hub network structures. As a real data example, we demonstrate the efficiency of the proposed method in estimating the gene regulatory networks of the ovarian cancer study. PMID:26849781
Sparse recovery via convex optimization
NASA Astrophysics Data System (ADS)
Randall, Paige Alicia
This thesis considers the problem of estimating a sparse signal from a few (possibly noisy) linear measurements. In other words, we have y = Ax + z where A is a measurement matrix with more columns than rows, x is a sparse signal to be estimated, z is a noise vector, and y is a vector of measurements. This setup arises frequently in many problems ranging from MRI imaging to genomics to compressed sensing.We begin by relating our setup to an error correction problem over the reals, where a received encoded message is corrupted by a few arbitrary errors, as well as smaller dense errors. We show that under suitable conditions on the encoding matrix and on the number of arbitrary errors, one is able to accurately recover the message.We next show that we are able to achieve oracle optimality for x, up to a log factor and a factor of sqrt{s}, when we require the matrix A to obey an incoherence property. The incoherence property is novel in that it allows the coherence of A to be as large as O(1/ log n) and still allows sparsities as large as O(m/log n). This is in contrast to other existing results involving coherence where the coherence can only be as large as O(1/sqrt{m}) to allow sparsities as large as O(sqrt{m}). We also do not make the common assumption that the matrix A obeys a restricted eigenvalue condition.We then show that we can recover a (non-sparse) signal from a few linear measurements when the signal has an exactly sparse representation in an overcomplete dictionary. We again only require that the dictionary obey an incoherence property.Finally, we introduce the method of l_1 analysis and show that it is guaranteed to give good recovery of a signal from a few measurements, when the signal can be well represented in a dictionary. We require that the combined measurement/dictionary matrix satisfies a uniform uncertainty principle and we compare our results with the more standard l_1 synthesis approach.All our methods involve solving an l_1 minimization
Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing.
Yoshida, Ryo; West, Mike
2010-05-01
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.
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.
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
Sparse Coding for Alpha Matting.
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 the 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 the current stateoftheart in image and video matting.
Sparse Coding for Alpha Matting.
Johnson, Jubin; Varnousfaderani, Ehsan; Cholakkal, Hisham; Rajan, Deepu
2016-04-21
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.
Universal Priors for Sparse Modeling(PREPRINT)
2009-08-01
UNIVERSAL PRIORS FOR SPARSE MODELING By Ignacio Ramı́rez Federico Lecumberry and Guillermo Sapiro IMA Preprint Series # 2276 ( August 2009...8-98) Prescribed by ANSI Std Z39-18 Universal Priors for Sparse Modeling (Invited Paper) Ignacio Ramı́rez#1, Federico Lecumberry ∗2, Guillermo Sapiro...I. Ramirez, F. Lecumberry , and G. Sapiro. Sparse modeling with univer- sal priors and learned incoherent dictionaries. Submitted to NIPS, 2009. [22
Discriminative Sparse Representations in Hyperspectral Imagery
2010-03-01
classification , and unsupervised labeling (clustering) [2, 3, 4, 5, 6, 7, 8]. Recently, a non-parametric (Bayesian) approach to sparse modeling and com...DISCRIMINATIVE SPARSE REPRESENTATIONS IN HYPERSPECTRAL IMAGERY By Alexey Castrodad, Zhengming Xing John Greer, Edward Bosch Lawrence Carin and...00-00-2010 to 00-00-2010 4. TITLE AND SUBTITLE Discriminative Sparse Representations in Hyperspectral Imagery 5a. CONTRACT NUMBER 5b. GRANT
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.
Medical Image Fusion Based on Feature Extraction and Sparse Representation
Wei, Gao; Zongxi, Song
2017-01-01
As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods. PMID:28321246
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
Bello, Oscar Daniel; Cappa, Andrea Isabel; de Paola, Matilde; Zanetti, María Natalia; Fukuda, Mitsunori; Fissore, Rafael A; Mayorga, Luis S; Michaut, Marcela A
2016-09-10
Fusion of cortical granules with the oocyte plasma membrane is the most significant event to prevent polyspermy. This particular exocytosis, also known as cortical reaction, is regulated by calcium and its molecular mechanism is still not known. Rab3A, a member of the small GTP-binding protein superfamily, has been implicated in calcium-dependent exocytosis and is not yet clear whether Rab3A participates in cortical granules exocytosis. Here, we examine the involvement of Rab3A in the physiology of cortical granules, particularly, in their distribution during oocyte maturation and activation, and their participation in membrane fusion during cortical granule exocytosis. Immunofluorescence and Western blot analysis showed that Rab3A and cortical granules have a similar migration pattern during oocyte maturation, and that Rab3A is no longer detected after cortical granule exocytosis. These results suggested that Rab3A might be a marker of cortical granules. Overexpression of EGFP-Rab3A colocalized with cortical granules with a Pearson correlation coefficient of +0.967, indicating that Rab3A and cortical granules have almost a perfect colocalization in the egg cortical region. Using a functional assay, we demonstrated that microinjection of recombinant, prenylated and active GST-Rab3A triggered cortical granule exocytosis, indicating that Rab3A has an active role in this secretory pathway. To confirm this active role, we inhibited the function of endogenous Rab3A by microinjecting a polyclonal antibody raised against Rab3A prior to parthenogenetic activation. Our results showed that Rab3A antibody microinjection abolished cortical granule exocytosis in parthenogenetically activated oocytes. Altogether, our findings confirm that Rab3A might function as a marker of cortical granules and participates in cortical granule exocytosis in mouse eggs.
Self-organizing control for space-based sparse antennas
NASA Technical Reports Server (NTRS)
Hadaegh, Fred Y.; Jamnejad, Vaharaz; Scharf, Daniel P.; Ploen, Scott R.
2003-01-01
An integrated control and electromagnetic/antenna formulation is presented for evaluating the performance of a distributed antenna system as a function of formation geometry. A distributed and self-organizing control law for the control law for the control of multiple antennas in Low Earth Orbit (LEO) is presented. The control system provides collaborative commanding and performance optimization to configure and operate the distributed formation system. A large aperture antenna is thereby realized by a collection of miniature sparse antennas in formation. A case study consisting of a simulation of four antennas in Low Earth orbit (LEO)is presented to demonstrate the concept.
Optimizing the imaging of the monkey auditory cortex: sparse vs. continuous fMRI.
Petkov, Christopher I; Kayser, Christoph; Augath, Mark; Logothetis, Nikos K
2009-10-01
The noninvasive imaging of the monkey auditory system with functional magnetic resonance imaging (fMRI) can bridge the gap between electrophysiological studies in monkeys and imaging studies in humans. Some of the recent imaging of monkey auditory cortical and subcortical structures relies on a technique of "sparse imaging," which was developed in human studies to sidestep the negative influence of scanner noise by adding periods of silence in between volume acquisition. Among the various aspects that have gone into the ongoing optimization of fMRI of the monkey auditory cortex, replacing the more common continuous-imaging paradigm with sparse imaging seemed to us to make the most obvious difference in the amount of activity that we could reliably obtain from awake or anesthetized animals. Here, we directly compare the sparse- and continuous-imaging paradigms in anesthetized animals. We document a strikingly greater auditory response with sparse imaging, both quantitatively and qualitatively, which includes a more expansive and robust tonotopic organization. There were instances where continuous imaging could better reveal organizational properties that sparse imaging missed, such as aspects of the hierarchical organization of auditory cortex. We consider the choice of imaging paradigm as a key component in optimizing the fMRI of the monkey auditory cortex.
Sparse and stable Markowitz portfolios.
Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace
2009-07-28
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.
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.
Evolution of cortical neurogenesis.
Abdel-Mannan, Omar; Cheung, Amanda F P; Molnár, Zoltán
2008-03-18
The neurons of the mammalian neocortex are organised into six layers. By contrast, the reptilian and avian dorsal cortices only have three layers which are thought to be equivalent to layers I, V and VI of mammals. Increased repertoire of mammalian higher cognitive functions is likely a result of an expanded cortical surface area. The majority of cortical cell proliferation in mammals occurs in the ventricular zone (VZ) and subventricular zone (SVZ), with a small number of scattered divisions outside the germinal zone. Comparative developmental studies suggest that the appearance of SVZ coincides with the laminar expansion of the cortex to six layers, as well as the tangential expansion of the cortical sheet seen within mammals. In spite of great variation and further compartmentalisation in the mitotic compartments, the number of neurons in an arbitrary cortical column appears to be remarkably constant within mammals. The current challenge is to understand how the emergence and elaboration of the SVZ has contributed to increased cortical cell diversity, tangential expansion and gyrus formation of the mammalian neocortex. This review discusses neurogenic processes that are believed to underlie these major changes in cortical dimensions in vertebrates.
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.
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.
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.
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.
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.
Discovering Cortical Folding Patterns in Neonatal Cortical Surfaces Using Large-Scale Dataset
Meng, Yu; Li, Gang; Wang, Li; Lin, Weili; Gilmore, John H.
2017-01-01
The cortical folding of the human brain is highly complex and variable across individuals. Mining the major patterns of cortical folding from modern large-scale neuroimaging datasets is of great importance in advancing techniques for neuroimaging analysis and understanding the inter-individual variations of cortical folding and its relationship with cognitive function and disorders. As the primary cortical folding is genetically influenced and has been established at term birth, neonates with the minimal exposure to the complicated postnatal environmental influence are the ideal candidates for understanding the major patterns of cortical folding. In this paper, for the first time, we propose a novel method for discovering the major patterns of cortical folding in a large-scale dataset of neonatal brain MR images (N = 677). In our method, first, cortical folding is characterized by the distribution of sulcal pits, which are the locally deepest points in cortical sulci. Because deep sulcal pits are genetically related, relatively consistent across individuals, and also stable during brain development, they are well suitable for representing and characterizing cortical folding. Then, the similarities between sulcal pit distributions of any two subjects are measured from spatial, geometrical, and topological points of view. Next, these different measurements are adaptively fused together using a similarity network fusion technique, to preserve their common information and also catch their complementary information. Finally, leveraging the fused similarity measurements, a hierarchical affinity propagation algorithm is used to group similar sulcal folding patterns together. The proposed method has been applied to 677 neonatal brains (the largest neonatal dataset to our knowledge) in the central sulcus, superior temporal sulcus, and cingulate sulcus, and revealed multiple distinct and meaningful folding patterns in each region. PMID:28229131
Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhang, Tuo; Zhang, Shu; Guo, Lei; Liu, Tianming
2015-12-01
The recently publicly released Human Connectome Project (HCP) grayordinate-based fMRI data not only has high spatial and temporal resolution, but also offers group-corresponding fMRI signals across a large population for the first time in the brain imaging field, thus significantly facilitating mapping the functional brain architecture with much higher resolution and in a group-wise fashion. In this article, we adopt the HCP grayordinate task-based fMRI (tfMRI) data to systematically identify and characterize task-based heterogeneous functional regions (THFRs) on cortical surface, i.e., the regions that are activated during multiple tasks conditions and contribute to multiple task-evoked systems during a specific task performance, and to assess the spatial patterns of identified THFRs on cortical gyri and sulci by applying a computational framework of sparse representations of grayordinate brain tfMRI signals. Experimental results demonstrate that both consistent task-evoked networks and intrinsic connectivity networks across all subjects and tasks in HCP grayordinate data are effectively and robustly reconstructed via the proposed sparse representation framework. Moreover, it is found that there are relatively consistent THFRs locating at bilateral parietal lobe, frontal lobe, and visual association cortices across all subjects and tasks. Particularly, those identified THFRs locate significantly more on gyral regions than on sulcal regions. These results based on sparse representation of HCP grayordinate data reveal novel functional architecture of cortical gyri and sulci, and might provide a foundation to better understand functional mechanisms of the human cerebral cortex in the future.
Cortical Neural Computation by Discrete Results Hypothesis.
Castejon, Carlos; Nuñez, Angel
2016-01-01
One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called "Discrete Results" (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of "Discrete Results" is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel "Discrete Results" concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast-spiking (FS
Cortical Neural Computation by Discrete Results Hypothesis
Castejon, Carlos; Nuñez, Angel
2016-01-01
One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called “Discrete Results” (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of “Discrete Results” is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel “Discrete Results” concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast
A Max-Margin Perspective on Sparse Representation-Based Classification
2013-11-30
ABSTRACT 16. SECURITY CLASSIFICATION OF: 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13. SUPPLEMENTARY NOTES 12. DISTRIBUTION AVAILIBILITY...Perspective on Sparse Representation-Based Classification Sparse Representation-based Classification (SRC) is a powerful tool in distinguishing signal...a reconstructive perspective, which neither offer- s any guarantee on its classification performance nor pro- The views, opinions and/or findings
Postpartum cortical blindness.
Faiz, Shakeel Ahmed
2008-09-01
A 30-years-old third gravida with previous normal pregnancies and an unremarkable prenatal course had an emergency lower segment caesarean section at a periphery hospital for failure of labour to progress. She developed bilateral cortical blindness immediately after recovery from anesthesia due to cerebral angiopathy shown by CT and MR scan as cortical infarct cerebral angiopathy, which is a rare complication of a normal pregnancy.
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.
Non-convex Statistical Optimization for Sparse Tensor Graphical Model
Sun, Wei; Wang, Zhaoran; Liu, Han; Cheng, Guang
2016-01-01
We consider the estimation of sparse graphical models that characterize the dependency structure of high-dimensional tensor-valued data. To facilitate the estimation of the precision matrix corresponding to each way of the tensor, we assume the data follow a tensor normal distribution whose covariance has a Kronecker product structure. The penalized maximum likelihood estimation of this model involves minimizing a non-convex objective function. In spite of the non-convexity of this estimation problem, we prove that an alternating minimization algorithm, which iteratively estimates each sparse precision matrix while fixing the others, attains an estimator with the optimal statistical rate of convergence as well as consistent graph recovery. Notably, such an estimator achieves estimation consistency with only one tensor sample, which is unobserved in previous work. Our theoretical results are backed by thorough numerical studies.
Cortical hierarchy governs rat claustrocortical circuit organization.
White, Michael G; Cody, Patrick A; Bubser, Michael; Wang, Hui-Dong; Deutch, Ariel Y; Mathur, Brian N
2017-04-15
The claustrum is a telencephalic gray matter structure with various proposed functions, including sensory integration and attentional allocation. Underlying these concepts is the reciprocal connectivity of the claustrum with most, if not all, areas of the cortex. What remains to be elucidated to inform functional hypotheses further is whether a pattern exists in the strength of connectivity between a given cortical area and the claustrum. To this end, we performed a series of retrograde neuronal tract tracer injections into rat cortical areas along the cortical processing hierarchy, from primary sensory and motor to frontal cortices. We observed that the number of claustrocortical projections increased as a function of processing hierarchy; claustrum neurons projecting to primary sensory cortices were scant and restricted in distribution across the claustrum, whereas neurons projecting to the cingulate cortex were densely packed and more evenly distributed throughout the claustrum. This connectivity pattern suggests that the claustrum may preferentially subserve executive functions orchestrated by the cingulate cortex. J. Comp. Neurol. 525:1347-1362, 2017. © 2016 Wiley Periodicals, Inc.
Sparse-aperture adaptive optics
NASA Astrophysics Data System (ADS)
Tuthill, Peter; Lloyd, James; Ireland, Michael; Martinache, Frantz; Monnier, John; Woodruff, Henry; ten Brummelaar, Theo; Turner, Nils; Townes, Charles
2006-06-01
Aperture masking interferometry and Adaptive Optics (AO) are two of the competing technologies attempting to recover diffraction-limited performance from ground-based telescopes. However, there are good arguments that these techniques should be viewed as complementary, not competitive. Masking has been shown to deliver superior PSF calibration, rejection of atmospheric noise and robust recovery of phase information through the use of closure phases. However, this comes at the penalty of loss of flux at the mask, restricting the technique to bright targets. Adaptive optics, on the other hand, can reach a fainter class of objects but suffers from the difficulty of calibration of the PSF which can vary with observational parameters such as seeing, airmass and source brightness. Here we present results from a fusion of these two techniques: placing an aperture mask downstream of an AO system. The precision characterization of the PSF enabled by sparse-aperture interferometry can now be applied to deconvolution of AO images, recovering structure from the traditionally-difficult regime within the core of the AO-corrected transfer function. Results of this program from the Palomar and Keck adaptive optical systems are presented.
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.
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.
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
Zhang, Tian-Ting; Jiang, Ye-Qin; Zhou, Hong; Yang, Wan-Xi
2010-01-01
Cortical granules are secretory vesicles in oocytes that develop from the Golgi complex. In the freshwater shrimp, Macrobrachium nipponense, mitochondria participates in the formation of cortical granules. We investigated the structural changes of mitochondria and the distribution cortical granules in different stages of oocyte development. Transmission electron microscopy provided evidence for the involvement of mitochondria and a particular spiral lamellar organization and an electron-lucent area in internal cortical granules. The ooplasm provided material for the cortical granules in early oocyte development. We demonstrated that mitochondria play a role in coalescence and maturation of cortical granules in this species. Additionally, a concept of cortical granules regarded as a functional integration is put forward. The genesis of shrimp cortical granules exhibited a particular pathway of maturation. The outer shape and inner organization considering different taxa suggested general as well as specific features of the development of cortical granules.
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
Structured sparse priors for image classification.
Srinivas, Umamahesh; Suo, Yuanming; Dao, Minh; Monga, Vishal; Tran, Trac D
2015-06-01
Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for the design of better signal recovery algorithms. This information about structure is often captured in the form of a prior on the sparse coefficients, with the Laplacian being the most common such choice (leading to l1 -norm minimization). Recent work has exploited the discriminative capability of sparse representations for image classification by employing class-specific dictionaries in the CS framework. Our contribution is a logical extension of these ideas into structured sparsity for classification. We introduce the notion of discriminative class-specific priors in conjunction with class specific dictionaries, specifically the spike-and-slab prior widely applied in Bayesian sparse regression. Significantly, the proposed framework takes the burden off the demand for abundant training image samples necessary for the success of sparsity-based classification schemes. We demonstrate this practical benefit of our approach in important applications, such as face recognition and object categorization.
Robust feature point matching with sparse model.
Jiang, Bo; Tang, Jin; Luo, Bin; Lin, Liang
2014-12-01
Feature point matching that incorporates pairwise constraints can be cast as an integer quadratic programming (IQP) problem. Since it is NP-hard, approximate methods are required. The optimal solution for IQP matching problem is discrete, binary, and thus sparse in nature. This motivates us to use sparse model for feature point matching problem. The main advantage of the proposed sparse feature point matching (SPM) method is that it generates sparse solution and thus naturally imposes the discrete mapping constraints approximately in the optimization process. Therefore, it can optimize the IQP matching problem in an approximate discrete domain. In addition, an efficient algorithm can be derived to solve SPM problem. Promising experimental results on both synthetic points sets matching and real-world image feature sets matching tasks show the effectiveness of the proposed feature point matching method.
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
Online Dictionary Learning for Sparse Coding
2009-04-01
cessing tasks such as denoising (Elad & Aharon, 2006) as well as higher-level tasks such as classification (Raina et al., 2007; Mairal et al., 2008a...Bruckstein, A. M. (2006). The K- SVD : An algorithm for designing of overcomplete dic- tionaries for sparse representations. IEEE Trans. SP...Tibshirani, R. (2004). Least angle regression. Ann. Statist. Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations
Sparse CSEM inversion driven by seismic coherence
NASA Astrophysics Data System (ADS)
Guo, Zhenwei; Dong, Hefeng; Kristensen, Åge
2016-12-01
Marine controlled source electromagnetic (CSEM) data inversion for hydrocarbon exploration is often challenging due to high computational cost, physical memory requirement and low resolution of the obtained resistivity map. This paper aims to enhance both the speed and resolution of CSEM inversion by introducing structural geological information in the inversion algorithm. A coarse mesh is generated for Occam’s inversion, where the parameters are fewer than in the fine regular mesh. This sparse mesh is defined as a coherence-based irregular (IC) sparse mesh, which is based on vertices extracted from available geological information. Inversion results on synthetic data illustrate that the IC sparse mesh has a smaller inversion computational cost compared to the regular dense (RD) mesh. It also has a higher resolution than with a regular sparse (RS) mesh for the same number of estimated parameters. In order to study how the IC sparse mesh reduces the computational time, four different meshes are generated for Occam’s inversion. As a result, an IC sparse mesh can reduce the computational cost while it keeps the resolution as good as a fine regular mesh. The IC sparse mesh reduces the computational cost of the matrix operation for model updates. When the number of estimated parameters reduces to a limited value, the computational cost is independent of the number of parameters. For a testing model with two resistive layers, the inversion result using an IC sparse mesh has higher resolution in both horizontal and vertical directions. Overall, the model representing significant geological information in the IC mesh can improve the resolution of the resistivity models obtained from inversion of CSEM data.
Sparse Extreme Learning Machine for Classification
Bai, Zuo; Huang, Guang-Bin; Wang, Danwei; Wang, Han; Westover, M. Brandon
2016-01-01
Extreme learning machine (ELM) was initially proposed for single-hidden-layer feedforward neural networks (SLFNs). In the hidden layer (feature mapping), nodes are randomly generated independently of training data. Furthermore, a unified ELM was proposed, providing a single framework to simplify and unify different learning methods, such as SLFNs, least square support vector machines, proximal support vector machines, and so on. However, the solution of unified ELM is dense, and thus, usually plenty of storage space and testing time are required for large-scale applications. In this paper, a sparse ELM is proposed as an alternative solution for classification, reducing storage space and testing time. In addition, unified ELM obtains the solution by matrix inversion, whose computational complexity is between quadratic and cubic with respect to the training size. It still requires plenty of training time for large-scale problems, even though it is much faster than many other traditional methods. In this paper, an efficient training algorithm is specifically developed for sparse ELM. The quadratic programming problem involved in sparse ELM is divided into a series of smallest possible sub-problems, each of which are solved analytically. Compared with SVM, sparse ELM obtains better generalization performance with much faster training speed. Compared with unified ELM, sparse ELM achieves similar generalization performance for binary classification applications, and when dealing with large-scale binary classification problems, sparse ELM realizes even faster training speed than unified ELM. PMID:25222727
Visual tracking via robust multitask sparse prototypes
NASA Astrophysics Data System (ADS)
Zhang, Huanlong; Hu, Shiqiang; Yu, Junyang
2015-03-01
Sparse representation has been applied to an online subspace learning-based tracking problem. To handle partial occlusion effectively, some researchers introduce l1 regularization to principal component analysis (PCA) reconstruction. However, in these traditional tracking methods, the representation of each object observation is often viewed as an individual task so the inter-relationship between PCA basis vectors is ignored. We propose a new online visual tracking algorithm with multitask sparse prototypes, which combines multitask sparse learning with PCA-based subspace representation. We first extend a visual tracking algorithm with sparse prototypes in multitask learning framework to mine inter-relations between subtasks. Then, to avoid the problem that enforcing all subtasks to share the same structure may result in degraded tracking results, we impose group sparse constraints on the coefficients of PCA basis vectors and element-wise sparse constraints on the error coefficients, respectively. Finally, we show that the proposed optimization problem can be effectively solved using the accelerated proximal gradient method with the fast convergence. Experimental results compared with the state-of-the-art tracking methods demonstrate that the proposed algorithm achieves favorable performance when the object undergoes partial occlusion, motion blur, and illumination changes.
