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Sample records for cortical neural ensembles

  1. Imprinting and recalling cortical ensembles.

    Science.gov (United States)

    Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S; Yuste, Rafael

    2016-08-12

    Neuronal ensembles are coactive groups of neurons that may represent building blocks of cortical circuits. These ensembles could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from ensembles in the visual cortex of awake mice builds neuronal ensembles that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted ensembles can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. Copyright © 2016, American Association for the Advancement of Science.

  2. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  3. Cortical Neural Computation by Discrete Results Hypothesis

    Science.gov (United States)

    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

  4. Altered Cortical Ensembles in Mouse Models of Schizophrenia.

    Science.gov (United States)

    Hamm, Jordan P; Peterka, Darcy S; Gogos, Joseph A; Yuste, Rafael

    2017-04-05

    In schizophrenia, brain-wide alterations have been identified at the molecular and cellular levels, yet how these phenomena affect cortical circuit activity remains unclear. We studied two mouse models of schizophrenia-relevant disease processes: chronic ketamine (KET) administration and Df(16)A(+/-), modeling 22q11.2 microdeletions, a genetic variant highly penetrant for schizophrenia. Local field potential recordings in visual cortex confirmed gamma-band abnormalities similar to patient studies. Two-photon calcium imaging of local cortical populations revealed in both models a deficit in the reliability of neuronal coactivity patterns (ensembles), which was not a simple consequence of altered single-neuron activity. This effect was present in ongoing and sensory-evoked activity and was not replicated by acute ketamine administration or pharmacogenetic parvalbumin-interneuron suppression. These results are consistent with the hypothesis that schizophrenia is an "attractor" disease and demonstrate that degraded neuronal ensembles are a common consequence of diverse genetic, cellular, and synaptic alterations seen in chronic schizophrenia. Published by Elsevier Inc.

  5. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    Science.gov (United States)

    Ye, Qiang

    2010-01-01

    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  6. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    Science.gov (United States)

    Ye, Qiang

    2010-01-01

    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  7. Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

    Science.gov (United States)

    Staras, Kevin

    2016-01-01

    We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture. PMID:27760125

  8. Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

    Science.gov (United States)

    Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

    2016-10-01

    We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

  9. Studying the role of synchronized and chaotic spiking neural ensembles in neural information processing.

    Science.gov (United States)

    Rosselló, Josep L; Canals, Vicens; Oliver, Antoni; Morro, Antoni

    2014-08-01

    The brain is characterized by performing many diverse processing tasks ranging from elaborate processes such as pattern recognition, memory or decision making to more simple functionalities such as linear filtering in image processing. Understanding the mechanisms by which the brain is able to produce such a different range of cortical operations remains a fundamental problem in neuroscience. Here we show a study about which processes are related to chaotic and synchronized states based on the study of in-silico implementation of Stochastic Spiking Neural Networks (SSNN). The measurements obtained reveal that chaotic neural ensembles are excellent transmission and convolution systems since mutual information between signals is minimized. At the same time, synchronized cells (that can be understood as ordered states of the brain) can be associated to more complex nonlinear computations. In this sense, we experimentally show that complex and quick pattern recognition processes arise when both synchronized and chaotic states are mixed. These measurements are in accordance with in vivo observations related to the role of neural synchrony in pattern recognition and to the speed of the real biological process. We also suggest that the high-level adaptive mechanisms of the brain that are the Hebbian and non-Hebbian learning rules can be understood as processes devoted to generate the appropriate clustering of both synchronized and chaotic ensembles. The measurements obtained from the hardware implementation of different types of neural systems suggest that the brain processing can be governed by the superposition of these two complementary states with complementary functionalities (nonlinear processing for synchronized states and information convolution and parallelization for chaotic).

  10. Regularized negative correlation learning for neural network ensembles.

    Science.gov (United States)

    Chen, Huanhuan; Yao, Xin

    2009-12-01

    Negative correlation learning (NCL) is a neural network ensemble learning algorithm that introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean square error (MSE) together with the correlation of the ensemble. This paper analyzes NCL and reveals that the training of NCL (when lambda = 1) corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This analysis explains the reason why NCL is prone to overfitting the noise in the training set. This paper also demonstrates that tuning the correlation parameter lambda in NCL by cross validation cannot overcome the overfitting problem. The paper analyzes this problem and proposes the regularized negative correlation learning (RNCL) algorithm which incorporates an additional regularization term for the whole ensemble. RNCL decomposes the ensemble's training objectives, including MSE and regularization, into a set of sub-objectives, and each sub-objective is implemented by an individual neural network. In this paper, we also provide a Bayesian interpretation for RNCL and provide an automatic algorithm to optimize regularization parameters based on Bayesian inference. The RNCL formulation is applicable to any nonlinear estimator minimizing the MSE. The experiments on synthetic as well as real-world data sets demonstrate that RNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set.

  11. Reprogramming movements: Extraction of motor intentions from cortical ensemble activity when movement goals change

    Directory of Open Access Journals (Sweden)

    Peter James Ifft

    2012-07-01

    Full Text Available The ability to inhibit unwanted movements and change motor plans is essential for behaviors of advanced organisms. The neural mechanisms by which the primate motor system rejects undesired actions have received much attention during the last decade, but it is not well understood how this neural function could be utilized to improve the efficiency of brain-machine interfaces (BMIs. Here we employed linear discriminant analysis (LDA and a Wiener filter to extract motor plan transitions from the activity of ensembles of sensorimotor cortex neurons. Two rhesus monkeys, chronically implanted with multielectrode arrays in primary motor (M1 and primary sensory (S1 cortices, were overtrained to produce reaching movements with a joystick towards visual targets upon their presentation. Then, the behavioral task was modified to include a distracting target that flashed for 50, 150 or 250 ms (25% of trials each followed by the true target that appeared at a different screen location. In the remaining 25% of trials, the initial target stayed on the screen and was the target to be approached. M1 and S1 neuronal activity represented both the true and distracting targets, even for the shortest duration of the distracting event. This dual representation persisted both when the monkey initiated movements towards the distracting target and then made corrections and when they moved directly towards the second, true target. The Wiener filter effectively decoded the location of the true target, whereas the LDA classifier extracted the location of both targets from ensembles of 50-250 neurons. Based on these results, we suggest developing real-time BMIs that inhibit unwanted movements represented by brain activity while enacting the desired motor outcome concomitantly.

  12. Dual roles for spike signaling in cortical neural populations

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    Dana eBallard

    2011-06-01

    Full Text Available A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning post-stimulus histograms and exponential interval histograms. In addition it makes testable predictions that follow from the γ latency coding.

  13. Viability of dielectrophoretically trapped neural cortical cells in culture

    NARCIS (Netherlands)

    Heida, T.; Vulto, P.; Rutten, W.L.C.; Marani, E.

    2001-01-01

    Negative dielectrophoretic trapping of neural cells is an efficient way to position neural cells on the electrode sites of planar micro-electrode arrays. The preservation of viability of the neural cells is essential for this approach. This study investigates the viability of postnatal cortical rat

  14. An Ensemble of Neural Networks for Stock Trading Decision Making

    Science.gov (United States)

    Chang, Pei-Chann; Liu, Chen-Hao; Fan, Chin-Yuan; Lin, Jun-Lin; Lai, Chih-Ming

    Stock turning signals detection are very interesting subject arising in numerous financial and economic planning problems. In this paper, Ensemble Neural Network system with Intelligent Piecewise Linear Representation for stock turning points detection is presented. The Intelligent piecewise linear representation method is able to generate numerous stocks turning signals from the historic data base, then Ensemble Neural Network system will be applied to train the pattern and retrieve similar stock price patterns from historic data for training. These turning signals represent short-term and long-term trading signals for selling or buying stocks from the market which are applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the hybrid system can make a significant and constant amount of profit when compared with other approaches using stock data available in the market.

  15. Improved Discriminability of Spatiotemporal Neural Patterns in Rat Motor Cortical Areas as Directional Choice Learning Progresses

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    Hongwei eMao

    2015-03-01

    Full Text Available Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2-3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats’ behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task.

  16. Extracting the Neural Representation of Tone Onsets for Separate Voices of Ensemble Music Using Multivariate EEG Analysis

    DEFF Research Database (Denmark)

    Sturm, Irene; Treder, Matthias S.; Miklody, Daniel

    2015-01-01

    When listening to ensemble music even non-musicians can follow single instruments effortlessly. Electrophysiological indices for neural sensory encoding of separate streams have been described using oddball paradigms which utilize brain reactions to sound events that deviate from a repeating...... standard pattern. Obviously, these paradigms put constraints on the compositional complexity of the musical stimulus. Here, we apply a regression-based method of multivariate EEG analysis in order to reveal the neural encoding of separate voices of naturalistic ensemble music that is based on cortical...... responses to tone onsets, such as N1/P2 ERP components. Music clips (resembling minimalistic electro-pop) were presented to 11 subjects, either in an ensemble version (drums, bass, keyboard) or in the corresponding three solo versions. For each instrument we train a spatio-temporal regression filter...

  17. Characterization of Early Cortical Neural Network ...

    Science.gov (United States)

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentially absent on DIV 2 and developed rapidly between DIV 5 and 12. Spiking activity was primarily sporadic and unorganized at early DIV, and became progressively more organized with time in culture, with bursting parameters, synchrony and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity and principal components analysis using these features demonstrated a general segregation of data by age at both the well and plate levels. Using a combination of random forest classifiers and Support Vector Machines, we demonstrated that 4 features (CV of within burst ISI, CV of IBI, network spike rate and burst rate) were sufficient to predict the age (either DIV 5, 7, 9 or 12) of each well recording with >65% accuracy. When restricting the classification problem to a binary decision, we found that classification improved dramatically, e.g. 95% accuracy for discriminating DIV 5 vs DIV 12 wells. Further, we present a novel resampling approach to determine the number of wells that might be needed for conducting comparisons of different treatments using mwMEA plates. Overall, these results demonstrate that network development on mwMEA plates is similar to

  18. A cortical neural prosthesis for restoring and enhancing memory

    Science.gov (United States)

    Berger, Theodore W.; Hampson, Robert E.; Song, Dong; Goonawardena, Anushka; Marmarelis, Vasilis Z.; Deadwyler, Sam A.

    2011-08-01

    A primary objective in developing a neural prosthesis is to replace neural circuitry in the brain that no longer functions appropriately. Such a goal requires artificial reconstruction of neuron-to-neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. In this study, the application of a specially designed neural prosthesis using a multi-input/multi-output (MIMO) nonlinear model is demonstrated by using trains of electrical stimulation pulses to substitute for MIMO model derived ensemble firing patterns. Ensembles of CA3 and CA1 hippocampal neurons, recorded from rats performing a delayed-nonmatch-to-sample (DNMS) memory task, exhibited successful encoding of trial-specific sample lever information in the form of different spatiotemporal firing patterns. MIMO patterns, identified online and in real-time, were employed within a closed-loop behavioral paradigm. Results showed that the model was able to predict successful performance on the same trial. Also, MIMO model-derived patterns, delivered as electrical stimulation to the same electrodes, improved performance under normal testing conditions and, more importantly, were capable of recovering performance when delivered to animals with ensemble hippocampal activity compromised by pharmacologic blockade of synaptic transmission. These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.

  19. Is the Cortical Deficit in Amblyopia Due to Reduced Cortical Magnification, Loss of Neural Resolution, or Neural Disorganization?

    NARCIS (Netherlands)

    Clavagnier, Simon; Dumoulin, S.O.|info:eu-repo/dai/nl/314406514; Hess, Robert F.

    2015-01-01

    The neural basis of amblyopia is a matter of debate. The following possibilities have been suggested: loss of foveal cells, reduced cortical magnification, loss of spatial resolution of foveal cells, and topographical disarray in the cellular map. To resolve this we undertook a population receptive

  20. Is the Cortical Deficit in Amblyopia Due to Reduced Cortical Magnification, Loss of Neural Resolution, or Neural Disorganization?

    NARCIS (Netherlands)

    Clavagnier, Simon; Dumoulin, S.O.; Hess, Robert F.

    2015-01-01

    The neural basis of amblyopia is a matter of debate. The following possibilities have been suggested: loss of foveal cells, reduced cortical magnification, loss of spatial resolution of foveal cells, and topographical disarray in the cellular map. To resolve this we undertook a population receptive

  1. Neural Network Ensemble Residual Kriging Application for Spatial Variability of Soil Properties

    Institute of Scientific and Technical Information of China (English)

    SHEN Zhang-Quan; SHI Jie-Bin; WANG Ke; KONG Fan-Sheng; J. S. BAILEY

    2004-01-01

    High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN)ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area.

  2. An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.

    Science.gov (United States)

    Ranganayaki, V; Deepa, S N

    2016-01-01

    Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

  3. Stable propagation of synchronous spiking in cortical neural networks

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    Diesmann, Markus; Gewaltig, Marc-Oliver; Aertsen, Ad

    1999-12-01

    The classical view of neural coding has emphasized the importance of information carried by the rate at which neurons discharge action potentials. More recent proposals that information may be carried by precise spike timing have been challenged by the assumption that these neurons operate in a noisy fashion-presumably reflecting fluctuations in synaptic input-and, thus, incapable of transmitting signals with millisecond fidelity. Here we show that precisely synchronized action potentials can propagate within a model of cortical network activity that recapitulates many of the features of biological systems. An attractor, yielding a stable spiking precision in the (sub)millisecond range, governs the dynamics of synchronization. Our results indicate that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is possible within a cortical-like network.

  4. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancer diseases is challenging job in biomedical data engineering. The improving of classification of gene selection of cancer diseases various classifier are used, but the classification of classifier are not validate. So ensemble classifier is used for cancer gene classification using neural network classifier with random forest tree. The random forest tree is ensembling technique of classifier in this technique the number of classifier ensemble of their leaf node of class of classifier. In this paper we combined neural network with random forest ensemble classifier for classification of cancer gene selection for diagnose analysis of cancer diseases. The proposed method is different from most of the methods of ensemble classifier, which follow an input output paradigm of neural network, where the members of the ensemble are selected from a set of neural network classifier. the number of classifiers is determined during the rising procedure of the forest. Furthermore, the proposed method produces an ensemble not only correct, but also assorted, ensuring the two important properties that should characterize an ensemble classifier. For empirical evaluation of our proposed method we used UCI cancer diseases data set for classification. Our experimental result shows that better result in compression of random forest tree classification.

  5. Employing Neocognitron Neural Network Base Ensemble Classifiers To Enhance Efficiency Of Classification In Handwritten Digit Datasets

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    Neera Saxena

    2011-07-01

    Full Text Available This paper presents an ensemble of neo-cognitron neural network base classifiers to enhance the accuracy of the system, along the experimental results. The method offers lesser computational preprocessing in comparison to other ensemble techniques as it ex-preempts feature extraction process before feeding the data into base classifiers. This is achieved by the basic nature of neo-cognitron, it is a multilayer feed-forward neural network. Ensemble of such base classifiers gives class labels for each pattern that in turn is combined to give the final class label for that pattern. The purpose of this paper is not only to exemplify learning behaviour of neo-cognitron as base classifiers, but also to purport better fashion to combine neural network based ensemble classifiers.

  6. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    Science.gov (United States)

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  7. Neural synchrony in cortical networks: history, concept and current status

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    Peter Uhlhaas

    2009-07-01

    Full Text Available Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies.

  8. Degraded attentional modulation of cortical neural populations in strabismic amblyopia

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    Hou, Chuan; Kim, Yee-Joon; Lai, Xin Jie; Verghese, Preeti

    2016-01-01

    Behavioral studies have reported reduced spatial attention in amblyopia, a developmental disorder of spatial vision. However, the neural populations in the visual cortex linked with these behavioral spatial attention deficits have not been identified. Here, we use functional MRI–informed electroencephalography source imaging to measure the effect of attention on neural population activity in the visual cortex of human adult strabismic amblyopes who were stereoblind. We show that compared with controls, the modulatory effects of selective visual attention on the input from the amblyopic eye are substantially reduced in the primary visual cortex (V1) as well as in extrastriate visual areas hV4 and hMT+. Degraded attentional modulation is also found in the normal-acuity fellow eye in areas hV4 and hMT+ but not in V1. These results provide electrophysiological evidence that abnormal binocular input during a developmental critical period may impact cortical connections between the visual cortex and higher level cortices beyond the known amblyopic losses in V1 and V2, suggesting that a deficit of attentional modulation in the visual cortex is an important component of the functional impairment in amblyopia. Furthermore, we find that degraded attentional modulation in V1 is correlated with the magnitude of interocular suppression and the depth of amblyopia. These results support the view that the visual suppression often seen in strabismic amblyopia might be a form of attentional neglect of the visual input to the amblyopic eye. PMID:26885628

  9. An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines.

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    Amozegar, M; Khorasani, K

    2016-04-01

    In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies.

  10. Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

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    Manikumari, N.; Murugappan, A.; Vinodhini, G.

    2017-07-01

    Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

  11. Is the Cortical Deficit in Amblyopia Due to Reduced Cortical Magnification, Loss of Neural Resolution, or Neural Disorganization?

    Science.gov (United States)

    Clavagnier, Simon; Dumoulin, Serge O; Hess, Robert F

    2015-11-04

    The neural basis of amblyopia is a matter of debate. The following possibilities have been suggested: loss of foveal cells, reduced cortical magnification, loss of spatial resolution of foveal cells, and topographical disarray in the cellular map. To resolve this we undertook a population receptive field (pRF) functional magnetic resonance imaging analysis in the central field in humans with moderate-to-severe amblyopia. We measured the relationship between averaged pRF size and retinal eccentricity in retinotopic visual areas. Results showed that cortical magnification is normal in the foveal field of strabismic amblyopes. However, the pRF sizes are enlarged for the amblyopic eye. We speculate that the pRF enlargement reflects loss of cellular resolution or an increased cellular positional disarray within the representation of the amblyopic eye. The neural basis of amblyopia, a visual deficit affecting 3% of the human population, remains a matter of debate. We undertook the first population receptive field functional magnetic resonance imaging analysis in participants with amblyopia and compared the projections from the amblyopic and fellow normal eye in the visual cortex. The projection from the amblyopic eye was found to have a normal cortical magnification factor, enlarged population receptive field sizes, and topographic disorganization in all early visual areas. This is consistent with an explanation of amblyopia as an immature system with a normal complement of cells whose spatial resolution is reduced and whose topographical map is disordered. This bears upon a number of competing theories for the psychophysical defect and affects future treatment therapies. Copyright © 2015 the authors 0270-6474/15/3514740-16$15.00/0.

  12. An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts

    Directory of Open Access Journals (Sweden)

    Mahmoud Barghash

    2015-01-01

    Full Text Available Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN’s performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

  13. A shared neural ensemble links distinct contextual memories encoded close in time

    Science.gov (United States)

    Cai, Denise J.; Aharoni, Daniel; Shuman, Tristan; Shobe, Justin; Biane, Jeremy; Song, Weilin; Wei, Brandon; Veshkini, Michael; La-Vu, Mimi; Lou, Jerry; Flores, Sergio E.; Kim, Isaac; Sano, Yoshitake; Zhou, Miou; Baumgaertel, Karsten; Lavi, Ayal; Kamata, Masakazu; Tuszynski, Mark; Mayford, Mark; Golshani, Peyman; Silva, Alcino J.

    2016-06-01

    Recent studies suggest that a shared neural ensemble may link distinct memories encoded close in time. According to the memory allocation hypothesis, learning triggers a temporary increase in neuronal excitability that biases the representation of a subsequent memory to the neuronal ensemble encoding the first memory, such that recall of one memory increases the likelihood of recalling the other memory. Here we show in mice that the overlap between the hippocampal CA1 ensembles activated by two distinct contexts acquired within a day is higher than when they are separated by a week. Several findings indicate that this overlap of neuronal ensembles links two contextual memories. First, fear paired with one context is transferred to a neutral context when the two contexts are acquired within a day but not across a week. Second, the first memory strengthens the second memory within a day but not across a week. Older mice, known to have lower CA1 excitability, do not show the overlap between ensembles, the transfer of fear between contexts, or the strengthening of the second memory. Finally, in aged mice, increasing cellular excitability and activating a common ensemble of CA1 neurons during two distinct context exposures rescued the deficit in linking memories. Taken together, these findings demonstrate that contextual memories encoded close in time are linked by directing storage into overlapping ensembles. Alteration of these processes by ageing could affect the temporal structure of memories, thus impairing efficient recall of related information.

  14. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancerdiseasesis challenging job in biomedical dataengineering. The improving of classification of geneselection of cancer diseases various classifier areused, but the classification of classifier are notvalidate. So ensemble classifier is used for cancergene classification using neural network classifierwith random forest tree. The random forest tree isensembling technique of classifier in this techniquethe number of classifier ensemble of their leaf nodeof class of classifier. In this paper we combinedneuralnetwork with random forest ensembleclassifier for classification of cancer gene selectionfor diagnose analysis of cancer diseases.Theproposed method is different from most of themethods of ensemble classifier, which follow aninput output paradigm ofneural network, where themembers of the ensemble are selected from a set ofneural network classifier. the number of classifiersis determined during the rising procedure of theforest. Furthermore, the proposed method producesan ensemble not only correct, but also assorted,ensuring the two important properties that shouldcharacterize an ensemble classifier. For empiricalevaluation of our proposed method we used UCIcancer diseases data set for classification. Ourexperimental result shows that betterresult incompression of random forest tree classification

  15. Genetic feedback regulation of frontal cortical neuronal ensembles through activity-dependent Arc expression and dopaminergic input

    Directory of Open Access Journals (Sweden)

    Surjeet Mastwal

    2016-12-01

    Full Text Available Mental functions involve coordinated activities of specific neuronal ensembles that are embedded in complex brain circuits. Aberrant neuronal ensemble dynamics is thought to form the neurobiological basis of mental disorders. A major challenge in mental health research is to identify these cellular ensembles and determine what molecular mechanisms constrain their emergence and consolidation during development and learning. Here, we provide a perspective based on recent studies that use activity-dependent gene Arc/Arg3.1 as a cellular marker to identify neuronal ensembles and a molecular probe to modulate circuit functions. These studies have demonstrated that the transcription of Arc is activated in selective groups of frontal cortical neurons in response to specific behavioral tasks. Arc expression regulates the persistent firing of individual neurons and predicts the consolidation of neuronal ensembles during repeated learning. Therefore, the Arc pathway represents a prototypical example of activity-dependent genetic feedback regulation of neuronal ensembles. The activation of this pathway in the frontal cortex starts during early postnatal development and requires dopaminergic input. Conversely, genetic disruption of Arc leads to a hypoactive mesofrontal dopamine circuit and its related cognitive deficit. This mutual interaction suggests an auto-regulatory mechanism to amplify the impact of neuromodulators and activity-regulated genes during postnatal development. Such a mechanism may contribute to the association of mutations in dopamine and Arc pathways with neurodevelopmental psychiatric disorders. As the mesofrontal dopamine circuit shows extensive activity-dependent developmental plasticity, activity-guided modulation of dopaminergic projections or Arc ensembles during development may help to repair circuit deficits related to neuropsychiatric disorders.

  16. Rule Based Ensembles Using Pair Wise Neural Network Classifiers

    Directory of Open Access Journals (Sweden)

    Moslem Mohammadi Jenghara

    2015-03-01

    Full Text Available In value estimation, the inexperienced people's estimation average is good approximation to true value, provided that the answer of these individual are independent. Classifier ensemble is the implementation of mentioned principle in classification tasks that are investigated in two aspects. In the first aspect, feature space is divided into several local regions and each region is assigned with a highly competent classifier and in the second, the base classifiers are applied in parallel and equally experienced in some ways to achieve a group consensus. In this paper combination of two methods are used. An important consideration in classifier combination is that much better results can be achieved if diverse classifiers, rather than similar classifiers, are combined. To achieve diversity in classifiers output, the symmetric pairwise weighted feature space is used and the outputs of trained classifiers over the weighted feature space are combined to inference final result. In this paper MLP classifiers are used as the base classifiers. The Experimental results show that the applied method is promising.

  17. An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation

    DEFF Research Database (Denmark)

    Lyksborg, Mark; Puonti, Oula; Agn, Mikael

    2015-01-01

    Accurate tumor segmentation plays an important role in radiosurgery planning and the assessment of radiotherapy treatment efficacy. In this paper we propose a method combining an ensemble of 2D convolutional neural networks for doing a volumetric segmentation of magnetic resonance images....... The segmentation is done in three steps; first the full tumor region, is segmented from the background by a voxel-wise merging of the decisions of three networks learned from three orthogonal planes, next the segmentation is refined using a cellular automaton-based seed growing method known as growcut. Finally......, within-tumor sub-regions are segmented using an additional ensemble of networks trained for the task. We demonstrate the method on the MICCAI Brain Tumor Segmentation Challenge dataset of 2014, and show improved segmentation accuracy compared to an axially trained 2D network and an ensemble segmentation...

  18. A Paper Currency Recognition System Using Negatively Correlated Neural Network Ensemble

    Directory of Open Access Journals (Sweden)

    Kalyan Kumar Debnath

    2010-12-01

    Full Text Available This paper presents a currency recognition system using ensemble neural network (ENN. The individual neural networks (NNs in an ENN are trained via negative correlation learning. The objective of using negative correlation learning (NCL is to expertise the individuals on different parts or portion of input patterns in an ensemble. The available currencies in the market consist of new, old and noisy ones. It is often difficult for machine to recognize these currencies; therefore we propose a system that uses ENN to identify them efficiently. We performed our experiment for seven different types of TAKA (Bangladeshi currency which are 2, 5, 10, 20, 50, 100 and 500 TAKA. The image of different types note is converted in gray scale and compressed in the desired range. Each pixel of the compressed image is given as an input to the network. This system is able to recognize highly noisy or old image of TAKA. Ensemble network is very useful for theclassification of different types of currencies. It reduces the chances of misclassification than a single network and ensemble network with independent training. In experimental results we have shown this with artificially added noise in the test set. We also find good result for similar pattern available in market. 

  19. Efficient Pruning Method for Ensemble Self-Generating Neural Networks

    Directory of Open Access Journals (Sweden)

    Hirotaka Inoue

    2003-12-01

    Full Text Available Recently, multiple classifier systems (MCS have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. In this paper, we propose an efficient pruning method for the structure of the SGNN in the MCS. We compare the pruned MCS with two sampling methods. Experiments have been conducted to compare the pruned MCS with an unpruned MCS, the MCS based on C4.5, and k-nearest neighbor method. The results show that the pruned MCS can improve its classification accuracy as well as reducing the computation cost.

  20. Spin-mediated consciousness theory: possible roles of neural membrane nuclear spin ensembles and paramagnetic oxygen.

    Science.gov (United States)

    Hu, Huping; Wu, Maoxin

    2004-01-01

    A novel theory of consciousness is proposed in this paper. We postulate that consciousness is intrinsically connected to quantum spin since the latter is the origin of quantum effects in both Bohm and Hestenes quantum formulism and a fundamental quantum process associated with the structure of space-time. That is, spin is the "mind-pixel". The unity of mind is achieved by entanglement of the mind-pixels. Applying these ideas to the particular structures and dynamics of the brain, we theorize that human brain works as follows: through action potential modulated nuclear spin interactions and paramagnetic O2/NO driven activations, the nuclear spins inside neural membranes and proteins form various entangled quantum states some of which survive decoherence through quantum Zeno effects or in decoherence-free subspaces and then collapse contextually via irreversible and non-computable means producing consciousness and, in turn, the collective spin dynamics associated with said collapses have effects through spin chemistry on classical neural activities thus influencing the neural networks of the brain. Our proposal calls for extension of associative encoding of neural memories to the dynamical structures of neural membranes and proteins. Thus, according our theory, the nuclear spin ensembles are the "mind-screen" with nuclear spins as its pixels, the neural membranes and proteins are the mind-screen and memory matrices, and the biologically available paramagnetic species such as O2 and NO are pixel-activating agents. Together, they form the neural substrates of consciousness. We also present supporting evidence and make important predictions. We stress that our theory is experimentally verifiable with present technologies. Further, experimental realizations of intra-/inter-molecular nuclear spin coherence and entanglement, macroscopic entanglement of spin ensembles and NMR quantum computation, all in room temperatures, strongly suggest the possibility of a spin

  1. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

    Science.gov (United States)

    Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

    2014-01-01

    Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

  2. Neural correlates of cognitive impairment in posterior cortical atrophy.

    Science.gov (United States)

    Kas, Aurélie; de Souza, Leonardo Cruz; Samri, Dalila; Bartolomeo, Paolo; Lacomblez, Lucette; Kalafat, Michel; Migliaccio, Raffaella; Thiebaut de Schotten, Michel; Cohen, Laurent; Dubois, Bruno; Habert, Marie-Odile; Sarazin, Marie

    2011-05-01

    With the prospect of disease-modifying drugs that will target the physiopathological process of Alzheimer's disease, it is now crucial to increase the understanding of the atypical focal presentations of Alzheimer's disease, such as posterior cortical atrophy. This study aimed to (i) characterize the brain perfusion profile in posterior cortical atrophy using regions of interest and a voxel-based approach; (ii) study the influence of the disease duration on the clinical and imaging profiles; and (iii) explore the correlations between brain perfusion and cognitive deficits. Thirty-nine patients with posterior cortical atrophy underwent a specific battery of neuropsychological tests, mainly targeting visuospatial functions, and a brain perfusion scintigraphy with 99mTc-ethyl cysteinate dimer. The imaging analysis included a comparison with a group of 24 patients with Alzheimer's disease, matched for age, disease duration and Mini-Mental State Examination, and 24 healthy controls. The single-photon emission computed tomography profile in patients with posterior cortical atrophy was characterized by extensive and severe hypoperfusion in the occipital, parietal, posterior temporal cortices and in a smaller cortical area corresponding to the frontal eye fields (Brodmann areas 6/8). Compared with patients with Alzheimer's disease, the group with posterior cortical atrophy showed more severe occipitoparietal hypoperfusion and higher perfusion in the frontal, anterior cingulate and mesiotemporal regions. When considering the disease duration, the functional changes began and remained centred on the posterior lobes, even in the late stage. Correlation analyses of brain perfusion and neuropsychological scores in posterior cortical atrophy highlighted the prominent role of left inferior parietal damage in acalculia, Gerstmann's syndrome, left-right indistinction and limb apraxia, whereas damage to the bilateral dorsal occipitoparietal regions appeared to be involved in B

  3. Field-programmable gate array implementation of a probabilistic neural network for motor cortical decoding in rats.

    Science.gov (United States)

    Zhou, Fan; Liu, Jun; Yu, Yi; Tian, Xiang; Liu, Hui; Hao, Yaoyao; Zhang, Shaomin; Chen, Weidong; Dai, Jianhua; Zheng, Xiaoxiang

    2010-01-15

    A practical brain-machine interface (BMI) requires real-time decoding algorithms to be realised in a portable device rather than a personal computer. In this article, a field-programmable gate array (FPGA) implementation of a probabilistic neural network (PNN) is proposed and developed to decode motor cortical ensemble recordings in rats performing a lever-pressing task for water rewards. A chronic 16-channel microelectrode array was implanted into the primary motor cortex of the rat to record neural activity, and the pressure signal of the lever were recorded simultaneously. To decode the pressure value from neural activity, both Matlab-based and FPGA-based mapping algorithms using a PNN were implemented and evaluated. In the FPGA architecture, training data of the network were stored in random access memory (RAM) blocks and multiply-add operations were realised by on-chip DSP48E slices. In the approximation of the activation function, a Taylor series and a look-up table (LUT) are used to achieve an accurate approximation. The results of FPGA implementation are as accurate as the realisation of Matlab, but the running speed is 37.9 times faster. This novel and feasible method indicates that the performance of current FPGAs is competent for portable BMI applications.

  4. From Designing A Single Neural Network to Designing Neural Network Ensembles

    Institute of Scientific and Technical Information of China (English)

    Liu Yong; Zou Xiu-fer

    2003-01-01

    This paper introduces supervised learning model,and surveys related research work. The paper is organised as follows. A supervised learning model is firstly described. The bias variance trade-off is then discussed for the supervised learning model. Based on the bias variance trade-off, both the single neural network approaches and the neural network en semble approaches are overviewed, and problems with the existing approaches are indicated. Finally, the paper concludes with specifying potential future research directions.

  5. A CAD system to analyse mammogram images using fully complex-valued relaxation neural network ensembled classifier.

    Science.gov (United States)

    Saraswathi, D; Srinivasan, E

    2014-10-01

    This paper presents a new improved classification technique using the Fully Complex-Valued Relaxation Neural Networks (FCRN) based ensemble technique for classifying mammogram images. The system is developed based on three stages of Breast cancer, namely Normal, Benign and Malignant, defined by the MIAS database. Features like Binary object Features, RST Invariant Features, Histogram Features, Texture Features and Spectral Features are extracted from the MIAS database. Extracted features are then given to the proposed FCRN-based ensemble classifier. FCRN networks are ensembled together for improving the classification rate. Receiver Operating Characteristic (ROC) analysis is used for evaluating the system. The results illustrate the superior classification performance of the ensembled FCRN. Performance comparison of various sets of training and testing vectors are provided for FCRN classifier. The resultant ensembled FCRN approximates the desired output more accurately with a lower computational effort.

  6. Ischemia-Induced Neural Stem/Progenitor Cells in the Pia Mater Following Cortical Infarction

    NARCIS (Netherlands)

    Nakagomi, Takayuki; Molnar, Zoltan; Nakano-Doi, Akiko; Taguchi, Akihiko; Saino, Orie; Kubo, Shuji; Clausen, Martijn; Yoshikawa, Hiroo; Nakagomi, Nami; Matsuyama, Tomohiro

    2011-01-01

    Increasing evidence shows that neural stem/ progenitor cells (NSPCs) can be activated in the nonconventional neurogenic zones such as the cortex following ischemic stroke. However, the precise origin, identity, and subtypes of the ischemia-induced NSPCs (iNSPCs), which can contribute to cortical

  7. Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates

    Science.gov (United States)

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentiall...

  8. Network bursts in cortical neuronal cultures: 'noise - versus pacemaker'- driven neural network simulations

    NARCIS (Netherlands)

    Gritsun, T.; Stegenga, J.; le Feber, Jakob; Rutten, Wim

    2009-01-01

    In this paper we address the issue of spontaneous bursting activity in cortical neuronal cultures and explain what might cause this collective behavior using computer simulations of two different neural network models. While the common approach to acivate a passive network is done by introducing

  9. Ischemia-Induced Neural Stem/Progenitor Cells in the Pia Mater Following Cortical Infarction

    NARCIS (Netherlands)

    Nakagomi, Takayuki; Molnar, Zoltan; Nakano-Doi, Akiko; Taguchi, Akihiko; Saino, Orie; Kubo, Shuji; Clausen, Martijn; Yoshikawa, Hiroo; Nakagomi, Nami; Matsuyama, Tomohiro

    2011-01-01

    Increasing evidence shows that neural stem/ progenitor cells (NSPCs) can be activated in the nonconventional neurogenic zones such as the cortex following ischemic stroke. However, the precise origin, identity, and subtypes of the ischemia-induced NSPCs (iNSPCs), which can contribute to cortical neu

  10. Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates

    Science.gov (United States)

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentiall...

  11. Dynamics of cortical neuronal ensembles transit from decision making to storage for later report.

    Science.gov (United States)

    Ponce-Alvarez, Adrián; Nácher, Verónica; Luna, Rogelio; Riehle, Alexa; Romo, Ranulfo

    2012-08-29

    Decisions based on sensory evaluation during single trials may depend on the collective activity of neurons distributed across brain circuits. Previous studies have deepened our understanding of how the activity of individual neurons relates to the formation of a decision and its storage for later report. However, little is known about how decision-making and decision maintenance processes evolve in single trials. We addressed this problem by studying the activity of simultaneously recorded neurons from different somatosensory and frontal lobe cortices of monkeys performing a vibrotactile discrimination task. We used the hidden Markov model to describe the spatiotemporal pattern of activity in single trials as a sequence of firing rate states. We show that the animal's decision was reliably maintained in frontal lobe activity through a selective state sequence, initiated by an abrupt state transition, during which many neurons changed their activity in a concomitant way, and for which both latency and variability depended on task difficulty. Indeed, transitions were more delayed and more variable for difficult trials compared with easy trials. In contrast, state sequences in somatosensory cortices were weakly decision related, had less variable transitions, and were not affected by the difficulty of the task. In summary, our results suggest that the decision process and its subsequent maintenance are dynamically linked by a cascade of transient events in frontal lobe cortices.

  12. Cortical geometry as a determinant of brain activity eigenmodes: Neural field analysis

    Science.gov (United States)

    Gabay, Natasha C.; Robinson, P. A.

    2017-09-01

    Perturbation analysis of neural field theory is used to derive eigenmodes of neural activity on a cortical hemisphere, which have previously been calculated numerically and found to be close analogs of spherical harmonics, despite heavy cortical folding. The present perturbation method treats cortical folding as a first-order perturbation from a spherical geometry. The first nine spatial eigenmodes on a population-averaged cortical hemisphere are derived and compared with previous numerical solutions. These eigenmodes contribute most to brain activity patterns such as those seen in electroencephalography and functional magnetic resonance imaging. The eigenvalues of these eigenmodes are found to agree with the previous numerical solutions to within their uncertainties. Also in agreement with the previous numerics, all eigenmodes are found to closely resemble spherical harmonics. The first seven eigenmodes exhibit a one-to-one correspondence with their numerical counterparts, with overlaps that are close to unity. The next two eigenmodes overlap the corresponding pair of numerical eigenmodes, having been rotated within the subspace spanned by that pair, likely due to second-order effects. The spatial orientations of the eigenmodes are found to be fixed by gross cortical shape rather than finer-scale cortical properties, which is consistent with the observed intersubject consistency of functional connectivity patterns. However, the eigenvalues depend more sensitively on finer-scale cortical structure, implying that the eigenfrequencies and consequent dynamical properties of functional connectivity depend more strongly on details of individual cortical folding. Overall, these results imply that well-established tools from perturbation theory and spherical harmonic analysis can be used to calculate the main properties and dynamics of low-order brain eigenmodes.

  13. Stochastic resonance modulates neural synchronization within and between cortical sources.

    Directory of Open Access Journals (Sweden)

    Lawrence M Ward

    Full Text Available Neural synchronization is a mechanism whereby functionally specific brain regions establish transient networks for perception, cognition, and action. Direct addition of weak noise (fast random fluctuations to various neural systems enhances synchronization through the mechanism of stochastic resonance (SR. Moreover, SR also occurs in human perception, cognition, and action. Perception, cognition, and action are closely correlated with, and may depend upon, synchronized oscillations within specialized brain networks. We tested the hypothesis that SR-mediated neural synchronization occurs within and between functionally relevant brain areas and thus could be responsible for behavioral SR. We measured the 40-Hz transient response of the human auditory cortex to brief pure tones. This response arises when the ongoing, random-phase, 40-Hz activity of a group of tuned neurons in the auditory cortex becomes synchronized in response to the onset of an above-threshold sound at its "preferred" frequency. We presented a stream of near-threshold standard sounds in various levels of added broadband noise and measured subjects' 40-Hz response to the standards in a deviant-detection paradigm using high-density EEG. We used independent component analysis and dipole fitting to locate neural sources of the 40-Hz response in bilateral auditory cortex, left posterior cingulate cortex and left superior frontal gyrus. We found that added noise enhanced the 40-Hz response in all these areas. Moreover, added noise also increased the synchronization between these regions in alpha and gamma frequency bands both during and after the 40-Hz response. Our results demonstrate neural SR in several functionally specific brain regions, including areas not traditionally thought to contribute to the auditory 40-Hz transient response. In addition, we demonstrated SR in the synchronization between these brain regions. Thus, both intra- and inter-regional synchronization of neural

  14. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

    Science.gov (United States)

    Truccolo, Wilson; Eden, Uri T; Fellows, Matthew R; Donoghue, John P; Brown, Emery N

    2005-02-01

    Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.

  15. Estimation of soil saturated hydraulic conductivity by artificial neural networks ensemble in smectitic soils

    Science.gov (United States)

    Sedaghat, A.; Bayat, H.; Safari Sinegani, A. A.

    2016-03-01

    The saturated hydraulic conductivity ( K s ) of the soil is one of the main soil physical properties. Indirect estimation of this parameter using pedo-transfer functions (PTFs) has received considerable attention. The Purpose of this study was to improve the estimation of K s using fractal parameters of particle and micro-aggregate size distributions in smectitic soils. In this study 260 disturbed and undisturbed soil samples were collected from Guilan province, the north of Iran. The fractal model of Bird and Perrier was used to compute the fractal parameters of particle and micro-aggregate size distributions. The PTFs were developed by artificial neural networks (ANNs) ensemble to estimate K s by using available soil data and fractal parameters. There were found significant correlations between K s and fractal parameters of particles and microaggregates. Estimation of K s was improved significantly by using fractal parameters of soil micro-aggregates as predictors. But using geometric mean and geometric standard deviation of particles diameter did not improve K s estimations significantly. Using fractal parameters of particles and micro-aggregates simultaneously, had the most effect in the estimation of K s . Generally, fractal parameters can be successfully used as input parameters to improve the estimation of K s in the PTFs in smectitic soils. As a result, ANNs ensemble successfully correlated the fractal parameters of particles and micro-aggregates to K s .

  16. Flexible Neural Electrode Array Based-on Porous Graphene for Cortical Microstimulation and Sensing

    Science.gov (United States)

    Lu, Yichen; Lyu, Hongming; Richardson, Andrew G.; Lucas, Timothy H.; Kuzum, Duygu

    2016-09-01

    Neural sensing and stimulation have been the backbone of neuroscience research, brain-machine interfaces and clinical neuromodulation therapies for decades. To-date, most of the neural stimulation systems have relied on sharp metal microelectrodes with poor electrochemical properties that induce extensive damage to the tissue and significantly degrade the long-term stability of implantable systems. Here, we demonstrate a flexible cortical microelectrode array based on porous graphene, which is capable of efficient electrophysiological sensing and stimulation from the brain surface, without penetrating into the tissue. Porous graphene electrodes show superior impedance and charge injection characteristics making them ideal for high efficiency cortical sensing and stimulation. They exhibit no physical delamination or degradation even after 1 million biphasic stimulation cycles, confirming high endurance. In in vivo experiments with rodents, same array is used to sense brain activity patterns with high spatio-temporal resolution and to control leg muscles with high-precision electrical stimulation from the cortical surface. Flexible porous graphene array offers a minimally invasive but high efficiency neuromodulation scheme with potential applications in cortical mapping, brain-computer interfaces, treatment of neurological disorders, where high resolution and simultaneous recording and stimulation of neural activity are crucial.

  17. The Age of Cortical Neural Networks Affects Their Interactions with Magnetic Nanoparticles.

    Science.gov (United States)

    Tay, Andy; Kunze, Anja; Jun, Dukwoo; Hoek, Eric; Di Carlo, Dino

    2016-07-01

    Despite increasing use of nanotechnology in neuroscience, the characterization of interactions between magnetic nanoparticles (MNPs) and primary cortical neural networks remains underdeveloped. In particular, how the age of primary neural networks affects MNP uptake and endocytosis is critical when considering MNP-based therapies for age-related diseases. Here, primary cortical neural networks are cultured up to 4 weeks and with CCL11/eotaxin, an age-inducing chemokine, to create aged neural networks. As the neural networks are aged, their association with membrane-bound starch-coated ferromagnetic nanoparticles (fMNPs) increases while their endocytic mechanisms are impaired, resulting in reduced internalization of chitosan-coated fMNPs. The age of the neurons also negates the neuroprotective effects of chitosan coatings on fMNPs, attributing to decreased intracellular trafficking and increased colocalization of MNPs with lysosomes. These findings demonstrate the importance of age and developmental stage of primary neural cells when developing in vitro models for fMNP therapeutics targeting age-related diseases.

  18. A GPU-accelerated cortical neural network model for visually guided robot navigation.

    Science.gov (United States)

    Beyeler, Michael; Oros, Nicolas; Dutt, Nikil; Krichmar, Jeffrey L

    2015-12-01

    Humans and other terrestrial animals use vision to traverse novel cluttered environments with apparent ease. On one hand, although much is known about the behavioral dynamics of steering in humans, it remains unclear how relevant perceptual variables might be represented in the brain. On the other hand, although a wealth of data exists about the neural circuitry that is concerned with the perception of self-motion variables such as the current direction of travel, little research has been devoted to investigating how this neural circuitry may relate to active steering control. Here we present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the position of objects based on motion discontinuities, and combines these signals with the representation of a goal location to produce motor commands that successfully steer the robot around obstacles toward the goal. The model produces robot trajectories that closely match human behavioral data. This study demonstrates how neural signals in a model of cortical area MT might provide sufficient motion information to steer a physical robot on human-like paths around obstacles in a real-world environment, and exemplifies the importance of embodiment, as behavior is deeply coupled not only with the underlying model of brain function, but also with the anatomical constraints of the physical body it controls.

  19. STUDY ON THE METEOROLOGICAL PREDICTION MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS

    Institute of Scientific and Technical Information of China (English)

    WU Jian-sheng; JIN Long

    2009-01-01

    Because of the difficulty in deciding on the structure of BP neural network in operational meteorological application and the tendency tbr the network to transform to an issue of local solution,a hybrid Particle Swarm Optimization Algorithm based on Artificial Neural Network (PSO-BP) model is proposed for monthly mean rainfall of the whole area of Guangxi. It combines Particle Swarm Optimization (PSO) with BP,that is,the number of hidden nodes and connection weights are optimized by the implementation of PSO operation. The method produces a better network architecture and initial connection weights,trains the traditional backward propagation again by training samples. The ensemble strategy is carried out for the linear programming to calculate the best weights based on the "east sum of the error absolute value" as the optimal rule. The weighted coefficient of each ensemble individual is obtained. The results show that the method can effectively improve learning and generalization ability of the neural network.

  20. Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates

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    Chin Kim On

    2016-12-01

    Full Text Available In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP in combination with Ensemble Neural Network (ENN. We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN. This research aims to solve four main issues; (1 localization of car plates that has the same colour with the vehicle colour, (2 detection and recognition of car plates with varying sizes, (3 detection and recognition of car plates with different font types, and (4 detection and recognition of non-standardized car plates. The non-standardized Malaysian’s car plates are different from the normal plate as they contain italic characters, a combination of cursive characters, and different font types. The experimental results show that the combination of backpropagation and ENN can be effectively used to solve these four issues. The combination of BPP and ENN’s algorithm achieved a localization rate of 98% and a 97% in recognition rate. On the other hand, the combination of backpropagation and simple FFNN recorded a 96% recognition rate.

  1. The causal inference of cortical neural networks during music improvisations.

    Science.gov (United States)

    Wan, Xiaogeng; Crüts, Björn; Jensen, Henrik Jeldtoft

    2014-01-01

    We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.

  2. The causal inference of cortical neural networks during music improvisations.

    Directory of Open Access Journals (Sweden)

    Xiaogeng Wan

    Full Text Available We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME, to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.

  3. Democratic organization of the thalamocortical neural ensembles in nociceptive signal processing

    Institute of Scientific and Technical Information of China (English)

    LUO Fei; WANG Jin-Yan

    2008-01-01

    Acute pain is a warning protective sensation for any impending harm. However, chronic pain syndromes are often resistant diseases that may consume large amount of health care costs. It has been suggested by recent studies that pain perception may be formed in central neural networks via large-scale coding processes, which involves sensory, affective, and cognitive dimensions. Many central areas are involved in these processes, including structures from the spinal cord, the brain stem, the limbic system, to the cortices. Thus, chronic painful diseases may be the result of some abnormal coding within this network. A thorough investigation of coding mechanism of pain within the central neuromatrix will bring us great insight into the mechanisms responsible for the development of chronic pain, hence leading to novel therapeutic interventions for pain management.

  4. Temporal coupling between stimulus-evoked neural activity and hemodynamic responses from individual cortical columns

    Energy Technology Data Exchange (ETDEWEB)

    Bruyns-Haylett, Michael; Zheng Ying; Berwick, Jason; Jones, Myles [The Centre for Signal Processing in Neuroimaging and Systems Neuroscience (SPINSN), Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP (United Kingdom)], E-mail: m.jones@sheffield.ac.uk

    2010-04-21

    Using previously published data from the whisker barrel cortex of anesthetized rodents (Berwick et al 2008 J. Neurophysiol. 99 787-98) we investigated whether highly spatially localized stimulus-evoked cortical hemodynamics responses displayed a linear time-invariant (LTI) relationship with neural activity. Presentation of stimuli to individual whiskers of 2 s and 16 s durations produced hemodynamics and neural activity spatially localized to individual cortical columns. Two-dimensional optical imaging spectroscopy (2D-OIS) measured hemoglobin responses, while multi-laminar electrophysiology recorded neural activity. Hemoglobin responses to 2 s stimuli were deconvolved with underlying evoked neural activity to estimate impulse response functions which were then convolved with neural activity evoked by 16 s stimuli to generate predictions of hemodynamic responses. An LTI system more adequately described the temporal neuro-hemodynamics coupling relationship for these spatially localized sensory stimuli than in previous studies that activated the entire whisker cortex. An inability to predict the magnitude of an initial 'peak' in the total and oxy- hemoglobin responses was alleviated when excluding responses influenced by overlying arterial components. However, this did not improve estimation of the hemodynamic responses return to baseline post-stimulus cessation.

  5. Spike phase synchronization in multiplex cortical neural networks

    Science.gov (United States)

    Jalili, Mahdi

    2017-01-01

    In this paper we study synchronizability of two multiplex cortical networks: whole-cortex of hermaphrodite C. elegans and posterior cortex in male C. elegans. These networks are composed of two connection layers: network of chemical synapses and the one formed by gap junctions. This work studies the contribution of each layer on the phase synchronization of non-identical spiking Hindmarsh-Rose neurons. The network of male C. elegans shows higher phase synchronization than its randomized version, while it is not the case for hermaphrodite type. The random networks in each layer are constructed such that the nodes have the same degree as the original network, thus providing an unbiased comparison. In male C. elegans, although the gap junction network is sparser than the chemical network, it shows higher contribution in the synchronization phenomenon. This is not the case in hermaphrodite type, which is mainly due to significant less density of gap junction layer (0.013) as compared to chemical layer (0.028). Also, the gap junction network in this type has stronger community structure than the chemical network, and this is another driving factor for its weaker synchronizability.

  6. Gamma Oscillations and Neural Field DCMs Can Reveal Cortical Excitability and Microstructure

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    Dimitris Pinotsis

    2014-05-01

    Full Text Available This paper shows how gamma oscillations can be combined with neural population models and dynamic causal modeling (DCM to distinguish among alternative hypotheses regarding cortical excitability and microstructure. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. Neural field models are used to evaluate model evidence and obtain parameter estimates using invasive and non-invasive gamma recordings. Our overview comprises two parts: in the first part, we use neural fields to simulate neural activity and distinguish the effects of post synaptic filtering on predicted responses in terms of synaptic rate constants that correspond to different timescales and distinct neurotransmitters. We focus on model predictions of conductance and convolution based field models and show that these can yield spectral responses that are sensitive to biophysical properties of local cortical circuits like synaptic kinetics and filtering; we also consider two different mechanisms for this filtering: a nonlinear mechanism involving specific conductances and a linear convolution of afferent firing rates producing post synaptic potentials. In the second part of this paper, we use neural fields quantitatively—to fit empirical data recorded during visual stimulation. We present two studies of spectral responses obtained from the visual cortex during visual perception experiments: in the first study, MEG data were acquired during a task designed to show how activity in the gamma band is related to visual perception, while in the second study, we exploited high density electrocorticographic (ECoG data to study the effect of varying stimulus contrast on cortical excitability and gamma peak frequency.

