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Sample records for basis function neural

  1. Radial basis function neural networks applied to NASA SSME data

    Science.gov (United States)

    Wheeler, Kevin R.; Dhawan, Atam P.

    1993-01-01

    This paper presents a brief report on the application of Radial Basis Function Neural Networks (RBFNN) to the prediction of sensor values for fault detection and diagnosis of the Space Shuttle's Main Engines (SSME). The location of the Radial Basis Function (RBF) node centers was determined with a K-means clustering algorithm. A neighborhood operation about these center points was used to determine the variances of the individual processing notes.

  2. Implementation of Radial Basis Function Neural Network for Image Steganalysis

    OpenAIRE

    Sambasiva Rao Baragada; S. Ramakrishna; M.S. Rao; S. Purushothaman

    2008-01-01

    Steganographic tools and techniques are becoming more potential and widespread. Illegal use of steganography poses serious challenges to the law enforcement agencies. Limited work has been carried out on supervised steganalysis using neural network as a classifier. We present a combined method of identifying the presence of covert information in a carrier image using fisher’s linear discriminant (FLD) function followed by the radial basis function (RBF). Experiments show promising resu...

  3. Implementation of Radial Basis Function Neural Network for Image Steganalysis

    Directory of Open Access Journals (Sweden)

    Sambasiva Rao Baragada

    2008-09-01

    Full Text Available Steganographic tools and techniques are becoming more potential and widespread. Illegal use of steganography poses serious challenges to the law enforcement agencies. Limited work has been carried out on supervised steganalysis using neural network as a classifier. We present a combined method of identifying the presence of covert information in a carrier image using fisher’s linear discriminant (FLD function followed by the radial basis function (RBF. Experiments show promising results when compared to the existing supervised steganalysis methods, but arranging the retrieved information is still a challenging problem.

  4. Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHENG Xin; CHEN Tian-Lun

    2003-01-01

    In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear timeseries, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-meansclustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from thelocal minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glassequation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting resultsare obtained.

  5. Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHENGXin; CHENTian-Lun

    2003-01-01

    In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear time series, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-means clustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from the local minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glass equation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting results are obtained.

  6. Neuronal spike sorting based on radial basis function neural networks

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

    Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

  7. An Efficient Weather Forecasting System using Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Tiruvenkadam Santhanam

    2011-01-01

    Full Text Available Problem statement: Accurate weather forecasting plays a vital role for planning day to day activities. Neural network has been use in numerous meteorological applications including weather forecasting. Approach: A neural network model has been developed for weather forecasting, based on various factors obtained from meteorological experts. This study evaluates the performance of Radial Basis Function (RBF with Back Propagation (BPN neural network. The back propagation neural network and radial basis function neural network were used to test the performance in order to investigate effective forecasting technique. Results: The prediction accuracy of RBF was 88.49%. Conclusion: The results indicate that proposed radial basis function neural network is better than back propagation neural network.

  8. Satisfiability of logic programming based on radial basis function neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

    2014-07-10

    In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.

  9. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  10. The Rate of Approximation of Gaussian Radial Basis Neural Networks in Continuous Function Space

    Institute of Scientific and Technical Information of China (English)

    Ting Fan XIE; Fei Long CAO

    2013-01-01

    There have been many studies on the dense theorem of approximation by radial basis feedforword neural networks,and some approximation problems by Gaussian radial basis feedforward neural networks (GRBFNs) in some special function space have also been investigated.This paper considers the approximation by the GRBFNs in continuous function space.It is proved that the rate of approximation by GRNFNs with nd neurons to any continuous function f defined on a compact subset K (C) Rd can be controlled by ω(f,n-1/2),where ω(f,t) is the modulus of continuity of the function f.

  11. Radial basis function neural network for power system load-flow

    International Nuclear Information System (INIS)

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  12. Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network

    Science.gov (United States)

    Gómez-Mayorga, Margarita E.; Gallegos-Funes, Francisco J.; de-La-Rosa-Vázquez, José M.; Cruz-Santiago, Rene; Ponomaryov, Volodymyr

    The automatic analysis of Pap smear microscopic images is one of the most interesting fields in biomedical image processing. In this paper we present the capability of the Median M-Type Radial Basis Function (MMRBF) neural network in the classification of cervical cancer cells. From simulation results we observe that the MMRBF neural network has better classification capabilities in comparison with the Median RBF algorithm used as comparative.

  13. Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Wei Liu

    2013-01-01

    Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.

  14. Computing single step operators of logic programming in radial basis function neural networks

    International Nuclear Information System (INIS)

    Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks

  15. Computing single step operators of logic programming in radial basis function neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

    2014-07-10

    Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

  16. On the use of back propagation and radial basis function neural networks in surface roughness prediction

    Science.gov (United States)

    Markopoulos, Angelos P.; Georgiopoulos, Sotirios; Manolakos, Dimitrios E.

    2016-03-01

    Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, namely the adaptive back propagation algorithm of the steepest descent with the use of momentum term, the back propagation Levenberg-Marquardt algorithm and the back propagation Bayesian algorithm. Moreover, radial basis function neural networks are examined. All the aforementioned algorithms are used for the prediction of surface roughness in milling, trained with the same input parameters and output data so that they can be compared. The advantages and disadvantages, in terms of the quality of the results, computational cost and time are identified. An algorithm for the selection of the spread constant is applied and tests are performed for the determination of the neural network with the best performance. The finally selected neural networks can satisfactorily predict the quality of the manufacturing process performed, through simulation and input-output surfaces for combinations of the input data, which correspond to milling cutting conditions.

  17. Three Phase Induction Motor Faults Detection by Using Radial Basis Function Neural Network

    OpenAIRE

    Abd Alla, Ahmed N.

    2006-01-01

    In the present study the Artificial Neural Network (ANN) technique for the detection of (bearing and stator inter turn faults) incipient faults in an induction motor bas been explored. Radial basis function approach has been used for ANN Training and test. Three phase instantaneous currents and angular velocity depending on rotor speed are utilized in proposed approach. An experimental setup is used to implement an online fault defector

  18. Research on motion compensation method based on neural network of radial basis function

    Institute of Scientific and Technical Information of China (English)

    Zuo Yunbo

    2014-01-01

    The machining precision not only depends on accurate mechanical structure but also depends on motion compensation method. If manufacturing precision of mechanical structure cannot be improved, the motion compensation is a reasonable way to improve motion precision. A motion compensation method based on neural network of radial basis function (RBF) was presented in this paper. It utilized the infinite approximation advantage of RBF neural network to fit the motion error curve. The best hidden neural quantity was optimized by training the motion error data and calculating the total sum of squares. The best curve coefficient matrix was got and used to calculate motion compensation values. The experiments showed that the motion errors could be reduced obviously by utilizing the method in this paper.

  19. Improved Radio Frequency Identification Indoor Localization Method via Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Dongliang Guo

    2014-01-01

    Full Text Available Indoor localization technique has received much attention in recent years. Many techniques have been developed to solve the problem. Among the recent proposed methods, radio frequency identification (RFID indoor localization technology has the advantages of low-cost, noncontact, non-line-of-sight, and high precision. This paper proposed two radial basis function (RBF neural network based indoor localization methods. The RBF neural networks are trained to learn the mapping relationship between received signal strength indication values and position of objects. Traditional method used the received signal strength directly as the input of neural network; we added another input channel by taking the difference of the received signal strength, thus improving the reliability and precision of positioning. Fuzzy clustering is used to determine the center of radial basis function. In order to reduce the impact of signal fading due to non-line-of-sight and multipath transmission in indoor environment, we improved the Gaussian filter to process received signal strength values. The experimental results show that the proposed method outperforms the existing methods as well as improves the reliability and precision of the RFID indoor positioning system.

  20. DENSENESS OF RADIAL-BASIS FUNCTIONS IN L2(Rn) AND ITS APPLICATIONS IN NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    CHENTIANPING; CHENHONG

    1996-01-01

    The authors discuss problems of approximation to functions in L2 (Rn)and operators from L2(Rn1)to L2(Rn2)by Radial-Basis Functions. The results obtained solve the parblem of capability of RBF neural networks,a basic problem in neural networks.

  1. Identification of tartrazine and sunset yellow by fluorescence spectroscopy combined with radial basis function neural network

    Institute of Scientific and Technical Information of China (English)

    Jun Wang; Guoqing Chen; Tuo Zhu; Shumei Gao; Bailin Wei; Linna Bi

    2009-01-01

    @@ The fluorescence spectra of synthetic food dyes of sunset yellow and tartrazine are analyzed.The fluorescence peak wavelengths of sunset yellow and tartrazine are 576 and 569 nm, respectively, while the fluorescence spectra widths are 480-750 and 500-750 nm induced by ultraviolet light between 310-400 nm.The fluorescence spectra of sunset yellow overlap heavily with those of tartrazine, so it is diffic ult to distinguish them.Based on the principle of radial basis function neural network, a neural network is obtained from the training of the 14 groups of experimental data.The results show that the species of sunset yellow and tartrazine could be recognized accurately.This method has potential applications in other synthetic food dyes detection and food safety inspection.

  2. Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Xinyu Wei

    2016-01-01

    Full Text Available Pellet-clad interaction (PCI is one of the major issues in fuel rod design and reactor core operation in water cooled reactors. The prediction of fuel rod failure by PCI is studied in this paper by the method of radial basis function neural network (RBFNN. The neural network is built through the analysis of the existing experimental data. It is concluded that it is a suitable way to reduce the calculation complexity. A self-organized RBFNN is used in our study, which can vary its structure dynamically in order to maintain the prediction accuracy. For the purpose of the appropriate network complexity and overall computational efficiency, the hidden neurons in the RBFNN can be changed online based on the neuron activity and mutual information. The presented method is tested by the experimental data from the reference, and the results demonstrate its effectiveness.

  3. Simulation of Peak Ground Acceleration by Artificial Neural Network and Radial Basis Function Network

    Directory of Open Access Journals (Sweden)

    Ali Nasrollahnejad

    2014-10-01

    Full Text Available Recording of ground motions with high amplitudes of acceleration and velocity play a key role for designing engineering projects. Here we try to represent a reasonable prediction of peak ground acceleration which may create more than g acceleration in different regions. In this study, applying different structures of Neural Networks (NN and using four key parameters, moment magnitude, rupture distance, site class, and style of faulting which an earthquake may cause serious effects on a site. We introduced a radial basis function network (RBF with mean error of 0.014, as the best network for estimating the occurrence probability of an earthquake with large value of PGA ≥1g in a region. Also the results of applying back propagation in feed forward neural network (FFBP show a good coincidence with designed RBF results for predicting high value of PGA, with Mean error of 0.017.

  4. On-line Cutting Quality Recognition in Milling Using a Radical Basis Function Neural Network

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Tool wear, chatter vibration, chip breaking and built-up edge are main phenomena to be monitored in modern manufacturing processes, which are considered as important factors to the quality of products.They are closely related to the cutting parameters, which are to be selected in manufacturing process.However, it is very difficult to measure directly the cutting quality based on on-line monitoring.In this study, the relationship between the cutting parameters and cutting quality is analyzed.A Radical Basis Function (RBF) neural network based on-line quality recognition scheme is also presented, which monitors the level of surface roughness.The experimental results reveal that the RBF neural network has a high prediction success rate.

  5. Radial basis function neural networks with sequential learning MRAN and its applications

    CERN Document Server

    Sundararajan, N; Wei Lu Ying

    1999-01-01

    This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of t

  6. Noise reduction technique for images using radial basis function neural networks

    International Nuclear Information System (INIS)

    This paper presents a NN (Neural Network) based model for reducing the noise from images. This is a RBF (Radial Basis Function) network which is used to reduce the effect of noise and blurring from the captured images. The proposed network calculates the mean MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) of the noisy images. The proposed network has also been successfully applied to medical images. The performance of the trained RBF network has been compared with the MLP (Multilayer Perceptron) Network and it has been demonstrated that the performance of the RBF network is better than the MLP network. (author)

  7. Design of Radial Basis Function Neural Networks for Software Effort Estimation

    Directory of Open Access Journals (Sweden)

    Ali Idri

    2010-07-01

    Full Text Available In spite of the several software effort estimation models developed over the last 30 years, providing accurate estimates of the software project under development is still unachievable goal. Therefore, many researchers are working on the development of new models and the improvement of the existing ones using artificial intelligence techniques such as: case-based reasoning, decision trees, genetic algorithms and neural networks. This paper is devoted to the design of Radial Basis Function Networks for software cost estimation. It shows the impact of the RBFN network structure, especially the number of neurons in the hidden layer and the widths of the basis function, on the accuracy of the produced estimates measured by means of MMRE and Pred indicators. The empirical study uses two different software project datasets namely, artificial COCOMO'81 and Tukutuku datasets.

  8. Circular antenna array pattern analysis using radial basis function neural network

    International Nuclear Information System (INIS)

    A method is proposed to design circular antenna array for the given gain and beam width using Artificial Neural Networks. In optimizing circular arrays, the parameters to be controlled are excitation of the elements, their separation, lengths and the circle radius. This paper deals about finding the parameters of radiation pattern of given uniform circular antenna array. Initially, the network is trained with a set of input-output data pairs. The trained network is used for testing. The training data set is generated from MATLAB simulation with number of elements N=5, 10, 15 and 20 elements of uniform circular array, respectively, distributed over a given circle, assuming 20 training cases. The number of input nodes, hidden nodes and output nodes are 20, 20 and 1, respectively. Predicted values of the neural network are compared with those of MATLAB simulation results and are found to be in agreement. This work establishes the application of Radial Basis Function Neural Network (RBFNN) for circular array pattern optimization. RBFNN is able to predict the output values with 97% of accuracy. This work proves that RBFNN can be used for circular antenna array design.

  9. Radial basis function process neural network training based on generalized frechet distance and GA-SA hybrid strategy

    OpenAIRE

    Wang, Bing; Meng, Yao-hua; Yu, Xiao-Hong

    2014-01-01

    For learning problem of Radial Basis Function Process Neural Network (RBF-PNN), an optimization training method based on GA combined with SA is proposed in this paper. Through building generalized Fr\\'echet distance to measure similarity between time-varying function samples, the learning problem of radial basis centre functions and connection weights is converted into the training on corresponding discrete sequence coefficients. Network training objective function is constructed according to...

  10. Hybrid model decomposition of speech and noise in a radial basis function neural model framework

    DEFF Research Database (Denmark)

    Sørensen, Helge Bjarup Dissing; Hartmann, Uwe

    1994-01-01

    The aim of the paper is to focus on a new approach to automatic speech recognition in noisy environments where the noise has either stationary or non-stationary statistical characteristics. The aim is to perform automatic recognition of speech in the presence of additive car noise. The technique...... applied is based on a combination of the hidden Markov model (HMM) decomposition method, for speech recognition in noise, developed by Varga and Moore (1990) from DRA and the hybrid (HMM/RBF) recognizer containing hidden Markov models and radial basis function (RBF) neural networks, developed by Singer...... and Lippmann (1992) from MIT Lincoln Lab. The present authors modified the hybrid recognizer to fit into the decomposition method to achieve high performance speech recognition in noisy environments. The approach has been denoted the hybrid model decomposition method and it provides an optimal method...

  11. Radial Basis Function Neural Network Based Super-Resolution Restoration for an Underspled Image

    Institute of Scientific and Technical Information of China (English)

    苏秉华; 金伟其; 牛丽红

    2004-01-01

    To achieve restoration of high frequency information for an underspled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an underspled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an underspled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.

  12. Radial Basis Function Neural Networks Based QSPR for the Prediction of log P

    Institute of Scientific and Technical Information of China (English)

    YAO,Xiao-Jun(姚小军); LIU,Man-Cang(刘满仓); ZHANG,Xiao-Yun(张晓昀); ZHANG,Rui-Sheng(张瑞生); HU,Zhi-De(胡之德); FAN,Bo-Tao(范波涛)

    2002-01-01

    Quantitative structure-property relatioonship (QSPR) method is used to study the correlation models between the structures of a set of diverse organic compounds and their log P. Molecular descriptors calculated from structure alone are used to describe the molecular structures. A subset of the calculated descriptors, selected using forward stepwise regression, is used in the QSPR models development. Multiple linear regression (MLR)and radial basis function neural networks (RBFNNs) are urilized to construct the linear and non-linear correlation model,respectively. The optimal QSPR model developedis based on a 7-17-1 RBFNNs architecture using seven calculated molecular descriptors. The root mean square errorsin predictions for the training, predicting and overall data sets are 0.284, 0.327 and 0.291 log P units, respectively.

  13. Radial Basis Function Neural Networks Based QSPR for the Prediction of log P

    Institute of Scientific and Technical Information of China (English)

    姚小军; 范波涛; 等

    2002-01-01

    Quantitative structure-property relationship(QSPR) method is used to study the correlation models between the structures of a set of diverse organic compounds and their log P.Molecular descriptors calculated from strucure alone are used to describe the molecular structures.A subset of the calcualted descriptors,selected using forward stepwise regression,is used in the QSPR models development.Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) are utilied to construct the linear and non-linear correlation model,respectively,The optimal QSPR model developed is based on a 7-17-1 RBFNNs architecture using sever calculated molecular descriptors .The root mean square errors in predictions for the training,predicting and overall data sets are 0.284,0.327 and 0.291 log P units respectively.

  14. On-line Transient Stability Assessment through Generator Rotor Angles Prediction Using Radial Basis Function Neural Network

    OpenAIRE

    Shahbaz A. Siddiqui; Kusum Verma; K. R. Niazi; Manoj Fozdar

    2014-01-01

    On-line Transient Stability Assessment (TSA) is challenging task due to the large number of variables involved and continuously varying operating conditions. This study proposes an on-line transient stability assessment methodology based on the predicted values of generator rotor angles under varying operating conditions for predefined contingency set through Radial Basis Function Neural Network (RBFNN). The real and reactive power loads are taken as input features for training of the neural ...

  15. Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm.

    Science.gov (United States)

    Lu, Y; Sundararajan, N; Saratchandran, P

    1998-01-01

    This paper presents a detailed performance analysis of the minimal resource allocation network (M-RAN) learning algorithm, M-RAN is a sequential learning radial basis function neural network which combines the growth criterion of the resource allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RAN. The performance of this algorithm is compared with the multilayer feedforward networks (MFNs) trained with 1) a variant of the standard backpropagation algorithm, known as RPROP and 2) the dependence identification (DI) algorithm of Moody and Antsaklis on several benchmark problems in the function approximation and pattern classification areas. For all these problems, the M-RAN algorithm is shown to realize networks with far fewer hidden neurons with better or same approximation/classification accuracy. Further, the time taken for learning (training) is also considerably shorter as M-RAN does not require repeated presentation of the training data. PMID:18252454

  16. [Automated recognition of quasars based on adaptive radial basis function neural networks].

    Science.gov (United States)

    Zhao, Mei-Fang; Luo, A-Li; Wu, Fu-Chao; Hu, Zhan-Yi

    2006-02-01

    Recognizing and certifying quasars through the research on spectra is an important method in the field of astronomy. This paper presents a novel adaptive method for the automated recognition of quasars based on the radial basis function neural networks (RBFN). The proposed method is composed of the following three parts: (1) The feature space is reduced by the PCA (the principal component analysis) on the normalized input spectra; (2) An adaptive RBFN is constructed and trained in this reduced space. At first, the K-means clustering is used for the initialization, then based on the sum of squares errors and a gradient descent optimization technique, the number of neurons in the hidden layer is adaptively increased to improve the recognition performance; (3) The quasar spectra recognition is effectively carried out by the above trained RBFN. The author's proposed adaptive RBFN is shown to be able to not only overcome the difficulty of selecting the number of neurons in hidden layer of the traditional RBFN algorithm, but also increase the stability and accuracy of recognition of quasars. Besides, the proposed method is particularly useful for automatic voluminous spectra processing produced from a large-scale sky survey project, such as our LAMOST, due to its efficiency. PMID:16826929

  17. Motion planning for autonomous vehicle based on radial basis function neural network in unstructured environment.

    Science.gov (United States)

    Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao

    2014-01-01

    The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality. PMID:25237902

  18. Motion Planning for Autonomous Vehicle Based on Radial Basis Function Neural Network in Unstructured Environment

    Directory of Open Access Journals (Sweden)

    Jiajia Chen

    2014-09-01

    Full Text Available The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.

  19. Analysis of CT Brain Images using Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    T. Joshva Devadas

    2012-07-01

    Full Text Available Medical image processing and analysis is the tool to assist radiologists in the diagnosis process to obtain a more accurate and faster diagnosis. In this work, we have developed a neural network to classify the computer tomography (CT brain tumor image for automatic diagnosis. This system is divided into four steps namely enhancement, segmentation, feature extraction and classification. In the first phase, an edge-based selective median filter is used to improve the visibility of the loss of the gray-white matter interface in CT brain tumor images. Second phase uses a modified version of shift genetic algorithm for the segmentation. Next phase extracts the textural features using statistical texture analysis method. These features are fed into classifiers like BPN, Fuzzy k-NN, and radial basis function network. The performances of these classifiers are analyzed in the final phase with receiver operating characteristic and precision-recall curve. The result shows that the CAD system is only to develop the tool for brain tumor and proposed method is very accurate and computationally more efficient and less time consuming.

  20. Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

    Science.gov (United States)

    Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

    2015-09-01

    In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. PMID:26163042

  1. A radial basis function neural network based on artificial immune systems for remote sensing image classification

    Science.gov (United States)

    Yan, Qin; Zhong, Yanfei

    2008-12-01

    The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre

  2. Specific neural basis of Chinese idioms processing: an event-related functional MRI study

    International Nuclear Information System (INIS)

    Objective: To address the neural basis of Chinese idioms processing with different kinds of stimuli using an event-related fMRI design. Methods: Sixteen native Chinese speakers were asked to perform a semantic decision task during fMRI scanning. Three kinds of stimuli were used: Real idioms (Real-idiom condition); Literally plausible phrases (Pseudo-idiom condition, the last character of a real idiom was replaced by a character with similar meaning); Literally implausible strings (Non-idiom condition, the last character of a real idiom was replaced by a character with unrelated meaning). Reaction time and correct rate were recorded at the same time. Results: The error rate was 2.6%, 5.2% and 0.9% (F=3.51, P0.05) for real idioms, pseudo-idioms and wrong idioms, respectively. Similar neural network was activated in all of the three conditions. However, the right hippocampus was only activated in the real idiom condition, and significant activations were found in anterior portion of left inferior frontal gyms (BA47) in real-and pseudo-idiom conditions, but not in non-idiom condition. Conclusion: The right hippocampus plays a specific role in the particular wording of the Chinese idioms. And the left anterior inferior frontal gyms (BA47) may be engaged in the semantic processing of Chinese idioms. The results support the notion that there were specific neural bases for Chinese idioms processing. (authors)

  3. Evolutionary Basis of Human Running and Its Impact on Neural Function.

    Science.gov (United States)

    Schulkin, Jay

    2016-01-01

    Running is not unique to humans, but it is seemingly a basic human capacity. This article addresses the evolutionary origins of humans running long distances, the basic physical capability of running, and the neurogenesis of aerobic fitness. This article more specifically speaks to the conditions that set the stage for the act of running, and then looks at brain expression, and longer-term consequences of running within a context of specific morphological features and diverse information molecules that participate in our capacity for running and sport. While causal factors are not known, we do know that physiological factors are involved in running and underlie neural function. Multiple themes about running are discussed in this article, including neurogenesis, neural plasticity, and memory enhancement. Aerobic exercise increases anterior hippocampus size. This expansion is linked to the improvement of memory, which reflects the improvement of learning as a function of running activity in animal studies. Higher fitness is associated with greater expansion, not only of the hippocampus, but of several other brain regions. PMID:27462208

  4. Supervised Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Vidnerová, Petra

    Los Alamitos : IEEE Computer Society, 2008, s. 193-196. ISBN 978-1-4244-3430-5. [SIP 2008. International Symposium on Signal Processing, Image Processing and Pattern Recognition /1./. Hainan Island (CN), 13.12.2008-15.12.2008] R&D Projects: GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : regularization * radial basis function * training error Subject RIV: IN - Informatics, Computer Science

  5. Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study

    International Nuclear Information System (INIS)

    Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported

  6. Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

    Science.gov (United States)

    Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

    2010-02-01

    Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

  7. On-line Transient Stability Assessment through Generator Rotor Angles Prediction Using Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Shahbaz A. Siddiqui

    2014-10-01

    Full Text Available On-line Transient Stability Assessment (TSA is challenging task due to the large number of variables involved and continuously varying operating conditions. This study proposes an on-line transient stability assessment methodology based on the predicted values of generator rotor angles under varying operating conditions for predefined contingency set through Radial Basis Function Neural Network (RBFNN. The real and reactive power loads are taken as input features for training of the neural network. Principal Component Analysis (PCA is used for dimensionality reduction of the input data set to select informative features. The proposed method is tested on IEEE-39 bus test system and the results obtained for transient stability assessment through predicted rotor angles are promising.

  8. The neural basis of bounded rational behavior

    OpenAIRE

    Coricelli, Giorgio; Nagel, Rosemarie

    2010-01-01

    Bounded rational behaviour is commonly observed in experimental games and in real life situations. Neuroeconomics can help to understand the mental processing underlying bounded rationality and out-of-equilibrium behaviour. Here we report results from recent studies on the neural basis of limited steps of reasoning in a competitive setting —the beauty contest game. We use functional magnetic resonance imaging (fMRI) to study the neural correlates of human mental processes in strategic games. ...

  9. Analysis of CT Brain Images using Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    T. Joshva Devadas

    2012-07-01

    Full Text Available Medical image processing and analysis is the tool to assist radiologists in the diagnosis process to obtain a moreaccurate and faster diagnosis. In this work, we have developed a neural network to classify the computer tomography(CT brain tumor image for automatic diagnosis. This system is divided into four steps namely enhancement, segmentation, feature extraction and classification. In the first phase, an edge-based selective median filter is usedto improve the visibility of the loss of the gray-white matter interface in CT brain tumor images. Second phaseuses a modified version of shift genetic algorithm for the segmentation. Next phase extracts the textural featuresusing statistical texture analysis method. These features are fed into classifiers like BPN, Fuzzy k-NN, and radialbasis function network. The performances of these classifiers are analyzed in the final phase with receiver operating characteristic and precision-recall curve. The result shows that the CAD system is only to develop the tool for braintumor and proposed method is very accurate and computationally more efficient and less time consuming.Defence Science Journal, 2012, 62(4, pp.212-218, DOI:http://dx.doi.org/10.14429/dsj.62.1830

  10. Iterative Radial Basis Functions Neural Networks as Metamodels of Stochastic Simulations of the Quality of Search Engines in the World Wide Web.

    Science.gov (United States)

    Meghabghab, George

    2001-01-01

    Discusses the evaluation of search engines and uses neural networks in stochastic simulation of the number of rejected Web pages per search query. Topics include the iterative radial basis functions (RBF) neural network; precision; response time; coverage; Boolean logic; regression models; crawling algorithms; and implications for search engine…

  11. Radial and Sigmoid Basis Function Neural Networks in Wireless Sensor Routing Topology Control in Underground Mine Rescue Operation Based on Particle Swarm Optimization

    OpenAIRE

    Mary Opokua Ansong; Hong-Xing Yao; Jun Steed Huang

    2013-01-01

    The performance of a proposed compact radial basis function was compared with the sigmoid basis function and the gaussian-radial basis function neural networks in 3D wireless sensor routing topology control, in underground mine rescue operation. Optimised errors among other parameters were examined in addition to scalability and time efficiency. To make the routing path efficient in emergency situations, the sensor sequence and deployment as well as transmission range were carefully considere...

  12. Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Vidnerová, Petra

    2009-01-01

    Roč. 1, č. 2 (2009), s. 49-57. ISSN 2005-4262 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network * RBF networks * regularization * learning Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJGDC/vol2_no1/5.pdf

  13. The Application of Direction Basis Function Neural Networks to the Prediction of Chaotic Time Series

    Institute of Scientific and Technical Information of China (English)

    CAOWenming

    2004-01-01

    In this paper we have examined the ability of Direction basis function networks (DBFN) to predict the output of a chaotic time series generated from a model of a physical system. DBFNs are known to be universal approximators, and chaotic systems are known to exhibit “random” behavior. Therefore the challenge is to apply the DBFN to the prediction of the output of a chaotic system, which we have chosen here to be the Mackey-Glass delay differential equation. The DBFN has been trained with off-line supervised learning using a Recursive Least Squares optimization for obtaining weights. Key issues which are addressed are the estimation of the order of the system and dependence of prediction error on various factors such as placement of DBF centers, selection of perceptive widths, and number of training samples. Included in this study is an implementation of Moody and Darken's K Means Clustering approach to optimally place DBF centers and a heuristic nearest neighbor method for determining perceptive widths.