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.
Sparse extreme learning machine for classification.
Bai, Zuo; Huang, Guang-Bin; Wang, Danwei; Wang, Han; Westover, M Brandon
2014-10-01
Extreme learning machine (ELM) was initially proposed for single-hidden-layer feedforward neural networks (SLFNs). In the hidden layer (feature mapping), nodes are randomly generated independently of training data. Furthermore, a unified ELM was proposed, providing a single framework to simplify and unify different learning methods, such as SLFNs, least square support vector machines, proximal support vector machines, and so on. However, the solution of unified ELM is dense, and thus, usually plenty of storage space and testing time are required for large-scale applications. In this paper, a sparse ELM is proposed as an alternative solution for classification, reducing storage space and testing time. In addition, unified ELM obtains the solution by matrix inversion, whose computational complexity is between quadratic and cubic with respect to the training size. It still requires plenty of training time for large-scale problems, even though it is much faster than many other traditional methods. In this paper, an efficient training algorithm is specifically developed for sparse ELM. The quadratic programming problem involved in sparse ELM is divided into a series of smallest possible sub-problems, each of which are solved analytically. Compared with SVM, sparse ELM obtains better generalization performance with much faster training speed. Compared with unified ELM, sparse ELM achieves similar generalization performance for binary classification applications, and when dealing with large-scale binary classification problems, sparse ELM realizes even faster training speed than unified ELM.
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.
Treatment of non-sparse cratering in planetary surface dating
NASA Astrophysics Data System (ADS)
Kneissl, T.; Michael, G. G.; Schmedemann, N.
2016-10-01
We here propose a new technique to derive crater size-frequency distributions (CSFDs) from non-sparsely cratered surfaces, by accounting for the loss of craters due to subsequent crater/ejecta coverage. This approach, which we refer to as the buffered non-sparseness correction (BNSC), relates each crater to a measurement area found by excluding regions in the study area that have been resurfaced by larger craters and their ejecta blankets. The approach includes the well-known buffered crater counting (BCC) technique in order to consider the potential identification of craters whose centers are located outside the counting area. We demonstrate the new approach at two test sites on the Moon, one on the ancient lunar highlands outside the South Pole Aitken basin and the other on the much younger surface of lunar Mare Serenitatis. As expected, the correction has a much stronger effect on ancient, densely cratered surfaces than on younger, sparsely cratered surfaces. Furthermore, these first results indicate that the shapes of CSFDs on ancient terrains are actually very similar to the shapes of CSFDs on younger terrains.
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.
Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons.
Xiao, Dongsheng; Vanni, Matthieu P; Mitelut, Catalin C; Chan, Allen W; LeDue, Jeffrey M; Xie, Yicheng; Chen, Andrew Cn; Swindale, Nicholas V; Murphy, Timothy H
2017-02-04
Understanding the basis of brain function requires knowledge of cortical operations over wide-spatial scales, but also within the context of single neurons. In vivo, wide-field GCaMP imaging and sub-cortical/cortical cellular electrophysiology were used in mice to investigate relationships between spontaneous single neuron spiking and mesoscopic cortical activity. We make use of a rich set of cortical activity motifs that are present in spontaneous activity in anesthetized and awake animals. A mesoscale spike-triggered averaging procedure allowed the identification of motifs that are preferentially linked to individual spiking neurons by employing genetically targeted indicators of neuronal activity. Thalamic neurons predicted and reported specific cycles of wide-scale cortical inhibition/excitation. In contrast, spike-triggered maps derived from single cortical neurons yielded spatio-temporal maps expected for regional cortical consensus function. This approach can define network relationships between any point source of neuronal spiking and mesoscale cortical maps.
Relative species abundance of replicator dynamics with sparse interactions
NASA Astrophysics Data System (ADS)
Obuchi, Tomoyuki; Kabashima, Yoshiyuki; Tokita, Kei
2016-11-01
A theory of relative species abundance on sparsely-connected networks is presented by investigating the replicator dynamics with symmetric interactions. Sparseness of a network involves difficulty in analyzing the fixed points of the equation, and we avoid this problem by treating large self interaction u, which allows us to construct a perturbative expansion. Based on this perturbation, we find that the nature of the interactions is directly connected to the abundance distribution, and some characteristic behaviors, such as multiple peaks in the abundance distribution and all species coexistence at moderate values of u, are discovered in a wide class of the distribution of the interactions. The all species coexistence collapses at a critical value of u, u c , and this collapsing is regarded as a phase transition. To get more quantitative information, we also construct a non-perturbative theory on random graphs based on techniques of statistical mechanics. The result shows those characteristic behaviors are sustained well even for not large u. For even smaller values of u, extinct species start to appear and the abundance distribution becomes rounded and closer to a standard functional form. Another interesting finding is the non-monotonic behavior of diversity, which quantifies the number of coexisting species, when changing the ratio of mutualistic relations Δ . These results are examined by numerical simulations, which show that our theory is exact for the case without extinct species, but becomes less and less precise as the proportion of extinct species grows.
Efficient, sparse biological network determination
August, Elias; Papachristodoulou, Antonis
2009-01-01
Background Determining the interaction topology of biological systems is a topic that currently attracts significant research interest. Typical models for such systems take the form of differential equations that involve polynomial and rational functions. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data much harder. The use of linear dynamics and linearization techniques that have been proposed in the past can circumvent this, but the general problem of developing efficient algorithms for models that provide more accurate system descriptions remains open. Results We present a network determination algorithm that can treat model descriptions with polynomial and rational functions and which does not make use of linearization. For this purpose, we make use of the observation that biochemical networks are in general 'sparse' and minimize the 1-norm of the decision variables (sum of weighted network connections) while constraints keep the error between data and the network dynamics small. The emphasis of our methodology is on determining the interconnection topology rather than the specific reaction constants and it takes into account the necessary properties that a chemical reaction network should have – something that techniques based on linearization can not. The problem can be formulated as a Linear Program, a convex optimization problem, for which efficient algorithms are available that can treat large data sets efficiently and uncertainties in data or model parameters. Conclusion The presented methodology is able to predict with accuracy and efficiency the connectivity structure of a chemical reaction network with mass action kinetics and of a gene regulatory network from simulation data even if the dynamics of these systems are non-polynomial (rational) and uncertainties in the data are taken into account. It also produces a network structure that can explain the real experimental
Avants, Brian B.; Libon, David J.; Rascovsky, Katya; Boller, Ashley; McMillan, Corey T.; Massimo, Lauren; Coslett, H. Branch; Chatterjee, Anjan; Gross, Rachel G.; Grossman, Murray
2014-01-01
This study establishes that sparse canonical correlation analysis (SCCAN) identifies generalizable, structural MRI-derived cortical networks that relate to five distinct categories of cognition. We obtain multivariate psychometrics from the domain-specific sub-scales of the Philadelphia Brief Assessment of Cognition (PBAC). By using a training and separate testing stage, we find that PBAC-defined cognitive domains of language, visuospatial functioning, episodic memory, executive control, and social functioning correlate with unique and distributed areas of gray matter (GM). In contrast, a parallel univariate framework fails to identify, from the training data, regions that are also significant in the left-out test dataset. The cohort includes164 patients with Alzheimer’s disease, behavioral-variant frontotemporal dementia, semantic variant primary progressive aphasia, nonfluent/agrammatic primary progressive aphasia, or corticobasal syndrome. The analysis is implemented with open-source software for which we provide examples in the text. In conclusion, we show that multivariate techniques identify biologically-plausible brain regions supporting specific cognitive domains. The findings are identified in training data and confirmed in test data. PMID:24096125
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.
Synapse-Centric Mapping of Cortical Models to the SpiNNaker Neuromorphic Architecture
Knight, James C.; Furber, Steve B.
2016-01-01
While the adult human brain has approximately 8.8 × 1010 neurons, this number is dwarfed by its 1 × 1015 synapses. From the point of view of neuromorphic engineering and neural simulation in general this makes the simulation of these synapses a particularly complex problem. SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Current solutions for simulating spiking neural networks on SpiNNaker are heavily inspired by work on distributed high-performance computing. However, while SpiNNaker shares many characteristics with such distributed systems, its component nodes have much more limited resources and, as the system lacks global synchronization, the computation performed on each node must complete within a fixed time step. We first analyze the performance of the current SpiNNaker neural simulation software and identify several problems that occur when it is used to simulate networks of the type often used to model the cortex which contain large numbers of sparsely connected synapses. We then present a new, more flexible approach for mapping the simulation of such networks to SpiNNaker which solves many of these problems. Finally we analyze the performance of our new approach using both benchmarks, designed to represent cortical connectivity, and larger, functional cortical models. In a benchmark network where neurons receive input from 8000 STDP synapses, our new approach allows 4× more neurons to be simulated on each SpiNNaker core than has been previously possible. We also demonstrate that the largest plastic neural network previously simulated on neuromorphic hardware can be run in real time using our new approach: double the speed that was previously achieved. Additionally this network contains two types of plastic synapse which previously had to be trained separately but, using our new approach, can be trained simultaneously. PMID:27683540
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach
NASA Astrophysics Data System (ADS)
Khanduri, Prashant; Kailkhura, Bhavya; Thiagarajan, Jayaraman J.; Varshney, Pramod K.
2016-10-01
This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the Fusion Center (FC). We design optimal collaboration strategies which are universal for a class of deterministic signals. By establishing the equivalence between the collaboration strategy design problem and sparse PCA, we solve the problem efficiently and evaluate the impact of collaboration on detection performance.
Associative fear learning enhances sparse network coding in primary sensory cortex
Gdalyahu, Amos; Tring, Elaine; Polack, Pierre-Olivier; Gruver, Robin; Golshani, Peyman; Fanselow, Michael S.; Silva, Alcino J.; Trachtenberg, Joshua T.
2012-01-01
Summary Several models of associative learning predict that stimulus processing changes during association formation. How associative learning reconfigures neural circuits in primary sensory cortex to "learn" associative attributes of a stimulus remains unknown. Using 2-photon in-vivo calcium imaging to measure responses of networks of neurons in primary somatosensory cortex, we discovered that associative fear learning, in which whisker stimulation is paired with foot shock, enhances sparse population coding and robustness of the conditional stimulus, yet decreases total network activity. Fewer cortical neurons responded to stimulation of the trained whisker than in controls, yet their response strength was enhanced. These responses were not observed in mice exposed to a non-associative learning procedure. Our results define how the cortical representation of a sensory stimulus is shaped by associative fear learning. These changes are proposed to enhance efficient sensory processing after associative learning. PMID:22794266
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.
A novel sparse boosting method for crater detection in the high resolution planetary image
NASA Astrophysics Data System (ADS)
Wang, Yan; Yang, Gang; Guo, Lei
2015-09-01
Impact craters distributed on planetary surface become one of the main barriers during the soft landing of planetary probes. In order to accelerate the crater detection, in this paper, we present a new sparse boosting (SparseBoost) method for automatic detection of sub-kilometer craters. The SparseBoost method integrates an improved sparse kernel density estimator (RSDE-WL1) into the Boost algorithm and the RSDE-WL1 estimator is achieved by introducing weighted l1 penalty term into the reduced set density estimator. An iterative algorithm is proposed to implement the RSDE-WL1. The SparseBoost algorithm has the advantage of fewer selected features and simpler representation of the weak classifiers compared with the Boost algorithm. Our SparseBoost based crater detection method is evaluated on a large and high resolution image of Martian surface. Experimental results demonstrate that the proposed method can achieve less computational complexity in comparison with other crater detection methods in terms of selected features.
Objective sea level pressure analysis for sparse data areas
NASA Technical Reports Server (NTRS)
Druyan, L. M.
1972-01-01
A computer procedure was used to analyze the pressure distribution over the North Pacific Ocean for eleven synoptic times in February, 1967. Independent knowledge of the central pressures of lows is shown to reduce the analysis errors for very sparse data coverage. The application of planned remote sensing of sea-level wind speeds is shown to make a significant contribution to the quality of the analysis especially in the high gradient mid-latitudes and for sparse coverage of conventional observations (such as over Southern Hemisphere oceans). Uniform distribution of the available observations of sea-level pressure and wind velocity yields results far superior to those derived from a random distribution. A generalization of the results indicates that the average lower limit for analysis errors is between 2 and 2.5 mb based on the perfect specification of the magnitude of the sea-level pressure gradient from a known verification analysis. A less than perfect specification will derive from wind-pressure relationships applied to satellite observed wind speeds.
Cortical spreading depression-induced preconditioning in the brain
Shen, Ping-ping; Hou, Shuai; Ma, Di; Zhao, Ming-ming; Zhu, Ming-qin; Zhang, Jing-dian; Feng, Liang-shu; Cui, Li; Feng, Jia-chun
2016-01-01
Cortical spreading depression is a technique used to depolarize neurons. During focal or global ischemia, cortical spreading depression-induced preconditioning can enhance tolerance of further injury. However, the underlying mechanism for this phenomenon remains relatively unclear. To date, numerous issues exist regarding the experimental model used to precondition the brain with cortical spreading depression, such as the administration route, concentration of potassium chloride, induction time, duration of the protection provided by the treatment, the regional distribution of the protective effect, and the types of neurons responsible for the greater tolerance. In this review, we focus on the mechanisms underlying cortical spreading depression-induced tolerance in the brain, considering excitatory neurotransmission and metabolism, nitric oxide, genomic reprogramming, inflammation, neurotropic factors, and cellular stress response. Specifically, we clarify the procedures and detailed information regarding cortical spreading depression-induced preconditioning and build a foundation for more comprehensive investigations in the field of neural regeneration and clinical application in the future. PMID:28123433
Merlin/ERM proteins establish cortical asymmetry and centrosome position
Hebert, Alan M.; DuBoff, Brian; Casaletto, Jessica B.; Gladden, Andrew B.; McClatchey, Andrea I.
2012-01-01
The ability to generate asymmetry at the cell cortex underlies cell polarization and asymmetric cell division. Here we demonstrate a novel role for the tumor suppressor Merlin and closely related ERM proteins (Ezrin, Radixin, and Moesin) in generating cortical asymmetry in the absence of external cues. Our data reveal that Merlin functions to restrict the cortical distribution of the actin regulator Ezrin, which in turn positions the interphase centrosome in single epithelial cells and three-dimensional organotypic cultures. In the absence of Merlin, ectopic cortical Ezrin yields mispositioned centrosomes, misoriented spindles, and aberrant epithelial architecture. Furthermore, in tumor cells with centrosome amplification, the failure to restrict cortical Ezrin abolishes centrosome clustering, yielding multipolar mitoses. These data uncover fundamental roles for Merlin/ERM proteins in spatiotemporally organizing the cell cortex and suggest that Merlin's role in restricting cortical Ezrin may contribute to tumorigenesis by disrupting cell polarity, spindle orientation, and, potentially, genome stability. PMID:23249734
Lineage-specific laminar organization of cortical GABAergic interneurons.
Ciceri, Gabriele; Dehorter, Nathalie; Sols, Ignasi; Huang, Z Josh; Maravall, Miguel; Marín, Oscar
2013-09-01
In the cerebral cortex, pyramidal cells and interneurons are generated in distant germinal zones, and so the mechanisms that control their precise assembly into specific microcircuits remain an enigma. Here we report that cortical interneurons labeled at the clonal level do not distribute randomly but rather have a strong tendency to cluster in the mouse neocortex. This behavior is common to different classes of interneurons, independently of their origin. Interneuron clusters are typically contained within one or two adjacent cortical layers, are largely formed by isochronically generated neurons and populate specific layers, as revealed by unbiased hierarchical clustering methods. Our results suggest that different progenitor cells give rise to interneurons populating infra- and supragranular cortical layers, which challenges current views of cortical neurogenesis. Thus, specific lineages of cortical interneurons seem to be produced to primarily mirror the laminar structure of the cerebral cortex, rather than its columnar organization.
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.
Learning-Based Topological Correction for Infant Cortical Surfaces
Hao, Shijie; Li, Gang; Wang, Li; Meng, Yu
2017-01-01
Reconstruction of topologically correct and accurate cortical surfaces from infant MR images is of great importance in neuroimaging mapping of early brain development. However, due to rapid growth and ongoing myelination, infant MR images exhibit extremely low tissue contrast and dynamic appearance patterns, thus leading to much more topological errors (holes and handles) in the cortical surfaces derived from tissue segmentation results, in comparison to adult MR images which typically have good tissue contrast. Existing methods for topological correction either rely on the minimal correction criteria, or ad hoc rules based on image intensity priori, thus often resulting in erroneous correction and large anatomical errors in reconstructed infant cortical surfaces. To address these issues, we propose to correct topological errors by learning information from the anatomical references, i.e., manually corrected images. Specifically, in our method, we first locate candidate voxels of topologically defected regions by using a topology-preserving level set method. Then, by leveraging rich information of the corresponding patches from reference images, we build region-specific dictionaries from the anatomical references and infer the correct labels of candidate voxels using sparse representation. Notably, we further integrate these two steps into an iterative framework to enable gradual correction of large topological errors, which are frequently occurred in infant images and cannot be completely corrected using one-shot sparse representation. Extensive experiments on infant cortical surfaces demonstrate that our method not only effectively corrects the topological defects, but also leads to better anatomical consistency, compared to the state-of-the-art methods.
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
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.
Stochastic Computations in Cortical Microcircuit Models
Maass, Wolfgang
2013-01-01
Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving. PMID:24244126
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.
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.
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
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
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.
Hernandez-Castillo, Carlos R; Aguilar-Castañeda, Erika; Iglesias, Martin; Fernandez-Ruiz, Juan
2016-05-01
The objective of this study was to characterize the cortical activity pattern of one patient who received bilateral forearm transplants. Using fMRI we acquired motor and sensory brain activity every year after surgery and during three consecutive years while the patient underwent physical rehabilitation. The motor related cortical activity evaluated during the first year showed a sparse pattern involving several brain regions. Over time, the analysis showed a progressive delimitation of the motor-related areas that had significant activity. The results also showed continuous size reductions of the activated cluster in the motor cortex. The activation in the sensory cortex showed significant increases in cluster size over time. The intensity of both motor and sensory cortical activations correlated with the Disabilities of the Arm, Shoulder and Hand questionnaire. Our results show significant cortical reorganization of motor and sensory cortices after transplantation of bilateral forearm transplantation over a four-year period.
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.
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.
Learning Stable Multilevel Dictionaries for Sparse Representations.
Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas
2015-09-01
Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The increasing need for learning sparse models in large-scale applications motivates the development of efficient, robust, and provably good dictionary learning algorithms. Algorithmic stability and generalizability are desirable characteristics for dictionary learning algorithms that aim to build global dictionaries, which can efficiently model any test data similar to the training samples. In this paper, we propose an algorithm to learn dictionaries for sparse representations from large scale data, and prove that the proposed learning algorithm is stable and generalizable asymptotically. The algorithm employs a 1-D subspace clustering procedure, the K-hyperline clustering, to learn a hierarchical dictionary with multiple levels. We also propose an information-theoretic scheme to estimate the number of atoms needed in each level of learning and develop an ensemble approach to learn robust dictionaries. Using the proposed dictionaries, the sparse code for novel test data can be computed using a low-complexity pursuit procedure. We demonstrate the stability and generalization characteristics of the proposed algorithm using simulations. We also evaluate the utility of the multilevel dictionaries in compressed recovery and subspace learning applications.
SAR Image Despeckling Via Structural Sparse Representation
NASA Astrophysics Data System (ADS)
Lu, Ting; Li, Shutao; Fang, Leyuan; Benediktsson, Jón Atli
2016-12-01
A novel synthetic aperture radar (SAR) image despeckling method based on structural sparse representation is introduced. The proposed method utilizes the fact that different regions in SAR images correspond to varying terrain reflectivity. Therefore, SAR images can be split into a heterogeneous class (with a varied terrain reflectivity) and a homogeneous class (with a constant terrain reflectivity). In the proposed method, different sparse representation based despeckling schemes are designed by combining the different region characteristics in SAR images. For heterogeneous regions with rich structure and texture information, structural dictionaries are learned to appropriately represent varied structural characteristics. Specifically, each patch in these regions is sparsely coded with the best fitted structural dictionary, thus good structure preservation can be obtained. For homogenous regions without rich structure and texture information, the highly redundant photometric self-similarity is exploited to suppress speckle noise without introducing artifacts. That is achieved by firstly learning the sub-dictionary, then simultaneously sparsely coding for each group of photometrically similar image patches. Visual and objective experimental results demonstrate the superiority of the proposed method over the-state-of-the-art methods.
Maxdenominator Reweighted Sparse Representation for Tumor Classification
Li, Weibiao; Liao, Bo; Zhu, Wen; Chen, Min; Peng, Li; Wei, Xiaohui; Gu, Changlong; Li, Keqin
2017-01-01
The classification of tumors is crucial for the proper treatment of cancer. Sparse representation-based classifier (SRC) exhibits good classification performance and has been successfully used to classify tumors using gene expression profile data. In this study, we propose a three-step maxdenominator reweighted sparse representation classification (MRSRC) method to classify tumors. First, we extract a set of metagenes from the training samples. These metagenes can capture the structures inherent to the data and are more effective for classification than the original gene expression data. Second, we use a reweighted regularization method to obtain the sparse representation coefficients. Reweighted regularization can enhance sparsity and obtain better sparse representation coefficients. Third, we classify the data by utilizing a maxdenominator residual error function. Maxdenominator strategy can reduce the residual error and improve the accuracy of the final classification. Extensive experiments using publicly available gene expression profile data sets show that the performance of MRSRC is comparable with or better than many existing representative methods. PMID:28393883
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. PMID:27630923
The biology and dynamics of mammalian cortical granules.
Liu, Min
2011-11-17
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.
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
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
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.
Deploying temporary networks for upscaling of sparse network stations
NASA Astrophysics Data System (ADS)
Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Kelly, Victoria; Hall, Mark; Palecki, Michael A.; Temimi, Marouane
2016-10-01
Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, business and consumer applications, or even human health issues. The installation of soil moisture sensors as sparse, national networks is necessitated by limited financial resources. However, this results in the incomplete sampling of the local heterogeneity of soil type, vegetation cover, topography, and the fine spatial distribution of precipitation events. To this end, temporary networks can be installed in the areas surrounding a permanent installation within a sparse network. The temporary networks deployed in this study provide a more representative average at the 3 km and 9 km scales, localized about the permanent gauge. The value of such temporary networks is demonstrated at test sites in Millbrook, New York and Crossville, Tennessee. The capacity of a single U.S. Climate Reference Network (USCRN) sensor set to approximate the average of a temporary network at the 3 km and 9 km scales using a simple linear scaling function is tested. The capacity of a temporary network to provide reliable estimates with diminishing numbers of sensors, the temporal stability of those networks, and ultimately, the relationship of the variability of those networks to soil moisture conditions at the permanent sensor are investigated. In this manner, this work demonstrates the single-season installation of a temporary network as a mechanism to characterize the soil moisture variability at a permanent gauge within a sparse network.
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.
Sparse Poisson noisy image deblurring.