  7. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

    Directory of Open Access Journals (Sweden)

    Tilo Schwalger

    2017-04-01

    Full Text Available Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

  8. Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

    Science.gov (United States)

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.

  9. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

    Science.gov (United States)

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

  10. Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power

    Directory of Open Access Journals (Sweden)

    Mashud Rana

    2016-10-01

    Full Text Available Solar energy generated from PhotoVoltaic (PV systems is one of the most promising types of renewable energy. However, it is highly variable as it depends on the solar irradiance and other meteorological factors. This variability creates difficulties for the large-scale integration of PV power in the electricity grid and requires accurate forecasting of the electricity generated by PV systems. In this paper we consider 2D-interval forecasts, where the goal is to predict summary statistics for the distribution of the PV power values in a future time interval. 2D-interval forecasts have been recently introduced, and they are more suitable than point forecasts for applications where the predicted variable has a high variability. We propose a method called NNE2D that combines variable selection based on mutual information and an ensemble of neural networks, to compute 2D-interval forecasts, where the two interval boundaries are expressed in terms of percentiles. NNE2D was evaluated for univariate prediction of Australian solar PV power data for two years. The results show that it is a promising method, outperforming persistence baselines and other methods used for comparison in terms of accuracy and coverage probability.

  11. Robust visual track using an ensemble cascade of convolutional neural networks

    Science.gov (United States)

    Hu, Dan; Zhou, Xingshe; Yu, Xiaohao; Hou, Zhiqiang

    2015-12-01

    Convolutional Neural Networks (CNN) have dramatically boosted the performance of various computer vision tasks except visual tracking due to the lack of training data. In this paper, we pre-train a deep CNN offline to classify the 1 million images from 256 classes with very leaky non-saturating neurons for training acceleration, which is transformed to a discriminative classifier by adding an additional classification layer. In addition, we propose a novel approach for combining increasingly our CNN classifiers in a "cascade" structure through a modification of the AdaBoost framework, and then transfer the selected discriminative features from the ensemble of CNN classifiers to the robust visual tracking task, by updating online to robustly discard the background regions from promising object-like region to cope with appearance changes of the target. Extensive experimental evaluations on an open tracker benchmark demonstrate outstanding performance of our tracker by improving tracking success rate and tracking precision on an average of 9.2% and 13.9% at least over other state-of-the-art trackers.

  12. A thalamo-cortical neural mass model for the simulation of brain rhythms during sleep.

    Science.gov (United States)

    Cona, F; Lacanna, M; Ursino, M

    2014-08-01

    Cortico-thalamic interactions are known to play a pivotal role in many brain phenomena, including sleep, attention, memory consolidation and rhythm generation. Hence, simple mathematical models that can simulate the dialogue between the cortex and the thalamus, at a mesoscopic level, have a great cognitive value. In the present work we describe a neural mass model of a cortico-thalamic module, based on neurophysiological mechanisms. The model includes two thalamic populations (a thalamo-cortical relay cell population, TCR, and its related thalamic reticular nucleus, TRN), and a cortical column consisting of four connected populations (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow and fast kinetics). Moreover, thalamic neurons exhibit two firing modes: bursting and tonic. Finally, cortical synapses among pyramidal neurons incorporate a disfacilitation mechanism following prolonged activity. Simulations show that the model is able to mimic the different patterns of rhythmic activity in cortical and thalamic neurons (beta and alpha waves, spindles, delta waves, K-complexes, slow sleep waves) and their progressive changes from wakefulness to deep sleep, by just acting on modulatory inputs. Moreover, simulations performed by providing short sensory inputs to the TCR show that brain rhythms during sleep preserve the cortex from external perturbations, still allowing a high cortical activity necessary to drive synaptic plasticity and memory consolidation. In perspective, the present model may be used within larger cortico-thalamic networks, to gain a deeper understanding of mechanisms beneath synaptic changes during sleep, to investigate the specific role of brain rhythms, and to explore cortical synchronization achieved via thalamic influences.

  13. Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

    Science.gov (United States)

    Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

    2016-10-01

    Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

  14. Estimating the directed information to infer causal relationships in ensemble neural spike train recordings.

    Science.gov (United States)

    Quinn, Christopher J; Coleman, Todd P; Kiyavash, Negar; Hatsopoulos, Nicholas G

    2011-02-01

    Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures-most of which are variants of Granger causality-with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger's original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering

  15. A combinational feature selection and ensemble neural network method for classification of gene expression data

    Directory of Open Access Journals (Sweden)

    Jiang Tianzi

    2004-09-01

    Full Text Available Abstract Background Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for a particular disease. To date, this problem has received most attention in the context of cancer research, especially in tumor classification. Various feature selection methods and classifier design strategies also have been generally used and compared. However, most published articles on tumor classification have applied a certain technique to a certain dataset, and recently several researchers compared these techniques based on several public datasets. But, it has been verified that differently selected features reflect different aspects of the dataset and some selected features can obtain better solutions on some certain problems. At the same time, faced with a large amount of microarray data with little knowledge, it is difficult to find the intrinsic characteristics using traditional methods. In this paper, we attempt to introduce a combinational feature selection method in conjunction with ensemble neural networks to generally improve the accuracy and robustness of sample classification. Results We validate our new method on several recent publicly available datasets both with predictive accuracy of testing samples and through cross validation. Compared with the best performance of other current methods, remarkably improved results can be obtained using our new strategy on a wide range of different datasets. Conclusions Thus, we conclude that our methods can obtain more information in microarray data to get more accurate classification and also can help to extract the latent marker genes of the diseases for better diagnosis and treatment.

  16. Using an Artificial Neural Bypass to Restore Cortical Control of Rhythmic Movements in a Human with Quadriplegia

    Science.gov (United States)

    Sharma, Gaurav; Friedenberg, David A.; Annetta, Nicholas; Glenn, Bradley; Bockbrader, Marcie; Majstorovic, Connor; Domas, Stephanie; Mysiw, W. Jerry; Rezai, Ali; Bouton, Chad

    2016-09-01

    Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) hand movements in a paralyzed person. However, cortical control of rhythmic movements which originate in the brain but are coordinated by Central Pattern Generator (CPG) neural networks in the spinal cord has not been demonstrated previously. Here we show a demonstration of an artificial neural bypass technology that decodes cortical activity and emulates spinal cord CPG function allowing volitional rhythmic hand movement. The technology uses a combination of signals recorded from the brain, machine-learning algorithms to decode the signals, a numerical model of CPG network, and a neuromuscular electrical stimulation system to evoke rhythmic movements. Using the neural bypass, a quadriplegic participant was able to initiate, sustain, and switch between rhythmic and discrete finger movements, using his thoughts alone. These results have implications in advancing neuroprosthetic technology to restore complex movements in people living with paralysis.

  17. Neural Correlates of Indicators of Sound Change in Cantonese: Evidence from Cortical and Subcortical Processes

    Science.gov (United States)

    Maggu, Akshay R.; Liu, Fang; Antoniou, Mark; Wong, Patrick C. M.

    2016-01-01

    Across time, languages undergo changes in phonetic, syntactic, and semantic dimensions. Social, cognitive, and cultural factors contribute to sound change, a phenomenon in which the phonetics of a language undergo changes over time. Individuals who misperceive and produce speech in a slightly divergent manner (called innovators) contribute to variability in the society, eventually leading to sound change. However, the cause of variability in these individuals is still unknown. In this study, we examined whether such misperceptions are represented in neural processes of the auditory system. We investigated behavioral, subcortical (via FFR), and cortical (via P300) manifestations of sound change processing in Cantonese, a Chinese language in which several lexical tones are merging. Across the merging categories, we observed a similar gradation of speech perception abilities in both behavior and the brain (subcortical and cortical processes). Further, we also found that behavioral evidence of tone merging correlated with subjects' encoding at the subcortical and cortical levels. These findings indicate that tone-merger categories, that are indicators of sound change in Cantonese, are represented neurophysiologically with high fidelity. Using our results, we speculate that innovators encode speech in a slightly deviant neurophysiological manner, and thus produce speech divergently that eventually spreads across the community and contributes to sound change. PMID:28066218

  18. Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models.

    Science.gov (United States)

    Ba, Demba; Temereanca, Simona; Brown, Emery N

    2014-01-01

    Understanding how ensembles of neurons represent and transmit information in the patterns of their joint spiking activity is a fundamental question in computational neuroscience. At present, analyses of spiking activity from neuronal ensembles are limited because multivariate point process (MPP) models cannot represent simultaneous occurrences of spike events at an arbitrarily small time resolution. Solo recently reported a simultaneous-event multivariate point process (SEMPP) model to correct this key limitation. In this paper, we show how Solo's discrete-time formulation of the SEMPP model can be efficiently fit to ensemble neural spiking activity using a multinomial generalized linear model (mGLM). Unlike existing approximate procedures for fitting the discrete-time SEMPP model, the mGLM is an exact algorithm. The MPP time-rescaling theorem can be used to assess model goodness-of-fit. We also derive a new marked point-process (MkPP) representation of the SEMPP model that leads to new thinning and time-rescaling algorithms for simulating an SEMPP stochastic process. These algorithms are much simpler than multivariate extensions of algorithms for simulating a univariate point process, and could not be arrived at without the MkPP representation. We illustrate the versatility of the SEMPP model by analyzing neural spiking activity from pairs of simultaneously-recorded rat thalamic neurons stimulated by periodic whisker deflections, and by simulating SEMPP data. In the data analysis example, the SEMPP model demonstrates that whisker motion significantly modulates simultaneous spiking activity at the 1 ms time scale and that the stimulus effect is more than one order of magnitude greater for simultaneous activity compared with non-simultaneous activity. Together, the mGLM, the MPP time-rescaling theorem and the MkPP representation of the SEMPP model offer a theoretically sound, practical tool for measuring joint spiking propensity in a neuronal ensemble.

  19. Wind speed and wind power short and medium range predictions for complex terrain using artificial neural networks and ensemble calibration

    Science.gov (United States)

    Schicker, Irene; Papazek, Petrina; Kann, Alexander; Wang, Yong

    2017-04-01

    Reliable predictions of wind speed and wind power are vital for balancing the electricity network. Within the last two decades the amount of energy stemming from renewable sources increased substantially relying heavily on the prevailing synoptic conditions. Especially for regions with complex terrain and forested surfaces providing reliable predictions is a challenging task. Forecasts in the nowcasting as well as in the (two) day-ahead range are thus essential for the network balancing. Predictions of wind speed and wind power from the nowcasting to the +72-hour forecast range using NWP models in regions with complex terrain need a suitable horizontal, vertical and temporal resolution (e.g. 10 - 15 minute forecasts for the Nowcasting range) requiring high performance computing. To be able to provide sub-hourly to hourly forecasts different approaches such as model output statistics (MOS) or artificial neural networks (ANN) - including feed forward recurrent neural networks, fuzzy logic, particle swarm optimizations - are needed as computational costs are too high. To represent the forecast uncertainties additional probabilistic ensemble predictions are required increasing the computational needs. Ensemble prediction systems account for errors and uncertainties in the initial and boundary conditions, parameterizations, numeric, etc. Due to the underestimation of model and sampling errors ensemble predictions tend to be underdispersive and biased. They lack, too, sharpness and reliability. These shortcomings can be accounted for using statistical post-processing methods such as the non-homogeneous Gaussian regression (NGR) to calibrate an ensemble. These calibrated ensembles provide forecasts in the medium range for any arbitrary location where observations are available. In this study an ANN is used to provide forecasts for the nowcasting and medium-range with sub-hourly to hourly predictions for different Austrian sites, including high alpine sites as well as low

  20. The dynamic brain: from spiking neurons to neural masses and cortical fields.

    Directory of Open Access Journals (Sweden)

    Gustavo Deco

    2008-08-01

    Full Text Available The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space-time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI, electroencephalogram (EEG, and magnetoencephalogram (MEG. Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the

  1. A new bio-inspired stimulator to suppress hyper-synchronized neural firing in a cortical network.

    Science.gov (United States)

    Amiri, Masoud; Amiri, Mahmood; Nazari, Soheila; Faez, Karim

    2016-12-07

    Hyper-synchronous neural oscillations are the character of several neurological diseases such as epilepsy. On the other hand, glial cells and particularly astrocytes can influence neural synchronization. Therefore, based on the recent researches, a new bio-inspired stimulator is proposed which basically is a dynamical model of the astrocyte biophysical model. The performance of the new stimulator is investigated on a large-scale, cortical network. Both excitatory and inhibitory synapses are also considered in the simulated spiking neural network. The simulation results show that the new stimulator has a good performance and is able to reduce recurrent abnormal excitability which in turn avoids the hyper-synchronous neural firing in the spiking neural network. In this way, the proposed stimulator has a demand controlled characteristic and is a good candidate for deep brain stimulation (DBS) technique to successfully suppress the neural hyper-synchronization. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Imprinting of idiosyncratic experience in cortical sensory maps: neural substrates of representational remodeling and correlative perceptual changes.

    Science.gov (United States)

    Xerri, Christian

    2008-09-01

    Over the past 30 years, extensive research has been conducted in the field of cortical plasticity, under the impetus of seminal studies showing that the mature brain retains a capacity to reorganize the morphological and functional architecture of its neural circuits in order to adapt to environmental changes and mediate functional recovery following injury. Much effort has been focused on determining how idiosyncratic experience translates into molecular, structural and physiological changes in the sensory and motor representations embedded within cortical networks. The wealth of data generated by a broad spectrum of experimental manipulations has allowed unprecedented progress in our understanding of the physiological processes and neuroplasticity mechanisms underlying cortical representational remodeling. The objective of the present review is to put various facets of cortical map plasticity into perspective so as to examine possible links between changes occurring at multiple scales of the neural organization of the mature brain. The main focus is on neural substrates that mediate the instructive influence of experience and behavioral context on cortical reorganization, and perceptual correlates of representational remodeling.

  3. Cognitive-Neural Effects of Brush Writing of Chinese Characters: Cortical Excitation of Theta Rhythm

    Directory of Open Access Journals (Sweden)

    Min Xu

    2013-01-01

    Full Text Available Chinese calligraphy has been scientifically investigated within the contexts and principles of psychology, cognitive science, and the cognitive neuroscience. On the basis of vast amount of research in the last 30 years, we have developed a cybernetic theory of handwriting and calligraphy to account for the intricate interactions of several psychological dimensions involved in the dynamic act of graphic production. Central to this system of writing are the role of sensory, bio-, cognitive, and neurofeedback mechanisms for the initiation, guidance, and regulation of the writing motions vis-a-vis visual-geometric variations of Chinese characters. This experiment provided the first evidence of cortical excitation in EEG theta wave as a neural hub that integrates information coming from changes in the practitioner’s body, emotions, and cognition. In addition, it has also confirmed neurofeedback as an essential component of the cybernetic theory of handwriting and calligraphy.

  4. Cognitive-neural effects of brush writing of chinese characters: cortical excitation of theta rhythm.

    Science.gov (United States)

    Xu, Min; Kao, Henry S R; Zhang, Manlin; Lam, Stewart P W; Wang, Wei

    2013-01-01

    Chinese calligraphy has been scientifically investigated within the contexts and principles of psychology, cognitive science, and the cognitive neuroscience. On the basis of vast amount of research in the last 30 years, we have developed a cybernetic theory of handwriting and calligraphy to account for the intricate interactions of several psychological dimensions involved in the dynamic act of graphic production. Central to this system of writing are the role of sensory, bio-, cognitive, and neurofeedback mechanisms for the initiation, guidance, and regulation of the writing motions vis-a-vis visual-geometric variations of Chinese characters. This experiment provided the first evidence of cortical excitation in EEG theta wave as a neural hub that integrates information coming from changes in the practitioner's body, emotions, and cognition. In addition, it has also confirmed neurofeedback as an essential component of the cybernetic theory of handwriting and calligraphy.

  5. Sharpened cortical tuning and enhanced cortico-cortical communication contribute to the long-term neural mechanisms of visual motion perceptual learning.

    Science.gov (United States)

    Chen, Nihong; Bi, Taiyong; Zhou, Tiangang; Li, Sheng; Liu, Zili; Fang, Fang

    2015-07-15

    Much has been debated about whether the neural plasticity mediating perceptual learning takes place at the sensory or decision-making stage in the brain. To investigate this, we trained human subjects in a visual motion direction discrimination task. Behavioral performance and BOLD signals were measured before, immediately after, and two weeks after training. Parallel to subjects' long-lasting behavioral improvement, the neural selectivity in V3A and the effective connectivity from V3A to IPS (intraparietal sulcus, a motion decision-making area) exhibited a persistent increase for the trained direction. Moreover, the improvement was well explained by a linear combination of the selectivity and connectivity increases. These findings suggest that the long-term neural mechanisms of motion perceptual learning are implemented by sharpening cortical tuning to trained stimuli at the sensory processing stage, as well as by optimizing the connections between sensory and decision-making areas in the brain.

  6. Neural representations and the cortical body matrix: implications for sports medicine and future directions.

    Science.gov (United States)

    Wallwork, Sarah B; Bellan, Valeria; Catley, Mark J; Moseley, G Lorimer

    2016-08-01

    Neural representations, or neurotags, refer to the idea that networks of brain cells, distributed across multiple brain areas, work in synergy to produce outputs. The brain can be considered then, a complex array of neurotags, each influencing and being influenced by each other. The output of some neurotags act on other systems, for example, movement, or on consciousness, for example, pain. This concept of neurotags has sparked a new body of research into pain and rehabilitation. We draw on this research and the concept of a cortical body matrix-a network of representations that subserves the regulation and protection of the body and the space around it-to suggest important implications for rehabilitation of sports injury and for sports performance. Protective behaviours associated with pain have been reinterpreted in light of these conceptual models. With a particular focus on rehabilitation of the injured athlete, this review presents the theoretical underpinnings of the cortical body matrix and its application within the sporting context. Therapeutic approaches based on these ideas are discussed and the efficacy of the most tested approaches is addressed. By integrating current thought in pain and cognitive neuroscience related to sports rehabilitation, recommendations for clinical practice and future research are suggested.

  7. Geometric-attributes-based segmentation of cortical bone slides using optimized neural networks.

    Science.gov (United States)

    Hage, Ilige S; Hamade, Ramsey F

    2016-05-01

    In cortical bone, solid (lamellar and interstitial) matrix occupies space left over by porous microfeatures such as Haversian canals, lacunae, and canaliculi-containing clusters. In this work, pulse-coupled neural networks (PCNN) were used to automatically distinguish the microfeatures present in histology slides of cortical bone. The networks' parameters were optimized using particle swarm optimization (PSO). When forming the fitness functions for the PSO, we considered the microfeatures' geometric attributes-namely, their size (based on measures of elliptical perimeter or area), shape (based on measures of compactness or the ratio of minor axis length to major axis length), and a two-way combination of these two geometric attributes. This hybrid PCNN-PSO method was further enhanced for pulse evaluation by combination with yet another method, adaptive threshold (AT), where the PCNN algorithm is repeated until the best threshold is found corresponding to the maximum variance between two segmented regions. Together, this framework of using PCNN-PSO-AT constitutes, we believe, a novel framework in biomedical imaging. Using this framework and extracting microfeatures from only one training image, we successfully extracted microfeatures from other test images. The high fidelity of all resultant segments was established using quantitative metrics such as precision, specificity, and Dice indices.

  8. Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method

    CERN Document Server

    Barkaoui, Abdelwahed; Tarek, Merzouki; Hambli, Ridha; Ali, Mkaddem

    2014-01-01

    The complexity and heterogeneity of bone tissue require a multiscale modelling to understand its mechanical behaviour and its remodelling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network computation and homogenisation equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained neural network simulation. Finite element (FE) calculation is performed at nanoscopic levels to provide a database to train an in-house neural network program; (iii) in steps 2 to 10 from fibril to continuum cortical bone tissue, homogenisation equations are used to perform the computation at the higher s...

  9. Neural network models for spatial data mining, map production, and cortical direction selectivity

    Science.gov (United States)

    Parsons, Olga

    A family of ARTMAP neural networks for incremental supervised learning has been developed over the last decade. The Sensor Exploitation Group of MIT Lincoln Laboratory (LL) has incorporated an early version of this network as the recognition engine of a hierarchical system for fusion and data mining of multiple registered geospatial images. The LL system has been successfully fielded, but it is limited to target vs. non-target identifications and does not produce whole maps. This dissertation expands the capabilities of the LL system so that it learns to identify arbitrarily many target classes at once and can thus produce a whole map. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of a consistent procedure and a benchmark testbed has permitted the evaluation of candidate recognition networks as well as pre- and post-processing and feature extraction options. The resulting default ARTMAP network and mapping methodology set a standard for a variety of related mapping problems and application domains. The second part of the dissertation investigates the development of cortical direction selectivity. The possible role of visual experience and oculomotor behavior in the maturation of cells in the primary visual cortex is studied. The responses of neurons in the thalamus and cortex of the cat are modeled when natural scenes are scanned by several types of eye movements. Inspired by the Hebbian-like synaptic plasticity, which is based upon correlations between cell activations, the second-order statistical structure of thalamo-cortical activity is examined. In the simulations, patterns of neural activity that lead to a correct refinement of cell responses are observed during visual fixation, when small ocular movements occur, but are not observed in the presence of large saccades. Simulations also replicate experiments in which kittens are reared under stroboscopic

  10. Estimation of neural dynamics from MEG/EEG cortical current density maps: application to the reconstruction of large-scale cortical synchrony.

    Science.gov (United States)

    David, Olivier; Garnero, Line; Cosmelli, Diego; Varela, Francisco J

    2002-09-01

    There is a growing interest in elucidating the role of specific patterns of neural dynamics--such as transient synchronization between distant cell assemblies--in brain functions. Magnetoencephalography (MEG)/electroencephalography (EEG) recordings consist in the spatial integration of the activity from large and multiple remotely located populations of neurons. Massive diffusive effects and poor signal-to-noise ratio (SNR) preclude the proper estimation of indices related to cortical dynamics from nonaveraged MEG/EEG surface recordings. Source localization from MEG/EEG surface recordings with its excellent time resolution could contribute to a better understanding of the working brain. We propose a robust and original approach to the MEG/EEG distributed inverse problem to better estimate neural dynamics of cortical sources. For this, the surrogate data method is introduced in the MEG/EEG inverse problem framework. We apply this approach on nonaveraged data with poor SNR using the minimum norm estimator and find source localization results weakly sensitive to noise. Surrogates allow the reduction of the source space in order to reconstruct MEG/EEG data with reduced biases in both source localization and time-series dynamics. Monte Carlo simulations and results obtained from real MEG data indicate it is possible to estimate non invasively an important part of cortical source locations and dynamic and, therefore, to reveal brain functional networks.

  11. Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

    Science.gov (United States)

    Kasiviswanathan, K.; Sudheer, K.

    2013-05-01

    Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

  12. Stochastic resonance in an ensemble of single-electron neuromorphic devices and its application to competitive neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Oya, Takahide [Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Sapporo 060-0814 (Japan)]. E-mail: oya@sapiens-ei.eng.hokudai.ac.jp; Asai, Tetsuya [Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Sapporo 060-0814 (Japan); Amemiya, Yoshihito [Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Sapporo 060-0814 (Japan)

    2007-04-15

    Neuromorphic computing based on single-electron circuit technology is gaining prominence because of its massively increased computational efficiency and the increasing relevance of computer technology and nanotechnology [Likharev K, Mayr A, Muckra I, Tuerel O. CrossNets: High-performance neuromorphic architectures for CMOL circuits. Molec Electron III: Ann NY Acad Sci 1006;2003:146-63; Oya T, Schmid A, Asai T, Leblebici Y, Amemiya Y. On the fault tolerance of a clustered single-electron neural network for differential enhancement. IEICE Electron Expr 2;2005:76-80]. The maximum impact of these technologies will be strongly felt when single-electron circuits based on fault- and noise-tolerant neural structures can operate at room temperature. In this paper, inspired by stochastic resonance (SR) in an ensemble of spiking neurons [Collins JJ, Chow CC, Imhoff TT. Stochastic resonance without tuning. Nature 1995;376:236-8], we propose our design of a basic single-electron neural component and report how we examined its statistical results on a network.

  13. A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process%A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process

    Institute of Scientific and Technical Information of China (English)

    朱群雄; 赵乃伟; 徐圆

    2012-01-01

    Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.

  14. The niche factor syndecan-1 regulates the maintenance and proliferation of neural progenitor cells during mammalian cortical development.

    Directory of Open Access Journals (Sweden)

    Qingjie Wang

    Full Text Available Neural progenitor cells (NPCs divide and differentiate in a precisely regulated manner over time to achieve the remarkable expansion and assembly of the layered mammalian cerebral cortex. Both intrinsic signaling pathways and environmental factors control the behavior of NPCs during cortical development. Heparan sulphate proteoglycans (HSPG are critical environmental regulators that help modulate and integrate environmental cues and downstream intracellular signals. Syndecan-1 (Sdc1, a major transmembrane HSPG, is highly enriched in the early neural germinal zone, but its function in modulating NPC behavior and cortical development has not been explored. In this study we investigate the expression pattern and function of Sdc1 in the developing mouse cerebral cortex. We found that Sdc1 is highly expressed by cortical NPCs. Knockdown of Sdc1 in vivo by in utero electroporation reduces NPC proliferation and causes their premature differentiation, corroborated in isolated cells in vitro. We found that Sdc1 knockdown leads to reduced levels of β-catenin, indicating reduced canonical Wnt signaling. Consistent with this, GSK3β inhibition helps rescue the Sdc1 knockdown phenotype, partially restoring NPC number and proliferation. Moreover, exogenous Wnt protein promotes cortical NPC proliferation, but this is prevented by Sdc1 knockdown. Thus, Sdc1 in the germinal niche is a key HSPG regulating the maintenance and proliferation of NPCs during cortical neurogenesis, in part by modulating the ability of NPCs to respond to Wnt ligands.

  15. Using NCAR Yellowstone for PhotoVoltaic Power Forecasts with Artificial Neural Networks and an Analog Ensemble

    Science.gov (United States)

    Cervone, G.; Clemente-Harding, L.; Alessandrini, S.; Delle Monache, L.

    2016-12-01

    A methodology based on Artificial Neural Networks (ANN) and an Analog Ensemble (AnEn) is presented to generate 72-hour deterministic and probabilistic forecasts of power generated by photovoltaic (PV) power plants using input from a numerical weather prediction model and computed astronomical variables. ANN and AnEn are used individually and in combination to generate forecasts for three solar power plant located in Italy. The computational scalability of the proposed solution is tested using synthetic data simulating 4,450 PV power stations. The NCAR Yellowstone supercomputer is employed to test the parallel implementation of the proposed solution, ranging from 1 node (32 cores) to 4,450 nodes (141,140 cores). Results show that a combined AnEn + ANN solution yields best results, and that the proposed solution is well suited for massive scale computation.

  16. Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble

    Directory of Open Access Journals (Sweden)

    Rong Shan

    2016-06-01

    Full Text Available Society is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies without considering the complex global factors and their cross effects in biological systems. In this paper, the Markov property and Neural Network Ensemble (NNE are utilized to construct an estimated matrix that combines the interaction of the different local factors. With such an estimation matrix, we could obtain estimated variables that could reflect the global influence. The ensemble weights are trained by multiple population algorithms. Our prediction could fit the real trend of the two predicted measures, namely Morbidity Rate and Gross Domestic Product (GDP. It could be an effective method of reflecting the relationship between input factors and predicted measures of the health of ecosystems. The method can perform a sensitivity analysis, which could help determine the critical factors that could be adjusted to move the ecosystem in a sustainable direction.

  17. Permeability transition pore-mediated mitochondrial superoxide flashes regulate cortical neural progenitor differentiation.

    Science.gov (United States)

    Hou, Yan; Mattson, Mark P; Cheng, Aiwu

    2013-01-01

    In the process of neurogenesis, neural progenitor cells (NPCs) cease dividing and differentiate into postmitotic neurons that grow dendrites and an axon, become excitable, and establish synapses with other neurons. Mitochondrial biogenesis and aerobic metabolism provide energy substrates required to support the differentiation, growth and synaptic activity of neurons. Mitochondria may also serve signaling functions and, in this regard, it was recently reported that mitochondria can generate rapid bursts of superoxide (superoxide flashes), the frequency of which changes in response to environmental conditions and signals including oxygen levels and Ca(2+) fluxes. Here we show that the frequency of mitochondrial superoxide flashes increases as embryonic cerebral cortical neurons differentiate from NPCs, and provide evidence that the superoxide flashes serve a signaling function that is critical for the differentiation process. The superoxide flashes are mediated by mitochondrial permeability transition pore (mPTP) opening, and pharmacological inhibition of the mPTP suppresses neuronal differentiation. Moreover, superoxide flashes and neuronal differentiation are inhibited by scavenging of mitochondrial superoxide. Conversely, manipulations that increase superoxide flash frequency accelerate neuronal differentiation. Our findings reveal a regulatory role for mitochondrial superoxide flashes, mediated by mPTP opening, in neuronal differentiation.

  18. A mathematical model relating cortical oxygenated and deoxygenated hemoglobin flows and volumes to neural activity

    Science.gov (United States)

    Cornelius, Nathan R.; Nishimura, Nozomi; Suh, Minah; Schwartz, Theodore H.; Doerschuk, Peter C.

    2015-08-01

    Objective. To describe a toolkit of components for mathematical models of the relationship between cortical neural activity and space-resolved and time-resolved flows and volumes of oxygenated and deoxygenated hemoglobin motivated by optical intrinsic signal imaging (OISI). Approach. Both blood flow and blood volume and both oxygenated and deoxygenated hemoglobin and their interconversion are accounted for. Flow and volume are described by including analogies to both resistive and capacitive electrical circuit elements. Oxygenated and deoxygenated hemoglobin and their interconversion are described by generalization of Kirchhoff's laws based on well-mixed compartments. Main results. Mathematical models built from this toolkit are able to reproduce experimental single-stimulus OISI results that are described in papers from other research groups and are able to describe the response to multiple-stimuli experiments as a sublinear superposition of responses to the individual stimuli. Significance. The same assembly of tools from the toolkit but with different parameter values is able to describe effects that are considered distinctive, such as the presence or absence of an initial decrease in oxygenated hemoglobin concentration, indicating that the differences might be due to unique parameter values in a subject rather than different fundamental mechanisms.

  19. Effects of Exercise in Immersive Virtual Environments on Cortical Neural Oscillations and Mental State

    Science.gov (United States)

    Vogt, Tobias; Herpers, Rainer; Askew, Christopher D.; Scherfgen, David; Strüder, Heiko K.; Schneider, Stefan

    2015-01-01

    Virtual reality environments are increasingly being used to encourage individuals to exercise more regularly, including as part of treatment those with mental health or neurological disorders. The success of virtual environments likely depends on whether a sense of presence can be established, where participants become fully immersed in the virtual environment. Exposure to virtual environments is associated with physiological responses, including cortical activation changes. Whether the addition of a real exercise within a virtual environment alters sense of presence perception, or the accompanying physiological changes, is not known. In a randomized and controlled study design, moderate-intensity Exercise (i.e., self-paced cycling) and No-Exercise (i.e., automatic propulsion) trials were performed within three levels of virtual environment exposure. Each trial was 5 minutes in duration and was followed by posttrial assessments of heart rate, perceived sense of presence, EEG, and mental state. Changes in psychological strain and physical state were generally mirrored by neural activation patterns. Furthermore, these changes indicated that exercise augments the demands of virtual environment exposures and this likely contributed to an enhanced sense of presence. PMID:26366305

  20. Effects of Exercise in Immersive Virtual Environments on Cortical Neural Oscillations and Mental State

    Directory of Open Access Journals (Sweden)

    Tobias Vogt

    2015-01-01

    Full Text Available Virtual reality environments are increasingly being used to encourage individuals to exercise more regularly, including as part of treatment those with mental health or neurological disorders. The success of virtual environments likely depends on whether a sense of presence can be established, where participants become fully immersed in the virtual environment. Exposure to virtual environments is associated with physiological responses, including cortical activation changes. Whether the addition of a real exercise within a virtual environment alters sense of presence perception, or the accompanying physiological changes, is not known. In a randomized and controlled study design, moderate-intensity Exercise (i.e., self-paced cycling and No-Exercise (i.e., automatic propulsion trials were performed within three levels of virtual environment exposure. Each trial was 5 minutes in duration and was followed by posttrial assessments of heart rate, perceived sense of presence, EEG, and mental state. Changes in psychological strain and physical state were generally mirrored by neural activation patterns. Furthermore, these changes indicated that exercise augments the demands of virtual environment exposures and this likely contributed to an enhanced sense of presence.

  1. Experimental and Computational Studies of Cortical Neural Network Properties Through Signal Processing

    Science.gov (United States)

    Clawson, Wesley Patrick

    Previous studies, both theoretical and experimental, of network level dynamics in the cerebral cortex show evidence for a statistical phenomenon called criticality; a phenomenon originally studied in the context of phase transitions in physical systems and that is associated with favorable information processing in the context of the brain. The focus of this thesis is to expand upon past results with new experimentation and modeling to show a relationship between criticality and the ability to detect and discriminate sensory input. A line of theoretical work predicts maximal sensory discrimination as a functional benefit of criticality, which can then be characterized using mutual information between sensory input, visual stimulus, and neural response,. The primary finding of our experiments in the visual cortex in turtles and neuronal network modeling confirms this theoretical prediction. We show that sensory discrimination is maximized when visual cortex operates near criticality. In addition to presenting this primary finding in detail, this thesis will also address our preliminary results on change-point-detection in experimentally measured cortical dynamics.

  2. Backward renormalization-group inference of cortical dipole sources and neural connectivity efficacy.

    Science.gov (United States)

    Amaral, Selene da Rocha; Baccalá, Luiz A; Barbosa, Leonardo S; Caticha, Nestor

    2017-06-01

    Proper neural connectivity inference has become essential for understanding cognitive processes associated with human brain function. Its efficacy is often hampered by the curse of dimensionality. In the electroencephalogram case, which is a noninvasive electrophysiological monitoring technique to record electrical activity of the brain, a possible way around this is to replace multichannel electrode information with dipole reconstructed data. We use a method based on maximum entropy and the renormalization group to infer the position of the sources, whose success hinges on transmitting information from low- to high-resolution representations of the cortex. The performance of this method compares favorably to other available source inference algorithms, which are ranked here in terms of their performance with respect to directed connectivity inference by using artificially generated dynamic data. We examine some representative scenarios comprising different numbers of dynamically connected dipoles over distinct cortical surface positions and under different sensor noise impairment levels. The overall conclusion is that inverse problem solutions do not affect the correct inference of the direction of the flow of information as long as the equivalent dipole sources are correctly found.

  3. Optimal sensorimotor integration in recurrent cortical networks: a neural implementation of Kalman filters.

    Science.gov (United States)

    Denève, Sophie; Duhamel, Jean-René; Pouget, Alexandre

    2007-05-23

    Several behavioral experiments suggest that the nervous system uses an internal model of the dynamics of the body to implement a close approximation to a Kalman filter. This filter can be used to perform a variety of tasks nearly optimally, such as predicting the sensory consequence of motor action, integrating sensory and body posture signals, and computing motor commands. We propose that the neural implementation of this Kalman filter involves recurrent basis function networks with attractor dynamics, a kind of architecture that can be readily mapped onto cortical circuits. In such networks, the tuning curves to variables such as arm velocity are remarkably noninvariant in the sense that the amplitude and width of the tuning curves of a given neuron can vary greatly depending on other variables such as the position of the arm or the reliability of the sensory feedback. This property could explain some puzzling properties of tuning curves in the motor and premotor cortex, and it leads to several new predictions.

  4. Cortical gene expression in spinal cord injury and repair: insight into the functional complexity of the neural regeneration program

    Directory of Open Access Journals (Sweden)

    Fabian eKruse

    2011-09-01

    Full Text Available Traumatic spinal cord injury (SCI results in the formation of a fibrous scar acting as a growth barrier for regenerating axons at the lesion site. We have previously shown (Klapka et al., 2005 that transient suppression of the inhibitory lesion scar in rat spinal cord leads to long distance axon regeneration, retrograde rescue of axotomized cortical motoneurons and improvement of locomotor function. Here we applied a systemic approach to investigate for the first time specific and dynamic alterations in the cortical gene expression profile following both thoracic SCI and regeneration-promoting anti-scarring treatment (AST. In order to monitor cortical gene expression we carried out microarray analyses using total RNA isolated from layer V/VI of rat sensorimotor cortex at 1-60 days post-operation (dpo. We demonstrate that cortical neurons respond to injury by massive changes in gene expression, starting as early as 1 dpo. AST, in turn, results in profound modifications of the lesion-induced expression profile. The treatment attenuates SCI-triggered transcriptional changes of genes related to inhibition of axon growth and impairment of cell survival, while upregulating the expression of genes associated with axon outgrowth, cell protection and neural development. Thus, AST not only modifies the local environment impeding spinal cord regeneration by reduction of fibrous scarring in the injured spinal cord, but, in addition, strikingly changes the intrinsic capacity of cortical pyramidal neurons towards enhanced cell maintenance and axonal regeneration.

  5. Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

    OpenAIRE

    2015-01-01

    The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion ...

  6. Improved Calibration of Near-Infrared Spectra by Using Ensembles of Neural Network Models

    OpenAIRE

    Ukil, A.; Bernasconi, J.; Braendle, H.; Buijs, H.; Bonenfant, S.

    2015-01-01

    IR or near-infrared (NIR) spectroscopy is a method used to identify a compound or to analyze the composition of a material. Calibration of NIR spectra refers to the use of the spectra as multivariate descriptors to predict concentrations of the constituents. To build a calibration model, state-of-the-art software predominantly uses linear regression techniques. For nonlinear calibration problems, neural network-based models have proved to be an interesting alternative. In this paper, we propo...

  7. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tao Ma

    2016-10-01

    Full Text Available The development of intrusion detection systems (IDS that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC and deep neural network (DNN algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN, support vector machine (SVM, random forest (RF and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

  8. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

    Science.gov (United States)

    Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

    2016-10-13

    The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

  9. Cortical reorganization in patients with subcortical hemiparesis: neural mechanisms of functional recovery and prognostic implication.

    Science.gov (United States)

    Fujii, Yukihiko; Nakada, Tsutomu

    2003-01-01

    A systematic investigation on cortical reorganization in patients with hemiparesis of a subcortical origin, with special emphasis on functional correlates, was conducted using functional magnetic resonance (fMR) imaging performed on a 3-tesla system specifically optimized for fMR imaging investigation. The study group included 46 patients with hemiparesis (25 with right and 21 with left hemiparesis) and 30 age-matched healthy volunteers as controls. All study participants were originally right handed. The characteristics of the lesion were putaminal hemorrhage in 19 patients, thalamic hemorrhage in 10 patients, and striatocapsular bland infarction in 17 patients. Functional recovery in subcortical hemiparesis showed two distinct phases of the recovery process involving entirely different neural mechanisms. Phase I is characterized by the process of recovery and/or reorganization of the primary system. Successful recovery of this system is typically reached within 1 month after stroke onset. Its clinical correlate is a rapid recovery course and significant recovery of function within 1 month of stroke onset. Failure of recovery of the primary system shifts the recovery process to Phase II, during which reorganization involving the ipsilateral pathway takes place. The clinical correlate of Phase II is a slow recovery course with variable functional outcome. Effective functional organization of the ipsilateral pathway, as identified by linked activation of the ipsilateral primary sensorimotor cortex and contralateral anterior lobe of the cerebellum, is correlated with a good prognostic outcome for patients in the slow recovery group. A high degree of connectivity between supplementary motor areas, bilaterally, appears to influence functional recovery adversely.

  10. Self-Sustained Irregular Activity in an Ensemble of Neural Oscillators

    Directory of Open Access Journals (Sweden)

    Ekkehard Ullner

    2016-02-01

    Full Text Available An ensemble of pulse-coupled phase oscillators is thoroughly analyzed in the presence of a mean-field coupling and a dispersion of their natural frequencies. In spite of the analogies with the Kuramoto setup, a much richer scenario is observed. The “synchronized” phase, which emerges upon increasing the coupling strength, is characterized by highly irregular fluctuations: A time-series analysis reveals that the dynamics of the order parameter is indeed high dimensional. The complex dynamics appears to be the result of the nonperturbative action of a suitably shaped phase-response curve. Such a mechanism differs from the often-invoked balance between excitation and inhibition and might provide an alternative basis to account for the self-sustained brain activity in the resting state. The potential interest of this dynamical regime is further strengthened by its (microscopic linear stability, which makes it quite suited for computational tasks. The overall study has been performed by combining analytical and numerical studies, starting from the linear stability analysis of the asynchronous regime, to include the Fourier analysis of the Kuramoto order parameter, the computation of various types of Lyapunov exponents, and a microscopic study of the interspike intervals.

  11. One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

    Science.gov (United States)

    Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

    2015-04-01

    Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN

  12. Spin-Mediated Consciousness Theory Possible Roles of Oxygen Unpaired Electronic Spins and Neural Membrane Nuclear Spin Ensemble in Memory and Consciousness

    CERN Document Server

    Hu, H; Hu, Huping; Wu, Maoxin

    2002-01-01

    We postulate that consciousness is connected to quantum mechanical spin since said spin is embedded in the microscopic structure of spacetime and may be more fundamental than spacetime itself. Thus, we theorize that consciousness is connected with the fabric of spacetime through spin. That is, spin is the "pixel" and "antenna" of mind. The unity of mind is achieved by non-local means within the pre-spacetime domain interfaced with spacetime. Human mind is possible because of the particular structures and dynamics of our brain postulated working as follows: The unpaired electronic spins of highly lipid-soluble and rapidly diffusing oxygen molecules extract information from the dynamical neural membranes and communicate said information through strong spin-spin couplings to the nuclear spin ensemble in the membranes for consciousness-related quantum statistical processing which survives decoherence. In turn, the dynamics of the nuclear spin ensemble has effects through spin chemistry on the classical neural act...

  13. Improving ECG classification accuracy using an ensemble of neural network modules.

    Directory of Open Access Journals (Sweden)

    Mehrdad Javadi

    Full Text Available This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization.

  14. A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network

    Directory of Open Access Journals (Sweden)

    Jing Xu

    2015-10-01

    Full Text Available In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD and Probabilistic Neural Network (PNN is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method.

  15. Using a Large-scale Neural Model of Cortical Object Processing to Investigate the Neural Substrate for Managing Multiple Items in Short-term Memory.

    Science.gov (United States)

    Liu, Qin; Ulloa, Antonio; Horwitz, Barry

    2017-07-07

    Many cognitive and computational models have been proposed to help understand working memory. In this article, we present a simulation study of cortical processing of visual objects during several working memory tasks using an extended version of a previously constructed large-scale neural model [Tagamets, M. A., & Horwitz, B. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cerebral Cortex, 8, 310-320, 1998]. The original model consisted of arrays of Wilson-Cowan type of neuronal populations representing primary and secondary visual cortices, inferotemporal (IT) cortex, and pFC. We added a module representing entorhinal cortex, which functions as a gating module. We successfully implemented multiple working memory tasks using the same model and produced neuronal patterns in visual cortex, IT cortex, and pFC that match experimental findings. These working memory tasks can include distractor stimuli or can require that multiple items be retained in mind during a delay period (Sternberg's task). Besides electrophysiology data and behavioral data, we also generated fMRI BOLD time series from our simulation. Our results support the involvement of IT cortex in working memory maintenance and suggest the cortical architecture underlying the neural mechanisms mediating particular working memory tasks. Furthermore, we noticed that, during simulations of memorizing a list of objects, the first and last items in the sequence were recalled best, which may implicate the neural mechanism behind this important psychological effect (i.e., the primacy and recency effect).

  16. Neural correlate of subjective sensory experience gradually builds up across cortical areas

    Science.gov (United States)

    de Lafuente, Victor; Romo, Ranulfo

    2006-01-01

    When a sensory stimulus is presented, many cortical areas are activated, but how does the representation of a sensory stimulus evolve in time and across cortical areas during a perceptual judgment? We investigated this question by analyzing the responses from single neurons, recorded in several cortical areas of parietal and frontal lobes, while trained monkeys reported the presence or absence of a mechanical vibration of varying amplitude applied to the skin of one fingertip. Here we show that the strength of the covariations between neuronal activity and perceptual judgments progressively increases across cortical areas as the activity is transmitted from the primary somatosensory cortex to the premotor areas of the frontal lobe. This finding suggests that the neuronal correlates of subjective sensory experience gradually build up across somatosensory areas of the parietal lobe and premotor cortices of the frontal lobe. PMID:16924098

  17. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

    Science.gov (United States)

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-03-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by

  18. A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.

    Science.gov (United States)

    Zhou, Qingping; Jiang, Haiyan; Wang, Jianzhou; Zhou, Jianling

    2014-10-15

    Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems.

  19. Sensory stimuli reduce the dimensionality of cortical activity

    Science.gov (United States)

    Mazzucato, Luca; Fontanini, Alfredo; La Camera, Giancarlo

    Neural ensembles in alert animals generate complex patterns of activity. Although cortical activity unfolds in a space whose dimension is equal to the number of neurons, it is often restricted to a lower dimensional subspace. Dimensionality is the minimal number of dimensions that accurately capture neural dynamics, and may be related to the computational tasks supported by the neural circuit. Here, we investigate the dimensionality of neural ensembles from the insular cortex of alert rats during periods of `ongoing' (spontaneous) and stimulus-evoked activity. We find that the dimensionality grows with ensemble size, and does so significantly faster during ongoing compared to evoked activity. We explain both results using a recurrent spiking network with clustered architecture, and obtain analytical results on the dependence of dimensionality on ensemble size, number of clusters, and pair-wise noise correlations. The theory predicts a characteristic scaling with ensemble size and the existence of an upper bound on dimensionality, which grows with the number of clusters and decreases with the amount of noise correlations. To our knowledge, this is the first mechanistic model of neural dimensionality in cortex during both spontaneous and evoked activity.

  20. Analysis of Neural Stem Cells from Human Cortical Brain Structures In Vitro.

    Science.gov (United States)

    Aleksandrova, M A; Poltavtseva, R A; Marei, M V; Sukhikh, G T

    2016-05-01

    Comparative immunohistochemical analysis of the neocortex from human fetuses showed that neural stem and progenitor cells are present in the brain throughout the gestation period, at least from week 8 through 26. At the same time, neural stem cells from the first and second trimester fetuses differed by the distribution, morphology, growth, and quantity. Immunocytochemical analysis of neural stem cells derived from fetuses at different gestation terms and cultured under different conditions showed their differentiation capacity. Detailed analysis of neural stem cell populations derived from fetuses on gestation weeks 8-9, 18-20, and 26 expressing Lex/SSEA1 was performed.

  1. License Plate Recognition Based on Neural Network Ensemble%基于神经网络集成的车牌字符识别

    Institute of Scientific and Technical Information of China (English)

    马宁; 李斌

    2012-01-01

    针对车牌字符在车牌图象退化时识别率较低的问题,提出一种基于神经网络集成的车牌字符识别方法。基于小生境遗传算法在提高进化的局部搜索方面的良好性能来动态构建个体网络差异性大的神经网络集成,进而提高整个集成系统的泛化能力。将该方法应用于车牌字符的识别,实验结果表明,该方法能有效地生成差异度较大的个体网络,得到的神经网络集成能有效提高车牌字符的识别率。%Aiming at the current problem that license plate character recognition is at low recognition rate when the license plate image quality degrades, this paper presents a license plate character recognition method which is based on neural network ensemble. Using niche technique's good performance in improving local search of evolution to dynamically construct individual networks with diversity, and to improve the generalization ability of the neural network ensemble. At last, the method is applied in the license plate character rec- ognition. The results show that the method can generate individual networks with greater different degrees and the neural network ensemble it generates can effectively improve the rate of license plate character recognition.

  2. Model validation of untethered, ultrasonic neural dust motes for cortical recording.

    Science.gov (United States)

    Seo, Dongjin; Carmena, Jose M; Rabaey, Jan M; Maharbiz, Michel M; Alon, Elad

    2015-04-15

    A major hurdle in brain-machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a substantial fraction of the user's lifetime. Recently, sub-mm implantable, wireless electromagnetic (EM) neural interfaces have been demonstrated in an effort to extend system longevity. However, EM systems do not scale down in size well due to the severe inefficiency of coupling radio-waves at those scales within tissue. This paper explores fundamental system design trade-offs as well as size, power, and bandwidth scaling limits of neural recording systems built from low-power electronics coupled with ultrasonic power delivery and backscatter communication. Such systems will require two fundamental technology innovations: (1) 10-100 μm scale, free-floating, independent sensor nodes, or neural dust, that detect and report local extracellular electrophysiological data via ultrasonic backscattering and (2) a sub-cranial ultrasonic interrogator that establishes power and communication links with the neural dust. We provide experimental verification that the predicted scaling effects follow theory; (127 μm)(3) neural dust motes immersed in water 3 cm from the interrogator couple with 0.002064% power transfer efficiency and 0.04246 ppm backscatter, resulting in a maximum received power of ∼0.5 μW with ∼1 nW of change in backscatter power with neural activity. The high efficiency of ultrasonic transmission can enable the scaling of the sensing nodes down to 10s of micrometer. We conclude with a brief discussion of the application of neural dust for both central and peripheral nervous system recordings, and perspectives on future research directions.

  3. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex

    Science.gov (United States)

    Chestek, Cynthia A.; Gilja, Vikash; Nuyujukian, Paul; Foster, Justin D.; Fan, Joline M.; Kaufman, Matthew T.; Churchland, Mark M.; Rivera-Alvidrez, Zuley; Cunningham, John P.; Ryu, Stephen I.; Shenoy, Krishna V.

    2011-08-01

    Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability, the long-term operation of these systems must be better understood. In particular, the quality and stability of the electrically-recorded neural signals require further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from four intracortical arrays in three animals. Action potential amplitude decreased by 2.4% per month on average over the course of 9.4, 10.4, and 31.7 months in three animals. During most time periods, decoder performance was not well correlated with action potential amplitude (p > 0.05 for three of four arrays). In two arrays from one animal, action potential amplitude declined by an average of 37% over the first 2 months after implant. However, when using simple threshold-crossing events rather than well-isolated action potentials, no corresponding performance loss was observed during this time using an offline decoder. One of these arrays was effectively used for online prosthetic experiments over the following year. Substantial short-term variations in waveforms were quantified using a wireless system for contiguous recording in one animal, and compared within and between days for all three animals. Overall, this study suggests that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold-crossing events rather than isolated action potentials. This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials.

  4. Abnormal left-sided orbitomedial prefrontal cortical-amygdala connectivity during happy and fear face processing: a potential neural mechanism of female MDD

    Directory of Open Access Journals (Sweden)

    Jorge eAlmeida

    2011-12-01

    Full Text Available Background: Pathophysiologic processes supporting abnormal emotion regulation in major depressive disorder (MDD are poorly understood. We previously found abnormal inverse left-sided ventromedial prefrontal cortical- amygdala effective connectivity to happy faces in females with MDD. We aimed to replicate and expand this previous finding in an independent participant sample, using a more inclusive neural model, and a novel emotion-processing paradigm.Methods: Nineteen individuals with MDD in depressed episode (12 females, and nineteen healthy individuals, age and gender matched, performed an implicit emotion processing and automatic attentional control paradigm to examine abnormalities in prefrontal cortical-amygdala neural circuitry during happy, angry, fearful and sad face processing measured with functional magnetic resonance imaging in a 3Tesla scanner. Effective connectivity was estimated with Dynamic Causal Modelling in a trinodal neural model including two anatomically defined prefrontal cortical regions, ventromedial prefrontal cortex and subgenual cingulate cortex(sgACC, and the amygdala. Results: We replicated our previous finding of abnormal inverse left-sided inverse top-down ventromedial prefrontal cortical-amygdala connectivity to happy faces in females with MDD (p=.04, and also showed a similar pattern of abnormal inverse left-sided sgACC-amygdala connectivity to these stimuli (p=0.03. These findings were paralleled by abnormally reduced positive left-sided ventromedial prefrontal cortical-sgACC connectivity to happy faces in females with MDD (p=0.008, and abnormally increased positive left-sided sgACC-amygdala connectivity to fearful faces in females, and all individuals, with MDD (p=0.008;p=0.003.Conclusions: Different patterns of abnormal prefrontal cortical-amygdala connectivity to happy and fearful stimuli might represent neural mechanisms for the excessive self-reproach and comorbid anxiety that characterize female MDD.