  14. Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process: Hydrogen Recovery from Refinery Gases

    Institute of Scientific and Technical Information of China (English)

    Lei Wang; Cheng Shao; Hai Wang; Hong Wu

    2006-01-01

    Membrane technology has found wide applications in the petrochemical industry, mainly in the purification and recovery of the hydrogen resources. Accurate prediction of the membrane separation performance plays an important role in carrying out advanced process control (APC). For the first time, a soft-sensor model for the membrane separation process has been established based on the radial basis function (RBF) neural networks. The main performance parameters, i.e, permeate hydrogen concentration, permeate gas flux, and residue hydrogen concentration, are estimated quantitatively by measuring the operating temperature, feed-side pressure, permeate-side pressure, residue-side pressure, feed-gas flux, and feed-hydrogen concentration excluding flow structure, membrane parameters, and other compositions. The predicted results can gain the desired effects. The effectiveness of this novel approach lays a foundation for integrating control technology and optimizing the operation of the gas membrane separation process.

  15. R-Peak Detection using Daubechies Wavelet and ECG Signal Classification using Radial Basis Function Neural Network

    Science.gov (United States)

    Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.

    2014-01-01

    This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.

  16. Mutual connectivity analysis (MCA) using generalized radial basis function neural networks for nonlinear functional connectivity network recovery in resting-state functional MRI

    Science.gov (United States)

    D'Souza, Adora M.; Abidin, Anas Zainul; Nagarajan, Mahesh B.; Wismüller, Axel

    2016-03-01

    We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 +/- 0.037) as well as the underlying network structure (Rand index = 0.87 +/- 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

  17. Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction

    OpenAIRE

    2014-01-01

    This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW-PSO). Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW-PSO algorithm to determine the centers, widths, and connection weights of RBF neural network. To assess the performance of the proposed EDIW-PSO-RBF model, we choo...

  18. Design of radial basis function neural network classifier realized with the aid of data preprocessing techniques: design and analysis

    Science.gov (United States)

    Oh, Sung-Kwun; Kim, Wook-Dong; Pedrycz, Witold

    2016-05-01

    In this paper, we introduce a new architecture of optimized Radial Basis Function neural network classifier developed with the aid of fuzzy clustering and data preprocessing techniques and discuss its comprehensive design methodology. In the preprocessing part, the Linear Discriminant Analysis (LDA) or Principal Component Analysis (PCA) algorithm forms a front end of the network. The transformed data produced here are used as the inputs of the network. In the premise part, the Fuzzy C-Means (FCM) algorithm determines the receptive field associated with the condition part of the rules. The connection weights of the classifier are of functional nature and come as polynomial functions forming the consequent part. The Particle Swarm Optimization algorithm optimizes a number of essential parameters needed to improve the accuracy of the classifier. Those optimized parameters include the type of data preprocessing, the dimensionality of the feature vectors produced by the LDA (or PCA), the number of clusters (rules), the fuzzification coefficient used in the FCM algorithm and the orders of the polynomials of networks. The performance of the proposed classifier is reported for several benchmarking data-sets and is compared with the performance of other classifiers reported in the previous studies.

  19. [Neural basis of procedural memory].

    Science.gov (United States)

    Mochizuki-Kawai, Hiroko

    2008-07-01

    Procedural memory is acquired by trial and error. Our daily life is supported by a number of procedural memories such as those for riding bicycle, typing, reading words, etc. Procedural memory is divided into 3 types; motor, perceptual, and cognitive. Here, the author reviews the cognitive and neural basis of procedural memory according to these 3 types. It is reported that the basal ganglia or cerebellum dysfunction causes deficits in procedural memory. Compared with age-matched healthy participants, patients with Parkinson disease (PD), Huntington disease (HD) or spinocerebellar degeneration (SCD) show deterioration in improvements in motor-type procedural memory tasks. Previous neuroimaging studies have reported that motor-type procedural memory may be supported by multiple brain regions, including the frontal and parietal regions as well as the basal ganglia (cerebellum); this was found with a serial reaction time task (SRT task). Although 2 other types of procedural memory are also maintained by multiple brain regions, the related cerebral areas depend on the type of memory. For example, it was suggested that acquisition of the perceptual type of procedural memory (e.g., ability to read mirror images of words) might be maintained by the bilateral fusiform region, while the acquisition of cognitive procedural memory might be supported by the frontal, parietal, or cerebellar regions as well as the basal ganglia. In the future, we need to cleary understand the neural "network" related to the procedural memory. PMID:18646622

  20. Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

    Science.gov (United States)

    Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

    2013-03-01

    Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

  1. Time-varying functional connectivity for understanding the neural basis of behavioral microsleeps.

    Science.gov (United States)

    Toppi, J; Astolfi, L; Poudel, G R; Babiloni, F; Macchiusi, L; Mattia, D; Salinari, S; Jones, R D

    2012-01-01

    Episodes of complete failure to respond during attentive tasks--lapses of responsiveness ('lapses')--accompanied by behavioral signs of sleep such as slow-eye-closure are known as behavioral microsleeps (BMs). The occurrence of BMs can have serious/fatal consequences, particularly in the transport sectors, and therefore further investigations on neurophysiological correlates of BMs are highly desirable. In this paper we propose a combination of High Resolution EEG techniques and an advanced method for time-varying functional connectivity estimation for reconstructing the temporal evolution of causal relations between cortical regions of BMs occurring during a visuomotor tracking task. The preliminary results highlight connectivity patterns involving parietal and fronto-parietal areas both preceding and following the onset of a BM. PMID:23366979

  2. An expert system with radial basis function neural network based on decision trees for predicting sediment transport in sewers.

    Science.gov (United States)

    Ebtehaj, Isa; Bonakdari, Hossein; Zaji, Amir Hossein

    2016-01-01

    In this study, an expert system with a radial basis function neural network (RBF-NN) based on decision trees (DT) is designed to predict sediment transport in sewer pipes at the limit of deposition. First, sensitivity analysis is carried out to investigate the effect of each parameter on predicting the densimetric Froude number (Fr). The results indicate that utilizing the ratio of the median particle diameter to pipe diameter (d/D), ratio of median particle diameter to hydraulic radius (d/R) and volumetric sediment concentration (C(V)) as the input combination leads to the best Fr prediction. Subsequently, the new hybrid DT-RBF method is presented. The results of DT-RBF are compared with RBF and RBF-particle swarm optimization (PSO), which uses PSO for RBF training. It appears that DT-RBF is more accurate (R(2) = 0.934, MARE = 0.103, RMSE = 0.527, SI = 0.13, BIAS = -0.071) than the two other RBF methods. Moreover, the proposed DT-RBF model offers explicit expressions for use by practicing engineers. PMID:27386995

  3. Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials

    Science.gov (United States)

    Ai, Yuewei; Shao, Xinyu; Jiang, Ping; Li, Peigen; Liu, Yang; Yue, Chen

    2015-11-01

    The welded joints of dissimilar materials have been widely used in automotive, ship and space industries. The joint quality is often evaluated by weld seam geometry, microstructures and mechanical properties. To obtain the desired weld seam geometry and improve the quality of welded joints, this paper proposes a process modeling and parameter optimization method to obtain the weld seam with minimum width and desired depth of penetration for laser butt welding of dissimilar materials. During the process, Taguchi experiments are conducted on the laser welding of the low carbon steel (Q235) and stainless steel (SUS301L-HT). The experimental results are used to develop the radial basis function neural network model, and the process parameters are optimized by genetic algorithm. The proposed method is validated by a confirmation experiment. Simultaneously, the microstructures and mechanical properties of the weld seam generated from optimal process parameters are further studied by optical microscopy and tensile strength test. Compared with the unoptimized weld seam, the welding defects are eliminated in the optimized weld seam and the mechanical properties are improved. The results show that the proposed method is effective and reliable for improving the quality of welded joints in practical production.

  4. Neural plasticity in hypocretin neurons: the basis of hypocretinergic regulation of physiological and behavioral functions in animals

    OpenAIRE

    Xiao-Bing Gao; Gretchen Hermes

    2015-01-01

    The neuronal system that resides in the perifornical and lateral hypothalamus (Pf/LH) and synthesizes the neuropeptide hypocretin/orexin participates in critical brain functions across species from fish to human. The hypocretin system regulates neural activity responsible for daily functions (such as sleep/wake homeostasis, energy balance, appetite, etc.) and long-term behavioral changes (such as reward seeking and addiction, stress response, etc.) in animals. The most recent evidence suggest...

  5. Neural Basis of Interpersonal Traits in Neurodegenerative Diseases

    OpenAIRE

    Sollberger, Marc; Stanley, Christine M.; Wilson, Stephen M; Gyurak, Anett; Beckman, Victoria; Growdon, Matthew; Jang, Jung; Weiner, Michael W.; Miller, Bruce L.; Rankin, Katherine P.

    2009-01-01

    Several functional and structural imaging studies have investigated the neural basis of personality in healthy adults, but human lesions studies are scarce. Personality changes are a common symptom in patients with neurodegenerative diseases like frontotemporal dementia (FTD) and semantic dementia (SD), allowing a unique window into the neural basis of personality. In this study, we used the Interpersonal Adjective Scales to investigate the structural basis of eight interpersonal traits (domi...

  6. Neural Basis of Visual Distraction

    Science.gov (United States)

    Kim, So-Yeon; Hopfinger, Joseph B.

    2010-01-01

    The ability to maintain focus and avoid distraction by goal-irrelevant stimuli is critical for performing many tasks and may be a key deficit in attention-related problems. Recent studies have demonstrated that irrelevant stimuli that are consciously perceived may be filtered out on a neural level and not cause the distraction triggered by…

  7. Neural plasticity in hypocretin neurons: the basis of hypocretinergic regulation of physiological and behavioral functions in animals

    Directory of Open Access Journals (Sweden)

    Xiao-Bing eGao

    2015-10-01

    Full Text Available The neuronal system that resides in the perifornical and lateral hypothalamus (Pf/LH and synthesizes the neuropeptide hypocretin/orexin participates in critical brain functions across species from fish to human. The hypocretin system regulates neural activity responsible for daily functions (such as sleep/wake homeostasis, energy balance, appetite, etc and long-term behavioral changes (such as reward seeking and addiction, stress response, etc in animals. The most recent evidence suggests that the hypocretin system undergoes substantial plastic changes in response to both daily fluctuations (such as food intake and sleep-wake regulation and long-term changes (such as cocaine seeking in neuronal activity in the brain. The understanding of these changes in the hypocretin system is essential in addressing the role of the hypocretin system in normal physiological functions and pathological conditions in animals and humans. In this review, the evidence demonstrating that neural plasticity occurs in hypocretin-containing neurons in the Pf/LH will be presented and possible physiological behavioral, and mental health implications of these findings will be discussed.

  8. The neural basis of the imitation drive.

    Science.gov (United States)

    Hanawa, Sugiko; Sugiura, Motoaki; Nozawa, Takayuki; Kotozaki, Yuka; Yomogida, Yukihito; Ihara, Mizuki; Akimoto, Yoritaka; Thyreau, Benjamin; Izumi, Shinichi; Kawashima, Ryuta

    2016-01-01

    Spontaneous imitation is assumed to underlie the acquisition of important skills by infants, including language and social interaction. In this study, functional magnetic resonance imaging (fMRI) was used to examine the neural basis of 'spontaneously' driven imitation, which has not yet been fully investigated. Healthy participants were presented with movie clips of meaningless bimanual actions and instructed to observe and imitate them during an fMRI scan. The participants were subsequently shown the movie clips again and asked to evaluate the strength of their 'urge to imitate' (Urge) for each action. We searched for cortical areas where the degree of activation positively correlated with Urge scores; significant positive correlations were observed in the right supplementary motor area (SMA) and bilateral midcingulate cortex (MCC) under the imitation condition. These areas were not explained by explicit reasons for imitation or the kinematic characteristics of the actions. Previous studies performed in monkeys and humans have implicated the SMA and MCC/caudal cingulate zone in voluntary actions. This study also confirmed the functional connectivity between Urge and imitation performance using a psychophysiological interaction analysis. Thus, our findings reveal the critical neural components that underlie spontaneous imitation and provide possible reasons why infants imitate spontaneously. PMID:26168793

  9. A radial basis function neural network approach to determine the survival of Listeria monocytogenes in Katiki, a traditional Greek soft cheese.

    Science.gov (United States)

    Panagou, Efstathios Z

    2008-04-01

    A radial basis function neural network was developed to determine the kinetic behavior of Listeria monocytogenes in Katiki, a traditional white acid-curd soft spreadable cheese. The applicability of the neural network approach was compared with the reparameterized Gompertz, the modified Weibull, and the Geeraerd primary models. Model performance was assessed with the root mean square error of the residuals of the model (RMSE), the regression coefficient (R2), and the F test. Commercially prepared cheese samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 10(6) CFU g(-1) and stored at 5, 10, 15, and 20 degrees C for 40 days. At each storage temperature, a pathogen viability loss profile was evident and included a shoulder, a log-linear phase, and a tailing phase. The developed neural network described the survival of L. monocytogenes equally well or slightly better than did the three primary models. The performance indices for the training subset of the network were R2 = 0.993 and RMSE = 0.214. The relevant mean values for all storage temperatures were R2 = 0.981, 0.986, and 0.985 and RMSE = 0.344, 0.256, and 0.262 for the reparameterized Gompertz, modified Weibull, and Geeraerd models, respectively. The results of the F test indicated that none of the primary models were able to describe accurately the survival of the pathogen at 5 degrees C, whereas with the neural network all fvalues were significant. The neural network and primary models all were validated under constant temperature storage conditions (12 and 17 degrees C). First or second order polynomial models were used to relate the inactivation parameters to temperature, whereas the neural network was used a one-step modeling approach. Comparison of the prediction capability was based on bias and accuracy factors and on the goodness-of-fit index. The prediction performance of the neural network approach was equal to that of the primary

  10. Detection of bearing defects in three-phase induction motors using Park’s transform and radial basis function neural networks

    Indian Academy of Sciences (India)

    Izzet Y Önel; K Burak Dalci; İbrahim Senol

    2006-06-01

    This paper investigates the application of induction motor stator current signature analysis (MCSA) using Park’s transform for the detection of rolling element bearing damages in three-phase induction motor. The paper first discusses bearing faults and Park’s transform, and then gives a brief overview of the radial basis function (RBF) neural networks algorithm. Finally, system information and the experimental results are presented. Data acquisition and Park’s transform algorithm are achieved by using LabVIEW and the neural network algorithm is achieved by using MATLAB programming language. Experimental results show that it is possible to detect bearing damage in induction motors using an ANN algorithm.

  11. The neural basis of bounded rational behavior

    Directory of Open Access Journals (Sweden)

    Coricelli, Giorgio

    2012-03-01

    Full Text Available Bounded rational behaviour is commonly observed in experimental games and in real life situations. Neuroeconomics can help to understand the mental processing underlying bounded rationality and out-of-equilibrium behaviour. Here we report results from recent studies on the neural basis of limited steps of reasoning in a competitive setting —the beauty contest game. We use functional magnetic resonance imaging (fMRI to study the neural correlates of human mental processes in strategic games. We apply a cognitive hierarchy model to classify subject’s choices in the experimental game according to the degree of strategic reasoning so that we can identify the neural substrates of different levels of strategizing. We found a correlation between levels of strategic reasoning and activity in a neural network related to mentalizing, i.e. the ability to think about other’s thoughts and mental states. Moreover, brain data showed how complex cognitive processes subserve the higher level of reasoning about others. We describe how a cognitive hierarchy model fits both behavioural and brain data.

    La racionalidad limitada es un fenómeno observado de manera frecuente tanto en juegos experimentales como en situaciones cotidianas. La Neuroeconomía puede mejorar la comprensión de los procesos mentales que caracterizan la racionalidad limitada; en paralelo nos puede ayudar a comprender comportamientos que violan el equilibrio. Nuestro trabajo presenta resultados recientes sobre la bases neuronales del razonamiento estratégico (y sus límite en juegos competitivos —como el juego del “beauty contest”. Estudiamos las bases neuronales del comportamiento estratégico en juegos con interacción entre sujetos usando resonancia magnética funcional (fMRI. Las decisiones de los participantes se clasifican acorde al grado de razonamiento estratégico: el llamado modelo de Jerarquías Cognitivas. Los resultados muestran una correlación entre niveles de

  12. The neural basis of hand gesture comprehension: A meta-analysis of functional magnetic resonance imaging studies.

    Science.gov (United States)

    Yang, Jie; Andric, Michael; Mathew, Mili M

    2015-10-01

    Gestures play an important role in face-to-face communication and have been increasingly studied via functional magnetic resonance imaging. Although a large amount of data has been provided to describe the neural substrates of gesture comprehension, these findings have never been quantitatively summarized and the conclusion is still unclear. This activation likelihood estimation meta-analysis investigated the brain networks underpinning gesture comprehension while considering the impact of gesture type (co-speech gestures vs. speech-independent gestures) and task demand (implicit vs. explicit) on the brain activation of gesture comprehension. The meta-analysis of 31 papers showed that as hand actions, gestures involve a perceptual-motor network important for action recognition. As meaningful symbols, gestures involve a semantic network for conceptual processing. Finally, during face-to-face interactions, gestures involve a network for social emotive processes. Our finding also indicated that gesture type and task demand influence the involvement of the brain networks during gesture comprehension. The results highlight the complexity of gesture comprehension, and suggest that future research is necessary to clarify the dynamic interactions among these networks. PMID:26271719

  13. The neural basis of phantom limb pain.

    Science.gov (United States)

    Flor, Herta; Diers, Martin; Andoh, Jamila

    2013-07-01

    A recent study suggests that brain changes in amputees may be pain-induced, questioning maladaptive plasticity as a neural basis of phantom pain. These findings add valuable information on cortical reorganization after amputation. We suggest further lines of research to clarify the mechanisms that underlie phantom pain. PMID:23608362

  14. Functional gene polymorphisms in the serotonin system and traumatic life events modulate the neural basis of fear acquisition and extinction.

    Directory of Open Access Journals (Sweden)

    Andrea Hermann

    Full Text Available Fear acquisition and extinction are crucial mechanisms in the etiology and maintenance of anxiety disorders. Moreover, they might play a pivotal role in conveying the influence of genetic and environmental factors on the development of a (more or less stronger proneness for, or resilience against psychopathology. There are only few insights in the neurobiology of genetically and environmentally based individual differences in fear learning and extinction. In this functional magnetic resonance imaging study, 74 healthy subjects were investigated. These were invited according to 5-HTTLPR/rs25531 (S+ vs. L(AL(A; triallelic classification and TPH2 (G(-703T (T+ vs. T- genotype. The aim was to investigate the influence of genetic factors and traumatic life events on skin conductance responses (SCRs and neural responses (amygdala, insula, dorsal anterior cingulate cortex (dACC and ventromedial prefrontal cortex (vmPFC during acquisition and extinction learning in a differential fear conditioning paradigm. Fear acquisition was characterized by stronger late conditioned and unconditioned responses in the right insula in 5-HTTLPR S-allele carriers. During extinction traumatic life events were associated with reduced amygdala activation in S-allele carriers vs. non-carriers. Beyond that, T-allele carriers of the TPH2 (G(-703T polymorphism with a higher number of traumatic life events showed enhanced responsiveness in the amygdala during acquisition and in the vmPFC during extinction learning compared with non-carriers. Finally, a combined effect of the two polymorphisms with higher responses in S- and T-allele carriers was found in the dACC during extinction. The results indicate an increased expression of conditioned, but also unconditioned fear responses in the insula in 5-HTTLPR S-allele carriers. A combined effect of the two polymorphisms on dACC activation during extinction might be associated with prolonged fear expression. Gene

  15. The neural basis of tactile motion perception

    OpenAIRE

    Pei, Yu-Cheng; Sliman J Bensmaia

    2014-01-01

    The manipulation of objects commonly involves motion between object and skin. In this review, we discuss the neural basis of tactile motion perception and its similarities with its visual counterpart. First, much like in vision, the perception of tactile motion relies on the processing of spatiotemporal patterns of activation across populations of sensory receptors. Second, many neurons in primary somatosensory cortex are highly sensitive to motion direction, and the response properties of th...

  16. Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

    Institute of Scientific and Technical Information of China (English)

    Li-juan XIE; Xing-qian YE; Dong-hong LIU; Yi-bin YING

    2008-01-01

    Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.

  17. Quality changes and predictive models of radial basis function neural networks for brined common carp (Cyprinus carpio) fillets during frozen storage.

    Science.gov (United States)

    Kong, Chunli; Wang, Huiyi; Li, Dapeng; Zhang, Yuemei; Pan, Jinfeng; Zhu, Beiwei; Luo, Yongkang

    2016-06-15

    To investigate and predict quality of 2% brined common carp (Cyprinus carpio) fillets during frozen storage, free fatty acids (FFA), salt extractable protein (SEP), total sulfhydryl (SH) content, and Ca(2+)-ATPase activity were determined at 261K, 253K, and 245K, respectively. There was a dramatic increase (Pfirst 3weeks. SEP decreased to 67.31% after 17weeks at 245K, whereas it took about 7weeks and 13weeks to decrease to the same extent at 261K and 253K, respectively. Ca(2+)-ATPase activity kept decreasing to 18.28% after 7weeks at 261K. Furthermore, radial basis function neural networks (RBFNNs) were developed to predict quality (FFA, SEP, SH, and Ca(2+)-ATPase activity) of brined carp fillets during frozen storage with relative errors all within ±5%. Thus, RBFNN is a promising method to predict quality of carp fillets during storage at 245-261K. PMID:26868584

  18. Neural network of Gaussian radial basis functions applied to the problem of identification of nuclear accidents in a PWR nuclear power plant

    International Nuclear Information System (INIS)

    Highlights: • It is presented a new method based on Artificial Neural Network (ANN) developed to deal with accident identification in PWR nuclear power plants. • Obtained results have shown the efficiency of the referred technique. • Results obtained with this method are as good as or even better to similar optimization tools available in the literature. - Abstract: The task of monitoring a nuclear power plant consists on determining, continuously and in real time, the state of the plant’s systems in such a way to give indications of abnormalities to the operators and enable them to recognize anomalies in system behavior. The monitoring is based on readings of a large number of meters and alarm indicators which are located in the main control room of the facility. On the occurrence of a transient or of an accident on the nuclear power plant, even the most experienced operators can be confronted with conflicting indications due to the interactions between the various components of the plant systems; since a disturbance of a system can cause disturbances on another plant system, thus the operator may not be able to distinguish what is cause and what is the effect. This cognitive overload, to which operators are submitted, causes a difficulty in understanding clearly the indication of an abnormality in its initial phase of development and in taking the appropriate and immediate corrective actions to face the system failure. With this in mind, computerized monitoring systems based on artificial intelligence that could help the operators to detect and diagnose these failures have been devised and have been the subject of research. Among the techniques that can be used in such development, radial basis functions (RBFs) neural networks play an important role due to the fact that they are able to provide good approximations to functions of a finite number of real variables. This paper aims to present an application of a neural network of Gaussian radial basis

  19. Functional gene polymorphisms in the serotonin system and traumatic life events modulate the neural basis of fear acquisition and extinction

    OpenAIRE

    Hermann, Andrea; Küpper, Yvonne; Schmitz, Anja; Walter, Bertram; Vaitl, Dieter; Hennig, Jürgen; Stark, Rudolf; Tabbert, Katharina

    2012-01-01

    Fear acquisition and extinction are crucial mechanisms in the etiology and maintenance of anxiety disorders. Moreover, they might play a pivotal role in conveying the influence of genetic and environmental factors on the development of a (more or less) stronger proneness for, or resilience against psychopathology. There are only few insights in the neurobiology of genetically and environmentally based individual differences in fear learning and extinction. In this functional magnetic resonanc...

  20. The neural basis of olfactory function and its relationship with anhedonia in individuals with schizotypy: An exploratory study.

    Science.gov (United States)

    Zou, Lai-quan; Geng, Fu-lei; Liu, Wen-hua; Wei, Xin-hua; Jiang, Xin-qing; Wang, Yi; Shi, Hai-song; Lui, Simon S Y; Cheung, Eric F C; Chan, Raymond C K

    2015-11-30

    Previous studies have established a linkage between olfactory deficits and negative symptoms in schizophrenia. However, it is not known whether olfactory function is associated with hedonic traits in individuals with schizotypy. Seventeen individuals with schizotypy and 18 age- and sex-matched controls participated in this study. Hedonic traits were assessed with the Chapman Scales for Physical and Social Anhedonia (CSAS and CPAS). Olfactory function was assessed with the Sniffin' Stick Test (olfactory threshold, odour discrimination and odour identification). All participants undertook a structural imaging scan for grey matter volume measurements. Individuals with schizotypy had significantly higher CSAS and CPAS scores than healthy controls. They had normal olfactory function. Their odour identification ability was inversely correlated with physical and social anhedonia. The volume of the right parahippocampal gyrus was positively associated with odour identification ability, and negatively associated with physical and social anhedonia. Furthermore, mediation analysis suggested that odour identification ability influences anhedonia through its effect on the right parahippocampal gyrus. No such relationship was found in controls. These findings suggest that there is a relationship between odour identification and anhedonia in individuals with schizotypy, and the association may be mediated by parahippocampal gyrus volume. PMID:26404551

  1. Application of Near Infrared Diffuse Reflectance Spectroscopy with Radial Basis Function Neural Network to Determination of Rifampincin Isoniazid and Pyrazinamide Tablets

    Institute of Scientific and Technical Information of China (English)

    DU Lin-na; WU Li-hang; LU Jia-hui; GUO Wei-liang; MENG Qing-fan; JIANG Chao-jun; SHEN Si-le; TENG Li-rong

    2007-01-01

    Partial least squares(PLS), back-propagation neural network (BPNN) and radial basis function neural network(RBFNN) were respectively used for estalishing quantative analysis models with near infrared(NIR) diffuse reflectance spectra for determining the contents of rifampincin(RMP), isoniazid(INH) and pyrazinamide(PZA) in rifampicin isoniazid and pyrazinamide tablets. Savitzky-Golay smoothing, first derivative, second derivative, fast Fourier transform(FFT) and standard normal variate(SNV) transformation methods were applied to pretreating raw NIR diffuse reflectance spectra. The raw and pretreated spectra were divided into several regions, depending on the average spectrum and RSD spectrum. Principal component analysis(PCA) method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data. The optimum spectral regions and the models' parameters were chosen by comparing the root mean square error of cross-validation(RMSECV) values which were obtained by leave-one-out cross-validation method. The RMSECV values of the RBFNN models for determining the contents of RMP, INH and PZA were 0.00288, 0.00226 and 0.00341, respectively. Using these models for predicting the contents of INH, RMP and PZA in prediction set, the RMSEP values were 0.00266, 0.00227 and 0.00411, respectively. These results are better than those obtained from PLS models and BPNN models. With additional advantages of fast calculation speed and less dependence on the initial conditions, RBFNN is a suitable tool to model complex systems.

  2. Neural Basis of Learning and Preference during Social Decision Making

    OpenAIRE

    Seo, Hyojung; Lee, Daeyeol

    2012-01-01

    Social decision making is arguably the most complex cognitive function performed by the human brain. This is due to two unique features of social decision making. First, predicting the behaviors of others is extremely difficult. Second, humans often take into consideration the well-beings of others during decision making, but this is influenced by many contextual factors. Despite such complexity, studies on the neural basis of social decision making have made substantial progress in the last ...