Carlavan, Mikael; Blanc-Féraud, Laure
2012-04-01
Deblurring noisy Poisson images has recently been a subject of an increasing amount of works in many areas such as astronomy and biological imaging. In this paper, we focus on confocal microscopy, which is a very popular technique for 3-D imaging of biological living specimens that gives images with a very good resolution (several hundreds of nanometers), although degraded by both blur and Poisson noise. Deconvolution methods have been proposed to reduce these degradations, and in this paper, we focus on techniques that promote the introduction of an explicit prior on the solution. One difficulty of these techniques is to set the value of the parameter, which weights the tradeoff between the data term and the regularizing term. Only few works have been devoted to the research of an automatic selection of this regularizing parameter when considering Poisson noise; therefore, it is often set manually such that it gives the best visual results. We present here two recent methods to estimate this regularizing parameter, and we first propose an improvement of these estimators, which takes advantage of confocal images. Following these estimators, we secondly propose to express the problem of the deconvolution of Poisson noisy images as the minimization of a new constrained problem. The proposed constrained formulation is well suited to this application domain since it is directly expressed using the antilog likelihood of the Poisson distribution and therefore does not require any approximation. We show how to solve the unconstrained and constrained problems using the recent alternating-direction technique, and we present results on synthetic and real data using well-known priors, such as total variation and wavelet transforms. Among these wavelet transforms, we specially focus on the dual-tree complex wavelet transform and on the dictionary composed of curvelets and an undecimated wavelet transform.
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.
Margin based ontology sparse vector learning algorithm and applied in biology science.
Gao, Wei; Qudair Baig, Abdul; Ali, Haidar; Sajjad, Wasim; Reza Farahani, Mohammad
2017-01-01
In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.
Wang, Gang; Wang, Yalin
2017-02-15
In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.
Spatial learning and action planning in a prefrontal cortical network model.
Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo
2011-05-01
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.
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.
Time-frequency manifold sparse reconstruction: A novel method for bearing fault feature extraction
NASA Astrophysics Data System (ADS)
Ding, Xiaoxi; He, Qingbo
2016-12-01
In this paper, a novel transient signal reconstruction method, called time-frequency manifold (TFM) sparse reconstruction, is proposed for bearing fault feature extraction. This method introduces image sparse reconstruction into the TFM analysis framework. According to the excellent denoising performance of TFM, a more effective time-frequency (TF) dictionary can be learned from the TFM signature by image sparse decomposition based on orthogonal matching pursuit (OMP). Then, the TF distribution (TFD) of the raw signal in a reconstructed phase space would be re-expressed with the sum of learned TF atoms multiplied by corresponding coefficients. Finally, one-dimensional signal can be achieved again by the inverse process of TF analysis (TFA). Meanwhile, the amplitude information of the raw signal would be well reconstructed. The proposed technique combines the merits of the TFM in denoising and the atomic decomposition in image sparse reconstruction. Moreover, the combination makes it possible to express the nonlinear signal processing results explicitly in theory. The effectiveness of the proposed TFM sparse reconstruction method is verified by experimental analysis for bearing fault feature extraction.
Cortical control of anticipatory postural adjustments prior to stepping.
Varghese, J P; Merino, D M; Beyer, K B; McIlroy, W E
2016-01-28
Human bipedal balance control is achieved either reactively or predictively by a distributed network of neural areas within the central nervous system with a potential role for cerebral cortex. While the role of the cortex in reactive balance has been widely explored, only few studies have addressed the cortical activations related to predictive balance control. The present study investigated the cortical activations related to the preparation and execution of anticipatory postural adjustment (APA) that precede a step. This study also examined whether the preparatory cortical activations related to a specific movement is dependent on the context of control (postural component vs. focal component). Ground reaction forces and electroencephalographic (EEG) data were recorded from 14 healthy adults while they performed lateral weight shift and lateral stepping with and without initially preloading their weight to the stance leg. EEG analysis revealed that there were distinct movement-related potentials (MRPs) with concurrent event-related desynchronization (ERD) of mu and beta rhythms prior to the onset of APA and also to the onset of foot-off during lateral stepping in the fronto-central cortical areas. Also, the MRPs and ERD prior to the onset of APA and onset of lateral weight shift were not significantly different suggesting the comparable cortical activations for the generation of postural and focal movements. The present study reveals the occurrence of cortical activation prior to the execution of an APA that precedes a step. Importantly, this cortical activity appears independent of the context of the movement.
High-performance parallel sparse-direct triangular solves (Invited)
NASA Astrophysics Data System (ADS)
Poulson, J.; Ying, L.
2013-12-01
Geophysical inverse problems are increasingly posed in the frequency domain in a manner which requires solving many challenging heterogeneous 3D Helmholtz or linear elastic wave equations at each iteration. One effective means of solving such problems, at least when there is no large-scale internal resonance, is to use moving-PML "sweeping preconditioners". Each application of the sweeping preconditioner involves performing many modest-sized sparse-direct triangular solves -- unfortunately, one at a time. While P. et al. have shown that, with a careful implementation of a distributed sparse-direct solver [1,2], challenging 3D problems approaching a billion degrees of freedom can be solved in a few minutes using less than 10,000 cores, this talk discusses how to leverage the existence of many right-hand sides in order to increase the performance of the preconditioner applications by orders of magnitude. [1] http://github.com/poulson/Clique [2] http://github.com/poulson/PSP
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.
Bayesian modeling of temporal dependence in large sparse contingency tables
Kunihama, Tsuyoshi; Dunson, David B.
2013-01-01
In many applications, it is of interest to study trends over time in relationships among categorical variables, such as age group, ethnicity, religious affiliation, political party and preference for particular policies. At each time point, a sample of individuals provide responses to a set of questions, with different individuals sampled at each time. In such settings, there tends to be abundant missing data and the variables being measured may change over time. At each time point, one obtains a large sparse contingency table, with the number of cells often much larger than the number of individuals being surveyed. To borrow information across time in modeling large sparse contingency tables, we propose a Bayesian autoregressive tensor factorization approach. The proposed model relies on a probabilistic Parafac factorization of the joint pmf characterizing the categorical data distribution at each time point, with autocorrelation included across times. Efficient computational methods are developed relying on MCMC. The methods are evaluated through simulation examples and applied to social survey data. PMID:24482548
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.
Demixing Population Activity in Higher Cortical Areas
Machens, Christian K.
2009-01-01
Neural responses in higher cortical areas often display a baffling complexity. In animals performing behavioral tasks, single neurons will typically encode several parameters simultaneously, such as stimuli, rewards, decisions, etc. When dealing with this large heterogeneity of responses, cells are conventionally classified into separate response categories using various statistical tools. However, this classical approach usually fails to account for the distributed nature of representations in higher cortical areas. Alternatively, principal component analysis (PCA) or related techniques can be employed to reduce the complexity of a data set while retaining the distributional aspect of the population activity. These methods, however, fail to explicitly extract the task parameters from the neural responses. Here we suggest a coordinate transformation that seeks to ameliorate these problems by combining the advantages of both methods. Our basic insight is that variance in neural firing rates can have different origins (such as changes in a stimulus, a reward, or the passage of time), and that, instead of lumping them together, as PCA does, we need to treat these sources separately. We present a method that seeks an orthogonal coordinate transformation such that the variance captured from different sources falls into orthogonal subspaces and is maximized within these subspaces. Using simulated examples, we show how this approach can be used to demix heterogeneous neural responses. Our method may help to lift the fog of response heterogeneity in higher cortical areas. PMID:21031029
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 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.
Fingerprint Compression Based on Sparse Representation.
Shao, Guangqi; Wu, Yanping; A, Yong; Liu, Xiao; Guo, Tiande
2014-02-01
A new fingerprint compression algorithm based on sparse representation is introduced. Obtaining an overcomplete dictionary from a set of fingerprint patches allows us to represent them as a sparse linear combination of dictionary atoms. In the algorithm, we first construct a dictionary for predefined fingerprint image patches. For a new given fingerprint images, represent its patches according to the dictionary by computing l(0)-minimization and then quantize and encode the representation. In this paper, we consider the effect of various factors on compression results. Three groups of fingerprint images are tested. The experiments demonstrate that our algorithm is efficient compared with several competing compression techniques (JPEG, JPEG 2000, and WSQ), especially at high compression ratios. The experiments also illustrate that the proposed algorithm is robust to extract minutiae.
Feature selection using sparse Bayesian inference
NASA Astrophysics Data System (ADS)
Brandes, T. Scott; Baxter, James R.; Woodworth, Jonathan
2014-06-01
A process for selecting a sparse subset of features that maximize discrimination between target classes is described in a Bayesian framework. Demonstrated on high range resolution radar (HRR) signature data, this has the effect of selecting the most informative range bins for a classification task. The sparse Bayesian classifier (SBC) model is directly compared against Fisher's linear discriminant analysis (LDA), showing a clear performance gain with the Bayesian framework using HRRs from the publicly available MSTAR data set. The discriminative power of the selected features from the SBC is shown to be particularly dominant over LDA when only a few features are selected or when there is a shift in training and testing data sets, as demonstrated by training on a specific target type and testing on a slightly different target type.
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.
Causal Network Inference Via Group Sparse Regularization
Bolstad, Andrew; Van Veen, Barry D.; Nowak, Robert
2011-01-01
This paper addresses the problem of inferring sparse causal networks modeled by multivariate autoregressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a “false connection score” ψ. In particular, we show that consistent recovery is possible even when the number of observations of the network is far less than the number of parameters describing the network, provided that ψ < 1. The false connection score is also demonstrated to be a useful metric of recovery in nonasymptotic regimes. The conditions suggest a modified gLasso procedure which tends to improve the false connection score and reduce the chances of reversing the direction of causal influence. Computational experiments and a real network based electrocorticogram (ECoG) simulation study demonstrate the effectiveness of the approach. PMID:21918591
Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons
Xiao, Dongsheng; Vanni, Matthieu P; Mitelut, Catalin C; Chan, Allen W; LeDue, Jeffrey M; Xie, Yicheng; Chen, Andrew CN; Swindale, Nicholas V; Murphy, Timothy H
2017-01-01
Understanding the basis of brain function requires knowledge of cortical operations over wide-spatial scales, but also within the context of single neurons. In vivo, wide-field GCaMP imaging and sub-cortical/cortical cellular electrophysiology were used in mice to investigate relationships between spontaneous single neuron spiking and mesoscopic cortical activity. We make use of a rich set of cortical activity motifs that are present in spontaneous activity in anesthetized and awake animals. A mesoscale spike-triggered averaging procedure allowed the identification of motifs that are preferentially linked to individual spiking neurons by employing genetically targeted indicators of neuronal activity. Thalamic neurons predicted and reported specific cycles of wide-scale cortical inhibition/excitation. In contrast, spike-triggered maps derived from single cortical neurons yielded spatio-temporal maps expected for regional cortical consensus function. This approach can define network relationships between any point source of neuronal spiking and mesoscale cortical maps. DOI: http://dx.doi.org/10.7554/eLife.19976.001 PMID:28160463
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.
Hyperspectral Image Classification via Kernel Sparse Representation
2013-01-01
and sparse representations, image processing, wavelets, multirate systems , and filter banks . Nasser M. Nasrabadi (S’80–M’84–SM’92–F’01) received the...ery, sampling, multirate systems , filter banks , transforms, wavelets, and their applications in signal analysis, compression, processing, and...University of Pavia and the Center of Pavia images, are urban images acquired by the Reflective Optics System Imaging Spectrom- eter (ROSIS). The ROSIS
Sparse Representation for Time-Series Classification
2015-02-08
February 8, 2015 16:49 World Scientific Review Volume - 9in x 6in ” time - series classification” page 1 Chapter 1 Sparse Representation for Time - Series ...studies the problem of time - series classification and presents an overview of recent developments in the area of feature extraction and information...problem of target classification, and more generally time - series classification, in two main directions, feature extraction and information fusion. 1
Control and amplification of cortical neurodynamics
NASA Astrophysics Data System (ADS)
Liljenstroem, Hans; Aronsson, P.
1999-03-01
We investigate different mechanisms for the control and amplification of cortical neurodynamics, using a neural network model of a three layered cortical structure. We show that different dynamical states can be obtained by changing a control parameter of the input-output relation, or by changing the noise level. Point attractor, limit cycle, and strange attractor dynamics occur at different values of the control parameter. For certain, optimal noise levels, system performance is maximized, analogous to stochastic resonance phenomena. Noise can also be used to induce different dynamical states. A few noisy network units distributed in a network layer can result in global synchronous oscillations, or waves of activity moving across the network. We further demonstrate that fast synchronization of network activity can be obtained by implementing electromagnetic interactions between network units.
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.
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.
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<
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.
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.
Evaluating Mandibular Cortical Index Quantitatively
Yasar, Fusun; Akgunlu, Faruk
2008-01-01
Objectives The aim was to assess whether Fractal Dimension and Lacunarity analysis can discriminate patients having different mandibular cortical shape. Methods Panoramic radiographs of 52 patients were evaluated for mandibular cortical index. Weighted Kappa between the observations were varying between 0.718–0.805. These radiographs were scanned and converted to binary images. Fractal Dimension and Lacunarity were calculated from the regions where best represents the cortical morphology. Results It was found that there were statistically significant difference between the Fractal Dimension and Lacunarity of radiographs which were classified as having Cl 1 and Cl 2 (Fractal Dimension P:0.000; Lacunarity P:0.003); and Cl 1 and Cl 3 cortical morphology (Fractal Dimension P:0.008; Lacunarity P:0.001); but there was no statistically significant difference between Fractal Dimension and Lacunarity of radiographs which were classified as having Cl 2 and Cl 3 cortical morphology (Fractal Dimension P:1.000; Lacunarity P:0.758). Conclusions FD and L can differentiate Cl 1 mandibular cortical shape from both Cl 2 and Cl 3 mandibular cortical shape but cannot differentiate Cl 2 from Cl 3 mandibular cortical shape on panoramic radiographs. PMID:19212535
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
Noor, Amina; Ahmad, Aitzaz; Serpedin, Erchin
2015-10-27
Network component analysis (NCA) is an important method for inferring transcriptional regulatory networks (TRNs) and recovering transcription factor activities (TFAs) using gene expression data, and the prior information about the connectivity matrix. The algorithms currently available crucially depend on the completeness of this prior information. However, inaccuracies in the measurement process may render incompleteness in the available knowledge about the connectivity matrix. Hence, computationally efficient algorithms are needed to overcome the possible incompleteness in the available data. We present a sparse network component analysis algorithm (sparseNCA), which incorporates the effect of incompleteness in the estimation of TRNs by imposing an additional sparsity constraint using the `1 norm, which results in a greater estimation accuracy. In order to improve the computational efficiency, an iterative re-weighted `2 method is proposed for the NCA problem which not only promotes sparsity but is hundreds of times faster than the `1 norm based solution. The performance of sparseNCA is rigorously compared to that of FastNCA and NINCA using synthetic data as well as real data. It is shown that sparseNCA outperforms the existing state-of-the-art algorithms both in terms of estimation accuracy and consistency with the added advantage of low computational complexity. The performance of sparseNCA compared to its predecessors is particularly pronounced in case of incomplete prior information about the sparsity of the network. Subnetwork analysis is performed on the E.coli data which reiterates the superior consistency of the proposed algorithm.
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.
Cortical Control of Striatal Dopamine Transmission via Striatal Cholinergic Interneurons
Kosillo, Polina; Zhang, Yan-Feng; Threlfell, Sarah; Cragg, Stephanie J.
2016-01-01
Corticostriatal regulation of striatal dopamine (DA) transmission has long been postulated, but ionotropic glutamate receptors have not been localized directly to DA axons. Striatal cholinergic interneurons (ChIs) are emerging as major players in striatal function, and can govern DA transmission by activating nicotinic receptors (nAChRs) on DA axons. Cortical inputs to ChIs have historically been perceived as sparse, but recent evidence indicates that they strongly activate ChIs. We explored whether activation of M1/M2 corticostriatal inputs can consequently gate DA transmission, via ChIs. We reveal that optogenetic activation of channelrhodopsin-expressing corticostriatal axons can drive striatal DA release detected with fast-scan cyclic voltammetry and requires activation of nAChRs on DA axons and AMPA receptors on ChIs that promote short-latency action potentials. By contrast, DA release driven by optogenetic activation of intralaminar thalamostriatal inputs involves additional activation of NMDA receptors on ChIs and action potential generation over longer timescales. Therefore, cortical and thalamic glutamate inputs can modulate DA transmission by regulating ChIs as gatekeepers, through ionotropic glutamate receptors. The different use of AMPA and NMDA receptors by cortical versus thalamic inputs might lead to distinct input integration strategies by ChIs and distinct modulation of the function of DA and striatum. PMID:27566978
Yao, Jincao; Yu, Huimin; Hu, Roland
2017-01-01
This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.
Paroxysmal kinesigenic dyskinesia: cortical or non-cortical origin.
van Strien, Teun W; van Rootselaar, Anne-Fleur; Hilgevoord, Anthony A J; Linssen, Wim H J P; Groffen, Alexander J A; Tijssen, Marina A J
2012-06-01
Paroxysmal kinesigenic dyskinesia (PKD) is characterized by involuntary dystonia and/or chorea triggered by a sudden movement. Cases are usually familial with an autosomal dominant inheritance. Hypotheses regarding the pathogenesis of PKD focus on the controversy whether PKD has a cortical or non-cortical origin. A combined familial trait of PKD and benign familial infantile seizures has been reported as the infantile convulsions and paroxysmal choreoathetosis (ICCA) syndrome. Here, we report a family diagnosed with ICCA syndrome with an Arg217STOP mutation. The index patient showed interictal EEG focal changes compatible with paroxysmal dystonic movements of his contralateral leg. This might support cortical involvement in PKD.
The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks.
Landau, Itamar D; Egger, Robert; Dercksen, Vincent J; Oberlaender, Marcel; Sompolinsky, Haim
2016-12-07
Models of cortical dynamics often assume a homogeneous connectivity structure. However, we show that heterogeneous input connectivity can prevent the dynamic balance between excitation and inhibition, a hallmark of cortical dynamics, and yield unrealistically sparse and temporally regular firing. Anatomically based estimates of the connectivity of layer 4 (L4) rat barrel cortex and numerical simulations of this circuit indicate that the local network possesses substantial heterogeneity in input connectivity, sufficient to disrupt excitation-inhibition balance. We show that homeostatic plasticity in inhibitory synapses can align the functional connectivity to compensate for structural heterogeneity. Alternatively, spike-frequency adaptation can give rise to a novel state in which local firing rates adjust dynamically so that adaptation currents and synaptic inputs are balanced. This theory is supported by simulations of L4 barrel cortex during spontaneous and stimulus-evoked conditions. Our study shows how synaptic and cellular mechanisms yield fluctuation-driven dynamics despite structural heterogeneity in cortical circuits.
SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos
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
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.
ERIC Educational Resources Information Center
Bowers, Wayne A.
This monograph was written for the Conference of the New Instructional Materials in Physics, held at the University of Washington in summer, 1965. It is intended for students who have had an introductory college physics course. It seeks to provide an introduction to the idea of distributions in general, and to some aspects of the subject in…
Adaptive feature extraction using sparse coding for machinery fault diagnosis
NASA Astrophysics Data System (ADS)
Liu, Haining; Liu, Chengliang; Huang, Yixiang
2011-02-01
In the signal processing domain, there has been growing interest in sparse coding with a learned dictionary instead of a predefined one, which is advocated as an effective mathematical description for the underlying principle of mammalian sensory systems in processing information. In this paper, sparse coding is introduced as a feature extraction technique for machinery fault diagnosis and an adaptive feature extraction scheme is proposed based on it. The two core problems of sparse coding, i.e., dictionary learning and coefficients solving, are discussed in detail. A natural extension of sparse coding, shift-invariant sparse coding, is also introduced. Then, the vibration signals of rolling element bearings are taken as the target signals to verify the proposed scheme, and shift-invariant sparse coding is used for vibration analysis. With the purpose of diagnosing the different fault conditions of bearings, features are extracted following the proposed scheme: basis functions are separately learned from each class of vibration signals trying to capture the defective impulses; a redundant dictionary is built by merging all the learned basis functions; based on the redundant dictionary, the diagnostic information is made explicit in the solved sparse representations of vibration signals; sparse features are formulated in terms of activations of atoms. The multiclass linear discriminant analysis (LDA) classifier is used to test the discriminability of the extracted sparse features and the adaptability of the learned atoms. The experiments show that sparse coding is an effective feature extraction technique for machinery fault diagnosis.
Stochastic convex sparse principal component analysis.
Baytas, Inci M; Lin, Kaixiang; Wang, Fei; Jain, Anil K; Zhou, Jiayu
2016-12-01
Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data. However, there is an obvious disadvantage of traditional PCA when it is applied to analyze data where interpretability is important. In applications, where the features have some physical meanings, we lose the ability to interpret the principal components extracted by conventional PCA because each principal component is a linear combination of all the original features. For this reason, sparse PCA has been proposed to improve the interpretability of traditional PCA by introducing sparsity to the loading vectors of principal components. The sparse PCA can be formulated as an ℓ1 regularized optimization problem, which can be solved by proximal gradient methods. However, these methods do not scale well because computation of the exact gradient is generally required at each iteration. Stochastic gradient framework addresses this challenge by computing an expected gradient at each iteration. Nevertheless, stochastic approaches typically have low convergence rates due to the high variance. In this paper, we propose a convex sparse principal component analysis (Cvx-SPCA), which leverages a proximal variance reduced stochastic scheme to achieve a geometric convergence rate. We further show that the convergence analysis can be significantly simplified by using a weak condition which allows a broader class of objectives to be applied. The efficiency and effectiveness of the proposed method are demonstrated on a large-scale electronic medical record cohort.
Space-Time Approximation with Sparse Grids
Griebel, M; Oeltz, D; Vassilevski, P S
2005-04-14
In this article we introduce approximation spaces for parabolic problems which are based on the tensor product construction of a multiscale basis in space and a multiscale basis in time. Proper truncation then leads to so-called space-time sparse grid spaces. For a uniform discretization of the spatial space of dimension d with O(N{sup d}) degrees of freedom, these spaces involve for d > 1 also only O(N{sup d}) degrees of freedom for the discretization of the whole space-time problem. But they provide the same approximation rate as classical space-time Finite Element spaces which need O(N{sup d+1}) degrees of freedoms. This makes these approximation spaces well suited for conventional parabolic and for time-dependent optimization problems. We analyze the approximation properties and the dimension of these sparse grid space-time spaces for general stable multiscale bases. We then restrict ourselves to an interpolatory multiscale basis, i.e. a hierarchical basis. Here, to be able to handle also complicated spatial domains {Omega}, we construct the hierarchical basis from a given spatial Finite Element basis as follows: First we determine coarse grid points recursively over the levels by the coarsening step of the algebraic multigrid method. Then, we derive interpolatory prolongation operators between the respective coarse and fine grid points by a least squares approach. This way we obtain an algebraic hierarchical basis for the spatial domain which we then use in our space-time sparse grid approach. We give numerical results on the convergence rate of the interpolation error of these spaces for various space-time problems with two spatial dimensions. Also implementational issues, data structures and questions of adaptivity are addressed to some extent.