  5. A quantitative framework to evaluate modeling of cortical development by neural stem cells

    Science.gov (United States)

    Stein, Jason L.; de la Torre-Ubieta, Luis; Tian, Yuan; Parikshak, Neelroop N.; Hernandez, Israel A.; Marchetto, Maria C.; Baker, Dylan K.; Lu, Daning; Hinman, Cassidy R.; Lowe, Jennifer K.; Wexler, Eric M.; Muotri, Alysson R.; Gage, Fred H.; Kosik, Kenneth S.; Geschwind, Daniel H.

    2014-01-01

    Summary Neural stem cells have been adopted to model a wide range of neuropsychiatric conditions in vitro. However, how well such models correspond to in vivo brain has not been evaluated in an unbiased, comprehensive manner. We used transcriptomic analyses to compare in vitro systems to developing human fetal brain and observed strong conservation of in vivo gene expression and network architecture in differentiating primary human neural progenitor cells (phNPCs). Conserved modules are enriched in genes associated with ASD, supporting the utility of phNPCs for studying neuropsychiatric disease. We also developed and validated a machine learning approach called CoNTExT that identifies the developmental maturity and regional identity of in vitro models. We observed strong differences between in vitro models, including hiPSC-derived neural progenitors from multiple laboratories. This work provides a systems biology framework for evaluating in vitro systems and supports their value in studying the molecular mechanisms of human neurodevelopmental disease. PMID:24991955

  6. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates

    Science.gov (United States)

    Borton, David A.; Yin, Ming; Aceros, Juan; Nurmikko, Arto

    2013-04-01

    Objective. Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. Approach. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Main results. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile

  7. Pulsed DC Electric Field-Induced Differentiation of Cortical Neural Precursor Cells.

    Directory of Open Access Journals (Sweden)

    Hui-Fang Chang

    Full Text Available We report the differentiation of neural stem and progenitor cells solely induced by direct current (DC pulses stimulation. Neural stem and progenitor cells in the adult mammalian brain are promising candidates for the development of therapeutic neuroregeneration strategies. The differentiation of neural stem and progenitor cells depends on various in vivo environmental factors, such as nerve growth factor and endogenous EF. In this study, we demonstrated that the morphologic and phenotypic changes of mouse neural stem and progenitor cells (mNPCs could be induced solely by exposure to square-wave DC pulses (magnitude 300 mV/mm at frequency of 100-Hz. The DC pulse stimulation was conducted for 48 h, and the morphologic changes of mNPCs were monitored continuously. The length of primary processes and the amount of branching significantly increased after stimulation by DC pulses for 48 h. After DC pulse treatment, the mNPCs differentiated into neurons, astrocytes, and oligodendrocytes simultaneously in stem cell maintenance medium. Our results suggest that simple DC pulse treatment could control the fate of NPCs. With further studies, DC pulses may be applied to manipulate NPC differentiation and may be used for the development of therapeutic strategies that employ NPCs to treat nervous system disorders.

  8. Pulsed DC Electric Field–Induced Differentiation of Cortical Neural Precursor Cells

    Science.gov (United States)

    Chang, Hui-Fang; Lee, Ying-Shan; Tang, Tang K.; Cheng, Ji-Yen

    2016-01-01

    We report the differentiation of neural stem and progenitor cells solely induced by direct current (DC) pulses stimulation. Neural stem and progenitor cells in the adult mammalian brain are promising candidates for the development of therapeutic neuroregeneration strategies. The differentiation of neural stem and progenitor cells depends on various in vivo environmental factors, such as nerve growth factor and endogenous EF. In this study, we demonstrated that the morphologic and phenotypic changes of mouse neural stem and progenitor cells (mNPCs) could be induced solely by exposure to square-wave DC pulses (magnitude 300 mV/mm at frequency of 100-Hz). The DC pulse stimulation was conducted for 48 h, and the morphologic changes of mNPCs were monitored continuously. The length of primary processes and the amount of branching significantly increased after stimulation by DC pulses for 48 h. After DC pulse treatment, the mNPCs differentiated into neurons, astrocytes, and oligodendrocytes simultaneously in stem cell maintenance medium. Our results suggest that simple DC pulse treatment could control the fate of NPCs. With further studies, DC pulses may be applied to manipulate NPC differentiation and may be used for the development of therapeutic strategies that employ NPCs to treat nervous system disorders. PMID:27352251

  9. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Directory of Open Access Journals (Sweden)

    Omer Ziv

    2015-10-01

    Full Text Available Neural stem cells (NSCs are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  10. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Science.gov (United States)

    Ziv, Omer; Zaritsky, Assaf; Yaffe, Yakey; Mutukula, Naresh; Edri, Reuven; Elkabetz, Yechiel

    2015-10-01

    Neural stem cells (NSCs) are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture) and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM)--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII) reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  11. A chronometric functional sub-network in the thalamo-cortical system regulates the flow of neural information necessary for conscious cognitive processes.

    Science.gov (United States)

    León-Domínguez, Umberto; Vela-Bueno, Antonio; Froufé-Torres, Manuel; León-Carrión, Jose

    2013-06-01

    The thalamo-cortical system has been defined as a neural network associated with consciousness. While there seems to be wide agreement that the thalamo-cortical system directly intervenes in vigilance and arousal, a divergence of opinion persists regarding its intervention in the control of other cognitive processes necessary for consciousness. In the present manuscript, we provide a review of recent scientific findings on the thalamo-cortical system and its role in the control and regulation of the flow of neural information necessary for conscious cognitive processes. We suggest that the axis formed by the medial prefrontal cortex and different thalamic nuclei (reticular nucleus, intralaminar nucleus, and midline nucleus), represents a core component for consciousness. This axis regulates different cerebral structures which allow basic cognitive processes like attention, arousal and memory to emerge. In order to produce a synchronized coherent response, neural communication between cerebral structures must have exact timing (chronometry). Thus, a chronometric functional sub-network within the thalamo-cortical system keeps us in an optimal and continuous functional state, allowing high-order cognitive processes, essential to awareness and qualia, to take place.

  12. Nociceptive responses of anterior cingulate cortical ensembles in behaving rats%清醒大鼠前扣带皮层神经元群的伤害性反应

    Institute of Scientific and Technical Information of China (English)

    王锦琰; 罗非; 张含荑; 张景渝; Donald J.WOODWARD; 韩济生

    2004-01-01

    目的:利用多通道记录技术在清醒大鼠前扣带回(ACC)记录神经元群的痛反应模式,以证实ACC在痛觉情绪编码中的作用.方法:在5只成年雄性SD大鼠的双侧ACC脑区植入微电极阵列,在大鼠术后清醒状态下给予尾部及四肢足底伤害性辐射热刺激,神经元的放电信号经由电缆和连接器传送至多通道记录系统.结果:伤害性辐射热刺激在大鼠ACC引起以兴奋为主的反应,该反应持续时间较长,可能与痛情绪有关;刺激开始附近ACC神经元有与痛刺激相关的预期性反应,可能与准备逃避的动机有关;同侧和对侧肢体刺激引起的ACC神经元的反应差别不显著,表明神经元感受野大,不适合精确定位.结论:ACC主要参与编码痛觉的情绪动机成分.%Objective: To confirm the role of anterior cingulated cortices (ACC) in the coding of pain affect by exploring the neural ensemble coding pattern within the anterior cingulate cortex in behaving rats with a multichannel recording technique. Methods: In five adult male Sprague-Dawley rats, two arrays of eight stainless steel microwires were bilaterally implanted into ACC. Noxious radiant heat stimulation was applied to the tail, bilateral fore-paws and hind-paws of freely moving rats. Neuroelectric signals were obtained from the microwires and sent to a multichannel recording device via cables and connectors. The time stamps of neuronal activities were stored on a personal computer for off-line analysis. Results:Noxious heat stimuli evoked predominantly excitatory and sustained neural activity within ACC, reflecting the processing of pain unpleasantness; pain-related anticipatory responses could be seen near the stimulation start, indicating the behavioral preparation for escape; ACC neurons had broad receptive fields by showing quite similar pain-related responses to stimuli on either side of the hind-paw, suggesting that they are not eligible for the localization of a stimulus

  13. Lipidome of midbody released from neural stem and progenitor cells during mammalian cortical neurogenesis

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    Yoko eArai

    2015-08-01

    Full Text Available Midbody release from proliferative neural progenitor cells is tightly associated with the neuronal commitment of neural progenitor cells during the progression of neurogenesis in the mammalian cerebral cortex. While the central portion of the midbody, a cytoplasmic bridge between nascent daughter cells, is engulfed by one of the daughter cell by most cells in vitro, it is shown to be released into the extracellular cerebrospinal fluid in vivo in mouse embryos. Several proteins have been involved in midbody release; however, few studies have addressed the participation of the plasma membrane’s lipids in this process. Here, we show by Shotgun Lipidomic analysis that phosphatydylserine (PS, among other lipids, is enriched in the released midbodies compared to lipoparticles and cellular membranes, both collected from the cerebrospinal fluid of the developing mouse embryos. Moreover, the developing mouse embryo neural progenitor cells released two distinct types of midbodies carrying either internalized PS or externalized PS on their membrane. This strongly suggests that phagocytosis and an alternative fate of released midbodies exists. HeLa cells, which are known to mainly engulf the midbody show almost no PS exposure, if any, on the outer leaflet of the midbody membrane. These results point towards that PS exposure might be involved in the selection of recipients of released midbodies, either to be engulfed by daughter cells or phagocytosed by non-daughter cells or another cell type in the developing cerebral cortex.

  14. Cortical and brain stem changes in neural activity during static handgrip and postexercise ischemia in humans

    DEFF Research Database (Denmark)

    Sander, Mikael; Macefield, Vaughan G; Henderson, Luke A

    2010-01-01

    Static isometric exercise increases muscle sympathetic nerve activity (MSNA) and mean arterial pressure, both of which can be maintained at the conclusion of the exercise by occlusion of the arterial supply [postexercise ischemia (PEI)]. To identify the cortical and subcortical sites involved......, and to differentiate between central command and reflex inputs, we used blood oxygen level-dependent (BOLD) functional MRI (fMRI) of the whole brain (3 T). Subjects performed submaximal static handgrip exercise for 2 min followed by 6 min of PEI; MSNA was recorded on a separate day. During the contraction phase......, parallel increases in BOLD signal intensity occurred in the contralateral primary motor cortex and cerebellar nuclei and cortex; these matched the effort profile and ceased at the conclusion of the contraction. Progressive increases in the contralateral insula and primary and secondary somatosensory...

  15. Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System.

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    Luma Issa Abdul-Kreem

    Full Text Available The visual cortex analyzes motion information along hierarchically arranged visual areas that interact through bidirectional interconnections. This work suggests a bio-inspired visual model focusing on the interactions of the cortical areas in which a new mechanism of feedforward and feedback processing are introduced. The model uses a neuromorphic vision sensor (silicon retina that simulates the spike-generation functionality of the biological retina. Our model takes into account two main model visual areas, namely V1 and MT, with different feature selectivities. The initial motion is estimated in model area V1 using spatiotemporal filters to locally detect the direction of motion. Here, we adapt the filtering scheme originally suggested by Adelson and Bergen to make it consistent with the spike representation of the DVS. The responses of area V1 are weighted and pooled by area MT cells which are selective to different velocities, i.e. direction and speed. Such feature selectivity is here derived from compositions of activities in the spatio-temporal domain and integrating over larger space-time regions (receptive fields. In order to account for the bidirectional coupling of cortical areas we match properties of the feature selectivity in both areas for feedback processing. For such linkage we integrate the responses over different speeds along a particular preferred direction. Normalization of activities is carried out over the spatial as well as the feature domains to balance the activities of individual neurons in model areas V1 and MT. Our model was tested using different stimuli that moved in different directions. The results reveal that the error margin between the estimated motion and synthetic ground truth is decreased in area MT comparing with the initial estimation of area V1. In addition, the modulated V1 cell activations shows an enhancement of the initial motion estimation that is steered by feedback signals from MT cells.

  16. Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements.

    Science.gov (United States)

    Ma, Xuan; Ma, Chaolin; Huang, Jian; Zhang, Peng; Xu, Jiang; He, Jiping

    2017-01-01

    Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed

  17. Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements

    Science.gov (United States)

    Ma, Xuan; Ma, Chaolin; Huang, Jian; Zhang, Peng; Xu, Jiang; He, Jiping

    2017-01-01

    Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed

  18. Modeling astrocytoma pathogenesis in vitro and in vivo using cortical astrocytes or neural stem cells from conditional, genetically engineered mice.

    Science.gov (United States)

    McNeill, Robert S; Schmid, Ralf S; Bash, Ryan E; Vitucci, Mark; White, Kristen K; Werneke, Andrea M; Constance, Brian H; Huff, Byron; Miller, C Ryan

    2014-08-12

    Current astrocytoma models are limited in their ability to define the roles of oncogenic mutations in specific brain cell types during disease pathogenesis and their utility for preclinical drug development. In order to design a better model system for these applications, phenotypically wild-type cortical astrocytes and neural stem cells (NSC) from conditional, genetically engineered mice (GEM) that harbor various combinations of floxed oncogenic alleles were harvested and grown in culture. Genetic recombination was induced in vitro using adenoviral Cre-mediated recombination, resulting in expression of mutated oncogenes and deletion of tumor suppressor genes. The phenotypic consequences of these mutations were defined by measuring proliferation, transformation, and drug response in vitro. Orthotopic allograft models, whereby transformed cells are stereotactically injected into the brains of immune-competent, syngeneic littermates, were developed to define the role of oncogenic mutations and cell type on tumorigenesis in vivo. Unlike most established human glioblastoma cell line xenografts, injection of transformed GEM-derived cortical astrocytes into the brains of immune-competent littermates produced astrocytomas, including the most aggressive subtype, glioblastoma, that recapitulated the histopathological hallmarks of human astrocytomas, including diffuse invasion of normal brain parenchyma. Bioluminescence imaging of orthotopic allografts from transformed astrocytes engineered to express luciferase was utilized to monitor in vivo tumor growth over time. Thus, astrocytoma models using astrocytes and NSC harvested from GEM with conditional oncogenic alleles provide an integrated system to study the genetics and cell biology of astrocytoma pathogenesis in vitro and in vivo and may be useful in preclinical drug development for these devastating diseases.

  19. Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model.

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    Robert Becker

    2015-09-01

    Full Text Available Oscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG - functional magnetic resonance imaging (fMRI studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations, and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals.

  20. Research On Neural Network Ensemble Algorithm Abstract Based On Selection Techniques%基于特征选择的神经网络集成研究

    Institute of Scientific and Technical Information of China (English)

    王涛

    2012-01-01

    本文提出了一种基于交叉验证和ReliefF的神经网络集成学习算法(CVRNNEn算法),首先利用交叉验证选取个体网络的训练数据集,然后再对每个训练集进行特征选择,来降低数据集的规模,减少相关性低的特征的对个体网络预测结果的干扰,提高了个体网络的预测精度和个体网络之间的差异度。算法代码在weka3.5.6平台上实现,通过在UCI数据集上仿真实验,和单个RBF网络的预测结果进行比较,得出CVRNNEn算法预测性能更优,从实验上证实了该算法在预测性能上的优势。%The thesis introduces a neural network ensemble based on cross validation and ReliefF algorithm named CVRNNEn.First of all,we need to obtain different training sets to train the networks,so the different training sets are obtained by some kind of cross-validation,and then for each training sets,use feature selection to eliminate redundant features in order to reduce the size of the data sets and the interference of redundant features.In this way,we improve the generalization ability of individual networks and increase the ambiguity of the ensemble,and then make the ensemble model to obtain better generalization performance.This algorithm is realized in weka 3.5.6 platform,by applying the CVRNNEn on UCI data sets in weka 3.5.6.Experiments also show that CVRNNEn is able to generate a set of trained networks that is more accurate and diverse than RBF ensemble approaches and to improve the quality of its ensemble.

  1. Neurofeedback of slow cortical potentials: neural mechanisms and feasibility of a placebo-controlled design in healthy adults

    Directory of Open Access Journals (Sweden)

    Holger eGevensleben

    2014-12-01

    Full Text Available To elucidate basic mechanisms underlying neurofeedback we investigated neural mechanisms of training of slow cortical potentials by considering EEG- and fMRI. Additionally, we analyzed the feasibility of a double-blind, placebo-controlled design in NF research based on regulation performance during treatment sessions and self-assessment of the participants. Twenty healthy adults participated in 16 sessions of SCP training: 9 participants received regular SCP training, 11 participants received sham feedback. At three time points (pre, intermediate, post fMRI and EEG/ERP-measurements were conducted during a continuous performance test (CPT. Performance-data during the sessions (regulation performance in the treatment group and the placebo group were analyzed. Analysis of EEG-activity revealed in the SCP group a strong enhancement of the CNV (electrode Cz at the intermediate assessment, followed by a decrease back to baseline at the post-treatment assessment. In contrast, in the placebo group a continuous but smaller increase of the CNV could be obtained from pre to post assessment. The increase of the CNV in the SCP group at intermediate testing was superior to the enhancement in the placebo group. The changes of the CNV were accompanied by a continuous improvement in the test performance of the CPT from pre to intermediate to post assessment comparable in both groups. The change of the CNV in the SCP group is interpreted as an indicator of neural plasticity and efficiency while an increase of the CNV in the placebo group might reflect learning and improved timing due to the frequent task repetition.In the fMRI analysis evidence was obtained for neuronal plasticity. After regular SCP neurofeedback activation in the posterior parietal cortex decreased from the pre- to the intermediate measurement and increased again in the post measurement, inversely following the U-shaped increase and decrease of the tCNV EEG amplitude in the SCP-trained group

  2. The development of neural synchrony and large-scale cortical networks during adolescence: relevance for the pathophysiology of schizophrenia and neurodevelopmental hypothesis.

    Science.gov (United States)

    Uhlhaas, Peter J; Singer, Wolf

    2011-05-01

    Recent data from developmental cognitive neuroscience highlight the profound changes in the organization and function of cortical networks during the transition from adolescence to adulthood. While previous studies have focused on the development of gray and white matter, recent evidence suggests that brain maturation during adolescence extends to fundamental changes in the properties of cortical circuits that in turn promote the precise temporal coding of neural activity. In the current article, we will highlight modifications in the amplitude and synchrony of neural oscillations during adolescence that may be crucial for the emergence of cognitive deficits and psychotic symptoms in schizophrenia. Specifically, we will suggest that schizophrenia is associated with impaired parameters of synchronous oscillations that undergo changes during late brain maturation, suggesting an important role of adolescent brain development for the understanding, treatment, and prevention of the disorder.

  3. Homocysteine Aggravates Cortical Neural Cell Injury through Neuronal Autophagy Overactivation following Rat Cerebral Ischemia-Reperfusion

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    Yaqian Zhao

    2016-07-01

    Full Text Available Elevated homocysteine (Hcy levels have been reported to be involved in neurotoxicity after ischemic stroke. However, the underlying mechanisms remain incompletely understood to date. In the current study, we hypothesized that neuronal autophagy activation may be involved in the toxic effect of Hcy on cortical neurons following cerebral ischemia. Brain cell injury was determined by hematoxylin-eosin (HE staining and TdT-mediated dUTP Nick-End Labeling (TUNEL staining. The level and localization of autophagy were detected by transmission electron microscopy, western blot and immunofluorescence double labeling. The oxidative DNA damage was revealed by immunofluorescence of 8-Hydroxy-2′-deoxyguanosine (8-OHdG. Hcy treatment aggravated neuronal cell death, significantly increased the formation of autophagosomes and the expression of LC3B and Beclin-1 in the brain cortex after middle cerebral artery occlusion-reperfusion (MCAO. Immunofluorescence analysis of LC3B and Beclin-1 distribution indicated that their expression occurred mainly in neurons (NeuN-positive and hardly in astrocytes (GFAP-positive. 8-OHdG expression was also increased in the ischemic cortex of Hcy-treated animals. Conversely, LC3B and Beclin-1 overexpression and autophagosome accumulation caused by Hcy were partially blocked by the autophagy inhibitor 3-methyladenine (3-MA. Hcy administration enhanced neuronal autophagy, which contributes to cell death following cerebral ischemia. The oxidative damage-mediated autophagy may be a molecular mechanism underlying neuronal cell toxicity of elevated Hcy level.

  4. Neural Correlates of Body and Face Perception Following Bilateral Destruction of the Primary Visual Cortices

    Directory of Open Access Journals (Sweden)

    Jan eVan den Stock

    2014-02-01

    Full Text Available Non-conscious visual processing of different object categories was investigated in a rare patient with bilateral destruction of the visual cortex (V1 and clinical blindness over the entire visual field. Images of biological and non-biological object categories were presented consisting of human bodies, faces, butterflies, cars, and scrambles. Behaviorally, only the body shape induced higher perceptual sensitivity, as revealed by signal detection analysis. Passive exposure to bodies and faces activated amygdala and superior temporal sulcus. In addition, bodies also activated the extrastriate body area, insula, orbitofrontal cortex (OFC and cerebellum. The results show that following bilateral damage to the primary visual cortex and ensuing complete cortical blindness, the human visual system is able to process categorical properties of human body shapes. This residual vision may be based on V1-independent input to body-selective areas along the ventral stream, in concert with areas involved in the representation of bodily states, like insula, OFC and cerebellum.

  5. Curcuma longa L. extract improves the cortical neural connectivity during the aging process

    Science.gov (United States)

    Flores, Gonzalo

    2017-01-01

    Turmeric or Curcuma is a natural product that has anti-inflammatory, antioxidant and anti-apoptotic pharmacological properties. It can be used in the control of the aging process that involves oxidative stress, inflammation, and apoptosis. Aging is a physiological process that affects higher cortical and cognitive functions with a reduction in learning and memory, limited judgment and deficits in emotional control and social behavior. Moreover, aging is a major risk factor for the appearance of several disorders such as cerebrovascular disease, diabetes mellitus, and hypertension. At the brain level, the aging process alters the synaptic intercommunication by a reduction in the dendritic arbor as well as the number of the dendritic spine in the pyramidal neurons of the prefrontal cortex, hippocampus and basolateral amygdala, consequently reducing the size of these regions. The present review discusses the synaptic changes caused by the aging process and the neuroprotective role the Curcuma has through its anti-inflammatory, antioxidant and anti-apoptotic actions PMID:28761413

  6. Multiple bases of human intelligence revealed by cortical thickness and neural activation.

    Science.gov (United States)

    Choi, Yu Yong; Shamosh, Noah A; Cho, Sun Hee; DeYoung, Colin G; Lee, Min Joo; Lee, Jong-Min; Kim, Sun I; Cho, Zang-Hee; Kim, Kyungjin; Gray, Jeremy R; Lee, Kun Ho

    2008-10-08

    We hypothesized that individual differences in intelligence (Spearman's g) are supported by multiple brain regions, and in particular that fluid (gF) and crystallized (gC) components of intelligence are related to brain function and structure with a distinct profile of association across brain regions. In 225 healthy young adults scanned with structural and functional magnetic resonance imaging sequences, regions of interest (ROIs) were defined on the basis of a correlation between g and either brain structure or brain function. In these ROIs, gC was more strongly related to structure (cortical thickness) than function, whereas gF was more strongly related to function (blood oxygenation level-dependent signal during reasoning) than structure. We further validated this finding by generating a neurometric prediction model of intelligence quotient (IQ) that explained 50% of variance in IQ in an independent sample. The data compel a nuanced view of the neurobiology of intelligence, providing the most persuasive evidence to date for theories emphasizing multiple distributed brain regions differing in function.

  7. Curcuma longa L. extract improves the cortical neural connectivity during the aging process

    Directory of Open Access Journals (Sweden)

    Gonzalo Flores

    2017-01-01

    Full Text Available Turmeric or Curcuma is a natural product that has anti-inflammatory, antioxidant and anti-apoptotic pharmacological properties. It can be used in the control of the aging process that involves oxidative stress, inflammation, and apoptosis. Aging is a physiological process that affects higher cortical and cognitive functions with a reduction in learning and memory, limited judgment and deficits in emotional control and social behavior. Moreover, aging is a major risk factor for the appearance of several disorders such as cerebrovascular disease, diabetes mellitus, and hypertension. At the brain level, the aging process alters the synaptic intercommunication by a reduction in the dendritic arbor as well as the number of the dendritic spine in the pyramidal neurons of the prefrontal cortex, hippocampus and basolateral amygdala, consequently reducing the size of these regions. The present review discusses the synaptic changes caused by the aging process and the neuroprotective role the Curcuma has through its anti-inflammatory, antioxidant and anti-apoptotic actions

  8. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box.

    Science.gov (United States)

    Ciompi, Francesco; de Hoop, Bartjan; van Riel, Sarah J; Chung, Kaman; Scholten, Ernst Th; Oudkerk, Matthijs; de Jong, Pim A; Prokop, Mathias; van Ginneken, Bram

    2015-12-01

    In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.

  9. Self-renewal and differentiation of reactive astrocyte-derived neural stem/progenitor cells isolated from the cortical peri-infarct area after stroke.

    Science.gov (United States)

    Shimada, Issei S; LeComte, Matthew D; Granger, Jerrica C; Quinlan, Noah J; Spees, Jeffrey L

    2012-06-06

    In response to stroke, subpopulations of cortical reactive astrocytes proliferate and express proteins commonly associated with neural stem/progenitor cells such as glial fibrillary acidic protein (GFAP) and Nestin. To examine the stem cell-related properties of cortical reactive astrocytes after injury, we generated GFAP-CreER(TM);tdRFP mice to permanently label reactive astrocytes. We isolated cells from the cortical peri-infarct area 3 d after stroke, and cultured them in neural stem cell medium containing epidermal growth factor and basic fibroblast growth factor. We observed tdRFP-positive neural spheres in culture, suggestive of tdRFP-positive reactive astrocyte-derived neural stem/progenitor cells (Rad-NSCs). Cultured Rad-NSCs self-renewed and differentiated into neurons, astrocytes, and oligodendrocytes. Pharmacological inhibition and conditional knock-out mouse studies showed that Presenilin 1 and Notch 1 controlled neural sphere formation by Rad-NSCs after stroke. To examine the self-renewal and differentiation potential of Rad-NSCs in vivo, Rad-NSCs were transplanted into embryonic, neonatal, and adult mouse brains. Transplanted Rad-NSCs were observed to persist in the subventricular zone and secondary Rad-NSCs were isolated from the host brain 28 d after transplantation. In contrast with neurogenic postnatal day 4 NSCs and adult NSCs from the subventricular zone, transplanted Rad-NSCs differentiated into astrocytes and oligodendrocytes, but not neurons, demonstrating that Rad-NSCs had restricted differentiation in vivo. Our results indicate that Rad-NSCs are unlikely to be suitable for neuronal replacement in the absence of genetic or epigenetic modification.

  10. Rapid evaluation of the durability of cortical neural implants using accelerated aging with reactive oxygen species

    Science.gov (United States)

    Takmakov, Pavel; Ruda, Kiersten; Phillips, K Scott; Isayeva, Irada S; Krauthamer, Victor; Welle, Cristin G

    2017-01-01

    Objective A challenge for implementing high bandwidth cortical brain–machine interface devices in patients is the limited functional lifespan of implanted recording electrodes. Development of implant technology currently requires extensive non-clinical testing to demonstrate device performance. However, testing the durability of the implants in vivo is time-consuming and expensive. Validated in vitro methodologies may reduce the need for extensive testing in animal models. Approach Here we describe an in vitro platform for rapid evaluation of implant stability. We designed a reactive accelerated aging (RAA) protocol that employs elevated temperature and reactive oxygen species (ROS) to create a harsh aging environment. Commercially available microelectrode arrays (MEAs) were placed in a solution of hydrogen peroxide at 87 °C for a period of 7 days. We monitored changes to the implants with scanning electron microscopy and broad spectrum electrochemical impedance spectroscopy (1 Hz–1 MHz) and correlated the physical changes with impedance data to identify markers associated with implant failure. Main results RAA produced a diverse range of effects on the structural integrity and electrochemical properties of electrodes. Temperature and ROS appeared to have different effects on structural elements, with increased temperature causing insulation loss from the electrode microwires, and ROS concentration correlating with tungsten metal dissolution. All array types experienced impedance declines, consistent with published literature showing chronic (>30 days) declines in array impedance in vivo. Impedance change was greatest at frequencies <10 Hz, and smallest at frequencies 1 kHz and above. Though electrode performance is traditionally characterized by impedance at 1 kHz, our results indicate that an impedance change at 1 kHz is not a reliable predictive marker of implant degradation or failure. Significance ROS, which are known to be present in vivo, can create

  11. DYRK1A-mediated Cyclin D1 Degradation in Neural Stem Cells Contributes to the Neurogenic Cortical Defects in Down Syndrome

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    Sònia Najas

    2015-02-01

    Full Text Available Alterations in cerebral cortex connectivity lead to intellectual disability and in Down syndrome, this is associated with a deficit in cortical neurons that arises during prenatal development. However, the pathogenic mechanisms that cause this deficit have not yet been defined. Here we show that the human DYRK1A kinase on chromosome 21 tightly regulates the nuclear levels of Cyclin D1 in embryonic cortical stem (radial glia cells, and that a modest increase in DYRK1A protein in transgenic embryos lengthens the G1 phase in these progenitors. These alterations promote asymmetric proliferative divisions at the expense of neurogenic divisions, producing a deficit in cortical projection neurons that persists in postnatal stages. Moreover, radial glial progenitors in the Ts65Dn mouse model of Down syndrome have less Cyclin D1, and Dyrk1a is the triplicated gene that causes both early cortical neurogenic defects and decreased nuclear Cyclin D1 levels in this model. These data provide insights into the mechanisms that couple cell cycle regulation and neuron production in cortical neural stem cells, emphasizing that the deleterious effect of DYRK1A triplication in the formation of the cerebral cortex begins at the onset of neurogenesis, which is relevant to the search for early therapeutic interventions in Down syndrome.

  12. NEURAL CORRELATES FOR APATHY: FRONTAL - PREFRONTAL AND PARIETAL CORTICAL - SUBCORTICAL CIRCUITS

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    Rita Moretti

    2016-12-01

    Full Text Available Apathy is an uncertain nosographical entity, which includes reduced motivation, abulia, decreased empathy, and lack of emotional invovlement; it is an important and heavy-burden clinical condition which strongly impacts in every day life events, affects the common daily living abilities, reduced the inner goal directed behavior, and gives the heaviest burden on caregivers. Is a quite common comorbidity of many neurological disease, However, there is no definite consensus on the role of apathy in clinical practice, no definite data on anatomical circuits involved in its development, and no definite instrument to detect it at bedside. As a general observation, the occurrence of apathy is connected to damage of prefrontal cortex (PFC and basal ganglia; emotional affective apathy may be related to the orbitomedial PFC and ventral striatum; cognitive apathy may be associated with dysfunction of lateral PFC and dorsal caudate nuclei; deficit of autoactivation may be due to bilateral lesions of the internal portion of globus pallidus, bilateral paramedian thalamic lesions, or the dorsomedial portion of PFC. On the other hand, apathy severity has been connected to neurofibrillary tangles density in the anterior cingulate gyrus and to grey matter atrophy in the anterior cingulate (ACC and in the left medial frontal cortex, confirmed by functional imaging studies. These neural networks are linked to projects, judjing and planning, execution and selection common actions, and through the basolateral amygdala and nucleus accumbens projects to the frontostriatal and to the dorsolateral prefrontal cortex. Therefore, an alteration of these circuitry caused a lack of insight, a reduction of decision-making strategies and a reduced speedness in action decsion, major resposnible for apathy. Emergent role concerns also the parietal cortex, with its direct action motivation control.We will discuss the importance of these circuits in different pathologies

  13. Effects of glossy privet fruit on neural cell apoptosis in the cortical parietal lobe and hippocampal CA1 region of vascular dementia rats

    Institute of Scientific and Technical Information of China (English)

    Jing Cai; Fan Zhou; Jian Du

    2008-01-01

    BACKGROUND: Glossy privet fruit inhibits neural cell apoptosis following the onset of vascular dementia. OBJECTIVE: To confirm glossy privet fruit effects on neural cell apoptosis in the cortical parietal lobe and hippocampal CA1 region of rat models of vascular dementia using molecular biology techniques. DESIGN, TIME AND SETTING: The neural cell morphology experiment was performed at the Laboratory of Flow Cells and Biochemistry, Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, and the Basic Room of Pathology, Academy of Chinese Medicine from December 2006 to May 2008.MATERIALS: A total of 60 Wistar rats were used to establish vascular dementia models using a photochemical reaction method. Glossy privet fruit was purchased from Fujian, China. Hydergine was co-produced by Sandoz, Switzerland and Huajin, China. METHODS: The 60 Wistar rats were randomly divided into 6 equal sized groups (n = 10), I.e. Model, blank, high, moderate and low doses of Chinese medicine, and hydergine control groups. Rats in the model group were treated with distilled water (1 mL/100 g) by gavage following model establishment. Rats in the blank group underwent experimental procedures as for the model group, except that rat models were created without illumination. Rats in the high, moderate and low doses of Chinese medicine groups, and the hydergine control group respectively received high, moderate and low doses of glossy privet fruit, and hydergine suspension (1 mL/100 g) by gavage, once a day, for 30 days. MAIN OUTCOME MEASURES: Morphology of neural cells from the rat cortical parietal lobe and hippocampal CA1 region of all groups was observed with an electron microscope. Positive neural cells in the injury site of the rat cortical parietal lobe and hippocampal CA1 region were investigated using the Fas immunohistochemical method. Absorbance of Fas-positive neurons was detected by the MPIAS-500 multimedia color imaging analysis system. RESULTS: Neural

  14. 基于空间拟合和神经网络的机场噪声预测集成模型%Airport noise prediction ensemble model based on space fitting and neural network

    Institute of Scientific and Technical Information of China (English)

    徐涛; 苏瀚; 杨国庆

    2016-01-01

    将集成学习方法引入到机场噪声预测中,提出一种基于空间拟合和神经网络的机场噪声预测集成模型.该模型采用空间拟合算法和BP神经网络算法构建基学习器,然后通过所提出的基于观察学习的异构集成算法将基学习器集成起来,获得集成的机场噪声预测结果.该模型通过集成多个异构机场噪声预测基学习器,能够有效提升预测准确率.实验结果表明,本文所提出的基于观察学习的异构集成算法,较之其他异构集成算法,在解决机场噪声预测问题上准确性更高、容错性更强.%This paper proposes an airport noise ensemble prediction model based on space fitting and neural network by introducing ensemble learning method. Space fitting and BP neural network is used respectively to create the base learner and a heterogeneous ensemble algorithm based on observational learning is used to integrate these base learners. The final ensemble model thus can improve prediction accuracy effectively by integrating multiple heterogeneous base prediction learners. The experimental results shows that the proposed heterogeneous ensemble algorithm based on observational learning is better than other heterogeneous ensemble algorithms on accuracy and tolerance for solving the airport noise prediction problem.

  15. 基于神经网络集成的入侵检测系统%An Intrusion Detection System Based on Neural Network Ensembles

    Institute of Scientific and Technical Information of China (English)

    徐敏; 沈晓红; 顾颀

    2011-01-01

    With the problems of the low detection rate and the insufficient sensitivity to the new intrusion, the current intrusion detection systems affect the functions of the entre system. Based on the very deep research, this paper proposes a new neural network ensembles method for intrusion detection. The method is used to train the individual networks on the basis of data reduction. Neural network techniques are used to combine the different classification results. Theory and experiment show that the model is effective.%目前,较为成熟的入侵检测系统普遍存在检测率偏低、对新的入侵不够敏感等问题,影响了系统的整体性能.在深入研究的基础上,本文提出了一种基于神经网络集成的入侵检测方法.该方法采用神经网络集成分类技术,在去除冗余数据的基础上对成员网络进行训练,并通过动态的方法确定成员网络的个数,最终通过神经网络对成员网络结果进行融合,以提高系统的整体性能.理论和实验表明,该方法能在保证成员网络差异性的同时提高入侵的检测率,具有较好的应用前景.

  16. Various multistage ensembles for prediction of heating energy consumption

    Directory of Open Access Journals (Sweden)

    Radisa Jovanovic

    2015-04-01

    Full Text Available Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble.

  17. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine

    Science.gov (United States)

    Acharya, Nachiketa; Shrivastava, Nitin Anand; Panigrahi, B. K.; Mohanty, U. C.

    2014-09-01

    The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982-2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982-2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson's and Kendal's correlation coefficient and Wilmort's index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.

  18. Investigation of the effects of process variables on derived properties of spray dried solid-dispersions using polymer based response surface model and ensemble artificial neural network models.

    Science.gov (United States)

    Patel, Ashwinkumar D; Agrawal, Anjali; Dave, Rutesh H

    2014-04-01

    The objective of this study was to use different statistical tools to understand and optimize the spray drying process to prepare solid dispersions. In this study we investigated the relationship between input variables (inlet temperature, feed concentration, flow rate, solvent and atomization parameters) and quality attributes (yield, outlet temperature and mean particle size) of spray dried solid dispersions (SSDs) using response surface model and ensemble artificial neural network. The Box Behnken design was developed to investigate the effect of various input variables on quality attributes of final products. Moreover, Pearson correlation analysis, self organizing map, contour plots and response surface plot were used to illustrate the relationship between input variables and quality attributes. The influence of different physicochemical properties of solvent on the quality attributes of spray dried products was also investigated. Final validation of prepared models was done using binary SSDs of six model drugs with PVP. Results demonstrated the effectiveness of proposed PVP based model which can help scientists to gain detailed understanding of spray drying process of solid dispersion using minimal resources and time during early formulation development stage. It will also help them to ensure consistent quality of SSDs using broad range of input variables. Copyright © 2013 Elsevier B.V. All rights reserved.

  19. Brainlab: a Python toolkit to aid in the design, simulation, and analysis of spiking neural networks with the NeoCortical Simulator

    Directory of Open Access Journals (Sweden)

    Richard P Drewes

    2009-05-01

    Full Text Available Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading ``glue'' tool for managing all sorts of complex programmatictasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS environment in particular. Brainlab is an integrated model building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS (the NeoCortical Simulator.

  20. Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator.

    Science.gov (United States)

    Drewes, Rich; Zou, Quan; Goodman, Philip H

    2009-01-01

    Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading "glue" tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS.

  1. Ensemble Methods

    Science.gov (United States)

    Re, Matteo; Valentini, Giorgio

    2012-03-01

    Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote ensemble, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall ensemble [158]. In the literature, a plethora of terms other than ensembles has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been

  2. NYYD Ensemble

    Index Scriptorium Estoniae

    2002-01-01

    NYYD Ensemble'i duost Traksmann - Lukk E.-S. Tüüri teosega "Symbiosis", mis on salvestatud ka hiljuti ilmunud NYYD Ensemble'i CDle. 2. märtsil Rakvere Teatri väikeses saalis ja 3. märtsil Rotermanni Soolalaos, kavas Tüür, Kaumann, Berio, Reich, Yun, Hauta-aho, Buckinx

  3. Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ⁹-tetrahydrocannabinol administration.

    Science.gov (United States)

    Fetterhoff, Dustin; Opris, Ioan; Simpson, Sean L; Deadwyler, Sam A; Hampson, Robert E; Kraft, Robert A

    2015-04-15

    Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing. Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain-computer interfaces and nonlinear neuronal models. Neurons involved in memory processing ("Functional Cell Types" or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid type-1 receptor (CB1R) partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons. WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events. z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain-computer interfaces. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

    Science.gov (United States)

    Ertosun, Mehmet Günhan; Rubin, Daniel L

    2015-01-01

    Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository.

  5. "Paradox of slow frequencies" - Are slow frequencies in upper cortical layers a neural predisposition of the level/state of consciousness (NPC)?

    Science.gov (United States)

    Northoff, Georg

    2017-09-01

    Consciousness research has much focused on faster frequencies like alpha or gamma while neglecting the slower ones in the infraslow (0.001-0.1Hz) and slow (0.1-1Hz) frequency range. These slower frequency ranges have a "bad reputation" though; their increase in power can observed during the loss of consciousness as in sleep, anesthesia, and vegetative state. However, at the same time, slower frequencies have been conceived instrumental for consciousness. The present paper aims to resolve this paradox which I describe as "paradox of slow frequencies". I first show various data that suggest a central role of slower frequencies in integrating faster ones, i.e., "temporo-spatial integration and nestedness". Such "temporo-spatial integration and nestedness" is disrupted during the loss of consciousness as in anesthesia and sleep leading to "temporo-spatial fragmentation and isolation" between slow and fast frequencies. Slow frequencies are supposedly mediated by neural activity in upper cortical layers in higher-order associative regions as distinguished from lower cortical layers that are related to faster frequencies. Taken together, slower and faster frequencies take on different roles for the level/state of consciousness. Faster frequencies by themselves are sufficient and thus a neural correlate of consciousness (NCC) while slower frequencies are a necessary non-sufficient condition of possible consciousness, e.g., a neural predisposition of the level/state of consciousness (NPC). This resolves the "paradox of slow frequencies" in that it assigns different roles to slower and faster frequencies in consciousness, i.e., NCC and NPC. Taken as NCC and NPC, fast and slow frequencies including their relation as in "temporo-spatial integration and nestedness" can be considered a first "building bloc" of a future "temporo-spatial theory of consciousness" (TTC) (Northoff, 2013; Northoff, 2014b; Northoff & Huang, 2017). Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Sensory cortical population dynamics uniquely track behavior across learning and extinction.

    Science.gov (United States)

    Moran, Anan; Katz, Donald B

    2014-01-22

    Neural responses in many cortical regions encode information relevant to behavior: information that necessarily changes as that behavior changes with learning. Although such responses are reasonably theorized to be related to behavior causation, the true nature of that relationship cannot be clarified by simple learning studies, which show primarily that responses change with experience. Neural activity that truly tracks behavior (as opposed to simply changing with experience) will not only change with learning but also change back when that learning is extinguished. Here, we directly probed for this pattern, recording the activity of ensembles of gustatory cortical single neurons as rats that normally consumed sucrose avidly were trained first to reject it (i.e., conditioned taste aversion learning) and then to enjoy it again (i.e., extinction), all within 49 h. Both learning and extinction altered cortical responses, consistent with the suggestion (based on indirect evidence) that extinction is a novel form of learning. But despite the fact that, as expected, postextinction single-neuron responses did not resemble "naive responses," ensemble response dynamics changed with learning and reverted with extinction: both the speed of stimulus processing and the relationships among ensemble responses to the different stimuli tracked behavioral relevance. These data suggest that population coding is linked to behavior with a fidelity that single-neuron coding is not.

  7. Popular Ensemble Methods: An Empirical Study

    CERN Document Server

    Maclin, R; 10.1613/jair.614

    2011-01-01

    An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being exa...

  8. Optimizing finite element predictions of local subchondral bone structural stiffness using neural network-derived density-modulus relationships for proximal tibial subchondral cortical and trabecular bone.

    Science.gov (United States)

    Nazemi, S Majid; Amini, Morteza; Kontulainen, Saija A; Milner, Jaques S; Holdsworth, David W; Masri, Bassam A; Wilson, David R; Johnston, James D

    2017-01-01

    Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain. However, it is unclear what density-modulus equation(s) should be applied with subchondral cortical and subchondral trabecular bone when constructing finite element models of the tibia. Using a novel approach applying neural networks, optimization, and back-calculation against in situ experimental testing results, the objective of this study was to identify subchondral-specific equations that optimized finite element predictions of local structural stiffness at the proximal tibial subchondral surface. Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using multiple density-modulus equations (93 total variations) then mapped to corresponding finite element models. For each variation, root mean squared error was calculated between finite element prediction and in situ measured stiffness at 47 indentation sites. Resulting errors were used to train an artificial neural network, which provided an unlimited number of model variations, with corresponding error, for predicting stiffness at the subchondral bone surface. Nelder-Mead optimization was used to identify optimum density-modulus equations for predicting stiffness. Finite element modeling predicted 81% of experimental stiffness variance (with 10.5% error) using optimized equations for subchondral cortical and trabecular bone differentiated with a 0.5g/cm(3) density. In comparison with published density-modulus relationships, optimized equations offered improved predictions of local subchondral structural stiffness. Further research is needed with anisotropy inclusion, a smaller voxel size and de-blurring algorithms to improve predictions. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Heterogeneous versus Homogeneous Machine Learning Ensembles

    Directory of Open Access Journals (Sweden)

    Petrakova Aleksandra

    2015-12-01

    Full Text Available The research demonstrates efficiency of the heterogeneous model ensemble application for a cancer diagnostic procedure. Machine learning methods used for the ensemble model training are neural networks, random forest, support vector machine and offspring selection genetic algorithm. Training of models and the ensemble design is performed by means of HeuristicLab software. The data used in the research have been provided by the General Hospital of Linz, Austria.

  10. Role of cortical neurodynamics for understanding the neural basis of motivated behavior - lessons from auditory category learning.

    Science.gov (United States)

    Ohl, Frank W

    2015-04-01

    Rhythmic activity appears in the auditory cortex in both microscopic and macroscopic observables and is modulated by both bottom-up and top-down processes. How this activity serves both types of processes is largely unknown. Here we review studies that have recently improved our understanding of potential functional roles of large-scale global dynamic activity patterns in auditory cortex. The experimental paradigm of auditory category learning allowed critical testing of the hypothesis that global auditory cortical activity states are associated with endogenous cognitive states mediating the meaning associated with an acoustic stimulus rather than with activity states that merely represent the stimulus for further processing.

  11. Formation and reverberation of sequential neural activity patterns evoked by sensory stimulation are enhanced during cortical desynchronization.

    Science.gov (United States)

    Bermudez Contreras, Edgar J; Schjetnan, Andrea Gomez Palacio; Muhammad, Arif; Bartho, Peter; McNaughton, Bruce L; Kolb, Bryan; Gruber, Aaron J; Luczak, Artur

    2013-08-07

    Memory formation is hypothesized to involve the generation of event-specific neural activity patterns during learning and the subsequent spontaneous reactivation of these patterns. Here, we present evidence that these processes can also be observed in urethane-anesthetized rats and are enhanced by desynchronized brain state evoked by tail pinch, subcortical carbachol infusion, or systemic amphetamine administration. During desynchronization, we found that repeated tactile or auditory stimulation evoked unique sequential patterns of neural firing in somatosensory and auditory cortex and that these patterns then reoccurred during subsequent spontaneous activity, similar to what we have observed in awake animals. Furthermore, the formation of these patterns was blocked by an NMDA receptor antagonist, suggesting that the phenomenon depends on synaptic plasticity. These results suggest that anesthetized animals with a desynchronized brain state could serve as a convenient model for studying stimulus-induced plasticity to improve our understanding of memory formation and replay in the brain.

  12. Implementing the cellular mechanisms of synaptic transmission in a neural mass model of the thalamo-cortical circuitry

    Directory of Open Access Journals (Sweden)

    Basabdatta Sen Bhattacharya

    2013-07-01

    Full Text Available A novel direction to existing neural mass modelling technique is proposed where the commonly used `alpha function' for representing synaptic transmission is replaced by a kinetic framework of neurotransmitter and receptor dynamics. The aim is to underpin neuro-transmission dynamics associated with abnormal brain rhythms commonly observed in neurological and psychiatric disorders. An existing thalamocortical neural mass model is modified by using the kinetic framework for modelling synaptic transmission mediated by glutamatergic and GABA (gamma-aminobutyric-acid-ergic receptors. The model output is compared qualitatively with existing literature on in-vitro experimental studies of ferret thalamic slices, as well as on single-neuron-level model based studies of neuro-receptor and transmitter dynamics in the thalamocortical tissue. The results are consistent with these studies: the activation of ligand-gated GABA receptors is essential for generation of spindle waves in the model, while blocking this pathway leads to low-frequency synchronised oscillations such as observed in slow-wave sleep; the frequency of spindle oscillations increase with increased levels of post-synaptic membrane conductance for AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic-acid receptors, and blocking this pathway effects a quiescent model output. In terms of computational efficiency, the simulation time is improved by a factor of ten compared to a similar neural mass model based on alpha functions. This implies a dramatic improvement in computational resources for large-scale network simulation using this model. Thus, the model provides a platform for correlating high-level brain oscillatory activity with low-level synaptic attributes, and makes a significant contribution towards advancements in current neural mass modelling paradigm as a potential computational tool to better the understanding of brain oscillations in sickness and in health.

  13. Implementing the cellular mechanisms of synaptic transmission in a neural mass model of the thalamo-cortical circuitry.

    Science.gov (United States)

    Bhattacharya, Basabdatta S

    2013-01-01

    A novel direction to existing neural mass modeling technique is proposed where the commonly used "alpha function" for representing synaptic transmission is replaced by a kinetic framework of neurotransmitter and receptor dynamics. The aim is to underpin neuro-transmission dynamics associated with abnormal brain rhythms commonly observed in neurological and psychiatric disorders. An existing thalamocortical neural mass model is modified by using the kinetic framework for modeling synaptic transmission mediated by glutamatergic and GABA (gamma-aminobutyric-acid)-ergic receptors. The model output is compared qualitatively with existing literature on in vitro experimental studies of ferret thalamic slices, as well as on single-neuron-level model based studies of neuro-receptor and transmitter dynamics in the thalamocortical tissue. The results are consistent with these studies: the activation of ligand-gated GABA receptors is essential for generation of spindle waves in the model, while blocking this pathway leads to low-frequency synchronized oscillations such as observed in slow-wave sleep; the frequency of spindle oscillations increase with increased levels of post-synaptic membrane conductance for AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic-acid) receptors, and blocking this pathway effects a quiescent model output. In terms of computational efficiency, the simulation time is improved by a factor of 10 compared to a similar neural mass model based on alpha functions. This implies a dramatic improvement in computational resources for large-scale network simulation using this model. Thus, the model provides a platform for correlating high-level brain oscillatory activity with low-level synaptic attributes, and makes a significant contribution toward advancements in current neural mass modeling paradigm as a potential computational tool to better the understanding of brain oscillations in sickness and in health.

  14. Prediction of malignancy degree in brain glioma using selective neural networks ensemble%基于聚类算法的选择性神经网络集成在大脑胶质瘤诊断中的应用

    Institute of Scientific and Technical Information of China (English)

    刘天羽; 李国正; 吴耿锋

    2006-01-01

    A clustering algorithm based selective neural networks ensemble (CLUSEN) is proposed to predict the degree of malignancy in brain glioma.Since the degree prediction of malignancy is critical before brain surgery, many learning methods are used like rule induction algorithm, single neural networks, support vector machines, etc.Ensemble learning methods can improve the generalization of single learning machine, and are becoming popular in the machine learning and medical data processing communities.The procedure of CLUSEN can efficiently remove redundancy learning individuals and help improve the diversity of ensemble methods.CLUSEN is used to predict the degree of malignancy in brain glioma.Experimental results on a set of brain glioma data show that, compared to support vector machines, rule induction and single neural networks, the classification accuracy of CLUSEN is higher.