  3. A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

    Directory of Open Access Journals (Sweden)

    Zhiqiang Guo

    Full Text Available In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D2PCA and a Radial Basis Function Neural Network (RBFNN to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA and independent component analysis (ICA. The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

  4. A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

    Science.gov (United States)

    Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

    2015-01-01

    In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. PMID:25849483

  5. State of charge estimation of Li-ion batteries in an electric vehicle based on a radial-basis-function neural network

    International Nuclear Information System (INIS)

    The on-line estimation of the state of charge (SOC) of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network (RBF NN) has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhances the real-time performance of estimation. Finally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle

  6. Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

    International Nuclear Information System (INIS)

    On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent. (geophysics, astronomy, and astrophysics)

  7. Characterization, pore size measurement and wear model of a sintered Cu–W nano composite using radial basis functional neural network

    International Nuclear Information System (INIS)

    Highlights: • Wear tests were conducted on Cu–(5–20%) W nano composite. • SEM, TEM, XRD and EDS were used to evaluate the characteristics of composite. • Wear mechanism was discussed through the distribution map and SEM. • Best trained RBFNN was developed to predict the unknown values. - Abstract: Cu–(5–20%) W composite preforms, with a density of 94% were prepared through mechanical milling, mixing, compaction, sintering and hot extrusion. The X-ray Diffraction analysis, Particle Size analysis, Transmission Electron Microscope, Scanning Electron Microscope and Energy Dispersive Spectrum were used for the characterization studies. The pore size during different sintering atmospheres and the pore size reduction during extrusion, were studied through Auto CAD 2010 software. The wear experiments were conducted using the pin-on-disc wear tester. The various regions in the wear mechanisms were identified through the wear distribution map. The Radial Basis Functional Neural Network has been used in an attempt to predict the mechanical and tribological behavior of composites, and useful conclusions have been made

  8. Molecular basis of neural function

    International Nuclear Information System (INIS)

    The conference proceedings contain abstracts of plenary lectures, of young neurochemists' ESN honorary lectures, lectures at symposia and workshops and poster communications. Twenty abstracts were inputted in INIS. The subject of these were the use of autoradiography for the determination of receptors, cholecystokinin, nicotine, adrenaline, glutamate, aspartate, tranquilizers, for distribution and pharmacokinetics of obidoxime-chloride, for cell proliferation, mitosis of brain cells, DNA repair; radioimmunoassay of cholinesterase, tyrosinase; positron computed tomography of the brain; biological radiation effects on cholinesterase activity; tracer techniques for determination of adrenaline; and studies of the biological repair of nerves. (J.P.)

  9. Neural basis of extraordinary empathy and altruistic motivation.

    Science.gov (United States)

    Mathur, Vani A; Harada, Tokiko; Lipke, Trixie; Chiao, Joan Y

    2010-07-15

    A central evolutionary challenge for social groups is uniting a heterogeneous set of individuals towards common goals. One means by which social groups form and endure is by endowing group members with extraordinary prosocial proclivities, such as ingroup love, towards other group members. Here we examined the neural basis of extraordinary empathy and altruistic motivation in African-American and Caucasian-American individuals using functional magnetic resonance imaging. Our results indicate that empathy for ingroup members is neurally distinct from empathy for humankind, more generally. People showed greater response within anterior cingulate cortex and bilateral insula when observing the suffering of others, but African-American individuals additionally recruit medial prefrontal cortex when observing the suffering of members of their own social group. Moreover, neural activity within medial prefrontal cortex in response to pain expressed by ingroup relative to outgroup members predicted greater empathy and altruistic motivation for one's ingroup, suggesting that neurocognitive processes associated with self identity underlie extraordinary empathy and altruistic motivation for members of one's own social group. Taken together, our findings reveal distinct neural mechanisms of empathy and altruistic motivation in an intergroup context and may serve as a foundation for future research investigating the neural bases of intergroup prosociality, more broadly construed. PMID:20302945

  10. The neural basis of academic achievement motivation.

    Science.gov (United States)

    Mizuno, Kei; Tanaka, Masaaki; Ishii, Akira; Tanabe, Hiroki C; Onoe, Hirotaka; Sadato, Norihiro; Watanabe, Yasuyoshi

    2008-08-01

    We have used functional magnetic resonance imaging to study the neural correlates of motivation, concentrating on the motivation to learn and gain monetary rewards. We compared the activation in the brain obtained during reported high states of motivation for learning, with the ones observed when the motivation was based on monetary reward. Our results show that motivation to learn correlates with bilateral activity in the putamen, and that the higher the reported motivation, as derived from a questionnaire that each subject filled prior to scanning, the greater the change in the BOLD signals within the putamen. Monetary motivation also activated the putamen bilaterally, though the intensity of activity was not related to the monetary reward. We conclude that the putamen is critical for motivation in different domains and the extent of activity of the putamen may be pivotal to the motivation that drives academic achievement and thus academic successes. PMID:18550387

  11. The neural basis of human tool use

    Directory of Open Access Journals (Sweden)

    FaustoCaruana

    2014-04-01

    Full Text Available In this review, we propose that the neural basis for the spontaneous, diversified human tool use is an area devoted to the execution and observation of tool actions, located in the left anterior supramarginal gyrus (aSMG. The aSMG activation elicited by observing tool use is typical of human subjects, as macaques show no similar activation, even after an extensive training to use tools. The execution of tool actions, as well as their observation, requires the convergence upon aSMG of inputs from different parts of the dorsal and ventral visual streams. Non semantic features of the target object may be provided by the posterior parietal cortex (PPC for tool-object interaction, paralleling the well-known PPC input to AIP for hand-object interaction. Semantic information regarding tool identity, and knowledge of the typical manner of handling the tool, could be provided by inferior and middle regions of the temporal lobe. Somatosensory feedback and technical reasoning, as well as motor and intentional constraints also play roles during the planning of tool actions and consequently their signals likewise converge upon aSMG.We further propose that aSMG may have arisen though duplication of monkey AIP and invasion of the duplicate area by afferents from PPC providing distinct signals depending on the kinematics of the manipulative action. This duplication may have occurred when Homo Habilis or Homo Erectus emerged, generating the Oldowan or Acheulean Industrial complexes respectively. Hence tool use may have emerged during hominid evolution between bipedalism and language.We conclude that humans have two parietal systems involved in tool behavior: a biological circuit for grasping objects, including tools, and an artifactual system devoted specifically to tool use. Only the latter allows humans to understand the causal relationship between tool use and obtaining the goal, and is likely to be the basis of all technological developments.

  12. Neurally augmented sexual function.

    Science.gov (United States)

    Meloy, S

    2007-01-01

    Neurally Augmented Sexual Function (NASF) is a technique utilizing epidural electrodes to restore and improve sexual function. Orgasmic dysfunction is common in adult women, affecting roughly one quarter of populations studied. Many male patients suffering from erectile dysfunction are not candidates for phosphdiesterase therapy due to concomitant nitrate therapy. Positioning the electrodes at roughly the level of the cauda equina allows for stimulation of somatic efferents and afferents as well as modifying sympathetic and parasympathetic activity. Our series of women treated by NASF is described. Our experience shows that the evaluation of potential candidates for both correctable causes and psychological screening are important considerations. PMID:17691397

  13. Functional neural anatomy of talent.

    Science.gov (United States)

    Kalbfleisch, M Layne

    2004-03-01

    The terms gifted, talented, and intelligent all have meanings that suggest an individual's highly proficient or exceptional performance in one or more specific areas of strength. Other than Spearman's g, which theorizes about a general elevated level of potential or ability, more contemporary theories of intelligence are based on theoretical models that define ability or intelligence according to a priori categories of specific performance. Recent studies in cognitive neuroscience report on the neural basis of g from various perspectives such as the neural speed theory and the efficiency of prefrontal function. Exceptional talent is the result of interactions between goal-directed behavior and nonvolitional perceptual processes in the brain that have yet to be fully characterized and understood by the fields of psychology and cognitive neuroscience. Some developmental studies report differences in region-specific neural activation, recruitment patterns, and reaction times in subjects who are identified with high IQ scores according to traditional scales of assessment such as the WISC-III or Stanford-Binet. Although as cases of savants and prodigies illustrate, talent is not synonymous with high IQ. This review synthesizes information from the fields of psychometrics and gifted education, with findings from the neurosciences on the neural basis of intelligence, creativity, profiles of expert performers, cognitive function, and plasticity to suggest a paradigm for investigating talent as the maximal and productive use of either or both of one's high level of general intelligence or domain-specific ability. Anat Rec (Part B: New Anat) 277B:21-36, 2004. PMID:15052651

  14. The neural circuit basis of learning

    Science.gov (United States)

    Patrick, Kaifosh William John

    The astounding capacity for learning ranks among the nervous system's most impressive features. This thesis comprises studies employing varied approaches to improve understanding, at the level of neural circuits, of the brain's capacity for learning. The first part of the thesis contains investigations of hippocampal circuitry -- both theoretical work and experimental work in the mouse Mus musculus -- as a model system for declarative memory. To begin, Chapter 2 presents a theory of hippocampal memory storage and retrieval that reflects nonlinear dendritic processing within hippocampal pyramidal neurons. As a prelude to the experimental work that comprises the remainder of this part, Chapter 3 describes an open source software platform that we have developed for analysis of data acquired with in vivo Ca2+ imaging, the main experimental technique used throughout the remainder of this part of the thesis. As a first application of this technique, Chapter 4 characterizes the content of signaling at synapses between GABAergic neurons of the medial septum and interneurons in stratum oriens of hippocampal area CA1. Chapter 5 then combines these techniques with optogenetic, pharmacogenetic, and pharmacological manipulations to uncover inhibitory circuit mechanisms underlying fear learning. The second part of this thesis focuses on the cerebellum-like electrosensory lobe in the weakly electric mormyrid fish Gnathonemus petersii, as a model system for non-declarative memory. In Chapter 6, we study how short-duration EOD motor commands are recoded into a complex temporal basis in the granule cell layer, which can be used to cancel Purkinje-like cell firing to the longer duration and temporally varying EOD-driven sensory responses. In Chapter 7, we consider not only the temporal aspects of the granule cell code, but also the encoding of body position provided from proprioceptive and efference copy sources. Together these studies clarify how the cerebellum-like circuitry of the

  15. Combining radial basis function neural network with genetic algorithm to QSPR modeling of adsorption on multi-walled carbon nanotubes surface

    Science.gov (United States)

    Hassanzadeh, Zeinabe; Kompany-Zareh, Mohsen; Ghavami, Raouf; Gholami, Somayeh; Malek-Khatabi, Atefe

    2015-10-01

    The configuring of a radial basis function neural network (RBFN) consists of optimizing the architecture and the network parameters (centers, widths, and weights). Methods such as genetic algorithm (GA), K-means and cluster analysis (CA) are among center selection methods. In the most of reports on RBFN modeling optimum centers are selected among rows of descriptors matrix. A combination of RBFN and GA is introduced for better description of quantitative structure-property relationships (QSPR) models. In this method, centers are not exactly rows of the independent matrix and can be located in any point of the samples space. In the proposed approach, initial centers are randomly selected from the calibration set. Then GA changes the locations of the initially selected centers to find the optimum positions of centers from the whole space of scores matrix, in order to obtain highest prediction ability. This approach is called whole space GA-RBFN (wsGA-RBFN) and applied to predict the adsorption coefficients (logk), of 40 small molecules on the surface of multi-walled carbon nanotubes (MWCNTs). The data consists of five solute descriptors [R, π, α, β, V] of the molecules and known as data set1. Prediction ability of wsGA-RBFN is compared to GA-RBFN and MLR models. The obtained Q2 values for wsGA-RBFN, GA-RBFN and MLR are 0.95, 0.85, and 0.78, respectively, which shows the merit of wsGA-RBFN. The method is also applied on the logarithm of surface area normalized adsorption coefficients (logKSA), of organic compounds (OCs) on MWCNTs surface. The data set2 includes 69 aromatic molecules with 13 physicochemical properties of the OCs. Thirty-nine of these molecules were similar to those of data set1 and the others were aromatic compounds included of small and big molecules. Prediction ability of wsGA-RBFN for second data set was compared to GA-RBF. The Q2 values for wsGA-RBFN and GA-RBF are obtained as 0.89 and 0.80, respectively.

  16. The neural basis of stereotypic impact on multiple social categorization.

    Science.gov (United States)

    Hehman, Eric; Ingbretsen, Zachary A; Freeman, Jonathan B

    2014-11-01

    Perceivers extract multiple social dimensions from another's face (e.g., race, emotion), and these dimensions can become linked due to stereotypes (e.g., Black individuals → angry). The current research examined the neural basis of detecting and resolving conflicts between top-down stereotypes and bottom-up visual information in person perception. Participants viewed faces congruent and incongruent with stereotypes, via variations in race and emotion, while neural activity was measured using fMRI. Hand movements en route to race/emotion responses were recorded using mouse-tracking to behaviorally index individual differences in stereotypical associations during categorization. The medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC) showed stronger activation to faces that violated stereotypical expectancies at the intersection of multiple social categories (i.e., race and emotion). These regions were highly sensitive to the degree of incongruency, exhibiting linearly increasing responses as race and emotion became stereotypically more incongruent. Further, the ACC exhibited greater functional connectivity with the lateral fusiform cortex, a region implicated in face processing, when viewing stereotypically incongruent (relative to congruent) targets. Finally, participants with stronger behavioral tendencies to link race and emotion stereotypically during categorization showed greater dorsolateral prefrontal cortex activation to stereotypically incongruent targets. Together, the findings provide insight into how conflicting stereotypes at the nexus of multiple social dimensions are resolved at the neural level to accurately perceive other people. PMID:25094016

  17. Assisted diagnosis for infancy anorexia based on a radial basis function probabilistic neural network model%基于径向基概率神经网络模型的小儿厌食症状辅助诊断

    Institute of Scientific and Technical Information of China (English)

    翟红林; 陈晓峰; 陈兴国; 胡之德

    2004-01-01

    结合了径向基神经网络较强模式分类能力与概率神经网络运算简单的优点,提出了一种径向基概率神经网络模型,并应用于小儿厌食症的辅助诊断,通过对119例样本数据的处理,获得了92.4%的准确率.此外,偏最小二乘法的分析结果表明,Zn元素与小儿厌食症关系最为紧密.%Based on a radial basis function probabilistic neural network model, which combined the powerful capability of the pattern classification of radial basis function neural network and the simple operation of probabilistic neural network, a new approach of assisted diagnosis for infancy anorexia was developed and applied to 119 samples, with an accuracy rate of 92%. In addition, the result of partial least squares analysis indicated that Zn was the most important element that was closely related to infancy anorexia..

  18. Using a Mahalanobis-like distance to train Radial Basis Neural Networks

    OpenAIRE

    Valls, José M.; Aler, Ricardo; Fernández, Óscar

    2005-01-01

    Radial Basis Neural Networks (RBNN) can approximate any regular function and have a faster training phase than other similar neural networks. However, the activation of each neuron depends on the euclidean distance between a pattern and the neuron center. Therefore, the activation function is symmetrical and all attributes are considered equally relevant. This could be solved by altering the metric used in the activation function (i.e. using non-symmetrical metrics). The Mahalanobis distance ...

  19. Neural basis of disgust perception in racial prejudice.

    Science.gov (United States)

    Liu, Yunzhe; Lin, Wanjun; Xu, Pengfei; Zhang, Dandan; Luo, Yuejia

    2015-12-01

    Worldwide racial prejudice is originated from in-group/out-group discrimination. This prejudice can bias face perception at the very beginning of social interaction. However, little is known about the neurocognitive mechanism underlying the influence of racial prejudice on facial emotion perception. Here, we examined the neural basis of disgust perception in racial prejudice using a passive viewing task and functional magnetic resonance imaging. We found that compared with the disgusted faces of in-groups, the disgusted faces of out-groups result in increased amygdala and insular engagement, positive coupling of the insula with amygdala-based emotional system, and negative coupling of the insula with anterior cingulate cortex (ACC)-based regulatory system. Furthermore, machine-learning algorithms revealed that the level of implicit racial prejudice could be predicted by functional couplings of the insula with both the amygdala and the ACC, which suggests that the insula is largely involved in racially biased disgust perception through two distinct neural circuits. In addition, individual difference in disgust sensitivity was found to be predictive of implicit racial prejudice. Taken together, our results suggest a crucial role of insula-centered circuits for disgust perception in racial prejudice. PMID:26417673

  20. Identifying Emotions on the Basis of Neural Activation.

    Science.gov (United States)

    Kassam, Karim S; Markey, Amanda R; Cherkassky, Vladimir L; Loewenstein, George; Just, Marcel Adam

    2013-01-01

    We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing. PMID:23840392

  1. Genetic influences on the neural basis of social cognition

    OpenAIRE

    Skuse, David

    2006-01-01

    The neural basis of social cognition has been the subject of intensive research in both human and non-human primates. Exciting, provocative and yet consistent findings are emerging. A major focus of interest is the role of efferent and afferent connectivity between the amygdala and the neocortical brain regions, now believed to be critical for the processing of social and emotional perceptions. One possible component is a subcortical neural pathway, which permits rapid and preconscious proces...

  2. The neural circuit basis of Rett syndrome

    OpenAIRE

    Goffin, Darren; Zhou, Zhaolan

    2012-01-01

    Rett syndrome is an Autism Spectrum Disorder caused by mutations in the gene encoding methyl-CpG binding protein (MeCP2). Following a period of normal development, patients lose learned communication and motor skills, and develop a number of symptoms including motor disturbances, cognitive impairments and often seizures. In this review, we discuss the role of MeCP2 in regulating synaptic function and how synaptic dysfunctions lead to neuronal network impairments and alterations in sensory inf...

  3. The shared neural basis of empathy and facial imitation accuracy.

    Science.gov (United States)

    Braadbaart, L; de Grauw, H; Perrett, D I; Waiter, G D; Williams, J H G

    2014-01-01

    Empathy involves experiencing emotion vicariously, and understanding the reasons for those emotions. It may be served partly by a motor simulation function, and therefore share a neural basis with imitation (as opposed to mimicry), as both involve sensorimotor representations of intentions based on perceptions of others' actions. We recently showed a correlation between imitation accuracy and Empathy Quotient (EQ) using a facial imitation task and hypothesised that this relationship would be mediated by the human mirror neuron system. During functional Magnetic Resonance Imaging (fMRI), 20 adults observed novel 'blends' of facial emotional expressions. According to instruction, they either imitated (i.e. matched) the expressions or executed alternative, pre-prescribed mismatched actions as control. Outside the scanner we replicated the association between imitation accuracy and EQ. During fMRI, activity was greater during mismatch compared to imitation, particularly in the bilateral insula. Activity during imitation correlated with EQ in somatosensory cortex, intraparietal sulcus and premotor cortex. Imitation accuracy correlated with activity in insula and areas serving motor control. Overlapping voxels for the accuracy and EQ correlations occurred in premotor cortex. We suggest that both empathy and facial imitation rely on formation of action plans (or a simulation of others' intentions) in the premotor cortex, in connection with representations of emotional expressions based in the somatosensory cortex. In addition, the insula may play a key role in the social regulation of facial expression. PMID:24012546

  4. The neural basis of unwanted thoughts during resting state

    OpenAIRE

    Kühn, Simone; Vanderhasselt, Marie-Anne; Raedt, Rudi; Gallinar, J

    2013-01-01

    Human beings are constantly engaged in thought. Sometimes thoughts occur repetitively and can become distressing. Up to now the neural bases of these intrusive or unwanted thoughts is largely unexplored. To study the neural correlates of unwanted thoughts, we acquired resting-state fMRI data of 41 female healthy subjects and assessed the self-reported amount of unwanted thoughts during measurement. We analyzed local connectivity by means of regional homogeneity (ReHo) and functional connectiv...

  5. The neural basis of responsibility attribution in decision-making.

    Science.gov (United States)

    Li, Peng; Shen, Yue; Sui, Xue; Chen, Changming; Feng, Tingyong; Li, Hong; Holroyd, Clay

    2013-01-01

    Social responsibility links personal behavior with societal expectations and plays a key role in affecting an agent's emotional state following a decision. However, the neural basis of responsibility attribution remains unclear. In two previous event-related brain potential (ERP) studies we found that personal responsibility modulated outcome evaluation in gambling tasks. Here we conducted a functional magnetic resonance imaging (fMRI) study to identify particular brain regions that mediate responsibility attribution. In a context involving team cooperation, participants completed a task with their teammates and on each trial received feedback about team success and individual success sequentially. We found that brain activity differed between conditions involving team success vs. team failure. Further, different brain regions were associated with reinforcement of behavior by social praise vs. monetary reward. Specifically, right temporoparietal junction (RTPJ) was associated with social pride whereas dorsal striatum and dorsal anterior cingulate cortex (ACC) were related to reinforcement of behaviors leading to personal gain. The present study provides evidence that the RTPJ is an important region for determining whether self-generated behaviors are deserving of praise in a social context. PMID:24224053

  6. The neural basis of responsibility attribution in decision-making.

    Directory of Open Access Journals (Sweden)

    Peng Li

    Full Text Available Social responsibility links personal behavior with societal expectations and plays a key role in affecting an agent's emotional state following a decision. However, the neural basis of responsibility attribution remains unclear. In two previous event-related brain potential (ERP studies we found that personal responsibility modulated outcome evaluation in gambling tasks. Here we conducted a functional magnetic resonance imaging (fMRI study to identify particular brain regions that mediate responsibility attribution. In a context involving team cooperation, participants completed a task with their teammates and on each trial received feedback about team success and individual success sequentially. We found that brain activity differed between conditions involving team success vs. team failure. Further, different brain regions were associated with reinforcement of behavior by social praise vs. monetary reward. Specifically, right temporoparietal junction (RTPJ was associated with social pride whereas dorsal striatum and dorsal anterior cingulate cortex (ACC were related to reinforcement of behaviors leading to personal gain. The present study provides evidence that the RTPJ is an important region for determining whether self-generated behaviors are deserving of praise in a social context.

  7. Neural basis for recognition confidence in younger and older adults

    OpenAIRE

    Chua, Elizabeth F.; Schacter, Daniel L.; Sperling, Reisa A.

    2009-01-01

    Although several studies have examined the neural basis for age-related changes in objective memory performance, less is known about how the process of memory monitoring changes with aging. We used fMRI to examine retrospective confidence in memory performance in aging. During low confidence, both younger and older adults showed behavioral evidence that they were guessing during recognition, and that they were aware they were guessing when making confidence judgments. Similarly, both younger ...

  8. Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

    Science.gov (United States)

    Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

    2010-01-01

    Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

  9. Neural basis of scientific innovation induced by heuristic prototype.

    Directory of Open Access Journals (Sweden)

    Junlong Luo

    Full Text Available A number of major inventions in history have been based on bionic imitation. Heuristics, by applying biological systems to the creation of artificial devices and machines, might be one of the most critical processes in scientific innovation. In particular, prototype heuristics propositions that innovation may engage automatic activation of a prototype such as a biological system to form novel associations between a prototype's function and problem-solving. We speculated that the cortical dissociation between the automatic activation and forming novel associations in innovation is critical point to heuristic creativity. In the present study, novel and old scientific innovations (NSI and OSI were selected as experimental materials in using learning-testing paradigm to explore the neural basis of scientific innovation induced by heuristic prototype. College students were required to resolve NSI problems (to which they did not know the answers and OSI problems (to which they knew the answers. From two fMRI experiments, our results showed that the subjects could resolve NSI when provided with heuristic prototypes. In Experiment 1, it was found that the lingual gyrus (LG; BA18 might be related to prototype heuristics in college students resolving NSI after learning a relative prototype. In Experiment 2, the LG (BA18 and precuneus (BA31 were significantly activated for NSI compared to OSI when college students learned all prototypes one day before the test. In addition, the mean beta-values of these brain regions of NSI were all correlated with the behavior accuracy of NSI. As our hypothesis indicated, the findings suggested that the LG might be involved in forming novel associations using heuristic information, while the precuneus might be involved in the automatic activation of heuristic prototype during scientific innovation.

  10. Exploring the Neural Basis of Cognitive Reserve in Aging

    OpenAIRE

    Steffener, Jason; Stern, Yaakov

    2011-01-01

    The concept of reserve arose from the mismatch between the extent of brain changes or pathology and the clinical manifestations of these brain changes. The cognitive reserve hypothesis posits that individual differences in the flexibility and adaptability of brain networks underlying cognitive function may allow some people to cope better with brain changes than others. Although there is ample epidemiologic evidence for cognitive reserve, the neural substrate of reserve is still a topic of on...

  11. Exploring the neural basis of real-life joint action: measuring brain activation during joint table setting with functional near-infrared spectroscopy (fNIRS

    Directory of Open Access Journals (Sweden)

    Johanna Egetemeir

    2011-09-01

    Full Text Available Many everyday life situations require two or more individuals to execute actions together. Assessing brain activation during naturalistic tasks to uncover relevant processes underlying such real-life joint action situations has remained a methodological challenge. In the present study, we introduce a novel joint action paradigm that enables the assessment of brain activation during real-life joint action tasks using functional near-infrared spectroscopy (fNIRS. We monitored brain activation of participants who coordinated complex actions with a partner sitting opposite them. Participants performed table-setting tasks, either alone (solo action or in cooperation with a partner (joint action, or they observed the partner performing the task (action observation. Comparing joint action and solo action revealed stronger activation (higher [oxy-Hb]-concentration during joint action in a number of areas. Among these were areas in the inferior parietal lobule (IPL that additionally showed an overlap of activation during action observation and solo action. Areas with such a close link between action observation and action execution have been associated with action simulation processes. The magnitude of activation in these IPL areas also varied according to joint action type and its respective demand on action simulation. The results validate fNIRS as an imaging technique for exploring the functional correlates of interindividual action coordination in real-life settings and suggest that coordinating actions in real-life situations requires simulating the actions of the partner.

  12. Wind Speed Forecasting Based on Quantum-behaved Particle Swarm Optimization and Radial Basis Function Neural Network Model%基于量子粒子群-径向基神经网络模型的风速预测

    Institute of Scientific and Technical Information of China (English)

    赵高强; 傅(王乐)

    2011-01-01

    风速预测对风电场和电力系统的运行都具有重要意义.为了提高风速预测的精度,提出了一种基于量子粒子群-径向基神经网络模型,在确定网络隐含层节点数后,将RBF网络的参数编码成优化算法中的粒子个体进行优化,在全局空间搜索最优适应值的参数.用优化后的神经网络进行风速预测,实例结果表明该算法在预测速度和精度上都得到了提高.%Forecasting of wind speed is very important to the operation of wind power plants and power systems.To improve the wind speed forecasting accuracy, a model based on quantum- behaved particle swarm optimization and radial basis function neural network algorithm is proposed.After the number of nodes in hidden layer is confirmed and all parameters of RBF nets are coded to individual particles to optimize learning algorithm, the parameter of optimal-adaptive values can be searched in global space.Using the optimized neural network to forecast wind speed,and some calculation examples were abtained.The results showed that the new method can improve the speed and accuracy of prediction.

  13. 基于RBFNN的强化学习在机器人导航中的应用%Application of Reinforcement Learning Based on Radial Basis Function Neural Networks in Robot Navigation

    Institute of Scientific and Technical Information of China (English)

    吴洪岩; 刘淑华; 张嵛

    2009-01-01

    在复杂连续环境下,强化学习系统的状态空间面临维数灾难问题,需要采取量化的方法,降低输入空间的复杂度.径向基神经网络(RBFNN:Radial Basis Function Neural Networks)具有较强的函数逼近能力及泛化能力,由此提出了基于径向基神经网络的Q学习方法,并将其应用于单机器人的自主导航.在基于径向基神经网络的强化学习系统中,用径向基神经网络逼近状态空间和Q函数,使学习系统具有良好的泛化能力.仿真结果表明,该导航方法具有较强的避碰能力,提高了机器人对环境的适应能力.

  14. The Neural Basis of Deception in Strategic Interactions

    Directory of Open Access Journals (Sweden)

    Kirsten G Volz

    2015-02-01

    Full Text Available Communication based on informational asymmetries abounds in politics, business, and almost any other form of social interaction. Informational asymmetries may create incentives for the better-informed party to exploit her advantage by misrepresenting information. Using a game-theoretic setting, we investigate the neural basis of deception in human interaction. Unlike in most previous fMRI research on deception, the participants decide themselves whether to lie or not. We find activation within the right temporo-parietal junction (rTPJ, the dorsal anterior cingulate cortex (ACC, the (precuneus (CUN, and the anterior frontal gyrus (aFG when contrasting lying with truth telling. Notably, our design also allows for an investigation of the neural foundations of sophisticated deception through telling the truth—when the sender does not expect the receiver to believe her (true message. Sophisticated deception triggers activation within the same network as plain lies, i.e., we find activity within the rTPJ, the CUN, and aFG. We take this result to show that brain activation can reveal the sender’s veridical intention to deceive others, irrespective of whether in fact the sender utters the factual truth or not.