Effective dimension reduction for sparse functional data.
Yao, F; Lei, E; Wu, Y
2015-06-01
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.
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
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
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.
Partitioning sparse rectangular matrices for parallel processing
Kolda, T.G.
1998-05-01
The authors are interested in partitioning sparse rectangular matrices for parallel processing. The partitioning problem has been well-studied in the square symmetric case, but the rectangular problem has received very little attention. They will formalize the rectangular matrix partitioning problem and discuss several methods for solving it. They will extend the spectral partitioning method for symmetric matrices to the rectangular case and compare this method to three new methods -- the alternating partitioning method and two hybrid methods. The hybrid methods will be shown to be best.
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
The influence of natural scene dynamics on auditory cortical activity.
Chandrasekaran, Chandramouli; Turesson, Hjalmar K; Brown, Charles H; Ghazanfar, Asif A
2010-10-20
The efficient cortical encoding of natural scenes is essential for guiding adaptive behavior. Because natural scenes and network activity in cortical circuits share similar temporal scales, it is necessary to understand how the temporal structure of natural scenes influences network dynamics in cortical circuits and spiking output. We examined the relationship between the structure of natural acoustic scenes and its impact on network activity [as indexed by local field potentials (LFPs)] and spiking responses in macaque primary auditory cortex. Natural auditory scenes led to a change in the power of the LFP in the 2-9 and 16-30 Hz frequency ranges relative to the ongoing activity. In contrast, ongoing rhythmic activity in the 9-16 Hz range was essentially unaffected by the natural scene. Phase coherence analysis showed that scene-related changes in LFP power were at least partially attributable to the locking of the LFP and spiking activity to the temporal structure in the scene, with locking extending up to 25 Hz for some scenes and cortical sites. Consistent with distributed place and temporal coding schemes, a key predictor of phase locking and power changes was the overlap between the spectral selectivity of a cortical site and the spectral structure of the scene. Finally, during the processing of natural acoustic scenes, spikes were locked to LFP phase at frequencies up to 30 Hz. These results are consistent with an idea that the cortical representation of natural scenes emerges from an interaction between network activity and stimulus dynamics.
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.
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
Sparse spectrum model for a turbulent phase.
Charnotskii, Mikhail
2013-03-01
Monte Carlo (MC) simulation of phase front perturbations by atmospheric turbulence finds numerous applications for design and modeling of the adaptive optics systems, laser beam propagation simulations, and evaluating the performance of the various optical systems operating in the open air environment. Accurate generation of two-dimensional random fields of turbulent phase is complicated by the enormous diversity of scales that can reach five orders of magnitude in each coordinate. In addition there is a need for generation of the long "ribbons" of turbulent phase that are used to represent the time evolution of the wave front. This makes it unfeasible to use the standard discrete Fourier transform-based technique as a basis for the MC simulation algorithm. We propose a new model for turbulent phase: the sparse spectrum (SS) random field. The principal assumption of the SS model is that each realization of the random field has a discrete random spectral support. Statistics of the random amplitudes and wave vectors of the SS model are arranged to provide the required spectral and correlation properties of the random field. The SS-based MC model offers substantial reduction of computer costs for simulation of the wide-band random fields and processes, and is capable of generating long aperiodic phase "ribbons." We report the results of model trials that determine the number of sparse components, and the range of wavenumbers that is necessary to accurately reproduce the random field with a power-law spectrum.
Image reconstruction from photon sparse data
Mertens, Lena; Sonnleitner, Matthias; Leach, Jonathan; Agnew, Megan; Padgett, Miles J.
2017-01-01
We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the second spatial derivatives of the reconstructed image pixel intensities. The balance between these two terms is set by a bootstrapping technique where the target value of the log-likelihood term is deduced from a smoothed version of the original data. When compared to the original data, the processed images exhibit lower residuals with respect to the true object. We use photon-sparse data from two different experimental systems, one system based on a single-photon, avalanche photo-diode array and the other system on a time-gated, intensified camera. However, this same processing technique could most likely be applied to any low photon-number image irrespective of how the data is collected. PMID:28169363
Image reconstruction from photon sparse data
NASA Astrophysics Data System (ADS)
Mertens, Lena; Sonnleitner, Matthias; Leach, Jonathan; Agnew, Megan; Padgett, Miles J.
2017-02-01
We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the second spatial derivatives of the reconstructed image pixel intensities. The balance between these two terms is set by a bootstrapping technique where the target value of the log-likelihood term is deduced from a smoothed version of the original data. When compared to the original data, the processed images exhibit lower residuals with respect to the true object. We use photon-sparse data from two different experimental systems, one system based on a single-photon, avalanche photo-diode array and the other system on a time-gated, intensified camera. However, this same processing technique could most likely be applied to any low photon-number image irrespective of how the data is collected.
Learning doubly sparse transforms for images.
Ravishankar, Saiprasad; Bresler, Yoram
2013-12-01
The sparsity of images in a transform domain or dictionary has been exploited in many applications in image processing. For example, analytical sparsifying transforms, such as wavelets and discrete cosine transform (DCT), have been extensively used in compression standards. Recently, synthesis sparsifying dictionaries that are directly adapted to the data have become popular especially in applications such as image denoising. Following up on our recent research, where we introduced the idea of learning square sparsifying transforms, we propose here novel problem formulations for learning doubly sparse transforms for signals or image patches. These transforms are a product of a fixed, fast analytic transform such as the DCT, and an adaptive matrix constrained to be sparse. Such transforms can be learnt, stored, and implemented efficiently. We show the superior promise of our learnt transforms as compared with analytical sparsifying transforms such as the DCT for image representation. We also show promising performance in image denoising that compares favorably with approaches involving learnt synthesis dictionaries such as the K-SVD algorithm. The proposed approach is also much faster than K-SVD denoising.
Sparse Identification of Nonlinear Dynamics (SINDy)
NASA Astrophysics Data System (ADS)
Brunton, Steven; Proctor, Joshua; Kutz, Nathan
2016-11-01
This work develops a general new framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity techniques and machine learning. The so-called sparse identification of nonlinear dynamics (SINDy) method results in models that are parsimonious, balancing model complexity with descriptive ability while avoiding over fitting. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including the chaotic Lorenz system, to the canonical fluid vortex shedding behind an circular cylinder at Re=100. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing. With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an increasingly important role in the characterization and control of fluid dynamics.
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.
Fast Sparse Level Sets on Graphics Hardware.
Jalba, Andrei C; van der Laan, Wladimir J; Roerdink, Jos B T M
2013-01-01
The level-set method is one of the most popular techniques for capturing and tracking deformable interfaces. Although level sets have demonstrated great potential in visualization and computer graphics applications, such as surface editing and physically based modeling, their use for interactive simulations has been limited due to the high computational demands involved. In this paper, we address this computational challenge by leveraging the increased computing power of graphics processors, to achieve fast simulations based on level sets. Our efficient, sparse GPU level-set method is substantially faster than other state-of-the-art, parallel approaches on both CPU and GPU hardware. We further investigate its performance through a method for surface reconstruction, based on GPU level sets. Our novel multiresolution method for surface reconstruction from unorganized point clouds compares favorably with recent, existing techniques and other parallel implementations. Finally, we point out that both level-set computations and rendering of level-set surfaces can be performed at interactive rates, even on large volumetric grids. Therefore, many applications based on level sets can benefit from our sparse level-set method.
Sparse and accurate high resolution SAR imaging
NASA Astrophysics Data System (ADS)
Vu, Duc; Zhao, Kexin; Rowe, William; Li, Jian
2012-05-01
We investigate the usage of an adaptive method, the Iterative Adaptive Approach (IAA), in combination with a maximum a posteriori (MAP) estimate to reconstruct high resolution SAR images that are both sparse and accurate. IAA is a nonparametric weighted least squares algorithm that is robust and user parameter-free. IAA has been shown to reconstruct SAR images with excellent side lobes suppression and high resolution enhancement. We first reconstruct the SAR images using IAA, and then we enforce sparsity by using MAP with a sparsity inducing prior. By coupling these two methods, we can produce a sparse and accurate high resolution image that are conducive for feature extractions and target classification applications. In addition, we show how IAA can be made computationally efficient without sacrificing accuracies, a desirable property for SAR applications where the size of the problems is quite large. We demonstrate the success of our approach using the Air Force Research Lab's "Gotcha Volumetric SAR Data Set Version 1.0" challenge dataset. Via the widely used FFT, individual vehicles contained in the scene are barely recognizable due to the poor resolution and high side lobe nature of FFT. However with our approach clear edges, boundaries, and textures of the vehicles are obtained.
Image reconstruction from photon sparse data.
Mertens, Lena; Sonnleitner, Matthias; Leach, Jonathan; Agnew, Megan; Padgett, Miles J
2017-02-07
We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the second spatial derivatives of the reconstructed image pixel intensities. The balance between these two terms is set by a bootstrapping technique where the target value of the log-likelihood term is deduced from a smoothed version of the original data. When compared to the original data, the processed images exhibit lower residuals with respect to the true object. We use photon-sparse data from two different experimental systems, one system based on a single-photon, avalanche photo-diode array and the other system on a time-gated, intensified camera. However, this same processing technique could most likely be applied to any low photon-number image irrespective of how the data is collected.
Sparse regularization techniques provide novel insights into outcome integration processes.
Mohr, Holger; Wolfensteller, Uta; Frimmel, Steffi; Ruge, Hannes
2015-01-01
By exploiting information that is contained in the spatial arrangement of neural activations, multivariate pattern analysis (MVPA) can detect distributed brain activations which are not accessible by standard univariate analysis. Recent methodological advances in MVPA regularization techniques have made it feasible to produce sparse discriminative whole-brain maps with highly specific patterns. Furthermore, the most recent refinement, the Graph Net, explicitly takes the 3D-structure of fMRI data into account. Here, these advanced classification methods were applied to a large fMRI sample (N=70) in order to gain novel insights into the functional localization of outcome integration processes. While the beneficial effect of differential outcomes is well-studied in trial-and-error learning, outcome integration in the context of instruction-based learning has remained largely unexplored. In order to examine neural processes associated with outcome integration in the context of instruction-based learning, two groups of subjects underwent functional imaging while being presented with either differential or ambiguous outcomes following the execution of varying stimulus-response instructions. While no significant univariate group differences were found in the resulting fMRI dataset, L1-regularized (sparse) classifiers performed significantly above chance and also clearly outperformed the standard L2-regularized (dense) Support Vector Machine on this whole-brain between-subject classification task. Moreover, additional L2-regularization via the Elastic Net and spatial regularization by the Graph Net improved interpretability of discriminative weight maps but were accompanied by reduced classification accuracies. Most importantly, classification based on sparse regularization facilitated the identification of highly specific regions differentially engaged under ambiguous and differential outcome conditions, comprising several prefrontal regions previously associated with
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.
A gradient in cortical pathology in multiple sclerosis by in vivo quantitative 7 T imaging.
Mainero, Caterina; Louapre, Céline; Govindarajan, Sindhuja T; Giannì, Costanza; Nielsen, A Scott; Cohen-Adad, Julien; Sloane, Jacob; Kinkel, Revere P
2015-04-01
We used a surface-based analysis of T2* relaxation rates at 7 T magnetic resonance imaging, which allows sampling quantitative T2* throughout the cortical width, to map in vivo the spatial distribution of intracortical pathology in multiple sclerosis. Ultra-high resolution quantitative T2* maps were obtained in 10 subjects with clinically isolated syndrome/early multiple sclerosis (≤ 3 years disease duration), 18 subjects with relapsing-remitting multiple sclerosis (≥ 4 years disease duration), 13 subjects with secondary progressive multiple sclerosis, and in 17 age-matched healthy controls. Quantitative T2* maps were registered to anatomical cortical surfaces for sampling T2* at 25%, 50% and 75% depth from the pial surface. Differences in laminar quantitative T2* between each patient group and controls were assessed using general linear model (P < 0.05 corrected for multiple comparisons). In all 41 multiple sclerosis cases, we tested for associations between laminar quantitative T2*, neurological disability, Multiple Sclerosis Severity Score, cortical thickness, and white matter lesions. In patients, we measured, T2* in intracortical lesions and in the intracortical portion of leukocortical lesions visually detected on 7 T scans. Cortical lesional T2* was compared with patients' normal-appearing cortical grey matter T2* (paired t-test) and with mean cortical T2* in controls (linear regression using age as nuisance factor). Subjects with multiple sclerosis exhibited relative to controls, independent from cortical thickness, significantly increased T2*, consistent with cortical myelin and iron loss. In early disease, T2* changes were focal and mainly confined at 25% depth, and in cortical sulci. In later disease stages T2* changes involved deeper cortical laminae, multiple cortical areas and gyri. In patients, T2* in intracortical and leukocortical lesions was increased compared with normal-appearing cortical grey matter (P < 10(-10) and P < 10(-7)), and mean
Analysis of Cortical Flow Models In Vivo
Benink, Hélène A.; Mandato, Craig A.; Bement, William M.
2000-01-01
Cortical flow, the directed movement of cortical F-actin and cortical organelles, is a basic cellular motility process. Microtubules are thought to somehow direct cortical flow, but whether they do so by stimulating or inhibiting contraction of the cortical actin cytoskeleton is the subject of debate. Treatment of Xenopus oocytes with phorbol 12-myristate 13-acetate (PMA) triggers cortical flow toward the animal pole of the oocyte; this flow is suppressed by microtubules. To determine how this suppression occurs and whether it can control the direction of cortical flow, oocytes were subjected to localized manipulation of either the contractile stimulus (PMA) or microtubules. Localized PMA application resulted in redirection of cortical flow toward the site of application, as judged by movement of cortical pigment granules, cortical F-actin, and cortical myosin-2A. Such redirected flow was accelerated by microtubule depolymerization, showing that the suppression of cortical flow by microtubules is independent of the direction of flow. Direct observation of cortical F-actin by time-lapse confocal analysis in combination with photobleaching showed that cortical flow is driven by contraction of the cortical F-actin network and that microtubules suppress this contraction. The oocyte germinal vesicle serves as a microtubule organizing center in Xenopus oocytes; experimental displacement of the germinal vesicle toward the animal pole resulted in localized flow away from the animal pole. The results show that 1) cortical flow is directed toward areas of localized contraction of the cortical F-actin cytoskeleton; 2) microtubules suppress cortical flow by inhibiting contraction of the cortical F-actin cytoskeleton; and 3) localized, microtubule-dependent suppression of actomyosin-based contraction can control the direction of cortical flow. We discuss these findings in light of current models of cortical flow. PMID:10930453
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.
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
2011-01-01
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
Cortical and trabecular load sharing in the human femoral neck.
Nawathe, Shashank; Nguyen, Bich Phuong; Barzanian, Nasim; Akhlaghpour, Hosna; Bouxsein, Mary L; Keaveny, Tony M
2015-03-18
The relative role of the cortical vs trabecular bone in the load-carrying capacity of the proximal femur-a fundamental issue in both basic-science and clinical biomechanics-remains unclear. To gain insight into this issue, we performed micro-CT-based, linear elastic finite element analysis (61.5-micron-sized elements; ~280 million elements per model) on 18 proximal femurs (5M, 13F, ages 61-93 years) to quantify the fraction of frontal-plane bending moment shared by the cortical vs trabecular bone in the femoral neck, as well as the associated spatial distributions of stress. Analyses were performed separately for a sideways fall and stance loading. For both loading modes and across all 18 bones, we found consistent patterns of load-sharing in the neck: most proximally, the trabecular bone took most of the load; moving distally, the cortical bone took increasingly more of the load; and more distally, there was a region of uniform load-sharing, the cortical bone taking the majority of the load. This distal region of uniform load-sharing extended more for fall than stance loading (77 ± 8% vs 51 ± 6% of the neck length for fall vs. stance; mean ± SD) but the fraction of total load taken by the cortical bone in that region was greater for stance loading (88 ± 5% vs. 64 ± 9% for stance vs. fall). Locally, maximum stress levels occurred in the cortical bone distally, but in the trabecular bone proximally. Although the distal cortex showed qualitative stress distributions consistent with the behavior of an Euler-type beam, quantitatively beam theory did not apply. We conclude that consistent and well-delineated regions of uniform load-sharing and load-transfer between the cortical and trabecular bone exist within the femoral neck, the details of which depend on the external loading conditions.
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
Improving sparse representation algorithms for maritime video processing
NASA Astrophysics Data System (ADS)
Smith, L. N.; Nichols, J. M.; Waterman, J. R.; Olson, C. C.; Judd, K. P.
2012-06-01
We present several improvements to published algorithms for sparse image modeling with the goal of improving processing of imagery of small watercraft in littoral environments. The first improvement is to the K-SVD algorithm for training over-complete dictionaries, which are used in sparse representations. It is shown that the training converges significantly faster by incorporating multiple dictionary (i.e., codebook) update stages in each training iteration. The paper also provides several useful and practical lessons learned from our experience with sparse representations. Results of three applications of sparse representation are presented and compared to the state-of-the-art methods; image compression, image denoising, and super-resolution.
Local structure preserving sparse coding for infrared target recognition
Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa
2017-01-01
Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions. PMID:28323824
Multimodal visual dictionary learning via heterogeneous latent semantic sparse coding
NASA Astrophysics Data System (ADS)
Li, Chenxiao; Ding, Guiguang; Zhou, Jile; Guo, Yuchen; Liu, Qiang
2014-11-01
Visual dictionary learning as a crucial task of image representation has gained increasing attention. Specifically, sparse coding is widely used due to its intrinsic advantage. In this paper, we propose a novel heterogeneous latent semantic sparse coding model. The central idea is to bridge heterogeneous modalities by capturing their common sparse latent semantic structure so that the learned visual dictionary is able to describe both the visual and textual properties of training data. Experiments on both image categorization and retrieval tasks demonstrate that our model shows superior performance over several recent methods such as K-means and Sparse Coding.
M-estimation for robust sparse unmixing of hyperspectral images
NASA Astrophysics Data System (ADS)
Toomik, Maria; Lu, Shijian; Nelson, James D. B.
2016-10-01
Hyperspectral unmixing methods often use a conventional least squares based lasso which assumes that the data follows the Gaussian distribution. The normality assumption is an approximation which is generally invalid for real imagery data. We consider a robust (non-Gaussian) approach to sparse spectral unmixing of remotely sensed imagery which reduces the sensitivity of the estimator to outliers and relaxes the linearity assumption. The method consists of several appropriate penalties. We propose to use an lp norm with 0 < p < 1 in the sparse regression problem, which induces more sparsity in the results, but makes the problem non-convex. On the other hand, the problem, though non-convex, can be solved quite straightforwardly with an extensible algorithm based on iteratively reweighted least squares. To deal with the huge size of modern spectral libraries we introduce a library reduction step, similar to the multiple signal classification (MUSIC) array processing algorithm, which not only speeds up unmixing but also yields superior results. In the hyperspectral setting we extend the traditional least squares method to the robust heavy-tailed case and propose a generalised M-lasso solution. M-estimation replaces the Gaussian likelihood with a fixed function ρ(e) that restrains outliers. The M-estimate function reduces the effect of errors with large amplitudes or even assigns the outliers zero weights. Our experimental results on real hyperspectral data show that noise with large amplitudes (outliers) often exists in the data. This ability to mitigate the influence of such outliers can therefore offer greater robustness. Qualitative hyperspectral unmixing results on real hyperspectral image data corroborate the efficacy of the proposed method.
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
2014-08-01
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.
Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data
Lin, Youzuo; Huang, Lianjie
2015-01-28
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 models produced with our new method using a sparse source array are almost as accurate as those obtained using a dense source array.
The development of cortical circuits for motion discrimination
Smith, Gordon B.; Sederberg, Audrey; Elyada, Yishai M.; Van Hooser, Stephen D.; Kaschube, Matthias; Fitzpatrick, David
2014-01-01
Stimulus discrimination depends on the selectivity and variability of neural responses, as well as the size and correlation structure of the responsive population. For direction discrimination in visual cortex, only the selectivity of neurons has been well characterized across development. Here we show in ferrets that at eye opening, the cortical response to visual stimulation exhibits several immaturities, including: a high density of active neurons that display prominent wave-like activity, a high degree of variability, and strong noise correlations. Over the next three weeks, the population response becomes increasingly sparse, wave-like activity disappears, and variability and noise correlations are markedly reduced. Similar changes are observed in identified neuronal populations imaged repeatedly over days. Furthermore, experience with a moving stimulus is capable of driving a reduction in noise correlations over a matter of hours. These changes in variability and correlation contribute significantly to a marked improvement in direction discriminability over development. PMID:25599224
Reser, David H; Majka, Piotr; Snell, Shakira; Chan, Jonathan M H; Watkins, Kirsty; Worthy, Katrina; Quiroga, Maria Del Mar; Rosa, Marcello G P
2017-04-15
The claustrum has been the subject of intense research interest in recent years, driven in large part by its extensive connections with various regions of the cerebral cortex and by hypotheses surrounding its possible role in multimodal sensory and/or sensory-emotional integration. Here we employed neuroanatomical tracers to map projections from the claustrum-insular region to the medial prefrontal and anterior cingulate cortex of the common marmoset (Callithrx jacchus). These areas were selected based on their identification as "hub" areas of the default mode and cortical salience networks, respectively. Microinjections of fluorescent tracers, along with gold-nanoparticle-conjugated cholera toxin B-subunit and biotinylated dextran amine, were placed in subdivisions of the anterior cingulate area 24b/c and in medial prefrontal areas 32 and 32V. The resulting distribution of transported label showed rostral-caudal and dorsal-ventral topographic arrangement of claustrum connections and clear rostral-caudal topography of insular projections. Medial prefrontal connections were restricted mainly to a ventromedial strip located in the rostral half of the claustrum, with a second, smaller patch of cells in the caudal, ventrolateral portion. In contrast, injections into area 24 yielded dense, widespread connections from the dorsal claustrum, extending along its entire rostral-caudal length. Projections from the "classical" agranular, disgranular, and granular insular areas were sparse or nonexistent in areas 32 and 32V, with progressively increasing connections observed in more caudal tracer injections (i.e., in subdivisions of area 24). Transported label was observed in rostral peri-insular areas orbital periallocortex, orbital proisocortex, and insular proisocortex following all prefrontal injections. These data provide a structural connectivity foundation for interpretation of functional imaging studies, which often indicate activity in the "anterior insula" that may
Xu, Kailiang; Minonzio, Jean-Gabriel; Ta, Dean; Hu, Bo; Wang, Weiqi; Laugier, Pascal
2016-10-01
The 2-D Fourier transform analysis of multichannel signals is a straightforward method to extract the dispersion curves of guided modes. Basically, the time signals recorded at several positions along the waveguide are converted to the wavenumber-frequency space, so that the dispersion curves (i.e., the frequency-dependent wavenumbers) of the guided modes can be extracted by detecting peaks of energy trajectories. In order to improve the dispersion curve extraction of low-amplitude modes propagating in a cortical bone, a multiemitter and multireceiver transducer array has been developed together with an effective singular vector decomposition (SVD)-based signal processing method. However, in practice, the limited number of positions where these signals are recorded results in a much lower resolution in the wavenumber axis than in the frequency axis. This prevents a clear identification of overlapping dispersion curves. In this paper, a sparse SVD (S-SVD) method, which combines the signal-to-noise ratio improvement of the SVD-based approach with the high wavenumber resolution advantage of the sparse optimization, is presented to overcome the above-mentioned limitation. Different penalty constraints, i.e., l1 -norm, Frobenius norm, and revised Cauchy norm, are compared with the sparse characteristics. The regularization parameters are investigated with respect to the convergence property and wavenumber resolution. The proposed S-SVD method is investigated using synthetic wideband signals and experimental data obtained from a bone-mimicking phantom and from an ex-vivo human radius. The analysis of the results suggests that the S-SVD method has the potential to significantly enhance the wavenumber resolution and to improve the extraction of the dispersion curves.