  15. Neural basis of moral elevation demonstrated through inter-subject synchronization of cortical activity during free-viewing.

    Directory of Open Access Journals (Sweden)

    Zoë A Englander

    Full Text Available BACKGROUND: Most research investigating the neural basis of social emotions has examined emotions that give rise to negative evaluations of others (e.g. anger, disgust. Emotions triggered by the virtues and excellences of others have been largely ignored. Using fMRI, we investigated the neural basis of two "other-praising" emotions--Moral Elevation (a response to witnessing acts of moral beauty, and Admiration (which we restricted to admiration for physical skill. METHODOLOGY/PRINCIPAL FINDINGS: Ten participants viewed the same nine video clips. Three clips elicited moral elevation, three elicited admiration, and three were emotionally neutral. We then performed pair-wise voxel-by-voxel correlations of the BOLD signal between individuals for each video clip and a separate resting-state run. We observed a high degree of inter-subject synchronization, regardless of stimulus type, across several brain regions during free-viewing of videos. Videos in the elevation condition evoked significant inter-subject synchronization in brain regions previously implicated in self-referential and interoceptive processes, including the medial prefrontal cortex, precuneus, and insula. The degree of synchronization was highly variable over the course of the videos, with the strongest synchrony occurring during portions of the videos that were independently rated as most emotionally arousing. Synchrony in these same brain regions was not consistently observed during the admiration videos, and was absent for the neutral videos. CONCLUSIONS/SIGNIFICANCE: Results suggest that the neural systems supporting moral elevation are remarkably consistent across subjects viewing the same emotional content. We demonstrate that model-free techniques such as inter-subject synchronization may be a useful tool for studying complex, context dependent emotions such as self-transcendent emotion.

  16. Neural Basis of Moral Elevation Demonstrated through Inter-Subject Synchronization of Cortical Activity during Free-Viewing

    Science.gov (United States)

    Englander, Zoë A.; Haidt, Jonathan; Morris, James P.

    2012-01-01

    Background Most research investigating the neural basis of social emotions has examined emotions that give rise to negative evaluations of others (e.g. anger, disgust). Emotions triggered by the virtues and excellences of others have been largely ignored. Using fMRI, we investigated the neural basis of two “other-praising" emotions – Moral Elevation (a response to witnessing acts of moral beauty), and Admiration (which we restricted to admiration for physical skill). Methodology/Principal Findings Ten participants viewed the same nine video clips. Three clips elicited moral elevation, three elicited admiration, and three were emotionally neutral. We then performed pair-wise voxel-by-voxel correlations of the BOLD signal between individuals for each video clip and a separate resting-state run. We observed a high degree of inter-subject synchronization, regardless of stimulus type, across several brain regions during free-viewing of videos. Videos in the elevation condition evoked significant inter-subject synchronization in brain regions previously implicated in self-referential and interoceptive processes, including the medial prefrontal cortex, precuneus, and insula. The degree of synchronization was highly variable over the course of the videos, with the strongest synchrony occurring during portions of the videos that were independently rated as most emotionally arousing. Synchrony in these same brain regions was not consistently observed during the admiration videos, and was absent for the neutral videos. Conclusions/Significance Results suggest that the neural systems supporting moral elevation are remarkably consistent across subjects viewing the same emotional content. We demonstrate that model-free techniques such as inter-subject synchronization may be a useful tool for studying complex, context dependent emotions such as self-transcendent emotion. PMID:22745745

  17. 中国入境旅游需求预测的神经网络集成模型研究%Neural Network Ensemble for Chinese Inbound Tourism Demand Prediction

    Institute of Scientific and Technical Information of China (English)

    张郴; 张捷

    2011-01-01

    Accurate prediction of the tourism demand is important for tourism management. The state-of-art methods for predictive modeling used in tourism research include traditional statistical methods, soft Computing methods, and artificial intelligence methods. Note that artificial intelligence methods, which were introdueod to tourism research inthe 1990s, have greatly improved the predictive accuracy of modeling methods. Machine learning is an important area of artificial intelligence, which has been widely recognized as a powerful tool for ,intelligent data analysis. BP neural network, as one of the traditional machine learning technique, has been widely used to construct predictive model for intelligent data analysis. However, BP neural network suffers from several drawbacks, such as overfitting, difficulties in setting parameters, and local minima problem, and hence the per- formance of BP neural network is very unstable in practical applications. This paper combines an advanced machine learning paradigm named ensemble learning with BP neural network to build neural network ensemble for tourism demand prediction. This study conducts predictive modeling for tourism demand of three important tourist source countries of US, Britain and Australia for travel to Chinese mainland. The results show that, by eombi, n ing a number., of diverse neural networks, neural network ensemble significantly improves the predictive accuracy over traditional statistical methods and traditional machine learning methods including single BP neural network, Such method provides a better choice for more accurate predictive modeling for tourism demand.%对入境旅游需求进行科学合理的预测直接关系到中国入境旅游发展战略的制定和实施,具有积极的现实意义。目前,BP神经网络作为一种常见的传统机器学习方法,被广泛用于旅游需求预测建模。然而,由于BP神经网络存在诸如易过配、参数设置难、获得全局最优

  18. Ensembl 2017

    Science.gov (United States)

    Aken, Bronwen L.; Achuthan, Premanand; Akanni, Wasiu; Amode, M. Ridwan; Bernsdorff, Friederike; Bhai, Jyothish; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.; Janacek, Sophie H.; Juettemann, Thomas; Keenan, Stephen; Laird, Matthew R.; Lavidas, Ilias; Maurel, Thomas; McLaren, William; Moore, Benjamin; Murphy, Daniel N.; Nag, Rishi; Newman, Victoria; Nuhn, Michael; Ong, Chuang Kee; Parker, Anne; Patricio, Mateus; Riat, Harpreet Singh; Sheppard, Daniel; Sparrow, Helen; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Walts, Brandon; Wilder, Steven P.; Zadissa, Amonida; Kostadima, Myrto; Martin, Fergal J.; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Staines, Daniel M.; Trevanion, Stephen J.; Cunningham, Fiona; Yates, Andrew; Zerbino, Daniel R.; Flicek, Paul

    2017-01-01

    Ensembl (www.ensembl.org) is a database and genome browser for enabling research on vertebrate genomes. We import, analyse, curate and integrate a diverse collection of large-scale reference data to create a more comprehensive view of genome biology than would be possible from any individual dataset. Our extensive data resources include evidence-based gene and regulatory region annotation, genome variation and gene trees. An accompanying suite of tools, infrastructure and programmatic access methods ensure uniform data analysis and distribution for all supported species. Together, these provide a comprehensive solution for large-scale and targeted genomics applications alike. Among many other developments over the past year, we have improved our resources for gene regulation and comparative genomics, and added CRISPR/Cas9 target sites. We released new browser functionality and tools, including improved filtering and prioritization of genome variation, Manhattan plot visualization for linkage disequilibrium and eQTL data, and an ontology search for phenotypes, traits and disease. We have also enhanced data discovery and access with a track hub registry and a selection of new REST end points. All Ensembl data are freely released to the scientific community and our source code is available via the open source Apache 2.0 license. PMID:27899575

  19. Dampened neural activity and abolition of epileptic-like activity in cortical slices by active ingredients of spices.

    Science.gov (United States)

    Pezzoli, Maurizio; Elhamdani, Abdeladim; Camacho, Susana; Meystre, Julie; González, Stephanie Michlig; le Coutre, Johannes; Markram, Henry

    2014-10-31

    Active ingredients of spices (AIS) modulate neural response in the peripheral nervous system, mainly through interaction with TRP channel/receptors. The present study explores how different AIS modulate neural response in layer 5 pyramidal neurons of S1 neocortex. The AIS tested are agonists of TRPV1/3, TRPM8 or TRPA1. Our results demonstrate that capsaicin, eugenol, menthol, icilin and cinnamaldehyde, but not AITC dampen the generation of APs in a voltage- and time-dependent manner. This effect was further tested for the TRPM8 ligands in the presence of a TRPM8 blocker (BCTC) and on TRPM8 KO mice. The observable effect was still present. Finally, the influence of the selected AIS was tested on in vitro gabazine-induced seizures. Results coincide with the above observations: except for cinnamaldehyde, the same AIS were able to reduce the number, duration of the AP bursts and increase the concentration of gabazine needed to elicit them. In conclusion, our data suggests that some of these AIS can modulate glutamatergic neurons in the brain through a TRP-independent pathway, regardless of whether the neurons are stimulated intracellularly or by hyperactive microcircuitry.

  20. Cortical region-specific engraftment of embryonic stem cell-derived neural progenitor cells restores axonal sprouting to a subcortical target and achieves motor functional recovery in a mouse model of neonatal hypoxic-ischemic brain injury

    Directory of Open Access Journals (Sweden)

    Mizuya eShinoyama

    2013-08-01

    Full Text Available Hypoxic–ischemic encephalopathy (HIE at birth could cause cerebral palsy, mental retardation, and epilepsy, which last throughout the individual’s lifetime. However, few restorative treatments for ischemic tissue are currently available. Cell replacement therapy offers the potential to rescue brain damage caused by HI and to restore motor function. In the present study, we evaluated the ability of embryonic stem cell-derived neural progenitor cells (ES-NPCs to become cortical deep layer neurons, to restore the neural network, and to repair brain damage in an HIE mouse model. ES cells stably expressing the reporter gene GFP are induced to a neural precursor state by stromal cell co-culture. Forty-hours after the induction of HIE, animals were grafted with ES-NPCs targeting the deep layer of the motor cortex in the ischemic brain. Motor function was evaluated 3 weeks after transplantation. Immunohistochemistry and neuroanatomical tracing with GFP were used to analyze neuronal differentiation and axonal sprouting. ES-NPCs could differentiate to cortical neurons with pyramidal morphology and expressed the deep layer-specific marker, Ctip2. The graft showed good survival and an appropriate innervation pattern via axonal sprouting from engrafted cells in the ischemic brain. The motor functions of the transplanted HIE mice also improved significantly compared to the sham-transplanted group. These findings suggest that cortical region specific engraftment of preconditioned cortical precursor cells could support motor functional recovery in the HIE model. It is not clear whether this is a direct effect of the engrafted cells or due to neurotrophic factors produced by these cells. These results suggest that cortical region-specific NPC engraftment is a promising therapeutic approach for brain repair.

  1. Cortical region-specific engraftment of embryonic stem cell-derived neural progenitor cells restores axonal sprouting to a subcortical target and achieves motor functional recovery in a mouse model of neonatal hypoxic-ischemic brain injury.

    Science.gov (United States)

    Shinoyama, Mizuya; Ideguchi, Makoto; Kida, Hiroyuki; Kajiwara, Koji; Kagawa, Yoshiteru; Maeda, Yoshihiko; Nomura, Sadahiro; Suzuki, Michiyasu

    2013-01-01

    Hypoxic-ischemic encephalopathy (HIE) at birth could cause cerebral palsy (CP), mental retardation, and epilepsy, which last throughout the individual's lifetime. However, few restorative treatments for ischemic tissue are currently available. Cell replacement therapy offers the potential to rescue brain damage caused by HI and to restore motor function. In the present study, we evaluated the ability of embryonic stem cell-derived neural progenitor cells (ES-NPCs) to become cortical deep layer neurons, to restore the neural network, and to repair brain damage in an HIE mouse model. ES cells stably expressing the reporter gene GFP are induced to a neural precursor state by stromal cell co-culture. Forty-hours after the induction of HIE, animals were grafted with ES-NPCs targeting the deep layer of the motor cortex in the ischemic brain. Motor function was evaluated 3 weeks after transplantation. Immunohistochemistry and neuroanatomical tracing with GFP were used to analyze neuronal differentiation and axonal sprouting. ES-NPCs could differentiate to cortical neurons with pyramidal morphology and expressed the deep layer-specific marker, Ctip2. The graft showed good survival and an appropriate innervation pattern via axonal sprouting from engrafted cells in the ischemic brain. The motor functions of the transplanted HIE mice also improved significantly compared to the sham-transplanted group. These findings suggest that cortical region specific engraftment of preconditioned cortical precursor cells could support motor functional recovery in the HIE model. It is not clear whether this is a direct effect of the engrafted cells or due to neurotrophic factors produced by these cells. These results suggest that cortical region-specific NPC engraftment is a promising therapeutic approach for brain repair.

  2. 基于神经网络集成方法预测膜蛋白类型%Prediction of Membrane Protein Types Based on Neural Network Ensemble Strategy

    Institute of Scientific and Technical Information of China (English)

    方海洋; 赵静; 汪益川; 宗福兴

    2013-01-01

    由Chou等人提出的预测膜蛋白分类的机器学习算法在近年来不断改进,使得预测膜蛋白类型的准确率越来越高。但是由于膜蛋白类分布不均衡而导致少数类的预测准确率非常低,使用神经网络集成方法能解决此问题。该方法中Bagging算法通过对多数类欠采样和少数类过采样来解决膜蛋白训练数据集不均衡问题。此外,用神经网络集成方法对已训练数据集和独立数据集进行分类测试,得出神经网络集成方法预测效果优于单个最好神经网络。该方法为解决蛋白质分类预测问题提供了一种新的策略,特别是训练数据集不均衡时,该方法的优势更加明显。%As a continuous effort to develop machine learning algorithms to predict membrane protein types that was initiated by Chou and Elrod,this study focuses on dealing with the problem of imbalanced training set of membrane protein types with the neural network ensemble. Bagging algorithm of the neural network ensemble has the advantage of dealing with imbalanced training set of membrane protein types by over-sampling minority classes and under-sampling majority classes. Furthermore,the perfor-mance of the neural network ensemble is found to be superior to the single best model from the results obtained through resubstitu-tion and independent dataset tests. The current approach represents a new strategy to deal with the problems of protein attribute pre-diction,especially when the training set is imbalanced,and hence is quite promising in the area of proteomics.

  3. The Physics of Decision Making:. Stochastic Differential Equations as Models for Neural Dynamics and Evidence Accumulation in Cortical Circuits

    Science.gov (United States)

    Holmes, Philip; Eckhoff, Philip; Wong-Lin, K. F.; Bogacz, Rafal; Zacksenhouse, Miriam; Cohen, Jonathan D.

    2010-03-01

    We describe how drift-diffusion (DD) processes - systems familiar in physics - can be used to model evidence accumulation and decision-making in two-alternative, forced choice tasks. We sketch the derivation of these stochastic differential equations from biophysically-detailed models of spiking neurons. DD processes are also continuum limits of the sequential probability ratio test and are therefore optimal in the sense that they deliver decisions of specified accuracy in the shortest possible time. This leaves open the critical balance of accuracy and speed. Using the DD model, we derive a speed-accuracy tradeoff that optimizes reward rate for a simple perceptual decision task, compare human performance with this benchmark, and discuss possible reasons for prevalent sub-optimality, focussing on the question of uncertain estimates of key parameters. We present an alternative theory of robust decisions that allows for uncertainty, and show that its predictions provide better fits to experimental data than a more prevalent account that emphasises a commitment to accuracy. The article illustrates how mathematical models can illuminate the neural basis of cognitive processes.

  4. Human Cortical Neural Stem Cells Expressing Insulin-Like Growth Factor-I: A Novel Cellular Therapy for Alzheimer's Disease.

    Science.gov (United States)

    McGinley, Lisa M; Sims, Erika; Lunn, J Simon; Kashlan, Osama N; Chen, Kevin S; Bruno, Elizabeth S; Pacut, Crystal M; Hazel, Tom; Johe, Karl; Sakowski, Stacey A; Feldman, Eva L

    2016-03-01

    Alzheimer's disease (AD) is the most prevalent age-related neurodegenerative disorder and a leading cause of dementia. Current treatment fails to modify underlying disease pathologies and very little progress has been made to develop effective drug treatments. Cellular therapies impact disease by multiple mechanisms, providing increased efficacy compared with traditional single-target approaches. In amyotrophic lateral sclerosis, we have shown that transplanted spinal neural stem cells (NSCs) integrate into the spinal cord, form synapses with the host, improve inflammation, and reduce disease-associated pathologies. Our current goal is to develop a similar "best in class" cellular therapy for AD. Here, we characterize a novel human cortex-derived NSC line modified to express insulin-like growth factor-I (IGF-I), HK532-IGF-I. Because IGF-I promotes neurogenesis and synaptogenesis in vivo, this enhanced NSC line offers additional environmental enrichment, enhanced neuroprotection, and a multifaceted approach to treating complex AD pathologies. We show that autocrine IGF-I production does not impact the cell secretome or normal cellular functions, including proliferation, migration, or maintenance of progenitor status. However, HK532-IGF-I cells preferentially differentiate into gamma-aminobutyric acid-ergic neurons, a subtype dysregulated in AD; produce increased vascular endothelial growth factor levels; and display an increased neuroprotective capacity in vitro. We also demonstrate that HK532-IGF-I cells survive peri-hippocampal transplantation in a murine AD model and exhibit long-term persistence in targeted brain areas. In conclusion, we believe that harnessing the benefits of cellular and IGF-I therapies together will provide the optimal therapeutic benefit to patients, and our findings support further preclinical development of HK532-IGF-I cells into a disease-modifying intervention for AD. ©AlphaMed Press.

  5. Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks.

    Science.gov (United States)

    Triggiani, Antonio I; Bevilacqua, Vitoantonio; Brunetti, Antonio; Lizio, Roberta; Tattoli, Giacomo; Cassano, Fabio; Soricelli, Andrea; Ferri, Raffaele; Nobili, Flavio; Gesualdo, Loreto; Barulli, Maria R; Tortelli, Rosanna; Cardinali, Valentina; Giannini, Antonio; Spagnolo, Pantaleo; Armenise, Silvia; Stocchi, Fabrizio; Buenza, Grazia; Scianatico, Gaetano; Logroscino, Giancarlo; Lacidogna, Giordano; Orzi, Francesco; Buttinelli, Carla; Giubilei, Franco; Del Percio, Claudio; Frisoni, Giovanni B; Babiloni, Claudio

    2016-01-01

    Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2-4 Hz), theta (4-8 Hz Hz), alpha1 (8-10.5 Hz), and alpha2 (10.5-13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification.

  6. Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks

    Science.gov (United States)

    Triggiani, Antonio I.; Bevilacqua, Vitoantonio; Brunetti, Antonio; Lizio, Roberta; Tattoli, Giacomo; Cassano, Fabio; Soricelli, Andrea; Ferri, Raffaele; Nobili, Flavio; Gesualdo, Loreto; Barulli, Maria R.; Tortelli, Rosanna; Cardinali, Valentina; Giannini, Antonio; Spagnolo, Pantaleo; Armenise, Silvia; Stocchi, Fabrizio; Buenza, Grazia; Scianatico, Gaetano; Logroscino, Giancarlo; Lacidogna, Giordano; Orzi, Francesco; Buttinelli, Carla; Giubilei, Franco; Del Percio, Claudio; Frisoni, Giovanni B.; Babiloni, Claudio

    2017-01-01

    Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2–4 Hz), theta (4–8 Hz Hz), alpha1 (8–10.5 Hz), and alpha2 (10.5–13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification. PMID:28184183

  7. Composed ensembles of random unitary ensembles

    CERN Document Server

    Pozniak, M; Kus, M; Pozniak, Marcin; Zyczkowski, Karol; Kus, Marek

    1997-01-01

    Composed ensembles of random unitary matrices are defined via products of matrices, each pertaining to a given canonical circular ensemble of Dyson. We investigate statistical properties of spectra of some composed ensembles and demonstrate their physical relevance. We discuss also the methods of generating random matrices distributed according to invariant Haar measure on the orthogonal and unitary group.

  8. Non-local quantum evolution of entangled ensemble states in neural nets and its significance for brain function and a theory of consciousness

    CERN Document Server

    Bieberich, E

    1999-01-01

    Current quantum theories of consciousness suggest a configuration space of an entangled ensemble state as global work space for conscious experience. This study will describe a procedure for adjustment of the singlet evolution of a quantum computation to a classical signal input by action potentials. The computational output of an entangled state in a single neuron will be selected in a network environment by "survival of the fittest" coupling with other neurons. Darwinian evolution of this coupling will result in a binding of action potentials to a convoluted orbit of phase-locked oscillations with harmonic, m-adic, or fractal periodicity. Progressive integration of signal inputs will evolve a present memory space independent from the history of construction. Implications for mental processes, e.g., associative memory, creativity, and consciousness will be discussed. A model for the generation of quantum coherence in a single neuron will be suggested.

  9. Layered Ensemble Architecture for Time Series Forecasting.

    Science.gov (United States)

    Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

    2016-01-01

    Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

  10. Genetic dissection of GABAergic neural circuits in mouse neocortex

    Directory of Open Access Journals (Sweden)

    Hiroki eTaniguchi

    2014-01-01

    Full Text Available Diverse and flexible cortical functions rely on the ability of neural circuits to perform multiple types of neuronal computations. GABAergic inhibitory interneurons significantly contribute to this task by regulating the balance of activity, synaptic integration, spiking, synchrony, and oscillation in a neural ensemble. GABAergic interneruons display a high degree of cellular diversity in morphology, physiology, connectivity, and gene expression. A considerable number of subtypes of GABAergic interneurons diversify modes of cortical inhibition, enabling various types of information processing in the cortex. Thus, comprehensively understanding fate specification, circuit assembly and physiological function of GABAergic interneurons is a key to elucidate the principles of cortical wiring and function. Recent advances in genetically encoded molecular tools have made a breakthrough to systematically study cortical circuitry at the molecular, cellular, circuit, and whole animal levels. However, the biggest obstacle to fully applying the power of these to analysis of GABAergic circuits was that there were no efficient and reliable methods to express them in subtypes of GABAergic interneurons. Here, I first summarize cortical interneuron diversity and current understanding of mechanisms, by which distinct classes of GABAergic interneurons are generated. I then review recent development in genetically encoded molecular tools for neural circuit research, and genetic targeting of GABAergic interneuron subtypes, particulary focusing on our recent effort to develop and characterize Cre/CreER knockin lines. Finally, I highlight recent success in genetic targeting of chandelier cells (ChCs, the most unique and distinct GABAergic interneuron subtype, and discuss what kind of questions need to be addressed to understand development and function of cortical inhibitory circuits.

  11. Infinite ensemble of support vector machines for prediction of ...

    African Journals Online (AJOL)

    user

    Many researchers have demonstrated the use of artificial neural networks (ANNs) to ..... Following section discusses the effect of infinite ensemble approach ..... major problem with artificial intelligence-based modeling approaches is their ...

  12. [Cortical blindness].

    Science.gov (United States)

    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.

  13. Electro-acupuncture exerts beneficial effects against cerebral ischemia and promotes the proliferation of neural progenitor cells in the cortical peri-infarct area through the Wnt/β-catenin signaling pathway.

    Science.gov (United States)

    Chen, Bin; Tao, Jing; Lin, Yukun; Lin, Ruhui; Liu, Weilin; Chen, Lidian

    2015-11-01

    Electro-acupuncture (EA) is a novel therapy based on combining traditional acupuncture with modern electrotherapy, and it is currently being investigated as a treatment for ischemic stroke. In the present study, we aimed to investigate the mechanisms through which EA regulates the proliferation of neural progenitor cells (NPCs) in the cortical peri‑infarct area after stroke. The neuroprotective effects of EA on ischemic rats were evaluated by determining the neurological deficit scores and cerebral infarct volumes. The proliferation of the NPCs and the activation of the Wnt/β‑catenin signaling pathway in the cortical peri‑infarct area were examined. Our results revealed that EA significantly alleviated neurological deficits, reduced the infarct volume and enhanced NPC proliferation [nestin/glial fibrillary acidic protein (GFAP)‑double positive] in the cortex of rats subjected to middle cerebral artery occlusion (MCAO). Moreover, the Wnt1 and β‑catenin mRNA and protein levels were increased, while glycogen synthase kinase‑3 (GSK3) transcription was suppressed by EA. These results suggest that the upregulatory effects of EA on the Wnt/β‑catenin signaling pathway may promote NPC proliferation in the cortical peri-infarct area after stroke, consequently providing a therapeutic effect against cerebral ischemia.

  14. Exploring ensemble visualization

    Science.gov (United States)

    Phadke, Madhura N.; Pinto, Lifford; Alabi, Oluwafemi; Harter, Jonathan; Taylor, Russell M., II; Wu, Xunlei; Petersen, Hannah; Bass, Steffen A.; Healey, Christopher G.

    2012-01-01

    An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data.

  15. Cortical plasticity and rehabilitation.

    Science.gov (United States)

    Moucha, Raluca; Kilgard, Michael P

    2006-01-01

    The brain is constantly adapting to environmental and endogenous changes (including injury) that occur at every stage of life. The mechanisms that regulate neural plasticity have been refined over millions of years. Motivation and sensory experience directly shape the rewiring that makes learning and neurological recovery possible. Guiding neural reorganization in a manner that facilitates recovery of function is a primary goal of neurological rehabilitation. As the rules that govern neural plasticity become better understood, it will be possible to manipulate the sensory and motor experience of patients to induce specific forms of plasticity. This review summarizes our current knowledge regarding factors that regulate cortical plasticity, illustrates specific forms of reorganization induced by control of each factor, and suggests how to exploit these factors for clinical benefit.

  16. A new ensemble feature selection and its application to pattern classification

    Institute of Scientific and Technical Information of China (English)

    Dongbo ZHANG; Yaonan WANG

    2009-01-01

    Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.

  17. Neural correlates of admiration and compassion

    Science.gov (United States)

    Immordino-Yang, Mary Helen; McColl, Andrea; Damasio, Hanna; Damasio, Antonio

    2009-01-01

    In an fMRI experiment, participants were exposed to narratives based on true stories designed to evoke admiration and compassion in 4 distinct categories: admiration for virtue (AV), admiration for skill (AS), compassion for social/psychological pain (CSP), and compassion for physical pain (CPP). The goal was to test hypotheses about recruitment of homeostatic, somatosensory, and consciousness-related neural systems during the processing of pain-related (compassion) and non-pain-related (admiration) social emotions along 2 dimensions: emotions about other peoples' social/psychological conditions (AV, CSP) and emotions about others' physical conditions (AS, CPP). Consistent with theoretical accounts, the experience of all 4 emotions engaged brain regions involved in interoceptive representation and homeostatic regulation, including anterior insula, anterior cingulate, hypothalamus, and mesencephalon. However, the study also revealed a previously undescribed pattern within the posteromedial cortices (the ensemble of precuneus, posterior cingulate cortex, and retrosplenial region), an intriguing territory currently known for its involvement in the default mode of brain operation and in self-related/consciousness processes: emotions pertaining to social/psychological and physical situations engaged different networks aligned, respectively, with interoceptive and exteroceptive neural systems. Finally, within the anterior insula, activity correlated with AV and CSP peaked later and was more sustained than that associated with CPP. Our findings contribute insights on the functions of the posteromedial cortices and on the recruitment of the anterior insula in social emotions concerned with physical versus psychological pain. PMID:19414310

  18. Prenatal Exposure to Autism-Specific Maternal Autoantibodies Alters Proliferation of Cortical Neural Precursor Cells, Enlarges Brain, and Increases Neuronal Size in Adult Animals.

    Science.gov (United States)

    Martínez-Cerdeño, Verónica; Camacho, Jasmin; Fox, Elizabeth; Miller, Elaine; Ariza, Jeanelle; Kienzle, Devon; Plank, Kaela; Noctor, Stephen C; Van de Water, Judy

    2016-01-01

    Autism spectrum disorders (ASDs) affect up to 1 in 68 children. Autism-specific autoantibodies directed against fetal brain proteins have been found exclusively in a subpopulation of mothers whose children were diagnosed with ASD or maternal autoantibody-related autism. We tested the impact of autoantibodies on brain development in mice by transferring human antigen-specific IgG directly into the cerebral ventricles of embryonic mice during cortical neurogenesis. We show that autoantibodies recognize radial glial cells during development. We also show that prenatal exposure to autism-specific maternal autoantibodies increased stem cell proliferation in the subventricular zone (SVZ) of the embryonic neocortex, increased adult brain size and weight, and increased the size of adult cortical neurons. We propose that prenatal exposure to autism-specific maternal autoantibodies directly affects radial glial cell development and presents a viable pathologic mechanism for the maternal autoantibody-related prenatal ASD risk factor.

  19. Making Tree Ensembles Interpretable

    OpenAIRE

    Hara, Satoshi; Hayashi, Kohei

    2016-01-01

    Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited. In this paper, we propose a post processing method that improves the model interpretability of tree ensembles. After learning a complex tree ensembles in a standard way, we approximate it by a simpler model that is interpretable for human. To obtain the simpler model, we derive the EM algorithm minimizing the KL divergence from the ...

  20. Neural connections of the posteromedial cortex in the macaque

    Science.gov (United States)

    Parvizi, Josef; Van Hoesen, Gary W.; Buckwalter, Joseph; Damasio, Antonio

    2006-01-01

    The posterior cingulate and the medial parietal cortices constitute an ensemble known as the posteromedial cortex (PMC), which consists of Brodmann areas 23, 29, 30, 31, and 7m. To understand the neural relationship of the PMC with the rest of the brain, we injected its component areas with four different anterograde and retrograde tracers in the cynomolgus monkey and found that all PMC areas are interconnected with each other and with the anterior cingulate, the mid-dorsolateral prefrontal, the lateral parietal cortices, and area TPO, as well as the thalamus, where projections from some of the PMC areas traverse in an uninterrupted bar-like manner, the dorsum of this structure from the posteriormost nuclei to its rostralmost tip. All PMC regions also receive projections from the claustrum and the basal forebrain and project to the caudate, the basis pontis, and the zona incerta. Moreover, the posterior cingulate areas are interconnected with the parahippocampal regions, whereas the medial parietal cortex projects only sparsely to the presubiculum. Although local interconnections and shared remote connections of all PMC components suggest a functional relationship among them, the distinct connections of each area with different neural structures suggests that distinct functional modules may be operating within the PMC. Our study provides a large-scale map of the PMC connections with the rest of the brain, which may serve as a useful tool for future studies of this cortical region and may contribute to elucidating its intriguing pattern of activity seen in recent functional imaging studies. PMID:16432221

  1. Multilevel ensemble Kalman filter

    KAUST Repository

    Chernov, Alexey

    2016-01-06

    This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.

  2. The Ensembl REST API: Ensembl Data for Any Language.

    Science.gov (United States)

    Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R S; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul

    2015-01-01

    We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest. © The Author 2014. Published by Oxford University Press.

  3. Grid cells and cortical representation.

    Science.gov (United States)

    Moser, Edvard I; Roudi, Yasser; Witter, Menno P; Kentros, Clifford; Bonhoeffer, Tobias; Moser, May-Britt

    2014-07-01

    One of the grand challenges in neuroscience is to comprehend neural computation in the association cortices, the parts of the cortex that have shown the largest expansion and differentiation during mammalian evolution and that are thought to contribute profoundly to the emergence of advanced cognition in humans. In this Review, we use grid cells in the medial entorhinal cortex as a gateway to understand network computation at a stage of cortical processing in which firing patterns are shaped not primarily by incoming sensory signals but to a large extent by the intrinsic properties of the local circuit.

  4. Activation of expression of brain-derived neurotrophic factor at the site of implantation of allogenic and xenogenic neural stem (progenitor) cells in rats with ischemic cortical stroke.

    Science.gov (United States)

    Chekhonin, V P; Lebedev, S V; Volkov, A I; Pavlov, K A; Ter-Arutyunyants, A A; Volgina, N E; Savchenko, E A; Grinenko, N F; Lazarenko, I P

    2011-02-01

    Ischemic stroke was modeled in the sensorimotor zone of the brain cortex in adult rats. Rat embryonic nervous tissue, neural stem cells from human olfactory epithelium, and rat fibroblasts (cell control) were implanted into the peri-infarction area of rats of different groups immediately after stroke modeling. Expression of BDNF mRNA was analyzed 7 days after surgery by real-time PCR. BDNF expression in cell preparation before their implantation was minimum. The expression of BDNF mRNA increased by 5-6 times in the areas of implantation of rat fibroblasts and human olfactory epithelium and by 23 times in the area of implantation of rat embryonic nervous tissue compared to periinfarction areas without cell implantation. These findings confirm the possibility of realization of the therapeutic effects of neural stem cells via expression of trophic factors.

  5. Ensemble Deep Learning for Biomedical Time Series Classification

    Directory of Open Access Journals (Sweden)

    Lin-peng Jin

    2016-01-01

    Full Text Available Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

  6. Ensemble Deep Learning for Biomedical Time Series Classification.

    Science.gov (United States)

    Jin, Lin-Peng; Dong, Jun

    2016-01-01

    Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

  7. Ensemble Deep Learning for Biomedical Time Series Classification

    Science.gov (United States)

    2016-01-01

    Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

  8. Modulation depth estimation and variable selection in state-space models for neural interfaces.

    Science.gov (United States)

    Malik, Wasim Q; Hochberg, Leigh R; Donoghue, John P; Brown, Emery N

    2015-02-01

    Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems.

  9. Ensemble Data Mining Methods

    Science.gov (United States)

    Oza, Nikunj C.

    2004-01-01

    Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

  10. Ensemble Data Mining Methods

    Data.gov (United States)

    National Aeronautics and Space Administration — Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve...

  11. Extended Ensemble Monte Carlo

    OpenAIRE

    Iba, Yukito

    2000-01-01

    ``Extended Ensemble Monte Carlo''is a generic term that indicates a set of algorithms which are now popular in a variety of fields in physics and statistical information processing. Exchange Monte Carlo (Metropolis-Coupled Chain, Parallel Tempering), Simulated Tempering (Expanded Ensemble Monte Carlo), and Multicanonical Monte Carlo (Adaptive Umbrella Sampling) are typical members of this family. Here we give a cross-disciplinary survey of these algorithms with special emphasis on the great f...

  12. Ensembl variation resources

    Directory of Open Access Journals (Sweden)

    Marin-Garcia Pablo

    2010-05-01

    Full Text Available Abstract Background The maturing field of genomics is rapidly increasing the number of sequenced genomes and producing more information from those previously sequenced. Much of this additional information is variation data derived from sampling multiple individuals of a given species with the goal of discovering new variants and characterising the population frequencies of the variants that are already known. These data have immense value for many studies, including those designed to understand evolution and connect genotype to phenotype. Maximising the utility of the data requires that it be stored in an accessible manner that facilitates the integration of variation data with other genome resources such as gene annotation and comparative genomics. Description The Ensembl project provides comprehensive and integrated variation resources for a wide variety of chordate genomes. This paper provides a detailed description of the sources of data and the methods for creating the Ensembl variation databases. It also explores the utility of the information by explaining the range of query options available, from using interactive web displays, to online data mining tools and connecting directly to the data servers programmatically. It gives a good overview of the variation resources and future plans for expanding the variation data within Ensembl. Conclusions Variation data is an important key to understanding the functional and phenotypic differences between individuals. The development of new sequencing and genotyping technologies is greatly increasing the amount of variation data known for almost all genomes. The Ensembl variation resources are integrated into the Ensembl genome browser and provide a comprehensive way to access this data in the context of a widely used genome bioinformatics system. All Ensembl data is freely available at http://www.ensembl.org and from the public MySQL database server at ensembldb.ensembl.org.

  13. Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

    Science.gov (United States)

    Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

    2017-09-01

    To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

  14. The cortical eye proprioceptive signal modulates neural activity in higher-order visual cortex as predicted by the variation in visual sensitivity

    DEFF Research Database (Denmark)

    Balslev, Daniela; Siebner, Hartwig R; Paulson, Olaf B

    2012-01-01

    Whereas the links between eye movements and the shifts in visual attention are well established, less is known about how eye position affects the prioritization of visual space. It was recently observed that visual sensitivity varies with the direction of gaze and the level of excitability...... in the eye proprioceptive representation in human left somatosensory cortex (S1(EYE)), so that after 1Hz repetitive transcranial magnetic stimulation (rTMS) over S1(EYE), targets presented nearer the center of the orbit are detected more accurately. Here we used whole-brain functional magnetic resonance...... target when the right eye was rotated leftwards as compared with when it was rotated rightwards. This effect was larger after S1(EYE)-rTMS than after rTMS of a control area in the motor cortex. The neural response to retinally identical stimuli in this area could be predicted from the changes in visual...

  15. Human cortical responses to slow and fast binaural beats reveal multiple mechanisms of binaural hearing

    National Research Council Canada - National Science Library

    Ross, Bernhard; Miyazaki, Takahiro; Thompson, Jessica; Jamali, Shahab; Fujioka, Takako

    2014-01-01

    .... To examine the neural representations underlying these different perceptions, we recorded neuromagnetic cortical responses while participants listened to binaural beats at a continuously varying rate...

  16. Cortical Correlates of Fitts’ Law

    Directory of Open Access Journals (Sweden)

    Peter eIfft

    2011-12-01

    Full Text Available Fitts' law describes the fundamental trade-off between movement accuracy and speed: It states that the duration of reaching movements is a function of target size and distance. While Fitts' law has been extensively studied in ergonomics and has guided the design of human-computer interfaces, there have been few studies on its neuronal correlates. To elucidate sensorimotor cortical activity underlying Fitts’ law, we implanted two monkeys with multielectrode arrays in the primary motor (M1 and primary somatosensory (S1 cortices. The monkeys performed reaches with a joystick-controlled cursor towards targets of different size. The reaction time, movement time and movement velocity changed with target size, and M1 and S1 activity reflected these changes. Moreover, modifications of cortical activity could not be explained by changes of movement parameters alone, but required target size as an additional parameter. Neuronal representation of target size was especially prominent during the early reaction time period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was mostly correlated with movement velocity. Neural decoders were applied to simultaneously decode target size and motor parameters from cortical modulations. We suggest using such classifiers to improve neuroprosthetic control.

  17. The semantic similarity ensemble

    Directory of Open Access Journals (Sweden)

    Andrea Ballatore

    2013-12-01

    Full Text Available Computational measures of semantic similarity between geographic terms provide valuable support across geographic information retrieval, data mining, and information integration. To date, a wide variety of approaches to geo-semantic similarity have been devised. A judgment of similarity is not intrinsically right or wrong, but obtains a certain degree of cognitive plausibility, depending on how closely it mimics human behavior. Thus selecting the most appropriate measure for a specific task is a significant challenge. To address this issue, we make an analogy between computational similarity measures and soliciting domain expert opinions, which incorporate a subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we define the semantic similarity ensemble (SSE as a composition of different similarity measures, acting as a panel of experts having to reach a decision on the semantic similarity of a set of geographic terms. The approach is evaluated in comparison to human judgments, and results indicate that an SSE performs better than the average of its parts. Although the best member tends to outperform the ensemble, all ensembles outperform the average performance of each ensemble's member. Hence, in contexts where the best measure is unknown, the ensemble provides a more cognitively plausible approach.

  18. Evolutionary Ensemble for In Silico Prediction of Ames Test Mutagenicity

    Science.gov (United States)

    Chen, Huanhuan; Yao, Xin

    Driven by new regulations and animal welfare, the need to develop in silico models has increased recently as alternative approaches to safety assessment of chemicals without animal testing. This paper describes a novel machine learning ensemble approach to building an in silico model for the prediction of the Ames test mutagenicity, one of a battery of the most commonly used experimental in vitro and in vivo genotoxicity tests for safety evaluation of chemicals. Evolutionary random neural ensemble with negative correlation learning (ERNE) [1] was developed based on neural networks and evolutionary algorithms. ERNE combines the method of bootstrap sampling on training data with the method of random subspace feature selection to ensure diversity in creating individuals within an initial ensemble. Furthermore, while evolving individuals within the ensemble, it makes use of the negative correlation learning, enabling individual NNs to be trained as accurate as possible while still manage to maintain them as diverse as possible. Therefore, the resulting individuals in the final ensemble are capable of cooperating collectively to achieve better generalization of prediction. The empirical experiment suggest that ERNE is an effective ensemble approach for predicting the Ames test mutagenicity of chemicals.

  19. Cortical Visual Impairment

    Science.gov (United States)

    ... 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 visual ...

  20. HIV-1-infected and immune-activated macrophages induce astrocytic differentiation of human cortical neural progenitor cells via the STAT3 pathway.

    Directory of Open Access Journals (Sweden)

    Hui Peng

    Full Text Available Diminished adult neurogenesis is considered a potential mechanism in the pathogenesis of HIV-1-associated dementia (HAD. In HAD, HIV-1-infected and immune-activated brain mononuclear phagocytes (MP; perivascular macrophages and microglia drive central nervous system (CNS inflammation and may alter normal neurogenesis. We previously demonstrated HIV-1-infected and lipopolysaccharide (LPS activated monocyte-derived macrophages (MDM inhibit human neural progenitor cell (NPC neurogenesis, while enhancing astrogliogenesis through the secretion of the inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α, in vitro and in vivo. Here we further test the hypothesis that HIV-1-infected/activated MDM promote NPC astrogliogenesis via activation of the transcription factor signal transducer and activator of transcription 3 (STAT3, a critical factor for astrogliogenesis. Our results show that LPS-activated MDM-conditioned medium (LPS-MCM and HIV-infected/LPS-activated MDM-conditioned medium (LPS+HIV-MCM induced Janus kinase 1 (Jak1 and STAT3 activation. Induction of the Jak-STAT3 activation correlated with increased glia fibrillary acidic protein (GFAP expression, demonstrating an induction of astrogliogenesis. Moreover, STAT3-targeting siRNA (siSTAT3 decreased MCM-induced STAT3 activation and NPC astrogliogenesis. Furthermore, inflammatory cytokines (including IL-6, IL-1β and TNF-α produced by LPS-activated and/or HIV-1-infected MDM may contribute to MCM-induced STAT3 activation and astrocytic differentiation. These observations were confirmed in severe combined immunodeficient (SCID mice with HIV-1 encephalitis (HIVE. In HIVE mice, siRNA control (without target sequence, sicon pre-transfected NPCs injected with HIV-1-infected MDM showed more astrocytic differentiation and less neuronal differentiation of NPCs as compared to NPC injection alone. siSTAT3 abrogated HIV-1-infected MDM-induced astrogliogenesis of injected NPCs. Collectively, these

  1. Protective Garment Ensemble

    Science.gov (United States)

    Wakefield, M. E.

    1982-01-01

    Protective garment ensemble with internally-mounted environmental- control unit contains its own air supply. Alternatively, a remote-environmental control unit or an air line is attached at the umbilical quick disconnect. Unit uses liquid air that is vaporized to provide both breathing air and cooling. Totally enclosed garment protects against toxic substances.

  2. Music Ensemble: Course Proposal.

    Science.gov (United States)

    Kovach, Brian

    A proposal is presented for a Music Ensemble course to be offered at the Community College of Philadelphia for music students who have had previous vocal or instrumental training. A standardized course proposal cover form is followed by a statement of purpose for the course, a list of major course goals, a course outline, and a bibliography. Next,…

  3. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Haakon

    2016-01-08

    The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.

  4. Subspace projection approaches to classification and visualization of neural network-level encoding patterns.

    Directory of Open Access Journals (Sweden)

    Remus Oşan

    Full Text Available Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA, Principal Components Analysis (PCA and Artificial Neural Networks (ANN, and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD. Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD.

  5. Efficient decoding with steady-state Kalman filter in neural interface systems.

    Science.gov (United States)

    Malik, Wasim Q; Truccolo, Wilson; Brown, Emery N; Hochberg, Leigh R

    2011-02-01

    The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

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  13. File list: His.Neu.50.AllAg.Cortical_neuron [Chip-atlas[Archive

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  15. File list: His.Neu.20.AllAg.Cortical_neuron [Chip-atlas[Archive

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  16. File list: ALL.Neu.05.AllAg.Cortical_neuron [Chip-atlas[Archive

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  17. File list: InP.Neu.05.AllAg.Cortical_neuron [Chip-atlas[Archive

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  18. File list: His.Neu.05.AllAg.Cortical_neuron [Chip-atlas[Archive

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  19. File list: Pol.Neu.10.AllAg.Cortical_neuron [Chip-atlas[Archive

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  20. File list: His.Neu.10.AllAg.Cortical_neuron [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  1. File list: Pol.Neu.50.AllAg.Cortical_neuron [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  2. File list: Oth.Neu.20.AllAg.Cortical_neuron [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  3. File list: ALL.Neu.10.AllAg.Cortical_neuron [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  4. File list: Oth.Neu.05.AllAg.Cortical_neuron [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  5. File list: Oth.Neu.50.AllAg.Cortical_neuron [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available Oth.Neu.50.AllAg.Cortical_neuron mm9 TFs and others Neural Cortical neuron SRX67167...3,SRX1057051 http://dbarchive.biosciencedbc.jp/kyushu-u/mm9/assembled/Oth.Neu.50.AllAg.Cortical_neuron.bed ...

  6. Effective Visualization of Temporal Ensembles.

    Science.gov (United States)

    Hao, Lihua; Healey, Christopher G; Bass, Steffen A

    2016-01-01

    An ensemble is a collection of related datasets, called members, built from a series of runs of a simulation or an experiment. Ensembles are large, temporal, multidimensional, and multivariate, making them difficult to analyze. Another important challenge is visualizing ensembles that vary both in space and time. Initial visualization techniques displayed ensembles with a small number of members, or presented an overview of an entire ensemble, but without potentially important details. Recently, researchers have suggested combining these two directions, allowing users to choose subsets of members to visualization. This manual selection process places the burden on the user to identify which members to explore. We first introduce a static ensemble visualization system that automatically helps users locate interesting subsets of members to visualize. We next extend the system to support analysis and visualization of temporal ensembles. We employ 3D shape comparison, cluster tree visualization, and glyph based visualization to represent different levels of detail within an ensemble. This strategy is used to provide two approaches for temporal ensemble analysis: (1) segment based ensemble analysis, to capture important shape transition time-steps, clusters groups of similar members, and identify common shape changes over time across multiple members; and (2) time-step based ensemble analysis, which assumes ensemble members are aligned in time by combining similar shapes at common time-steps. Both approaches enable users to interactively visualize and analyze a temporal ensemble from different perspectives at different levels of detail. We demonstrate our techniques on an ensemble studying matter transition from hadronic gas to quark-gluon plasma during gold-on-gold particle collisions.

  7. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Hakon

    2016-06-14

    This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.

  8. Critical behavior in topological ensembles

    CERN Document Server

    Bulycheva, K; Nechaev, S

    2014-01-01

    We consider the relation between three physical problems: 2D directed lattice random walks in an external magnetic field, ensembles of torus knots, and 5d Abelian SUSY gauge theory with massless hypermultiplet in $\\Omega$ background. All these systems exhibit the critical behavior typical for the "area+length" statistics of grand ensembles of 2D directed paths. In particular, using the combinatorial description, we have found the new critical behavior in the ensembles of the torus knots and in the instanton ensemble in 5d gauge theory. The relation with the integrable model is discussed.

  9. Cortical activation elicited by unrecognized stimuli

    Directory of Open Access Journals (Sweden)

    Badgaiyan Rajendra D

    2006-05-01

    Full Text Available Abstract Background It is unclear whether a stimulus that cannot be recognized consciously, could elicit a well-processed cognitive response. Methods We used functional imaging to examine the pattern of cortical activation elicited by unrecognized stimuli during memory processing. Subjects were given a recognition task using recognizable and non-recognizable subliminal stimuli. Results Unrecognized stimuli activated the cortical areas that are associated with retrieval attempt (left prefrontal, and novelty detection (left hippocampus. This indicates that the stimuli that were not consciously recognized, activated neural network associated with aspects of explicit memory processing. Conclusion Results suggest that conscious recognition of stimuli is not necessary for activation of cognitive processing.

  10. Flexibility of neural stem cells

    Directory of Open Access Journals (Sweden)

    Eumorphia eRemboutsika

    2011-04-01

    Full Text Available Embryonic cortical neural stem cells are self-renewing progenitors that can differentiate into neurons and glia. We generated neurospheres from the developing cerebral cortex using a mouse genetic model that allows for lineage selection and found that the self-renewing neural stem cells are restricted to Sox2 expressing cells. Under normal conditions, embryonic cortical neurospheres are heterogeneous with regard to Sox2 expression and contain astrocytes, neural stem cells and neural progenitor cells sufficiently plastic to give rise to neural crest cells when transplanted into the hindbrain of E1.5 chick and E8 mouse embryos. However, when neurospheres are maintained under lineage selection, such that all cells express Sox2, neural stem cells maintain their Pax6+ cortical radial glia identity and exhibit a more restricted fate in vitro and after transplantation. These data demonstrate that Sox2 preserves the cortical identity and regulates the plasticity of self-renewing Pax6+ radial glia cells.

  11. ESPC Coupled Global Ensemble Design

    Science.gov (United States)

    2014-09-30

    coupled system infrastructure and forecasting capabilities. Initial operational capability is targeted for 2018. APPROACH 1. It is recognized...provided will be the probability distribution function (PDF) of environmental conditions. It is expected that this distribution will have skill. To...system would be the initial capability for ensemble forecasts . Extensions to fully coupled ensembles would be the next step. 2. Develop an extended

  12. Botnet analysis using ensemble classifier

    Directory of Open Access Journals (Sweden)

    Anchit Bijalwan

    2016-09-01

    Full Text Available This paper analyses the botnet traffic using Ensemble of classifier algorithm to find out bot evidence. We used ISCX dataset for training and testing purpose. We extracted the features of both training and testing datasets. After extracting the features of this dataset, we bifurcated these features into two classes, normal traffic and botnet traffic and provide labelling. Thereafter using modern data mining tool, we have applied ensemble of classifier algorithm. Our experimental results show that the performance for finding bot evidence using ensemble of classifiers is better than single classifier. Ensemble based classifiers perform better than single classifier by either combining powers of multiple algorithms or introducing diversification to the same classifier by varying input in bot analysis. Our results are showing that by using voting method of ensemble based classifier accuracy is increased up to 96.41% from 93.37%.

  13. On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles

    KAUST Repository

    Luo, Xiaodong

    2010-09-19

    The ensemble square root filter (EnSRF) [1, 2, 3, 4] is a popular method for data assimilation in high dimensional systems (e.g., geophysics models). Essentially the EnSRF is a Monte Carlo implementation of the conventional Kalman filter (KF) [5, 6]. It is mainly different from the KF at the prediction steps, where it is some ensembles, rather then the means and covariance matrices, of the system state that are propagated forward. In doing this, the EnSRF is computationally more efficient than the KF, since propagating a covariance matrix forward in high dimensional systems is prohibitively expensive. In addition, the EnSRF is also very convenient in implementation. By propagating the ensembles of the system state, the EnSRF can be directly applied to nonlinear systems without any change in comparison to the assimilation procedures in linear systems. However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].