  15. The neural basis of deception in strategic interactions.

    Science.gov (United States)

    Volz, Kirsten G; Vogeley, Kai; Tittgemeyer, Marc; von Cramon, D Yves; Sutter, Matthias

    2015-01-01

    Communication based on informational asymmetries abounds in politics, business, and almost any other form of social interaction. Informational asymmetries may create incentives for the better-informed party to exploit her advantage by misrepresenting information. Using a game-theoretic setting, we investigate the neural basis of deception in human interaction. Unlike in most previous fMRI research on deception, the participants decide themselves whether to lie or not. We find activation within the right temporo-parietal junction (rTPJ), the dorsal anterior cingulate cortex (ACC), the (pre)cuneus (CUN), and the anterior frontal gyrus (aFG) when contrasting lying with truth telling. Notably, our design also allows for an investigation of the neural foundations of sophisticated deception through telling the truth-when the sender does not expect the receiver to believe her (true) message. Sophisticated deception triggers activation within the same network as plain lies, i.e., we find activity within the rTPJ, the CUN, and aFG. We take this result to show that brain activation can reveal the sender's veridical intention to deceive others, irrespective of whether in fact the sender utters the factual truth or not. PMID:25729358

  16. fMRI of Simultaneous Interpretation Reveals the Neural Basis of Extreme Language Control

    OpenAIRE

    Hervais-Adelman, Alexis; Moser-Mercer, Barbara; Michel, Christoph; Golestani, Narly

    2015-01-01

    We used functional magnetic resonance imaging (fMRI) to examine the neural basis of extreme multilingual language control in a group of 50 multilingual participants. Comparing brain responses arising during simultaneous interpretation (SI) with those arising during simultaneous repetition revealed activation of regions known to be involved in speech perception and production, alongside a network incorporating the caudate nucleus that is known to be implicated in domain-general cognitive contr...

  17. Emotional moments across time: a possible neural basis for time perception in the anterior insula

    OpenAIRE

    Craig, A.D. (Bud)

    2009-01-01

    A model of awareness based on interoceptive salience is described, which has an endogenous time base that might provide a basis for the human capacity to perceive and estimate time intervals in the range of seconds to subseconds. The model posits that the neural substrate for awareness across time is located in the anterior insular cortex, which fits with recent functional imaging evidence relevant to awareness and time perception. The time base in this model is adaptive and emotional, and th...

  18. The neural basis of unwanted thoughts during resting state.

    Science.gov (United States)

    Kühn, Simone; Vanderhasselt, Marie-Anne; De Raedt, Rudi; Gallinat, Jürgen

    2014-09-01

    Human beings are constantly engaged in thought. Sometimes thoughts occur repetitively and can become distressing. Up to now the neural bases of these intrusive or unwanted thoughts is largely unexplored. To study the neural correlates of unwanted thoughts, we acquired resting-state fMRI data of 41 female healthy subjects and assessed the self-reported amount of unwanted thoughts during measurement. We analyzed local connectivity by means of regional homogeneity (ReHo) and functional connectivity of a seed region. More unwanted thoughts (state) were associated with lower ReHo in right dorsolateral prefrontal cortex (DLPFC) and higher ReHo in left striatum (putamen). Additional seed-based analysis revealed higher functional connectivity of the left striatum with left inferior frontal gyrus (IFG) in participants reporting more unwanted thoughts. The state-dependent higher connectivty in left striatum was positively correlated with rumination assessed with a dedicated questionnaire focussing on trait aspects. Unwanted thoughts are associated with activity in the fronto-striatal brain circuitry. The reduction of local connectivity in DLPFC could reflect deficiencies in thought suppression processes, whereas the hightened activity in left striatum could imply an imbalance of gating mechanisms housed in basal ganglia. Its functional connectivity to left IFG is discussed as the result of thought-related speech processes. PMID:23929943

  19. A Novel Algorithm of Network Trade Customer Classification Based on Fourier Basis Functions

    Directory of Open Access Journals (Sweden)

    Li Xinwu

    2013-11-01

    Full Text Available Learning algorithm of neural network is always an important research contents in neural network theory research and application field, learning algorithm about the feed-forward neural network has no satisfactory solution in particular for its defects in calculation speed. The paper presents a new Fourier basis functions neural network algorithm and applied it to classify network trade customer. First, 21 customer classification indicators are designed, based on characteristics and behaviors analysis of network trade customer, including customer characteristics type variables and customer behaviors type variables,; Second, Fourier basis functions is used to improve the calculation flow and algorithm structure of original BP neural network algorithm to speed up its convergence and then a new Fourier basis neural network model is constructed. Finally the experimental results show that the problem of convergence speed can been solved, and the accuracy of the customer classification are ensured when the new algorithm is used in network trade customer classification practically.

  20. Self-organization: the basic principle of neural functions.

    Science.gov (United States)

    Szentágothai, J

    1993-06-01

    Recent neurophysiological observations are giving rise to the expectation that in the near future genuine biological experiments may contribute more than will premature speculations to the understanding of global and cognitive functions. The classical reflex principle--as the basis of neural functions--has to yield to new ideas, like autopoiesis and/or self-organization, as the basic paradigm in the framework of which the essence of the neural can be better understood. Neural activity starts in the very earliest stages of development well before receptors and afferent input become functional. Under suitable conditions, both in nervous tissue cultures and in embryonic tissue recombination experiments, the conditions of such initial autopoietic activity can be studied. This paper tries to generalize this elementary concept for various neural centers, notably for the spinal segmental apparatus and the cerebral cortex. PMID:8236059

  1. A Neural Basis for the Acquired Capability for Suicide

    Science.gov (United States)

    Deshpande, Gopikrishna; Baxi, Madhura; Witte, Tracy; Robinson, Jennifer L.

    2016-01-01

    The high rate of fatal suicidal behavior (SB) in men is an urgent issue as highlighted in the public eye via news sources and media outlets. In this study, we have attempted to address this issue and understand the neural substrates underlying the gender differences in the rate of fatal SB. The Interpersonal–Psychological Theory of Suicide has proposed an explanation for the seemingly paradoxical relationship between gender and SB, i.e., greater non-fatal suicide attempts by women but higher number of deaths by suicide in men. This theory states that possessing suicidal desire (due to conditions such as depression) alone is not sufficient for a lethal suicide attempt. It is imperative for an individual to have the acquired capability for suicide (ACS) along with suicidal desire in order to die by suicide. Therefore, higher levels of ACS in men may explain why men are more likely to die by suicide than women, despite being less likely to experience suicidal ideation or depression. In this study, we used activation likelihood estimation meta-analysis to investigate a potential ACS network that involves neural substrates underlying emotional stoicism, sensation-seeking, pain tolerance, and fearlessness of death, along with a potential depression network that involves neural substrates that underlie clinical depression. Brain regions commonly found in ACS and depression networks for males and females were further used as seeds to obtain regions functionally and structurally connected to them. We found that the male-specific networks were more widespread and diverse than the female-specific ones. Also, while the former involved motor regions, such as the premotor cortex and cerebellum, the latter was dominated by limbic regions. This may support the fact that suicidal desire generally leads to fatal/decisive action in males, while, in females, it manifests as depression, ideation, and generally non-fatal actions. The proposed model is a first attempt to characterize

  2. 集对分析径向基函数神经网络预测模型%Prediction Model of Radial Basis Function Neural Network Based on Set Pair Analysis

    Institute of Scientific and Technical Information of China (English)

    陈晶; 王文圣; 李跃清

    2011-01-01

    将集对分析与径向基函数神经网络结合,提出了集对分析径向基函数神经网络预测模型.模型思路是将研究对象t-1时和t时的影响因子集构造为集对并计算联系度,由联系度的同一度、差异度、对立度及研究对象t-1时的值为输入,研究对象t时的值为输出,构建径向基函数神经网络.以年径流预测为例研究表明,模型结构清晰、步骤明确、预测精度较高,为集对分析应用于水文预测提供了新思路.%The proposed SPA-RBFNN prediction model is a combination of set pair analysis (SPA) and radial basis function neural network (RBFNN). The idea of SPA-RBFNN, firstly sets the impact factors of research object in both t-1 and t period of time as a pair, and calculates the connection degree of the pair, then uses its calculated homology degree, difference degree and antinomy degree, along with the situation of research object in t-1 period of time as model input, the situation of research object in t period of time as model output, finally finishes the model establishment. The case study of annual runoff prediction shows that SPARBFNN prediction model is characterized by explicit structure, easy realization and good prediction ability. The model construction idea provides a new thinking for the application of SPA in solving the hydrological prediction problems.

  3. The neural basis of predicting the outcomes of planned actions

    Directory of Open Access Journals (Sweden)

    Andrew Jahn

    2011-11-01

    Full Text Available A key feature of human intelligence is the ability to predict the outcomes of one’s own actions prior to executing them. Action values are thought to be represented in part in the dorsal and ventral medial prefrontal cortex, yet current studies have focused on the value of executed actions rather than the anticipated value of a planned action. Thus, little is known about the neural basis of how individuals think (or fail to think about their actions and the potential consequences before they act. We scanned individuals with fMRI while they thought about performing actions that they knew would likely be correct or incorrect. Here we show that merely imagining an error, as opposed to imagining a correct outcome, increases activity in the dorsal anterior cingulate cortex, independently of subsequent actions. This activity overlaps with regions that respond to actual error commission. The findings show a distinct network that signals the prospective outcomes of one’s planned actions. A number of clinical disorders such as schizophrenia and drug abuse involve a failure to take the potential consequences of an action into account prior to acting. Our results thus suggest how dysfunctions of the medial prefrontal cortex may contribute to such failures.

  4. Dynamic programming using radial basis functions

    OpenAIRE

    Junge, Oliver; Schreiber, Alex

    2014-01-01

    We propose a discretization of the optimality principle in dynamic programming based on radial basis functions and Shepard's moving least squares approximation method. We prove convergence of the approximate optimal value function to the true one and present several numerical experiments.

  5. Speech Recognition Oriented Vowel Classification Using Temporal Radial Basis Functions

    CERN Document Server

    Guezouri, Mustapha; Benyettou, Abdelkader

    2009-01-01

    The recent resurgence of interest in spatio-temporal neural network as speech recognition tool motivates the present investigation. In this paper an approach was developed based on temporal radial basis function "TRBF" looking to many advantages: few parameters, speed convergence and time invariance. This application aims to identify vowels taken from natural speech samples from the Timit corpus of American speech. We report a recognition accuracy of 98.06 percent in training and 90.13 in test on a subset of 6 vowel phonemes, with the possibility to expend the vowel sets in future.

  6. Wave forecasting in near real time basis by neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Rao, S.; Mandal, S.; Prabaharan, N.

    ., forecasting of waves become an important aspect of marine environment. This paper presents application of the neural network (NN) with better update algorithms, namely rprop, quickprop and superSAB for wave forecasting. Measured waves off Marmagoa, Goa, India...

  7. Analysis of radial basis function interpolation approach

    Institute of Scientific and Technical Information of China (English)

    Zou You-Long; Hu Fa-Long; Zhou Can-Can; Li Chao-Liu; Dunn Keh-Jim

    2013-01-01

    The radial basis function (RBF) interpolation approach proposed by Freedman is used to solve inverse problems encountered in well-logging and other petrophysical issues. The approach is to predict petrophysical properties in the laboratory on the basis of physical rock datasets, which include the formation factor, viscosity, permeability, and molecular composition. However, this approach does not consider the effect of spatial distribution of the calibration data on the interpolation result. This study proposes a new RBF interpolation approach based on the Freedman's RBF interpolation approach, by which the unit basis functions are uniformly populated in the space domain. The inverse results of the two approaches are comparatively analyzed by using our datasets. We determine that although the interpolation effects of the two approaches are equivalent, the new approach is more flexible and beneficial for reducing the number of basis functions when the database is large, resulting in simplification of the interpolation function expression. However, the predicted results of the central data are not sufficiently satisfied when the data clusters are far apart.

  8. Neural stem cell sex dimorphism in aromatase (CYP19 expression: a basis for differential neural fate

    Directory of Open Access Journals (Sweden)

    Jay Waldron

    2010-11-01

    Full Text Available Jay Waldron1, Althea McCourty1, Laurent Lecanu1,21The Research Institute of the McGill University Health Centre, Montreal, Canada; 2Department of Medicine, McGill University, Quebec, CanadaPurpose: Neural stem cell (NSC transplantation and pharmacologic activation of endogenous neurogenesis are two approaches that trigger a great deal of interest as brain repair strategies. However, the success rate of clinical attempts using stem cells to restore neurologic functions altered either after traumatic brain injury or as a consequence of neurodegenerative disease remains rather disappointing. This suggests that factors affecting the fate of grafted NSCs are largely understudied and remain to be characterized. We recently reported that aging differentially affects the neurogenic properties of male and female NSCs. Although the sex steroids androgens and estrogens participate in the regulation of neurogenesis, to our knowledge, research on how gender-based differences affect the capacity of NSCs to differentiate and condition their neural fate is lacking. In the present study, we explored further the role of cell sex as a determining factor of the neural fate followed by differentiating NSCs and its relationship with a potential differential expression of aromatase (CYP19, the testosterone-metabolizing enzyme.Results: Using NSCs isolated from the subventricular zone of three-month-old male and female Long-Evans rats and maintained as neurospheres, we showed that differentiation triggered by retinoic acid resulted in a neural phenotype that depends on cell sex. Differentiated male NSCs mainly expressed markers of neuronal fate, including ßIII-tubulin, microtubule associated protein 2, growth-associated protein 43, and doublecortin. In contrast, female NSCs essentially expressed the astrocyte marker glial fibrillary acidic protein. Quantification of the expression of aromatase showed a very low level of expression in undifferentiated female NSCs

  9. Development of neural basis for chinese orthographic neighborhood size effect.

    Science.gov (United States)

    Zhao, Jing; Li, Qing-Lin; Ding, Guo-Sheng; Bi, Hong-Yan

    2016-02-01

    The brain activity of orthographic neighborhood size (N size) effect in Chinese character naming has been studied in adults, meanwhile behavioral studies have revealed a developmental trend of Chinese N-size effect in developing readers. However, it is unclear whether and how the neural mechanism of N-size effect changes in Chinese children along with development. Here we address this issue using functional magnetic resonance imaging. Forty-four students from the 3(rd) , 5(th) , and 7(th) grades were scanned during silent naming of Chinese characters. After scanning, all participants took part in an overt naming test outside the scanner, and results of the naming task showed that the 3(rd) graders named characters from large neighborhoods faster than those from small neighborhoods, revealing a facilitatory N-size effect; the 5(th) graders showed null N-size effect while the 7(th) graders showed an inhibitory N-size effect. Neuroimaging results revealed that only the 3(rd) graders exhibited a significant N-size effect in the left middle occipital activity, with greater activation for large N-size characters. Results of 5(th) and 7(th) graders showed significant N-size effects in the left middle frontal gyrus, in which 5(th) graders induced greater activation in large N-size condition than in small N-size condition, while 7(th) graders exhibited an opposite effect which was similar to the adult pattern reported in a previous study. The current findings suggested the transition from broadly tuned to finely tuned orthographic representation with reading development, and the inhibition from neighbors' phonology for higher graders. Hum Brain Mapp 37:632-647, 2016. © 2015 Wiley Periodicals, Inc. PMID:26777875

  10. Neural Basis of Intrinsic Motivation: Evidence from Event-Related Potentials

    OpenAIRE

    Jia Jin; Liping Yu; Qingguo Ma

    2015-01-01

    Human intrinsic motivation is of great importance in human behavior. However, although researchers have focused on this topic for decades, its neural basis was still unclear. The current study employed event-related potentials to investigate the neural disparity between an interesting stop-watch (SW) task and a boring watch-stop task (WS) to understand the neural mechanisms of intrinsic motivation. Our data showed that, in the cue priming stage, the cue of the SW task elicited smaller N2 ampl...

  11. Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

    Science.gov (United States)

    Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

    2015-04-01

    The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

  12. The neural basis of the speed-accuracy tradeoff

    NARCIS (Netherlands)

    R. Bogacz; E.J. Wagenmakers; B.U. Forstmann; S. Nieuwenhuis

    2010-01-01

    In many situations, decision makers need to negotiate between the competing demands of response speed and response accuracy, a dilemma generally known as the speed-accuracy tradeoff (SAT). Despite the ubiquity of SAT, the question of how neural decision circuits implement SAT has received little att

  13. Towards a neural basis of interactive alignment in conversation

    Directory of Open Access Journals (Sweden)

    Laura eMenenti

    2012-06-01

    Full Text Available The interactive-alignment account of dialogue proposes that interlocutors achieve conversational success by aligning their understanding of the situation under discussion. Such alignment occurs because they prime each other at different levels of representation (e.g., phonology, syntax, semantics, and this is possible because these representations are shared across production and comprehension. In this paper, we briefly review the behavioural evidence, and then consider how findings from cognitive neuroscience might lend support to this account, on the assumption that alignment of neural activity corresponds to alignment of mental states. We first review work supporting representational parity between production and comprehension, and suggest that neural activity associated with phonological, lexical, and syntactic aspects of production and comprehension are closely related. We next consider evidence for the neural bases of the activation and use of situation models during production and comprehension, and how these demonstrate the activation of non-linguistic conceptual representations associated with language use. We then review evidence for alignment of neural mechanisms that are specific to the act of communication. Finally, we suggest some avenues of further research that need to be explored to test crucial predictions of the interactive alignment account.

  14. Dynamic social power modulates neural basis of math calculation

    OpenAIRE

    Tokiko eHarada; Donna eBridge; Chiao, Joan Y.

    2013-01-01

    Both situational (e.g., perceived power) and sustained social factors (e.g., cultural stereotypes) are known to affect how people academically perform, particularly in the domain of mathematics. The ability to compute even simple mathematics, such as addition, relies on distinct neural circuitry within the inferior parietal and inferior frontal lobes, brain regions where magnitude representation and addition are performed. Despite prior behavioral evidence of social influence on academic pe...

  15. The Interpolation Theory of Radial Basis Functions

    CERN Document Server

    Baxter, Brad

    2010-01-01

    In this dissertation, it is first shown that, when the radial basis function is a $p$-norm and $1 2$. Specifically, for every $p > 2$, we construct a set of different points in some $\\Rd$ for which the interpolation matrix is singular. The greater part of this work investigates the sensitivity of radial basis function interpolants to changes in the function values at the interpolation points. Our early results show that it is possible to recast the work of Ball, Narcowich and Ward in the language of distributional Fourier transforms in an elegant way. We then use this language to study the interpolation matrices generated by subsets of regular grids. In particular, we are able to extend the classical theory of Toeplitz operators to calculate sharp bounds on the spectra of such matrices. Applying our understanding of these spectra, we construct preconditioners for the conjugate gradient solution of the interpolation equations. Our main result is that the number of steps required to achieve solution of the lin...

  16. Polarized DIS Structure Functions from Neural Networks

    International Nuclear Information System (INIS)

    We present a parametrization of polarized Deep-Inelastic-Scattering (DIS) structure functions based on Neural Networks. The parametrization provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. As an example we discuss the application of this method to the study of the structure function g1p(x,Q2)

  17. Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot%两自由度闭链机器人的神经网络自适应控制

    Institute of Scientific and Technical Information of China (English)

    陈正洪; 王勇; 李艳

    2008-01-01

    A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical use. This paper presents an adaptive proportional integral differential (PID) control algorithm based on radial basis function (RBF) neural network for trajectory tracking of a two-degree-of-freedom (2-DOF) closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

  18. Spherical radial basis functions, theory and applications

    CERN Document Server

    Hubbert, Simon; Morton, Tanya M

    2015-01-01

    This book is the first to be devoted to the theory and applications of spherical (radial) basis functions (SBFs), which is rapidly emerging as one of the most promising techniques for solving problems where approximations are needed on the surface of a sphere. The aim of the book is to provide enough theoretical and practical details for the reader to be able to implement the SBF methods to solve real world problems. The authors stress the close connection between the theory of SBFs and that of the more well-known family of radial basis functions (RBFs), which are well-established tools for solving approximation theory problems on more general domains. The unique solvability of the SBF interpolation method for data fitting problems is established and an in-depth investigation of its accuracy is provided. Two chapters are devoted to partial differential equations (PDEs). One deals with the practical implementation of an SBF-based solution to an elliptic PDE and another which describes an SBF approach for solvi...

  19. Neural Basis of Repetition Priming during Mathematical Cognition: Repetition Suppression or Repetition Enhancement?

    Science.gov (United States)

    Salimpoor, Valorie N.; Chang, Catie; Menon, Vinod

    2010-01-01

    We investigated the neural basis of repetition priming (RP) during mathematical cognition. Previous studies of RP have focused on repetition suppression as the basis of behavioral facilitation, primarily using word and object identification and classification tasks. More recently, researchers have suggested associative stimulus-response learning…

  20. Affinely Recursive Functions and Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra; Kainen, P.C.

    Atlanta : Georgia Institute of Technology, 1994 - ( Ames , W.), s. 776-779 [IMACS World Congress /14./. Atlanta (US), 11.07.1994-15.07.1994] R&D Projects: GA AV ČR IA23057; GA ČR GA201/93/0427 Keywords : neural networks * affinely recursive functions

  1. Neural basis of quasi-rational decision making.

    Science.gov (United States)

    Lee, Daeyeol

    2006-04-01

    Standard economic theories conceive homo economicus as a rational decision maker capable of maximizing utility. In reality, however, people tend to approximate optimal decision-making strategies through a collection of heuristic routines. Some of these routines are driven by emotional processes, and others are adjusted iteratively through experience. In addition, routines specialized for social decision making, such as inference about the mental states of other decision makers, might share their origins and neural mechanisms with the ability to simulate or imagine outcomes expected from alternative actions that an individual can take. A recent surge of collaborations across economics, psychology and neuroscience has provided new insights into how such multiple elements of decision making interact in the brain. PMID:16531040

  2. The neural basis of predicate-argument structure.

    Science.gov (United States)

    Hurford, James R

    2003-06-01

    Neural correlates exist for a basic component of logical formulae, PREDICATE(x). Vision and audition research in primates and humans shows two independent neural pathways; one locates objects in body-centered space, the other attributes properties, such as colour, to objects. In vision these are the dorsal and ventral pathways. In audition, similarly separable "where" and "what" pathways exist. PREDICATE(x) is a schematic representation of the brain's integration of the two processes of delivery by the senses of the location of an arbitrary referent object, mapped in parietal cortex, and analysis of the properties of the referent by perceptual subsystems. The brain computes actions using a few "deictic" variables pointing to objects. Parallels exist between such nonlinguistic variables and linguistic deictic devices. Indexicality and reference have linguistic and nonlinguistic (e.g., visual) versions, sharing the concept of attention. The individual variables of logical formulae are interpreted as corresponding to these mental variables. In computing action, the deictic variables are linked with "semantic" information about the objects, corresponding to logical predicates. Mental scene descriptions are necessary for practical tasks of primates, and preexist language phylogenetically. The type of scene descriptions used by nonhuman primates would be reused for more complex cognitive, ultimately linguistic, purposes. The provision by the brain's sensory/perceptual systems of about four variables for temporary assignment to objects, and the separate processes of perceptual categorization of the objects so identified, constitute a pre-adaptive platform on which an early system for the linguistic description of scenes developed. PMID:14968690

  3. Artificial neural network modeling of fixed bed biosorption using radial basis approach

    Science.gov (United States)

    Saha, Dipendu; Bhowal, Avijit; Datta, Siddhartha

    2010-04-01

    In modern day scenario, biosorption is a cost effective separation technology for the removal of various pollutants from wastewater and waste streams from various process industries. The difficulties associated in rigorous mathematical modeling of a fixed bed bio-adsorbing systems due to the complexities of the process often makes the development of pure black-box artificial neural network (ANN) models particularly useful in this field. In this work, radial basis function network has been employed as ANN to model the breakthrough curves in fixed bed biosorption. The prediction has been compared to the experimental breakthrough curves of Cadmium, Lanthanum and a dye available in the literature. Results show that this network gives fairly accurate representation of the actual breakthrough curves. The results obtained from ANN modeling approach shows the better agreement between experimental and predicted breakthrough curves as the error for all these situations are within 6%.

  4. The statistical basis of neural network algorithms: theory, applications, and caveats

    International Nuclear Information System (INIS)

    There is a growing interest in the use of algorithms based on artificial neural networks and related methods in order to perform data analyses, and even triggering, in modern high energy physics experiments. For the increasingly complex tasks faced by the HELP community, they are becoming more and more attractive, especially in view of their apparent power and case of implementation. Common to the methods under consideration is their use of correlations between input variables. Frequently, the algorithms are treated as black-boxes which are essentially incomprehensible, but provide almost magically good results. This paper describes in some detail the statistical basis by which these techniques actually function, and its connection to classical methods. Strong emphases are made on: the dangers of the blind application of these newer techniques and some methods to use them more wisely. Specific examples of relevance to high energy physics, and, in particular, to particle identification, are constructed to illustrate the importance of the ideas presented. (author)

  5. Symbolic functions from neural computation.

    Science.gov (United States)

    Smolensky, Paul

    2012-07-28

    Is thought computation over ideas? Turing, and many cognitive scientists since, have assumed so, and formulated computational systems in which meaningful concepts are encoded by symbols which are the objects of computation. Cognition has been carved into parts, each a function defined over such symbols. This paper reports on a research program aimed at computing these symbolic functions without computing over the symbols. Symbols are encoded as patterns of numerical activation over multiple abstract neurons, each neuron simultaneously contributing to the encoding of multiple symbols. Computation is carried out over the numerical activation values of such neurons, which individually have no conceptual meaning. This is massively parallel numerical computation operating within a continuous computational medium. The paper presents an axiomatic framework for such a computational account of cognition, including a number of formal results. Within the framework, a class of recursive symbolic functions can be computed. Formal languages defined by symbolic rewrite rules can also be specified, the subsymbolic computations producing symbolic outputs that simultaneously display central properties of both facets of human language: universal symbolic grammatical competence and statistical, imperfect performance. PMID:22711873

  6. fMRI of Simultaneous Interpretation Reveals the Neural Basis of Extreme Language Control.

    Science.gov (United States)

    Hervais-Adelman, Alexis; Moser-Mercer, Barbara; Michel, Christoph M; Golestani, Narly

    2015-12-01

    We used functional magnetic resonance imaging (fMRI) to examine the neural basis of extreme multilingual language control in a group of 50 multilingual participants. Comparing brain responses arising during simultaneous interpretation (SI) with those arising during simultaneous repetition revealed activation of regions known to be involved in speech perception and production, alongside a network incorporating the caudate nucleus that is known to be implicated in domain-general cognitive control. The similarity between the networks underlying bilingual language control and general executive control supports the notion that the frequently reported bilingual advantage on executive tasks stems from the day-to-day demands of language control in the multilingual brain. We examined neural correlates of the management of simultaneity by correlating brain activity during interpretation with the duration of simultaneous speaking and hearing. This analysis showed significant modulation of the putamen by the duration of simultaneity. Our findings suggest that, during SI, the caudate nucleus is implicated in the overarching selection and control of the lexico-semantic system, while the putamen is implicated in ongoing control of language output. These findings provide the first clear dissociation of specific dorsal striatum structures in polyglot language control, roles that are consistent with previously described involvement of these regions in nonlinguistic executive control. PMID:25037924

  7. Neural basis of music knowledge: evidence from the dementias.

    Science.gov (United States)

    Hsieh, Sharpley; Hornberger, Michael; Piguet, Olivier; Hodges, John R

    2011-09-01

    The study of patients with semantic dementia has revealed important insights into the cognitive and neural architecture of semantic memory. Patients with semantic dementia are known to have difficulty understanding the meanings of environmental sounds from an early stage but little is known about their knowledge for famous tunes, which might be preserved in some cases. Patients with semantic dementia (n = 13), Alzheimer's disease (n = 14) as well as matched healthy control participants (n = 20) underwent a battery of tests designed to assess knowledge of famous tunes, environmental sounds and famous faces, as well as volumetric magnetic resonance imaging. As a group, patients with semantic dementia were profoundly impaired in the recognition of everyday environmental sounds and famous tunes with consistent performance across testing modalities, which is suggestive of a central semantic deficit. A few notable individuals (n = 3) with semantic dementia demonstrated clear preservation of knowledge of known melodies and famous people. Defects in auditory semantics were mild in patients with Alzheimer's disease. Voxel-based morphometry of magnetic resonance brain images showed that the recognition of famous tunes correlated with the degree of right anterior temporal lobe atrophy, particularly in the temporal pole. This area was segregated from the region found to be involved in the recognition of everyday sounds but overlapped considerably with the area that was correlated with the recognition of famous faces. The three patients with semantic dementia with sparing of musical knowledge had significantly less atrophy of the right temporal pole in comparison to the other patients in the semantic dementia group. These findings highlight the role of the right temporal pole in the processing of known tunes and faces. Overlap in this region might reflect that having a unique identity is a quality that is common to both melodies and people. PMID:21857031

  8. Psycho-neural Identity as the Basis for Empirical Research and Theorization in Psychology: An Interview with Mario A. Bunge

    Science.gov (United States)

    Virues-Ortega, Javier; Hurtado-Parrado, Camilo; Martin, Toby L.; Julio, Flávia

    2012-10-01

    Mario Bunge is one of the most prolific philosophers of our time. Over the past sixty years he has written extensively about semantics, ontology, epistemology, philosophy of science and ethics. Bunge has been interested in the philosophical and methodological implications of modern psychology and more specifically in the philosophies of the relation between the neural and psychological realms. According to Bunge, functionalism, the philosophical stand of current psychology, has limited explanatory power in that neural processes are not explicitly acknowledged as components or factors of psychological phenomena. In Matter and Mind (2010), Bunge has elaborated in great detail the philosophies of the mind-brain dilemma and the basis of the psychoneural identity hypothesis, which suggests that all psychological processes can be analysed in terms of neural and physical phenomena. This article is the result of a long interview with Dr. Bunge on psychoneural identity and brain-behaviour relations.