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
Surface reconstruction from sparse fringe contours
Cong, G.; Parvin, B.
1998-08-10
A new approach for reconstruction of 3D surfaces from 2D cross-sectional contours is presented. By using the so-called ''Equal Importance Criterion,'' we reconstruct the surface based on the assumption that every point in the region contributes equally to the surface reconstruction process. In this context, the problem is formulated in terms of a partial differential equation (PDE), and we show that the solution for dense contours can be efficiently derived from distance transform. In the case of sparse contours, we add a regularization term to insure smoothness in surface recovery. The proposed technique allows for surface recovery at any desired resolution. The main advantage of the proposed method is that inherent problems due to correspondence, tiling, and branching are avoided. Furthermore, the computed high resolution surface is better represented for subsequent geometric analysis. We present results on both synthetic and real data.
Preserving sparseness in multivariate polynominal factorization
NASA Technical Reports Server (NTRS)
Wang, P. S.
1977-01-01
Attempts were made to factor these ten polynomials on MACSYMA. However it did not get very far with any of the larger polynomials. At that time, MACSYMA used an algorithm created by Wang and Rothschild. This factoring algorithm was also implemented for the symbolic manipulation system, SCRATCHPAD of IBM. A closer look at this old factoring algorithm revealed three problem areas, each of which contribute to losing sparseness and intermediate expression growth. This study led to effective ways of avoiding these problems and actually to a new factoring algorithm. The three problems are known as the extraneous factor problem, the leading coefficient problem, and the bad zero problem. These problems are examined separately. Their causes and effects are set forth in detail; the ways to avoid or lessen these problems are described.
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.
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).
A scalable sparse eigensolver for petascale applications
NASA Astrophysics Data System (ADS)
Keceli, Murat; Zhang, Hong; Zapol, Peter; Dixon, David; Wagner, Albert
2015-03-01
Exploiting locality of chemical interactions and therefore sparsity is necessary to push the limits of quantum simulations beyond petascale. However, sparse numerical algorithms are known to have poor strong scaling. Here, we show that shift-and-invert parallel spectral transformations (SIPs) method can scale up to two-hundred thousand cores for density functional based tight-binding (DFTB), or semi-empirical molecular orbital (SEMO) applications. We demonstrated the robustness and scalability of the SIPs method on various kinds of systems including metallic carbon nanotubes, diamond crystals and water clusters. We analyzed how sparsity patterns and eigenvalue spectrums of these different type of applications affect the computational performance of the SIPs. The SIPs method enables us to perform simulations with more than five hundred thousands of basis functions utilizing more than hundreds of thousands of cores. SIPs has a better scaling for memory and computational time in contrast to dense eigensolvers, and it does not require fast interconnects.
Blind source separation by sparse decomposition
NASA Astrophysics Data System (ADS)
Zibulevsky, Michael; Pearlmutter, Barak A.
2000-04-01
The blind source separation problem is to extract the underlying source signals from a set of their linear mixtures, where the mixing matrix is unknown. This situation is common, eg in acoustics, radio, and medical signal processing. We exploit the property of the sources to have a sparse representation in a corresponding signal dictionary. Such a dictionary may consist of wavelets, wavelet packets, etc., or be obtained by learning from a given family of signals. Starting from the maximum a posteriori framework, which is applicable to the case of more sources than mixtures, we derive a few other categories of objective functions, which provide faster and more robust computations, when there are an equal number of sources and mixtures. Our experiments with artificial signals and with musical sounds demonstrate significantly better separation than other known techniques.
Using sparse photometric data sets for asteroid lightcurve studies
NASA Astrophysics Data System (ADS)
Warner, Brian D.; Harris, Alan W.
2011-12-01
With the advent of wide-field imagers, it has become possible to conduct a photometric lightcurve survey of many asteroids simultaneously, either for that single purpose (e.g., Dermawan, B., Nakamura, T., Yoshida, F. [2011]. Publ. Astron. Soc. Japan 63, S555-S576; Masiero, J., Jedicke, R., Ďurech, J., Gwyn, S., Denneau, L., Larsen, J. [2009]. Icarus 204, 145-171), or as a part of a multipurpose survey (e.g., Pan-STARRS, LSST). Such surveys promise to yield photometric data for many thousands of asteroids, but these data sets will be “sparse” compared to most of those taken in a “targeted” mode directed to one asteroid at a time. We consider the potential limitations of sparse data sets using different sampling rates with respect to specific research questions that might be addressed with lightcurve data. For our study we created synthetic sparse data sets similar to those from wide-field surveys by generating more than 380,000 individual lightcurves that were combined into more than 47,000 composite lightcurves. The variables in generating the data included the number of observations per night, number of nights, noise, and the intervals between observations and nights, in addition to periods ranging from 0.1 to 400 h and amplitudes ranging from 0.1 to 2.0 mag. A Fourier analysis pipeline was used to find the period for each composite lightcurve and then review the derived period and period spectrum to gauge how well an automated analysis of sparse data sets would perform in finding the true period. For this part of the analysis, a normally distributed noise level of 0.03 mag was added to the data, regardless of amplitude, thus simulating a relatively high SNR for the observations. For the second part of the analysis, a smaller set of composite curves was generated with fixed core parameters of eight observations per night, 8 nights within a 14-day span, periods ranging from 2 to 6 h, and an amplitude of either 0.3 mag or 0.4 mag. Individual data sets using
A Circuit for Motor Cortical Modulation of Auditory Cortical Activity
Nelson, Anders; Schneider, David M.; Takatoh, Jun; Sakurai, Katsuyasu; Wang, Fan
2013-01-01
Normal hearing depends on the ability to distinguish self-generated sounds from other sounds, and this ability is thought to involve neural circuits that convey copies of motor command signals to various levels of the auditory system. Although such interactions at the cortical level are believed to facilitate auditory comprehension during movements and drive auditory hallucinations in pathological states, the synaptic organization and function of circuitry linking the motor and auditory cortices remain unclear. Here we describe experiments in the mouse that characterize circuitry well suited to transmit motor-related signals to the auditory cortex. Using retrograde viral tracing, we established that neurons in superficial and deep layers of the medial agranular motor cortex (M2) project directly to the auditory cortex and that the axons of some of these deep-layer cells also target brainstem motor regions. Using in vitro whole-cell physiology, optogenetics, and pharmacology, we determined that M2 axons make excitatory synapses in the auditory cortex but exert a primarily suppressive effect on auditory cortical neuron activity mediated in part by feedforward inhibition involving parvalbumin-positive interneurons. Using in vivo intracellular physiology, optogenetics, and sound playback, we also found that directly activating M2 axon terminals in the auditory cortex suppresses spontaneous and stimulus-evoked synaptic activity in auditory cortical neurons and that this effect depends on the relative timing of motor cortical activity and auditory stimulation. These experiments delineate the structural and functional properties of a corticocortical circuit that could enable movement-related suppression of auditory cortical activity. PMID:24005287
A circuit for motor cortical modulation of auditory cortical activity.
Nelson, Anders; Schneider, David M; Takatoh, Jun; Sakurai, Katsuyasu; Wang, Fan; Mooney, Richard
2013-09-04
Normal hearing depends on the ability to distinguish self-generated sounds from other sounds, and this ability is thought to involve neural circuits that convey copies of motor command signals to various levels of the auditory system. Although such interactions at the cortical level are believed to facilitate auditory comprehension during movements and drive auditory hallucinations in pathological states, the synaptic organization and function of circuitry linking the motor and auditory cortices remain unclear. Here we describe experiments in the mouse that characterize circuitry well suited to transmit motor-related signals to the auditory cortex. Using retrograde viral tracing, we established that neurons in superficial and deep layers of the medial agranular motor cortex (M2) project directly to the auditory cortex and that the axons of some of these deep-layer cells also target brainstem motor regions. Using in vitro whole-cell physiology, optogenetics, and pharmacology, we determined that M2 axons make excitatory synapses in the auditory cortex but exert a primarily suppressive effect on auditory cortical neuron activity mediated in part by feedforward inhibition involving parvalbumin-positive interneurons. Using in vivo intracellular physiology, optogenetics, and sound playback, we also found that directly activating M2 axon terminals in the auditory cortex suppresses spontaneous and stimulus-evoked synaptic activity in auditory cortical neurons and that this effect depends on the relative timing of motor cortical activity and auditory stimulation. These experiments delineate the structural and functional properties of a corticocortical circuit that could enable movement-related suppression of auditory cortical activity.
Rate and Temporal Coding Convey Multisensory Information in Primary Sensory Cortices.
Bieler, Malte; Sieben, Kay; Cichon, Nicole; Schildt, Sandra; Röder, Brigitte; Hanganu-Opatz, Ileana L
2017-01-01
Optimal behavior and survival result from integration of information across sensory systems. Modulation of network activity at the level of primary sensory cortices has been identified as a mechanism of cross-modal integration, yet its cellular substrate is still poorly understood. Here, we uncover the mechanisms by which individual neurons in primary somatosensory (S1) and visual (V1) cortices encode visual-tactile stimuli. For this, simultaneous extracellular recordings were performed from all layers of the S1 barrel field and V1 in Brown Norway rats in vivo and units were clustered and assigned to pyramidal neurons (PYRs) and interneurons (INs). We show that visual-tactile stimulation modulates the firing rate of a relatively low fraction of neurons throughout all cortical layers. Generally, it augments the firing of INs and decreases the activity of PYRs. Moreover, bimodal stimulation shapes the timing of neuronal firing by strengthening the phase-coupling between neuronal discharge and theta-beta band network oscillations as well as by modulating spiking onset. Sparse direct axonal projections between neurons in S1 and V1 seem to time the spike trains between the two cortical areas and, thus, may act as a substrate of cross-modal modulation. These results indicate that few cortical neurons mediate multisensory effects in primary sensory areas by directly encoding cross-modal information by their rate and timing of firing.
Rate and Temporal Coding Convey Multisensory Information in Primary Sensory Cortices
Sieben, Kay; Cichon, Nicole; Schildt, Sandra; Röder, Brigitte
2017-01-01
Abstract Optimal behavior and survival result from integration of information across sensory systems. Modulation of network activity at the level of primary sensory cortices has been identified as a mechanism of cross-modal integration, yet its cellular substrate is still poorly understood. Here, we uncover the mechanisms by which individual neurons in primary somatosensory (S1) and visual (V1) cortices encode visual-tactile stimuli. For this, simultaneous extracellular recordings were performed from all layers of the S1 barrel field and V1 in Brown Norway rats in vivo and units were clustered and assigned to pyramidal neurons (PYRs) and interneurons (INs). We show that visual-tactile stimulation modulates the firing rate of a relatively low fraction of neurons throughout all cortical layers. Generally, it augments the firing of INs and decreases the activity of PYRs. Moreover, bimodal stimulation shapes the timing of neuronal firing by strengthening the phase-coupling between neuronal discharge and theta–beta band network oscillations as well as by modulating spiking onset. Sparse direct axonal projections between neurons in S1 and V1 seem to time the spike trains between the two cortical areas and, thus, may act as a substrate of cross-modal modulation. These results indicate that few cortical neurons mediate multisensory effects in primary sensory areas by directly encoding cross-modal information by their rate and timing of firing. PMID:28374008
Bunce, Jamie G.; Zikopoulos, Basilis; Feinberg, Marcia; Barbas, Helen
2013-01-01
To investigate how prefrontal cortices impinge on medial temporal cortices we labeled pathways from the anterior cingulate cortex (ACC) and posterior orbitofrontal cortex (pOFC) in rhesus monkeys to compare their relationship with excitatory and inhibitory systems in rhinal cortices. The ACC pathway terminated mostly in areas 28 and 35 with a high proportion of large terminals, whereas the pOFC pathway terminated mostly through small terminals in area 36 and sparsely in areas 28 and 35. Both pathways terminated in all layers. Simultaneous labeling of pathways and distinct neurochemical classes of inhibitory neurons, followed by analyses of appositions of presynaptic and postsynaptic fluorescent signal, or synapses, showed overall predominant association with spines of putative excitatory neurons, but also significant interactions with presumed inhibitory neurons labeled for calretinin, calbindin or parvalbumin. In the upper layers of areas 28 and 35 the ACC pathway was associated with dendrites of neurons labeled with calretinin, which are thought to disinhibit neighboring excitatory neurons, suggesting facilitated hippocampal access. In contrast, in area 36 pOFC axons were associated with dendrites of calbindin neurons, which are poised to reduce noise and enhance signal. In the deep layers, both pathways innervated mostly dendrites of parvalbumin neurons, which strongly inhibit neighboring excitatory neurons, suggesting gating of hippocampal output to other cortices. These findings suggest that the ACC, associated with attention and context, and the pOFC, associated with emotional valuation, have distinct contributions to memory in rhinal cortices, in processes that are disrupted in psychiatric diseases. PMID:23839697
Clonidine and Cortical Plasticity: Possible Evidence for Noradrenergic Involvement.
1984-10-31
D 616 CLONIDINE AND CORTICAL PLASTICITY POSSIBLE EVIDENCE / FOR NORADRENERGIC IN..(U) BROWIN UNIV PROVIDENCE RICENTER FOR NEURAL SCIENCE S B NELSON...plasticity in kitten visual cortex, we monocularly deprived kittens while administering the at-2 adrenergic agonist clonidine (CLON). To avoid bias in...Distribution/ AvailabilitY Codes Avail and/or Dist Special I. S N 0102- LF. 014- 6601 SECuRITY CLASSIVrCATION OF TMIS PA*S St’e DOI@ MAIN Clonidine and Cortical
Components of vestibular cortical function.
Klingner, Carsten M; Volk, Gerd F; Flatz, Claudia; Brodoehl, Stefan; Dieterich, Marianne; Witte, Otto W; Guntinas-Lichius, Orlando
2013-01-01
It is known that the functional response (e.g., nystagmus) to caloric vestibular stimulation is delayed and prolonged compared with the stimulus-response timing of other sensory systems. Imaging studies have used different models to predict cortical responses and to determine the areas of the brain that are involved. These studies have revealed a widespread network of vestibular brain regions. However, there is some disagreement regarding the brain areas involved, which may partly be caused by differences in the models used. This disagreement indicates the possible existence of multiple cortical components with different temporal characteristics that underlie cortical vestibular processing. However, data-driven methods have yet to be used to analyze the underlying hemodynamic components during and after vestibular stimulation. We performed functional magnetic resonance imaging (fMRI) on 12 healthy subjects during caloric stimulation and analyzed these data using a model-free analysis method (ICA). We found seven independent stimulus-induced components that outline a robust pattern of cortical activation and deactivation. These independent components demonstrated significant differences in their time courses. No single-modeled response function was able to cover the entire range of these independent components. The response functions determined in the present study should improve model-based studies investigating vestibular cortical processing.
Pontabry, J; Rousseau, F; Studholme, C; Koob, M; Dietemann, J-L
2017-01-01
The development of post-processing reconstruction techniques has opened new possibilities for the study of in-utero fetal brain MRI data. Recent cortical surface analysis have led to the computation of quantitative maps characterizing brain folding of the developing brain. In this paper, we describe a novel feature selection-based approach that is used to extract the most discriminative and sparse set of features of a given dataset. The proposed method is used to sparsely characterize cortical folding patterns of an in-utero fetal MR dataset, labeled with heterogeneous gestational age ranging from 26 weeks to 34 weeks. The proposed algorithm is validated on a synthetic dataset with both linear and non-linear dynamics, supporting its ability to capture deformation patterns across the dataset within only a few features. Results on the fetal brain dataset show that the temporal process of cortical folding related to brain maturation can be characterized by a very small set of points, located in anatomical regions changing across time. Quantitative measurements of growth against time are extracted from the set selected features to compare multiple brain regions (e.g. lobes and hemispheres) during the considered period of gestation.
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.
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
Sparse representations for online-learning-based hyperspectral image compression.
Ülkü, İrem; Töreyin, Behçet Uğur
2015-10-10
Sparse models provide data representations in the fewest possible number of nonzero elements. This inherent characteristic enables sparse models to be utilized for data compression purposes. Hyperspectral data is large in size. In this paper, a framework for sparsity-based hyperspectral image compression methods using online learning is proposed. There are various sparse optimization models. A comparative analysis of sparse representations in terms of their hyperspectral image compression performance is presented. For this purpose, online-learning-based hyperspectral image compression methods are proposed using four different sparse representations. Results indicate that, independent of the sparsity models, online-learning-based hyperspectral data compression schemes yield the best compression performances for data rates of 0.1 and 0.3 bits per sample, compared to other state-of-the-art hyperspectral data compression techniques, in terms of image quality measured as average peak signal-to-noise ratio.
Cortical myoclonus in Huntington's disease.
Thompson, P D; Bhatia, K P; Brown, P; Davis, M B; Pires, M; Quinn, N P; Luthert, P; Honovar, M; O'Brien, M D; Marsden, C D
1994-11-01
We describe three patients with Huntington's disease, from two families, in whom myoclonus was the predominant clinical feature. The diagnosis was confirmed at autopsy in two cases and by DNA analysis in all three. These patients all presented before the age of 30 years and were the offspring of affected fathers. Neurophysiological studies documented generalised and multifocal action myoclonus of cortical origin that was strikingly stimulus sensitive, without enlargement of the cortical somatosensory evoked potential. The myoclonus improved with piracetam therapy in one patient and a combination of sodium valproate and clonazepam in the other two. Cortical reflex myoclonus is a rare but disabling component of the complex movement disorder of Huntington's disease, which may lead to substantial diagnostic difficulties.
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.
Orderly cortical representation of vowel categories presented by multiple exemplars.
Shestakova, Anna; Brattico, Elvira; Soloviev, Alexei; Klucharev, Vasily; Huotilainen, Minna
2004-11-01
This study aimed at determining how the human brain automatically processes phoneme categories irrespective of the large acoustic inter-speaker variability. Subjects were presented with 450 different speech stimuli, equally distributed across the [a], [i], and [u] vowel categories, and each uttered by a different male speaker. A 306-channel magnetoencephalogram (MEG) was used to record N1m, the magnetic counterpart of the N1 component of the auditory event-related potential (ERP). The N1m amplitude and source locations differed between vowel categories. We also found that the spectrum dissimilarities were reproduced in the cortical representations of the large set of the phonemes used in this study: vowels with similar spectral envelopes had closer cortical representations than those whose spectral differences were the largest. Our data further extend the notion of differential cortical representations in response to vowel categories, previously demonstrated by using only one or a few tokens representing each category.
Hatanaka, Yumiko; Namikawa, Tomohiro; Yamauchi, Kenta; Kawaguchi, Yasuo
2016-05-01
Excitatory cortical neurons project to various subcortical and intracortical regions, and exhibit diversity in their axonal connections. Although this diversity may develop from primary axons, how many types of axons initially occur remains unknown. Using a sparse-labeling in utero electroporation method, we investigated the axonal outgrowth of these neurons in mice and correlated the data with axonal projections in adults. Examination of lateral cortex neurons labeled during the main period of cortical neurogenesis (E11.5-E15.5) indicated that axonal outgrowth commonly occurs in the intermediate zone. Conversely, the axonal direction varied; neurons labeled before E12.5 and the earliest cortical plate neurons labeled at E12.5 projected laterally, whereas neurons labeled thereafter projected medially. The expression of Ctip2 and Satb2 and the layer destinations of these neurons support the view that lateral and medial projection neurons are groups of prospective subcortical and callosal projection neurons, respectively. Consistently, birthdating experiments demonstrated that presumptive lateral projection neurons were generated earlier than medial projection neurons, even within the same layer. These results suggest that the divergent axonal connections of excitatory cortical neurons begin from two types of primary axons, which originate from two sequentially generated distinct subpopulations: early-born lateral (subcortical) and later-born medial (callosal) projection neuron groups.
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…
Colibazzi, Tiziano; Wexler, Bruce E.; Bansal, Ravi; Hao, Xuejun; Liu, Jun; Sanchez-Peña, Juan; Corcoran, Cheryl; Lieberman, Jeffrey A.; Peterson, Bradley S.
2013-01-01
Background Although schizophrenia has been associated with abnormalities in brain anatomy, imaging studies have not fully determined the nature and relative contributions of gray matter (GM) and white matter (WM) disturbances underlying these findings. We sought to determine the pattern and distribution of these GM and WM abnormalities. Furthermore, we aimed to clarify the contribution of abnormalities in cortical thickness and cortical surface area to the reduced GM volumes reported in schizophrenia. Methods We recruited 76 persons with schizophrenia and 57 healthy controls from the community and obtained measures of cortical and WM surface areas, of local volumes along the brain and WM surfaces, and of cortical thickness. Results We detected reduced local volumes in patients along corresponding locations of the brain and WM surfaces in addition to bilateral greater thickness of perisylvian cortices and thinner cortex in the superior frontal and cingulate gyri. Total cortical and WM surface areas were reduced. Patients with worse performance on the serial-position task, a measure of working memory, had a higher burden of WM abnormalities. Conclusions Reduced local volumes along the surface of the brain mirrored the locations of abnormalities along the surface of the underlying WM, rather than of abnormalities of cortical thickness. Moreover, anatomical features of white matter, but not cortical thickness, correlated with measures of working memory. We propose that reductions in WM and smaller total cortical surface area could be central anatomical abnormalities in schizophrenia, driving, at least partially, the reduced regional GM volumes often observed in this illness. PMID:23418459
A mechanism for the cortical computation of hierarchical linguistic structure.
Martin, Andrea E; Doumas, Leonidas A A
2017-03-01
Biological systems often detect species-specific signals in the environment. In humans, speech and language are species-specific signals of fundamental biological importance. To detect the linguistic signal, human brains must form hierarchical representations from a sequence of perceptual inputs distributed in time. What mechanism underlies this ability? One hypothesis is that the brain repurposed an available neurobiological mechanism when hierarchical linguistic representation became an efficient solution to a computational problem posed to the organism. Under such an account, a single mechanism must have the capacity to perform multiple, functionally related computations, e.g., detect the linguistic signal and perform other cognitive functions, while, ideally, oscillating like the human brain. We show that a computational model of analogy, built for an entirely different purpose-learning relational reasoning-processes sentences, represents their meaning, and, crucially, exhibits oscillatory activation patterns resembling cortical signals elicited by the same stimuli. Such redundancy in the cortical and machine signals is indicative of formal and mechanistic alignment between representational structure building and "cortical" oscillations. By inductive inference, this synergy suggests that the cortical signal reflects structure generation, just as the machine signal does. A single mechanism-using time to encode information across a layered network-generates the kind of (de)compositional representational hierarchy that is crucial for human language and offers a mechanistic linking hypothesis between linguistic representation and cortical computation.