  14. Extradural cortical stimulation for neural network recovery in stroke patients%硬膜外植入式皮质刺激脑卒中患者提高神经网络功能

    Institute of Scientific and Technical Information of China (English)

    赵健乐; 李景琦; 牛森林; 高坚

    2014-01-01

    背景:硬膜外植入式皮质刺激兼顾了经颅磁刺激、经颅直流电刺激、硬膜下皮质刺激和深部脑刺激的优点,可显著改善脑卒中后的肢体运动与语言功能。目的:综述近年来有关硬膜外植入式皮质刺激在脑卒中康复中的研究及其临床应用。方法:由第一作者应用计算机检索1995年1月至2014年4月PubMed 数据库及中国期刊全文数据库文献,检索关键词为“cortical stimulation,extradural motor cortex stimulation,extradural cortical implants, extradural cortical stimulation,stroke,rehabilitation;皮质刺激,硬膜外电刺激,硬膜外皮质植入,硬膜外皮质刺激,脑卒中,康复”。纳入有关硬膜外植入式皮质刺激在脑卒中后运动与言语障碍中应用的文章。结果与结论:硬膜外皮质刺激是植入式皮质刺激,其优势是侵入性小、高度精确性和经硬膜与大脑密切接触,对缺乏有效治疗的脑卒中慢性期运动和语言障碍患者来说,这有可能是一种新的治疗方法。硬膜外皮质刺激通过促进神经可塑性、促进病灶周围结构与功能改变、提高神经网络功能、促进大脑半球间功能平衡及增加感觉输入来改善脑卒中后的肢体运动功能与语言功能。%BACKGROUND:Extradural cortical stimulation combines the advantages of repetitive transcranial magnetic stimulation, transcranial direct current stimulation, subdural cortical stimulation and deep brain stimulation, which can significantly improve motor and language function after stroke. OBJECTIVE:To review the theoretical research and clinical application of extradural cortical stimulation for stroke recovery. METHODS:An online retrieval of PubMed database and CNKI database between January 1995 and April 2014 was performed for articles on theoretical research and clinical application of extradural cortical stimulation for stroke recovery, with the key words of“cortical

  15. Assortment of GABAergic plasticity in the cortical interneuron melting pot.

    Science.gov (United States)

    Méndez, Pablo; Bacci, Alberto

    2011-01-01

    Cortical structures of the adult mammalian brain are characterized by a spectacular diversity of inhibitory interneurons, which use GABA as neurotransmitter. GABAergic neurotransmission is fundamental for integrating and filtering incoming information and dictating postsynaptic neuronal spike timing, therefore providing a tight temporal code used by each neuron, or ensemble of neurons, to perform sophisticated computational operations. However, the heterogeneity of cortical GABAergic cells is associated to equally diverse properties governing intrinsic excitability as well as strength, dynamic range, spatial extent, anatomical localization, and molecular components of inhibitory synaptic connections that they form with pyramidal neurons. Recent studies showed that similarly to their excitatory (glutamatergic) counterparts, also inhibitory synapses can undergo activity-dependent changes in their strength. Here, some aspects related to plasticity and modulation of adult cortical and hippocampal GABAergic synaptic transmission will be reviewed, aiming at providing a fresh perspective towards the elucidation of the role played by specific cellular elements of cortical microcircuits during both physiological and pathological operations.

  16. 基于RB F神经网络的集成增量学习算法%RESEARCH ON RBF NEURAL NETWORK-BASED ENSEMBLE INCREMENTAL LEARNING ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    彭玉青; 赵翠翠; 高晴晴

    2016-01-01

    针对增量学习的遗忘性问题和集成增量学习的网络增长过快问题,提出基于径向基神经网络(RBF)的集成增量学习方法。为了避免网络的遗忘性,每次学习新类别知识时都训练一个RBF神经网络,把新训练的RBF神经网络加入到集成系统中,从而组建成一个大的神经网络系统。分别采用最近中心法、最大概率法、最近中心与最大概率相结合的方法进行确定获胜子网络,最终结果由获胜子网络进行输出。在最大概率法中引入自组织映(SOM)的原型向量来解决类中心相近问题。为了验证网络的增量学习,用UCI机器学习库中Statlog(Landsat Satellite)数据集做实验,结果显示该网络在学习新类别知识后,既获得了新类别的知识也没有遗忘已学知识。%Aiming at the forgetfulness problem of incremental learning and the excessive network growth problem of the integrated incremental learning,this paper proposes an integrated incremental learning method which is based on the radial basis function (RBF)neural network.In order to avoid the forgetfulness of the network,for each knowledge learning of new category we all trained an RBF neural network,and added the newly trained RBF neural network to the integrated system so as to form a large system of neural networks.To determine the winning sub-network,we adopted the nearest centre method,the maximum probability method and the combination of these two methods,and the final result was outputted by the winning sub-network.Moreover,we introduced the prototype vectors of self-organising map to maximum probability method for solving the problem of class centre similarity.For verifying the proposed network incremental learning,we made the experiments using the Statlog (Landsat Satellite)dataset in UCI machine learning library.Experimental results showed that after learning the knowledge of new category,this network could accept the new without

  17. A study of fuzzy logic ensemble system performance on face recognition problem

    Science.gov (United States)

    Polyakova, A.; Lipinskiy, L.

    2017-02-01

    Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.

  18. A morpho-density approach to estimating neural connectivity.

    Directory of Open Access Journals (Sweden)

    Michael P McAssey

    Full Text Available Neuronal signal integration and information processing in cortical neuronal networks critically depend on the organization of synaptic connectivity. Because of the challenges involved in measuring a large number of neurons, synaptic connectivity is difficult to determine experimentally. Current computational methods for estimating connectivity typically rely on the juxtaposition of experimentally available neurons and applying mathematical techniques to compute estimates of neural connectivity. However, since the number of available neurons is very limited, these connectivity estimates may be subject to large uncertainties. We use a morpho-density field approach applied to a vast ensemble of model-generated neurons. A morpho-density field (MDF describes the distribution of neural mass in the space around the neural soma. The estimated axonal and dendritic MDFs are derived from 100,000 model neurons that are generated by a stochastic phenomenological model of neurite outgrowth. These MDFs are then used to estimate the connectivity between pairs of neurons as a function of their inter-soma displacement. Compared with other density-field methods, our approach to estimating synaptic connectivity uses fewer restricting assumptions and produces connectivity estimates with a lower standard deviation. An important requirement is that the model-generated neurons reflect accurately the morphology and variation in morphology of the experimental neurons used for optimizing the model parameters. As such, the method remains subject to the uncertainties caused by the limited number of neurons in the experimental data set and by the quality of the model and the assumptions used in creating the MDFs and in calculating estimating connectivity. In summary, MDFs are a powerful tool for visualizing the spatial distribution of axonal and dendritic densities, for estimating the number of potential synapses between neurons with low standard deviation, and for obtaining

  19. Ensemble manifold regularization.

    Science.gov (United States)

    Geng, Bo; Tao, Dacheng; Xu, Chao; Yang, Linjun; Hua, Xian-Sheng

    2012-06-01

    We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, cross validation is applied, but it does not necessarily scale up. Other problems derive from the suboptimality incurred by discrete grid search and the overfitting. Therefore, we develop an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR carefully so it 1) learns both the composite manifold and the semi-supervised learner jointly, 2) is fully automatic for learning the intrinsic manifold hyperparameters implicitly, 3) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption, and 4) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Furthermore, we prove the convergence property of EMR to the deterministic matrix at rate root-n. Extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework.

  20. Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning

    DEFF Research Database (Denmark)

    Aakerberg, Andreas; Nasrollahi, Kamal; Heder, Thomas

    2017-01-01

    Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored...... in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments...

  1. Diurnal Ensemble Surface Meteorology Statistics

    Data.gov (United States)

    U.S. Environmental Protection Agency — Excel file containing diurnal ensemble statistics of 2-m temperature, 2-m mixing ratio and 10-m wind speed. This Excel file contains figures for Figure 2 in the...

  2. Text classification based on improved Fuzzy clustering RBF neural network ensemble%基于改进的模糊聚类RBF网络集成的文本分类方法

    Institute of Scientific and Technical Information of China (English)

    张爱科; 符保龙; 李辉

    2012-01-01

    Web文本分类是数据挖掘研究的一个热点问题.针对文本向量维度过高的特点,提出一种改进的模糊聚类RBF网络集成的文本分类方法,该方法利用模糊C均值聚类算法对文本特征向量进行简化、抽取,引入自适应遗传算法优化RBF神经网络的权值,构建RBF网络集成模型对文本进行分类.实验结果表明,该方法具有更高的分类效率和正确率.%Web text classification is a hot issue in the research on data mining. In view of the characteristics of high dimension text vector, the paper proposes an improved text classification method of fuzzy cluster RBF network integration. The method uses fuzzy c-means clustering algorithm to simplify and extract the text eigenvector, introduces adaptive genetic algorithm for optimization of RBF Neural network weights, and builds a RBF network model for text classification. Experimental results show that the method possesses a higher classification efficiency and accuracy.

  3. PSO-Ensemble Demo Application

    DEFF Research Database (Denmark)

    2004-01-01

    Within the framework of the PSO-Ensemble project (FU2101) a demo application has been created. The application use ECMWF ensemble forecasts. Two instances of the application are running; one for Nysted Offshore and one for the total production (except Horns Rev) in the Eltra area. The output...... is available via two password-protected web-pages hosted at IMM and is used daily by Elsam and E2....

  4. Similarity measures for protein ensembles

    DEFF Research Database (Denmark)

    Lindorff-Larsen, Kresten; Ferkinghoff-Borg, Jesper

    2009-01-01

    Analyses of similarities and changes in protein conformation can provide important information regarding protein function and evolution. Many scores, including the commonly used root mean square deviation, have therefore been developed to quantify the similarities of different protein conformations...... a synthetic example from molecular dynamics simulations. We then apply the algorithms to revisit the problem of ensemble averaging during structure determination of proteins, and find that an ensemble refinement method is able to recover the correct distribution of conformations better than standard single...

  5. PSO-Ensemble Demo Application

    DEFF Research Database (Denmark)

    2004-01-01

    Within the framework of the PSO-Ensemble project (FU2101) a demo application has been created. The application use ECMWF ensemble forecasts. Two instances of the application are running; one for Nysted Offshore and one for the total production (except Horns Rev) in the Eltra area. The output is a...... is available via two password-protected web-pages hosted at IMM and is used daily by Elsam and E2....

  6. Cortical control of whisker movement.

    Science.gov (United States)

    Petersen, Carl C H

    2014-01-01

    Facial muscles drive whisker movements, which are important for active tactile sensory perception in mice and rats. These whisker muscles are innervated by cholinergic motor neurons located in the lateral facial nucleus. The whisker motor neurons receive synaptic inputs from premotor neurons, which are located within the brain stem, the midbrain, and the neocortex. Complex, distributed neural circuits therefore regulate whisker movement during behavior. This review focuses specifically on cortical whisker motor control. The whisker primary motor cortex (M1) strongly innervates brain stem reticular nuclei containing whisker premotor neurons, which might form a central pattern generator for rhythmic whisker protraction. In a parallel analogous pathway, the whisker primary somatosensory cortex (S1) strongly projects to the brain stem spinal trigeminal interpolaris nucleus, which contains whisker premotor neurons innervating muscles for whisker retraction. These anatomical pathways may play important functional roles, since stimulation of M1 drives exploratory rhythmic whisking, whereas stimulation of S1 drives whisker retraction.

  7. Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Adnan O. M. Abuassba

    2017-01-01

    Full Text Available Extreme Learning Machine (ELM is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN. It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2, and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble. The validity of AELME is confirmed through classification on several real-world benchmark datasets.

  8. Emergence of spatiotemporal invariance in large neuronal ensembles in rat barrel cortex

    Science.gov (United States)

    Jacobs, Nathan S.; Chen-Bee, Cynthia H.; Frostig, Ron D.

    2015-01-01

    Invariant sensory coding is the robust coding of some sensory information (e.g., stimulus type) despite major changes in other sensory parameters (e.g., stimulus strength). The contribution of large populations of neurons (ensembles) to invariant sensory coding is not well understood, but could offer distinct advantages over invariance in single cell receptive fields. To test invariant sensory coding in neuronal ensembles evoked by single whisker stimulation as early as primary sensory cortex, we recorded detailed spatiotemporal movies of evoked ensemble activity through the depth of rat barrel cortex using microelectrode arrays. We found that an emergent property of whisker evoked ensemble activity, its spatiotemporal profile, was notably invariant across major changes in stimulus amplitude (up to >200-fold). Such ensemble-based invariance was found for single whisker stimulation as well as for the integrated profile of activity evoked by the more naturalistic stimulation of the entire whisker array. Further, the integrated profile of whisker array evoked ensemble activity and its invariance to stimulus amplitude shares striking similarities to “funneled” tactile perception in humans. We therefore suggest that ensemble-based invariance could provide a robust neurobiological substrate for invariant sensory coding and integration at an early stage of cortical sensory processing already in primary sensory cortex. PMID:26217194

  9. Emergence of spatiotemporal invariance in large neuronal ensembles in rat barrel cortex.

    Directory of Open Access Journals (Sweden)

    Nathan S Jacobs

    2015-07-01

    Full Text Available Invariant sensory coding is the robust coding of some sensory information (e.g. stimulus type despite major changes in other sensory parameters (e.g. stimulus strength. The contribution of large populations of neurons (ensembles to invariant sensory coding is not well understood, but could offer distinct advantages over invariance in single cell receptive fields. To test invariant sensory coding in neuronal ensembles evoked by single whisker stimulation as early as primary sensory cortex, we recorded detailed spatiotemporal movies of evoked ensemble activity through the depth of rat barrel cortex using microelectrode arrays. We found that an emergent property of whisker evoked ensemble activity, its spatiotemporal profile, was notably invariant across major changes in stimulus amplitude (up to >200 fold. Such ensemble-based invariance was found for single whisker stimulation as well as for the integrated profile of activity evoked by the more naturalistic stimulation of the entire whisker array. Further, the integrated profile of whisker array evoked ensemble activity and its invariance to stimulus amplitude shares striking similarities to 'funneled' tactile perception in humans. We therefore suggest that ensemble-based invariance could provide a robust neurobiological substrate for invariant sensory coding and integration at an early stage of cortical sensory processing already in primary sensory cortex.

  10. Cortical and thalamic contributions to response dynamics across layers of the primary somatosensory cortex during tactile discrimination.

    Science.gov (United States)

    Pais-Vieira, Miguel; Kunicki, Carolina; Tseng, Po-He; Martin, Joel; Lebedev, Mikhail; Nicolelis, Miguel A L

    2015-09-01

    Tactile information processing in the rodent primary somatosensory cortex (S1) is layer specific and involves modulations from both thalamocortical and cortico-cortical loops. However, the extent to which these loops influence the dynamics of the primary somatosensory cortex while animals execute tactile discrimination remains largely unknown. Here, we describe neural dynamics of S1 layers across the multiple epochs defining a tactile discrimination task. We observed that neuronal ensembles within different layers of the S1 cortex exhibited significantly distinct neurophysiological properties, which constantly changed across the behavioral states that defined a tactile discrimination. Neural dynamics present in supragranular and granular layers generally matched the patterns observed in the ventral posterior medial nucleus of the thalamus (VPM), whereas the neural dynamics recorded from infragranular layers generally matched the patterns from the posterior nucleus of the thalamus (POM). Selective inactivation of contralateral S1 specifically switched infragranular neural dynamics from POM-like to those resembling VPM neurons. Meanwhile, ipsilateral M1 inactivation profoundly modulated the firing suppression observed in infragranular layers. This latter effect was counterbalanced by contralateral S1 block. Tactile stimulus encoding was layer specific and selectively affected by M1 or contralateral S1 inactivation. Lastly, causal information transfer occurred between all neurons in all S1 layers but was maximal from infragranular to the granular layer. These results suggest that tactile information processing in the S1 of awake behaving rodents is layer specific and state dependent and that its dynamics depend on the asynchronous convergence of modulations originating from ipsilateral M1 and contralateral S1.

  11. Cortical control of facial expression.

    Science.gov (United States)

    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.

  12. 基于神经网络集成的女下装号型归档模型构建%Construction of female's bottom size grading model based on neural network ensemble

    Institute of Scientific and Technical Information of China (English)

    孙洁; 金娟凤; 倪世明; 叶玲; 刘焘; 邹奉元

    2013-01-01

    如何实现大样本的快速、精确号型归档是服装数字化批量定制系统构建的关键问题.结合下装结构设计的关键部位尺寸,在国家标准基础上将下装控制部位扩展为10项;以女裤装为例,分别构建身高和腰围的简单BP神经网络归档模型,通过平均影响值(MIV)筛选出对归档结果影响较大的5个控制部位,并将其作为集成归档模型的输入层,其中身高、腰围、臀围、腰围高、后裆长为身高归档模型的输入变量,腰围、臀围、大腿围、腰长、身高为腰围归档模型的输入变量;通过Adaboost算法集成10个简单BP神经网络,获得具有高精度和强泛化能力的女裤装号型集成归档模型.%How to achieve fast and precise size-grading for large numbers of garment is a key issue to the construction of digitalized mass customization system.Combining with key measurement in designing the pivotal parts of women pants or bottoms,this paper expanded the control parts of bottoms into 10 items based on the national standard.Taking female trousers as an example,after constructing a simple size grading model with BP neutral network,the five control parts that have a major impact on the result of grading were selected based on mean impact value (MIV) and then they were used as the input layer of garment size grading model.In the model,height,waist circumference,hip circumference,waist height and back gore length worked as input variables of the height model,and waist circumference,hip circumference,thigh circumference,waist length and height used as input variables of the waist circumference model.Then,after ensembing 10 simple BP neural networks through Adaboost algorithm,it achieved the size-grading model with high precision and strong generalization ability.

  13. Cortical inactivation by cooling in small animals

    Directory of Open Access Journals (Sweden)

    Ben eCoomber

    2011-06-01

    Full Text Available Reversible inactivation of the cortex by surface cooling is a powerful method for studying the function of a particular area. Implanted cooling cryoloops have been used to study the role of individual cortical areas in auditory processing of awake-behaving cats. Cryoloops have also been used in rodents for reversible inactivation of the cortex, but recently there has been a concern that the cryoloop may also cool non-cortical structures either directly or via the perfusion of blood, cooled as it passed close to the cooling loop. In this study we have confirmed that the loop can inactivate most of the auditory cortex without causing a significant reduction in temperature of the auditory thalamus or other sub-cortical structures. We placed a cryoloop on the surface of the guinea pig cortex, cooled it to 2°C and measured thermal gradients across the neocortical surface. We found that the temperature dropped to 20-24°C among cells within a radius of about 2.5mm away from the loop. This temperature drop was sufficient to reduce activity of most cortical cells and led to the inactivation of almost the entire auditory region. When the temperature of thalamus, midbrain, and middle ear were measured directly during cortical cooling, there was a small drop in temperature (about 4°C but this was not sufficient to directly reduce neural activity. In an effort to visualise the extent of neural inactivation we measured the uptake of thallium ions following an intravenous injection. This confirmed that there was a large reduction of activity across much of the ipsilateral cortex and only a small reduction in subcortical structures.

  14. Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

    Science.gov (United States)

    Rajangam, Sankaranarayani; Tseng, Po-He; Yin, Allen; Lehew, Gary; Schwarz, David; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

    2016-03-01

    Several groups have developed brain-machine-interfaces (BMIs) that allow primates to use cortical activity to control artificial limbs. Yet, it remains unknown whether cortical ensembles could represent the kinematics of whole-body navigation and be used to operate a BMI that moves a wheelchair continuously in space. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair, using their cortical activity as the main control signal. Two monkeys were chronically implanted with multichannel microelectrode arrays that allowed wireless recordings from ensembles of premotor and sensorimotor cortical neurons. Initially, while monkeys remained seated in the robotic wheelchair, passive navigation was employed to train a linear decoder to extract 2D wheelchair kinematics from cortical activity. Next, monkeys employed the wireless BMI to translate their cortical activity into the robotic wheelchair’s translational and rotational velocities. Over time, monkeys improved their ability to navigate the wheelchair toward the location of a grape reward. The navigation was enacted by populations of cortical neurons tuned to whole-body displacement. During practice with the apparatus, we also noticed the presence of a cortical representation of the distance to reward location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future.

  15. Evaluating the trade-offs between ensemble size and ensemble resolution in an ensemble-variational data assimilation system

    Science.gov (United States)

    Lei, Lili; Whitaker, Jeffrey S.

    2017-06-01

    The current NCEP operational four-dimensional ensemble-variational data assimilation system uses a control forecast at T1534 resolution coupled with an 80 member ensemble at T574 resolution. Given an increase in computing resources, and assuming the control forecast resolution is fixed, would it be better to increase the ensemble size and keep the ensemble resolution the same, or increase the ensemble resolution and keep the ensemble size the same? To answer this question, experiments are conducted at reduced resolutions. Two sets of experiments are conducted which both use approximately four times more computational resources than the control experiment that uses a control forecast at T670 and an 80 member ensemble at T254. One increases the ensemble size to 320 but keeps the ensemble resolution at T254; and the other increases the ensemble resolution to T670 but retains an 80 ensemble size. When ensemble size increases to 320, turning off the static component of the background-error covariance does not degrade performance. When the data assimilation parameters are tuned for optimal performance, increasing either ensemble size or ensemble resolution can improve the forecast performance. Increasing ensemble resolution is slightly, but significantly better than increasing ensemble size for these experiments, particularly when considering errors at smaller scales. Much of the benefit of increasing ensemble resolution comes about by eliminating the need for a deterministic control forecast and running all of the background forecasts at the same resolution. In this "single-resolution" mode, the control forecast is replaced by an ensemble average, which reduces small-scale errors significantly.

  16. Algorithms on ensemble quantum computers.

    Science.gov (United States)

    Boykin, P Oscar; Mor, Tal; Roychowdhury, Vwani; Vatan, Farrokh

    2010-06-01

    In ensemble (or bulk) quantum computation, all computations are performed on an ensemble of computers rather than on a single computer. Measurements of qubits in an individual computer cannot be performed; instead, only expectation values (over the complete ensemble of computers) can be measured. As a result of this limitation on the model of computation, many algorithms cannot be processed directly on such computers, and must be modified, as the common strategy of delaying the measurements usually does not resolve this ensemble-measurement problem. Here we present several new strategies for resolving this problem. Based on these strategies we provide new versions of some of the most important quantum algorithms, versions that are suitable for implementing on ensemble quantum computers, e.g., on liquid NMR quantum computers. These algorithms are Shor's factorization algorithm, Grover's search algorithm (with several marked items), and an algorithm for quantum fault-tolerant computation. The first two algorithms are simply modified using a randomizing and a sorting strategies. For the last algorithm, we develop a classical-quantum hybrid strategy for removing measurements. We use it to present a novel quantum fault-tolerant scheme. More explicitly, we present schemes for fault-tolerant measurement-free implementation of Toffoli and σ(z)(¼) as these operations cannot be implemented "bitwise", and their standard fault-tolerant implementations require measurement.

  17. CME Ensemble Forecasting - A Primer

    Science.gov (United States)

    Pizzo, V. J.; de Koning, C. A.; Cash, M. D.; Millward, G. H.; Biesecker, D. A.; Codrescu, M.; Puga, L.; Odstrcil, D.

    2014-12-01

    SWPC has been evaluating various approaches for ensemble forecasting of Earth-directed CMEs. We have developed the software infrastructure needed to support broad-ranging CME ensemble modeling, including composing, interpreting, and making intelligent use of ensemble simulations. The first step is to determine whether the physics of the interplanetary propagation of CMEs is better described as chaotic (like terrestrial weather) or deterministic (as in tsunami propagation). This is important, since different ensemble strategies are to be pursued under the two scenarios. We present the findings of a comprehensive study of CME ensembles in uniform and structured backgrounds that reveals systematic relationships between input cone parameters and ambient flow states and resulting transit times and velocity/density amplitudes at Earth. These results clearly indicate that the propagation of single CMEs to 1 AU is a deterministic process. Thus, the accuracy with which one can forecast the gross properties (such as arrival time) of CMEs at 1 AU is determined primarily by the accuracy of the inputs. This is no tautology - it means specifically that efforts to improve forecast accuracy should focus upon obtaining better inputs, as opposed to developing better propagation models. In a companion paper (deKoning et al., this conference), we compare in situ solar wind data with forecast events in the SWPC operational archive to show how the qualitative and quantitative findings presented here are entirely consistent with the observations and may lead to improved forecasts of arrival time at Earth.

  18. Estimating preselected and postselected ensembles

    Energy Technology Data Exchange (ETDEWEB)

    Massar, Serge [Laboratoire d' Information Quantique, C.P. 225, Universite libre de Bruxelles (U.L.B.), Av. F. D. Rooselvelt 50, B-1050 Bruxelles (Belgium); Popescu, Sandu [H. H. Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL (United Kingdom); Hewlett-Packard Laboratories, Stoke Gifford, Bristol BS12 6QZ (United Kingdom)

    2011-11-15

    In analogy with the usual quantum state-estimation problem, we introduce the problem of state estimation for a pre- and postselected ensemble. The problem has fundamental physical significance since, as argued by Y. Aharonov and collaborators, pre- and postselected ensembles are the most basic quantum ensembles. Two new features are shown to appear: (1) information is flowing to the measuring device both from the past and from the future; (2) because of the postselection, certain measurement outcomes can be forced never to occur. Due to these features, state estimation in such ensembles is dramatically different from the case of ordinary, preselected-only ensembles. We develop a general theoretical framework for studying this problem and illustrate it through several examples. We also prove general theorems establishing that information flowing from the future is closely related to, and in some cases equivalent to, the complex conjugate information flowing from the past. Finally, we illustrate our approach on examples involving covariant measurements on spin-1/2 particles. We emphasize that all state-estimation problems can be extended to the pre- and postselected situation. The present work thus lays the foundations of a much more general theory of quantum state estimation.

  19. Virtual Calibration of Cosmic Ray Sensor: Using Supervised Ensemble Machine Learning

    Directory of Open Access Journals (Sweden)

    Ritaban Dutta

    2013-09-01

    Full Text Available In this paper an ensemble of supervised machine learning methods has been investigated to virtually and dynamically calibrate the cosmic ray sensors measuring area wise bulk soil moisture. Main focus of this study was to find an alternative to the currently available field calibration method; based on expensive and time consuming soil sample collection methodology. Data from the Australian Water Availability Project (AWAP database was used as independent soil moisture ground truth and results were compared against the conventionally estimated soil moisture using a Hydroinnova CRS-1000 cosmic ray probe deployed in Tullochgorum, Australia. Prediction performance of a complementary ensemble of four supervised estimators, namely Sugano type Adaptive Neuro-Fuzzy Inference System (S-ANFIS, Cascade Forward Neural Network (CFNN, Elman Neural Network (ENN and Learning Vector Quantization Neural Network (LVQN was evaluated using training and testing paradigms. An AWAP trained ensemble of four estimators was able to predict bulk soil moisture directly from cosmic ray neutron counts with 94.4% as best accuracy. The ensemble approach outperformed the individual performances from these networks. This result proved that an ensemble machine learning based paradigm could be a valuable alternative data driven calibration method for cosmic ray sensors against the current expensive and hydrological assumption based field calibration method.

  20. Dopamine replacement modulates oscillatory coupling between premotor and motor cortical areas in Parkinson's disease

    DEFF Research Database (Denmark)

    Herz, Damian Marc; Florin, Esther; Christensen, Mark Schram;

    2014-01-01

    Efficient neural communication between premotor and motor cortical areas is critical for manual motor control. Here, we used high-density electroencephalography to study cortical connectivity in patients with Parkinson's disease (PD) and age-matched healthy controls while they performed repetitive...

  1. Linking neuronal ensembles by associative synaptic plasticity.

    Directory of Open Access Journals (Sweden)

    Qi Yuan

    Full Text Available Synchronized activity in ensembles of neurons recruited by excitatory afferents is thought to contribute to the coding information in the brain. However, the mechanisms by which neuronal ensembles are generated and modified are not known. Here we show that in rat hippocampal slices associative synaptic plasticity enables ensembles of neurons to change by incorporating neurons belonging to different ensembles. Associative synaptic plasticity redistributes the composition of different ensembles recruited by distinct inputs such as to specifically increase the similarity between the ensembles. These results show that in the hippocampus, the ensemble of neurons recruited by a given afferent projection is fluid and can be rapidly and persistently modified to specifically include neurons from different ensembles. This linking of ensembles may contribute to the formation of associative memories.

  2. Evolution of cortical neurogenesis.

    Science.gov (United States)

    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.

  3. A mollified Ensemble Kalman filter

    CERN Document Server

    Bergemann, Kay

    2010-01-01

    It is well recognized that discontinuous analysis increments of sequential data assimilation systems, such as ensemble Kalman filters, might lead to spurious high frequency adjustment processes in the model dynamics. Various methods have been devised to continuously spread out the analysis increments over a fixed time interval centered about analysis time. Among these techniques are nudging and incremental analysis updates (IAU). Here we propose another alternative, which may be viewed as a hybrid of nudging and IAU and which arises naturally from a recently proposed continuous formulation of the ensemble Kalman analysis step. A new slow-fast extension of the popular Lorenz-96 model is introduced to demonstrate the properties of the proposed mollified ensemble Kalman filter.

  4. Excitation energies from ensemble DFT

    Science.gov (United States)

    Borgoo, Alex; Teale, Andy M.; Helgaker, Trygve

    2015-12-01

    We study the evaluation of the Gross-Oliveira-Kohn expression for excitation energies E1-E0=ɛ1-ɛ0+∂E/xc,w[ρ] ∂w | ρ =ρ0. This expression gives the difference between an excitation energy E1 - E0 and the corresponding Kohn-Sham orbital energy difference ɛ1 - ɛ0 as a partial derivative of the exchange-correlation energy of an ensemble of states Exc,w[ρ]. Through Lieb maximisation, on input full-CI density functions, the exchange-correlation energy is evaluated accurately and the partial derivative is evaluated numerically using finite difference. The equality is studied numerically for different geometries of the H2 molecule and different ensemble weights. We explore the adiabatic connection for the ensemble exchange-correlation energy. The latter may prove useful when modelling the unknown weight dependence of the exchange-correlation energy.

  5. Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

    Science.gov (United States)

    Fujii, Keisuke; Nakajima, Kohei

    2017-08-01

    The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.

  6. Cortical Lewy Body Dementia

    Directory of Open Access Journals (Sweden)

    W. R. G. Gibb

    1990-01-01

    Full Text Available In cortical Lewy body dementia the distribution of Lewy bodies in the nervous system follows that of Parkinson's disease, except for their greater profusion in the cerebral cortex. The cortical tangles and plaques of Alzheimer pathology are often present, the likely explanation being that Alzheimer pathology provokes dementia in many patients. Pure cortical Lewy body dementia without Alzheimer pathology is uncommon. The age of onset reflects that of Parkinson's disease, and clinical features, though not diagnostic, include aphasias, apraxias, agnosias, paranoid delusions and visual hallucinations. Parkinsonism may present before or after the dementia, and survival duration is approximately half that seen in Parkinson's disease without dementia.

  7. The Partition Ensemble Fallacy Fallacy

    CERN Document Server

    Nemoto, K; Nemoto, Kae; Braunstein, Samuel L.

    2002-01-01

    The Partition Ensemble Fallacy was recently applied to claim no quantum coherence exists in coherent states produced by lasers. We show that this claim relies on an untestable belief of a particular prior distribution of absolute phase. One's choice for the prior distribution for an unobservable quantity is a matter of `religion'. We call this principle the Partition Ensemble Fallacy Fallacy. Further, we show an alternative approach to construct a relative-quantity Hilbert subspace where unobservability of certain quantities is guaranteed by global conservation laws. This approach is applied to coherent states and constructs an approximate relative-phase Hilbert subspace.

  8. Broadcasting of cortical activity to the olfactory bulb.

    Science.gov (United States)

    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.

  9. Ensemble regression model-based anomaly detection for cyber-physical intrusion detection in smart grids

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena; Gehrke, Oliver

    2016-01-01

    on an ensemble of non-linear artificial neural network DER models which detect and evaluate anomalies in DER operation. The proposed method is validated against measurement data which yields a precision of 0.947 and an accuracy of 0.976. This improves the precision and accuracy of a classic model-based anomaly...

  10. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  11. Object recognition by artificial cortical maps.

    Science.gov (United States)

    Plebe, Alessio; Domenella, Rosaria Grazia

    2007-09-01

    Object recognition is one of the most important functions of the human visual system, yet one of the least understood, this despite the fact that vision is certainly the most studied function of the brain. We understand relatively well how several processes in the cortical visual areas that support recognition capabilities take place, such as orientation discrimination and color constancy. This paper proposes a model of the development of object recognition capability, based on two main theoretical principles. The first is that recognition does not imply any sort of geometrical reconstruction, it is instead fully driven by the two dimensional view captured by the retina. The second assumption is that all the processing functions involved in recognition are not genetically determined or hardwired in neural circuits, but are the result of interactions between epigenetic influences and basic neural plasticity mechanisms. The model is organized in modules roughly related to the main visual biological areas, and is implemented mainly using the LISSOM architecture, a recent neural self-organizing map model that simulates the effects of intercortical lateral connections. This paper shows how recognition capabilities, similar to those found in brain ventral visual areas, can develop spontaneously by exposure to natural images in an artificial cortical model.

  12. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.

    Science.gov (United States)

    Hochberg, Leigh R; Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y; Simeral, John D; Vogel, Joern; Haddadin, Sami; Liu, Jie; Cash, Sydney S; van der Smagt, Patrick; Donoghue, John P

    2012-05-16

    Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices. Able-bodied monkeys have used a neural interface system to control a robotic arm, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

  13. Focal cortical dysplasia - review.

    Science.gov (United States)

    Kabat, Joanna; Król, Przemysław

    2012-04-01

    Focal cortical dysplasia is a malformation of cortical development, which is the most common cause of medically refractory epilepsy in the pediatric population and the second/third most common etiology of medically intractable seizures in adults.Both genetic and acquired factors are involved in the pathogenesis of cortical dysplasia. Numerous classifications of the complex structural abnormalities of focal cortical dysplasia have been proposed - from Taylor et al. in 1971 to the last modification of Palmini classification made by Blumcke in 2011. In general, three types of cortical dysplasia are recognized.Type I focal cortical dysplasia with mild symptomatic expression and late onset, is more often seen in adults, with changes present in the temporal lobe.Clinical symptoms are more severe in type II of cortical dysplasia usually seen in children. In this type, more extensive changes occur outside the temporal lobe with predilection for the frontal lobes.New type III is one of the above dysplasias with associated another principal lesion as hippocampal sclerosis, tumor, vascular malformation or acquired pathology during early life.Brain MRI imaging shows abnormalities in the majority of type II dysplasias and in only some of type I cortical dysplasias.THE MOST COMMON FINDINGS ON MRI IMAGING INCLUDE: focal cortical thickening or thinning, areas of focal brain atrophy, blurring of the gray-white junction, increased signal on T2- and FLAIR-weighted images in the gray and subcortical white matter often tapering toward the ventricle. On the basis of the MRI findings, it is possible to differentiate between type I and type II cortical dysplasia. A complete resection of the epileptogenic zone is required for seizure-free life. MRI imaging is very helpful to identify those patients who are likely to benefit from surgical treatment in a group of patients with drug-resistant epilepsy.However, in type I cortical dysplasia, MR imaging is often normal, and also in both types

  14. Greedy and Linear Ensembles of Machine Learning Methods Outperform Single Approaches for QSPR Regression Problems.

    Science.gov (United States)

    Kew, William; Mitchell, John B O

    2015-09-01

    The application of Machine Learning to cheminformatics is a large and active field of research, but there exist few papers which discuss whether ensembles of different Machine Learning methods can improve upon the performance of their component methodologies. Here we investigated a variety of methods, including kernel-based, tree, linear, neural networks, and both greedy and linear ensemble methods. These were all tested against a standardised methodology for regression with data relevant to the pharmaceutical development process. This investigation focused on QSPR problems within drug-like chemical space. We aimed to investigate which methods perform best, and how the 'wisdom of crowds' principle can be applied to ensemble predictors. It was found that no single method performs best for all problems, but that a dynamic, well-structured ensemble predictor would perform very well across the board, usually providing an improvement in performance over the best single method. Its use of weighting factors allows the greedy ensemble to acquire a bigger contribution from the better performing models, and this helps the greedy ensemble generally to outperform the simpler linear ensemble. Choice of data preprocessing methodology was found to be crucial to performance of each method too. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Postpartum cortical blindness.

    Science.gov (United States)

    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.

  16. Modulation of Cortical Oscillations by Low-Frequency Direct Cortical Stimulation Is State-Dependent.

    Directory of Open Access Journals (Sweden)

    Sankaraleengam Alagapan

    2016-03-01

    Full Text Available Cortical oscillations play a fundamental role in organizing large-scale functional brain networks. Noninvasive brain stimulation with temporally patterned waveforms such as repetitive transcranial magnetic stimulation (rTMS and transcranial alternating current stimulation (tACS have been proposed to modulate these oscillations. Thus, these stimulation modalities represent promising new approaches for the treatment of psychiatric illnesses in which these oscillations are impaired. However, the mechanism by which periodic brain stimulation alters endogenous oscillation dynamics is debated and appears to depend on brain state. Here, we demonstrate with a static model and a neural oscillator model that recurrent excitation in the thalamo-cortical circuit, together with recruitment of cortico-cortical connections, can explain the enhancement of oscillations by brain stimulation as a function of brain state. We then performed concurrent invasive recording and stimulation of the human cortical surface to elucidate the response of cortical oscillations to periodic stimulation and support the findings from the computational models. We found that (1 stimulation enhanced the targeted oscillation power, (2 this enhancement outlasted stimulation, and (3 the effect of stimulation depended on behavioral state. Together, our results show successful target engagement of oscillations by periodic brain stimulation and highlight the role of nonlinear interaction between endogenous network oscillations and stimulation. These mechanistic insights will contribute to the design of adaptive, more targeted stimulation paradigms.

  17. Multimodel ensembles of wheat growth

    DEFF Research Database (Denmark)

    Martre, Pierre; Wallach, Daniel; Asseng, Senthold

    2015-01-01

    , but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24...

  18. Neuregulin 3 Mediates Cortical Plate Invasion and Laminar Allocation of GABAergic Interneurons

    Directory of Open Access Journals (Sweden)

    Giorgia Bartolini

    2017-01-01

    Full Text Available Neural circuits in the cerebral cortex consist of excitatory pyramidal cells and inhibitory interneurons. These two main classes of cortical neurons follow largely different genetic programs, yet they assemble into highly specialized circuits during development following a very precise choreography. Previous studies have shown that signals produced by pyramidal cells influence the migration of cortical interneurons, but the molecular nature of these factors has remained elusive. Here, we identified Neuregulin 3 (Nrg3 as a chemoattractive factor expressed by developing pyramidal cells that guides the allocation of cortical interneurons in the developing cortical plate. Gain- and loss-of-function approaches reveal that Nrg3 modulates the migration of interneurons into the cortical plate in a process that is dependent on the tyrosine kinase receptor ErbB4. Perturbation of Nrg3 signaling in conditional mutants leads to abnormal lamination of cortical interneurons. Nrg3 is therefore a critical mediator in the assembly of cortical inhibitory circuits.

  19. Control and amplification of cortical neurodynamics

    Science.gov (United States)

    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.

  20. Relearning to See in Cortical Blindness.

    Science.gov (United States)

    Melnick, Michael D; Tadin, Duje; Huxlin, Krystel R

    2016-04-01

    The incidence of cortically induced blindness is increasing as our population ages. The major cause of cortically induced blindness is stroke affecting the primary visual cortex. While the impact of this form of vision loss is devastating to quality of life, the development of principled, effective rehabilitation strategies for this condition lags far behind those used to treat motor stroke victims. Here we summarize recent developments in the still emerging field of visual restitution therapy, and compare the relative effectiveness of different approaches. We also draw insights into the properties of recovered vision, its limitations and likely neural substrates. We hope that these insights will guide future research and bring us closer to the goal of providing much-needed rehabilitation solutions for this patient population.

  1. Visual Dysfunction in Posterior Cortical Atrophy

    Directory of Open Access Journals (Sweden)

    Mari N. Maia da Silva

    2017-08-01

    Full Text Available Posterior cortical atrophy (PCA is a syndromic diagnosis. It is characterized by progressive impairment of higher (cortical visual function with imaging evidence of degeneration affecting the occipital, parietal, and posterior temporal lobes bilaterally. Most cases will prove to have Alzheimer pathology. The aim of this review is to summarize the development of the concept of this disorder since it was first introduced. A critical discussion of the evolving diagnostic criteria is presented and the differential diagnosis with regard to the underlying pathology is reviewed. Emphasis is given to the visual dysfunction that defines the disorder, and the classical deficits, such as simultanagnosia and visual agnosia, as well as the more recently recognized visual field defects, are reviewed, along with the evidence on their neural correlates. The latest developments on the imaging of PCA are summarized, with special attention to its role on the differential diagnosis with related conditions.

  2. Visual Dysfunction in Posterior Cortical Atrophy

    Science.gov (United States)

    da Silva, Mari N. Maia; Millington, Rebecca S.; Bridge, Holly; James-Galton, Merle; Plant, Gordon T.

    2017-01-01

    Posterior cortical atrophy (PCA) is a syndromic diagnosis. It is characterized by progressive impairment of higher (cortical) visual function with imaging evidence of degeneration affecting the occipital, parietal, and posterior temporal lobes bilaterally. Most cases will prove to have Alzheimer pathology. The aim of this review is to summarize the development of the concept of this disorder since it was first introduced. A critical discussion of the evolving diagnostic criteria is presented and the differential diagnosis with regard to the underlying pathology is reviewed. Emphasis is given to the visual dysfunction that defines the disorder, and the classical deficits, such as simultanagnosia and visual agnosia, as well as the more recently recognized visual field defects, are reviewed, along with the evidence on their neural correlates. The latest developments on the imaging of PCA are summarized, with special attention to its role on the differential diagnosis with related conditions. PMID:28861031

  3. Global Ensemble Forecast System (GEFS) [1 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  4. Attention-dependent early cortical suppression contributes to crowding.

    Science.gov (United States)

    Chen, Juan; He, Yingchen; Zhu, Ziyun; Zhou, Tiangang; Peng, Yujia; Zhang, Xilin; Fang, Fang

    2014-08-01

    Crowding, the identification difficulty for a target in the presence of nearby flankers, is ubiquitous in spatial vision and is considered a bottleneck of object recognition and visual awareness. Despite its significance, the neural mechanisms of crowding are still unclear. Here, we performed event-related potential and fMRI experiments to measure the cortical interaction between the target and flankers in human subjects. We found that the magnitude of the crowding effect was closely associated with an early suppressive cortical interaction. The cortical suppression was reflected in the earliest event-related potential component (C1), which originated in V1, and in the BOLD signal in V1, but not other higher cortical areas. Intriguingly, spatial attention played a critical role in the manifestation of the suppression. These findings provide direct and converging evidence that attention-dependent V1 suppression contributes to crowding at a very early stage of visual processing.

  5. Squeezing of Collective Excitations in Spin Ensembles

    DEFF Research Database (Denmark)

    Kraglund Andersen, Christian; Mølmer, Klaus

    2012-01-01

    We analyse the possibility to create two-mode spin squeezed states of two separate spin ensembles by inverting the spins in one ensemble and allowing spin exchange between the ensembles via a near resonant cavity field. We investigate the dynamics of the system using a combination of numerical an...

  6. Cortical connective field estimates from resting state fMRI activity

    NARCIS (Netherlands)

    Gravel, Nicolas; Harvey, Ben; Nordhjem, Barbara; Haak, Koen V.; Dumoulin, Serge O.; Renken, Remco; Curcic-Blake, Branisalava; Cornelissen, Frans W.

    2014-01-01

    One way to study connectivity in visual cortical areas is by examining spontaneous neural activity. In the absence of visual input, such activity remains shaped by the underlying neural architecture and, presumably, may still reflect visuotopic organization. Here, we applied population connective fi

  7. Relating Cortical Wave Dynamics to Learning and Remembering

    Directory of Open Access Journals (Sweden)

    Eduardo Mercado III

    2014-12-01

    Full Text Available Electrical waves propagate across sensory and motor cortices in stereotypical patterns. These waves have been described as potentially facilitating sensory processing when they travel through sensory cortex, as guiding movement preparation and performance when they travel across motor cortex, and as possibly promoting synaptic plasticity and the consolidation of memory traces, especially during sleep. Here, an alternative theoretical framework is suggested that integrates Pavlovian hypotheses about learning and cortical function with concepts from contemporary proceduralist theories of memory. The proposed framework postulates that sensory-evoked cortical waves are gradually modified across repeated experiences such that the waves more effectively differentiate sensory events, and so that the waves are more likely to reverberate. It is argued that the qualities of cortical waves—their origins, form, intensity, speed, periodicity, extent, and trajectories —are a function of both the structural organization of neural circuits and ongoing reverberations resulting from previously experienced events. It is hypothesized that experience-dependent cortical plasticity, both in the short- and long-term, modulates the qualities of cortical waves, thereby enabling individuals to make progressively more precise distinctions between complex sensory events, and to reconstruct components of previously experienced events. Unlike most current neurobiological theories of learning and memory mechanisms, this hypothesis does not assume that synaptic plasticity, or any other form of neural plasticity, serves to store physical records of previously experienced events for later reactivation. Rather, the reorganization of cortical circuits may alter the potential for certain wave patterns to arise and persist. Understanding what factors determine the spatiotemporal dynamics of cortical waves, how structural changes affect their qualities, and how wave dynamics

  8. The neural correlates of crowding-induced changes in appearance

    National Research Council Canada - National Science Library

    Anderson, Elaine J; Dakin, Steven C; Schwarzkopf, D Samuel; Rees, Geraint; Greenwood, John A

    2012-01-01

    .... Despite its severity, little is known about the cortical locus of crowding. Here, we examined the neural correlates of crowding by combining event-related fMRI adaptation with a change-detection paradigm...

  9. Neural correlates of auditory temporal predictions during sensorimotor synchronization

    Directory of Open Access Journals (Sweden)

    Nadine ePecenka

    2013-08-01

    Full Text Available Musical ensemble performance requires temporally precise interpersonal action coordination. To play in synchrony, ensemble musicians presumably rely on anticipatory mechanisms that enable them to predict the timing of sounds produced by co-performers. Previous studies have shown that individuals differ in their ability to predict upcoming tempo changes in paced finger-tapping tasks (indexed by cross-correlations between tap timing and pacing events and that the degree of such prediction influences the accuracy of sensorimotor synchronization (SMS and interpersonal coordination in dyadic tapping tasks. The current functional magnetic resonance imaging study investigated the neural correlates of auditory temporal predictions during SMS in a within-subject design. Hemodynamic responses were recorded from 18 musicians while they tapped in synchrony with auditory sequences containing gradual tempo changes under conditions of varying cognitive load (achieved by a simultaneous visual n-back working-memory task comprising three levels of difficulty: observation only, 1-back, and 2-back object comparisons. Prediction ability during SMS decreased with increasing cognitive load. Results of a parametric analysis revealed that the generation of auditory temporal predictions during SMS recruits (1 a distributed network in cortico-cerebellar motor-related brain areas (left dorsal premotor and motor cortex, right lateral cerebellum, SMA proper and bilateral inferior parietal cortex and (2 medial cortical areas (medial prefrontal cortex, posterior cingulate cortex. While the first network is presumably involved in basic sensory prediction, sensorimotor integration, motor timing, and temporal adaptation, activation in the second set of areas may be related to higher-level social-cognitive processes elicited during action coordination with auditory signals that resemble music performed by human agents.

  10. Classical and Quantum Ensembles via Multiresolution. II. Wigner Ensembles

    OpenAIRE

    2004-01-01

    We present the application of the variational-wavelet analysis to the analysis of quantum ensembles in Wigner framework. (Naive) deformation quantization, the multiresolution representations and the variational approach are the key points. We construct the solutions of Wigner-like equations via the multiscale expansions in the generalized coherent states or high-localized nonlinear eigenmodes in the base of the compactly supported wavelets and the wavelet packets. We demonstrate the appearanc...

  11. A novel Bayesian learning method for information aggregation in modular neural networks

    DEFF Research Database (Denmark)

    Wang, Pan; Xu, Lida; Zhou, Shang-Ming;

    2010-01-01

    Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight ...

  12. Measuring the quality of neuronal identification in ensemble recordings.

    Science.gov (United States)

    Neymotin, Samuel A; Lytton, William W; Olypher, Andrey V; Fenton, André A

    2011-11-09

    Technological advances in electrode construction and digital signal processing now allow recording simultaneous extracellular action potential discharges from many single neurons, with the potential to revolutionize understanding of the neural codes for sensory, motor, and cognitive variables. Such studies have revealed the importance of ensemble neural codes, encoding information in the dynamic relationships among the action potential spike trains of multiple single neurons. Although the success of this research depends on the accurate classification of extracellular action potentials to individual neurons, there are no widely used quantitative methods for assessing the quality of the classifications. Here we describe information theoretic measures of action potential waveform isolation applicable to any dataset that have an intuitive, universal interpretation, that are not dependent on the methods or choice of parameters for single-unit isolation, and that have been validated using a dataset of simultaneous intracellular and extracellular neuronal recordings from Sprague Dawley rats.

  13. Cortical control of anticipatory postural adjustments prior to stepping.

    Science.gov (United States)

    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.

  14. Hydrological Ensemble Prediction System (HEPS)

    Science.gov (United States)

    Thielen-Del Pozo, J.; Schaake, J.; Martin, E.; Pailleux, J.; Pappenberger, F.

    2010-09-01

    Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Following on the success of the use of ensembles for weather forecasting, the hydrological community now moves increasingly towards Hydrological Ensemble Prediction Systems (HEPS) for improved flood forecasting using operationally available NWP products as inputs. However, these products are often generated on relatively coarse scales compared to hydrologically relevant basin units and suffer systematic biases that may have considerable impact when passed through the non-linear hydrological filters. Therefore, a better understanding on how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes is necessary. The "Hydrologic Ensemble Prediction Experiment" (HEPEX), is an international initiative consisting of hydrologists, meteorologist and end-users to advance probabilistic hydrologic forecast techniques for flood, drought and water management applications. Different aspects of the hydrological ensemble processor are being addressed including • Production of useful meteorological products relevant for hydrological applications, ranging from nowcasting products to seasonal forecasts. The importance of hindcasts that are consistent with the operational weather forecasts will be discussed to support bias correction and downscaling, statistically meaningful verification of HEPS, and the development and testing of operating rules; • Need for downscaling and post-processing of weather ensembles to reduce bias before entering hydrological applications; • Hydrological model and parameter uncertainty and how to correct and

  15. From Neural Plate to Cortical Arousal—A Neuronal Network Theory of Sleep Derived from in Vitro “Model” Systems for Primordial Patterns of Spontaneous Bioelectric Activity in the Vertebrate Central Nervous System

    Directory of Open Access Journals (Sweden)

    Michael A. Corner

    2013-05-01

    Full Text Available In the early 1960s intrinsically generated widespread neuronal discharges were discovered to be the basis for the earliest motor behavior throughout the animal kingdom. The pattern generating system is in fact programmed into the developing nervous system, in a regionally specific manner, already at the early neural plate stage. Such rhythmically modulated phasic bursts were next discovered to be a general feature of developing neural networks and, largely on the basis of experimental interventions in cultured neural tissues, to contribute significantly to their morpho-physiological maturation. In particular, the level of spontaneous synchronized bursting is homeostatically regulated, and has the effect of constraining the development of excessive network excitability. After birth or hatching, this “slow-wave” activity pattern becomes sporadically suppressed in favor of sensory oriented “waking” behaviors better adapted to dealing with environmental contingencies. It nevertheless reappears periodically as “sleep” at several species-specific points in the diurnal/nocturnal cycle. Although this “default” behavior pattern evolves with development, its essential features are preserved throughout the life cycle, and are based upon a few simple mechanisms which can be both experimentally demonstrated and simulated by computer modeling. In contrast, a late onto- and phylogenetic aspect of sleep, viz., the intermittent “paradoxical” activation of the forebrain so as to mimic waking activity, is much less well understood as regards its contribution to brain development. Some recent findings dealing with this question by means of cholinergically induced “aroused” firing patterns in developing neocortical cell cultures, followed by quantitative electrophysiological assays of immediate and longterm sequelae, will be discussed in connection with their putative implications for sleep ontogeny.