  9. Neural basis of an inherited speech and language disorder

    OpenAIRE

    Vargha-Khadem, F; Watkins, K. E.; Price, C. J.; Ashburner, J.; Alcock, K. J.; Connelly, A; Frackowiak, R. S. J.; Friston, K. J.; Pembrey, M. E.; Mishkin, M.; Gadian, D. G.; Passingham, R. E.

    1998-01-01

    Investigation of the three-generation KE family, half of whose members are affected by a pronounced verbal dyspraxia, has led to identification of their core deficit as one involving sequential articulation and orofacial praxis. A positron emission tomography activation study revealed functional abnormalities in both cortical and subcortical motor-related areas of the frontal lobe, while quantitative analyses of magnetic resonance imaging scans revealed structural abnormalities in several of ...

  10. Neural basis of irony comprehension in children with autism: the role of prosody and context

    OpenAIRE

    Wang, A. Ting; Lee, Susan S.; Sigman, Marian; Dapretto, Mirella

    2006-01-01

    While individuals with autism spectrum disorders (ASD) are typically impaired in interpreting the communicative intent of others, little is known about the neural bases of higher-level pragmatic impairments. Here, we used functional MRI (fMRI) to examine the neural circuitry underlying deficits in understanding irony in high-functioning children with ASD. Participants listened to short scenarios and decided whether the speaker was sincere or ironic. Three types of scenarios were used in which...

  11. Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks

    Institute of Scientific and Technical Information of China (English)

    顾成奎; 王正欧; 孙雅明

    2003-01-01

    A new method for identifying nonlinear time-varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non-linearity of the system, characterize time-varying dynamics of the system by the time-varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black-box modeling ability of neural networks, the presented method can identify nonlinear time-varying systems with unknown structure. In order to improve the real-time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.

  12. Neural basis of reward anticipation and its genetic determinants.

    Science.gov (United States)

    Jia, Tianye; Macare, Christine; Desrivières, Sylvane; Gonzalez, Dante A; Tao, Chenyang; Ji, Xiaoxi; Ruggeri, Barbara; Nees, Frauke; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia J; Dove, Rachel; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny A; Heinz, Andreas; Ittermann, Bernd; Lathrop, Mark; Lemaitre, Hervé; Martinot, Jean-Luc; Paus, Tomáš; Pausova, Zdenka; Poline, Jean-Baptiste; Rietschel, Marcella; Robbins, Trevor; Smolka, Michael N; Müller, Christian P; Feng, Jianfeng; Rothenfluh, Adrian; Flor, Herta; Schumann, Gunter

    2016-04-01

    Dysfunctional reward processing is implicated in various mental disorders, including attention deficit hyperactivity disorder (ADHD) and addictions. Such impairments might involve different components of the reward process, including brain activity during reward anticipation. We examined brain nodes engaged by reward anticipation in 1,544 adolescents and identified a network containing a core striatal node and cortical nodes facilitating outcome prediction and response preparation. Distinct nodes and functional connections were preferentially associated with either adolescent hyperactivity or alcohol consumption, thus conveying specificity of reward processing to clinically relevant behavior. We observed associations between the striatal node, hyperactivity, and the vacuolar protein sorting-associated protein 4A (VPS4A) gene in humans, and the causal role of Vps4 for hyperactivity was validated in Drosophila Our data provide a neurobehavioral model explaining the heterogeneity of reward-related behaviors and generate a hypothesis accounting for their enduring nature. PMID:27001827

  13. Neural basis of an inherited speech and language disorder

    Science.gov (United States)

    Vargha-Khadem, F.; Watkins, K. E.; Price, C. J.; Ashburner, J.; Alcock, K. J.; Connelly, A.; Frackowiak, R. S. J.; Friston, K. J.; Pembrey, M. E.; Mishkin, M.; Gadian, D. G.; Passingham, R. E.

    1998-01-01

    Investigation of the three-generation KE family, half of whose members are affected by a pronounced verbal dyspraxia, has led to identification of their core deficit as one involving sequential articulation and orofacial praxis. A positron emission tomography activation study revealed functional abnormalities in both cortical and subcortical motor-related areas of the frontal lobe, while quantitative analyses of magnetic resonance imaging scans revealed structural abnormalities in several of these same areas, particularly the caudate nucleus, which was found to be abnormally small bilaterally. A recent linkage study [Fisher, S., Vargha-Khadem, F., Watkins, K. E., Monaco, A. P. & Pembry, M. E. (1998) Nat. Genet. 18, 168–170] localized the abnormal gene (SPCH1) to a 5.6-centiMorgan interval in the chromosomal band 7q31. The genetic mutation or deletion in this region has resulted in the abnormal development of several brain areas that appear to be critical for both orofacial movements and sequential articulation, leading to marked disruption of speech and expressive language. PMID:9770548

  14. Yearning to yawn: the neural basis of contagious yawning.

    Science.gov (United States)

    Schürmann, Martin; Hesse, Maike D; Stephan, Klaas E; Saarela, Miiamaaria; Zilles, Karl; Hari, Riitta; Fink, Gereon R

    2005-02-15

    Yawning is contagious: Watching another person yawn may trigger us to do the same. Here we studied brain activation with functional magnetic resonance imaging (fMRI) while subjects watched videotaped yawns. Significant increases in the blood oxygen level dependent (BOLD) signal, specific to yawn viewing as contrasted to viewing non-nameable mouth movements, were observed in the right posterior superior temporal sulcus (STS) and bilaterally in the anterior STS, in agreement with the high affinity of STS to social cues. However, no additional yawn-specific activation was observed in Broca's area, the core region of the human mirror-neuron system (MNS) that matches action observation and execution. Thus, activation associated with viewing another person yawn seems to circumvent the essential parts of the MNS, in line with the nature of contagious yawns as automatically released behavioural acts-rather than truly imitated motor patterns that would require detailed action understanding. The subjects' self-reported tendency to yawn covaried negatively with activation of the left periamygdalar region, suggesting a connection between yawn contagiousness and amygdalar activation. PMID:15670705

  15. The neural basis of risky choice with affective outcomes.

    Directory of Open Access Journals (Sweden)

    Renata S Suter

    Full Text Available Both normative and many descriptive theories of decision making under risk are based on the notion that outcomes are weighted by their probability, with subsequent maximization of the (subjective expected outcome. Numerous investigations from psychology, economics, and neuroscience have produced evidence consistent with this notion. However, this research has typically investigated choices involving relatively affect-poor, monetary outcomes. We compared choice in relatively affect-poor, monetary lottery problems with choice in relatively affect-rich medical decision problems. Computational modeling of behavioral data and model-based neuroimaging analyses provide converging evidence for substantial differences in the respective decision mechanisms. Relative to affect-poor choices, affect-rich choices yielded a more strongly curved probability weighting function of cumulative prospect theory, thus signaling that the psychological impact of probabilities is strongly diminished for affect-rich outcomes. Examining task-dependent brain activation, we identified a region-by-condition interaction indicating qualitative differences of activation between affect-rich and affect-poor choices. Moreover, brain activation in regions that were more active during affect-poor choices (e.g., the supramarginal gyrus correlated with individual trial-by-trial decision weights, indicating that these regions reflect processing of probabilities. Formal reverse inference Neurosynth meta-analyses suggested that whereas affect-poor choices seem to be based on brain mechanisms for calculative processes, affect-rich choices are driven by the representation of outcomes' emotional value and autobiographical memories associated with them. These results provide evidence that the traditional notion of expectation maximization may not apply in the context of outcomes laden with affective responses, and that understanding the brain mechanisms of decision making requires the domain

  16. Representation of Functional Data in Neural Networks

    CERN Document Server

    Rossi, Fabrice; Conan-Guez, Brieuc; Verleysen, Michel

    2005-01-01

    Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, Functional Principal Component Analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data ana...

  17. An improved radial basis function network for structural reliability analysis

    International Nuclear Information System (INIS)

    Approximation methods such as response surface method and artificial neural network (ANN) method are widely used to alleviate the computation costs in structural reliability analysis. However most of the ANN methods proposed in the literature suffer various drawbacks such as poor choice of parameter setting, poor generalization and local minimum. In this study, a support vector machine-based radial basis function (RBF) network method is proposed, in which the improved RBF model is used to approximate the limit state function and then is connected to a reliability method to estimate failure probability. Since the learning algorithm of RBF network is replaced by the support vector algorithm, the advantage of the latter, such as good generalization ability and global optimization are propagated to the former, thus the inherent drawback of RBF network can be defeated. Numerical examples are given to demonstrate the applicability of the improved RBF network method in structural reliability analysis, as well as to illustrate the validity and effectiveness of the proposed method

  18. Vector control of wind turbine on the basis of the fuzzy selective neural net*

    Science.gov (United States)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

  19. Molecular basis of whey protein functionality

    OpenAIRE

    Zoran Herceg; Anet Režek; Suzana Rimac Brnčić

    2008-01-01

    Whey proteins constitute 18-20% of total milk protein content. Their nutritive value, accompanied by diverse physico-chemical and functional properties, make whey proteins widely applicable in food industry. Highly risen demands of consumers for tastier, healthier, suitable and more natural food products have given dairy industry the opportunity for development and enrichment of food products with whey protein supplements in order to increase their functional and nutritive properties. Develop...

  20. Neural Basis of Impaired Cognitive Flexibility in Patients with Anorexia Nervosa

    OpenAIRE

    Sato, Yasuhiro; Saito, Naohiro; Utsumi, Atsushi; Aizawa, Emiko; Shoji, Tomotaka; Izumiyama, Masahiro; Mushiake, Hajime; Hongo, Michio; Fukudo, Shin

    2013-01-01

    Background Impaired cognitive flexibility in anorexia nervosa (AN) causes clinical problems and makes the disease hard to treat, but its neural basis has yet to be fully elucidated. The purpose of this study was to evaluate the brain activity of individuals with AN while performing a task requiring cognitive flexibility on the Wisconsin Card Sorting Test (WCST), which is one of the most frequently used neurocognitive measures of cognitive flexibility and problem-solving ability. Methods Parti...

  1. Neural origins of psychosocial functioning impairments in major depression.

    Science.gov (United States)

    Pulcu, Erdem; Elliott, Rebecca

    2015-09-01

    Major depressive disorder, a complex neuropsychiatric condition, is associated with psychosocial functioning impairments that could become chronic even after symptoms remit. Social functioning impairments in patients could also pose coping difficulties to individuals around them. In this Personal View, we trace the potential neurobiological origins of these impairments down to three candidate domains-namely, social perception and emotion processing, motivation and reward value processing, and social decision making. We argue that the neural basis of abnormalities in these domains could be detectable at different temporal stages during social interactions (eg, before and after decision stages), particularly within frontomesolimbic networks (ie, frontostriatal and amygdala-striatal circuitries). We review some of the experimental designs used to probe these circuits and suggest novel, integrative approaches. We propose that an understanding of the interactions between these domains could provide valuable insights for the clinical stratification of major depressive disorder subtypes and might inform future developments of novel treatment options in return. PMID:26360902

  2. EEG Source Reconstruction using Sparse Basis Function Representations

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hansen, Lars Kai

    2014-01-01

    State of the art performance of 3D EEG imaging is based on reconstruction using spatial basis function representations. In this work we augment the Variational Garrote (VG) approach for sparse approximation to incorporate spatial basis functions. As VG handles the bias variance trade-off with cross...

  3. Neural Basis of Intrinsic Motivation: Evidence from Event-Related Potentials

    Directory of Open Access Journals (Sweden)

    Jia Jin

    2015-01-01

    Full Text Available Human intrinsic motivation is of great importance in human behavior. However, although researchers have focused on this topic for decades, its neural basis was still unclear. The current study employed event-related potentials to investigate the neural disparity between an interesting stop-watch (SW task and a boring watch-stop task (WS to understand the neural mechanisms of intrinsic motivation. Our data showed that, in the cue priming stage, the cue of the SW task elicited smaller N2 amplitude than that of the WS task. Furthermore, in the outcome feedback stage, the outcome of the SW task induced smaller FRN amplitude and larger P300 amplitude than that of the WS task. These results suggested that human intrinsic motivation did exist and that it can be detected at the neural level. Furthermore, intrinsic motivation could be quantitatively indexed by the amplitude of ERP components, such as N2, FRN, and P300, in the cue priming stage or feedback stage. Quantitative measurements would also be convenient for intrinsic motivation to be added as a candidate social factor in the construction of a machine learning model.

  4. A Discrete Adapted Hierarchical Basis Solver For Radial Basis Function Interpolation

    CERN Document Server

    Castrillon-Candas, Julio Enrique; Eijkhout, Victor

    2011-01-01

    In this paper we develop a discrete Hierarchical Basis (HB) to efficiently solve the Radial Basis Function (RBF) interpolation problem with variable polynomial order. The HB forms an orthogonal set and is adapted to the kernel seed function and the placement of the interpolation nodes. Moreover, this basis is orthogonal to a set of polynomials up to a given order defined on the interpolating nodes. We are thus able to decouple the RBF interpolation problem for any order of the polynomial interpolation and solve it in two steps: (1) The polynomial orthogonal RBF interpolation problem is efficiently solved in the transformed HB basis with a GMRES iteration and a diagonal, or block SSOR preconditioner. (2) The residual is then projected onto an orthonormal polynomial basis. We apply our approach on several test cases to study its effectiveness, including an application to the Best Linear Unbiased Estimator regression problem.

  5. Behavioural and neural basis of anomalous motor learning in children with autism

    Science.gov (United States)

    Crocetti, Deana; Hulst, Thomas; Donchin, Opher; Shadmehr, Reza; Mostofsky, Stewart H.

    2015-01-01

    Autism spectrum disorder is a developmental disorder characterized by deficits in social and communication skills and repetitive and stereotyped interests and behaviours. Although not part of the diagnostic criteria, individuals with autism experience a host of motor impairments, potentially due to abnormalities in how they learn motor control throughout development. Here, we used behavioural techniques to quantify motor learning in autism spectrum disorder, and structural brain imaging to investigate the neural basis of that learning in the cerebellum. Twenty children with autism spectrum disorder and 20 typically developing control subjects, aged 8–12, made reaching movements while holding the handle of a robotic manipulandum. In random trials the reach was perturbed, resulting in errors that were sensed through vision and proprioception. The brain learned from these errors and altered the motor commands on the subsequent reach. We measured learning from error as a function of the sensory modality of that error, and found that children with autism spectrum disorder outperformed typically developing children when learning from errors that were sensed through proprioception, but underperformed typically developing children when learning from errors that were sensed through vision. Previous work had shown that this learning depends on the integrity of a region in the anterior cerebellum. Here we found that the anterior cerebellum, extending into lobule VI, and parts of lobule VIII were smaller than normal in children with autism spectrum disorder, with a volume that was predicted by the pattern of learning from visual and proprioceptive errors. We suggest that the abnormal patterns of motor learning in children with autism spectrum disorder, showing an increased sensitivity to proprioceptive error and a decreased sensitivity to visual error, may be associated with abnormalities in the cerebellum. PMID:25609685

  6. Estimation of furnace exit gas temperature (FEGT) using optimized radial basis and back-propagation neural networks

    International Nuclear Information System (INIS)

    The boiler is a very important component of a thermal power plant, and its efficient operation requires continuous online information of various relevant parameters. Furnace exit gas temperature (FEGT) is one such important design/operating parameter. Knowledge of FEGT is not only useful for design of convective heating surface but also helpful for operating actions and decision making. Its online information ensures improvement in economic benefit of the power plant. Non-availability of FEGT on the operator desk greatly limits efficient operation. In this study, a novel method of estimating FEGT using neural network is presented. The training data are first generated by calculating FEGT using heat balances through various heat exchangers. Prediction accuracy and fast response are major advantages in using neural network for estimating FEGT for operator information. Two types of feed forward neural modeling networks, radial basis function and back-propagation network, were applied and compared based on their network simplicity, model building and prediction accuracy. Results are verified on practical data obtained from a 210 MW boiler of a thermal power plant

  7. Construction of `Wachspress Type' Rational Basis Functions over Rectangles

    Indian Academy of Sciences (India)

    P L Powar; S S Rana

    2000-02-01

    In the present paper, we have constructed rational basis functions of 0 class over rectangular elements with wider choice of denominator function. This construction yields additional number of interior nodes. Hence, extra nodal points and the flexibility of denominator function suggest better approximation.

  8. Brain–immune interactions and the neural basis of disease-avoidant ingestive behaviour

    OpenAIRE

    Pacheco-López, Gustavo; Bermúdez-Rattoni, Federico

    2011-01-01

    Neuro–immune interactions are widely manifested in animal physiology. Since immunity competes for energy with other physiological functions, it is subject to a circadian trade-off between other energy-demanding processes, such as neural activity, locomotion and thermoregulation. When immunity is challenged, this trade-off is tilted to an adaptive energy protecting and reallocation strategy that is identified as ‘sickness behaviour’. We review diverse disease-avoidant behaviours in the context...

  9. Neural basis of stereotype-induced shifts in women's mental rotation performance

    OpenAIRE

    Wraga, Maryjane; Helt, Molly; Jacobs, Emily; Sullivan, Kerry

    2007-01-01

    Recent negative focus on women's academic abilities has fueled disputes over gender disparities in the sciences. The controversy derives, in part, from women's relatively poorer performance in aptitude tests, many of which require skills of spatial reasoning. We used functional magnetic imaging to examine the neural structure underlying shifts in women's performance of a spatial reasoning task induced by positive and negative stereotypes. Three groups of participants performed a task involvin...

  10. Density functional and neural network analysis

    DEFF Research Database (Denmark)

    Jalkanen, K. J.; Suhai, S.; Bohr, Henrik

    1997-01-01

    dichroism (VCD) intensities. The large changes due to hydration on the structures, relative stability of conformers, and in the VA and VCD spectra observed experimentally are reproduced by the DFT calculations. Furthermore a neural network was constructed for reproducing the inverse scattering data (infer...... the structural coordinates from spectroscopic data) that the DFT method could produce. Finally the neural network performances are used to monitor a sensitivity or dependence analysis of the importance of secondary structures....

  11. Dynamic Analysis of Wind Turbine Blades Using Radial Basis Functions

    OpenAIRE

    Ming-Hung Hsu

    2011-01-01

    Wind turbine blades play important roles in wind energy generation. The dynamic problems associated with wind turbine blades are formulated using radial basis functions. The radial basis function procedure is used to transform partial differential equations, which represent the dynamic behavior of wind turbine blades, into a discrete eigenvalue problem. Numerical results demonstrate that rotational speed significantly impacts the first frequency of a wind turbine blade. Moreover, the...

  12. Behavioral sensitization to ethanol: Neural basis and factors that influence its acquisition and expression.

    Science.gov (United States)

    Camarini, Rosana; Pautassi, Ricardo Marcos

    2016-07-01

    Ethanol-induced behavioral sensitization (EBS) was first described in 1980, approximately 10 years after the phenomenon was described for psychostimulants. Ethanol acts on γ-aminobutyric acid (GABA) and glutamate receptors as an allosteric agonist and antagonist, respectively, but it also affects many other molecular targets. The multiplicity of factors involved in the behavioral and neurochemical effects of ethanol and the ensuing complexity may explain much of the apparent disparate results, found across different labs, regarding ethanol-induced behavioral sensitization. Although the mesocorticolimbic dopamine system plays an important role in EBS, we provide evidence of the involvement of other neurotransmitter systems, mainly the glutamatergic, GABAergic, and opioidergic systems. This review also analyses the neural underpinnings (e.g., induction of cellular transcription factors such as cyclic adenosine monophosphate response element binding protein and growth factors, such as the brain-derived neurotrophic factor) and other factors that influence the phenomenon, including age, sex, dose, and protocols of drug administration. One of the reasons that make EBS an attractive phenomenon is the assumption, firmly based on empirical evidence, that EBS and addiction-related processes have common molecular and neural basis. Therefore, EBS has been used as a model of addiction processes. We discuss the association between different measures of ethanol-induced reward and EBS. Parallels between the pharmacological basis of EBS and acute motor effects of ethanol are also discussed. PMID:27093941

  13. Approximating Multivariable Functions by Feedforward Neural Nets

    Czech Academy of Sciences Publication Activity Database

    Kainen, P.C.; Kůrková, Věra; Sanguineti, M.

    Berlin : Springer, 2013 - (Bianchini, M.; Maggini, M.; Jain, L.), s. 143-181 ISBN 978-3-642-36656-7. - (Intelligent Systems Reference Library. 49) R&D Projects: GA ČR GAP202/11/1368; GA MŠk(CZ) ME10023 Grant ostatní: CNR-AV ČR(CZ) Project 2010–2012 “Complexity of Neural-Network and Kernel Computational Models Institutional support: RVO:67985807 Keywords : multivariable approximation * feedforward neural networks * network complexity * approximation rates * variational norm * best approximation * tractability of approximation Subject RIV: IN - Informatics, Computer Science

  14. Density functional and neural network analysis

    DEFF Research Database (Denmark)

    Jalkanen, K. J.; Suhai, S.; Bohr, Henrik

    dichroism (VCD) intensities. The large changes due to hydration on the structures, relative stability of conformers, and in the VA and VCD spectra observed experimentally are reproduced by the DFT calculations. Furthermore a neural network was constructed for reproducing the inverse scattering data (infer...

  15. Functional modeling of neural-glial interaction

    DEFF Research Database (Denmark)

    Postnov, D.E.; Ryazanova, L.S.; Sosnovtseva, Olga

    2007-01-01

    We propose a generalized mathematical model for a small neural-glial ensemble. The model incorporates subunits of the tripartite synapse that includes a presynaptic neuron, the synaptic terminal itself, a postsynaptic neuron, and a glial cell. The glial cell is assumed to be activated via two dif...

  16. Acoustics of a flanged cylindrical pipe using singular basis functions

    Science.gov (United States)

    Amir; Matzner; Shtrikman

    2000-02-01

    The problem of acoustic radiation from a cylindrical pipe with an infinite flange has been discussed in a number of papers. The most common approach is to decompose the field inside the pipe over a basis of Bessel functions. A very large number of basis functions is usually required, with a large degree of ripple appearing as an artifact in the solution. In this paper it is shown that a close analysis of the velocity field near the corner yields a new family of functions, which are called "edge functions." Using this set of functions as test functions and applying the moment method on the boundary between the waveguide and free space, a solution is obtained with greatly improved convergence properties and no ripple. PMID:10687680

  17. Neural basis of first and second language processing of sentence-level linguistic prosody.

    Science.gov (United States)

    Gandour, Jackson; Tong, Yunxia; Talavage, Thomas; Wong, Donald; Dzemidzic, Mario; Xu, Yisheng; Li, Xiaojian; Lowe, Mark

    2007-02-01

    A fundamental question in multilingualism is whether the neural substrates are shared or segregated for the two or more languages spoken by polyglots. This study employs functional MRI to investigate the neural substrates underlying the perception of two sentence-level prosodic phenomena that occur in both Mandarin Chinese (L1) and English (L2): sentence focus (sentence-initial vs. -final position of contrastive stress) and sentence type (declarative vs. interrogative modality). Late-onset, medium proficiency Chinese-English bilinguals were asked to selectively attend to either sentence focus or sentence type in paired three-word sentences in both L1 and L2 and make speeded-response discrimination judgments. L1 and L2 elicited highly overlapping activations in frontal, temporal, and parietal lobes. Furthermore, region of interest analyses revealed that for both languages the sentence focus task elicited a leftward asymmetry in the supramarginal gyrus; both tasks elicited a rightward asymmetry in the mid-portion of the middle frontal gyrus. A direct comparison between L1 and L2 did not show any difference in brain activation in the sentence type task. In the sentence focus task, however, greater activation for L2 than L1 occurred in the bilateral anterior insula and superior frontal sulcus. The sentence focus task also elicited a leftward asymmetry in the posterior middle temporal gyrus for L1 only. Differential activation patterns are attributed primarily to disparities between L1 and L2 in the phonetic manifestation of sentence focus. Such phonetic divergences lead to increased computational demands for processing L2. These findings support the view that L1 and L2 are mediated by a unitary neural system despite late age of acquisition, although additional neural resources may be required in task-specific circumstances for unequal bilinguals. PMID:16718651

  18. Control your anger! The neural basis of aggression regulation in response to negative social feedback.

    Science.gov (United States)

    Achterberg, Michelle; van Duijvenvoorde, Anna C K; Bakermans-Kranenburg, Marian J; Crone, Eveline A

    2016-05-01

    Negative social feedback often generates aggressive feelings and behavior. Prior studies have investigated the neural basis of negative social feedback, but the underlying neural mechanisms of aggression regulation following negative social feedback remain largely undiscovered. In the current study, participants viewed pictures of peers with feedback (positive, neutral or negative) to the participant's personal profile. Next, participants responded to the peer feedback by pressing a button, thereby producing a loud noise toward the peer, as an index of aggression. Behavioral analyses showed that negative feedback led to more aggression (longer noise blasts). Conjunction neuroimaging analyses revealed that both positive and negative feedback were associated with increased activity in the medial prefrontal cortex (PFC) and bilateral insula. In addition, more activation in the right dorsal lateral PFC (dlPFC) during negative feedback vs neutral feedback was associated with shorter noise blasts in response to negative social feedback, suggesting a potential role of dlPFC in aggression regulation, or top-down control over affective impulsive actions. This study demonstrates a role of the dlPFC in the regulation of aggressive social behavior. PMID:26755768

  19. The neural basis of learning to spell again: An fMRI study of spelling training in acquired dysgraphia.

    OpenAIRE

    Jeremy Purcell

    2015-01-01

    Introduction: In acquired dysgraphia, the spelling network is disrupted, typically causing difficulty in correctly spelling some words more than others. Various studies have demonstrated that training can be effective for recovery (e.g., Beeson et al., 2002; Rapp, 2005). However, little is known regarding the neural basis of this recovery. Studying the neural changes associated with recovery can improve understanding of how the damaged brain responds to behavioral treatment, and will be relev...

  20. Point Set Denoising Using Bootstrap-Based Radial Basis Function

    Science.gov (United States)

    Ramli, Ahmad; Abd. Majid, Ahmad

    2016-01-01

    This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study. PMID:27315105

  1. Speech/Nonspeech Detection Using Minimal Walsh Basis Functions

    Directory of Open Access Journals (Sweden)

    Pwint Moe

    2007-01-01

    Full Text Available This paper presents a new method to detect speech/nonspeech components of a given noisy signal. Employing the combination of binary Walsh basis functions and an analysis-synthesis scheme, the original noisy speech signal is modified first. From the modified signals, the speech components are distinguished from the nonspeech components by using a simple decision scheme. Minimal number of Walsh basis functions to be applied is determined using singular value decomposition (SVD. The main advantages of the proposed method are low computational complexity, less parameters to be adjusted, and simple implementation. It is observed that the use of Walsh basis functions makes the proposed algorithm efficiently applicable in real-world situations where processing time is crucial. Simulation results indicate that the proposed algorithm achieves high-speech and nonspeech detection rates while maintaining a low error rate for different noisy conditions.