Prostate segmentation by sparse representation based classification
Gao, Yaozong; Liao, Shu; Shen, Dinggang
2012-01-01
Purpose: The segmentation of prostate in CT images is of essential importance to external beam radiotherapy, which is one of the major treatments for prostate cancer nowadays. During the radiotherapy, the prostate is radiated by high-energy x rays from different directions. In order to maximize the dose to the cancer and minimize the dose to the surrounding healthy tissues (e.g., bladder and rectum), the prostate in the new treatment image needs to be accurately localized. Therefore, the effectiveness and efficiency of external beam radiotherapy highly depend on the accurate localization of the prostate. However, due to the low contrast of the prostate with its surrounding tissues (e.g., bladder), the unpredicted prostate motion, and the large appearance variations across different treatment days, it is challenging to segment the prostate in CT images. In this paper, the authors present a novel classification based segmentation method to address these problems. Methods: To segment the prostate, the proposed method first uses sparse representation based classification (SRC) to enhance the prostate in CT images by pixel-wise classification, in order to overcome the limitation of poor contrast of the prostate images. Then, based on the classification results, previous segmented prostates of the same patient are used as patient-specific atlases to align onto the current treatment image and the majority voting strategy is finally adopted to segment the prostate. In order to address the limitations of the traditional SRC in pixel-wise classification, especially for the purpose of segmentation, the authors extend SRC from the following four aspects: (1) A discriminant subdictionary learning method is proposed to learn a discriminant and compact representation of training samples for each class so that the discriminant power of SRC can be increased and also SRC can be applied to the large-scale pixel-wise classification. (2) The L1 regularized sparse coding is replaced by
Local Sparse Structure Denoising for Low-Light-Level Image.
Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa
2015-12-01
Sparse and redundant representations perform well in image denoising. However, sparsity-based methods fail to denoise low-light-level (LLL) images because of heavy and complex noise. They consider sparsity on image patches independently and tend to lose the texture structures. To suppress noises and maintain textures simultaneously, it is necessary to embed noise invariant features into the sparse decomposition process. We, therefore, used a local structure preserving sparse coding (LSPSc) formulation to explore the local sparse structures (both the sparsity and local structure) in image. It was found that, with the introduction of spatial local structure constraint into the general sparse coding algorithm, LSPSc could improve the robustness of sparse representation for patches in serious noise. We further used a kernel LSPSc (K-LSPSc) formulation, which extends LSPSc into the kernel space to weaken the influence of linear structure constraint in nonlinear data. Based on the robust LSPSc and K-LSPSc algorithms, we constructed a local sparse structure denoising (LSSD) model for LLL images, which was demonstrated to give high performance in the natural LLL images denoising, indicating that both the LSPSc- and K-LSPSc-based LSSD models have the stable property of noise inhibition and texture details preservation.
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.
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.
Sparse Superpixel Unmixing for Hyperspectral Image Analysis
NASA Technical Reports Server (NTRS)
Castano, Rebecca; Thompson, David R.; Gilmore, Martha
2010-01-01
Software was developed that automatically detects minerals that are present in each pixel of a hyperspectral image. An algorithm based on sparse spectral unmixing with Bayesian Positive Source Separation is used to produce mineral abundance maps from hyperspectral images. A superpixel segmentation strategy enables efficient unmixing in an interactive session. The algorithm computes statistically likely combinations of constituents based on a set of possible constituent minerals whose abundances are uncertain. A library of source spectra from laboratory experiments or previous remote observations is used. A superpixel segmentation strategy improves analysis time by orders of magnitude, permitting incorporation into an interactive user session (see figure). Mineralogical search strategies can be categorized as supervised or unsupervised. Supervised methods use a detection function, developed on previous data by hand or statistical techniques, to identify one or more specific target signals. Purely unsupervised results are not always physically meaningful, and may ignore subtle or localized mineralogy since they aim to minimize reconstruction error over the entire image. This algorithm offers advantages of both methods, providing meaningful physical interpretations and sensitivity to subtle or unexpected minerals.
A sparse Ising model with covariates.
Cheng, Jie; Levina, Elizaveta; Wang, Pei; Zhu, Ji
2014-12-01
There has been a lot of work fitting Ising models to multivariate binary data in order to understand the conditional dependency relationships between the variables. However, additional covariates are frequently recorded together with the binary data, and may influence the dependence relationships. Motivated by such a dataset on genomic instability collected from tumor samples of several types, we propose a sparse covariate dependent Ising model to study both the conditional dependency within the binary data and its relationship with the additional covariates. This results in subject-specific Ising models, where the subject's covariates influence the strength of association between the genes. As in all exploratory data analysis, interpretability of results is important, and we use ℓ1 penalties to induce sparsity in the fitted graphs and in the number of selected covariates. Two algorithms to fit the model are proposed and compared on a set of simulated data, and asymptotic results are established. The results on the tumor dataset and their biological significance are discussed in detail.
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.
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.
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.
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.
Visual Exploration of Sparse Traffic Trajectory Data.
Wang, Zuchao; Ye, Tangzhi; Lu, Min; Yuan, Xiaoru; Qu, Huamin; Yuan, Jacky; Wu, Qianliang
2014-12-01
In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a city. Therefore it is very suitable for macro-traffic analysis. However, the vehicle movements are recorded only when they pass through the cells. The exact tracks between two consecutive cells are unknown. To deal with such uncertainties, we first design a local animation, showing the vehicle movements only in the vicinity of cells. Besides, we ignore the micro-behaviors of individual vehicles, and focus on the macro-traffic patterns. We apply existing trajectory aggregation techniques to the dataset, studying cell status pattern and inter-cell flow pattern. Beyond that, we propose to study the correlation between these two patterns with dynamic graph visualization techniques. It allows us to check how traffic congestion on one cell is correlated with traffic flows on neighbouring links, and with route selection in its neighbourhood. Case studies show the effectiveness of our system.
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.
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.
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
[Infantile cortical hyperostosis: Case report].
Rodríguez, Mónica; Martínez, Luz Elena; Cortés, José; de Uña, Armando; Vega, Valentina; Acosta, Mario
Infantile Cortical Hyperostosis, or Caffey-Silverman disease, is a rare condition characterised by generalised bone proliferation mediated by an acute inflammatory process. Diagnosis can be made through clinical evaluation and X-ray studies. The course is generally self-limiting and prognosis is excellent.
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
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
Real-time Algorithms for Sparse Neuronal System Identification.
Sheikhattar, Alireza; Babadi, Behtash
2016-08-01
We consider the problem of sparse adaptive neuronal system identification, where the goal is to estimate the sparse time-varying neuronal model parameters in an online fashion from neural spiking observations. We develop two adaptive filters based on greedy estimation techniques and regularized log-likelihood maximization. We apply the proposed algorithms to simulated spiking data as well as experimentally recorded data from the ferret's primary auditory cortex during performance of auditory tasks. Our results reveal significant performance gains achieved by the proposed algorithms in terms of sparse identification and trackability, compared to existing algorithms.
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.
Stacked Predictive Sparse Decomposition for Classification of Histology Sections.
Chang, Hang; Zhou, Yin; Borowsky, Alexander; Barner, Kenneth; Spellman, Paul; Parvin, Bahram
2015-05-01
Image-based classification of histology sections, in terms of distinct components (e.g., tumor, stroma, normal), provides a series of indices for histology composition (e.g., the percentage of each distinct components in histology sections), and enables the study of nuclear properties within each component. Furthermore, the study of these indices, constructed from each whole slide image in a large cohort, has the potential to provide predictive models of clinical outcome. For example, correlations can be established between the constructed indices and the patients' survival information at cohort level, which is a fundamental step towards personalized medicine. However, performance of the existing techniques is hindered as a result of large technical variations (e.g., variations of color/textures in tissue images due to non-standard experimental protocols) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. We propose a system that automatically learns a series of dictionary elements for representing the underlying spatial distribution using stacked predictive sparse decomposition. The learned representation is then fed into the spatial pyramid matching framework with a linear support vector machine classifier. The system has been evaluated for classification of distinct histological components for two cohorts of tumor types. Throughput has been increased by using of graphical processing unit (GPU), and evaluation indicates a superior performance results, compared with previous research.
Stacked Predictive Sparse Decomposition for Classification of Histology Sections
Zhou, Yin; Borowsky, Alexander; Barner, Kenneth; Spellman, Paul
2016-01-01
Image-based classification of histology sections, in terms of distinct components (e.g., tumor, stroma, normal), provides a series of indices for histology composition (e.g., the percentage of each distinct components in histology sections), and enables the study of nuclear properties within each component. Furthermore, the study of these indices, constructed from each whole slide image in a large cohort, has the potential to provide predictive models of clinical outcome. For example, correlations can be established between the constructed indices and the patients’ survival information at cohort level, which is a fundamental step towards personalized medicine. However, performance of the existing techniques is hindered as a result of large technical variations (e.g., variations of color/textures in tissue images due to non-standard experimental protocols) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. We propose a system that automatically learns a series of dictionary elements for representing the underlying spatial distribution using stacked predictive sparse decomposition. The learned representation is then fed into the spatial pyramid matching framework with a linear support vector machine classifier. The system has been evaluated for classification of distinct histological components for two cohorts of tumor types. Throughput has been increased by using of graphical processing unit (GPU), and evaluation indicates a superior performance results, compared with previous research. PMID:27721567
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).
Sparse diffraction imaging method using an adaptive reweighting homotopy algorithm
NASA Astrophysics Data System (ADS)
Yu, Caixia; Zhao, Jingtao; Wang, Yanfei; Qiu, Zhen
2017-02-01
Seismic diffractions carry valuable information from subsurface small-scale geologic discontinuities, such as faults, cavities and other features associated with hydrocarbon reservoirs. However, seismic imaging methods mainly use reflection theory for constructing imaging models, which means a smooth constraint on imaging conditions. In fact, diffractors occupy a small account of distributions in an imaging model and possess discontinuous characteristics. In mathematics, this kind of phenomena can be described by the sparse optimization theory. Therefore, we propose a diffraction imaging method based on a sparsity-constraint model for studying diffractors. A reweighted L 2-norm and L 1-norm minimization model is investigated, where the L 2 term requests a least-square error between modeled diffractions and observed diffractions and the L 1 term imposes sparsity on the solution. In order to efficiently solve this model, we use an adaptive reweighting homotopy algorithm that updates the solutions by tracking a path along inexpensive homotopy steps. Numerical examples and field data application demonstrate the feasibility of the proposed method and show its significance for detecting small-scale discontinuities in a seismic section. The proposed method has an advantage in improving the focusing ability of diffractions and reducing the migration artifacts.
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.
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.
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
Crack growth resistance in cortical bone: concept of microcrack toughening.
Vashishth, D; Behiri, J C; Bonfield, W
1997-08-01
The role of microcracking in cortical bone as a toughening mechanism has been investigated in conjunction with the variation in fracture toughness with crack length. Fracture toughness tests were conducted on miniaturised compact tension specimens made from human and bovine cortical bone and the resultant microstructural damage, present in the form of microcracking on the surface, was analysed around the main propagating crack. It was found that the fracture toughness (Kc) and the cumulative number of microcracks increased linearly with crack extension in human and bovine cortical bone, although both Kc and number of microcracks were considerably higher in the latter case. Based on these results, a mechanism, derived from the resistance (R) curve concept developed for microcracking brittle solids, is proposed to explain the fracture of cortical bone, with microcracking distributed between a frontal process zone and a significant process zone wake. Evidence to support this mechanism is given from the existing bone literature, detailed scanning electron microscopical observations and the distribution of microcracks in the process zone wake.
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.
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
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}.
Optical double-image encryption and authentication by sparse representation.
Mohammed, Emad A; Saadon, H L
2016-12-10
An optical double-image encryption and authentication method by sparse representation is proposed. The information from double-image encryption can be integrated into a sparse representation. Unlike the traditional double-image encryption technique, only sparse (partial) data from the encrypted data is adopted for the authentication process. Simulation results demonstrate that the correct authentication results are achieved even with partial information from the encrypted data. The randomly selected sparse encrypted information will be used as an effective key for a security system. Therefore, the proposed method is feasible, effective, and can provide an additional security layer for optical security systems. In addition, the method also achieved the general requirements of storage and transmission due to a high reduction of the encrypted information.
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 denoising via group Sparse representation over learned dictionary
NASA Astrophysics Data System (ADS)
Cheng, Pan; Deng, Chengzhi; Wang, Shengqian; Zhang, Chunfeng
2013-10-01
Images are one of vital ways to get information for us. However, in the practical application, images are often subject to a variety of noise, so that solving the problem of image denoising becomes particularly important. The K-SVD algorithm can improve the denoising effect by sparse coding atoms instead of the traditional method of sparse coding dictionary. In order to further improve the effect of denoising, we propose to extended the K-SVD algorithm via group sparse representation. The key point of this method is dividing the sparse coefficients into groups, so that adjusts the correlation among the elements by controlling the size of the groups. This new approach can improve the local constraints between adjacent atoms, thereby it is very important to increase the correlation between the atoms. The experimental results show that our method has a better effect on image recovery, which is efficient to prevent the block effect and can get smoother images.
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.
Roy, Asim
2017-01-01
The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings - in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the
Roy, Asim
2017-01-01
The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings – in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the
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.
Visual tracking based on extreme learning machine and sparse representation.
Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen
2015-10-22
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.
Sparseness Analysis in the Pretraining of Deep Neural Networks.
Li, Jun; Zhang, Tong; Luo, Wei; Yang, Jian; Yuan, Xiao-Tong; Zhang, Jian
2016-03-31
A major progress in deep multilayer neural networks (DNNs) is the invention of various unsupervised pretraining methods to initialize network parameters which lead to good prediction accuracy. This paper presents the sparseness analysis on the hidden unit in the pretraining process. In particular, we use the L₁-norm to measure sparseness and provide some sufficient conditions for that pretraining leads to sparseness with respect to the popular pretraining models--such as denoising autoencoders (DAEs) and restricted Boltzmann machines (RBMs). Our experimental results demonstrate that when the sufficient conditions are satisfied, the pretraining models lead to sparseness. Our experiments also reveal that when using the sigmoid activation functions, pretraining plays an important sparseness role in DNNs with sigmoid (Dsigm), and when using the rectifier linear unit (ReLU) activation functions, pretraining becomes less effective for DNNs with ReLU (Drelu). Luckily, Drelu can reach a higher recognition accuracy than DNNs with pretraining (DAEs and RBMs), as it can capture the main benefit (such as sparseness-encouraging) of pretraining in Dsigm. However, ReLU is not adapted to the different firing rates in biological neurons, because the firing rate actually changes along with the varying membrane resistances. To address this problem, we further propose a family of rectifier piecewise linear units (RePLUs) to fit the different firing rates. The experimental results show that the performance of RePLU is better than ReLU, and is comparable with those with some pretraining techniques, such as RBMs and DAEs.
Prediction of brain maturity based on cortical thickness at different spatial resolutions.
Khundrakpam, Budhachandra S; Tohka, Jussi; Evans, Alan C
2015-05-01
Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n=308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R=0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity.
Kameda, Hiroshi; Hioki, Hiroyuki; Tanaka, Yasuyo H; Tanaka, Takuma; Sohn, Jaerin; Sonomura, Takahiro; Furuta, Takahiro; Fujiyama, Fumino; Kaneko, Takeshi
2012-03-01
To examine inputs to parvalbumin (PV)-producing interneurons, we generated transgenic mice expressing somatodendritic membrane-targeted green fluorescent protein specifically in the interneurons, and completely visualized their dendrites and somata. Using immunolabeling for vesicular glutamate transporter (VGluT)1, VGluT2, and vesicular GABA transporter, we found that VGluT1-positive terminals made contacts 4- and 3.1-fold more frequently with PV-producing interneurons than VGluT2-positive and GABAergic terminals, respectively, in the primary somatosensory cortex. Even in layer 4, where VGluT2-positive terminals were most densely distributed, VGluT1-positive inputs to PV-producing interneurons were 2.4-fold more frequent than VGluT2-positive inputs. Furthermore, although GABAergic inputs to PV-producing interneurons were as numerous as VGluT2-positive inputs in most cortical layers, GABAergic inputs clearly preferred the proximal dendrites and somata of the interneurons, indicating that the sites of GABAergic inputs were more optimized than those of VGluT2-positive inputs. Simulation analysis with a PV-producing interneuron model compatible with the present morphological data revealed a plausible reason for this observation, by showing that GABAergic and glutamatergic postsynaptic potentials evoked by inputs to distal dendrites were attenuated to 60 and 87%, respectively, of those evoked by somatic inputs. As VGluT1-positive and VGluT2-positive axon terminals were presumed to be cortical and thalamic glutamatergic inputs, respectively, cortical excitatory inputs to PV-producing interneurons outnumbered the thalamic excitatory and intrinsic inhibitory inputs more than two-fold in any cortical layer. Although thalamic inputs are known to evoke about two-fold larger unitary excitatory postsynaptic potentials than cortical ones, the present results suggest that cortical inputs control PV-producing interneurons at least as strongly as thalamic inputs.
Hierarchical Sparse Learning with Spectral-Spatial Information for Hyperspectral Imagery Denoising
Liu, Shuai; Jiao, Licheng; Yang, Shuyuan
2016-01-01
During the acquisition process hyperspectral images (HSI) are inevitably corrupted by various noises, which greatly influence their visual impression and subsequent applications. In this paper, a novel Bayesian approach integrating hierarchical sparse learning and spectral-spatial information is proposed for HSI denoising. Based on the structure correlations, spectral bands with similar and continuous features are segmented into the same band-subset. To exploit local similarity, each subset is then divided into overlapping cubic patches. All patches can be regarded as consisting of clean image component, Gaussian noise component and sparse noise component. The first term is depicted by a linear combination of dictionary elements, where Gaussian process with Gamma distribution is applied to impose spatial consistency on dictionary. The last two terms are utilized to fully depict the noise characteristics. Furthermore, the sparseness of the model is adaptively manifested through Beta-Bernoulli process. Calculated by Gibbs sampler, the proposed model can directly predict the noise and dictionary without priori information of the degraded HSI. The experimental results on both synthetic and real HSI demonstrate that the proposed approach can better suppress the existing various noises and preserve the structure/spectral-spatial information than the compared state-of-art approaches. PMID:27763511
Model-based imaging of damage with Lamb waves via sparse reconstruction.
Levine, Ross M; Michaels, Jennifer E
2013-03-01
Ultrasonic guided waves are gaining acceptance for structural health monitoring and nondestructive evaluation of plate-like structures. One configuration of interest is a spatially distributed array of fixed piezoelectric devices. Typical operation consists of recording signals from all transmit-receive pairs and subtracting pre-recorded baselines to detect changes, possibly due to damage or other effects. While techniques such as delay-and-sum imaging as applied to differential signals are both simple and capable of detecting flaws, their performance is limited, particularly when there are multiple damage sites. Here a very different approach to imaging is considered that exploits the expected sparsity of structural damage; i.e., the structure is mostly damage-free. Differential signals are decomposed into a sparse linear combination of location-based components, which are pre-computed from a simple propagation model. The sparse reconstruction techniques of basis pursuit denoising and orthogonal matching pursuit are applied to achieve this decomposition, and a hybrid reconstruction method is also proposed and evaluated. Noisy simulated data and experimental data recorded on an aluminum plate with artificial damage are considered. Results demonstrate the efficacy of all three methods by producing very sparse indications of damage at the correct locations even in the presence of model mismatch and significant noise.
A sparse model based detection of copy number variations from exome sequencing data
Duan, Junbo; Wan, Mingxi; Deng, Hong-Wen; Wang, Yu-Ping
2016-01-01
Goal Whole-exome sequencing provides a more cost-effective way than whole-genome sequencing for detecting genetic variants such as copy number variations (CNVs). Although a number of approaches have been proposed to detect CNVs from whole-genome sequencing, a direct adoption of these approaches to whole-exome sequencing will often fail because exons are separately located along a genome. Therefore, an appropriate method is needed to target the specific features of exome sequencing data. Methods In this paper a novel sparse model based method is proposed to discover CNVs from multiple exome sequencing data. First, exome sequencing data are represented with a penalized matrix approximation, and technical variability and random sequencing errors are assumed to follow a generalized Gaussian distribution. Second, an iteratively re-weighted least squares algorithm is used to estimate the solution. Results The method is tested and validated on both synthetic and real data, and compared with other approaches including CoNIFER, XHMM and cn.MOPS. The test demonstrates that the proposed method outperform other approaches. Conclusion The proposed sparse model can detect CNVs from exome sequencing data with high power and precision. Significance Sparse model can target the specific features of exome sequencing data. The software codes are freely available at http://www.tulane.edu/wyp/software/ExonCNV.m PMID:26258935
Extrathalamic Modulation of Cortical Function
1990-07-27
and c7rtico-cortical systems. For example, we have shown that primate LC-NA neurons are more acti during waking than sleep and exhibit bursts of...infusion needle. Infusion of the alpha-adrenergic agonist clonidine (CLON), in concentrations ranging from 5-20 uM (67-270pg/50 nl injection...ind hippocampal EEG (HEEG) typically exhibit activity similar to that of a lightly sleeping animal. However, periods of "waking" EEG are sometimes
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
[Parietal Cortices and Body Information].
Naito, Eiichi; Amemiya, Kaoru; Morita, Tomoyo
2016-11-01
Proprioceptive signals originating from skeletal muscles and joints contribute to the formation of both the human body schema and the body image. In this chapter, we introduce various types of bodily illusions that are elicited by proprioceptive inputs, and we discuss distinct functions implemented by different parietal cortices. First, we illustrate the primary importance of the motor network in the processing of proprioceptive (kinesthetic) signals originating from muscle spindles. Next, we argue that the right inferior parietal cortex, in concert with the inferior frontal cortex (both regions connected by the inferior branch of the superior longitudinal fasciculus-SLF III), may be involved in the conscious experience of body image. Further, we hypothesize other functions of distinct parietal regions: the association between internal hand motor representation with external object representation in the left inferior parietal cortex, visuo-kinesthetic processing in the bilateral posterior parietal cortices, and the integration of somatic signals from different body parts in the higher-order somatosensory parietal cortices. Our results indicate that a distinct parietal region, in concert with its anatomically and functionally connected frontal regions, probably plays specialized roles in the processing of body-related information.
Zhang, Guoqing; Sun, Huaijiang; Xia, Guiyu; Sun, Quansen
2016-07-07
Sparse representation based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. [10] devised a SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) [22] has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier (MKSRC), and then we use it as a criterion to design a multiple kernel sparse representation based orthogonal discriminative projection method (MK-SR-ODP). The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method [33]. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.
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.
Sensory-evoked perturbations of locomotor activity by sparse sensory input: a computational study
Brownstone, Robert M.