  16. From neural plate to cortical arousal-a neuronal network theory of sleep derived from in vitro "model" systems for primordial patterns of spontaneous bioelectric activity in the vertebrate central nervous system.

    Science.gov (United States)

    Corner, Michael A

    2013-05-22

    In the early 1960s intrinsically generated widespread neuronal discharges were discovered to be the basis for the earliest motor behavior throughout the animal kingdom. The pattern generating system is in fact programmed into the developing nervous system, in a regionally specific manner, already at the early neural plate stage. Such rhythmically modulated phasic bursts were next discovered to be a general feature of developing neural networks and, largely on the basis of experimental interventions in cultured neural tissues, to contribute significantly to their morpho-physiological maturation. In particular, the level of spontaneous synchronized bursting is homeostatically regulated, and has the effect of constraining the development of excessive network excitability. After birth or hatching, this "slow-wave" activity pattern becomes sporadically suppressed in favor of sensory oriented "waking" behaviors better adapted to dealing with environmental contingencies. It nevertheless reappears periodically as "sleep" at several species-specific points in the diurnal/nocturnal cycle. Although this "default" behavior pattern evolves with development, its essential features are preserved throughout the life cycle, and are based upon a few simple mechanisms which can be both experimentally demonstrated and simulated by computer modeling. In contrast, a late onto- and phylogenetic aspect of sleep, viz., the intermittent "paradoxical" activation of the forebrain so as to mimic waking activity, is much less well understood as regards its contribution to brain development. Some recent findings dealing with this question by means of cholinergically induced "aroused" firing patterns in developing neocortical cell cultures, followed by quantitative electrophysiological assays of immediate and longterm sequelae, will be discussed in connection with their putative implications for sleep ontogeny.

  17. Predicting the Severity of Breast Masses with Ensemble of Bayesian Classifiers

    Directory of Open Access Journals (Sweden)

    Alaa M. Elsayad

    2010-01-01

    Full Text Available Problem statement: This study evaluated two different Bayesian classifiers; tree augmented Naive Bayes and Markov blanket estimation networks in order to build an ensemble model for prediction the severity of breast masses. The objective of the proposed algorithm was to help physicians in their decisions to perform a breast biopsy on a suspicious lesion seen in a mammogram image or to perform a short term follow-up examination instead. While, mammography is the most effective and available tool for breast cancer screening, mammograms do not detect all breast cancers. Also, a small portion of mammograms show that a cancer could probably be present when it is not (called a false-positive result. Approach: Apply ensemble of Bayesian classifiers to predict the severity of breast masses. Bayesian classifiers had been selected as they were able to produce probability estimates rather than predictions. These estimated allow predictions to be ranked and their expected costs to be minimized. The proposed ensemble used the confidence scores where the highest confidence wins to combine the predictions of individual classifiers. Results: The prediction accuracies of Bayesian ensemble was benchmarked against the well-known multilayer perceptron neural network and the ensemble had achieved a remarkable performance with 91.83% accuracy on training subset and 90.63% of test one and outperformed the neural network model. Conclusion: Experimental results showed that the Bayesian classifiers are competitive techniques in the problem of prediction the severity of breast masses.

  18. Spectral diagonal ensemble Kalman filters

    CERN Document Server

    Kasanický, Ivan; Vejmelka, Martin

    2015-01-01

    A new type of ensemble Kalman filter is developed, which is based on replacing the sample covariance in the analysis step by its diagonal in a spectral basis. It is proved that this technique improves the aproximation of the covariance when the covariance itself is diagonal in the spectral basis, as is the case, e.g., for a second-order stationary random field and the Fourier basis. The method is extended by wavelets to the case when the state variables are random fields, which are not spatially homogeneous. Efficient implementations by the fast Fourier transform (FFT) and discrete wavelet transform (DWT) are presented for several types of observations, including high-dimensional data given on a part of the domain, such as radar and satellite images. Computational experiments confirm that the method performs well on the Lorenz 96 problem and the shallow water equations with very small ensembles and over multiple analysis cycles.

  19. Symanzik flow on HISQ ensembles

    CERN Document Server

    Bazavov, A; Brown, N; DeTar, C; Foley, J; Gottlieb, Steven; Heller, U M; Hetrick, J E; Laiho, J; Levkova, L; Oktay, M; Sugar, R L; Toussaint, D; Van de Water, R S; Zhou, R

    2013-01-01

    We report on a scale determination with gradient-flow techniques on the $N_f = 2 + 1 + 1$ HISQ ensembles generated by the MILC collaboration. The lattice scale $w_0/a$, originally proposed by the BMW collaboration, is computed using Symanzik flow at four lattice spacings ranging from 0.15 to 0.06 fm. With a Taylor series ansatz, the results are simultaneously extrapolated to the continuum and interpolated to physical quark masses. We give a preliminary determination of the scale $w_0$ in physical units, along with associated systematic errors, and compare with results from other groups. We also present a first estimate of autocorrelation lengths as a function of flowtime for these ensembles.

  20. Statistical Analysis of Protein Ensembles

    Science.gov (United States)

    Máté, Gabriell; Heermann, Dieter

    2014-04-01

    As 3D protein-configuration data is piling up, there is an ever-increasing need for well-defined, mathematically rigorous analysis approaches, especially that the vast majority of the currently available methods rely heavily on heuristics. We propose an analysis framework which stems from topology, the field of mathematics which studies properties preserved under continuous deformations. First, we calculate a barcode representation of the molecules employing computational topology algorithms. Bars in this barcode represent different topological features. Molecules are compared through their barcodes by statistically determining the difference in the set of their topological features. As a proof-of-principle application, we analyze a dataset compiled of ensembles of different proteins, obtained from the Ensemble Protein Database. We demonstrate that our approach correctly detects the different protein groupings.

  1. Statistical Analysis of Protein Ensembles

    Directory of Open Access Journals (Sweden)

    Gabriell eMáté

    2014-04-01

    Full Text Available As 3D protein-configuration data is piling up, there is an ever-increasing need for well-defined, mathematically rigorous analysis approaches, especially that the vast majority of the currently available methods rely heavily on heuristics. We propose an analysis framework which stems from topology, the field of mathematics which studies properties preserved under continuous deformations. First, we calculate a barcode representation of the molecules employing computational topology algorithms. Bars in this barcode represent different topological features. Molecules are compared through their barcodes by statistically determining the difference in the set of their topological features. As a proof-of-principle application, we analyze a dataset compiled of ensembles of different proteins, obtained from the Ensemble Protein Database. We demonstrate that our approach correctly detects the different protein groupings.

  2. Time-dependent statistical and correlation properties of neural signals during handwriting.

    Directory of Open Access Journals (Sweden)

    Valery I Rupasov

    Full Text Available To elucidate the cortical control of handwriting, we examined time-dependent statistical and correlational properties of simultaneously recorded 64-channel electroencephalograms (EEGs and electromyograms (EMGs of intrinsic hand muscles. We introduced a statistical method, which offered advantages compared to conventional coherence methods. In contrast to coherence methods, which operate in the frequency domain, our method enabled us to study the functional association between different neural regions in the time domain. In our experiments, subjects performed about 400 stereotypical trials during which they wrote a single character. These trials provided time-dependent EMG and EEG data capturing different handwriting epochs. The set of trials was treated as a statistical ensemble, and time-dependent correlation functions between neural signals were computed by averaging over that ensemble. We found that trial-to-trial variability of both the EMGs and EEGs was well described by a log-normal distribution with time-dependent parameters, which was clearly distinguished from the normal (Gaussian distribution. We found strong and long-lasting EMG/EMG correlations, whereas EEG/EEG correlations, which were also quite strong, were short-lived with a characteristic correlation durations on the order of 100 ms or less. Our computations of correlation functions were restricted to the [Formula: see text] spectral range (13-30 Hz of EEG signals where we found the strongest effects related to handwriting. Although, all subjects involved in our experiments were right-hand writers, we observed a clear symmetry between left and right motor areas: inter-channel correlations were strong if both channels were located over the left or right hemispheres, and 2-3 times weaker if the EEG channels were located over different hemispheres. Although we observed synchronized changes in the mean energies of EEG and EMG signals, we found that EEG/EMG correlations were much

  3. Muscle synergy patterns as physiological markers of motor cortical damage.

    Science.gov (United States)

    Cheung, Vincent C K; Turolla, Andrea; Agostini, Michela; Silvoni, Stefano; Bennis, Caoimhe; Kasi, Patrick; Paganoni, Sabrina; Bonato, Paolo; Bizzi, Emilio

    2012-09-04

    The experimental findings herein reported are aimed at gaining a perspective on the complex neural events that follow lesions of the motor cortical areas. Cortical damage, whether by trauma or stroke, interferes with the flow of descending signals to the modular interneuronal structures of the spinal cord. These spinal modules subserve normal motor behaviors by activating groups of muscles as individual units (muscle synergies). Damage to the motor cortical areas disrupts the orchestration of the modules, resulting in abnormal movements. To gain insights into this complex process, we recorded myoelectric signals from multiple upper-limb muscles in subjects with cortical lesions. We used a factorization algorithm to identify the muscle synergies. Our factorization analysis revealed, in a quantitative way, three distinct patterns of muscle coordination-including preservation, merging, and fractionation of muscle synergies-that reflect the multiple neural responses that occur after cortical damage. These patterns varied as a function of both the severity of functional impairment and the temporal distance from stroke onset. We think these muscle-synergy patterns can be used as physiological markers of the status of any patient with stroke or trauma, thereby guiding the development of different rehabilitation approaches, as well as future physiological experiments for a further understanding of postinjury mechanisms of motor control and recovery.

  4. Lateral thinking, from the Hopfield model to cortical dynamics.

    Science.gov (United States)

    Akrami, Athena; Russo, Eleonora; Treves, Alessandro

    2012-01-24

    Self-organizing attractor networks may comprise the building blocks for cortical dynamics, providing the basic operations of categorization, including analog-to-digital conversion, association and auto-association, which are then expressed as components of distinct cognitive functions depending on the contents of the neural codes in each region. To assess the viability of this scenario, we first review how a local cortical patch may be modeled as an attractor network, in which memory representations are not artificially stored as prescribed binary patterns of activity as in the Hopfield model, but self-organize as continuously graded patterns induced by afferent input. Recordings in macaques indicate that such cortical attractor networks may express retrieval dynamics over cognitively plausible rapid time scales, shorter than those dominated by neuronal fatigue. A cortical network encompassing many local attractor networks, and incorporating a realistic description of adaptation dynamics, may be captured by a Potts model. This network model has the capacity to engage long-range associations into sustained iterative attractor dynamics at a cortical scale, in what may be regarded as a mathematical model of spontaneous lateral thought. This article is part of a Special Issue entitled: Neural Coding.

  5. Distinct neural patterns enable grasp types decoding in monkey dorsal premotor cortex

    Science.gov (United States)

    Hao, Yaoyao; Zhang, Qiaosheng; Controzzi, Marco; Cipriani, Christian; Li, Yue; Li, Juncheng; Zhang, Shaomin; Wang, Yiwen; Chen, Weidong; Chiara Carrozza, Maria; Zheng, Xiaoxiang

    2014-12-01

    Objective. Recent studies have shown that dorsal premotor cortex (PMd), a cortical area in the dorsomedial grasp pathway, is involved in grasp movements. However, the neural ensemble firing property of PMd during grasp movements and the extent to which it can be used for grasp decoding are still unclear. Approach. To address these issues, we used multielectrode arrays to record both spike and local field potential (LFP) signals in PMd in macaque monkeys performing reaching and grasping of one of four differently shaped objects. Main results. Single and population neuronal activity showed distinct patterns during execution of different grip types. Cluster analysis of neural ensemble signals indicated that the grasp related patterns emerged soon (200-300 ms) after the go cue signal, and faded away during the hold period. The timing and duration of the patterns varied depending on the behaviors of individual monkey. Application of support vector machine model to stable activity patterns revealed classification accuracies of 94% and 89% for each of the two monkeys, indicating a robust, decodable grasp pattern encoded in the PMd. Grasp decoding using LFPs, especially the high-frequency bands, also produced high decoding accuracies. Significance. This study is the first to specify the neuronal population encoding of grasp during the time course of grasp. We demonstrate high grasp decoding performance in PMd. These findings, combined with previous evidence for reach related modulation studies, suggest that PMd may play an important role in generation and maintenance of grasp action and may be a suitable locus for brain-machine interface applications.

  6. Intelligent Ensemble Forecasting System of Stock Market Fluctuations Based on Symetric and Asymetric Wavelet Functions

    Science.gov (United States)

    Lahmiri, Salim; Boukadoum, Mounir

    2015-08-01

    We present a new ensemble system for stock market returns prediction where continuous wavelet transform (CWT) is used to analyze return series and backpropagation neural networks (BPNNs) for processing CWT-based coefficients, determining the optimal ensemble weights, and providing final forecasts. Particle swarm optimization (PSO) is used for finding optimal weights and biases for each BPNN. To capture symmetry/asymmetry in the underlying data, three wavelet functions with different shapes are adopted. The proposed ensemble system was tested on three Asian stock markets: The Hang Seng, KOSPI, and Taiwan stock market data. Three statistical metrics were used to evaluate the forecasting accuracy; including, mean of absolute errors (MAE), root mean of squared errors (RMSE), and mean of absolute deviations (MADs). Experimental results showed that our proposed ensemble system outperformed the individual CWT-ANN models each with different wavelet function. In addition, the proposed ensemble system outperformed the conventional autoregressive moving average process. As a result, the proposed ensemble system is suitable to capture symmetry/asymmetry in financial data fluctuations for better prediction accuracy.

  7. Classical and Quantum Ensembles via Multiresolution. II. Wigner Ensembles

    CERN Document Server

    Fedorova, A N; Fedorova, Antonina N.; Zeitlin, Michael G.

    2004-01-01

    We present the application of the variational-wavelet analysis to the analysis of quantum ensembles in Wigner framework. (Naive) deformation quantization, the multiresolution representations and the variational approach are the key points. We construct the solutions of Wigner-like equations via the multiscale expansions in the generalized coherent states or high-localized nonlinear eigenmodes in the base of the compactly supported wavelets and the wavelet packets. We demonstrate the appearance of (stable) localized patterns (waveletons) and consider entanglement and decoherence as possible applications.

  8. Cortical myoclonus and cerebellar pathology

    NARCIS (Netherlands)

    Tijssen, MAJ; Thom, M; Ellison, DW; Wilkins, P; Barnes, D; Thompson, PD; Brown, P

    2000-01-01

    Objective To study the electrophysiologic and pathologic findings in three patients with cortical myoclonus. In two patients the myoclonic ataxic syndrome was associated with proven celiac disease. Background: The pathologic findings in conditions associated with cortical myoclonus commonly involve

  9. Cortical Abnormalities in ADHD

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2003-12-01

    Full Text Available Grey-matter abnormalities at the cortical surface and regional brain size were mapped by high-resolution MRI and surface-based, computational image analytical techniques in a group of 27 children and adolescents with attention deficit hyperactivity disorder (ADHD and 46 controls, matched by age and sex, at the University of California at Los Angeles.

  10. Ensembles

    OpenAIRE

    2012-01-01

    Licence; En 1935, un groupe de mathématiciens français eut l'ambition de reconstruire tout l'édifice mathématique (sans S pour bien montrer l'unité) selon la pensée formaliste de Hilbert. Les membres fondateurs ont été Henri Cartan, Claude Chevalley, Jean Delsarte, Jean Dieudonné, André Weil auxquels se joindra René de Possel. En juillet 1935 fut donc créé, lors d'un séminaire en Auvergne le groupe 'Nicolas Bourbaki'. Le nom de cette association fait référence en fait à une anecdote qui se pa...

  11. High-frequency neural activity and human cognition: past, present and possible future of intracranial EEG research

    Science.gov (United States)

    Lachaux, Jean-Philippe; Axmacher, Nikolai; Mormann, Florian; Halgren, Eric; Crone, Nathan E.

    2013-01-01

    Human intracranial EEG (iEEG) recordings are primarily performed in epileptic patients for presurgical mapping. When patients perform cognitive tasks, iEEG signals reveal high-frequency neural activities (HFA, between around 40 Hz and 150 Hz) with exquisite anatomical, functional and temporal specificity. Such HFA were originally interpreted in the context of perceptual or motor binding, in line with animal studies on gamma-band (‘40Hz’) neural synchronization. Today, our understanding of HFA has evolved into a more general index of cortical processing: task-induced HFA reveals, with excellent spatial and time resolution, the participation of local neural ensembles in the task-at-hand, and perhaps the neural communication mechanisms allowing them to do so. This review promotes the claim that studying HFA with iEEG provides insights into the neural bases of cognition that cannot be derived as easily from other approaches, such as fMRI. We provide a series of examples supporting that claim, drawn from studies on memory, language and default-mode networks, and successful attempts of real-time functional mapping. These examples are followed by several guidelines for HFA research, intended for new groups interested by this approach. Overall, iEEG research on HFA should play an increasing role in cognitive neuroscience in humans, because it can be explicitly linked to basic research in animals. We conclude by discussing the future evolution of this field, which might expand that role even further, for instance through the use of multi-scale electrodes and the fusion of iEEG with MEG and fMRI. PMID:22750156

  12. EEG-guided transcranial magnetic stimulation reveals rapid shifts in motor cortical excitability during the human sleep slow oscillation

    DEFF Research Database (Denmark)

    Bergmann, Til O; Mölle, Matthias; Schmidt, Marlit A

    2012-01-01

    Evoked cortical responses do not follow a rigid input-output function but are dynamically shaped by intrinsic neural properties at the time of stimulation. Recent research has emphasized the role of oscillatory activity in determining cortical excitability. Here we employed EEG-guided transcranial...... magnetic stimulation (TMS) during non-rapid eye movement sleep to examine whether the spontaneous...

  13. Electrophysiological Data and the Biophysical Modelling of Local Cortical Circuits

    Directory of Open Access Journals (Sweden)

    Dimitris Pinotsis

    2014-03-01

    Full Text Available This paper shows how recordings of gamma oscillations – under different experimental conditions or from different subjects – can be combined with a class of population models called neural fields and dynamic causal modeling (DCM to distinguish among alternative hypotheses regarding cortical structure and function. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. It draws on the computational power of Bayesian model inversion, when applied to neural field models of cortical dynamics. Bayesian model comparison allows one to adjudicate among different mechanistic hypotheses about cortical excitability, synaptic kinetics and the cardinal topographic features of local cortical circuits. It also provides optimal parameter estimates that quantify neuromodulation and the spatial dispersion of axonal connections or summation of receptive fields in the visual cortex. This paper provides an overview of a family of neural field models that have been recently implemented using the DCM toolbox of the academic freeware Statistical Parametric Mapping (SPM. The SPM software is a popular platform for analyzing neuroimaging data, used by several neuroscience communities worldwide. DCM allows for a formal (Bayesian statistical analysis of cortical network connectivity, based upon realistic biophysical models of brain responses. It is this particular feature of DCM – the unique combination of generative models with optimization techniques based upon (variational Bayesian principles – that furnishes a novel way to characterize functional brain architectures. In particular, it provides answers to questions about how the brain is wired and how it responds to different experimental manipulations. For a review of the general role of neural fields in SPM the reader can consult e.g. see [1]. Neural fields have a long and illustrious history in mathematical

  14. Explicit neural representations, recursive neural networks and conscious visual perception.

    Science.gov (United States)

    Pollen, Daniel A

    2003-08-01

    The fundamental question as to whether the neural correlates of any given conscious visual experience are expressed locally within a given cortical area or more globally within some widely distributed network remains unresolved. We inquire as to whether recursive processing-by which we mean the combined flow and integrated outcome of afferent and recurrent activity across a series of cortical areas-is essential for the emergence of conscious visual experience. If so, we further inquire as to whether such recursive processing is essential only for loops between extrastriate cortical areas explicitly representing experiences such as color or motion back to V1 or whether it is processing between still higher levels and the areas computing such explicit representations that is exclusively or additionally essential for visual experience. If recursive processing is not essential for the emergence of conscious visual experience, then it should also be possible to determine whether it is only the intracortical sensory processing within areas computing explicit sensory representations that is required for perceptual experience or whether it is the subsequent processing of the output of such areas within more anterior cortical regions that engenders perception. The present analysis suggests that the questions posed here may ultimately become experimentally resolvable. Whatever the outcome, the results will likely open new approaches to identify the neural correlates of conscious visual perception.

  15. Analysis of mesoscale forecasts using ensemble methods

    CERN Document Server

    Gross, Markus

    2016-01-01

    Mesoscale forecasts are now routinely performed as elements of operational forecasts and their outputs do appear convincing. However, despite their realistic appearance at times the comparison to observations is less favorable. At the grid scale these forecasts often do not compare well with observations. This is partly due to the chaotic system underlying the weather. Another key problem is that it is impossible to evaluate the risk of making decisions based on these forecasts because they do not provide a measure of confidence. Ensembles provide this information in the ensemble spread and quartiles. However, running global ensembles at the meso or sub mesoscale involves substantial computational resources. National centers do run such ensembles, but the subject of this publication is a method which requires significantly less computation. The ensemble enhanced mesoscale system presented here aims not at the creation of an improved mesoscale forecast model. Also it is not to create an improved ensemble syste...

  16. Measuring social interaction in music ensembles.

    Science.gov (United States)

    Volpe, Gualtiero; D'Ausilio, Alessandro; Badino, Leonardo; Camurri, Antonio; Fadiga, Luciano

    2016-05-05

    Music ensembles are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music ensembles offer a broad variety of scenarios which are particularly suitable for investigation. Small ensembles, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger ensembles, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music ensembles with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of ensemble music performances.

  17. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin

    2014-01-01

    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  18. Gibbs Ensembles of Nonintersecting Paths

    CERN Document Server

    Borodin, Alexei

    2008-01-01

    We consider a family of determinantal random point processes on the two-dimensional lattice and prove that members of our family can be interpreted as a kind of Gibbs ensembles of nonintersecting paths. Examples include probability measures on lozenge and domino tilings of the plane, some of which are non-translation-invariant. The correlation kernels of our processes can be viewed as extensions of the discrete sine kernel, and we show that the Gibbs property is a consequence of simple linear relations satisfied by these kernels. The processes depend on infinitely many parameters, which are closely related to parametrization of totally positive Toeplitz matrices.

  19. Wind Power Prediction using Ensembles

    DEFF Research Database (Denmark)

    Giebel, Gregor; Badger, Jake; Landberg, Lars

    2005-01-01

    offshore wind farm and the whole Jutland/Funen area. The utilities used these forecasts for maintenance planning, fuel consumption estimates and over-the-weekend trading on the Leipzig power exchange. Othernotable scientific results include the better accuracy of forecasts made up from a simple...... superposition of two NWP provider (in our case, DMI and DWD), an investigation of the merits of a parameterisation of the turbulent kinetic energy within thedelivered wind speed forecasts, and the finding that a “naïve” downscaling of each of the coarse ECMWF ensemble members with higher resolution HIRLAM did...

  20. Ensemble Methods Foundations and Algorithms

    CERN Document Server

    Zhou, Zhi-Hua

    2012-01-01

    An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity a

  1. Quantum Repeaters and Atomic Ensembles

    DEFF Research Database (Denmark)

    Borregaard, Johannes

    a previous protocol, thereby enabling fast local processing, which greatly enhances the distribution rate. We then move on to describe our work on improving the stability of atomic clocks using entanglement. Entanglement can potentially push the stability of atomic clocks to the so-called Heisenberg limit......, which is the absolute upper limit of the stability allowed by the Heisenberg uncertainty relation. It has, however, been unclear whether entangled state’s enhanced sensitivity to noise would prevent reaching this limit. We have developed an adaptive measurement protocol, which circumvents this problem...... based on atomic ensembles....

  2. A Localized Ensemble Kalman Smoother

    Science.gov (United States)

    Butala, Mark D.

    2012-01-01

    Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized ensemble Kalman smoother.

  3. Assessing cortical network properties using TMS-EEG.

    Science.gov (United States)

    Rogasch, Nigel C; Fitzgerald, Paul B

    2013-07-01

    The past decade has seen significant developments in the concurrent use of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to directly assess cortical network properties such as excitability and connectivity in humans. New hardware solutions, improved EEG amplifier technology, and advanced data processing techniques have allowed substantial reduction of the TMS-induced artifact, which had previously rendered concurrent TMS-EEG impossible. Various physiological artifacts resulting from TMS have also been identified, and methods are being developed to either minimize or remove these sources of artifact. With these developments, TMS-EEG has unlocked regions of the cortex to researchers that were previously inaccessible to TMS. By recording the TMS-evoked response directly from the cortex, TMS-EEG provides information on the excitability, effective connectivity, and oscillatory tuning of a given cortical area, removing the need to infer such measurements from indirect measures. In the following review, we investigate the different online and offline methods for reducing artifacts in TMS-EEG recordings and the physiological information contained within the TMS-evoked cortical response. We then address the use of TMS-EEG to assess different cortical mechanisms such as cortical inhibition and neural plasticity, before briefly reviewing studies that have utilized TMS-EEG to explore cortical network properties at rest and during different functional brain states.

  4. Somatostatin-expressing inhibitory interneurons in cortical circuits

    Directory of Open Access Journals (Sweden)

    Iryna Yavorska

    2016-09-01

    Full Text Available Cortical inhibitory neurons exhibit remarkable diversity in their morphology, connectivity, and synaptic properties. Here, we review the function of somatostatin-expressing (SOM inhibitory interneurons, focusing largely on sensory cortex. SOM neurons also comprise a number of subpopulations that can be distinguished by their morphology, input and output connectivity, laminar location, firing properties, and expression of molecular markers. Several of these classes of SOM neurons show unique dynamics and characteristics, such as facilitating synapses, specific axonal projections, intralaminar input, and top-down modulation, which suggest possible computational roles. SOM cells can be differentially modulated by behavioral state depending on their class, sensory system, and behavioral paradigm. The functional effects of such modulation have been studied with optogenetic manipulation of SOM cells, which produces effects on learning and memory, task performance, and the integration of cortical activity. Different classes of SOM cells participate in distinct disinhibitory circuits with different inhibitory partners and in different cortical layers. Through these disinhibitory circuits, SOM cells help encode the behavioral relevance of sensory stimuli by regulating the activity of cortical neurons based on subcortical and intracortical modulatory input. Associative learning leads to long-term changes in the strength of connectivity of SOM cells with other neurons, often influencing the strength of inhibitory input they receive. Thus despite their heterogeneity and variability across cortical areas, current evidence shows that SOM neurons perform unique neural computations, forming not only distinct molecular but also functional subclasses of cortical inhibitory interneurons.

  5. Interpreting Tree Ensembles with inTrees

    OpenAIRE

    Deng, Houtao

    2014-01-01

    Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based learner, referred to as the simplified tree ensemble learner (STEL), can also be formed and used for future prediction. The inTrees framework can applied to both classification an...

  6. Analysis of peeling decoder for MET ensembles

    CERN Document Server

    Hinton, Ryan

    2009-01-01

    The peeling decoder introduced by Luby, et al. allows analysis of LDPC decoding for the binary erasure channel (BEC). For irregular ensembles, they analyze the decoder state as a Markov process and present a solution to the differential equations describing the process mean. Multi-edge type (MET) ensembles allow greater precision through specifying graph connectivity. We generalize the the peeling decoder for MET ensembles and derive analogous differential equations. We offer a new change of variables and solution to the node fraction evolutions in the general (MET) case. This result is preparatory to investigating finite-length ensemble behavior.

  7. Comparison of the gene expression profiles of human fetal cortical astrocytes with pluripotent stem cell derived neural stem cells identifies human astrocyte markers and signaling pathways and transcription factors active in human astrocytes.

    Science.gov (United States)

    Malik, Nasir; Wang, Xiantao; Shah, Sonia; Efthymiou, Anastasia G; Yan, Bin; Heman-Ackah, Sabrina; Zhan, Ming; Rao, Mahendra

    2014-01-01

    Astrocytes are the most abundant cell type in the central nervous system (CNS) and have a multitude of functions that include maintenance of CNS homeostasis, trophic support of neurons, detoxification, and immune surveillance. It has only recently been appreciated that astrocyte dysfunction is a primary cause of many neurological disorders. Despite their importance in disease very little is known about global gene expression for human astrocytes. We have performed a microarray expression analysis of human fetal astrocytes to identify genes and signaling pathways that are important for astrocyte development and maintenance. Our analysis confirmed that the fetal astrocytes express high levels of the core astrocyte marker GFAP and the transcription factors from the NFI family which have been shown to play important roles in astrocyte development. A group of novel markers were identified that distinguish fetal astrocytes from pluripotent stem cell-derived neural stem cells (NSCs) and NSC-derived neurons. As in murine astrocytes, the Notch signaling pathway appears to be particularly important for cell fate decisions between the astrocyte and neuronal lineages in human astrocytes. These findings unveil the repertoire of genes expressed in human astrocytes and serve as a basis for further studies to better understand astrocyte biology, especially as it relates to disease.

  8. Hierarchical Bayes Ensemble Kalman Filtering

    CERN Document Server

    Tsyrulnikov, Michael

    2015-01-01

    Ensemble Kalman filtering (EnKF), when applied to high-dimensional systems, suffers from an inevitably small affordable ensemble size, which results in poor estimates of the background error covariance matrix ${\\bf B}$. The common remedy is a kind of regularization, usually an ad-hoc spatial covariance localization (tapering) combined with artificial covariance inflation. Instead of using an ad-hoc regularization, we adopt the idea by Myrseth and Omre (2010) and explicitly admit that the ${\\bf B}$ matrix is unknown and random and estimate it along with the state (${\\bf x}$) in an optimal hierarchical Bayes analysis scheme. We separate forecast errors into predictability errors (i.e. forecast errors due to uncertainties in the initial data) and model errors (forecast errors due to imperfections in the forecast model) and include the two respective components ${\\bf P}$ and ${\\bf Q}$ of the ${\\bf B}$ matrix into the extended control vector $({\\bf x},{\\bf P},{\\bf Q})$. Similarly, we break the traditional backgrou...

  9. Visualizing ensembles in structural biology.

    Science.gov (United States)

    Melvin, Ryan L; Salsbury, Freddie R

    2016-06-01

    Displaying a single representative conformation of a biopolymer rather than an ensemble of states mistakenly conveys a static nature rather than the actual dynamic personality of biopolymers. However, there are few apparent options due to the fixed nature of print media. Here we suggest a standardized methodology for visually indicating the distribution width, standard deviation and uncertainty of ensembles of states with little loss of the visual simplicity of displaying a single representative conformation. Of particular note is that the visualization method employed clearly distinguishes between isotropic and anisotropic motion of polymer subunits. We also apply this method to ligand binding, suggesting a way to indicate the expected error in many high throughput docking programs when visualizing the structural spread of the output. We provide several examples in the context of nucleic acids and proteins with particular insights gained via this method. Such examples include investigating a therapeutic polymer of FdUMP (5-fluoro-2-deoxyuridine-5-O-monophosphate) - a topoisomerase-1 (Top1), apoptosis-inducing poison - and nucleotide-binding proteins responsible for ATP hydrolysis from Bacillus subtilis. We also discuss how these methods can be extended to any macromolecular data set with an underlying distribution, including experimental data such as NMR structures.

  10. Breast Cancer Classification From Histological Images with Multiple Features and Random Subspace Classifier Ensemble

    Science.gov (United States)

    Zhang, Yungang; Zhang, Bailing; Lu, Wenjin

    2011-06-01

    Histological image is important for diagnosis of breast cancer. In this paper, we present a novel automatic breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co-occurence Matrix (GLCM) and Completed Local Binary Patterns (CLBP), respectively. The three different features are combined together and used for classification. A classifier ensemble approach, called Random Subspace Ensemble (RSE), are used to select and aggregate a set of base neural network classifiers for classification. The proposed multiple features and random subspace ensemble offer the classification rate 95.22% on a publically available breast cancer image dataset, which compares favorably with the previously published result 93.4%.

  11. Purely Cortical Anaplastic Ependymoma

    Directory of Open Access Journals (Sweden)

    Flávio Ramalho Romero

    2012-01-01

    Full Text Available Ependymomas are glial tumors derived from ependymal cells lining the ventricles and the central canal of the spinal cord. It may occur outside the ventricular structures, representing the extraventicular form, or without any relationship of ventricular system, called ectopic ependymona. Less than fifteen cases of ectopic ependymomas were reported and less than five were anaplastic. We report a rare case of pure cortical ectopic anaplastic ependymoma.

  12. [Posterior cortical atrophy].

    Science.gov (United States)

    Solyga, Volker Moræus; Western, Elin; Solheim, Hanne; Hassel, Bjørnar; Kerty, Emilia

    2015-06-02

    Posterior cortical atrophy is a neurodegenerative condition with atrophy of posterior parts of the cerebral cortex, including the visual cortex and parts of the parietal and temporal cortices. It presents early, in the 50s or 60s, with nonspecific visual disturbances that are often misinterpreted as ophthalmological, which can delay the diagnosis. The purpose of this article is to present current knowledge about symptoms, diagnostics and treatment of this condition. The review is based on a selection of relevant articles in PubMed and on the authors' own experience with the patient group. Posterior cortical atrophy causes gradually increasing impairment in reading, distance judgement, and the ability to perceive complex images. Examination of higher visual functions, neuropsychological testing, and neuroimaging contribute to diagnosis. In the early stages, patients do not have problems with memory or insight, but cognitive impairment and dementia can develop. It is unclear whether the condition is a variant of Alzheimer's disease, or whether it is a separate disease entity. There is no established treatment, but practical measures such as the aid of social care workers, telephones with large keypads, computers with voice recognition software and audiobooks can be useful. Currently available treatment has very limited effect on the disease itself. Nevertheless it is important to identify and diagnose the condition in its early stages in order to be able to offer patients practical assistance in their daily lives.

  13. Emerging roles of Axin in cerebral cortical development

    Directory of Open Access Journals (Sweden)

    Tao eYe

    2015-06-01

    Full Text Available Proper functioning of the cerebral cortex depends on the appropriate production and positioning of neurons, establishment of axon–dendrite polarity, and formation of proper neuronal connectivity. Deficits in any of these processes greatly impair neural functions and are associated with various human neurodevelopmental disorders including microcephaly, cortical heterotopias, and autism. The application of in vivo manipulation techniques such as in utero electroporation has resulted in significant advances in our understanding of the cellular and molecular mechanisms that underlie neural development in vivo. Axin is a scaffold protein that regulates neuronal differentiation and morphogenesis in vitro. Recent studies provide novel insights into the emerging roles of Axin in gene expression and cytoskeletal regulation during neurogenesis, neuronal polarization, and axon formation. This review summarizes current knowledge on Axin as a key molecular controller of cerebral cortical development.

  14. Oxytocin Enables Maternal Behavior by Balancing Cortical Inhibition

    Science.gov (United States)

    Marlin, Bianca J.; Mitre, Mariela; D’amour, James A.; Chao, Moses V.; Froemke, Robert C.

    2015-01-01

    Oxytocin is important for social interactions and maternal behavior. However, little is known about when, where, and how oxytocin modulates neural circuits to improve social cognition. Here we show how oxytocin enables pup retrieval behavior in female mice by enhancing auditory cortical pup call responses. Retrieval behavior required left but not right auditory cortex, was accelerated by oxytocin in left auditory cortex, and oxytocin receptors were preferentially expressed in left auditory cortex. Neural responses to pup calls were lateralized, with co-tuned and temporally-precise excitatory and inhibitory responses in left cortex of maternal but not pup-naive adults. Finally, pairing calls with oxytocin enhanced responses by balancing the magnitude and timing of inhibition with excitation. Our results describe fundamental synaptic mechanisms by which oxytocin increases the salience of acoustic social stimuli. Furthermore, oxytocin-induced plasticity provides a biological basis for lateralization of auditory cortical processing. PMID:25874674

  15. Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble

    Directory of Open Access Journals (Sweden)

    Pham Tuan D

    2011-04-01

    ensemble model produces better classification performance compared to the component neural networks trained. For the three images sets HeLa, CHO and RNAi, the Random Subspace Ensembles offers the classification rates 91.20%, 98.86% and 91.03% respectively, which compares sharply with the published result 84%, 93% and 82% from a multi-purpose image classifier WND-CHARM which applied wavelet transforms and other feature extraction methods. We investigated the problem of estimation of ensemble parameters and found that satisfactory performance improvement could be brought by a relative medium dimensionality of feature subsets and small ensemble size. Conclusions The characteristics of curvelet transform of being multiscale and multidirectional suit the description of microscopy images very well. It is empirically demonstrated that the curvelet-based feature is clearly preferred to wavelet-based feature for bioimage descriptions. The random subspace ensemble of MLPs is much better than a number of commonly applied multi-class classifiers in the investigated application of phenotype recognition.

  16. Neural modeling of prefrontal executive function

    Energy Technology Data Exchange (ETDEWEB)

    Levine, D.S. [Univ. of Texas, Arlington, TX (United States)

    1996-12-31

    Brain executive function is based in a distributed system whereby prefrontal cortex is interconnected with other cortical. and subcortical loci. Executive function is divided roughly into three interacting parts: affective guidance of responses; linkage among working memory representations; and forming complex behavioral schemata. Neural network models of each of these parts are reviewed and fit into a preliminary theoretical framework.

  17. Improved customer choice predictions using ensemble methods

    NARCIS (Netherlands)

    M.C. van Wezel (Michiel); R. Potharst (Rob)

    2005-01-01

    textabstractIn this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to

  18. Ensemble methods for handwritten digit recognition

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Liisberg, Christian; Salamon, P.

    1992-01-01

    . It is further shown that it is possible to estimate the ensemble performance as well as the learning curve on a medium-size database. In addition the authors present preliminary analysis of experiments on a large database and show that state-of-the-art performance can be obtained using the ensemble approach...

  19. Nonextensivity in magnetic nanoparticle ensembles

    Science.gov (United States)

    Binek, Ch.; Polisetty, S.; He, Xi; Mukherjee, T.; Rajesh, R.; Redepenning, J.

    2006-08-01

    A superconducting quantum interference device and Faraday rotation technique are used to study dipolar interacting nanoparticles embedded in a polystyrene matrix. Magnetization isotherms are measured for three cylindrically shaped samples of constant diameter but various heights. Detailed analysis of the isotherms supports Tsallis’ conjecture of a magnetic equation of state that involves temperature and magnetic field variables scaled by the logarithm of the number of magnetic nanoparticles. This unusual scaling of thermodynamic variables, which are conventionally considered to be intensive, originates from the nonextensivity of the Gibbs free energy in three-dimensional dipolar interacting particle ensembles. Our experimental evidence for nonextensivity is based on the data collapse of various isotherms that require scaling of the field variable in accordance with Tsallis’ equation of state.

  20. Perception of ensemble statistics requires attention.

    Science.gov (United States)

    Jackson-Nielsen, Molly; Cohen, Michael A; Pitts, Michael A

    2017-02-01

    To overcome inherent limitations in perceptual bandwidth, many aspects of the visual world are represented as summary statistics (e.g., average size, orientation, or density of objects). Here, we investigated the relationship between summary (ensemble) statistics and visual attention. Recently, it was claimed that one ensemble statistic in particular, color diversity, can be perceived without focal attention. However, a broader debate exists over the attentional requirements of conscious perception, and it is possible that some form of attention is necessary for ensemble perception. To test this idea, we employed a modified inattentional blindness paradigm and found that multiple types of summary statistics (color and size) often go unnoticed without attention. In addition, we found attentional costs in dual-task situations, further implicating a role for attention in statistical perception. Overall, we conclude that while visual ensembles may be processed efficiently, some amount of attention is necessary for conscious perception of ensemble statistics.

  1. Multimodal Sensory Responses of Nucleus Reticularis Gigantocellularis and the Responses' Relation to Cortical and Motor Activation

    OpenAIRE

    Martin, Eugene M.; Pavlides, Constantine; Pfaff, Donald

    2010-01-01

    The connectivity of large neurons of the nucleus reticularis gigantocellularis (NRGc) in the medullary reticular formation potentially allows both for the integration of stimuli, in several modalities, that would demand immediate action, and for coordinated activation of cortical and motoric activity. We have simultaneously recorded cortical local field potentials, neck muscle electromyograph (EMG), and the neural activity of medullary NRGc neurons in unrestrained, unanesthetized rats to dete...

  2. Generation of scenarios from calibrated ensemble forecasts with a dynamic ensemble copula coupling approach

    CERN Document Server

    Bouallegue, Zied Ben; Theis, Susanne E; Pinson, Pierre

    2015-01-01

    Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost.For example, the ensemble copula coupling (ECC) method consists in rebuilding the multivariate aspect of the forecast from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new approach which preserves the dynamical development of the ensemble members is called dynamic ensemble copula coupling (...

  3. Electrical coupling in ensembles of nonexcitable cells: modeling the spatial map of single cell potentials.

    Science.gov (United States)

    Cervera, Javier; Manzanares, Jose Antonio; Mafe, Salvador

    2015-02-19

    We analyze the coupling of model nonexcitable (non-neural) cells assuming that the cell membrane potential is the basic individual property. We obtain this potential on the basis of the inward and outward rectifying voltage-gated channels characteristic of cell membranes. We concentrate on the electrical coupling of a cell ensemble rather than on the biochemical and mechanical characteristics of the individual cells, obtain the map of single cell potentials using simple assumptions, and suggest procedures to collectively modify this spatial map. The response of the cell ensemble to an external perturbation and the consequences of cell isolation, heterogeneity, and ensemble size are also analyzed. The results suggest that simple coupling mechanisms can be significant for the biophysical chemistry of model biomolecular ensembles. In particular, the spatiotemporal map of single cell potentials should be relevant for the uptake and distribution of charged nanoparticles over model cell ensembles and the collective properties of droplet networks incorporating protein ion channels inserted in lipid bilayers.

  4. Rainfall downscaling of weekly ensemble forecasts using self-organising maps

    Directory of Open Access Journals (Sweden)

    Masamichi Ohba

    2016-03-01

    Full Text Available This study presents an application of self-organising maps (SOMs to downscaling medium-range ensemble forecasts and probabilistic prediction of local precipitation in Japan. SOM was applied to analyse and connect the relationship between atmospheric patterns over Japan and local high-resolution precipitation data. Multiple SOM was simultaneously employed on four variables derived from the JRA-55 reanalysis over the area of study (south-western Japan, and a two-dimensional lattice of weather patterns (WPs was obtained. Weekly ensemble forecasts can be downscaled to local precipitation using the obtained multiple SOM. The downscaled precipitation is derived by the five SOM lattices based on the WPs of the global model ensemble forecasts for a particular day in 2009–2011. Because this method effectively handles the stochastic uncertainties from the large number of ensemble members, a probabilistic local precipitation is easily and quickly obtained from the ensemble forecasts. This downscaling of ensemble forecasts provides results better than those from a 20-km global spectral model (i.e. capturing the relatively detailed precipitation distribution over the region. To capture the effect of the detailed pattern differences in each SOM node, a statistical model is additionally concreted for each SOM node. The predictability skill of the ensemble forecasts is significantly improved under the neural network-statistics hybrid-downscaling technique, which then brings a much better skill score than the traditional method. It is expected that the results of this study will provide better guidance to the user community and contribute to the future development of dam-management models.

  5. Cortical Entropy, Mutual Information and Scale-Free Dynamics in Waking Mice

    Science.gov (United States)

    Fagerholm, Erik D.; Scott, Gregory; Shew, Woodrow L.; Song, Chenchen; Leech, Robert; Knöpfel, Thomas; Sharp, David J.

    2016-01-01

    Some neural circuits operate with simple dynamics characterized by one or a few well-defined spatiotemporal scales (e.g. central pattern generators). In contrast, cortical neuronal networks often exhibit richer activity patterns in which all spatiotemporal scales are represented. Such “scale-free” cortical dynamics manifest as cascades of activity with cascade sizes that are distributed according to a power-law. Theory and in vitro experiments suggest that information transmission among cortical circuits is optimized by scale-free dynamics. In vivo tests of this hypothesis have been limited by experimental techniques with insufficient spatial coverage and resolution, i.e., restricted access to a wide range of scales. We overcame these limitations by using genetically encoded voltage imaging to track neural activity in layer 2/3 pyramidal cells across the cortex in mice. As mice recovered from anesthesia, we observed three changes: (a) cortical information capacity increased, (b) information transmission among cortical regions increased and (c) neural activity became scale-free. Our results demonstrate that both information capacity and information transmission are maximized in the awake state in cortical regions with scale-free network dynamics. PMID:27384059

  6. Calculations of canonical averages from the grand canonical ensemble.

    Science.gov (United States)

    Kosov, D S; Gelin, M F; Vdovin, A I

    2008-02-01

    Grand canonical and canonical ensembles become equivalent in the thermodynamic limit, but when the system size is finite the results obtained in the two ensembles deviate from each other. In many important cases, the canonical ensemble provides an appropriate physical description but it is often much easier to perform the calculations in the corresponding grand canonical ensemble. We present a method to compute averages in the canonical ensemble based on calculations of the expectation values in the grand canonical ensemble. The number of particles, which is fixed in the canonical ensemble, is not necessarily the same as the average number of particles in the grand canonical ensemble.

  7. Error awareness and salience processing in the oddball task: Shared neural mechanisms.

    Directory of Open Access Journals (Sweden)

    Helga A Harsay

    2012-08-01

    Full Text Available A body of work suggests that there are similarities in the way we become aware of an error and process motivationally salient events. Yet, evidence for a shared neural mechanism has not been provided. A within-subject investigation of the brain regions involved in error awareness and salience processing has not been reported. While the neural response to motivationally salient events is classically studied during target detection after longer target-to-target intervals in an oddball task and engages a widespread insula-thalamo-cortical brain network, error awareness has recently been linked to, most prominently, anterior insula cortex. Here we explore whether the anterior insula activation for error awareness is related to salience processing, by testing for activation overlap in subjects undergoing two different task settings. Using a within-subjects design, we show activation overlap in six major brain areas during aware errors in an antisaccade task and during target detection (which were associated with longer target-to-target interval conditions in an oddball task: anterior insula, anterior cingulate, supplementary motor area, thalamus, brainstem and parietal lobe. Within subject analyses shows that the insula is engaged in both error awareness and the processing of salience, and that the anterior insula is more involved in both processes than the posterior insula. The results of a fine-grained spatial pattern overlap analysis between active clusters in the same subjects indicated that even if the anterior insula is activated for both error awareness and salience processing, the two types of processes might tend to activate non-identical neural ensembles on a finer-grained spatial level. Together, these outcomes suggest a similar functional phenomenon in the two different task settings. Error awareness and salience processing share a functional anatomy, with a tendency towards subregional dorsal and ventral specialization within the

  8. Predicting artificailly drained areas by means of selective model ensemble

    DEFF Research Database (Denmark)

    Møller, Anders Bjørn; Beucher, Amélie; Iversen, Bo Vangsø

    . The approaches employed include decision trees, discriminant analysis, regression models, neural networks and support vector machines amongst others. Several models are trained with each method, using variously the original soil covariates and principal components of the covariates. With a large ensemble...... out since the mid-19th century, and it has been estimated that half of the cultivated area is artificially drained (Olesen, 2009). A number of machine learning approaches can be used to predict artificially drained areas in geographic space. However, instead of choosing the most accurate model....... The study aims firstly to train a large number of models to predict the extent of artificially drained areas using various machine learning approaches. Secondly, the study will develop a method for selecting the models, which give a good prediction of artificially drained areas, when used in conjunction...

  9. Decoding of covert vowel articulation using electroencephalography cortical currents

    Directory of Open Access Journals (Sweden)

    Natsue eYoshimura

    2016-05-01

    Full Text Available 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.

  10. Cortical spreading depression-induced preconditioning in the brain

    Institute of Scientific and Technical Information of China (English)

    Ping-ping Shen; Shuai Hou; Di Ma; Ming-ming Zhao; Ming-qin Zhu; Jing-dian Zhang; Liang-shu Feng; Li Cui; Jia-chun Feng

    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. Howev-er, 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 cor-tical spreading depression-induced tolerance in the brain, considering excitatory neurotransmission and metabolism, nitric oxide, genomic reprogramming, inlfammation, neurotropic factors, and cellular stress response. Speciifcally, we clarify the procedures and detailed information regarding cortical spreading de-pression-induced preconditioning and build a foundation for more comprehensive investigations in the ifeld of neural regeneration and clinical application in the future.

  11. Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents

    Science.gov (United States)

    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

  12. Force estimation from ensembles of Golgi tendon organs

    Science.gov (United States)

    Mileusnic, M. P.; Loeb, G. E.

    2009-06-01

    Golgi tendon organs (GTOs) located in the skeletal muscles provide the central nervous system with information about muscle tension. The ensemble firing of all GTO receptors in the muscle has been hypothesized to represent a reliable measure of the whole muscle force but the precision and accuracy of that information are largely unknown because it is impossible to record activity simultaneously from all GTOs in a muscle. In this study, we combined a new mathematical model of force sampling and transduction in individual GTOs with various models of motor unit (MU) organization and recruitment simulating various normal, pathological and neural prosthetic conditions. Our study suggests that in the intact muscle the ensemble GTO activity accurately encodes force information according to a nonlinear, monotonic relationship that has its steepest slope for low force levels and tends to saturate at the highest force levels. The relationship between the aggregate GTO activity and whole muscle tension under some pathological conditions is similar to one seen in the intact muscle during rapidly modulated, phasic excitation of the motor pool (typical for many natural movements) but quite different when the muscle is activated slowly or held at a given force level. Substantial deviations were also observed during simulated functional electrical stimulation.

  13. Hybrid Data Assimilation without Ensemble Filtering

    Science.gov (United States)

    Todling, Ricardo; Akkraoui, Amal El

    2014-01-01

    The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the ensemble is generated using a square-root ensemble Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member ensemble solution close to the variational solution; we also found it necessary to re-center the members of the ensemble about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the ensemble. This led us to consider a hybrid strategy in which the members of the ensemble are generated by simply converting the variational analysis to the resolution of the ensemble and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure.

  14. MSEBAG: a dynamic classifier ensemble generation based on `minimum-sufficient ensemble' and bagging

    Science.gov (United States)

    Chen, Lei; Kamel, Mohamed S.

    2016-01-01

    In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient ensemble' and bagging at the ensemble level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient ensemble', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient ensemble', a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.

  15. Neural inhibition enables selection during language processing.

    Science.gov (United States)

    Snyder, Hannah R; Hutchison, Natalie; Nyhus, Erika; Curran, Tim; Banich, Marie T; O'Reilly, Randall C; Munakata, Yuko

    2010-09-21

    Whether grocery shopping or choosing words to express a thought, selecting between options can be challenging, especially for people with anxiety. We investigate the neural mechanisms supporting selection during language processing and its breakdown in anxiety. Our neural network simulations demonstrate a critical role for competitive, inhibitory dynamics supported by GABAergic interneurons. As predicted by our model, we find that anxiety (associated with reduced neural inhibition) impairs selection among options and associated prefrontal cortical activity, even in a simple, nonaffective verb-generation task, and the GABA agonist midazolam (which increases neural inhibition) improves selection, whereas retrieval from semantic memory is unaffected when selection demands are low. Neural inhibition is key to choosing our words.