  2. Functional expansion representations of artificial neural networks

    Science.gov (United States)

    Gray, W. Steven

    1992-01-01

    In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.

  3. Optimal Piecewise Linear Basis Functions in Two Dimensions

    Energy Technology Data Exchange (ETDEWEB)

    Brooks III, E D; Szoke, A

    2009-01-26

    We use a variational approach to optimize the center point coefficients associated with the piecewise linear basis functions introduced by Stone and Adams [1], for polygonal zones in two Cartesian dimensions. Our strategy provides optimal center point coefficients, as a function of the location of the center point, by minimizing the error induced when the basis function interpolation is used for the solution of the time independent diffusion equation within the polygonal zone. By using optimal center point coefficients, one expects to minimize the errors that occur when these basis functions are used to discretize diffusion equations, or transport equations in optically thick zones (where they approach the solution of the diffusion equation). Our optimal center point coefficients satisfy the requirements placed upon the basis functions for any location of the center point. We also find that the location of the center point can be optimized, but this requires numerical calculations. Curiously, the optimum center point location is independent of the values of the dependent variable on the corners only for quadrilaterals.

  4. Adult neural stem cells-Functional potential and therapeutic applications

    Institute of Scientific and Technical Information of China (English)

    YANG Lin; ZHU Jianhong

    2004-01-01

    The adult brain has been thought traditionally as a structure with a very limited regenerative capacity. It is now evident that neurogenesis in adult mammalian brain is a prevailing phenomenon. Neural stem cells with the ability to self-renew, differentiate into neurons, astrocytes and oligodendrocytes reside in some regions of the adult brain. Adult neurogenesis can be stimulated by many physiological factors including pregnancy. More strikingly, newborn neurons in hippocampus integrally function with local neurons, thus neural stem cells might play important roles in memory and learning function. It seems that neural stem cells could transdifferentiate into other tissues, such as blood cells and muscles. Although there are some impediments in this field, some attempts have been made to employ adult neural stem cells in the cell replacement therapy for traumatic and ischemic brain injuries.

  5. Collision avoidance for a mobile robot based on radial basis function hybrid force control technique

    Institute of Scientific and Technical Information of China (English)

    Wen Shu-Huan

    2009-01-01

    Collision avoidance is always difficult in the planning path for a mobile robot. In this paper, the virtual force field between a mobile robot and an obstacle is formed and regulated to maintain a desired distance by hybrid force control algorithm. Since uncertainties from robot dynamics and obstacle degrade the performance of a collision avoidance task, intelligent control is used to compensate for the uncertainties. A radial basis function (RBF) neural network is used to regulate the force field of an accurate distance between a robot and an obstacle in this paper and then simulation studies are conducted to confirm that the proposed algorithm is effective.

  6. Basis convergence of range-separated density-functional theory

    International Nuclear Information System (INIS)

    Range-separated density-functional theory (DFT) is an alternative approach to Kohn-Sham density-functional theory. The strategy of range-separated density-functional theory consists in separating the Coulomb electron-electron interaction into long-range and short-range components and treating the long-range part by an explicit many-body wave-function method and the short-range part by a density-functional approximation. Among the advantages of using many-body methods for the long-range part of the electron-electron interaction is that they are much less sensitive to the one-electron atomic basis compared to the case of the standard Coulomb interaction. Here, we provide a detailed study of the basis convergence of range-separated density-functional theory. We study the convergence of the partial-wave expansion of the long-range wave function near the electron-electron coalescence. We show that the rate of convergence is exponential with respect to the maximal angular momentum L for the long-range wave function, whereas it is polynomial for the case of the Coulomb interaction. We also study the convergence of the long-range second-order Møller-Plesset correlation energy of four systems (He, Ne, N2, and H2O) with cardinal number X of the Dunning basis sets cc − p(C)V XZ and find that the error in the correlation energy is best fitted by an exponential in X. This leads us to propose a three-point complete-basis-set extrapolation scheme for range-separated density-functional theory based on an exponential formula

  7. Spatial Neural Networks and their Functional Samples: Similarities and Differences

    OpenAIRE

    Antiqueira, Lucas; Zhao, Liang

    2014-01-01

    Models of neural networks have proven their utility in the development of learning algorithms in computer science and in the theoretical study of brain dynamics in computational neuroscience. We propose in this paper a spatial neural network model to analyze the important class of functional networks, which are commonly employed in computational studies of clinical brain imaging time series. We developed a simulation framework inspired by multichannel brain surface recordings (more specifical...

  8. Shared neural basis of social and non-social reward deficits in chronic cocaine users.

    Science.gov (United States)

    Tobler, Philippe N; Preller, Katrin H; Campbell-Meiklejohn, Daniel K; Kirschner, Matthias; Kraehenmann, Rainer; Stämpfli, Philipp; Herdener, Marcus; Seifritz, Erich; Quednow, Boris B

    2016-06-01

    Changed reward functions have been proposed as a core feature of stimulant addiction, typically observed as reduced neural responses to non-drug-related rewards. However, it was unclear yet how specific this deficit is for different types of non-drug rewards arising from social and non-social reinforcements. We used functional neuroimaging in cocaine users to investigate explicit social reward as modeled by agreement of music preferences with music experts. In addition, we investigated non-social reward as modeled by winning desired music pieces. The study included 17 chronic cocaine users and 17 matched stimulant-naive healthy controls. Cocaine users, compared with controls, showed blunted neural responses to both social and non-social reward. Activation differences were located in the ventromedial prefrontal cortex overlapping for both reward types and, thus, suggesting a non-specific deficit in the processing of non-drug rewards. Interestingly, in the posterior lateral orbitofrontal cortex, social reward responses of cocaine users decreased with the degree to which they were influenced by social feedback from the experts, a response pattern that was opposite to that observed in healthy controls. The present results suggest that cocaine users likely suffer from a generalized impairment in value representation as well as from an aberrant processing of social feedback. PMID:26969866

  9. Data Fusion Using Different Activation Functions in Artificial Neural Networks for Vehicular Navigation

    Directory of Open Access Journals (Sweden)

    MALLESWARAN M,

    2010-12-01

    Full Text Available Global positioning System (GPS and Inertial Navigation System (INS data can be integrated together to provide a reliable navigation. GPS/INS data integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and increase in position errors with time for INS. This paper aims to provide GPS/INS data integration utilizing Artificial Neural Network (ANN architecture. This architecture is based on Feed Forward Neural Networks, which generally includes Radial Basis Function (RBF neural network and Back Propagation neural network (BPN. These are systematic methods for training multi-layer artificial networks. The BPN-ANN and RBF-ANN modules are trained to predict the INS position error and provide accurate positioning of the moving vehicle. This paper also compares performance of theGPS/INS data integration system by using different activation function like Bipolar Sigmoidal Function (BPSF, Binary Sigmoidal Function (BISF, Hyperbolic Tangential Function (HTF and Gaussian Function (GF in BPN-ANN and using Gaussian function in RBF-ANN.

  10. Lag Synchronization of Switched Neural Networks via Neural Activation Function and Applications in Image Encryption.

    Science.gov (United States)

    Wen, Shiping; Zeng, Zhigang; Huang, Tingwen; Meng, Qinggang; Yao, Wei

    2015-07-01

    This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the controller based on the neuron activation function. Comparing the results, in this paper, with the existing ones shows that we improve and generalize the results derived in the previous literature. Several examples are also given to illustrate the effectiveness and potential applications in image encryption. PMID:25594985

  11. Neural basis of feature-based contextual effects on visual search behavior

    Directory of Open Access Journals (Sweden)

    Kelly eShen

    2012-01-01

    Full Text Available Searching for a visual object is known to be adaptable to context, and it is thought to result from the selection of neural representations distributed on a visual salience map, wherein stimulus-driven and goal-directed signals are combined. Here we investigated the neural basis of this adaptability by recording superior colliculus (SC neurons while three female rhesus monkeys (Macaca mulatta searched with saccadic eye movements for a target presented in an array of visual stimuli whose feature composition varied from trial to trial. We found that sensory-motor activity associated with distracters was enhanced or suppressed depending on the search array composition and that it corresponded to the monkey's search strategy, as assessed by the distribution of the occasional errant saccades. This feature-related modulation occurred independently from the saccade goal and facilitated the process of saccade target selection. We also observed feature-related enhancement in the activity associated with distracters that had been the search target during the previous session. Consistent with recurrent processing, both feature-related neuronal modulations occurred more than 60 ms after the onset of the visually evoked responses, and their near coincidence with the time of saccade target selection suggests that they are integral to this process. These results suggest that SC neuronal activity is shaped by the visual context as dictated by both stimulus-driven and goal-directed signals. Given the close proximity of the SC to the motor circuit, our findings suggest a direct link between perception and action and no need for distinct salience and motor maps.

  12. The Functional Requirements and Design Basis for Information Barriers

    Energy Technology Data Exchange (ETDEWEB)

    Fuller, James L.

    2012-05-01

    This report summarizes the results of the Information Barrier Working Group workshop held at Sandia National Laboratory in Albuquerque, NM, February 2-4, 1999. This workshop was convened to establish the functional requirements associated with warhead radiation signature information barriers, to identify the major design elements of any such system or approach, and to identify a design basis for each of these major elements. Such information forms the general design basis to be used in designing, fabricating, and evaluating the complete integrated systems developed for specific purposes.

  13. A T Matrix Method Based upon Scalar Basis Functions

    Science.gov (United States)

    Mackowski, D.W.; Kahnert, F. M.; Mishchenko, Michael I.

    2013-01-01

    A surface integral formulation is developed for the T matrix of a homogenous and isotropic particle of arbitrary shape, which employs scalar basis functions represented by the translation matrix elements of the vector spherical wave functions. The formulation begins with the volume integral equation for scattering by the particle, which is transformed so that the vector and dyadic components in the equation are replaced with associated dipole and multipole level scalar harmonic wave functions. The approach leads to a volume integral formulation for the T matrix, which can be extended, by use of Green's identities, to the surface integral formulation. The result is shown to be equivalent to the traditional surface integral formulas based on the VSWF basis.

  14. Algebraic evaluation of matrix elements in the Laguerre function basis

    Science.gov (United States)

    McCoy, A. E.; Caprio, M. A.

    2016-02-01

    The Laguerre functions constitute one of the fundamental basis sets for calculations in atomic and molecular electron-structure theory, with applications in hadronic and nuclear theory as well. While similar in form to the Coulomb bound-state eigenfunctions (from the Schrödinger eigenproblem) or the Coulomb-Sturmian functions (from a related Sturm-Liouville problem), the Laguerre functions, unlike these former functions, constitute a complete, discrete, orthonormal set for square-integrable functions in three dimensions. We construct the SU(1, 1) × SO(3) dynamical algebra for the Laguerre functions and apply the ideas of factorization (or supersymmetric quantum mechanics) to derive shift operators for these functions. We use the resulting algebraic framework to derive analytic expressions for matrix elements of several basic radial operators (involving powers of the radial coordinate and radial derivative) in the Laguerre function basis. We illustrate how matrix elements for more general spherical tensor operators in three dimensional space, such as the gradient, may then be constructed from these radial matrix elements.

  15. Nonlinear Image Restoration Using a Radial Basis Function Network

    Directory of Open Access Journals (Sweden)

    Iiguni Youji

    2004-01-01

    Full Text Available We propose a nonlinear image restoration method that uses the generalized radial basis function network (GRBFN and a regularization method. The GRBFN is used to estimate the nonlinear blurring function. The regularization method is used to recover the original image from the nonlinearly degraded image. We alternately use the two estimation methods to restore the original image from the degraded image. Since the GRBFN approximates the nonlinear blurring function itself, the existence of the inverse of the blurring process does not need to be assured. A method of adjusting the regularization parameter according to image characteristics is also presented for improving restoration performance.

  16. Neural basis of stereotype-induced shifts in women's mental rotation performance.

    Science.gov (United States)

    Wraga, Maryjane; Helt, Molly; Jacobs, Emily; Sullivan, Kerry

    2007-03-01

    Recent negative focus on women's academic abilities has fueled disputes over gender disparities in the sciences. The controversy derives, in part, from women's relatively poorer performance in aptitude tests, many of which require skills of spatial reasoning. We used functional magnetic imaging to examine the neural structure underlying shifts in women's performance of a spatial reasoning task induced by positive and negative stereotypes. Three groups of participants performed a task involving imagined rotations of the self. Prior to scanning, the positive stereotype group was exposed to a false but plausible stereotype of women's superior perspective-taking abilities; the negative stereotype group was exposed to the pervasive stereotype that men outperform women on spatial tasks; and the control group received neutral information. The significantly poorer performance we found in the negative stereotype group corresponded to increased activation in brain regions associated with increased emotional load. In contrast, the significantly improved performance we found in the positive stereotype group was associated with increased activation in visual processing areas and, to a lesser degree, complex working memory processes. These findings suggest that stereotype messages affect the brain selectively, with positive messages producing relatively more efficient neural strategies than negative messages. PMID:18985116

  17. The Neural Basis of Reversible Sentence Comprehension: Evidence from Voxel-Based Lesion Symptom Mapping in Aphasia

    Science.gov (United States)

    Thothathiri, Malathi; Kimberg, Daniel Y.; Schwartz, Myrna F.

    2012-01-01

    We explored the neural basis of reversible sentence comprehension in a large group of aphasic patients (n = 79). Voxel-based lesion symptom mapping revealed a significant association between damage in temporo-parietal cortex and impaired sentence comprehension. This association remained after we controlled for phonological working memory. We…

  18. The Neural Basis of the Right Visual Field Advantage in Reading: An MEG Analysis Using Virtual Electrodes

    Science.gov (United States)

    Barca, Laura; Cornelissen, Piers; Simpson, Michael; Urooj, Uzma; Woods, Will; Ellis, Andrew W.

    2011-01-01

    Right-handed participants respond more quickly and more accurately to written words presented in the right visual field (RVF) than in the left visual field (LVF). Previous attempts to identify the neural basis of the RVF advantage have had limited success. Experiment 1 was a behavioral study of lateralized word naming which established that the…

  19. Characteristic functions and process identification by neural networks

    CERN Document Server

    Dente, J A

    1997-01-01

    Principal component analysis (PCA) algorithms use neural networks to extract the eigenvectors of the correlation matrix from the data. However, if the process is non-Gaussian, PCA algorithms or their higher order generalisations provide only incomplete or misleading information on the statistical properties of the data. To handle such situations we propose neural network algorithms, with an hybrid (supervised and unsupervised) learning scheme, which constructs the characteristic function of the probability distribution and the transition functions of the stochastic process. Illustrative examples are presented, which include Cauchy and Levy-type processes

  20. The Neural Basis of Economic Decision-Making in the Ultimatum Game

    Science.gov (United States)

    Sanfey, Alan G.; Rilling, James K.; Aronson, Jessica A.; Nystrom, Leigh E.; Cohen, Jonathan D.

    2003-06-01

    The nascent field of neuroeconomics seeks to ground economic decision- making in the biological substrate of the brain. We used functional magnetic resonance imaging of Ultimatum Game players to investigate neural substrates of cognitive and emotional processes involved in economic decision-making. In this game, two players split a sum of money; one player proposes a division and the other can accept or reject this. We scanned players as they responded to fair and unfair proposals. Unfair offers elicited activity in brain areas related to both emotion (anterior insula) and cognition (dorsolateral prefrontal cortex). Further, significantly heightened activity in anterior insula for rejected unfair offers suggests an important role for emotions in decision-making.

  1. Application of the Characteristic Basis Function Method Using CUDA

    Directory of Open Access Journals (Sweden)

    Juan Ignacio Pérez

    2014-01-01

    Full Text Available The characteristic basis function method (CBFM is a popular technique for efficiently solving the method of moments (MoM matrix equations. In this work, we address the adaptation of this method to a relatively new computing infrastructure provided by NVIDIA, the Compute Unified Device Architecture (CUDA, and take into account some of the limitations which appear when the geometry under analysis becomes too big to fit into the Graphics Processing Unit’s (GPU’s memory.

  2. Learning of Radial Basis Function Networks: Experimental Results

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman

    World Scientific and Engineering Society Press, 2002 - (Mastorakis, N.; Mladenov, V.), s. 241-246 ISBN 960-8052-62-9. [World Multi-Conference on Circuits, Systems, Communications and Computeers /6./. Rethymno (GR), 07.07.2002-12.07.2002] R&D Projects: GA ČR GA201/01/1192; GA AV ČR IAB1030006 Institutional research plan: AV0Z1030915 Keywords : radial basis function networks * hybrid learning * soft computing Subject RIV: BA - General Mathematics

  3. Improving Genetic Optimization by Means of Radial Basis Function Networks

    Czech Academy of Sciences Publication Activity Database

    Bajer, L.; Holeňa, Martin

    Seňa : Pont, 2009 - (Vojtáš, P.). s. 95-96 ISBN 978-80-970179-1-0. [ITAT 2009. Conference on Theory and Practice of Information Theory. 25.09.2009-29.09.2009, Kráľova studňa] Institutional research plan: CEZ:AV0Z10300504 Keywords : black-box optimization * evolutionary optimization * genetic algorithms * surrogate modelling * radial basis function networks Subject RIV: IN - Informatics, Computer Science

  4. Neural basis of music imagery and the effect of musical expertise.

    Science.gov (United States)

    Herholz, Sibylle C; Lappe, Claudia; Knief, Arne; Pantev, Christo

    2008-12-01

    Although the influence of long-term musical training on the processing of heard music has been the subject of many studies, the neural basis of music imagery and the effect of musical expertise remain insufficiently understood. By means of magnetoencephalography (MEG) we compared musicians and nonmusicians in a musical imagery task with familiar melodies. Subjects listened to the beginnings of the melodies, continued them in their imagination and then heard a tone which was either a correct or an incorrect further continuation of the melody. Only in musicians was the imagery of these melodies strong enough to elicit an early preattentive brain response to unexpected incorrect continuations of the imagined melodies; this response, the imagery mismatch negativity (iMMN), peaked approximately 175 ms after tone onset and was right-lateralized. In contrast to previous studies the iMMN was not based on a heard but on a purely imagined memory trace. Our results suggest that in trained musicians imagery and perception rely on similar neuronal correlates, and that the musicians' intense musical training has modified this network to achieve a superior ability for imagery and preattentive processing of music. PMID:19046375

  5. Neurotrophin regulation of neural circuit development and function.

    Science.gov (United States)

    Park, Hyungju; Poo, Mu-ming

    2013-01-01

    Brain-derived neurotrophic factor (BDNF)--a member of a small family of secreted proteins that includes nerve growth factor, neurotrophin 3 and neurotrophin 4--has emerged as a key regulator of neural circuit development and function. The expression, secretion and actions of BDNF are directly controlled by neural activity, and secreted BDNF is capable of mediating many activity-dependent processes in the mammalian brain, including neuronal differentiation and growth, synapse formation and plasticity, and higher cognitive functions. This Review summarizes some of the recent progress in understanding the cellular and molecular mechanisms underlying neurotrophin regulation of neural circuits. The focus of the article is on BDNF, as this is the most widely expressed and studied neurotrophin in the mammalian brain. PMID:23254191

  6. Glial Contributions to Neural Function and Disease.

    Science.gov (United States)

    Rasband, Matthew N

    2016-02-01

    The nervous system consists of neurons and glial cells. Neurons generate and propagate electrical and chemical signals, whereas glia function mainly to modulate neuron function and signaling. Just as there are many different kinds of neurons with different roles, there are also many types of glia that perform diverse functions. For example, glia make myelin; modulate synapse formation, function, and elimination; regulate blood flow and metabolism; and maintain ionic and water homeostasis to name only a few. Although proteomic approaches have been used extensively to understand neurons, the same cannot be said for glia. Importantly, like neurons, glial cells have unique protein compositions that reflect their diverse functions, and these compositions can change depending on activity or disease. Here, I discuss the major classes and functions of glial cells in the central and peripheral nervous systems. I describe proteomic approaches that have been used to investigate glial cell function and composition and the experimental limitations faced by investigators working with glia. PMID:26342039

  7. An fMRI study of the neural basis hand postures specific to tool use. Presidential award proceedings

    International Nuclear Information System (INIS)

    Patients with apraxia are often unable to mimic the use of a tool, even when it is presented visually. Such mimicking involves various cognitive and motor processes, including the visual perception of a tool and the manipulation of imagined tools. Although previous studies reported the involvement of several brain areas, including the left inferior parietal lobule, in such tool-use action, the details of each process have not been well understood. To clarify the neural basis of the process involved in forming hand postures for using tools, we used functional magnetic resonance imaging (fMRI) in normal volunteers to investigate brain activation while they formed hand postures for tool manipulation. Three conditions were evaluated in separate block-designed fMRI series, formation of hand posture (A) using a tool, (B) imitating such a hand posture, and (C) to imitate the shape of a tool. Subjects formed their right hand in a manner specified according to the task conditions. Hand posturing for condition (A) induced activation in the left inferior frontal gyrus (BA 45), left inferior parietal lobule (BA 40), and the premotor area compared with the imitative posturing of condition (B). Activation in these areas might be related to processes shared by tool-use pantomime. On the other hand, comparison between conditions (A) and (C) demonstrated activation in the right superior parietal lobule (BA 7). This activation may reflect spatial regulation, in which the subject was prepared to hold and manipulate the tool. Formation of static hand postures to prepare for tool use may employ a neural network shared by various tool-use actions, such as pantomime. In addition, forming hand postures may require close coordination between the tool and hand. (author)

  8. Neural basis of the time window for subjective motor-auditory integration

    Directory of Open Access Journals (Sweden)

    Koichi Toida

    2016-01-01

    Full Text Available Temporal contiguity between an action and corresponding auditory feedback is crucial to the perception of self-generated sound. However, the neural mechanisms underlying motor–auditory temporal integration are unclear. Here, we conducted four experiments with an oddball paradigm to examine the specific event-related potentials (ERPs elicited by delayed auditory feedback for a self-generated action. The first experiment confirmed that a pitch-deviant auditory stimulus elicits mismatch negativity (MMN and P300, both when it is generated passively and by the participant’s action. In our second and third experiments, we investigated the ERP components elicited by delayed auditory feedback of for a self-generated action. We found that delayed auditory feedback elicited an enhancement of P2 (enhanced-P2 and a N300 component, which were apparently different from the MMN and P300 components observed in the first experiment. We further investigated the sensitivity of the enhanced-P2 and N300 to delay length in our fourth experiment. Strikingly, the amplitude of the N300 increased as a function of the delay length. Additionally, the N300 amplitude was significantly correlated with the conscious detection of the delay (the 50% detection point was around 200 ms, and hence reduction in the feeling of authorship of the sound (the sense of agency. In contrast, the enhanced-P2 was most prominent in short-delay (≤ 200 ms conditions and diminished in long-delay conditions. Our results suggest that different neural mechanisms are employed for the processing of temporally-deviant and pitch-deviant auditory feedback. Additionally, the temporal window for subjective motor–auditory integration is likely about 200 ms, as indicated by these auditory ERP components.

  9. A functional clustering algorithm for the analysis of neural relationships

    CERN Document Server

    Feldt, S; Hetrick, V L; Berke, J D; Zochowski, M

    2008-01-01

    We formulate a novel technique for the detection of functional clusters in neural data. In contrast to prior network clustering algorithms, our procedure progressively combines spike trains and derives the optimal clustering cutoff in a simple and intuitive manner. To demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. We observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.

  10. Functional Modeling of Neural-Glia Interaction

    DEFF Research Database (Denmark)

    Postnov, D.E.; Brazhe, N.A.; Sosnovtseva, Olga

    2012-01-01

    Functional modeling is an approach that focuses on the representation of the qualitative dynamics of the individual components (e.g. cells) of a system and on the structure of the interaction network.......Functional modeling is an approach that focuses on the representation of the qualitative dynamics of the individual components (e.g. cells) of a system and on the structure of the interaction network....

  11. Practical auxiliary basis implementation of Rung 3.5 functionals.

    Science.gov (United States)

    Janesko, Benjamin G; Scalmani, Giovanni; Frisch, Michael J

    2014-07-21

    Approximate exchange-correlation functionals for Kohn-Sham density functional theory often benefit from incorporating exact exchange. Exact exchange is constructed from the noninteracting reference system's nonlocal one-particle density matrix γ(r(->), r(->)'). Rung 3.5 functionals attempt to balance the strengths and limitations of exact exchange using a new ingredient, a projection of γ(r(->), r(->)') onto a semilocal model density matrix γ(SL)(ρ(r(->)), ∇ρ(r(->)), r(->) - r(->)'). γSL depends on the electron density ρ(r(->) at reference point r(->), and is closely related to semilocal model exchange holes. We present a practical implementation of Rung 3.5 functionals, expanding the r(->) - r(->)' dependence of γSL in an auxiliary basis set. Energies and energy derivatives are obtained from 3D numerical integration as in standard semilocal functionals. We also present numerical tests of a range of properties, including molecular thermochemistry and kinetics, geometries and vibrational frequencies, and bandgaps and excitation energies. Rung 3.5 functionals typically provide accuracy intermediate between semilocal and hybrid approximations. Nonlocal potential contributions from γSL yield interesting successes and failures for band structures and excitation energies. The results enable and motivate continued exploration of Rung 3.5 functional forms. PMID:25053297

  12. Product design on the basis of fuzzy quality function deployment

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In the implementation of quality function deployment (QFD), the determination of the target values of engineering characteristics is a complex decision process with multiple variables and multiple objectives that should trade off, and optimize all kinds of conflicts and constraints. A fuzzy linear programming model (FLP) is proposed. On the basis of the inherent fuzziness of QFD system, triangular fuzzy numbers are used to represent all the relationships and correlations, and then, the functional relationships between the customer needs and engineering characteristics and the functional correlations among the engineering characteristics are determined with the information in the house of quality (HoQ) fully used. The fuzzy linear programming (FLP) model aims to find the optimal target values of the engineering characteristics to maximize the customer satisfaction. Finally, the proposed method is illustrated by a numerical example.

  13. Fabric Quality Optimization by Using Desirability Function and Neural Networks

    Directory of Open Access Journals (Sweden)

    Hajer Souid

    2012-01-01

    Full Text Available The present paper presents a new method to estimate objective reflection of Denim fabric quality by using desirability function and neural networks. The global fabric quality was defined through one index belonging to the closed interval [0, 1]. For this reason, we have created a first algorithm that is modified when the definition of fabric quality is changed. This prediction would allow fabric producer to estimate customer’s quality satisfaction level. The present approach has conferred a good evaluation and prediction of the all-encompassing denim fabric quality. In the second stage of the study, we developed a model to predict global fabric quality from fiber, yarn, weaving parameters and finishing characteristics by using neural networks. The neural network model is accomplished by using a second algorithm based on back-propagation concept. The results have shown that the neuronal networks could predict global fabric quality of the untrained fabrics with better precision

  14. Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease

    OpenAIRE

    Voytek, Bradley; Robert T Knight

    2015-01-01

    Perception, cognition, and social interaction depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at multiple timescales. These networks are built upon a structural scaffolding with intrinsic neuroplasticity that changes with development, aging, disease, and personal experience. In this paper we begin from the perspective that successful interregional communication relies upon the transient synchronization be...

  15. The Basis of Hyperspecificity in Autism: A Preliminary Suggestion Based on Properties of Neural Nets.

    Science.gov (United States)

    McClelland, James L.

    2000-01-01

    This article discusses representation of information in neural networks and the apparent hyperspecificity that is often seen in the application of previously acquired information by children with autism. Hyperspecificity is seen as reflecting a possible feature of the neural codes used to represent concepts in the autistic brain. (Contains 12…

  16. Intelligent Control of Welding Gun Pose for Pipeline Welding Robot Based on Improved Radial Basis Function Network and Expert System

    OpenAIRE

    Jingwen Tian; Meijuan Gao; Yonggang He

    2013-01-01

    Since the control system of the welding gun pose in whole‐position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN) and expert system (ES) is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN...