2015-01-01
Sensory inputs from muscle, cutaneous, and joint afferents project to the spinal cord, where they are able to affect ongoing locomotor activity. Activation of sensory input can initiate or prolong bouts of locomotor activity depending on the identity of the sensory afferent activated and the timing of the activation within the locomotor cycle. However, the mechanisms by which afferent activity modifies locomotor rhythm and the distribution of sensory afferents to the spinal locomotor networks have not been determined. Considering the many sources of sensory inputs to the spinal cord, determining this distribution would provide insights into how sensory inputs are integrated to adjust ongoing locomotor activity. We asked whether a sparsely distributed set of sensory inputs could modify ongoing locomotor activity. To address this question, several computational models of locomotor central pattern generators (CPGs) that were mechanistically diverse and generated locomotor-like rhythmic activity were developed. We show that sensory inputs restricted to a small subset of the network neurons can perturb locomotor activity in the same manner as seen experimentally. Furthermore, we show that an architecture with sparse sensory input improves the capacity to gate sensory information by selectively modulating sensory channels. These data demonstrate that sensory input to rhythm-generating networks need not be extensively distributed. PMID:25673740
Cortical thickness in a case of congenital unilateral perisylvian syndrome.
Kotini, A; Camposano, S; Hara, K; Salat, D; Cole, A; Stufflebeam, S; Halgren, E
2004-11-30
In congenital perisylvian syndrome, there is polymicrogyric cortex distributed in variable extensions around the sylvian fissure. Unilateral cases usually present with congenital hemiparesis, while bilateral cases have pseudobulbar paralysis of the oropharingoglossal region. Both unilateral and bilateral cases have a high rate of epilepsy. Polymicrogyric cortex is characterized by too many small convolutions. Often there are no intervening sulci, and almost no white matter can be seen under them. On MRI they appear to have increased thickness. Bilateral and symmetric polimycrogiria can be hard to recognize on standard MRIs. Accurate and automated methods for measuring the thickness of cerebral cortex are available. They have mainly been used to study a variety of disorders with diminished cortical thickness. We studied a case of right perisylvian polymicrogyria, who presented in adult life with epilepsy and had a normal neurological exam. Fischl and Dale's automated cortical thickness analysis rendered a very clear picture of increased cortical thickness with values up to 9 mm in the affected areas (normal cortical thickness varies between 1 and 4.5 mm). The thickest areas were seen over grossly abnormal gyri on the reconstructed cerebral cortex. On MEG he presented a prominent and monotonous 9 Hz activity that was located within the limits of a thick gyrus. There was a significant difference of thickness between homologous hemispheric areas. To our surprise some areas of the left hemisphere also appeared to have increased thickness, raising the question of a bilateral asymmetric case.
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
A mechanism for the cortical computation of hierarchical linguistic structure
Doumas, Leonidas A. A.
2017-01-01
Biological systems often detect species-specific signals in the environment. In humans, speech and language are species-specific signals of fundamental biological importance. To detect the linguistic signal, human brains must form hierarchical representations from a sequence of perceptual inputs distributed in time. What mechanism underlies this ability? One hypothesis is that the brain repurposed an available neurobiological mechanism when hierarchical linguistic representation became an efficient solution to a computational problem posed to the organism. Under such an account, a single mechanism must have the capacity to perform multiple, functionally related computations, e.g., detect the linguistic signal and perform other cognitive functions, while, ideally, oscillating like the human brain. We show that a computational model of analogy, built for an entirely different purpose—learning relational reasoning—processes sentences, represents their meaning, and, crucially, exhibits oscillatory activation patterns resembling cortical signals elicited by the same stimuli. Such redundancy in the cortical and machine signals is indicative of formal and mechanistic alignment between representational structure building and “cortical” oscillations. By inductive inference, this synergy suggests that the cortical signal reflects structure generation, just as the machine signal does. A single mechanism—using time to encode information across a layered network—generates the kind of (de)compositional representational hierarchy that is crucial for human language and offers a mechanistic linking hypothesis between linguistic representation and cortical computation. PMID:28253256
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.
An enhanced sparse representation strategy for signal classification
NASA Astrophysics Data System (ADS)
Zhou, Yin; Gao, Jinglun; Barner, Kenneth E.
2012-06-01
Sparse representation based classification (SRC) has achieved state-of-the-art results on face recognition. It is hence desired to extend its power to a broader range of classification tasks in pattern recognition. SRC first encodes a query sample as a linear combination of a few atoms from a predefined dictionary. It then identifies the label by evaluating which class results in the minimum reconstruction error. The effectiveness of SRC is limited by an important assumption that data points from different classes are not distributed along the same radius direction. Otherwise, this approach will lose their discrimination ability, even though data from different classes are actually well-separated in terms of Euclidean distance. This assumption is reasonable for face recognition as images of the same subject under different intensity levels are still considered to be of same-class. However, the assumption is not always satisfied when dealing with many other real-world data, e.g., the Iris dataset, where classes are stratified along the radius direction. In this paper, we propose a new coding strategy, called Nearest- Farthest Neighbors based SRC (NF-SRC), to effectively overcome the limitation within SRC. The dictionary is composed of both the Nearest Neighbors and the Farthest Neighbors. While the Nearest Neighbors are used to narrow the selection of candidate samples, the Farthest Neighbors are employed to make the dictionary more redundant. NF-SRC encodes each query signal in a greedy way similar to OMP. The proposed approach is evaluated over extensive experiments. The encouraging results demonstrate the feasibility of the proposed method.
Environment identification in flight using sparse approximation of wing strain
NASA Astrophysics Data System (ADS)
Manohar, Krithika; Brunton, Steven L.; Kutz, J. Nathan
2017-04-01
This paper addresses the problem of identifying different flow environments from sparse data collected by wing strain sensors. Insects regularly perform this feat using a sparse ensemble of noisy strain sensors on their wing. First, we obtain strain data from numerical simulation of a Manduca sexta hawkmoth wing undergoing different flow environments. Our data-driven method learns low-dimensional strain features originating from different aerodynamic environments using proper orthogonal decomposition (POD) modes in the frequency domain, and leverages sparse approximation to classify a set of strain frequency signatures using a dictionary of POD modes. This bio-inspired machine learning architecture for dictionary learning and sparse classification permits fewer costly physical strain sensors while being simultaneously robust to sensor noise. A measurement selection algorithm identifies frequencies that best discriminate the different aerodynamic environments in low-rank POD feature space. In this manner, sparse and noisy wing strain data can be exploited to robustly identify different aerodynamic environments encountered in flight, providing insight into the stereotyped placement of neurons that act as strain sensors on a Manduca sexta hawkmoth wing.
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.
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.
A temporal channel for information in sparse sensory coding
Gupta, Nitin; Stopfer, Mark
2014-01-01
Summary Background Sparse codes are found in nearly every sensory system, but the role of spike timing in sparse sensory coding is unclear. Here we used the olfactory system of awake locusts to test whether the timing of spikes in Kenyon cells, a population of neurons that responds sparsely to odors, carries sensory information to, and influences the responses of, follower neurons. Results We characterized two major classes of direct followers of Kenyon cells. With paired intracellular and field potential recordings made during odor presentations, we found these followers contain information about odor identity in the temporal patterns of their spikes, rather than in the spike rate, the spike phase or the identities of the responsive neurons. Subtly manipulating the relative timing of Kenyon cell spikes with temporally and spatially structured microstimulation reliably altered the response patterns of the followers. Conclusions Our results show that even remarkably sparse spiking responses can provide information through stimulus-specific variations in timing on the order of tens to hundreds of milliseconds, and that these variations can determine the responses of downstream neurons. These results establish the importance of spike timing in a sparse sensory code. PMID:25264257
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. PMID:27630710
A sparse matrix based full-configuration interaction algorithm.
Rolik, Zoltán; Szabados, Agnes; Surján, Péter R
2008-04-14
We present an algorithm related to the full-configuration interaction (FCI) method that makes complete use of the sparse nature of the coefficient vector representing the many-electron wave function in a determinantal basis. Main achievements of the presented sparse FCI (SFCI) algorithm are (i) development of an iteration procedure that avoids the storage of FCI size vectors; (ii) development of an efficient algorithm to evaluate the effect of the Hamiltonian when both the initial and the product vectors are sparse. As a result of point (i) large disk operations can be skipped which otherwise may be a bottleneck of the procedure. At point (ii) we progress by adopting the implementation of the linear transformation by Olsen et al. [J. Chem Phys. 89, 2185 (1988)] for the sparse case, getting the algorithm applicable to larger systems and faster at the same time. The error of a SFCI calculation depends only on the dropout thresholds for the sparse vectors, and can be tuned by controlling the amount of system memory passed to the procedure. The algorithm permits to perform FCI calculations on single node workstations for systems previously accessible only by supercomputers.
Cortical microtubule rearrangements and cell wall patterning
Oda, Yoshihisa
2015-01-01
Plant cortical microtubules, which form a highly ordered array beneath the plasma membrane, play essential roles in determining cell shape and function by directing the arrangement of cellulosic and non-cellulosic compounds on the cell surface. Interphase transverse arrays of cortical microtubules self-organize through their dynamic instability and inter-microtubule interactions, and by branch-form microtubule nucleation and severing. Recent studies revealed that distinct spatial signals including ROP GTPase, cellular geometry, and mechanical stress regulate the behavior of cortical microtubules at the subcellular and supercellular levels, giving rise to dramatic rearrangements in the cortical microtubule array in response to internal and external cues. Increasing evidence indicates that negative regulators of microtubules also contribute to the rearrangement of the cortical microtubule array. In this review, I summarize recent insights into how the rearrangement of the cortical microtubule array leads to proper, flexible cell wall patterning. PMID:25904930
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
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.
MR appearance of distal femoral cortical irregularity (cortical desmoid)
Suh, Jin-Suck; Cho, Jae-Hyun; Shin, Kyoo-Ho
1996-03-01
Our goal was to describe the MR appearance of distal femoral cortical irregularity (DFCI). With plain radiographs and MR images of 100 knees, the presence of DFCIs was determined, and the shapes of DFCIs were classified into three subgroups: concave, convex, and divergent cortical shapes. Radiographic and MR shapes of DFCIs were compared. DFCIs were shown in various shapes on both the radiographs and the MR images. Forty-four DFCIs were found both on radiograph and by MR image. An additional 14 DFCIs were identifiable only on MR images. However, the majority of DFCIs showed an association between radiographic and MR shapes. MRI revealed that all 58 DFCIs were located at the attachment site of the media gastrocnemius muscle. DFCIs were enhanced in three of the four patients who underwent postcontrast MR study. A good understanding of radiographic and MR findings of the DFCI may be of great help in the differential diagnosis of distal femoral lesions. 16 refs., 6 figs., 1 tab.
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.
A Rare Hydrocephalus Complication: Cortical Blindness.
Ünal, Emre; Göçmen, Rahşan; Işıkay, Ayşe İlksen; Tekşam, Özlem
2015-01-01
Cortical blindness related to bilateral occipital lobe infarction is an extremely rare complication of hydrocephalus. Compression of the posterior cerebral artery, secondary to tentorial herniation, is the cause of occipital infarction. Particularly in children and mentally ill patients, cortical blindness may be missed. Therefore, early diagnosis and treatment of hydrocephalus is important. We present herein a child of ventricular shunt malfunction complicated by cortical blindness.
[Posterior cortical atrophy (Benson-syndrome)].
Rózsa, Anikó; Szilvássy, Ildikó; Kovács, Krisztina; Boór, Krisztina; Gács, Gyula
2010-01-30
We present the characteristics of posterior cortical atrophy--a very rare cortical dementia--in a 69 year old woman's case. Our patient's symptoms began with a visual problem which was initially explained by ophthalmological disorder. After neurological exam visual agnosia was diagnosed apart from other cognitive disorder (alexia without agraphia, acalculia, prosopagnosia, constructional disorder, clock-time recognition disorder, dressing apraxia, visuospatial disorientation). The brain MRI showed bilateral asymmetric parieto-occipital atrophy which is characteristic of posterior cortical atrophy.
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
Posttraumatic cortical defect of femur.
Nadarajah, Jeyaseelan; Srivastava, Deep N; Malhotra, Rajesh; Palaniswamy, Aravindh
2013-01-01
Posttraumatic cortical defect of bone is a rare entity which occurs in a maturing skeleton following green stick or torus fracture. Most of the cases are asymptomatic and they are detected incidentally on radiograph. These lesions usually require no treatment. However, the appearance of these lesions can mimic various pathological conditions affecting bone. Knowledge about this entity is important as it avoids unnecessary investigations. We present this case as the occurrence of this entity in femur is very rare and the child was symptomatic.
Zhu, Min; Zhang, Wenbo; Dickens, Deanna; Ding, Lei
2012-01-01
The aim of the present study is to evaluate the capability of a recently proposed l(1)-norm based regularization method, named as variation-based sparse cortical current density (VB-SCCD) algorithm, in estimating location and spatial coverage of extensive brain sources. Its performance was compared to the conventional minimum norm estimate (MNE) using both simulations and clinical interictal spike MEG data from epilepsy patients. Four metrics were adopted to evaluate two regularization methods for EEG/MEG inverse problems from different aspects in simulation study. Both methods were further compared in reconstructing epileptic sources and validated using results from clinical diagnosis. Both simulation and experimental results suggest VB-SCCD has better performance in localization and estimation of source extents, as well as less spurious sources than MNE, which makes it a promising noninvasive tool to assist presurgical evaluation for surgical treatment in epilepsy patients.
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
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 representation-based image restoration via nonlocal supervised coding
NASA Astrophysics Data System (ADS)
Li, Ao; Chen, Deyun; Sun, Guanglu; Lin, Kezheng
2016-10-01
Sparse representation (SR) and nonlocal technique (NLT) have shown great potential in low-level image processing. However, due to the degradation of the observed image, SR and NLT may not be accurate enough to obtain a faithful restoration results when they are used independently. To improve the performance, in this paper, a nonlocal supervised coding strategy-based NLT for image restoration is proposed. The novel method has three main contributions. First, to exploit the useful nonlocal patches, a nonnegative sparse representation is introduced, whose coefficients can be utilized as the supervised weights among patches. Second, a novel objective function is proposed, which integrated the supervised weights learning and the nonlocal sparse coding to guarantee a more promising solution. Finally, to make the minimization tractable and convergence, a numerical scheme based on iterative shrinkage thresholding is developed to solve the above underdetermined inverse problem. The extensive experiments validate the effectiveness of the proposed method.
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.
A note on rank reduction in sparse multivariate regression.
Chen, Kun; Chan, Kung-Sik
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.
Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging
Zhang, Yichun; Shi, Tielin; Su, Lei; Wang, Xiao; Hong, Yuan; Chen, Kepeng; Liao, Guanglan
2016-01-01
Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l1-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging. PMID:27783040
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.
Combining sparseness and smoothness improves classification accuracy and interpretability.
de Brecht, Matthew; Yamagishi, Noriko
2012-04-02
Sparse logistic regression (SLR) has been shown to be a useful method for decoding high-dimensional fMRI and MEG data by automatically selecting relevant feature dimensions. However, when applied to signals with high spatio-temporal correlations, SLR often over-prunes the feature space, which can result in overfitting and weight vectors that are difficult to interpret. To overcome this problem, we investigate a modification of ℓ₁-normed sparse logistic regression, called smooth sparse logistic regression (SSLR), which has a spatio-temporal "smoothing" prior that encourages weights that are close in time and space to have similar values. This causes the classifier to select spatio-temporally continuous groups of features, whereas SLR classifiers often select a scattered collection of independent features. We applied the method to both simulation data and real MEG data. We found that SSLR consistently increases classification accuracy, and produces weight vectors that are more meaningful from a neuroscientific perspective.
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.
Sparse nonnegative matrix factorization with ℓ0-constraints
Peharz, Robert; Pernkopf, Franz
2012-01-01
Although nonnegative matrix factorization (NMF) favors a sparse and part-based representation of nonnegative data, there is no guarantee for this behavior. Several authors proposed NMF methods which enforce sparseness by constraining or penalizing the ℓ1-norm of the factor matrices. On the other hand, little work has been done using a more natural sparseness measure, the ℓ0-pseudo-norm. In this paper, we propose a framework for approximate NMF which constrains the ℓ0-norm of the basis matrix, or the coefficient matrix, respectively. For this purpose, techniques for unconstrained NMF can be easily incorporated, such as multiplicative update rules, or the alternating nonnegative least-squares scheme. In experiments we demonstrate the benefits of our methods, which compare to, or outperform existing approaches. PMID:22505792
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.
Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging.
Zhang, Yichun; Shi, Tielin; Su, Lei; Wang, Xiao; Hong, Yuan; Chen, Kepeng; Liao, Guanglan
2016-10-24
Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l₁-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging.
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.
Competition for shortest paths on sparse graphs.
Yeung, Chi Ho; Saad, David
2012-05-18
Optimal paths connecting randomly selected network nodes and fixed routers are studied analytically in the presence of a nonlinear overlap cost that penalizes congestion. Routing becomes more difficult as the number of selected nodes increases and exhibits ergodicity breaking in the case of multiple routers. The ground state of such systems reveals nonmonotonic complex behaviors in average path length and algorithmic convergence, depending on the network topology, and densities of communicating nodes and routers. A distributed linearly scalable routing algorithm is also devised.
Removal of Nuisance Signals from Limited and Sparse 1H MRSI Data Using a Union-of-Subspaces Model
Ma, Chao; Lam, Fan; Johnson, Curtis L.; Liang, Zhi-Pei
2015-01-01
Purpose To remove nuisance signals (e.g., water and lipid signals) for 1H MRSI data collected from the brain with limited and/or sparse (k, t)-space coverage. Methods A union-of-subspace model is proposed for removing nuisance signals. The model exploits the partial separability of both the nuisance signals and the metabolite signal, and decomposes an MRSI dataset into several sets of generalized voxels that share the same spectral distributions. This model enables the estimation of the nuisance signals from an MRSI dataset that has limited and/or sparse (k, t)-space coverage. Results The proposed method has been evaluated using in vivo MRSI data. For conventional CSI data with limited k-space coverage, the proposed method produced “lipid-free” spectra without lipid suppression during data acquisition at 130 ms echo time. For sparse (k, t)-space data acquired with conventional pulses for water and lipid suppression, the proposed method was also able to remove the remaining water and lipid signals with negligible residuals. Conclusions Nuisance signals in 1H MRSI data reside in low-dimensional subspaces. This property can be utilized for estimation and removal of nuisance signals from 1H MRSI data even when they have limited and/or sparse coverage of (k, t)-space. The proposed method should prove useful especially for accelerated high-resolution 1H MRSI of the brain. PMID:25762370
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
The MUSIC algorithm for sparse objects: a compressed sensing analysis
NASA Astrophysics Data System (ADS)
Fannjiang, Albert C.
2011-03-01
The multiple signal classification (MUSIC) algorithm, and its extension for imaging sparse extended objects, with noisy data is analyzed by compressed sensing (CS) techniques. A thresholding rule is developed to augment the standard MUSIC algorithm. The notion of restricted isometry property (RIP) and an upper bound on the restricted isometry constant (RIC) are employed to establish sufficient conditions for the exact localization by MUSIC with or without noise. In the noiseless case, the sufficient condition gives an upper bound on the numbers of random sampling and incident directions necessary for exact localization. In the noisy case, the sufficient condition assumes additionally an upper bound for the noise-to-object ratio in terms of the RIC and the dynamic range of objects. This bound points to the super-resolution capability of the MUSIC algorithm. Rigorous comparison of performance between MUSIC and the CS minimization principle, basis pursuit denoising (BPDN), is given. In general, the MUSIC algorithm guarantees to recover, with high probability, s scatterers with n= {O}(s^2) random sampling and incident directions and sufficiently high frequency. For the favorable imaging geometry where the scatterers are distributed on a transverse plane MUSIC guarantees to recover, with high probability, s scatterers with a median frequency and n= {O}(s) random sampling/incident directions. Moreover, for the problems of spectral estimation and source localizations both BPDN and MUSIC guarantee, with high probability, to identify exactly the frequencies of random signals with the number n= {O}(s) of sampling times. However, in the absence of abundant realizations of signals, BPDN is the preferred method for spectral estimation. Indeed, BPDN can identify the frequencies approximately with just one realization of signals with the recovery error at worst linearly proportional to the noise level. Numerical results confirm that BPDN outperforms MUSIC in the well
Samargia, Sharyl; Schmidt, Rebekah; Kimberley, Teresa Jacobson
2015-01-01
Background The pathophysiology of adductor spasmodic dysphonia (AdSD), like other focal dystonias, is largely unknown. Objective The purposes of this study were to determine 1) cortical excitability differences between AdSD, muscle tension dysphonia (MTD) and healthy controls 2) distribution of potential differences in cranial or skeletal muscle, and 3) if cortical excitability measures assist in the differential diagnosis of AdSD and MTD. Methods 10 participants with adductor spasmodic dysphonia, 8 with muscle tension dysphonia and 10 healthy controls received single and paired pulse transcranial magnetic stimulation (TMS) to the primary motor cortex contralateral to tested muscles, first dorsal interosseus (FDI) and masseter. We tested the hypothesis that cortical excitability measures in AdSD would be significantly different than in MTD and healthy. In addition, we hypothesized there would be a correlation between cortical excitability measures and clinical voice severity in AdSD. Results Cortical silent period (CSP) duration in masseter and FDI was significantly shorter in AdSD than MTD and healthy controls. Other measures failed to demonstrate differences. Conclusion There are differences in cortical excitability between AdSD, MTD and healthy controls. These differences in the cortical measure of both the FDI and masseter muscles in AdSD suggest widespread dysfunction of the GABAB mechanism may be a pathophysiologic feature of AdSD, similar to other forms of focal dystonia. Further exploration of the use of TMS to assist in the differential diagnosis of AdSD and MTD is warranted. PMID:26089309
Cortical Specializations Underlying Fast Computations.
Volgushev, Maxim
2016-04-01
The time course of behaviorally relevant environmental events sets temporal constraints on neuronal processing. How does the mammalian brain make use of the increasingly complex networks of the neocortex, while making decisions and executing behavioral reactions within a reasonable time? The key parameter determining the speed of computations in neuronal networks is a time interval that neuronal ensembles need to process changes at their input and communicate results of this processing to downstream neurons. Theoretical analysis identified basic requirements for fast processing: use of neuronal populations for encoding, background activity, and fast onset dynamics of action potentials in neurons. Experimental evidence shows that populations of neocortical neurons fulfil these requirements. Indeed, they can change firing rate in response to input perturbations very quickly, within 1 to 3 ms, and encode high-frequency components of the input by phase-locking their spiking to frequencies up to 300 to 1000 Hz. This implies that time unit of computations by cortical ensembles is only few, 1 to 3 ms, which is considerably faster than the membrane time constant of individual neurons. The ability of cortical neuronal ensembles to communicate on a millisecond time scale allows for complex, multiple-step processing and precise coordination of neuronal activity in parallel processing streams, while keeping the speed of behavioral reactions within environmentally set temporal constraints.
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
Cortical thickness in untreated transsexuals.