  16. Effects of Parecoxib and Fentanyl on nociception-induced cortical activity

    Directory of Open Access Journals (Sweden)

    Wang Ying-Wei

    2010-01-01

    Full Text Available Abstract Background Analgesics, including opioids and non-steroid anti-inflammatory drugs reduce postoperative pain. However, little is known about the quantitative effects of these drugs on cortical activity induced by nociceptive stimulation. The aim of the present study was to determine the neural activity in response to a nociceptive stimulus and to investigate the effects of fentanyl (an opioid agonist and parecoxib (a selective cyclooxygenase-2 inhibitor on this nociception-induced cortical activity evoked by tail pinch. Extracellular recordings (electroencephalogram and multi-unit signals were performed in the area of the anterior cingulate cortex while intracellular recordings were made in the primary somatosensory cortex. The effects of parecoxib and fentanyl on induced cortical activity were compared. Results Peripheral nociceptive stimulation in anesthetized rats produced an immediate electroencephalogram (EEG desynchronization resembling the cortical arousal (low-amplitude, fast-wave activity, while the membrane potential switched into a persistent depolarization state. The induced cortical activity was abolished by fentanyl, and the fentanyl's effect was reversed by the opioid receptor antagonist, naloxone. Parecoxib, on the other hand, did not significantly affect the neural activity. Conclusion Cortical activity was modulated by nociceptive stimulation in anesthetized rats. Fentanyl showed a strong inhibitory effect on the nociceptive-stimulus induced cortical activity while parecoxib had no significant effect.

  17. Theory of cortical function.

    Science.gov (United States)

    Heeger, David J

    2017-02-21

    Most models of sensory processing in the brain have a feedforward architecture in which each stage comprises simple linear filtering operations and nonlinearities. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence (with deep convolutional neural nets) are based on this architecture. However, neocortex is not a feedforward architecture. This paper proposes a first step toward an alternative computational framework in which neural activity in each brain area depends on a combination of feedforward drive (bottom-up from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation). The relative contributions of feedforward drive, feedback drive, and prior drive are controlled by a handful of state parameters, which I hypothesize correspond to neuromodulators and oscillatory activity. In some states, neural responses are dominated by the feedforward drive and the theory is identical to a conventional feedforward model, thereby preserving all of the desirable features of those models. In other states, the theory is a generative model that constructs a sensory representation from an abstract representation, like memory recall. In still other states, the theory combines prior expectation with sensory input, explores different possible perceptual interpretations of ambiguous sensory inputs, and predicts forward in time. The theory, therefore, offers an empirically testable framework for understanding how the cortex accomplishes inference, exploration, and prediction.

  18. 4DVAR by ensemble Kalman smoother

    CERN Document Server

    Mandel, Jan; Gratton, Serge

    2013-01-01

    We propose to use the ensemble Kalman smoother (EnKS) as linear least squares solver in the Gauss-Newton method for the large nonlinear least squares in incremental 4DVAR. The ensemble approach is naturally parallel over the ensemble members and no tangent or adjoint operators are needed. Further, adding a regularization term results in replacing the Gauss-Newton method, which may diverge, by^M the Levenberg-Marquardt method, which is known to be convergent. The regularization is implemented efficiently as an additional observation in the EnKS.

  19. Derivation of Mayer Series from Canonical Ensemble

    Science.gov (United States)

    Wang, Xian-Zhi

    2016-02-01

    Mayer derived the Mayer series from both the canonical ensemble and the grand canonical ensemble by use of the cluster expansion method. In 2002, we conjectured a recursion formula of the canonical partition function of a fluid (X.Z. Wang, Phys. Rev. E 66 (2002) 056102). In this paper we give a proof for this formula by developing an appropriate expansion of the integrand of the canonical partition function. We further derive the Mayer series solely from the canonical ensemble by use of this recursion formula.

  20. The influence of lexical statistics on temporal lobe cortical dynamics during spoken word listening

    Science.gov (United States)

    Cibelli, Emily S.; Leonard, Matthew K.; Johnson, Keith; Chang, Edward F.

    2015-01-01

    Neural representations of words are thought to have a complex spatio-temporal cortical basis. It has been suggested that spoken word recognition is not a process of feed-forward computations from phonetic to lexical forms, but rather involves the online integration of bottom-up input with stored lexical knowledge. Using direct neural recordings from the temporal lobe, we examined cortical responses to words and pseudowords. We found that neural populations were not only sensitive to lexical status (real vs. pseudo), but also to cohort size (number of words matching the phonetic input at each time point) and cohort frequency (lexical frequency of those words). These lexical variables modulated neural activity from the posterior to anterior temporal lobe, and also dynamically as the stimuli unfolded on a millisecond time scale. Our findings indicate that word recognition is not purely modular, but relies on rapid and online integration of multiple sources of lexical knowledge. PMID:26072003

  1. Nonextensivity in Magnetic Nanocluster Ensembles

    Science.gov (United States)

    Binek, Christian; Polisetty, Srinivas; He, Xi; Mukherjee, Tathagata; Rajasekeran, Rajesh; Redepenning, Jody

    2006-03-01

    We study the scaling behavior of dipolar interacting nanoparticles in 3D samples of various sizes but constant particle density. Ferromagnetic γ-Fe2O3 clusters embedded in a polystyrene matrix are fabricated by thermal decomposition of metal carbonyls. Transmission electron microscopy reveals a narrow size distribution of 12 nm clusters. They are randomly dispersed in the matrix with an average separation of 80 nm. Magnetization isotherms of these single domain particle ensembles are measured by SQUID magnetometry above the blocking temperature TB =115K where non-equilibrium effects are avoided. After demagnetization corrections which convert the applied magnetic fields into internal fields, H, a data collapse is achieved when scaling the magnetic moment, m, and H by appropriate factors. The latter are theoretically predicted functions of the number of particles and determined here numerically. Scaling of H takes into account the nonextensive (NE) behavior of dipolar interacting particles. In the case of long range interactions a scaling schema has been proposed by Tsallis and confirmed by simulations. The controversial field of NE thermodynamics requires however experimental evidence provided here.

  2. Ensemble Dynamics and Bred Vectors

    CERN Document Server

    Balci, Nusret; Restrepo, Juan M; Sell, George R

    2011-01-01

    We introduce the new concept of an EBV to assess the sensitivity of model outputs to changes in initial conditions for weather forecasting. The new algorithm, which we call the "Ensemble Bred Vector" or EBV, is based on collective dynamics in essential ways. By construction, the EBV algorithm produces one or more dominant vectors. We investigate the performance of EBV, comparing it to the BV algorithm as well as the finite-time Lyapunov Vectors. We give a theoretical justification to the observed fact that the vectors produced by BV, EBV, and the finite-time Lyapunov vectors are similar for small amplitudes. Numerical comparisons of BV and EBV for the 3-equation Lorenz model and for a forced, dissipative partial differential equation of Cahn-Hilliard type that arises in modeling the thermohaline circulation, demonstrate that the EBV yields a size-ordered description of the perturbation field, and is more robust than the BV in the higher nonlinear regime. The EBV yields insight into the fractal structure of th...

  3. Neural locus of color afterimages.

    Science.gov (United States)

    Zaidi, Qasim; Ennis, Robert; Cao, Dingcai; Lee, Barry

    2012-02-07

    After fixating on a colored pattern, observers see a similar pattern in complementary colors when the stimulus is removed [1-6]. Afterimages were important in disproving the theory that visual rays emanate from the eye, in demonstrating interocular interactions, and in revealing the independence of binocular vision from eye movements. Afterimages also prove invaluable in exploring selective attention, filling in, and consciousness. Proposed physiological mechanisms for color afterimages range from bleaching of cone photopigments to cortical adaptation [4-9], but direct neural measurements have not been reported. We introduce a time-varying method for evoking afterimages, which provides precise measurements of adaptation and a direct link between visual percepts and neural responses [10]. We then use in vivo electrophysiological recordings to show that all three classes of primate retinal ganglion cells exhibit subtractive adaptation to prolonged stimuli, with much slower time constants than those expected of photoreceptors. At the cessation of the stimulus, ganglion cells generate rebound responses that can provide afterimage signals for later neurons. Our results indicate that afterimage signals are generated in the retina but may be modified like other retinal signals by cortical processes, so that evidence presented for cortical generation of color afterimages is explainable by spatiotemporal factors that modify all signals.

  4. Cortico-Cortical Receptive Field Estimates in Human Visual Cortex

    Directory of Open Access Journals (Sweden)

    Koen V Haak

    2012-05-01

    Full Text Available Human visual cortex comprises many visual areas that contain a map of the visual field (Wandell et al 2007, Neuron 56, 366–383. These visual field maps can be identified readily in individual subjects with functional magnetic resonance imaging (fMRI during experimental sessions that last less than an hour (Wandell and Winawer 2011, Vis Res 718–737. Hence, visual field mapping with fMRI has been, and still is, a heavily used technique to examine the organisation of both normal and abnormal human visual cortex (Haak et al 2011, ACNR, 11(3, 20–21. However, visual field mapping cannot reveal every aspect of human visual cortex organisation. For example, the information processed within a visual field map arrives from somewhere and is sent to somewhere, and visual field mapping does not derive these input/output relationships. Here, we describe a new, model-based analysis for estimating the dependence between signals in distinct cortical regions using functional magnetic resonance imaging (fMRI data. Just as a stimulus-referred receptive field predicts the neural response as a function of the stimulus contrast, the neural-referred receptive field predicts the neural response as a function of responses elsewhere in the nervous system. When applied to two cortical regions, this function can be called the cortico-cortical receptive field (CCRF. We model the CCRF as a Gaussian-weighted region on the cortical surface and apply the model to data from both stimulus-driven and resting-state experimental conditions in visual cortex.

  5. Evaluating mandibular cortical index quantitatively.

    Science.gov (United States)

    Yasar, Fusun; Akgunlu, Faruk

    2008-10-01

    The aim was to assess whether Fractal Dimension and Lacunarity analysis can discriminate patients having different mandibular cortical shape. 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. 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). 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.

  6. Cortico-cortical communication dynamics

    Directory of Open Access Journals (Sweden)

    Per E Roland

    2014-05-01

    Full Text Available IIn 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.

  7. A 4D-Ensemble-Variational System for Data Assimilation and Ensemble Initialization

    Science.gov (United States)

    Bowler, Neill; Clayton, Adam; Jardak, Mohamed; Lee, Eunjoo; Jermey, Peter; Lorenc, Andrew; Piccolo, Chiara; Pring, Stephen; Wlasak, Marek; Barker, Dale; Inverarity, Gordon; Swinbank, Richard

    2016-04-01

    The Met Office has been developing a four-dimensional ensemble variational (4DEnVar) data assimilation system over the past four years. The 4DEnVar system is intended both as data assimilation system in its own right and also an improved means of initializing the Met Office Global and Regional Ensemble Prediction System (MOGREPS). The global MOGREPS ensemble has been initialized by running an ensemble of 4DEnVars (En-4DEnVar). The scalability and maintainability of ensemble data assimilation methods make them increasingly attractive, and 4DEnVar may be adopted in the context of the Met Office's LFRic project to redevelop the technical infrastructure to enable its Unified Model (MetUM) to be run efficiently on massively parallel supercomputers. This presentation will report on the results of the 4DEnVar development project, including experiments that have been run using ensemble sizes of up to 200 members.

  8. Modeling of cortical signals using echo state networks

    Science.gov (United States)

    Zhou, Hanying; Wang, Yongji; Huang, Jiangshuai

    2009-10-01

    Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have, it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully. What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this paper we train this net with a refined version of its typical training method and get a better model.

  9. Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

    Science.gov (United States)

    Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

  10. Modeling cortical circuits.

    Energy Technology Data Exchange (ETDEWEB)

    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.

  11. Cortical and spinal assessment

    DEFF Research Database (Denmark)

    Fischer, I W; Gram, Mikkel; Hansen, T M

    2017-01-01

    BACKGROUND: Standardized objective methods to assess the analgesic effects of opioids, enable identification of underlying mechanisms of drug actions in the central nervous system. Opioids may exert their effect on both cortical and spinal levels. In this study actions of morphine at both levels...... subjects was included in the data analysis. There was no change in the activity in resting EEG (P>0.05) after morphine administration as compared to placebo. During cold pressor stimulation, morphine significantly lowered the relative activity in the delta (1-4Hz) band (P=0.03) and increased the activity...... morphine administration (P>0.05). CONCLUSIONS: Cold pressor EEG and the nociceptive reflex were more sensitive to morphine analgesia than resting EEG and can be used as standardized objective methods to assess opioid effects. However, no correlation between the analgesic effect of morphine on the spinal...

  12. Hiperostosis cortical infantil

    OpenAIRE

    Salvador Javier Santos Medina; Orelvis Pérez Duerto

    2015-01-01

    La enfermedad de Caffey, o hiperostosis cortical infantil, es una rara enfermedad ósea autolimitada, que aparece de preferencia en lactantes con signos inespecíficos sistémicos; el más relevante es la reacción subperióstica e hiperostosis en varios huesos del cuerpo, con predilección en el 75-80 % de los casos por la mandíbula. Su pronóstico es bueno, la mayoría no deja secuelas. El propósito del presente trabajo es describir las características clínicas, presentes en un lactante de cinco mes...

  13. Progressive posterior cortical dysfunction

    Directory of Open Access Journals (Sweden)

    Fábio Henrique de Gobbi Porto

    Full Text Available Abstract Progressive posterior cortical dysfunction (PPCD is an insidious syndrome characterized by prominent disorders of higher visual processing. It affects both dorsal (occipito-parietal and ventral (occipito-temporal pathways, disturbing visuospatial processing and visual recognition, respectively. We report a case of a 67-year-old woman presenting with progressive impairment of visual functions. Neurologic examination showed agraphia, alexia, hemispatial neglect (left side visual extinction, complete Balint's syndrome and visual agnosia. Magnetic resonance imaging showed circumscribed atrophy involving the bilateral parieto-occipital regions, slightly more predominant to the right . Our aim was to describe a case of this syndrome, to present a video showing the main abnormalities, and to discuss this unusual presentation of dementia. We believe this article can contribute by improving the recognition of PPCD.

  14. Transition from Poisson to circular unitary ensemble

    Indian Academy of Sciences (India)

    Vinayak; Akhilesh Pandey

    2009-09-01

    Transitions to universality classes of random matrix ensembles have been useful in the study of weakly-broken symmetries in quantum chaotic systems. Transitions involving Poisson as the initial ensemble have been particularly interesting. The exact two-point correlation function was derived by one of the present authors for the Poisson to circular unitary ensemble (CUE) transition with uniform initial density. This is given in terms of a rescaled symmetry breaking parameter Λ. The same result was obtained for Poisson to Gaussian unitary ensemble (GUE) transition by Kunz and Shapiro, using the contour-integral method of Brezin and Hikami. We show that their method is applicable to Poisson to CUE transition with arbitrary initial density. Their method is also applicable to the more general ℓ CUE to CUE transition where CUE refers to the superposition of ℓ independent CUE spectra in arbitrary ratio.

  15. Ensemble treatments of thermal pairing in nuclei

    Science.gov (United States)

    Hung, Nguyen Quang; Dang, Nguyen Dinh

    2009-10-01

    A systematic comparison is conducted for pairing properties of finite systems at nonzero temperature as predicted by the exact solutions of the pairing problem embedded in three principal statistical ensembles, namely the grandcanonical ensemble, canonical ensemble and microcanonical ensemble, as well as the unprojected (FTBCS1+SCQRPA) and Lipkin-Nogami projected (FTLN1+SCQRPA) theories that include the quasiparticle number fluctuation and coupling to pair vibrations within the self-consistent quasiparticle random-phase approximation. The numerical calculations are performed for the pairing gap, total energy, heat capacity, entropy, and microcanonical temperature within the doubly-folded equidistant multilevel pairing model. The FTLN1+SCQRPA predictions are found to agree best with the exact grand-canonical results. In general, all approaches clearly show that the superfluid-normal phase transition is smoothed out in finite systems. A novel formula is suggested for extracting the empirical pairing gap in reasonable agreement with the exact canonical results.

  16. Ensemble Machine Learning Methods and Applications

    CERN Document Server

    Ma, Yunqian

    2012-01-01

    It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...

  17. Ensemble Learning for Free with Evolutionary Algorithms ?

    CERN Document Server

    Gagné, Christian; Schoenauer, Marc; Tomassini, Marco

    2007-01-01

    Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-line) or incrementally along evolution (On-line). Experiments on a set of benchmark problems show that Off-line outperforms single-hypothesis evolutionary learning and state-of-art ...

  18. Dynamic neuronal ensembles: Issues in representing structure change in object-oriented, biologically-based brain models

    Energy Technology Data Exchange (ETDEWEB)

    Vahie, S.; Zeigler, B.P.; Cho, H. [Univ. of Arizona, Tucson, AZ (United States)

    1996-12-31

    This paper describes the structure of dynamic neuronal ensembles (DNEs). DNEs represent a new paradigm for learning, based on biological neural networks that use variable structures. We present a computational neural element that demonstrates biological neuron functionality such as neurotransmitter feedback absolute refractory period and multiple output potentials. More specifically, we will develop a network of neural elements that have the ability to dynamically strengthen, weaken, add and remove interconnections. We demonstrate that the DNE is capable of performing dynamic modifications to neuron connections and exhibiting biological neuron functionality. In addition to its applications for learning, DNEs provide an excellent environment for testing and analysis of biological neural systems. An example of habituation and hyper-sensitization in biological systems, using a neural circuit from a snail is presented and discussed. This paper provides an insight into the DNE paradigm using models developed and simulated in DEVS.

  19. Reversible Projective Measurement in Quantum Ensembles

    CERN Document Server

    Khitrin, Anatoly; Lee, Jae-Seung

    2010-01-01

    We present experimental NMR demonstration of a scheme of reversible projective measurement, which allows extracting information on outcomes and probabilities of a projective measurement in a non-destructive way, with a minimal net effect on the quantum state of an ensemble. The scheme uses reversible dynamics and weak measurement of the intermediate state. The experimental system is an ensemble of 133Cs (S = 7/2) nuclei in a liquid-crystalline matrix.

  20. Ozone ensemble forecast with machine learning algorithms

    OpenAIRE

    Mallet, Vivien; Stoltz, Gilles; Mauricette, Boris

    2009-01-01

    International audience; We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrati...

  1. Cluster Ensemble-based Image Segmentation

    OpenAIRE

    Xiaoru Wang; Junping Du; Shuzhe Wu; Xu Li; Fu Li

    2013-01-01

    Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories ...

  2. Calibrating ensemble reliability whilst preserving spatial structure

    Directory of Open Access Journals (Sweden)

    Jonathan Flowerdew

    2014-03-01

    Full Text Available Ensemble forecasts aim to improve decision-making by predicting a set of possible outcomes. Ideally, these would provide probabilities which are both sharp and reliable. In practice, the models, data assimilation and ensemble perturbation systems are all imperfect, leading to deficiencies in the predicted probabilities. This paper presents an ensemble post-processing scheme which directly targets local reliability, calibrating both climatology and ensemble dispersion in one coherent operation. It makes minimal assumptions about the underlying statistical distributions, aiming to extract as much information as possible from the original dynamic forecasts and support statistically awkward variables such as precipitation. The output is a set of ensemble members preserving the spatial, temporal and inter-variable structure from the raw forecasts, which should be beneficial to downstream applications such as hydrological models. The calibration is tested on three leading 15-d ensemble systems, and their aggregation into a simple multimodel ensemble. Results are presented for 12 h, 1° scale over Europe for a range of surface variables, including precipitation. The scheme is very effective at removing unreliability from the raw forecasts, whilst generally preserving or improving statistical resolution. In most cases, these benefits extend to the rarest events at each location within the 2-yr verification period. The reliability and resolution are generally equivalent or superior to those achieved using a Local Quantile-Quantile Transform, an established calibration method which generalises bias correction. The value of preserving spatial structure is demonstrated by the fact that 3×3 averages derived from grid-scale precipitation calibration perform almost as well as direct calibration at 3×3 scale, and much better than a similar test neglecting the spatial relationships. Some remaining issues are discussed regarding the finite size of the output

  3. Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

    Science.gov (United States)

    Liu, Li; Xu, Yue-Ping

    2017-04-01

    Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

  4. Towards a GME ensemble forecasting system: Ensemble initialization using the breeding technique

    Directory of Open Access Journals (Sweden)

    Jan D. Keller

    2008-12-01

    Full Text Available The quantitative forecast of precipitation requires a probabilistic background particularly with regard to forecast lead times of more than 3 days. As only ensemble simulations can provide useful information of the underlying probability density function, we built a new ensemble forecasting system (GME-EFS based on the GME model of the German Meteorological Service (DWD. For the generation of appropriate initial ensemble perturbations we chose the breeding technique developed by Toth and Kalnay (1993, 1997, which develops perturbations by estimating the regions of largest model error induced uncertainty. This method is applied and tested in the framework of quasi-operational forecasts for a three month period in 2007. The performance of the resulting ensemble forecasts are compared to the operational ensemble prediction systems ECMWF EPS and NCEP GFS by means of ensemble spread of free atmosphere parameters (geopotential and temperature and ensemble skill of precipitation forecasting. This comparison indicates that the GME ensemble forecasting system (GME-EFS provides reasonable forecasts with spread skill score comparable to that of the NCEP GFS. An analysis with the continuous ranked probability score exhibits a lack of resolution for the GME forecasts compared to the operational ensembles. However, with significant enhancements during the 3 month test period, the first results of our work with the GME-EFS indicate possibilities for further development as well as the potential for later operational usage.

  5. Speech perception in the child brain: cortical timing and its relevance to literacy acquisition.

    Science.gov (United States)

    Parviainen, Tiina; Helenius, Päivi; Poskiparta, Elisa; Niemi, Pekka; Salmelin, Riitta

    2011-12-01

    Speech processing skills go through intensive development during mid-childhood, providing basis also for literacy acquisition. The sequence of auditory cortical processing of speech has been characterized in adults, but very little is known about the neural representation of speech sound perception in the developing brain. We used whole-head magnetoencephalography (MEG) to record neural responses to speech and nonspeech sounds in first-graders (7-8-year-old) and compared the activation sequence to that in adults. In children, the general location of neural activity in the superior temporal cortex was similar to that in adults, but in the time domain the sequence of activation was strikingly different. Cortical differentiation between sound types emerged in a prolonged response pattern at about 250 ms after sound onset, in both hemispheres, clearly later than the corresponding effect at about 100 ms in adults that was detected specifically in the left hemisphere. Better reading skills were linked with shorter-lasting neural activation, speaking for interdependence of the maturing neural processes of auditory perception and developing linguistic skills. This study uniquely utilized the potential of MEG in comparing both spatial and temporal characteristics of neural activation between adults and children. Besides depicting the group-typical features in cortical auditory processing, the results revealed marked interindividual variability in children.

  6. Minimal redefinition of the OSV ensemble

    CERN Document Server

    Parvizi, S; Parvizi, Shahrokh; Tavanfar, Alireza

    2005-01-01

    In the interesting conjecture, Z_{BH}=|Z_{top}|^2, proposed by Ooguri, Strominger and Vafa (OSV), the black hole ensemble is a mixed ensemble, and resulting degeneracy of states as obtained from the ensemble inverse-Laplace integration, suffer from prefactors which do not respect the (relevant) electric-magnetic dualities. One idea to overcome this deficiency, as claimed recently, is imposing a nontrivial measure for the ensemble sum. We address this problem and upon a redefinition of the OSV ensemble whose variables are as numerous as the electric potentials, show that for restoring the symmetry no non-Euclidean measure is needful. In detail, we rewrite the OSV free energy as a function of new variables which are combinations of the electric-potentials and the black hole charges. Subsequently the Legendre transformation which bridges between the entropy and the black hole free energy in terms of these variables, points to a generalized ensemble. In this context we will consider all the cases of relevance: sm...

  7. Level density for deformations of the Gaussian orthogonal ensemble

    CERN Document Server

    Bertuola, A C; Hussein, M S; Pato, M P; Sargeant, A J

    2004-01-01

    Formulas are derived for the average level density of deformed, or transition, Gaussian orthogonal random matrix ensembles. After some general considerations about Gaussian ensembles we derive formulas for the average level density for (i) the transition from the Gaussian orthogonal ensemble (GOE) to the Poisson ensemble and (ii) the transition from the GOE to $m$ GOEs.

  8. Semantic Segmentation of Aerial Images with AN Ensemble of Cnns

    Science.gov (United States)

    Marmanis, D.; Wegner, J. D.; Galliani, S.; Schindler, K.; Datcu, M.; Stilla, U.

    2016-06-01

    This paper describes a deep learning approach to semantic segmentation of very high resolution (aerial) images. Deep neural architectures hold the promise of end-to-end learning from raw images, making heuristic feature design obsolete. Over the last decade this idea has seen a revival, and in recent years deep convolutional neural networks (CNNs) have emerged as the method of choice for a range of image interpretation tasks like visual recognition and object detection. Still, standard CNNs do not lend themselves to per-pixel semantic segmentation, mainly because one of their fundamental principles is to gradually aggregate information over larger and larger image regions, making it hard to disentangle contributions from different pixels. Very recently two extensions of the CNN framework have made it possible to trace the semantic information back to a precise pixel position: deconvolutional network layers undo the spatial downsampling, and Fully Convolution Networks (FCNs) modify the fully connected classification layers of the network in such a way that the location of individual activations remains explicit. We design a FCN which takes as input intensity and range data and, with the help of aggressive deconvolution and recycling of early network layers, converts them into a pixelwise classification at full resolution. We discuss design choices and intricacies of such a network, and demonstrate that an ensemble of several networks achieves excellent results on challenging data such as the ISPRS semantic labeling benchmark, using only the raw data as input.

  9. Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods

    Directory of Open Access Journals (Sweden)

    Saadia Zahid

    2015-01-01

    Full Text Available Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount of training data, which handles noise and is suitable for use for real-time applications. Noise in an audio stream is segmented out as environment sound. A hybrid classification approach is used, bagged support vector machines (SVMs with artificial neural networks (ANNs. Audio stream is classified, firstly, into speech and nonspeech segment by using bagged support vector machines; nonspeech segment is further classified into music and environment sound by using artificial neural networks and lastly, speech segment is classified into silence and pure-speech segments on the basis of rule-based classifier. Minimum data is used for training classifier; ensemble methods are used for minimizing misclassification rate and approximately 98% accurate segments are obtained. A fast and efficient algorithm is designed that can be used with real-time multimedia applications.

  10. Loss of Ensemble Segregation in Dentate Gyrus, but Not in Somatosensory Cortex, during Contextual Fear Memory Generalization

    Directory of Open Access Journals (Sweden)

    Marie Yokoyama

    2016-11-01

    Full Text Available The details of contextual or episodic memories are lost and generalized with the passage of time. Proper generalization may underlie the formation and assimilation of semantic memories and enable animals to adapt to ever-changing environments, whereas overgeneralization of fear memory evokes maladaptive fear responses to harmless stimuli, which is a symptom of anxiety disorders such as post-traumatic stress disorder (PTSD. To understand the neural basis of fear memory generalization, we investigated the patterns of neuronal ensemble reactivation during memory retrieval when contextual fear memory expression is generalized using transgenic mice that allowed us to visualize specific neuronal ensembles activated during memory encoding and retrieval. We found preferential reactivations of neuronal ensembles in the primary somatosensory cortex, when mice were returned to the conditioned context to retrieve their memory 1 day after conditioning. In the hippocampal dentate gyrus (DG, exclusively separated ensemble reactivation was observed when mice were exposed to a novel context. These results suggest that the DG as well as the somatosensory cortex were likely to distinguish the two different contexts at the ensemble activity level when memory is not generalized at the behavioral level. However, 9 days after conditioning when animals exhibited generalized fear, the unique reactivation pattern in the DG, but not in the somatosensory cortex, was lost. Our results suggest that the alternations in the ensemble representation within the DG, or in upstream structures that link the sensory cortex to the hippocampus, may underlie generalized contextual fear memory expression.

  11. The classicality and quantumness of a quantum ensemble

    CERN Document Server

    Zhu, Xuanmin; Wu, Shengjun; Liu, Quanhui

    2010-01-01

    In this paper, we investigate the classicality and quantumness of a quantum ensemble. We define a quantity called classicality to characterize how classical a quantum ensemble is. An ensemble of commuting states that can be manipulated classically has a unit classicality, while a general ensemble has a classicality less than 1. We also study how quantum an ensemble is by defining a related quantity called quantumness. We find that the classicality of an ensemble is closely related to how perfectly the ensemble can be cloned, and that the quantumness of an ensemble is essentially responsible for the security of quantum key distribution(QKD) protocols using that ensemble. Furthermore, we show that the quantumness of an ensemble used in a QKD protocol is exactly the attainable lower bound of the error rate in the sifted key.

  12. Regional vulnerability of longitudinal cortical association connectivity

    Directory of Open Access Journals (Sweden)

    Rafael Ceschin

    2015-01-01

    Full Text Available Preterm born children with spastic diplegia type of cerebral palsy and white matter injury or periventricular leukomalacia (PVL, are known to have motor, visual and cognitive impairments. Most diffusion tensor imaging (DTI studies performed in this group have demonstrated widespread abnormalities using averaged deterministic tractography and voxel-based DTI measurements. Little is known about structural network correlates of white matter topography and reorganization in preterm cerebral palsy, despite the availability of new therapies and the need for brain imaging biomarkers. Here, we combined novel post-processing methodology of probabilistic tractography data in this preterm cohort to improve spatial and regional delineation of longitudinal cortical association tract abnormalities using an along-tract approach, and compared these data to structural DTI cortical network topology analysis. DTI images were acquired on 16 preterm children with cerebral palsy (mean age 5.6 ± 4 and 75 healthy controls (mean age 5.7 ± 3.4. Despite mean tract analysis, Tract-Based Spatial Statistics (TBSS and voxel-based morphometry (VBM demonstrating diffusely reduced fractional anisotropy (FA reduction in all white matter tracts, the along-tract analysis improved the detection of regional tract vulnerability. The along-tract map-structural network topology correlates revealed two associations: (1 reduced regional posterior–anterior gradient in FA of the longitudinal visual cortical association tracts (inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, optic radiation, posterior thalamic radiation correlated with reduced posterior–anterior gradient of intra-regional (nodal efficiency metrics with relative sparing of frontal and temporal regions; and (2 reduced regional FA within frontal–thalamic–striatal white matter pathways (anterior limb/anterior thalamic radiation, superior longitudinal fasciculus and cortical spinal tract

  13. Ensemble postprocessing for probabilistic quantitative precipitation forecasts

    Science.gov (United States)

    Bentzien, S.; Friederichs, P.

    2012-12-01

    Precipitation is one of the most difficult weather variables to predict in hydrometeorological applications. In order to assess the uncertainty inherent in deterministic numerical weather prediction (NWP), meteorological services around the globe develop ensemble prediction systems (EPS) based on high-resolution NWP systems. With non-hydrostatic model dynamics and without parameterization of deep moist convection, high-resolution NWP models are able to describe convective processes in more detail and provide more realistic mesoscale structures. However, precipitation forecasts are still affected by displacement errors, systematic biases and fast error growth on small scales. Probabilistic guidance can be achieved from an ensemble setup which accounts for model error and uncertainty of initial and boundary conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) provides such an ensemble system based on the German-focused limited-area model COSMO-DE. With a horizontal grid-spacing of 2.8 km, COSMO-DE is the convection-permitting high-resolution part of the operational model chain at DWD. The COSMO-DE-EPS consists of 20 realizations of COSMO-DE, driven by initial and boundary conditions derived from 4 global models and 5 perturbations of model physics. Ensemble systems like COSMO-DE-EPS are often limited with respect to ensemble size due to the immense computational costs. As a consequence, they can be biased and exhibit insufficient ensemble spread, and probabilistic forecasts may be not well calibrated. In this study, probabilistic quantitative precipitation forecasts are derived from COSMO-DE-EPS and evaluated at more than 1000 rain gauges located all over Germany. COSMO-DE-EPS is a frequently updated ensemble system, initialized 8 times a day. We use the time-lagged approach to inexpensively increase ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Moreover, we will show that statistical

  14. Development and maturation of embryonic cortical neurons grafted into the damaged adult motor cortex

    Directory of Open Access Journals (Sweden)

    Nissrine Ballout

    2016-08-01

    Full Text Available Injury to the human central nervous system can lead to devastating consequences due to its poor ability to self-repair. Neural transplantation aimed at replacing lost neurons and restore functional circuitry has proven to be a promising therapeutical avenue. We previously reported in adult rodent animal models with cortical lesions that grafted fetal cortical neurons could effectively re-establish specific patterns of projections and synapses. The current study was designed to provide a detailed characterization of the spatio-temporal in vivo development of fetal cortical transplanted cells within the lesioned adult motor cortex and their corresponding axonal projections. We show here that as early as two weeks after grafting, cortical neuroblasts transplanted into damaged adult motor cortex developed appropriate projections to cortical and subcortical targets. Grafted cells initially exhibited characteristics of immature neurons, which then differentiated into mature neurons with appropriate cortical phenotypes where most were glutamatergic and few were GABAergic. All cortical subtypes identified with the specific markers CTIP2, Cux1, FOXP2 and Tbr1 were generated after grafting as evidenced with BrdU co-labeling.The set of data provided here is of interest as it sets biological standards for future studies aimed at replacing fetal cells with embryonic stem cells as a source of cortical neurons.

  15. Modeling brand choice using boosted and stacked neural networks

    NARCIS (Netherlands)

    R. Potharst (Rob); M. van Rijthoven; M.C. van Wezel (Michiel)

    2005-01-01

    textabstractThe brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and stacking have never been applied to the

  16. A 32-channel fully implantable wireless neurosensor for simultaneous recording from two cortical regions.

    Science.gov (United States)

    Aceros, Juan; Yin, Ming; Borton, David A; Patterson, William R; Nurmikko, Arto V

    2011-01-01

    We present a fully implantable, wireless, neurosensor for multiple-location neural interface applications. The device integrates two independent 16-channel intracortical microelectrode arrays and can simultaneously acquire 32 channels of broadband neural data from two separate cortical areas. The system-on-chip implantable sensor is built on a flexible Kapton polymer substrate and incorporates three very low power subunits: two cortical subunits connected to a common subcutaneous subunit. Each cortical subunit has an ultra-low power 16-channel preamplifier and multiplexer integrated onto a cortical microelectrode array. The subcutaneous epicranial unit has an inductively coupled power supply, two analog-to-digital converters, a low power digital controller chip, and microlaser-based infrared telemetry. The entire system is soft encapsulated with biocompatible flexible materials for in vivo applications. Broadband neural data is conditioned, amplified, and analog multiplexed by each of the cortical subunits and passed to the subcutaneous component, where it is digitized and combined with synchronization data and wirelessly transmitted transcutaneously using high speed infrared telemetry.

  17. Effects of sensory behavioral tasks on pain threshold and cortical excitability.

    Science.gov (United States)

    Volz, Magdalena Sarah; Suarez-Contreras, Vanessa; Mendonca, Mariana E; Pinheiro, Fernando Santos; Merabet, Lotfi B; Fregni, Felipe

    2013-01-01

    Transcutaneous electrical stimulation has been proven to modulate nervous system activity, leading to changes in pain perception, via the peripheral sensory system, in a bottom up approach. We tested whether different sensory behavioral tasks induce significant effects in pain processing and whether these changes correlate with cortical plasticity. This randomized parallel designed experiment included forty healthy right-handed males. Three different somatosensory tasks, including learning tasks with and without visual feedback and simple somatosensory input, were tested on pressure pain threshold and motor cortex excitability using transcranial magnetic stimulation (TMS). Sensory tasks induced hand-specific pain modulation effects. They increased pain thresholds of the left hand (which was the target to the sensory tasks) and decreased them in the right hand. TMS showed that somatosensory input decreased cortical excitability, as indexed by reduced MEP amplitudes and increased SICI. Although somatosensory tasks similarly altered pain thresholds and cortical excitability, there was no significant correlation between these variables and only the visual feedback task showed significant somatosensory learning. Lack of correlation between cortical excitability and pain thresholds and lack of differential effects across tasks, but significant changes in pain thresholds suggest that analgesic effects of somatosensory tasks are not primarily associated with motor cortical neural mechanisms, thus, suggesting that subcortical neural circuits and/or spinal cord are involved with the observed effects. Identifying the neural mechanisms of somatosensory stimulation on pain may open novel possibilities for combining different targeted therapies for pain control.

  18. Human cortical responses to slow and fast binaural beats reveal multiple mechanisms of binaural hearing.

    Science.gov (United States)

    Ross, Bernhard; Miyazaki, Takahiro; Thompson, Jessica; Jamali, Shahab; Fujioka, Takako

    2014-10-15

    When two tones with slightly different frequencies are presented to both ears, they interact in the central auditory system and induce the sensation of a beating sound. At low difference frequencies, we perceive a single sound, which is moving across the head between the left and right ears. The percept changes to loudness fluctuation, roughness, and pitch with increasing beat rate. To examine the neural representations underlying these different perceptions, we recorded neuromagnetic cortical responses while participants listened to binaural beats at a continuously varying rate between 3 Hz and 60 Hz. Binaural beat responses were analyzed as neuromagnetic oscillations following the trajectory of the stimulus rate. Responses were largest in the 40-Hz gamma range and at low frequencies. Binaural beat responses at 3 Hz showed opposite polarity in the left and right auditory cortices. We suggest that this difference in polarity reflects the opponent neural population code for representing sound location. Binaural beats at any rate induced gamma oscillations. However, the responses were largest at 40-Hz stimulation. We propose that the neuromagnetic gamma oscillations reflect postsynaptic modulation that allows for precise timing of cortical neural firing. Systematic phase differences between bilateral responses suggest that separate sound representations of a sound object exist in the left and right auditory cortices. We conclude that binaural processing at the cortical level occurs with the same temporal acuity as monaural processing whereas the identification of sound location requires further interpretation and is limited by the rate of object representations. Copyright © 2014 the American Physiological Society.

  19. Asymmetrical Body Perception: A Possible Role for Neural Body Representations

    OpenAIRE

    2009-01-01

    Perception of one's body is related not only to the physical appearance of the body, but also to the neural representation of the body. The brain contains many body maps that systematically differ between right- and left-handed people. In general, the cortical representations of the right arm and right hand tend to be of greater area in the left hemisphere than in the right hemisphere for right-handed people, whereas these cortical representations tend to be symmetrical across hemispheres for...

  20. An Overview of Bayesian Methods for Neural Spike Train Analysis

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

    Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  1. Longitudinal quantitative MRI assessment of cortical damage in multiple sclerosis: A pilot study.

    Science.gov (United States)

    Gracien, René-Maxime; Reitz, Sarah C; Hof, Stephanie-Michelle; Fleischer, Vinzenz; Droby, Amgad; Wahl, Mathias; Steinmetz, Helmuth; Groppa, Sergiu; Deichmann, Ralf; Klein, Johannes C

    2017-02-27

    Quantitative MRI (qMRI) allows assessing cortical pathology in multiple sclerosis (MS) on a microstructural level, where cortical damage has been shown to prolong T1 -relaxation time and increase proton density (PD) compared to controls. However, the evolution of these changes in MS over time has not been investigated so far. In this pilot study we used an advanced method for the longitudinal assessment of cortical tissue change in MS patients with qMRI in comparison to cortical atrophy, as derived from conventional MRI. Twelve patients with relapsing-remitting MS underwent 3T T1 /PD-mapping at two timepoints with a mean interval of 12 months. The respective cortical T1 /PD-values were extracted from the middle of the cortical layer and the cortical thickness was measured for surface-based identification of clusters with increasing/decreasing values. Statistical analysis showed clusters with increasing PD- and T1 -values over time (annualized rate for T1 /PD increase in these clusters: 3.4 ± 2.56% for T1 , P = 0.0007; 2.3 ± 2.59% for PD, P = 0.01). Changes are heterogeneous across the cortex and different patterns of longitudinal PD and T1 increase emerged. Analysis of the cortical thickness yielded only one small cluster indicating a decrease of cortical thickness. Changes of cortical tissue composition in MS seem to be reflected by a spatially inhomogeneous, multifocal increase of the PD values, indicating replacement of neural tissue by water, and of the T1 -relaxation time, a surrogate of demyelination, axonal loss, and gliosis. qMRI changes were more prominent than cortical atrophy, showing the potential of qMRI techniques to quantify microstructural alterations that remain undetected by conventional MRI. 1 J. Magn. Reson. Imaging 2017. © 2017 International Society for Magnetic Resonance in Medicine.

  2. Cortical Response Variability as a Developmental Index of Selective Auditory Attention

    Science.gov (United States)

    Strait, Dana L.; Slater, Jessica; Abecassis, Victor; Kraus, Nina

    2014-01-01

    Attention induces synchronicity in neuronal firing for the encoding of a given stimulus at the exclusion of others. Recently, we reported decreased variability in scalp-recorded cortical evoked potentials to attended compared with ignored speech in adults. Here we aimed to determine the developmental time course for this neural index of auditory…

  3. Dissociation of Subtraction and Multiplication in the Right Parietal Cortex: Evidence from Intraoperative Cortical Electrostimulation

    Science.gov (United States)

    Yu, Xiaodan; Chen, Chuansheng; Pu, Song; Wu, Chenxing; Li, Yongnian; Jiang, Tao; Zhou, Xinlin

    2011-01-01

    Previous research has consistently shown that the left parietal cortex is critical for numerical processing, but the role of the right parietal lobe has been much less clear. This study used the intraoperative cortical electrical stimulation approach to investigate neural dissociation in the right parietal cortex for subtraction and…

  4. Dissociation of Subtraction and Multiplication in the Right Parietal Cortex: Evidence from Intraoperative Cortical Electrostimulation

    Science.gov (United States)

    Yu, Xiaodan; Chen, Chuansheng; Pu, Song; Wu, Chenxing; Li, Yongnian; Jiang, Tao; Zhou, Xinlin

    2011-01-01

    Previous research has consistently shown that the left parietal cortex is critical for numerical processing, but the role of the right parietal lobe has been much less clear. This study used the intraoperative cortical electrical stimulation approach to investigate neural dissociation in the right parietal cortex for subtraction and…

  5. Recurrent cortico-cortical Interactions in neural disease

    NARCIS (Netherlands)

    Lamme, V.A.F.; Pletson, J.E.

    2005-01-01

    The cerebral cortex consists of a large number of areas, each subserving a more or less distinct function. This view has its roots in the early work of Penfield, and today is reflected in the body of functional MRI literature describing the regions of the brain that are activated during particular t

  6. Potentials of Endogenous Neural Stem cells in Cortical Repair

    Directory of Open Access Journals (Sweden)

    Bhaskar eSaha

    2012-04-01

    Full Text Available In the last few decades great thrust has been put in the area of regenerative neurobiology research to combat brain injuries and neurodegenerative diseases. The recent discovery of neurogenic niches in the adult brain has led researchers to study how to mobilize these cells to orchestrate an endogenous repair mechanism. The brain can minimize injury-induced damage by means of an immediate glial response and by initiating repair mechanisms that involve the generation and mobilization of new neurons to the site of injury where they can integrate into the existing circuit. This review highlights the current status of research in this field. Here, we discuss the changes that take place in the neurogenic milieu following injury. We will focus, in particular, on the cellular and molecular controls that lead to increased proliferation in the Sub ventricular Zone (SVZ as well as neurogenesis. We will also concentrate on how these cellular and molecular mechanisms influence the migration of new cells to the affected area and their differentiation into neuronal/glial lineage that initiate the repair mechanism. Next, we will discuss some of the different factors that limit/retard the repair process and highlight future lines of research that can help to overcome these limitations. A clear understanding of the underlying molecular mechanisms and physiological changes following brain damage and the subsequent endogenous repair should help us develop better strategies to repair damaged brains.

  7. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

    KAUST Repository

    Toye, Habib

    2017-05-26

    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

  8. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

    Science.gov (United States)

    Toye, Habib; Zhan, Peng; Gopalakrishnan, Ganesh; Kartadikaria, Aditya R.; Huang, Huang; Knio, Omar; Hoteit, Ibrahim

    2017-07-01

    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

  9. Towards an optimal paradigm for simultaneously recording cortical and brainstem auditory evoked potentials.

    Science.gov (United States)

    Bidelman, Gavin M

    2015-02-15

    Simultaneous recording of brainstem and cortical event-related brain potentials (ERPs) may offer a valuable tool for understanding the early neural transcription of behaviorally relevant sounds and the hierarchy of signal processing operating at multiple levels of the auditory system. To date, dual recordings have been challenged by technological and physiological limitations including different optimal parameters necessary to elicit each class of ERP (e.g., differential adaptation/habitation effects and number of trials to obtain adequate response signal-to-noise ratio). We investigated a new stimulus paradigm for concurrent recording of the auditory brainstem frequency-following response (FFR) and cortical ERPs. The paradigm is "optimal" in that it uses a clustered stimulus presentation and variable interstimulus interval (ISI) to (i) achieve the most ideal acquisition parameters for eliciting subcortical and cortical responses, (ii) obtain an adequate number of trials to detect each class of response, and (iii) minimize neural adaptation/habituation effects. Comparison between clustered and traditional (fixed, slow ISI) stimulus paradigms revealed minimal change in amplitude or latencies of either the brainstem FFR or cortical ERP. The clustered paradigm offered over a 3× increase in recording efficiency compared to conventional (fixed ISI presentation) and thus, a more rapid protocol for obtaining dual brainstem-cortical recordings in individual listeners. We infer that faster recording of subcortical and cortical potentials might allow more complete and sensitive testing of neurophysiological function and aid in the differential assessment of auditory function. Copyright © 2014 Elsevier B.V. All rights reserved.

  10. Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

    Science.gov (United States)

    Drzewiecki, Wojciech

    2016-12-01

    In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

  11. Credit scoring using ensemble of various classifiers on reduced feature set

    Directory of Open Access Journals (Sweden)

    Dahiya Shashi

    2015-01-01

    Full Text Available Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the customers, overall business by the customer with bank, and loan payment history of the loan applicants. The advantages of using credit scoring models include reducing the cost of credit analysis, enabling faster credit decisions and diminishing possible risk. Many statistical and machine learning techniques such as Logistic Regression, Support Vector Machines, Neural Networks and Decision tree algorithms have been used independently and as hybrid credit scoring models. This paper proposes an ensemble based technique combining seven individual models to increase the classification accuracy. Feature selection has also been used for selecting important attributes for classification. Cross classification was conducted using three data partitions. German credit dataset having 1000 instances and 21 attributes is used in the present study. The results of the experiments revealed that the ensemble model yielded a very good accuracy when compared to individual models. In all three different partitions, the ensemble model was able to classify more than 80% of the loan customers as good creditors correctly. Also, for 70:30 partition there was a good impact of feature selection on the accuracy of classifiers. The results were improved for almost all individual models including the ensemble model.

  12. Coherent delta-band oscillations between cortical areas correlate with decision making

    Science.gov (United States)

    Nácher, Verónica; Ledberg, Anders; Deco, Gustavo; Romo, Ranulfo

    2013-01-01

    Coherent oscillations in the theta-to-gamma frequency range have been proposed as a mechanism that coordinates neural activity in large-scale cortical networks in sensory, motor, and cognitive tasks. Whether this mechanism also involves coherent oscillations at delta frequencies (1–4 Hz) is not known. Rather, delta oscillations have been associated with slow-wave sleep. Here, we show coherent oscillations in the delta frequency band between parietal and frontal cortices during the decision-making component of a somatosensory discrimination task. Importantly, the magnitude of this delta-band coherence is modulated by the different decision alternatives. Furthermore, during control conditions not requiring decision making, delta-band coherences are typically much reduced. Our work indicates an important role for synchronous activity in the delta frequency band when large-scale, distant cortical networks coordinate their neural activity during decision making. PMID:23980180

  13. Coherent delta-band oscillations between cortical areas correlate with decision making.

    Science.gov (United States)

    Nácher, Verónica; Ledberg, Anders; Deco, Gustavo; Romo, Ranulfo

    2013-09-10

    Coherent oscillations in the theta-to-gamma frequency range have been proposed as a mechanism that coordinates neural activity in large-scale cortical networks in sensory, motor, and cognitive tasks. Whether this mechanism also involves coherent oscillations at delta frequencies (1-4 Hz) is not known. Rather, delta oscillations have been associated with slow-wave sleep. Here, we show coherent oscillations in the delta frequency band between parietal and frontal cortices during the decision-making component of a somatosensory discrimination task. Importantly, the magnitude of this delta-band coherence is modulated by the different decision alternatives. Furthermore, during control conditions not requiring decision making, delta-band coherences are typically much reduced. Our work indicates an important role for synchronous activity in the delta frequency band when large-scale, distant cortical networks coordinate their neural activity during decision making.

  14. The cortical motor system of the marmoset monkey (Callithrix jacchus).

    Science.gov (United States)

    Bakola, Sophia; Burman, Kathleen J; Rosa, Marcello G P

    2015-04-01

    Precise descriptions of the anatomical pathways that link different areas of the cerebral cortex are essential to the understanding of the sensorimotor and association processes that underlie human actions, and their impairment in pathological situations. Many years of research in macaque monkeys have critically shaped how we currently think about cortical motor function in humans. However, it is important to obtain additional understanding about the homologies between cortical areas in human and various non-human primates, and in particular how evolutionary changes in connectivity within specific neural circuits impact on the capacity for different behaviors. Current research has converged on the New World marmoset monkey as an important animal model for cortical function and dysfunction, emphasizing advantages unique to this species. However, the motor repertoire of the marmoset differs from that of the macaque in many ways, including the capacity for skilled use of the hands. Here, we review current knowledge about the cortical frontal areas in marmosets, which are key to the generation and control of motor behaviors, with focus on comparative analyses. We note significant parallels with the macaque monkey, as well as a few potentially important differences, which suggest future directions for work involving architectonic and functional analyses.

  15. Generation of scenarios from calibrated ensemble forecasts with a dynamic ensemble copula coupling approach

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.

    2015-01-01

    Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts...... is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost.For example, the ensemble copula coupling (ECC) method consists in rebuilding the multivariate aspect of the forecast...... from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error...

  16. Generation of scenarios from calibrated ensemble forecasts with a dual ensemble copula coupling approach

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.

    2016-01-01

    Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts...... is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost. For example, the ensemble copula coupling (ECC) method rebuilds the multivariate aspect of the forecast from...... the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new...

  17. Optimizing working memory with heterogeneity of recurrent cortical excitation.

    Science.gov (United States)

    Kilpatrick, Zachary P; Ermentrout, Bard; Doiron, Brent

    2013-11-27

    A neural correlate of parametric working memory is a stimulus-specific rise in neuron firing rate that persists long after the stimulus is removed. Network models with local excitation and broad inhibition support persistent neural activity, linking network architecture and parametric working memory. Cortical neurons receive noisy input fluctuations that cause persistent activity to diffusively wander about the network, degrading memory over time. We explore how cortical architecture that supports parametric working memory affects the diffusion of persistent neural activity. Studying both a spiking network and a simplified potential well model, we show that spatially heterogeneous excitatory coupling stabilizes a discrete number of persistent states, reducing the diffusion of persistent activity over the network. However, heterogeneous coupling also coarse-grains the stimulus representation space, limiting the storage capacity of parametric working memory. The storage errors due to coarse-graining and diffusion trade off so that information transfer between the initial and recalled stimulus is optimized at a fixed network heterogeneity. For sufficiently long delay times, the optimal number of attractors is less than the number of possible stimuli, suggesting that memory networks can under-represent stimulus space to optimize performance. Our results clearly demonstrate the combined effects of network architecture and stochastic fluctuations on parametric memory storage.