  17. Dynamics of learning near singularities in radial basis function networks.

    Science.gov (United States)

    Wei, Haikun; Amari, Shun-Ichi

    2008-09-01

    The radial basis function (RBF) networks are one of the most widely used models for function approximation in the regression problem. In the learning paradigm, the best approximation is recursively or iteratively searched for based on observed data (teacher signals). One encounters difficulties in such a process when two component basis functions become identical, or when the magnitude of one component becomes null. In this case, the number of the components reduces by one, and then the reduced component recovers as the learning process proceeds further, provided such a component is necessary for the best approximation. Strange behaviors, especially the plateau phenomena, have been observed in dynamics of learning when such reduction occurs. There exist singularities in the space of parameters, and the above reduction takes place at the singular regions. This paper focuses on a detailed analysis of the dynamical behaviors of learning near the overlap and elimination singularities in RBF networks, based on the averaged learning equation that is applicable to both on-line and batch mode learning. We analyze the stability on the overlap singularity by solving the eigenvalues of the Hessian explicitly. Based on the stability analysis, we plot the analytical dynamic vector fields near the singularity, which are then compared to those real trajectories obtained by a numeric method. We also confirm the existence of the plateaus in both batch and on-line learning by simulation. PMID:18693082

  18. Radial Basis Functional Link Network and Hamilton Jacobi Issacs for Force/Position Control in Robotic Manipulation

    Directory of Open Access Journals (Sweden)

    Shuhuan Wen

    2012-01-01

    Full Text Available This paper works on hybrid force/position control in robotic manipulation and proposes an improved radial basis functional (RBF neural network, which is a robust relying on the Hamilton Jacobi Issacs principle of the force control loop. The method compensates uncertainties in a robot system by using the property of RBF neural network. The error approximation of neural network is regarded as an external interference of the system, and it is eliminated by the robust control method. Since the conventionally fixed structure of RBF network is not optimal, resource allocating network (RAN is proposed in this paper to adjust the network structure in time and avoid the underfit. Finally the advantage of system stability and transient performance is demonstrated by the numerical simulations.

  19. The Gaussian Radial Basis Function Method for Plasma Kinetic Theory

    CERN Document Server

    Hirvijoki, Eero; Belli, Emily; Embréus, Ola

    2015-01-01

    A fundamental macroscopic description of a magnetized plasma is the Vlasov equation supplemented by the nonlinear inverse-square force Fokker-Planck collision operator [Rosenbluth et al., Phys. Rev., 107, 1957]. The Vlasov part describes advection in a six-dimensional phase space whereas the collision operator involves friction and diffusion coefficients that are weighted velocity-space integrals of the particle distribution function. The Fokker-Planck collision operator is an integro-differential, bilinear operator, and numerical discretization of the operator is far from trivial. In this letter, we describe a new approach to discretize the entire kinetic system based on an expansion in Gaussian Radial Basis functions (RBFs). This approach is particularly well-suited to treat the collision operator because the friction and diffusion coefficients can be analytically calculated. Although the RBF method is known to be a powerful scheme for the interpolation of scattered multidimensional data, Gaussian RBFs also...

  20. Learning Mixtures of Truncated Basis Functions from Data

    DEFF Research Database (Denmark)

    Langseth, Helge; Nielsen, Thomas Dyhre; Pérez-Bernabé, Inmaculada;

    2014-01-01

    significantly faster, and therefore indicate that the MoTBF framework can be used for inference and learning in reasonably sized domains. Furthermore, we show how a particular sub- class of MoTBF potentials (learnable by the proposed methods) can be exploited to significantly reduce complexity during inference.......In this paper we investigate methods for learning hybrid Bayesian networks from data. First we utilize a kernel density estimate of the data in order to translate the data into a mixture of truncated basis functions (MoTBF) representation using a convex optimization technique. When utilizing a...... propose an alternative learning method that relies on the cumulative distribution function of the data. Empirical results demonstrate the usefulness of the approaches: Even though the methods produce estimators that are slightly poorer than the state of the art (in terms of log-likelihood), they are...

  1. Neural basis of a multidimensional model of body image distortion in anorexia nervosa.

    Science.gov (United States)

    Gaudio, Santino; Quattrocchi, Carlo Cosimo

    2012-09-01

    Body image distortion is a key symptom of anorexia nervosa (AN). The majority of the neuroimaging studies on body image distortion in AN conceptualized it as an unidimensional symptom. However, behavioural research considers such symptom as a multidimensional construct. Our paper systematically reviews the functional magnetic resonance (fMRI) studies on body image distortion in AN and classifies them according to a speculative model of body image distortion, that consists of the three most widely accepted components in the behavioural research: perceptive, affective and cognitive. We found that: (1) the perceptive component is mainly related to alterations of the precuneus and the inferior parietal lobe; (2) the affective component is mainly related to alterations of the prefrontal cortex, the insula and the amygdala; (3) the cognitive component has been weakly explored. These evidences seem to confirm that specific neural alterations are related to the components of the body image distortion in AN. Further neuroimaging studies are needed to better understand the complexity of the body image distortion in AN. PMID:22613629

  2. Age and experience shape developmental changes in the neural basis of language-related learning.

    Science.gov (United States)

    McNealy, Kristin; Mazziotta, John C; Dapretto, Mirella

    2011-11-01

    Very little is known about the neural underpinnings of language learning across the lifespan and how these might be modified by maturational and experiential factors. Building on behavioral research highlighting the importance of early word segmentation (i.e. the detection of word boundaries in continuous speech) for subsequent language learning, here we characterize developmental changes in brain activity as this process occurs online, using data collected in a mixed cross-sectional and longitudinal design. One hundred and fifty-six participants, ranging from age 5 to adulthood, underwent functional magnetic resonance imaging (fMRI) while listening to three novel streams of continuous speech, which contained either strong statistical regularities, strong statistical regularities and speech cues, or weak statistical regularities providing minimal cues to word boundaries. All age groups displayed significant signal increases over time in temporal cortices for the streams with high statistical regularities; however, we observed a significant right-to-left shift in the laterality of these learning-related increases with age. Interestingly, only the 5- to 10-year-old children displayed significant signal increases for the stream with low statistical regularities, suggesting an age-related decrease in sensitivity to more subtle statistical cues. Further, in a sample of 78 10-year-olds, we examined the impact of proficiency in a second language and level of pubertal development on learning-related signal increases, showing that the brain regions involved in language learning are influenced by both experiential and maturational factors. PMID:22010887

  3. Neural substrate expansion for the restoration of brain function

    Directory of Open Access Journals (Sweden)

    Han-Chiao Isaac Chen

    2016-01-01

    Full Text Available Restoring neurological and cognitive function in individuals who have suffered brain damage is one of the principal objectives of modern translational neuroscience. Electrical stimulation approaches, such as deep-brain stimulation, have achieved the most clinical success, but they ultimately may be limited by the computational capacity of the residual cerebral circuitry. An alternative strategy is brain substrate expansion, in which the computational capacity of the brain is augmented through the addition of new processing units and the reconstitution of network connectivity. This latter approach has been explored to some degree using both biological and electronic means but thus far has not demonstrated the ability to reestablish the function of large-scale neuronal networks. In this review, we contend that fulfilling the potential of brain substrate expansion will require a significant shift from current methods that emphasize direct manipulations of the brain (e.g., injections of cellular suspensions and the implantation of multi-electrode arrays to the generation of more sophisticated neural tissues and neural-electric hybrids in vitro that are subsequently transplanted into the brain. Drawing from neural tissue engineering, stem cell biology, and neural interface technologies, this strategy makes greater use of the manifold techniques available in the laboratory to create biocompatible constructs that recapitulate brain architecture and thus are more easily recognized and utilized by brain networks.

  4. Binary higher order neural networks for realizing Boolean functions.

    Science.gov (United States)

    Zhang, Chao; Yang, Jie; Wu, Wei

    2011-05-01

    In order to more efficiently realize Boolean functions by using neural networks, we propose a binary product-unit neural network (BPUNN) and a binary π-ς neural network (BPSNN). The network weights can be determined by one-step training. It is shown that the addition " σ," the multiplication " π," and two kinds of special weighting operations in BPUNN and BPSNN can implement the logical operators " ∨," " ∧," and " ¬" on Boolean algebra 〈Z(2),∨,∧,¬,0,1〉 (Z(2)={0,1}), respectively. The proposed two neural networks enjoy the following advantages over the existing networks: 1) for a complete truth table of N variables with both truth and false assignments, the corresponding Boolean function can be realized by accordingly choosing a BPUNN or a BPSNN such that at most 2(N-1) hidden nodes are needed, while O(2(N)), precisely 2(N) or at most 2(N), hidden nodes are needed by existing networks; 2) a new network BPUPS based on a collaboration of BPUNN and BPSNN can be defined to deal with incomplete truth tables, while the existing networks can only deal with complete truth tables; and 3) the values of the weights are all simply -1 or 1, while the weights of all the existing networks are real numbers. Supporting numerical experiments are provided as well. Finally, we present the risk bounds of BPUNN, BPSNN, and BPUPS, and then analyze their probably approximately correct learnability. PMID:21427020

  5. Neural Substrate Expansion for the Restoration of Brain Function.

    Science.gov (United States)

    Chen, H Isaac; Jgamadze, Dennis; Serruya, Mijail D; Cullen, D Kacy; Wolf, John A; Smith, Douglas H

    2016-01-01

    Restoring neurological and cognitive function in individuals who have suffered brain damage is one of the principal objectives of modern translational neuroscience. Electrical stimulation approaches, such as deep-brain stimulation, have achieved the most clinical success, but they ultimately may be limited by the computational capacity of the residual cerebral circuitry. An alternative strategy is brain substrate expansion, in which the computational capacity of the brain is augmented through the addition of new processing units and the reconstitution of network connectivity. This latter approach has been explored to some degree using both biological and electronic means but thus far has not demonstrated the ability to reestablish the function of large-scale neuronal networks. In this review, we contend that fulfilling the potential of brain substrate expansion will require a significant shift from current methods that emphasize direct manipulations of the brain (e.g., injections of cellular suspensions and the implantation of multi-electrode arrays) to the generation of more sophisticated neural tissues and neural-electric hybrids in vitro that are subsequently transplanted into the brain. Drawing from neural tissue engineering, stem cell biology, and neural interface technologies, this strategy makes greater use of the manifold techniques available in the laboratory to create biocompatible constructs that recapitulate brain architecture and thus are more easily recognized and utilized by brain networks. PMID:26834579

  6. Tools for Resolving Functional Activity and Connectivity within Intact Neural Circuits

    OpenAIRE

    Jennings, Joshua H.; Stuber, Garret D.

    2014-01-01

    Mammalian neural circuits are sophisticated biological systems that choreograph behavioral processes vital for survival. While the inherent complexity of discrete neural circuits has proven difficult to decipher, many parallel methodological developments promise to help delineate the function and connectivity of molecularly defined neural circuits. Here, we review recent technological advances designed to precisely monitor and manipulate neural circuit activity. We propose a holistic, multifa...

  7. Practical auxiliary basis implementation of Rung 3.5 functionals

    Energy Technology Data Exchange (ETDEWEB)

    Janesko, Benjamin G., E-mail: b.janesko@tcu.edu [Department of Chemistry, Texas Christian University, Fort Worth, Texas 76129 (United States); Scalmani, Giovanni; Frisch, Michael J. [Gaussian, Inc., 340 Quinnipiac St., Bldg. 40, Wallingford, Connecticut 06492 (United States)

    2014-07-21

    Approximate exchange-correlation functionals for Kohn-Sham density functional theory often benefit from incorporating exact exchange. Exact exchange is constructed from the noninteracting reference system's nonlocal one-particle density matrix γ(r{sup -vector},r{sup -vector}′). Rung 3.5 functionals attempt to balance the strengths and limitations of exact exchange using a new ingredient, a projection of γ(r{sup -vector},r{sup -vector} ′) onto a semilocal model density matrix γ{sub SL}(ρ(r{sup -vector}),∇ρ(r{sup -vector}),r{sup -vector}−r{sup -vector} ′). γ{sub SL} depends on the electron density ρ(r{sup -vector}) at reference point r{sup -vector}, and is closely related to semilocal model exchange holes. We present a practical implementation of Rung 3.5 functionals, expanding the r{sup -vector}−r{sup -vector} ′ dependence of γ{sub SL} in an auxiliary basis set. Energies and energy derivatives are obtained from 3D numerical integration as in standard semilocal functionals. We also present numerical tests of a range of properties, including molecular thermochemistry and kinetics, geometries and vibrational frequencies, and bandgaps and excitation energies. Rung 3.5 functionals typically provide accuracy intermediate between semilocal and hybrid approximations. Nonlocal potential contributions from γ{sub SL} yield interesting successes and failures for band structures and excitation energies. The results enable and motivate continued exploration of Rung 3.5 functional forms.

  8. Neural basis for the ability of atypical antipsychotic drugs to improve cognition in schizophrenia

    Directory of Open Access Journals (Sweden)

    Tomiki eSumiyoshi

    2013-10-01

    Full Text Available Cognitive impairments are considered to largely affect functional outcome in patients with schizophrenia, other psychotic illnesses, or mood disorders. Specifically, there is much attention to the role of psychotropic compounds acting on serotonin (5-HT receptors in ameliorating cognitive deficits of schizophrenia.It is noteworthy that atypical antipsychotic drugs, e.g. clozapine, melperone, risperidone, olanzapine, quetiapine, aripiprazole, perospirone, blonanserin, and lurasidone, have variable affinities for these receptors. Among the 5-HT receptor subtypes, the 5-HT1A receptor is attracting particular interests as a potential target for enhancing cognition, based on preclinical and clinical evidence.The neural network underlying the ability of 5-HT1A agonists to treat cognitive impairments of schizophrenia likely includes dopamine, glutamate, and GABA neurons. A novel strategy for cognitive enhancement in psychosis may be benefitted by focusing on energy metabolism in the brain. In this context, lactate plays a major role, and has been shown to protect neurons against oxidative and other stressors. In particular, our data indicate chronic treatment with tandospirone, a partial 5-HT1A agonist, recover stress-induced lactate production in the prefrontal cortex of a rat model of schizophrenia. Recent advances of electrophysiological measures, e.g. event-related potentials, and their imaging have provided insights into facilitative effects on cognition of some atypical antipsychotic drugs acting directly or indirectly on 5-HT1A receptors.These findings are expected to promote the development of novel therapeutics for the improvement of functional outcome in people with schizophrenia.

  9. Extension of the basis set of linearized augmented plane wave (LAPW) method by using supplemented tight binding basis functions

    Science.gov (United States)

    Nikolaev, A. V.; Lamoen, D.; Partoens, B.

    2016-07-01

    In order to increase the accuracy of the linearized augmented plane wave (LAPW) method, we present a new approach where the plane wave basis function is augmented by two different atomic radial components constructed at two different linearization energies corresponding to two different electron bands (or energy windows). We demonstrate that this case can be reduced to the standard treatment within the LAPW paradigm where the usual basis set is enriched by the basis functions of the tight binding type, which go to zero with zero derivative at the sphere boundary. We show that the task is closely related with the problem of extended core states which is currently solved by applying the LAPW method with local orbitals (LAPW+LO). In comparison with LAPW+LO, the number of supplemented basis functions in our approach is doubled, which opens up a new channel for the extension of the LAPW and LAPW+LO basis sets. The appearance of new supplemented basis functions absent in the LAPW+LO treatment is closely related with the existence of the u ˙ l -component in the canonical LAPW method. We discuss properties of additional tight binding basis functions and apply the extended basis set for computation of electron energy bands of lanthanum (face and body centered structures) and hexagonal close packed lattice of cadmium. We demonstrate that the new treatment gives lower total energies in comparison with both canonical LAPW and LAPW+LO, with the energy difference more pronounced for intermediate and poor LAPW basis sets.

  10. Finite basis representations with nondirect product basis functions having structure similar to that of spherical harmonics

    Science.gov (United States)

    Czakó, Gábor; Szalay, Viktor; Császár, Attila G.

    2006-01-01

    The currently most efficient finite basis representation (FBR) method [Corey et al., in Numerical Grid Methods and Their Applications to Schrödinger Equation, NATO ASI Series C, edited by C. Cerjan (Kluwer Academic, New York, 1993), Vol. 412, p. 1; Bramley et al., J. Chem. Phys. 100, 6175 (1994)] designed specifically to deal with nondirect product bases of structures ϕnl(s)fl(u), χml(t)ϕnl(s)fl(u), etc., employs very special l-independent grids and results in a symmetric FBR. While highly efficient, this method is not general enough. For instance, it cannot deal with nondirect product bases of the above structure efficiently if the functions ϕnl(s) [and/or χml(t)] are discrete variable representation (DVR) functions of the infinite type. The optimal-generalized FBR(DVR) method [V. Szalay, J. Chem. Phys. 105, 6940 (1996)] is designed to deal with general, i.e., direct and/or nondirect product, bases and grids. This robust method, however, is too general, and its direct application can result in inefficient computer codes [Czakó et al., J. Chem. Phys. 122, 024101 (2005)]. It is shown here how the optimal-generalized FBR method can be simplified in the case of nondirect product bases of structures ϕnl(s)fl(u), χml(t)ϕnl(s)fl(u), etc. As a result the commonly used symmetric FBR is recovered and simplified nonsymmetric FBRs utilizing very special l-dependent grids are obtained. The nonsymmetric FBRs are more general than the symmetric FBR in that they can be employed efficiently even when the functions ϕnl(s) [and/or χml(t)] are DVR functions of the infinite type. Arithmetic operation counts and a simple numerical example presented show unambiguously that setting up the Hamiltonian matrix requires significantly less computer time when using one of the proposed nonsymmetric FBRs than that in the symmetric FBR. Therefore, application of this nonsymmetric FBR is more efficient than that of the symmetric FBR when one wants to diagonalize the Hamiltonian matrix

  11. Losing Neutrality: The Neural Basis of Impaired Emotional Control without Sleep.

    Science.gov (United States)

    Simon, Eti Ben; Oren, Noga; Sharon, Haggai; Kirschner, Adi; Goldway, Noam; Okon-Singer, Hadas; Tauman, Rivi; Deweese, Menton M; Keil, Andreas; Hendler, Talma

    2015-09-23

    Sleep deprivation has been shown recently to alter emotional processing possibly associated with reduced frontal regulation. Such impairments can ultimately fail adaptive attempts to regulate emotional processing (also known as cognitive control of emotion), although this hypothesis has not been examined directly. Therefore, we explored the influence of sleep deprivation on the human brain using two different cognitive-emotional tasks, recorded using fMRI and EEG. Both tasks involved irrelevant emotional and neutral distractors presented during a competing cognitive challenge, thus creating a continuous demand for regulating emotional processing. Results reveal that, although participants showed enhanced limbic and electrophysiological reactions to emotional distractors regardless of their sleep state, they were specifically unable to ignore neutral distracting information after sleep deprivation. As a consequence, sleep deprivation resulted in similar processing of neutral and negative distractors, thus disabling accurate emotional discrimination. As expected, these findings were further associated with a decrease in prefrontal connectivity patterns in both EEG and fMRI signals, reflecting a profound decline in cognitive control of emotion. Notably, such a decline was associated with lower REM sleep amounts, supporting a role for REM sleep in overnight emotional processing. Altogether, our findings suggest that losing sleep alters emotional reactivity by lowering the threshold for emotional activation, leading to a maladaptive loss of emotional neutrality. Significance statement: Sleep loss is known as a robust modulator of emotional reactivity, leading to increased anxiety and stress elicited by seemingly minor triggers. In this work, we aimed to portray the neural basis of these emotional impairments and their possible association with frontal regulation of emotional processing, also known as cognitive control of emotion. Using specifically suited EEG and f

  12. Nuclear charge radii: Density functional theory meets Bayesian neural networks

    CERN Document Server

    Utama, Raditya; Piekarewicz, Jorge

    2016-01-01

    The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. We explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonst...

  13. A spiking neural network architecture for nonlinear function approximation.

    Science.gov (United States)

    Iannella, N; Back, A D

    2001-01-01

    Multilayer perceptrons have received much attention in recent years due to their universal approximation capabilities. Normally, such models use real valued continuous signals, although they are loosely based on biological neuronal networks that encode signals using spike trains. Spiking neural networks are of interest both from a biological point of view and in terms of a method of robust signaling in particularly noisy or difficult environments. It is important to consider networks based on spike trains. A basic question that needs to be considered however, is what type of architecture can be used to provide universal function approximation capabilities in spiking networks? In this paper, we propose a spiking neural network architecture using both integrate-and-fire units as well as delays, that is capable of approximating a real valued function mapping to within a specified degree of accuracy. PMID:11665783

  14. Radial basis function network learns ceramic processing and predicts related strength and density

    Energy Technology Data Exchange (ETDEWEB)

    Cios, K.J.; Baaklini, G.Y.; Vary, A.; Tjia, R.E.

    1993-05-01

    Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials.

  15. Radial basis function network learns ceramic processing and predicts related strength and density

    Energy Technology Data Exchange (ETDEWEB)

    Cios, K.J.; Baaklini, G.Y.; Vary, A. (NASA Lewis Research Center, Cleveland, OH (United States)); Tjia, R.E. (Univ. of Toledo, OH (United States))

    1994-07-01

    Radial basis function (RBF) neural networks were trained using the data from 273 Si[sub 3]N[sub 4] modulus of rupture (MOR) bars that were tested at room temperature and 135 MOR bars that were tested at 1,370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used s the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The nodes at data points'' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12% and density with an average error of less than 2%. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of emerging ceramic materials.

  16. Radial basis function network learns ceramic processing and predicts related strength and density

    Science.gov (United States)

    Cios, Krzysztof J.; Baaklini, George Y.; Vary, Alex; Tjia, Robert E.

    1993-01-01

    Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials.

  17. Dynamics of on-line learning in radial basis function networks

    Science.gov (United States)

    Freeman, Jason A. S.; Saad, David

    1997-07-01

    On-line learning is examined for the radial basis function network, an important and practical type of neural network. The evolution of generalization error is calculated within a framework which allows the phenomena of the learning process, such as the specialization of the hidden units, to be analyzed. The distinct stages of training are elucidated, and the role of the learning rate described. The three most important stages of training, the symmetric phase, the symmetry-breaking phase, and the convergence phase, are analyzed in detail; the convergence phase analysis allows derivation of maximal and optimal learning rates. As well as finding the evolution of the mean system parameters, the variances of these parameters are derived and shown to be typically small. Finally, the analytic results are strongly confirmed by simulations.

  18. Explicit transverse leakage treatment using an analytic basis function expansion

    International Nuclear Information System (INIS)

    An explicit method for calculating the transverse leakage is presented in this paper. The method is based upon the use of analytic basis functions, which represent individual eigenfunctions of the neutron diffusion equation. The intranodal flux solution is expressed as an eigenspace, and can be solved by using the already calculated surface currents and flux moments as boundary conditions. The salient feature of the method, therefore, is that no ad hoc presumptions are made with regard to the leakage shape. The individual eigenfunctions are calculated based upon already calculated parameters from the flux solution and response matrix solution, and therefore no additional parameters are introduced into the problem, which could lead to an unwanted increase in computation time. The new transverse leakage method is implemented in PSU's NEM code and is tested against the OECD/NEA 3D C5G7 rodded MOX benchmark and the C3 benchmark. (author)

  19. Optimized Radial Basis Function Classifier for Multi Modal Biometrics

    Directory of Open Access Journals (Sweden)

    Anand Viswanathan

    2014-07-01

    Full Text Available Biometric systems can be used for the identification or verification of humans based on their physiological or behavioral features. In these systems the biometric characteristics such as fingerprints, palm-print, iris or speech can be recorded and are compared with the samples for the identification or verification. Multimodal biometrics is more accurate and solves spoof attacks than the single modal bio metrics systems. In this study, a multimodal biometric system using fingerprint images and finger-vein patterns is proposed and also an optimized Radial Basis Function (RBF kernel classifier is proposed to identify the authorized users. The extracted features from these modalities are selected by PCA and kernel PCA and combined to classify by RBF classifier. The parameters of RBF classifier is optimized by using BAT algorithm with local search. The performance of the proposed classifier is compared with the KNN classifier, Naïve Bayesian classifier and non-optimized RBF classifier.

  20. Adaptive radial basis function mesh deformation using data reduction

    Science.gov (United States)

    Gillebaart, T.; Blom, D. S.; van Zuijlen, A. H.; Bijl, H.

    2016-09-01

    Radial Basis Function (RBF) mesh deformation is one of the most robust mesh deformation methods available. Using the greedy (data reduction) method in combination with an explicit boundary correction, results in an efficient method as shown in literature. However, to ensure the method remains robust, two issues are addressed: 1) how to ensure that the set of control points remains an accurate representation of the geometry in time and 2) how to use/automate the explicit boundary correction, while ensuring a high mesh quality. In this paper, we propose an adaptive RBF mesh deformation method, which ensures the set of control points always represents the geometry/displacement up to a certain (user-specified) criteria, by keeping track of the boundary error throughout the simulation and re-selecting when needed. Opposed to the unit displacement and prescribed displacement selection methods, the adaptive method is more robust, user-independent and efficient, for the cases considered. Secondly, the analysis of a single high aspect ratio cell is used to formulate an equation for the correction radius needed, depending on the characteristics of the correction function used, maximum aspect ratio, minimum first cell height and boundary error. Based on the analysis two new radial basis correction functions are derived and proposed. This proposed automated procedure is verified while varying the correction function, Reynolds number (and thus first cell height and aspect ratio) and boundary error. Finally, the parallel efficiency is studied for the two adaptive methods, unit displacement and prescribed displacement for both the CPU as well as the memory formulation with a 2D oscillating and translating airfoil with oscillating flap, a 3D flexible locally deforming tube and deforming wind turbine blade. Generally, the memory formulation requires less work (due to the large amount of work required for evaluating RBF's), but the parallel efficiency reduces due to the limited

  1. Exploring the mechanism of neural-function reconstruction by reinnervated nerves in targeted muscles

    Institute of Scientific and Technical Information of China (English)

    Hui ZHOU; Lin YANG; Feng-xia WU; Jian-ping HUANG; Liang-qing ZHANG; Ying-jian YANG; Guang-lin LI

    2014-01-01

    A lack of myoelectric sources after limb amputation is a critical challenge in the control of multifunctional motorized prostheses. To reconstruct myoelectric sources physiologically related to lost limbs, a newly proposed neural-function construc-tion method, targeted muscle reinnervation (TMR), appears promising. Recent advances in the TMR technique suggest that TMR could provide additional motor command information for the control of multifunctional myoelectric prostheses. However, little is known about the nature of the physiological functional recovery of the reinnervated muscles. More understanding of the under-lying mechanism of TMR could help us fine tune the technique to maximize its capability to achieve a much higher performance in the control of multifunctional prostheses. In this study, rats were used as an animal model for TMR surgery involving transferring a median nerve into the pectoralis major, which served as the target muscle. Intramuscular myoelectric signals reconstructed following TMR were recorded by implanted wire electrodes and analyzed to explore the nature of the neural-function recon-struction achieved by reinnervation of targeted muscles. Our results showed that the active myoelectric signal reconstructed in the targeted muscle was acquired one week after TMR surgery, and its amplitude gradually became stronger over time. These pre-liminary results from rats may serve as a basis for exploring the mechanism of neural-function reconstruction by the TMR tech-nique in human subjects.