Zubiaurre-Elorza, Leire; Junque, Carme; Gómez-Gil, Esther; Segovia, Santiago; Carrillo, Beatriz; Rametti, Giuseppina; Guillamon, Antonio
2013-12-01
Sex differences in cortical thickness (CTh) have been extensively investigated but as yet there are no reports on CTh in transsexuals. Our aim was to determine whether the CTh pattern in transsexuals before hormonal treatment follows their biological sex or their gender identity. We performed brain magnetic resonance imaging on 94 subjects: 24 untreated female-to-male transsexuals (FtMs), 18 untreated male-to-female transsexuals (MtFs), and 29 male and 23 female controls in a 3-T TIM-TRIO Siemens scanner. T1-weighted images were analyzed to obtain CTh and volumetric subcortical measurements with FreeSurfer software. CTh maps showed control females have thicker cortex than control males in the frontal and parietal regions. In contrast, males have greater right putamen volume. FtMs had a similar CTh to control females and greater CTh than males in the parietal and temporal cortices. FtMs had larger right putamen than females but did not differ from males. MtFs did not differ in CTh from female controls but had greater CTh than control males in the orbitofrontal, insular, and medial occipital regions. In conclusion, FtMs showed evidence of subcortical gray matter masculinization, while MtFs showed evidence of CTh feminization. In both types of transsexuals, the differences with respect to their biological sex are located in the right hemisphere.
Sparse grid techniques for particle-in-cell schemes
NASA Astrophysics Data System (ADS)
Ricketson, L. F.; Cerfon, A. J.
2017-02-01
We propose the use of sparse grids to accelerate particle-in-cell (PIC) schemes. By using the so-called ‘combination technique’ from the sparse grids literature, we are able to dramatically increase the size of the spatial cells in multi-dimensional PIC schemes while paying only a slight penalty in grid-based error. The resulting increase in cell size allows us to reduce the statistical noise in the simulation without increasing total particle number. We present initial proof-of-principle results from test cases in two and three dimensions that demonstrate the new scheme’s efficiency, both in terms of computation time and memory usage.
High Angular Resolution Microwave Sensing with Large, Sparse, Random Arrays.
1982-12-01
b.cnuainas saldaatv an quired at microwaves to achieve the rec0n(pwro cam’ forming or seti -colternng or phas. synchronzing. After the moo optical...AD A126 866 HIGH ANGULAR RESOLUTICN MICROWAVE SENSING WITH LARGE 1/ SPARSE RANDOM ARRAYS..U) MOORE SCHOOL OF ELECTRICAL ENGINEERING PHILADELPHIAPA...RESOLUTION TEST CHART N4ATIONAL BUREAU Of SrANDARDS 1963 A iOSR-TR- 83-0225 HIGH ANGULAR RESOLUTION MICROWAVE SENSING WITH LARGE, SPARSE, RANDOM ARRAYS Annual
Moving target detection for frequency agility radar by sparse reconstruction.
Quan, Yinghui; Li, YaChao; Wu, Yaojun; Ran, Lei; Xing, Mengdao; Liu, Mengqi
2016-09-01
Frequency agility radar, with randomly varied carrier frequency from pulse to pulse, exhibits superior performance compared to the conventional fixed carrier frequency pulse-Doppler radar against the electromagnetic interference. A novel moving target detection (MTD) method is proposed for the estimation of the target's velocity of frequency agility radar based on pulses within a coherent processing interval by using sparse reconstruction. Hardware implementation of orthogonal matching pursuit algorithm is executed on Xilinx Virtex-7 Field Programmable Gata Array (FPGA) to perform sparse optimization. Finally, a series of experiments are performed to evaluate the performance of proposed MTD method for frequency agility radar systems.
Towards an Accurate Performance Modeling of Parallel SparseFactorization
Grigori, Laura; Li, Xiaoye S.
2006-05-26
We present a performance model to analyze a parallel sparseLU factorization algorithm on modern cached-based, high-end parallelarchitectures. Our model characterizes the algorithmic behavior bytakingaccount the underlying processor speed, memory system performance, aswell as the interconnect speed. The model is validated using theSuperLU_DIST linear system solver, the sparse matrices from realapplications, and an IBM POWER3 parallel machine. Our modelingmethodology can be easily adapted to study performance of other types ofsparse factorizations, such as Cholesky or QR.
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.
Sparse coding based feature representation method for remote sensing images
NASA Astrophysics Data System (ADS)
Oguslu, Ender
In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation systems based on the sparse signal model are computationally expensive, requiring to solve a convex optimization problem to learn a dictionary. A sparse coding feature representation framework for the classification of HSI is presented that alleviates the complexity of sparse coding through sub-band construction, dictionary learning, and encoding steps. In the framework, we construct the dictionary based upon the extracted sub-bands from the spectral representation of a pixel. In the encoding step, we utilize a soft threshold function to obtain sparse feature representations for HSI. Experimental results showed that a randomly selected dictionary could be as effective as a dictionary learned from optimization. The new representation usually has a very high dimensionality requiring a lot of computational resources. In addition, the spatial information of the HSI data has not been included in the representation. Thus, we modify the framework by incorporating the spatial information of the HSI pixels and reducing the dimension of the new sparse representations. The enhanced model, called sparse coding based dense feature representation (SC-DFR), is integrated with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) classifiers to discriminate different types of land cover. We evaluated the proposed algorithm on three well known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit (SOMP) and image fusion and recursive filtering (IFRF). The results from the experiments showed that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification. To further
An evaluation of GPU acceleration for sparse reconstruction
NASA Astrophysics Data System (ADS)
Braun, Thomas R.
2010-04-01
Image processing applications typically parallelize well. This gives a developer interested in data throughput several different implementation options, including multiprocessor machines, general purpose computation on the graphics processor, and custom gate-array designs. Herein, we will investigate these first two options for dictionary learning and sparse reconstruction, specifically focusing on the K-SVD algorithm for dictionary learning and the Batch Orthogonal Matching Pursuit for sparse reconstruction. These methods have been shown to provide state of the art results for image denoising, classification, and object recognition. We'll explore the GPU implementation and show that GPUs are not significantly better or worse than CPUs for this application.
Multiscale Sparse Image Representation with Learned Dictionaries (PREPRINT)
2007-01-01
age processing, e.g., image denoising [5]. In [1] the K- SVD is proposed for learning a single-scale dic- tionary for sparse representation of image...performance we obtain. 2. THE SINGLE-SCALE K- SVD DENOISING ALGORITHM In this section, we briefly review the main ideas of the K- SVD frame- work for sparse...weighted average: x̂ = “ λI + X ij R T ijRij ”−1“ λy + X ij R T ijD̂α̂ij ” . (4) Fig. 1. The single-scale K- SVD -based image denoising algorithm. Fig
Accelerated nonlinear multichannel ultrasonic tomographic imaging using target sparseness.
Chengdong Dong; Yuanwei Jin; Enyue Lu
2014-03-01
This paper presents an accelerated iterative Landweber method for nonlinear ultrasonic tomographic imaging in a multiple-input multiple-output (MIMO) configuration under a sparsity constraint on the image. The proposed method introduces the emerging MIMO signal processing techniques and target sparseness constraints in the traditional computational imaging field, thus significantly improves the speed of image reconstruction compared with the conventional imaging method while producing high quality images. Using numerical examples, we demonstrate that incorporating prior knowledge about the imaging field such as target sparseness accelerates significantly the convergence of the iterative imaging method, which provides considerable benefits to real-time tomographic imaging applications.
Moving target detection for frequency agility radar by sparse reconstruction
NASA Astrophysics Data System (ADS)
Quan, Yinghui; Li, YaChao; Wu, Yaojun; Ran, Lei; Xing, Mengdao; Liu, Mengqi
2016-09-01
Frequency agility radar, with randomly varied carrier frequency from pulse to pulse, exhibits superior performance compared to the conventional fixed carrier frequency pulse-Doppler radar against the electromagnetic interference. A novel moving target detection (MTD) method is proposed for the estimation of the target's velocity of frequency agility radar based on pulses within a coherent processing interval by using sparse reconstruction. Hardware implementation of orthogonal matching pursuit algorithm is executed on Xilinx Virtex-7 Field Programmable Gata Array (FPGA) to perform sparse optimization. Finally, a series of experiments are performed to evaluate the performance of proposed MTD method for frequency agility radar systems.
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
Assessment criteria for MEG/EEG cortical patch tests
NASA Astrophysics Data System (ADS)
Im, Chang-Hwan; An, Kwang-Ok; Jung, Hyun-Kyo; Kwon, Hyukchan; Lee, Yong-Ho
2003-08-01
To validate newly developed methods or implemented software for magnetoencephalography/electroencephalography (MEG/EEG) source localization problems, many researchers have used human skull phantom experiments or artificially constructed forward data sets. Between the two methods, the use of an artificial data set constructed with forward calculation attains superiority over the use of a human skull phantom in that it is simple to implement, adjust and control various conditions. Nowadays, for the forward calculation, especially for the cortically distributed source models, generating artificial activation patches on a brain cortical surface has been popularized instead of activating some point dipole sources. However, no well-established assessment criterion to validate the reconstructed results quantitatively has yet been introduced. In this paper, we suggest some assessment criteria to compare and validate the various MEG/EEG source localization techniques or implemented software applied to the cortically distributed source model. Four different criteria can be used to measure accuracy, degrees of focalization, noise-robustness, existence of spurious sources and so on. To verify the usefulness of the proposed criteria, four different results from two different noise conditions and two different reconstruction techniques were compared for several patches. The simulated results show that the new criteria can provide us with a reliable index to validate the MEG/EEG source localization techniques.
Models, structure, function: the transformation of cortical signals in the dentate gyrus.
Acsády, László; Káli, Szabolcs
2007-01-01
Our central question is why the hippocampal CA3 region is the only cortical area capable of forming interference-free representations of complex environmental events (episodes), given that apparently all cortical regions have recurrent excitatory circuits with modifiable synapses, the basic substrate for autoassociative memory networks. We review evidence for the radical (but classic) view that a unique transformation of incoming cortical signals by the dentate gyrus and the subsequent faithful transfer of the resulting code by the mossy fibers are absolutely critical for the appropriate association of memory items by CA3 and, in general, for hippocampal function. In particular, at the gate of the hippocampal formation, the dentate gyrus possesses a set of unusual properties, which selectively evolved for the task of code transformation between cortical afferents and the hippocampus. These evolutionarily conserved anatomical features enable the dentate gyrus to translate the noisy signal of the upstream cortical areas into the sparse and specific code of hippocampal formation, which is indispensable for the efficient storage and recall of multiple, multidimensional memory items. To achieve this goal the mossy fiber pathway maximally utilizes the opportunity to differentially regulate its postsynaptic partners. Selective innervation of CA3 pyramidal cells and interneurons by distinct terminal types creates a favorable condition to differentially regulate the short-term and long-term plasticity and the motility of various mossy terminal types. The utility of this highly dynamic system appears to be the frequency-dependent fine-tuning the excitation and inhibition evoked by the large and the small mossy terminals respectively. This will determine exactly which CA3 cell population is active and induces permanent modification in the autoassociational network of the CA3 region.
Xu, Jiansong
2015-01-01
Blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies often report inconsistent findings, probably due to brain properties such as balanced excitation and inhibition and functional heterogeneity. These properties indicate that different neurons in the same voxels may show variable activities including concurrent activation and deactivation, that the relationships between BOLD signal and neural activity (i.e., neurovascular coupling) are complex, and that increased BOLD signal may reflect reduced deactivation, increased activation, or both. The traditional general-linear-model-based-analysis (GLM-BA) is a univariate approach, cannot separate different components of BOLD signal mixtures from the same voxels, and may contribute to inconsistent findings of fMRI. Spatial independent component analysis (sICA) is a multivariate approach, can separate the BOLD signal mixture from each voxel into different source signals and measure each separately, and thus may reconcile previous conflicting findings generated by GLM-BA. We propose that methods capable of separating mixed signals such as sICA should be regularly used for more accurately and completely extracting information embedded in fMRI datasets. PMID:26341939
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
Furchtgott, Leon A; Melton, Samuel; Menon, Vilas; Ramanathan, Sharad
2017-01-01
Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships. DOI: http://dx.doi.org/10.7554/eLife.20488.001 PMID:28296636
Estimation of Cortical Connectivity From EEG Using State-Space Models
Cheung, Bing Leung Patrick; Riedner, Brady; Tononi, Giulio; Van Veen, Barry D.
2010-01-01
A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp recorded EEG. A state equation represents the MVAR model of cortical dynamics while an observation equation describes the physics relating the cortical signals to the measured EEG and the presence of spatially correlated noise. We assume the cortical signals originate from known regions of cortex, but that the spatial distribution of activity within each region is unknown. An expectation maximization algorithm is developed to directly estimate the MVAR model parameters, the spatial activity distribution components, and the spatial covariance matrix of the noise from the measured EEG. Simulation and analysis demonstrate that this integrated approach is less sensitive to noise than two-stage approaches in which the cortical signals are first estimated from EEG measurements, and next an MVAR model is fit to the estimated cortical signals. The method is further demonstrated by estimating conditional Granger causality using EEG data collected while subjects passively watch a movie. PMID:20501341
Estimation of cortical connectivity from EEG using state-space models.
Cheung, Bing Leung Patrick; Riedner, Brady Alexander; Tononi, Giulio; Van Veen, Barry D
2010-09-01
A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp-recorded EEG. A state equation represents the MVAR model of cortical dynamics, while an observation equation describes the physics relating the cortical signals to the measured EEG and the presence of spatially correlated noise. We assume that the cortical signals originate from known regions of cortex, but the spatial distribution of activity within each region is unknown. An expectation-maximization algorithm is developed to directly estimate the MVAR model parameters, the spatial activity distribution components, and the spatial covariance matrix of the noise from the measured EEG. Simulation and analysis demonstrate that this integrated approach is less sensitive to noise than two-stage approaches in which the cortical signals are first estimated from EEG measurements, and next, an MVAR model is fit to the estimated cortical signals. The method is further demonstrated by estimating conditional Granger causality using EEG data collected while subjects passively watch a movie.
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.
Highly optimized tolerance and power laws in dense and sparse resource regimes.
Manning, M; Carlson, J M; Doyle, J
2005-07-01
Power law cumulative frequency (P) versus event size (l) distributions P > or =l) approximately l(-alpha) are frequently cited as evidence for complexity and serve as a starting point for linking theoretical models and mechanisms with observed data. Systems exhibiting this behavior present fundamental mathematical challenges in probability and statistics. The broad span of length and time scales associated with heavy tailed processes often require special sensitivity to distinctions between discrete and continuous phenomena. A discrete highly optimized tolerance (HOT) model, referred to as the probability, loss, resource (PLR) model, gives the exponent alpha=1/d as a function of the dimension d of the underlying substrate in the sparse resource regime. This agrees well with data for wildfires, web file sizes, and electric power outages. However, another HOT model, based on a continuous (dense) distribution of resources, predicts alpha=1+1/d . In this paper we describe and analyze a third model, the cuts model, which exhibits both behaviors but in different regimes. We use the cuts model to show all three models agree in the dense resource limit. In the sparse resource regime, the continuum model breaks down, but in this case, the cuts and PLR models are described by the same exponent.
Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.
Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen
2016-07-27
Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.
Cortical thickness gradients in structural hierarchies
Wagstyl, Konrad; Ronan, Lisa; Goodyer, Ian M.; Fletcher, Paul C.
2015-01-01
MRI, enabling in vivo analysis of cortical morphology, offers a powerful tool in the assessment of brain development and pathology. One of the most ubiquitous measures used—the thickness of the cortex—shows abnormalities in a number of diseases and conditions, but the functional and biological correlates of such alterations are unclear. If the functional connotations of structural MRI measures are to be understood, we must strive to clarify the relationship between measures such as cortical thickness and their cytoarchitectural determinants. We therefore sought to determine whether patterns of cortical thickness mirror a key motif of the cortex, specifically its structural hierarchical organisation. We delineated three sensory hierarchies (visual, somatosensory and auditory) in two species—macaque and human—and explored whether cortical thickness was correlated with specific cytoarchitectural characteristics. Importantly, we controlled for cortical folding which impacts upon thickness and may obscure regional differences. Our results suggest that an easily measurable macroscopic brain parameter, namely, cortical thickness, is systematically related to cytoarchitecture and to the structural hierarchical organisation of the cortex. We argue that the measurement of cortical thickness gradients may become an important way to develop our understanding of brain structure–function relationships. The identification of alterations in such gradients may complement the observation of regionally localised cortical thickness changes in our understanding of normal development and neuropsychiatric illnesses. PMID:25725468
Familiarity with Speech Affects Cortical Processing of Auditory Distance Cues and Increases Acuity
Wisniewski, Matthew G.; Mercado, Eduardo; Gramann, Klaus; Makeig, Scott
2012-01-01
Several acoustic cues contribute to auditory distance estimation. Nonacoustic cues, including familiarity, may also play a role. We tested participants’ ability to distinguish the distances of acoustically similar sounds that differed in familiarity. Participants were better able to judge the distances of familiar sounds. Electroencephalographic (EEG) recordings collected while participants performed this auditory distance judgment task revealed that several cortical regions responded in different ways depending on sound familiarity. Surprisingly, these differences were observed in auditory cortical regions as well as other cortical regions distributed throughout both hemispheres. These data suggest that learning about subtle, distance-dependent variations in complex speech sounds involves processing in a broad cortical network that contributes both to speech recognition and to how spatial information is extracted from speech. PMID:22911734
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
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
Cortical Odor Processing in Health and Disease
Wilson, Donald A.; Xu, Wenjin; Sadrian, Benjamin; Courtiol, Emmanuelle; Cohen, Yaniv; Barnes, Dylan C.
2014-01-01
The olfactory system has a rich cortical representation, including a large archicortical component present in most vertebrates, and in mammals neocortical components including the entorhinal and orbitofrontal cortices. Together, these cortical components contribute to normal odor perception and memory. They help transform the physicochemical features of volatile molecules inhaled or exhaled through the nose into the perception of odor objects with rich associative and hedonic aspects. This chapter focuses on how olfactory cortical areas contribute to odor perception and begins to explore why odor perception is so sensitive to disease and pathology. Odor perception is disrupted by a wide range of disorders including Alzheimer’s disease, Parkinson’s disease, schizophrenia, depression, autism, and early life exposure to toxins. This olfactory deficit often occurs despite maintained functioning in other sensory systems. Does the unusual network of olfactory cortical structures contribute to this sensitivity? PMID:24767487
Sparse feature fidelity for perceptual image quality assessment.
Chang, Hua-Wen; Yang, Hua; Gan, Yong; Wang, Ming-Hui
2013-10-01
The prediction of an image quality metric (IQM) should be consistent with subjective human evaluation. As the human visual system (HVS) is critical to visual perception, modeling of the HVS is regarded as the most suitable way to achieve perceptual quality predictions. Sparse coding that is equivalent to independent component analysis (ICA) can provide a very good description of the receptive fields of simple cells in the primary visual cortex, which is the most important part of the HVS. With this inspiration, a quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA) on the basis of transformation of images into sparse representations in the primary visual cortex. The proposed method is based on the sparse features that are acquired by a feature detector, which is trained on samples of natural images by an ICA algorithm. In addition, two strategies are designed to simulate the properties of the visual perception: 1) visual attention and 2) visual threshold. The computation of SFF has two stages: training and fidelity computation, in addition, the fidelity computation consists of two components: feature similarity and luminance correlation. The feature similarity measures the structure differences between the two images, whereas the luminance correlation evaluates brightness distortions. SFF also reflects the chromatic properties of the HVS, and it is very effective for color IQA. The experimental results on five image databases show that SFF has a better performance in matching subjective ratings compared with the leading IQMs.
Stable Restoration and Separation of Approximately Sparse Signals
2011-07-02
dictionary. Particular applications covered by our framework include the restoration of signals impaired by impulse noise , narrowband interference, or...representation in a second general dictionary. Particular applications covered by our framework include the restoration of signals impaired by impulse noise ...applications (see [1–17] and references therein), including: • Impulse noise : The recovery of approximately sparse signals corrupted by impulse noise [13
Robust Methods for Sensing and Reconstructing Sparse Signals
ERIC Educational Resources Information Center
Carrillo, Rafael E.
2012-01-01
Compressed sensing (CS) is an emerging signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are…
Feature-Enhanced, Model-Based Sparse Aperture Imaging
2008-03-01
imaging, anisotropy characterization, feature-enhanced imaging, inverse problems, superresolution , anisotropy, sparse signal representation, overcomplete...number of such activities ourselves, and we provide very brief information on some of them here. We have developed a superresolution technique for...enhanced, superresolution image reconstruction. This framework provides a number of desirable features including preservation of anisotropic scatterers
Remote sensing image fusion via wavelet transform and sparse representation
NASA Astrophysics Data System (ADS)
Cheng, Jian; Liu, Haijun; Liu, Ting; Wang, Feng; Li, Hongsheng
2015-06-01
In this paper, we propose a remote sensing image fusion method which combines the wavelet transform and sparse representation to obtain fusion images with high spectral resolution and high spatial resolution. Firstly, intensity-hue-saturation (IHS) transform is applied to Multi-Spectral (MS) images. Then, wavelet transform is used to the intensity component of MS images and the Panchromatic (Pan) image to construct the multi-scale representation respectively. With the multi-scale representation, different fusion strategies are taken on the low-frequency and the high-frequency sub-images. Sparse representation with training dictionary is introduced into the low-frequency sub-image fusion. The fusion rule for the sparse representation coefficients of the low-frequency sub-images is defined by the spatial frequency maximum. For high-frequency sub-images with prolific detail information, the fusion rule is established by the images information fusion measurement indicator. Finally, the fused results are obtained through inverse wavelet transform and inverse IHS transform. The wavelet transform has the ability to extract the spectral information and the global spatial details from the original pairwise images, while sparse representation can extract the local structures of images effectively. Therefore, our proposed fusion method can well preserve the spectral information and the spatial detail information of the original images. The experimental results on the remote sensing images have demonstrated that our proposed method could well maintain the spectral characteristics of fusion images with a high spatial resolution.
Immunogenicity is preferentially induced in sparse dendritic cell cultures
Nasi, Aikaterini; Bollampalli, Vishnu Priya; Sun, Meng; Chen, Yang; Amu, Sylvie; Nylén, Susanne; Eidsmo, Liv; Rothfuchs, Antonio Gigliotti; Réthi, Bence
2017-01-01
We have previously shown that human monocyte-derived dendritic cells (DCs) acquired different characteristics in dense or sparse cell cultures. Sparsity promoted the development of IL-12 producing migratory DCs, whereas dense cultures increased IL-10 production. Here we analysed whether the density-dependent endogenous breaks could modulate DC-based vaccines. Using murine bone marrow-derived DC models we show that sparse cultures were essential to achieve several key functions required for immunogenic DC vaccines, including mobility to draining lymph nodes, recruitment and massive proliferation of antigen-specific CD4+ T cells, in addition to their TH1 polarization. Transcription analyses confirmed higher commitment in sparse cultures towards T cell activation, whereas DCs obtained from dense cultures up-regulated immunosuppressive pathway components and genes suggesting higher differentiation plasticity towards osteoclasts. Interestingly, we detected a striking up-regulation of fatty acid and cholesterol biosynthesis pathways in sparse cultures, suggesting an important link between DC immunogenicity and lipid homeostasis regulation. PMID:28276533
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...
Dictionary Learning and Sparse Coding for Unsupervised Clustering
2009-09-01
the fu