  18. Focal cortical dysplasia – review

    Science.gov (United States)

    Kabat, Joanna; Król, Przemysław

    2012-01-01

    Summary Focal cortical dysplasia is a malformation of cortical development, which is the most common cause of medically refractory epilepsy in the pediatric population and the second/third most common etiology of medically intractable seizures in adults. Both genetic and acquired factors are involved in the pathogenesis of cortical dysplasia. Numerous classifications of the complex structural abnormalities of focal cortical dysplasia have been proposed – from Taylor et al. in 1971 to the last modification of Palmini classification made by Blumcke in 2011. In general, three types of cortical dysplasia are recognized. Type I focal cortical dysplasia with mild symptomatic expression and late onset, is more often seen in adults, with changes present in the temporal lobe. Clinical symptoms are more severe in type II of cortical dysplasia usually seen in children. In this type, more extensive changes occur outside the temporal lobe with predilection for the frontal lobes. New type III is one of the above dysplasias with associated another principal lesion as hippocampal sclerosis, tumor, vascular malformation or acquired pathology during early life. Brain MRI imaging shows abnormalities in the majority of type II dysplasias and in only some of type I cortical dysplasias. The most common findings on MRI imaging include: focal cortical thickening or thinning, areas of focal brain atrophy, blurring of the gray-white junction, increased signal on T2- and FLAIR-weighted images in the gray and subcortical white matter often tapering toward the ventricle. On the basis of the MRI findings, it is possible to differentiate between type I and type II cortical dysplasia. A complete resection of the epileptogenic zone is required for seizure-free life. MRI imaging is very helpful to identify those patients who are likely to benefit from surgical treatment in a group of patients with drug-resistant epilepsy. However, in type I cortical dysplasia, MR imaging is often normal, and also

  19. Multiscale macromolecular simulation: role of evolving ensembles.

    Science.gov (United States)

    Singharoy, A; Joshi, H; Ortoleva, P J

    2012-10-22

    Multiscale analysis provides an algorithm for the efficient simulation of macromolecular assemblies. This algorithm involves the coevolution of a quasiequilibrium probability density of atomic configurations and the Langevin dynamics of spatial coarse-grained variables denoted order parameters (OPs) characterizing nanoscale system features. In practice, implementation of the probability density involves the generation of constant OP ensembles of atomic configurations. Such ensembles are used to construct thermal forces and diffusion factors that mediate the stochastic OP dynamics. Generation of all-atom ensembles at every Langevin time step is computationally expensive. Here, multiscale computation for macromolecular systems is made more efficient by a method that self-consistently folds in ensembles of all-atom configurations constructed in an earlier step, history, of the Langevin evolution. This procedure accounts for the temporal evolution of these ensembles, accurately providing thermal forces and diffusions. It is shown that efficiency and accuracy of the OP-based simulations is increased via the integration of this historical information. Accuracy improves with the square root of the number of historical timesteps included in the calculation. As a result, CPU usage can be decreased by a factor of 3-8 without loss of accuracy. The algorithm is implemented into our existing force-field based multiscale simulation platform and demonstrated via the structural dynamics of viral capsomers.

  20. Distance modulation of neural activity in the visual cortex.

    Science.gov (United States)

    Dobbins, A C; Jeo, R M; Fiser, J; Allman, J M

    1998-07-24

    Humans use distance information to scale the size of objects. Earlier studies demonstrated changes in neural response as a function of gaze direction and gaze distance in the dorsal visual cortical pathway to parietal cortex. These findings have been interpreted as evidence of the parietal pathway's role in spatial representation. Here, distance-dependent changes in neural response were also found to be common in neurons in the ventral pathway leading to inferotemporal cortex of monkeys. This result implies that the information necessary for object and spatial scaling is common to all visual cortical areas.

  1. Analysis of Cortical Flow Models In Vivo

    Science.gov (United States)

    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

  2. Self-organizing model of motor cortical activities during drawing

    Science.gov (United States)

    Lin, Siming H.; Si, Jennie; Schwartz, Andrew B.

    1996-05-01

    The population vector algorithm has been developed to combine the simultaneous direction- related activities of a population of motor cortical neurons to predict the trajectory of the arm movement. In our study, we consider a self-organizing model of a neural representation of the arm trajectory based on neuronal discharge rates. Self-organizing feature mapping (SOFM) is used to select the optimal set of weights in the model to determine the contribution of individual neuron to the overall movement. The correspondence between the movement directions and the discharge patterns of the motor cortical neurons is established in the output map. The topology preserving property of the SOFM is used to analyze real recorded data of a behavior monkey. The data used in this analysis were taken while the monkey was drawing spirals and doing the center out movement. Using such a statistical model, the monkey's arm moving directions could be well predicted based on the motor cortex neuronal firing information.

  3. Using neural networks for the synthesis of microwave devices

    Directory of Open Access Journals (Sweden)

    V. O. Adamenko

    2012-10-01

    Full Text Available In this work the advantages of neural networks for the synthesis of microwave devices are considered. The problems which can occur using neural networks as the universal approximating system in problems of frequency-selective microwave devices synthesis are characterized. The expediency of solving these problems by using ensemble of neural networks is substantiated. Offered the architecture group which used for the practical implementation metal dielectric microwave filters, taking into account their characteristics in the stop bands above and below the pass band. The expediency of the further investigation of the proposed architecture in problems of synthesis of material objects is shown.

  4. Analysis of neural activity in human motor cortex -- Towards brain machine interface system

    Science.gov (United States)

    Secundo, Lavi

    , the correlation of ECoG activity to kinematic parameters of arm movement is context-dependent, an important constraint to consider in future development of BMI systems. The third chapter delves into a fundamental organizational principle of the primate motor system---cortical control of contralateral limb movements. However, ipsilateral motor areas also appear to play a role in the control of ipsilateral limb movements. Several studies in monkeys have shown that individual neurons in ipsilateral primary motor cortex (M1) may represent, on average, the direction of movements of the ipsilateral arm. Given the increasing body of evidence demonstrating that neural ensembles can reliably represent information with a high temporal resolution, here we characterize the distributed neural representation of ipsilateral upper limb kinematics in both monkey and man. In two macaque monkeys trained to perform center-out reaching movements, we found that the ensemble spiking activity in M1 could continuously represent ipsilateral limb position. We also recorded cortical field potentials from three human subjects and also consistently found evidence of a neural representation for ipsilateral movement parameters. Together, our results demonstrate the presence of a high-fidelity neural representation for ipsilateral movement and illustrates that it can be successfully incorporated into a brain-machine interface.

  5. Hiperostosis cortical infantil

    Directory of Open Access Journals (Sweden)

    Salvador Javier Santos Medina

    2015-04-01

    Full Text Available La enfermedad de Caffey, o hiperostosis cortical infantil, es una rara enfermedad ósea autolimitada, que aparece de preferencia en lactantes con signos inespecíficos sistémicos; el más relevante es la reacción subperióstica e hiperostosis en varios huesos del cuerpo, con predilección en el 75-80 % de los casos por la mandíbula. Su pronóstico es bueno, la mayoría no deja secuelas. El propósito del presente trabajo es describir las características clínicas, presentes en un lactante de cinco meses de edad, atendido en el Hospital Pediátrico Provincial “Mártires de Las Tunas” con este diagnóstico, quien ingresó en el servicio de miscelánea B por una celulitis facial. Presentaba aumento de volumen en la región geniana izquierda, febrícola e inapetencia. Se impuso tratamiento con cefazolina y se egresó a los siete días. Acudió nuevamente con tumefacción blanda y difusa de ambas hemicaras, irritabilidad y fiebre. Se interconsultó con cirugía maxilofacial, se indicaron estudios sanguíneos y radiológicos. Se diagnosticó como enfermedad de Caffey, basado en la edad del niño, tumefacción facial sin signos inflamatorios agudos e hiperostosis en ambas corticales mandibulares a la radiografía AP mandíbula; unido a anemia ligera, leucocitosis y eritrosedimentación acelerada. El paciente se trató sintomáticamente y con antinflamatorios no esteroideos. Esta rara entidad se debe tener presente en casos de niños y lactantes con irritabilidad y fiebre inespecífica

  6. Control and Synchronization of Neuron Ensembles

    CERN Document Server

    Li, Jr-Shin; Ruths, Justin

    2011-01-01

    Synchronization of oscillations is a phenomenon prevalent in natural, social, and engineering systems. Controlling synchronization of oscillating systems is motivated by a wide range of applications from neurological treatment of Parkinson's disease to the design of neurocomputers. In this article, we study the control of an ensemble of uncoupled neuron oscillators described by phase models. We examine controllability of such a neuron ensemble for various phase models and, furthermore, study the related optimal control problems. In particular, by employing Pontryagin's maximum principle, we analytically derive optimal controls for spiking single- and two-neuron systems, and analyze the applicability of the latter to an ensemble system. Finally, we present a robust computational method for optimal control of spiking neurons based on pseudospectral approximations. The methodology developed here is universal to the control of general nonlinear phase oscillators.

  7. On large deviations for ensembles of distributions

    Science.gov (United States)

    Khrychev, D. A.

    2013-11-01

    The paper is concerned with the large deviations problem in the Freidlin-Wentzell formulation without the assumption of the uniqueness of the solution to the equation involving white noise. In other words, it is assumed that for each \\varepsilon>0 the nonempty set \\mathscr P_\\varepsilon of weak solutions is not necessarily a singleton. Analogues of a number of concepts in the theory of large deviations are introduced for the set \\{\\mathscr P_\\varepsilon,\\,\\varepsilon>0\\}, hereafter referred to as an ensemble of distributions. The ensembles of weak solutions of an n-dimensional stochastic Navier-Stokes system and stochastic wave equation with power-law nonlinearity are shown to be uniformly exponentially tight. An idempotent Wiener process in a Hilbert space and idempotent partial differential equations are defined. The accumulation points in the sense of large deviations of the ensembles in question are shown to be weak solutions of the corresponding idempotent equations. Bibliography: 14 titles.

  8. Cavity cooling of an ensemble spin system.

    Science.gov (United States)

    Wood, Christopher J; Borneman, Troy W; Cory, David G

    2014-02-07

    We describe how sideband cooling techniques may be applied to large spin ensembles in magnetic resonance. Using the Tavis-Cummings model in the presence of a Rabi drive, we solve a Markovian master equation describing the joint spin-cavity dynamics to derive cooling rates as a function of ensemble size. Our calculations indicate that the coupled angular momentum subspaces of a spin ensemble containing roughly 10(11) electron spins may be polarized in a time many orders of magnitude shorter than the typical thermal relaxation time. The described techniques should permit efficient removal of entropy for spin-based quantum information processors and fast polarization of spin samples. The proposed application of a standard technique in quantum optics to magnetic resonance also serves to reinforce the connection between the two fields, which has recently begun to be explored in further detail due to the development of hybrid designs for manufacturing noise-resilient quantum devices.

  9. Characteristic polynomials in real Ginibre ensembles

    Energy Technology Data Exchange (ETDEWEB)

    Akemann, G; Phillips, M J [Department of Mathematical Sciences and BURSt Research Centre, Brunel University West London, UB8 3PH Uxbridge (United Kingdom); Sommers, H-J [Fachbereich Physik, Universitaet Duisburg-Essen, 47048 Duisburg (Germany)], E-mail: Gernot.Akemann@brunel.ac.uk, E-mail: Michael.Phillips@brunel.ac.uk, E-mail: H.J.Sommers@uni-due.de

    2009-01-09

    We calculate the average of two characteristic polynomials for the real Ginibre ensemble of asymmetric random matrices, and its chiral counterpart. Considered as quadratic forms they determine a skew-symmetric kernel from which all complex eigenvalue correlations can be derived. Our results are obtained in a very simple fashion without going to an eigenvalue representation, and are completely new in the chiral case. They hold for Gaussian ensembles which are partly symmetric, with kernels given in terms of Hermite and Laguerre polynomials respectively, depending on an asymmetry parameter. This allows us to interpolate between the maximally asymmetric real Ginibre and the Gaussian orthogonal ensemble, as well as their chiral counterparts. (fast track communication)

  10. Embedded random matrix ensembles in quantum physics

    CERN Document Server

    Kota, V K B

    2014-01-01

    Although used with increasing frequency in many branches of physics, random matrix ensembles are not always sufficiently specific to account for important features of the physical system at hand. One refinement which retains the basic stochastic approach but allows for such features consists in the use of embedded ensembles.  The present text is an exhaustive introduction to and survey of this important field. Starting with an easy-to-read introduction to general random matrix theory, the text then develops the necessary concepts from the beginning, accompanying the reader to the frontiers of present-day research. With some notable exceptions, to date these ensembles have primarily been applied in nuclear spectroscopy. A characteristic example is the use of a random two-body interaction in the framework of the nuclear shell model. Yet, topics in atomic physics, mesoscopic physics, quantum information science and statistical mechanics of isolated finite quantum systems can also be addressed using these ensemb...

  11. Total probabilities of ensemble runoff forecasts

    Science.gov (United States)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2017-04-01

    Ensemble forecasting has a long history from meteorological modelling, as an indication of the uncertainty of the forecasts. However, it is necessary to calibrate and post-process the ensembles as the they often exhibit both bias and dispersion errors. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters varying in space and time, while giving a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, which makes it unsuitable for our purpose. Our post-processing method of the ensembles is developed in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu), where we are making forecasts for whole Europe, and based on observations from around 700 catchments. As the target is flood forecasting, we are also more interested in improving the forecast skill for high-flows rather than in a good prediction of the entire flow regime. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different meteorological forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to estimate the total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but we are adding a spatial penalty in the calibration process to force a spatial correlation of the parameters. The penalty takes

  12. Circular β ensembles, CMV representation, characteristic polynomials

    Institute of Scientific and Technical Information of China (English)

    SU ZhongGen

    2009-01-01

    In this note we first briefly review some recent progress in the study of the circular β ensemble on the unit circle, where 0 > 0 is a model parameter. In the special cases β = 1,2 and 4, this ensemble describes the joint probability density of eigenvalues of random orthogonal, unitary and sympletic matrices, respectively. For general β, Killip and Nenciu discovered a five-diagonal sparse matrix model, the CMV representation. This representation is new even in the case β = 2; and it has become a powerful tool for studying the circular β ensemble. We then give an elegant derivation for the moment identities of characteristic polynomials via the link with orthogonal polynomials on the unit circle.

  13. Statistical ensembles and fragmentation of finite nuclei

    Science.gov (United States)

    Das, P.; Mallik, S.; Chaudhuri, G.

    2017-09-01

    Statistical models based on different ensembles are very commonly used to describe the nuclear multifragmentation reaction in heavy ion collisions at intermediate energies. Canonical model results are more appropriate for finite nuclei calculations while those obtained from the grand canonical ones are more easily calculable. A transformation relation has been worked out for converting results of finite nuclei from grand canonical to canonical and vice versa. The formula shows that, irrespective of the particle number fluctuation in the grand canonical ensemble, exact canonical results can be recovered for observables varying linearly or quadratically with the number of particles. This result is of great significance since the baryon and charge conservation constraints can make the exact canonical calculations extremely difficult in general. This concept developed in this work can be extended in future for transformation to ensembles where analytical solutions do not exist. The applicability of certain equations (isoscaling, etc.) in the regime of finite nuclei can also be tested using this transformation relation.

  14. An ensemble-based approach for breast mass classification in mammography images

    Science.gov (United States)

    Ribeiro, Patricia B.; Papa, João. P.; Romero, Roseli A. F.

    2017-03-01

    Mammography analysis is an important tool that helps detecting breast cancer at the very early stages of the disease, thus increasing the quality of life of hundreds of thousands of patients worldwide. In Computer-Aided Detection systems, the identification of mammograms with and without masses (without clinical findings) is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest that may contain some suspicious content. In this work, the introduce a variant of the Optimum-Path Forest (OPF) classifier for breast mass identification, as well as we employed an ensemble-based approach that can enhance the effectiveness of individual classifiers aiming at dealing with the aforementioned purpose. The experimental results also comprise the naïve OPF and a traditional neural network, being the most accurate results obtained through the ensemble of classifiers, with an accuracy nearly to 86%.

  15. Sand-Dust Storm Ensemble Forecast Model Based on Rough Set

    Institute of Scientific and Technical Information of China (English)

    LU Zhiying; YANG Le; LI Yanying; ZHAO Zhichao

    2007-01-01

    To improve the accuracy of sand-dust storm forecast system, a sand-dust storm ensemble forecast model based on rough set (RS) is proposed. The feature data are extracted from the historical data sets using the self-organization map (SOM) clustering network and single fields forecast to form the feature values with low dimensions. Then, the unwanted attributes are reduced according to RS to discretize the continuous feature values. Lastly, the minimum decision rules are constructed according to the remainder attributes, namely sand-dust storm ensemble forecast model based on RS is constructed. Results comparison between the proposed model and the back propagation neural network model show that the sand-storm forecast model based on RS has better stability, faster running speed, and its forecasting accuracy ratio is increased from 17.1% to 86.21%.

  16. Depression of cortical activity in humans by mild hypercapnia.

    Science.gov (United States)

    Thesen, Thomas; Leontiev, Oleg; Song, Tao; Dehghani, Nima; Hagler, Donald J; Huang, Mingxiong; Buxton, Richard; Halgren, Eric

    2012-03-01

    The effects of neural activity on cerebral hemodynamics underlie human brain imaging with functional magnetic resonance imaging and positron emission tomography. However, the threshold and characteristics of the converse effects, wherein the cerebral hemodynamic and metabolic milieu influence neural activity, remain unclear. We tested whether mild hypercapnia (5% CO2 ) decreases the magnetoencephalogram response to auditory pattern recognition and visual semantic tasks. Hypercapnia induced statistically significant decreases in event-related fields without affecting behavioral performance. Decreases were observed in early sensory components in both auditory and visual modalities as well as later cognitive components related to memory and language. Effects were distributed across cortical regions. Decreases were comparable in evoked versus spontaneous spectral power. Hypercapnia is commonly used with hemodynamic models to calibrate the blood oxygenation level-dependent response. Modifying model assumptions to incorporate the current findings produce a modest but measurable decrease in the estimated cerebral metabolic rate for oxygen change with activation. Because under normal conditions, low cerebral pH would arise when bloodflow is unable to keep pace with neuronal activity, the cortical depression observed here may reflect a homeostatic mechanism by which neuronal activity is adjusted to a level that can be sustained by available bloodflow. Animal studies suggest that these effects may be mediated by pH-modulating presynaptic adenosine receptors. Although the data is not clear, comparable changes in cortical pH to those induced here may occur during sleep apnea, sleep, and exercise. If so, these results suggest that such activities may in turn have generalized depressive effects on cortical activity.

  17. Protective effects of berberine against amyloid beta-induced toxicity in cultured rat cortical neurons

    Institute of Scientific and Technical Information of China (English)

    Jing Wang; Yanjun Zhang; Shuai Du; Mixia Zhang

    2011-01-01

    Berberine, a major constituent of Coptidis rhizoma, exhibits neural protective effects. The present study analyzed the potential protective effect of berberine against amyloid G-induced cytotoxicity in rat cerebral cortical neurons. Alzheimer's disease cell models were treated with 0.5 and 2 μmol/Lberberine for 36 hours to inhibit amyloid G-induced toxicity. Methyl thiazolyl tetrazolium assay and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling staining results showed that berberine significantly increased cell viability and reduced cell apoptosis in primary cultured rat cortical neurons. In addition, western blot analysis revealed a protective effect of berberine against amyloid β-induced toxicity in cultured cortical neurons, which coincided with significantly decreased abnormal up-regulation of activated caspase-3. These results showed that berberine exhibited a protective effect against amyloid 13-induced cytotoxicity in cultured rat cortical neurons.

  18. Membrane potential dynamics of populations of cortical neurons during auditory streaming.

    Science.gov (United States)

    Farley, Brandon J; Noreña, Arnaud J

    2015-10-01

    How a mixture of acoustic sources is perceptually organized into discrete auditory objects remains unclear. One current hypothesis postulates that perceptual segregation of different sources is related to the spatiotemporal separation of cortical responses induced by each acoustic source or stream. In the present study, the dynamics of subthreshold membrane potential activity were measured across the entire tonotopic axis of the rodent primary auditory cortex during the auditory streaming paradigm using voltage-sensitive dye imaging. Consistent with the proposed hypothesis, we observed enhanced spatiotemporal segregation of cortical responses to alternating tone sequences as their frequency separation or presentation rate was increased, both manipulations known to promote stream segregation. However, across most streaming paradigm conditions tested, a substantial cortical region maintaining a response to both tones coexisted with more peripheral cortical regions responding more selectively to one of them. We propose that these coexisting subthreshold representation types could provide neural substrates to support the flexible switching between the integrated and segregated streaming percepts.

  19. Oxytocin mediates early experience-dependent cross-modal plasticity in the sensory cortices.

    Science.gov (United States)

    Zheng, Jing-Jing; Li, Shu-Jing; Zhang, Xiao-Di; Miao, Wan-Ying; Zhang, Dinghong; Yao, Haishan; Yu, Xiang

    2014-03-01

    Sensory experience is critical to development and plasticity of neural circuits. Here we report a new form of plasticity in neonatal mice, where early sensory experience cross-modally regulates development of all sensory cortices via oxytocin signaling. Unimodal sensory deprivation from birth through whisker deprivation or dark rearing reduced excitatory synaptic transmission in the correspondent sensory cortex and cross-modally in other sensory cortices. Sensory experience regulated synthesis and secretion of the neuropeptide oxytocin as well as its level in the cortex. Both in vivo oxytocin injection and increased sensory experience elevated excitatory synaptic transmission in multiple sensory cortices and significantly rescued the effects of sensory deprivation. Together, these results identify a new function for oxytocin in promoting cross-modal, experience-dependent cortical development. This link between sensory experience and oxytocin is particularly relevant to autism, where hypersensitivity or hyposensitivity to sensory inputs is prevalent and oxytocin is a hotly debated potential therapy.

  20. A neural theory of visual attention

    DEFF Research Database (Denmark)

    Bundesen, Claus; Habekost, Thomas; Kyllingsbæk, Søren

    2005-01-01

    A neural theory of visual attention (NTVA) is presented. NTVA is a neural interpretation of C. Bundesen's (1990) theory of visual attention (TVA). In NTVA, visual processing capacity is distributed across stimuli by dynamic remapping of receptive fields of cortical cells such that more processing...... resources (cells) are devoted to behaviorally important objects than to less important ones. By use of the same basic equations used in TVA, NTVA accounts for a wide range of known attentional effects in human performance (reaction times and error rates) and a wide range of effects observed in firing rates...

  1. Ensemble hidden Markov models with application to landmine detection

    Science.gov (United States)

    Hamdi, Anis; Frigui, Hichem

    2015-12-01

    We introduce an ensemble learning method for temporal data that uses a mixture of hidden Markov models (HMM). We hypothesize that the data are generated by K models, each of which reflects a particular trend in the data. The proposed approach, called ensemble HMM (eHMM), is based on clustering within the log-likelihood space and has two main steps. First, one HMM is fit to each of the N individual training sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This results in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per cluster. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE), and the variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the models' outputs using an artificial neural network. We propose both discrete and continuous versions of the eHMM. Our approach was evaluated on a real-world application for landmine detection using ground-penetrating radar (GPR). Results show that both the continuous and discrete eHMM can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. These attributes are reflected in the mixture model's parameters. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data.

  2. Cortical and Subcortical Contributions to Short-Term Memory for Orienting Movements.

    Science.gov (United States)

    Kopec, Charles D; Erlich, Jeffrey C; Brunton, Bingni W; Deisseroth, Karl; Brody, Carlos D

    2015-10-21

    Neural activity in frontal cortical areas has been causally linked to short-term memory (STM), but whether this activity is necessary for forming, maintaining, or reading out STM remains unclear. In rats performing a memory-guided orienting task, the frontal orienting fields in cortex (FOF) are considered critical for STM maintenance, and during each trial display a monotonically increasing neural encoding for STM. Here, we transiently inactivated either the FOF or the superior colliculus and found that the resulting impairments in memory-guided orienting performance followed a monotonically decreasing time course, surprisingly opposite to the neural encoding. A dynamical attractor model in which STM relies equally on cortical and subcortical regions reconciled the encoding and inactivation data. We confirmed key predictions of the model, including a time-dependent relationship between trial difficulty and perturbability, and substantial, supralinear, impairment following simultaneous inactivation of the FOF and superior colliculus during memory maintenance.

  3. Ensemble Enabled Weighted PageRank

    CERN Document Server

    Luo, Dongsheng; Hu, Renjun; Duan, Liang; Ma, Shuai

    2016-01-01

    This paper describes our solution for WSDM Cup 2016. Ranking the query independent importance of scholarly articles is a critical and challenging task, due to the heterogeneity and dynamism of entities involved. Our approach is called Ensemble enabled Weighted PageRank (EWPR). To do this, we first propose Time-Weighted PageRank that extends PageRank by introducing a time decaying factor. We then develop an ensemble method to assemble the authorities of the heterogeneous entities involved in scholarly articles. We finally propose to use external data sources to further improve the ranking accuracy. Our experimental study shows that our EWPR is a good choice for ranking scholarly articles.

  4. Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis.

    Directory of Open Access Journals (Sweden)

    Eduardo J Izquierdo

    Full Text Available Increased efforts in the assembly and analysis of connectome data are providing new insights into the principles underlying the connectivity of neural circuits. However, despite these considerable advances in connectomics, neuroanatomical data must be integrated with neurophysiological and behavioral data in order to obtain a complete picture of neural function. Due to its nearly complete wiring diagram and large behavioral repertoire, the nematode worm Caenorhaditis elegans is an ideal organism in which to explore in detail this link between neural connectivity and behavior. In this paper, we develop a neuroanatomically-grounded model of salt klinotaxis, a form of chemotaxis in which changes in orientation are directed towards the source through gradual continual adjustments. We identify a minimal klinotaxis circuit by systematically searching the C. elegans connectome for pathways linking chemosensory neurons to neck motor neurons, and prune the resulting network based on both experimental considerations and several simplifying assumptions. We then use an evolutionary algorithm to find possible values for the unknown electrophsyiological parameters in the network such that the behavioral performance of the entire model is optimized to match that of the animal. Multiple runs of the evolutionary algorithm produce an ensemble of such models. We analyze in some detail the mechanisms by which one of the best evolved circuits operates and characterize the similarities and differences between this mechanism and other solutions in the ensemble. Finally, we propose a series of experiments to determine which of these alternatives the worm may be using.

  5. Time to address the problems at the neural interface

    Science.gov (United States)

    Durand, Dominique M.; Ghovanloo, Maysam; Krames, Elliot

    2014-04-01

    Neural engineers have made significant, if not remarkable, progress in interfacing with the nervous system in the last ten years. In particular, neuromodulation of the brain has generated significant therapeutic benefits [1-5]. EEG electrodes can be used to communicate with patients with locked-in syndrome [6]. In the central nervous system (CNS), electrode arrays placed directly over or within the cortex can record neural signals related to the intent of the subject or patient [7, 8]. A similar technology has allowed paralyzed patients to control an otherwise normal skeletal system with brain signals [9, 10]. This technology has significant potential to restore function in these and other patients with neural disorders such as stroke [11]. Although there are several multichannel arrays described in the literature, the workhorse for these cortical interfaces has been the Utah array [12]. This 100-channel electrode array has been used in most studies on animals and humans since the 1990s and is commercially available. This array and other similar microelectrode arrays can record neural signals with high quality (high signal-to-noise ratio), but these signals fade and disappear after a few months and therefore the current technology is not reliable for extended periods of time. Therefore, despite these major advances in communicating with the brain, clinical translation cannot be implemented. The reasons for this failure are not known but clearly involve the interface between the electrode and the neural tissue. The Defense Advanced Research Project Agency (DARPA) as well as other federal funding agencies such as the National Science Foundation (NSF) and the National Institutes of Health have provided significant financial support to investigate this problem without much success. A recent funding program from DARPA was designed to establish the failure modes in order to generate a reliable neural interface technology and again was unsuccessful at producing a robust

  6. Ensemble Eclipse: A Process for Prefab Development Environment for the Ensemble Project

    Science.gov (United States)

    Wallick, Michael N.; Mittman, David S.; Shams, Khawaja, S.; Bachmann, Andrew G.; Ludowise, Melissa

    2013-01-01

    This software simplifies the process of having to set up an Eclipse IDE programming environment for the members of the cross-NASA center project, Ensemble. It achieves this by assembling all the necessary add-ons and custom tools/preferences. This software is unique in that it allows developers in the Ensemble Project (approximately 20 to 40 at any time) across multiple NASA centers to set up a development environment almost instantly and work on Ensemble software. The software automatically has the source code repositories and other vital information and settings included. The Eclipse IDE is an open-source development framework. The NASA (Ensemble-specific) version of the software includes Ensemble-specific plug-ins as well as settings for the Ensemble project. This software saves developers the time and hassle of setting up a programming environment, making sure that everything is set up in the correct manner for Ensemble development. Existing software (i.e., standard Eclipse) requires an intensive setup process that is both time-consuming and error prone. This software is built once by a single user and tested, allowing other developers to simply download and use the software

  7. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

    Science.gov (United States)

    Hochberg, Leigh R.; Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y.; Simeral, John D.; Vogel, Joern; Haddadin, Sami; Liu, Jie; Cash, Sydney S.; van der Smagt, Patrick; Donoghue, John P.

    2012-01-01

    Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS)1–5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices6–8. Able-bodied monkeys have used an NIS to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after CNS injury, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals. PMID:22596161

  8. [Research advances on cortical functional and structural deficits of amblyopia].

    Science.gov (United States)

    Wu, Y; Liu, L Q

    2017-05-11

    Previous studies have observed functional deficits in primary visual cortex. With the development of functional magnetic resonance imaging and electrophysiological technique, the research of the striate, extra-striate cortex and higher-order cortical deficit underlying amblyopia reaches a new stage. The neural mechanisms of amblyopia show that anomalous responses exist throughout the visual processing hierarchy, including the functional and structural abnormalities. This review aims to summarize the current knowledge about structural and functional deficits of brain regions associated with amblyopia. (Chin J Ophthalmol, 2017, 53: 392-395).

  9. Total probabilities of ensemble runoff forecasts

    Science.gov (United States)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2016-04-01

    Ensemble forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the ensembles often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative

  10. Associations between cortical thickness and verbal fluency in childhood, adolescence, and young adulthood.

    Science.gov (United States)

    Porter, James N; Collins, Paul F; Muetzel, Ryan L; Lim, Kelvin O; Luciana, Monica

    2011-04-15

    Neuroimaging studies of normative human brain development indicate that the brain matures at differing rates across time and brain regions, with some areas maturing into young adulthood. In particular, changes in cortical thickness may index maturational progressions from an overabundance of neuropil toward efficiently pruned neural networks. Developmental changes in structural MRI measures have rarely been examined in relation to discrete neuropsychological functions. In this study, healthy right-handed adolescents completed MRI scanning and the Controlled Oral Word Association Test (COWAT). Associations of task performance and cortical thickness were assessed with cortical-surface-based analyses. Significant correlations between increasing COWAT performances and decreasing cortical thickness were found in left hemisphere language regions, including perisylvian regions surrounding Wernicke's and Broca's areas. Task performance was also correlated with regions associated with effortful verbal processing, working memory, and performance monitoring. Structure-function associations were not significantly different between older and younger subjects. Decreases in cortical thicknesses in regions that comprise the language network likely reflect maturation toward adult-like cortical organization and processing efficiency. The changes in cortical thicknesses that support verbal fluency are apparent by middle childhood, but with regionally separate developmental trajectories for males and females, consistent with other studies of adolescent development.

  11. A Neural Basis for Contagious Yawning.

    Science.gov (United States)

    Brown, Beverley J; Kim, Soyoung; Saunders, Hannah; Bachmann, Clarissa; Thompson, Jessica; Ropar, Danielle; Jackson, Stephen R; Jackson, Georgina M

    2017-09-11

    Contagious yawning, in which yawning is triggered involuntarily when we observe another person yawn, is a common form of echophenomena-the automatic imitation of another's words (echolalia) or actions (echopraxia) [1]. The neural basis for echophenomena is unknown; however, it has been proposed that it is linked to disinhibition of the human mirror-neuron system [1-4] and hyper-excitability of cortical motor areas [1]. We investigated the neural basis for contagious yawning using transcranial magnetic stimulation (TMS). Thirty-six adults viewed video clips that showed another individual yawning and, in separate blocks, were instructed to either resist yawning or allow themselves to yawn. Participants were videoed throughout and their yawns or stifled yawns were counted. We used TMS to quantify motor cortical excitability and physiological inhibition for each participant, and these measures were then used to predict the propensity for contagious yawning across participants. We demonstrate that instructions to resist yawning increase the urge to yawn and alter how yawns are expressed (i.e., full versus stifled yawns) but do not alter the individual propensity for contagious yawning. By contrast, TMS measures of cortical excitability and physiological inhibition were significant predictors of contagious yawning and accounted for approximately 50% of the variability in contagious yawning. These data demonstrate that individual variability in the propensity for contagious yawning is determined by cortical excitability and physiological inhibition in the primary motor cortex. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  12. Cortical tracking of hierarchical linguistic structures in connected speech.

    Science.gov (United States)

    Ding, Nai; Melloni, Lucia; Zhang, Hang; Tian, Xing; Poeppel, David

    2016-01-01

    The most critical attribute of human language is its unbounded combinatorial nature: smaller elements can be combined into larger structures on the basis of a grammatical system, resulting in a hierarchy of linguistic units, such as words, phrases and sentences. Mentally parsing and representing such structures, however, poses challenges for speech comprehension. In speech, hierarchical linguistic structures do not have boundaries that are clearly defined by acoustic cues and must therefore be internally and incrementally constructed during comprehension. We found that, during listening to connected speech, cortical activity of different timescales concurrently tracked the time course of abstract linguistic structures at different hierarchical levels, such as words, phrases and sentences. Notably, the neural tracking of hierarchical linguistic structures was dissociated from the encoding of acoustic cues and from the predictability of incoming words. Our results indicate that a hierarchy of neural processing timescales underlies grammar-based internal construction of hierarchical linguistic structure.

  13. An Ensemble Approach for Expanding Queries

    Science.gov (United States)

    2012-11-01

    vincristine; thalidomide; painful; cisplatin; oxaliplatin; charcot -marie-tooth disease ; drugs; neuropathy Ensemble expansion child of, asthma, kids...system disorders; peripheral nerve diseases ; peripheral neuropathies; peripheral nervous system disorder; peripheral nervous system disease ...peripheral nerve disease ; peripheral nerve disorders, peripheral nerve disorder Relation expansion offspring, child of, of child, child find

  14. NYYD Ensemble ja Riho Sibul / Anneli Remme

    Index Scriptorium Estoniae

    Remme, Anneli, 1968-

    2001-01-01

    Gavin Bryarsi teos "Jesus' Blood Never Failed Me Yet" NYYD Ensemble'i ja Riho Sibula esituses 27. detsembril Pauluse kirikus Tartus ja 28. detsembril Rootsi- Mihkli kirikus Tallinnas. Kaastegevad Tartu Ülikooli Kammerkoor (Tartus) ja kammerkoor Voces Musicales (Tallinnas). Kunstiline juht Olari Elts

  15. A method for ensemble wildland fire simulation

    Science.gov (United States)

    Mark A. Finney; Isaac C. Grenfell; Charles W. McHugh; Robert C. Seli; Diane Trethewey; Richard D. Stratton; Stuart Brittain

    2011-01-01

    An ensemble simulation system that accounts for uncertainty in long-range weather conditions and two-dimensional wildland fire spread is described. Fuel moisture is expressed based on the energy release component, a US fire danger rating index, and its variation throughout the fire season is modeled using time series analysis of historical weather data. This analysis...

  16. NYYD Ensemble ja Riho Sibul / Anneli Remme

    Index Scriptorium Estoniae

    Remme, Anneli, 1968-

    2001-01-01

    Gavin Bryarsi teos "Jesus' Blood Never Failed Me Yet" NYYD Ensemble'i ja Riho Sibula esituses 27. detsembril Pauluse kirikus Tartus ja 28. detsembril Rootsi- Mihkli kirikus Tallinnas. Kaastegevad Tartu Ülikooli Kammerkoor (Tartus) ja kammerkoor Voces Musicales (Tallinnas). Kunstiline juht Olari Elts

  17. Eigenstate Gibbs ensemble in integrable quantum systems

    Science.gov (United States)

    Nandy, Sourav; Sen, Arnab; Das, Arnab; Dhar, Abhishek

    2016-12-01

    The eigenstate thermalization hypothesis conjectures that for a thermodynamically large system in one of its energy eigenstates, the reduced density matrix describing any finite subsystem is determined solely by a set of relevant conserved quantities. In a chaotic quantum system, only the energy is expected to play that role and hence eigenstates appear locally thermal. Integrable systems, on the other hand, possess an extensive number of such conserved quantities and therefore the reduced density matrix requires specification of all the corresponding parameters (generalized Gibbs ensemble). However, here we show by unbiased statistical sampling of the individual eigenstates with a given finite energy density that the local description of an overwhelming majority of these states of even such an integrable system is actually Gibbs-like, i.e., requires only the energy density of the eigenstate. Rare eigenstates that cannot be represented by the Gibbs ensemble can also be sampled efficiently by our method and their local properties are then shown to be described by appropriately truncated generalized Gibbs ensembles. We further show that the presence of these rare eigenstates differentiates the model from the chaotic case and leads to the system being described by a generalized Gibbs ensemble at long time under a unitary dynamics following a sudden quench, even when the initial state is a typical (Gibbs-like) eigenstate of the prequench Hamiltonian.

  18. Locally Accessible Information from Multipartite Ensembles

    Institute of Scientific and Technical Information of China (English)

    SONG Wei

    2009-01-01

    We present a universal Holevo-like upper bound on the locally accessible information for arbitrary multipartite ensembles.This bound allows us to analyze the indistinguishability of a set of orthogonal states under local operations and classical communication.We also derive the upper bound for the capacity of distributed dense coding with multipartite senders and multipartite receivers.

  19. Canonical Ensemble Model for Black Hole Radiation

    Indian Academy of Sciences (India)

    Jingyi Zhang

    2014-09-01

    In this paper, a canonical ensemble model for the black hole quantum tunnelling radiation is introduced. In this model the probability distribution function corresponding to the emission shell is calculated to second order. The formula of pressure and internal energy of the thermal system is modified, and the fundamental equation of thermodynamics is also discussed.

  20. A Hierarchical Bayes Ensemble Kalman Filter

    Science.gov (United States)

    Tsyrulnikov, Michael; Rakitko, Alexander

    2017-01-01

    A new ensemble filter that allows for the uncertainty in the prior distribution is proposed and tested. The filter relies on the conditional Gaussian distribution of the state given the model-error and predictability-error covariance matrices. The latter are treated as random matrices and updated in a hierarchical Bayes scheme along with the state. The (hyper)prior distribution of the covariance matrices is assumed to be inverse Wishart. The new Hierarchical Bayes Ensemble Filter (HBEF) assimilates ensemble members as generalized observations and allows ordinary observations to influence the covariances. The actual probability distribution of the ensemble members is allowed to be different from the true one. An approximation that leads to a practicable analysis algorithm is proposed. The new filter is studied in numerical experiments with a doubly stochastic one-variable model of "truth". The model permits the assessment of the variance of the truth and the true filtering error variance at each time instance. The HBEF is shown to outperform the EnKF and the HEnKF by Myrseth and Omre (2010) in a wide range of filtering regimes in terms of performance of its primary and secondary filters.

  1. Statistical theory of hierarchical avalanche ensemble

    OpenAIRE

    Olemskoi, Alexander I.

    1999-01-01

    The statistical ensemble of avalanche intensities is considered to investigate diffusion in ultrametric space of hierarchically subordinated avalanches. The stationary intensity distribution and the steady-state current are obtained. The critical avalanche intensity needed to initiate the global avalanche formation is calculated depending on noise intensity. The large time asymptotic for the probability of the global avalanche appearance is derived.

  2. Marking up lattice QCD configurations and ensembles

    CERN Document Server

    Coddington, P; Maynard, C M; Pleiter, D; Yoshié, T

    2007-01-01

    QCDml is an XML-based markup language designed for sharing QCD configurations and ensembles world-wide via the International Lattice Data Grid (ILDG). Based on the latest release, we present key ingredients of the QCDml in order to provide some starting points for colleagues in this community to markup valuable configurations and submit them to the ILDG.

  3. A Theoretical Analysis of Why Hybrid Ensembles Work

    Directory of Open Access Journals (Sweden)

    Kuo-Wei Hsu

    2017-01-01

    Full Text Available Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.

  4. Global Ensemble Forecast System (GEFS) [2.5 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  5. An educational model for ensemble streamflow simulation and uncertainty analysis

    National Research Council Canada - National Science Library

    AghaKouchak, A; Nakhjiri, N; Habib, E

    2013-01-01

    ...) are interconnected. The educational toolbox includes a MATLAB Graphical User Interface (GUI) and an ensemble simulation scheme that can be used for teaching uncertainty analysis, parameter estimation, ensemble simulation and model sensitivity...

  6. Ensemble-based Kalman Filters in Strongly Nonlinear Dynamics

    Institute of Scientific and Technical Information of China (English)

    Zhaoxia PU; Joshua HACKER

    2009-01-01

    This study examines the effectiveness of ensemble Kalman filters in data assimilation with the strongly nonlinear dynamics of the Lorenz-63 model, and in particular their use in predicting the regime transition that occurs when the model jumps from one basin of attraction to the other. Four configurations of the ensemble-based Kalman filtering data assimilation techniques, including the ensemble Kalman filter, ensemble adjustment Kalman filter, ensemble square root filter and ensemble transform Kalman filter, are evaluated with their ability in predicting the regime transition (also called phase transition) and also are compared in terms of their sensitivity to both observational and sampling errors. The sensitivity of each ensemble-based filter to the size of the ensemble is also examined.

  7. Space Applications for Ensemble Detection and Analysis Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Ensemble Detection is both a measurement technique and analysis tool. Like a prism that separates light into spectral bands, an ensemble detector mixes a signal with...

  8. Perceptual learning modifies the functional specializations of visual cortical areas.

    Science.gov (United States)

    Chen, Nihong; Cai, Peng; Zhou, Tiangang; Thompson, Benjamin; Fang, Fang

    2016-05-17

    Training can improve performance of perceptual tasks. This phenomenon, known as perceptual learning, is strongest for the trained task and stimulus, leading to a widely accepted assumption that the associated neuronal plasticity is restricted to brain circuits that mediate performance of the trained task. Nevertheless, learning does transfer to other tasks and stimuli, implying the presence of more widespread plasticity. Here, we trained human subjects to discriminate the direction of coherent motion stimuli. The behavioral learning effect substantially transferred to noisy motion stimuli. We used transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms underlying the transfer of learning. The TMS experiment revealed dissociable, causal contributions of V3A (one of the visual areas in the extrastriate visual cortex) and MT+ (middle temporal/medial superior temporal cortex) to coherent and noisy motion processing. Surprisingly, the contribution of MT+ to noisy motion processing was replaced by V3A after perceptual training. The fMRI experiment complemented and corroborated the TMS finding. Multivariate pattern analysis showed that, before training, among visual cortical areas, coherent and noisy motion was decoded most accurately in V3A and MT+, respectively. After training, both kinds of motion were decoded most accurately in V3A. Our findings demonstrate that the effects of perceptual learning extend far beyond the retuning of specific neural populations for the trained stimuli. Learning could dramatically modify the inherent functional specializations of visual cortical areas and dynamically reweight their contributions to perceptual decisions based on their representational qualities. These neural changes might serve as the neural substrate for the transfer of perceptual learning.

  9. Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    methodology. In a first stage, ensemble forecasts of meteorological variables are converted to power through a suitable power curve model. The relevance and benefits of employing a newly developed orthogonal fitting method for the power curve model over the traditional least-squares one are discussed...... predictive distributions. Such a methodology has the benefit of yielding predictive distributions that are of increased reliability (in a probabilistic sense) in comparison with the raw ensemble forecasts, while taking advantage of their high resolution....... of probabilistic forecasts, the resolution of which may be maximized by using meteorological ensemble predictions as input. The paper concentrates on the test case of the Horns Rev wind farm over a period of approximately one year, in order to describe, apply and discuss a complete ensemble-based forecasting...

  10. Theory and Practice of Phase-aware Ensemble Forecasting

    Science.gov (United States)

    Schulte, J. A.; Georgas, N.

    2016-12-01

    The timing of events represents a source of uncertainty in ensemble forecasting that can produce misleading ensemble statistics. A general theory is presented to overcome drawbacks of traditional ensemble forecasting statistics that perform poorly in the presence of timing disagreements among ensemble members. It was shown, in particular, that ensemble forecasts containing substantial uncertainty in timing can produce non-trivial higher-order statistical moments, rendering the ensemble mean inappropriate as a best available estimate of the future state of the forecast parameter in question. A set of theoretical experiments showed that the existence of large timing differences among ensemble members can produce negative ensemble skewness even when the ensemble members are sinusoids whose amplitudes are drawn from a normal distribution: Consistently, the ensemble mean will tend to fall on the left tail of the normal distribution representing the originally sampled amplitudes, rather than at the mean or median. To remedy the left-tail placement problem of the ensemble mean, a new generally applicable ensemble statistic - the phase-aware ensemble mean - is proposed that is more robust against ensemble skewness resulting from timing spread. The computation of the phase-aware mean involves the transformation of all ensemble members to wavelet space and the subsequent inverse wavelet transformation of the product of the ensemble mean wavelet phase and modulus back to the time domain. The new methods were applied to storm surge reforecasts for Hurricane Irene and Sandy at 8 stations located around the New York City metropolitan area. The phase-aware ensemble mean was found to perform better at detecting the magnitude of events compared to the traditional ensemble mean, consistent with the results from theoretical experiments. The ensemble mean, moreover, was found to be consistently located on the left tail of distributions representing future peak storm surge outcomes. A

  11. Consonance and dissonance of musical chords: neural correlates in auditory cortex of monkeys and humans.

    Science.gov (United States)

    Fishman, Y I; Volkov, I O; Noh, M D; Garell, P C; Bakken, H; Arezzo, J C; Howard, M A; Steinschneider, M

    2001-12-01

    , AEPs recorded in the planum temporale do not display significant phase-locked activity, suggesting functional differentiation of auditory cortical regions in humans. These findings support the relevance of synchronous phase-locked neural ensemble activity in A1 for the physiological representation of sensory dissonance in humans and highlight the merits of complementary monkey/human studies in the investigation of neural substrates underlying auditory perception.

  12. Cortical myoclonus in Huntington's disease.

    Science.gov (United States)

    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.

  13. Quantum canonical ensemble: A projection operator approach

    Science.gov (United States)

    Magnus, Wim; Lemmens, Lucien; Brosens, Fons

    2017-09-01

    Knowing the exact number of particles N, and taking this knowledge into account, the quantum canonical ensemble imposes a constraint on the occupation number operators. The constraint particularly hampers the systematic calculation of the partition function and any relevant thermodynamic expectation value for arbitrary but fixed N. On the other hand, fixing only the average number of particles, one may remove the above constraint and simply factorize the traces in Fock space into traces over single-particle states. As is well known, that would be the strategy of the grand-canonical ensemble which, however, comes with an additional Lagrange multiplier to impose the average number of particles. The appearance of this multiplier can be avoided by invoking a projection operator that enables a constraint-free computation of the partition function and its derived quantities in the canonical ensemble, at the price of an angular or contour integration. Introduced in the recent past to handle various issues related to particle-number projected statistics, the projection operator approach proves beneficial to a wide variety of problems in condensed matter physics for which the canonical ensemble offers a natural and appropriate environment. In this light, we present a systematic treatment of the canonical ensemble that embeds the projection operator into the formalism of second quantization while explicitly fixing N, the very number of particles rather than the average. Being applicable to both bosonic and fermionic systems in arbitrary dimensions, transparent integral representations are provided for the partition function ZN and the Helmholtz free energy FN as well as for two- and four-point correlation functions. The chemical potential is not a Lagrange multiplier regulating the average particle number but can be extracted from FN+1 -FN, as illustrated for a two-dimensional fermion gas.

  14. Relationships between behavior, brainstem and cortical encoding of seen and heard speech in musicians and non-musicians.

    Science.gov (United States)

    Musacchia, Gabriella; Strait, Dana; Kraus, Nina

    2008-07-01

    Musicians have a variety of perceptual and cortical specializations compared to non-musicians. Recent studies have shown that potentials evoked from primarily brainstem structures are enhanced in musicians, compared to non-musicians. Specifically, musicians have more robust representations of pitch periodicity and faster neural timing to sound onset when listening to sounds or both listening to and viewing a speaker. However, it is not known whether musician-related enhancements at the subcortical level are correlated with specializations in the cortex. Does musical training shape the auditory system in a coordinated manner or in disparate ways at cortical and subcortical levels? To answer this question, we recorded simultaneous brainstem and cortical evoked responses in musician and non-musician subjects. Brainstem response periodicity was related to early cortical response timing across all subjects, and this relationship was stronger in musicians. Peaks of the brainstem response evoked by sound onset and timbre cues were also related to cortical timing. Neurophysiological measures at both levels correlated with musical skill scores across all subjects. In addition, brainstem and cortical measures correlated with the age musicians began their training and the years of musical practice. Taken together, these data imply that neural representations of pitch, timing and timbre cues and cortical response timing are shaped in a coordinated manner, and indicate corticofugal modulation of subcortical afferent circuitry.

  15. Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

    OpenAIRE

    Jinyin Ye; Yuehong Shao; Zhijia Li

    2016-01-01

    TIGGE (THORPEX International Grand Global Ensemble) was a major part of the THORPEX (Observing System Research and Predictability Experiment). It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability fo...

  16. Relaxed genetic control of cortical organization in human brains compared with chimpanzees.

    Science.gov (United States)

    Gómez-Robles, Aida; Hopkins, William D; Schapiro, Steven J; Sherwood, Chet C

    2015-12-01

    The study of hominin brain evolution has focused largely on the neocortical expansion and reorganization undergone by humans as inferred from the endocranial fossil record. Comparisons of modern human brains with those of chimpanzees provide an additional line of evidence to define key neural traits that have emerged in human evolution and that underlie our unique behavioral specializations. In an attempt to identify fundamental developmental differences, we have estimated the genetic bases of brain size and cortical organization in chimpanzees and humans by studying phenotypic similarities between individuals with known kinship relationships. We show that, although heritability for brain size and cortical organization is high in chimpanzees, cerebral cortical anatomy is substantially less genetically heritable than brain size in humans, indicating greater plasticity and increased environmental influence on neurodevelopment in our species. This relaxed genetic control on cortical organization is especially marked in association areas and likely is related to underlying microstructural changes in neural circuitry. A major result of increased plasticity is that the development of neural circuits that underlie behavior is shaped by the environmental, social, and cultural context more intensively in humans than in other primate species, thus providing an anatomical basis for behavioral and cognitive evolution.

  17. Ensembles of signal transduction models using Pareto Optimal Ensemble Techniques (POETs).

    Science.gov (United States)

    Song, Sang Ok; Chakrabarti, Anirikh; Varner, Jeffrey D

    2010-07-01

    Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (64 ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an ensemble of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the ensemble following the addition of extracellular ligand. Also, the ensemble recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information.

  18. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering

    DEFF Research Database (Denmark)

    Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh

    2015-01-01

    Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...

  19. Exploring and Listening to Chinese Classical Ensembles in General Music

    Science.gov (United States)

    Zhang, Wenzhuo

    2017-01-01

    Music diversity is valued in theory, but the extent to which it is efficiently presented in music class remains limited. Within this article, I aim to bridge this gap by introducing four genres of Chinese classical ensembles--Qin and Xiao duets, Jiang Nan bamboo and silk ensembles, Cantonese ensembles, and contemporary Chinese orchestras--into the…

  20. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering

    DEFF Research Database (Denmark)

    Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh;

    2015-01-01

    Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...