  2. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

    Directory of Open Access Journals (Sweden)

    Lukas Falat

    2016-01-01

    Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  3. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

    Science.gov (United States)

    Falat, Lukas; Marcek, Dusan; Durisova, Maria

    2016-01-01

    This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

  4. Neural network parametrization of deep-inelastic structure functions

    International Nuclear Information System (INIS)

    We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We generate a Monte Carlo sample of pseudo-data configurations and we train an ensemble of neural networks on them. This effectively provides us with a probability measure in the space of structure functions, within the whole kinematic region where data are available. This measure can then be used to determine the value of the structure function, its error, point-to-point correlations and generally the value and uncertainty of any function of the structure function itself. We apply this technique to the determination of the structure function F2 of the proton and deuteron, and a precision determination of the isotriplet combination F2[p-d]. We discuss in detail these results, check their stability and accuracy, and make them available in various formats for applications. (author)

  5. Circadian gating of neuronal functionality: a basis for iterative metaplasticity.

    Science.gov (United States)

    Iyer, Rajashekar; Wang, Tongfei A; Gillette, Martha U

    2014-01-01

    Brain plasticity, the ability of the nervous system to encode experience, is a modulatory process leading to long-lasting structural and functional changes. Salient experiences induce plastic changes in neurons of the hippocampus, the basis of memory formation and recall. In the suprachiasmatic nucleus (SCN), the central circadian (~24-h) clock, experience with light at night induces changes in neuronal state, leading to circadian plasticity. The SCN's endogenous ~24-h time-generator comprises a dynamic series of functional states, which gate plastic responses. This restricts light-induced alteration in SCN state-dynamics and outputs to the nighttime. Endogenously generated circadian oscillators coordinate the cyclic states of excitability and intracellular signaling molecules that prime SCN receptivity to plasticity signals, generating nightly windows of susceptibility. We propose that this constitutes a paradigm of ~24-h iterative metaplasticity, the repeated, patterned occurrence of susceptibility to induction of neuronal plasticity. We detail effectors permissive for the cyclic susceptibility to plasticity. We consider similarities of intracellular and membrane mechanisms underlying plasticity in SCN circadian plasticity and in hippocampal long-term potentiation (LTP). The emerging prominence of the hippocampal circadian clock points to iterative metaplasticity in that tissue as well. Exploring these links holds great promise for understanding circadian shaping of synaptic plasticity, learning, and memory. PMID:25285070

  6. Possible scenarios of the influence of low-dose ionizing radiation on neural functioning.

    Science.gov (United States)

    Zakhvataev, Vladimir E

    2015-12-01

    Possible scenarios of the influence of ionizing radiation on neural functioning and the CNS are suggested. We argue that the radiation-induced bystander mechanisms associated with Ca(2+) flows, reactive nitrogen and oxygen species, and cytokines might lead to modulation of certain neuronal signaling pathways. The considered scenarios of conjugation of the bystander signaling and the neuronal signaling might result in modulation of certain synaptic receptors, neurogenesis, neurotransmission, channel conductance, synaptic signaling, different forms of neural plasticity, memory formation and storage, and learning. On this basis, corresponding new possible strategies for treating neurodegenerative deceases and mental disorders are proposed. The mechanisms considered might also be associated with neuronal survival and relevant to the treatment for brain injuries. At the same time, these mechanisms might be associated with detrimental effects and might facilitate the development of some neurological and psychiatric disorders. PMID:26526727

  7. Reconstructing the magnetosphere from data using radial basis functions

    Science.gov (United States)

    Andreeva, Varvara A.; Tsyganenko, Nikolai A.

    2016-03-01

    A new method is proposed to derive from data magnetospheric magnetic field configurations without any a priori assumptions on the geometry of electric currents. The approach utilizes large sets of archived satellite data and uses an advanced technique to represent the field as a sum of toroidal and poloidal parts, whose generating potentials Ψ1 and Ψ2 are expanded into series of radial basis functions (RBFs) with their nodes regularly distributed over the 3-D modeling domain. The method was tested by reconstructing the inner and high-latitude field within geocentric distances up to 12RE on the basis of magnetometer data of Geotail, Polar, Cluster, Time History of Events and Macroscale Interactions during Substorms, and Van Allen space probes, taken during 1995-2015. Four characteristic states of the magnetosphere before and during a disturbance have been modeled: a quiet prestorm period, storm deepening phase with progressively decreasing SYM-H index, the storm maximum around the negative peak of SYM-H, and the recovery phase. Fitting the RBF model to data faithfully resolved contributions to the total magnetic field from all principal sources, including the westward and eastward ring current, the tail current, diamagnetic currents associated with the polar cusps, and the large-scale effect of the field-aligned currents. For two main phase conditions, the model field exhibited a strong dawn-dusk asymmetry of the low-latitude magnetic depression, extending to low altitudes and partly spreading sunward from the terminator plane in the dusk sector. The RBF model was found to resolve even finer details, such as the bifurcation of the innermost tail current. The method can be further developed into a powerful tool for data-based studies of the magnetospheric currents.

  8. Dimensionality reduction in conic section function neural network

    Indian Academy of Sciences (India)

    Tulay Yildirim; Lale Ozyilmaz

    2002-12-01

    This paper details how dimensionality can be reduced in conic section function neural networks (CSFNN). This is particularly important for hardware implementation of networks. One of the main problems to be solved when considering the hardware design is the high connectivity requirement. If the effect that each of the network inputs has on the network output after training a neural network is known, then some inputs can be removed from the network. Consequently, the dimensionality of the network, and hence, the connectivity and the training time can be reduced. Sensitivity analysis, which extracts the cause and effect relationship between the inputs and outputs of the network, has been proposed as a method to achieve this and is investigated for Iris plant, thyroid disease and ionosphere databases. Simulations demonstrate the validity of the method used.

  9. Age and Experience Shape Developmental Changes in the Neural Basis of Language-Related Learning

    Science.gov (United States)

    McNealy, Kristin; Mazziotta, John C.; Dapretto, Mirella

    2011-01-01

    Very little is known about the neural underpinnings of language learning across the lifespan and how these might be modified by maturational and experiential factors. Building on behavioral research highlighting the importance of early word segmentation (i.e. the detection of word boundaries in continuous speech) for subsequent language learning,…

  10. The Neural Basis of Sustained and Transient Attentional Control in Young Adults with ADHD

    Science.gov (United States)

    Banich, Marie T.; Burgess, Gregory C.; Depue, Brendan E.; Ruzic, Luka; Bidwell, L. Cinnamon; Hitt-Laustsen, Sena; Du, Yiping P.; Willcutt, Erik G.

    2009-01-01

    Differences in neural activation during performance on an attentionally demanding Stroop task were examined between 23 young adults with ADHD carefully selected to not be co-morbid for other psychiatric disorders and 23 matched controls. A hybrid blocked/single-trial design allowed for examination of more sustained vs. more transient aspects of…

  11. Neural basis for brain responses to TV commercials: a high-resolution EEG study.

    Science.gov (United States)

    Astolfi, Laura; De Vico Fallani, F; Cincotti, F; Mattia, D; Bianchi, L; Marciani, M G; Salinari, S; Colosimo, A; Tocci, A; Soranzo, R; Babiloni, F

    2008-12-01

    We investigated brain activity during the observation of TV commercials by tracking the cortical activity and the functional connectivity changes in normal subjects. The aim was to elucidate if the TV commercials that were remembered by the subjects several days after their first observation elicited particular brain activity and connectivity compared with those generated during the observation of TV commercials that were quickly forgotten. High-resolution electroencephalogram (EEG) recordings were performed in a group of healthy subjects and the cortical activity during the observation of TV commercials was evaluated in several regions of interest coincident with the Brodmann areas (BAs). The patterns of cortical connectivity were obtained in the four principal frequency bands, Theta (3-7 Hz), Alpha (8-12 Hz), Beta (13-30 Hz), Gamma (30-40 Hz) and the directed influences between any given pair of the estimated cortical signals were evaluated by use of a multivariate spectral technique known as partial directed coherence. The topology of the cortical networks has been identified with tools derived from graph theory. Results suggest that the cortical activity and connectivity elicited by the viewing of the TV commercials that were remembered by the experimental subjects are markedly different from the brain activity elicited during the observation of the TV commercials that were forgotten. In particular, during the observation of the TV commercials that were remembered, the amount of cortical spectral activity from the frontal areas (BA 8 and 9) and from the parietal areas (BA 5, 7, and 40) is higher compared with the activity elicited by the observation of TV commercials that were forgotten. In addition, network analysis suggests a clear role of the parietal areas as a target of the incoming flow of information from all the other parts of the cortex during the observation of TV commercials that have been remembered. The techniques presented here shed new light on

  12. A posteriori error estimator for adaptive local basis functions to solve Kohn-Sham density functional theory

    CERN Document Server

    Kaye, Jason; Yang, Chao

    2014-01-01

    Kohn-Sham density functional theory is one of the most widely used electronic structure theories. The recently developed adaptive local basis functions form an accurate and systematically improvable basis set for solving Kohn-Sham density functional theory using discontinuous Galerkin methods, requiring a small number of basis functions per atom. In this paper we develop residual-based a posteriori error estimates for the adaptive local basis approach, which can be used to guide non-uniform basis refinement for highly inhomogeneous systems such as surfaces and large molecules. The adaptive local basis functions are non-polynomial basis functions, and standard a posteriori error estimates for $hp$-refinement using polynomial basis functions do not directly apply. We generalize the error estimates for $hp$-refinement to non-polynomial basis functions. We demonstrate the practical use of the a posteriori error estimator in performing three-dimensional Kohn-Sham density functional theory calculations for quasi-2D...

  13. CAD and mesh repair with Radial Basis Functions

    Science.gov (United States)

    Marchandise, E.; Piret, C.; Remacle, J.-F.

    2012-03-01

    In this paper we present a process that includes both model/mesh repair and mesh generation. The repair algorithm is based on an initial mesh that may be either an initial mesh of a dirty CAD model or STL triangulation with many errors such as gaps, overlaps and T-junctions. This initial mesh is then remeshed by computing a discrete parametrization with Radial Basis Functions (RBF's). We showed in [1] that a discrete parametrization can be computed by solving Partial Differential Equations (PDE's) on an initial correct mesh using finite elements. Paradoxically, the meshless character of the RBF's makes it an attractive numerical method for solving the PDE's for the parametrization in the case where the initial mesh contains errors or holes. In this work, we implement the Orthogonal Gradients method to be described in [2], as a RBF solution method for solving PDE's on arbitrary surfaces. Different examples show that the presented method is able to deal with errors such as gaps, overlaps, T-junctions and that the resulting meshes are of high quality. Moreover, the presented algorithm can be used as a hole-filling algorithm to repair meshes with undesirable holes. The overall procedure is implemented in the open-source mesh generator Gmsh [3].

  14. A quantitative overview of biophysical forces impinging on neural function

    International Nuclear Information System (INIS)

    The fundamentals of neuronal membrane excitability are globally described using the Hodgkin-Huxley (HH) model. The HH model, however, does not account for a number of biophysical phenomena associated with action potentials or propagating nerve impulses. Physical mechanisms underlying these processes, such as reversible heat transfer and axonal swelling, have been compartmentalized and separately investigated to reveal neuronal activity is not solely influenced by electrical or biochemical factors. Instead, mechanical forces and thermodynamics also govern neuronal excitability and signaling. To advance our understanding of neuronal function and dysfunction, compartmentalized analyses of electrical, chemical, and mechanical processes need to be revaluated and integrated into more comprehensive theories. The present perspective is intended to provide a broad overview of biophysical forces that can influence neural function, but which have been traditionally underappreciated in neuroscience. Further, several examples where mechanical forces have been shown to exert their actions on nervous system development, signaling, and plasticity are highlighted to underscore their importance in sculpting neural function. By considering the collective actions of biophysical forces influencing neuronal activity, our working models can be expanded and new paradigms can be applied to the investigation and characterization of brain function and dysfunction. (topical review)

  15. Adaptive, associative, and self-organizing functions in neural computing.

    Science.gov (United States)

    Kohonen, T

    1987-12-01

    This paper contains an attempt to describe certain adaptive and cooperative functions encountered in neural networks. The approach is a compromise between biological accuracy and mathematical clarity. two types of differential equation seem to describe the basic effects underlying the information of these functions: the equation for the electrical activity of the neuron and the adaptation equation that describes changes in its input connectivities. Various phenomena and operations are derivable from them: clustering of activity in a laterally interconnected nework; adaptive formation of feature detectors; the autoassociative memory function; and self-organized formation of ordered sensory maps. The discussion tends to reason what functions are readily amenable to analytical modeling and which phenomena seem to ensue from the more complex interactions that take place in the brain. PMID:20523469

  16. Sleep quality and neural circuit function supporting emotion regulation

    Directory of Open Access Journals (Sweden)

    Minkel Jared D

    2012-12-01

    Full Text Available Abstract Background Recent laboratory studies employing an extended sleep deprivation model have mapped sleep-related changes in behavior onto functional alterations in specific brain regions supporting emotion, suggesting possible biological mechanisms for an association between sleep difficulties and deficits in emotion regulation. However, it is not yet known if similar behavioral and neural changes are associated with the more modest variability in sleep observed in daily life. Methods We examined relationships between sleep and neural circuitry of emotion using the Pittsburgh Sleep Quality Index and fMRI data from a widely used emotion regulation task focusing on cognitive reappraisal of negative emotional stimuli in an unselected sample of 97 adult volunteers (48 women; mean age 42.78±7.37 years, range 30–54 years old. Results Emotion regulation was associated with greater activation in clusters located in the dorsomedial prefrontal cortex (dmPFC, left dorsolateral prefrontal cortex (dlPFC, and inferior parietal cortex. Only one subscale from the Pittsburgh Sleep Quality Index, use of sleep medications, was related to BOLD responses in the dmPFC and dlPFC during cognitive reappraisal. Use of sleep medications predicted lesser BOLD responses during reappraisal, but other aspects of sleep, including sleep duration and subjective sleep quality, were not related to neural activation in this paradigm. Conclusions The relatively modest variability in sleep that is common in the general community is unlikely to cause significant disruption in neural circuits supporting reactivity or regulation by cognitive reappraisal of negative emotion. Use of sleep medication however, may influence emotion regulation circuitry, but additional studies are necessary to determine if such use plays a causal role in altering emotional responses.

  17. The role of BDNF in depression on the basis of its location in the neural circuitry

    Institute of Scientific and Technical Information of China (English)

    Hui YU; Zhe-yu CHEN

    2011-01-01

    Depression is one of the most prevalent and life-threatening forms of mental illnesses and the neural circuitry underlying depression remains incompletely understood. Most attention in the field has focused on hippocampal and frontal cortical regions for their roles in depression and antidepressant action. While these regions no doubt play important roles in the mental illness, there is compelling evi-dence that other brain regions are also involved. Brain-derived neurotrophic factor (BDNF) is broadly expressed in the developing and adult mammalian brain and has been implicated in development, neural regeneration, synaptic transmission, synaptic plasticity and neurogenesis. Recently BDNF has been shown to play an important role in the pathophysiology of depression, however there are con-troversial reports about the effects of BDNF on depression. Here, we present an overview of the current knowledge concerning BDNF actions and associated intracellular signaling in hippocampus, prefrontal cortex, nucleus accumbens (NAc) and amygdala as their rela-tion to depression.

  18. Patterns of theta oscillation reflect the neural basis of individual differences in epistemic motivation.

    Science.gov (United States)

    Mussel, Patrick; Ulrich, Natalie; Allen, John J B; Osinsky, Roman; Hewig, Johannes

    2016-01-01

    Theta oscillations in the EEG have been shown to reflect ongoing cognitive processes related to mental effort. Here, we show that the pattern of theta oscillation in response to varying cognitive demands reflects stable individual differences in the personality trait epistemic motivation: Individuals with high levels of epistemic motivation recruit relatively more cognitive resources in response to situations possessing high, compared to low, cognitive demand; individuals with low levels do not show such a specific response. Our results provide direct evidence for the theory of the construct need for cognition and add to our understanding of the neural processes underlying theta oscillations. More generally, we provide an explanation how individual differences in personality traits might be represented on a neural level. PMID:27380648

  19. Neural basis of motivational approach and withdrawal behaviors in neurodegenerative disease

    OpenAIRE

    Shinagawa, Shunichiro; Babu, Adhimoolam; Sturm, Virginia; Shany-Ur, Tal; Toofanian Ross, Parnian; Zackey, Diana; Poorzand, Pardis; Grossman, Scott; Miller, Bruce L.; Rankin, Katherine P.

    2015-01-01

    Introduction The Behavioral Inhibition System (BIS) and the Behavioral Activation System (BAS) have been theorized as neural systems that regulate approach/withdrawal behaviors. Behavioral activation/inhibition balance may change in neurodegenerative disease based on underlying alterations in systems supporting motivation and approach/withdrawal behaviors, which may in turn be reflected in neuropsychiatric symptoms. Method A total of 187 participants (31 patients diagnosed with behavioral var...

  20. The neural basis of social decision making in patients with major depressive disorder

    OpenAIRE

    Zhang, Huijun; 張慧君

    2014-01-01

    Social decision making is a complex process of selecting an optimal option with the most desirable outcome in the interpersonal context. Because of the involvement of human interactions, social decision making usually demands heavily on the affective neural system. Within the system mood plays a vital role in the social interaction. In this connection, given the fact that depression is characterized as a stable state of low mood, researches have begun focusing on exploring the relationship be...

  1. The Neural and Behavioral Basis of Empathy for Positive and Negative Emotions

    OpenAIRE

    Morelli, Sylvia Annette

    2012-01-01

    Empathy provides a window into another person's mind, creating a shared experience between two individuals. This intimate view of another person's emotional world often makes the empathizer feel more connected to the other person and may motivate the empathizer to respond to the other's emotional needs. While past studies have investigated the underlying psychological and neural mechanisms for this fundamental human experience, researchers have predominantly focused on examining empathy for...

  2. Estimation of State of Charge of Lead Acid Battery using Radial Basis Function

    OpenAIRE

    Sauradip, M; Sinha, SK; K Muthukumar

    2001-01-01

    A Radial Basis Function based learning system method has been proposed for estimation of State of Charge (SOC) of Lead Acid Battery. Coulomb metric method is used for SOC estimation with correction factor computed by Radial Basis Function Method. Radial basis function based technique is used for learning battery performance variation with time and other parameters. Experimental results are included.

  3. The neural basis of one's own conscious and unconscious emotional states.

    Science.gov (United States)

    Smith, Ryan; Lane, Richard D

    2015-10-01

    The study of emotional states has recently received considerable attention within the cognitive and neural sciences. However, limited work has been done to synthesize this growing body of literature within a coherent hierarchical, neuro-cognitive framework. In this article, we review evidence pertaining to three interacting hierarchical neural systems associated with the generation, perception and regulation of one's own emotional state. In the framework we propose, emotion generation proceeds through a series of appraisal mechanisms - some of which appear to require more cognitively sophisticated computational processing (and hence more time) than others - that ultimately trigger iterative adjustments to one's bodily state (as well as to the modes of processing in other cognitive systems). Perceiving one's own emotions then involves a multi-stage interoceptive/somatosensory process by which these body state patterns are detected and assigned conceptual emotional meaning. Finally, emotion regulation can be understood as a hierarchical control system that, at various levels, modulates autonomic reactions, appraisal mechanisms, attention, the contents of working memory, and goal-directed action selection. We highlight implications this integrative model may have for competing theories of emotion and emotional consciousness and for guiding future research. PMID:26363579

  4. Three learning phases for radial-basis-function networks.

    Science.gov (United States)

    Schwenker, F; Kestler, H A; Palm, G

    2001-05-01

    In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where

  5. Towards a neural basis of music perception -- A review and updated model

    Directory of Open Access Journals (Sweden)

    Stefan eKoelsch

    2011-06-01

    Full Text Available Music perception involves acoustic analysis, auditory memory, auditoryscene analysis, processing of interval relations, of musical syntax and semantics,and activation of (premotor representations of actions. Moreover, music percep-tion potentially elicits emotions, thus giving rise to the modulation of emotionaleffector systems such as the subjective feeling system, the autonomic nervoussystem, the hormonal, and the immune system. Building on a previous article(Koelsch & Siebel, 2005, this review presents an updated model of music percep-tion and its neural correlates. The article describes processes involved in musicperception, and reports EEG and fMRI studies that inform about the time courseof these processes, as well as about where in the brain these processes might belocated.

  6. Neural basis of attachment-caregiving systems interaction:insights from neuroimaging

    Directory of Open Access Journals (Sweden)

    Delia eLenzi

    2015-08-01

    Full Text Available The attachment and the caregiving system are complementary systems which are active simultaneously in infant and mother interactions. This ensures the infant survival and optimal social, emotional and cognitive development. In this brief review we first define the characteristics of these two behavioral systems and the theory that links them, according to what Bowlby called the attachment-caregiving social bond (Bowlby, 1969. We then follow with those neuroimaging studies that have focused on this particular issue, i.e. those which have studied the activation of the careging system in women (using infant stimuli and have explored how the individual attachment model (through the Adult Attachment Interview modulates its activity. Studies report altered activation in limbic and prefrontal areas and in basal ganglia and hypothalamus/pituitary regions. These altered activations are thought to be the neural substrate of the attachment-caregiving systems interaction.

  7. Defining the neural basis of appetite and obesity: from genes to behaviour.

    Science.gov (United States)

    Farooqi, I Sadaf

    2014-06-01

    Obesity represents one of the biggest public health challenges facing us today. Urbanisation, sedentary lifestyles and the availability of inexpensive, highly palatable foods have promoted the increasing prevalence of obesity over the past 30 years. However, some people gain weight more easily than others, and there is strong evidence that, within a given environment, this variance in body weight is influenced by genetic factors. This article discusses how genetic studies have contributed to our understanding of the mechanisms involved in the regulation of body weight. We now understand that weight is regulated by neural mechanisms that regulate appetite and energy expenditure and that disruption of these pathways can result in severe obesity in some patients. These studies provide a framework for investigating patients and ultimately may guide the development of more rational, targeted therapies for genetically susceptible individuals with severe obesity. PMID:24889574

  8. Assessing the Neural Basis of Uncertainty in Perceptual Category Learning through Varying Levels of Distortion

    Science.gov (United States)

    Daniel, Reka; Wagner, Gerd; Koch, Kathrin; Reichenbach, Jurgen R.; Sauer, Heinrich; Schlosser, Ralf G. M.

    2011-01-01

    The formation of new perceptual categories involves learning to extract that information from a wide range of often noisy sensory inputs, which is critical for selecting between a limited number of responses. To identify brain regions involved in visual classification learning under noisy conditions, we developed a task on the basis of the…

  9. Estimating dollar-value outcomes of workman`s compensation claims using radial basis function networks

    Energy Technology Data Exchange (ETDEWEB)

    Hancock, M.F. Jr. [Rollins College, Winter Park, FL (United States)

    1995-12-31

    The National Council on Compensation Insurance (NCCI) maintains a national data base of outcomes of workers` compensation claims. We consider whether a radial basis function network can predict the total dollar value of a claim based upon medical and demographic indicators (MDI`s). This work used data from 12,130 workers` compensation claims collected over a period of four years from the state of New Mexico. Two problems were addressed: (1) How well can the total incurred medical expense for all claims be predicted from available MDI`s? For individual claims? (2) How well can the duration of disability be predicted from available MDI`s? The available features intuitively correlated with total medical cost were selected, including type of injury, part of body injured, person`s age at time of injury, gender, marital status, etc. These features were statistically standardized and sorted by correlation with outcome valuation. Principal component analysis was applied. A radial basis function neural network was applied to the feature sets in both supervised and unsupervised training modes. For sets used in training, individual case valuations could consistently be predicted to within $1000 over 98% of the time. For these sets, it was possible to predict total medical expense for the training sets themselves to within 10%. When applied as blind tests against sets which were NOT part of the training data, the prediction was within 15% on the whole sets. Results on individual cases were very poor in only 30% of the cases were the predictions for the training sets within $1000 of their actual valuations. Single-factor analysis suggested that the presence of an attorney strongly decorrelated the data. A simple stratification was performed to remove cases involving attorneys and contested claims, and the procedures above repeated. Preliminary results based upon the very limited effort applied indicate that NCCI data support population estimates, but not single-point estimates.

  10. Novel Progesterone Receptors: Neural Localization and Possible Functions

    Directory of Open Access Journals (Sweden)

    SandraLPetersen

    2013-09-01

    Full Text Available Progesterone (P4 regulates a wide range of neural functions and likely acts through multiple receptors. Over the past 30 years, most studies investigating neural effects of P4 focused on genomic and non-genomic actions of the classical progestin receptor (PGR. More recently the focus has widened to include two groups of non-classical P4 signaling molecules. Members of the Class II progestin and adipoQ receptor (PAQR family are called membrane progestin receptors (mPRs and include: mPRα (PAQR7, mPRβ (PAQR8, mPRγ (PAQR5, mPRδ (PAQR6 and mPRε (PAQR9. Members of the b5-like heme/steroid-binding protein family include progesterone receptor membrane component 1 (PGRMC1, PGRMC2, neudesin and neuferricin. Results of our recent mapping studies show that members of the PGRMC1/S2R family, but not mPRs, are quite abundant in forebrain structures important for neuroendocrine regulation and other non-genomic effects of P4. Herein we describe the structures, neuroanatomical localization and signaling mechanisms of these molecules. We also discuss possible roles for Pgrmc1/S2R in gonadotropin release, feminine sexual behaviors, fluid balance and neuroprotection, as well as catamenial epilepsy.

  11. Intraoperative Neural Response Telemetry and Neural Recovery Function: a Comparative Study between Adults and Children

    Science.gov (United States)

    Carvalho, Bettina; Hamerschmidt, Rogerio; Wiemes, Gislaine

    2014-01-01

    Introduction Neural response telemetry (NRT) is a method of capturing the action potential of the distal portion of the auditory nerve in cochlear implant (CI) users, using the CI itself to elicit and record the answers. In addition, it can also measure the recovery function of the auditory nerve (REC), that is, the refractory properties of the nerve. It is not clear in the literature whether the responses from adults are the same as those from children. Objective To compare the results of NRT and REC between adults and children undergoing CI surgery. Methods Cross-sectional, descriptive, and retrospective study of the results of NRT and REC for patients undergoing IC at our service. The NRT is assessed by the level of amplitude (microvolts) and REC as a function of three parameters: A (saturation level, in microvolts), t0 (absolute refractory period, in seconds), and tau (curve of the model function), measured in three electrodes (apical, medial, and basal). Results Fifty-two patients were evaluated with intraoperative NRT (26 adults and 26 children), and 24 with REC (12 adults and 12 children). No statistically significant difference was found between intraoperative responses of adults and children for NRT or for REC's three parameters, except for parameter A of the basal electrode. Conclusion The results of intraoperative NRT and REC were not different between adults and children, except for parameter A of the basal electrode. PMID:25992145

  12. Radial Basis Neural Networks Based Fault Detection and Isolation Scheme for Pneumatic Actuator

    OpenAIRE

    K. Prabakaran; S, Kaushik; R, Mouleeshuwarapprabu

    2014-01-01

    Fault diagnosis is an ongoing significant research field due to the constantly increasing need for maintainability, reliability and safety of industrial plants. The pneumatic actuators are installed in harsh environment: high temperature, pressure, aggressive media and vibration, etc. This influenced the pneumatic actuator predicted life time. The failures in pneumatic actuator cause forces the installation shut down and may also determine the final quality of the product. A Radial Basis Neur...

  13. Neural basis of decision-making and assessment: Issues on testability and philosophical relevance

    Directory of Open Access Journals (Sweden)

    Gabriel José Corrêa Mograbi

    2011-03-01

    Full Text Available Decision-making is an intricate subject in neuroscience. It is often argued that laboratorial research is not capable of dealing with the necessary complexity to study the issue. Whereas philosophers in general neglect the physiological features that constitute the main aspects of thought and behaviour, I advocate that cutting-edge neuroscientific experiments can offer us a framework to explain human behaviour in its relationship with will, self-control, inhibition, emotion and reasoning. It is my contention that self-control mechanisms can modulate more basic stimuli. Assuming the aforementioned standpoints, I show the physiological mechanisms underlying social assessment and decision-making. I also establish a difference between veridical and adaptive decision-making, useful to create experimental designs that can better mimic the complexity of our day-by-day decisions in more ecologically relevant laboratorial research. Moreover, I analyse some experiments in order to develop an epistemological reflection about the necessary neural mechanisms to social assessment and decision-making.

  14. Transgenerational influence of sensorimotor training on offspring behavior and its neural basis in Drosophila.

    Science.gov (United States)

    Williams, Ziv M

    2016-05-01

    Whether specific learning experiences by parents influence the behavior of subsequent generations remains unclear. This study examines whether and what aspects of parental sensorimotor training prior to conception affect the behavior of subsequent generations and identifies the neural circuitries in Drosophila responsible for mediating these effects. Using genetic and anatomic techniques, I find that both first- and second-generation offspring of parents who underwent prolonged olfactory training over many days displayed a weak but selective approach bias to the same trained odors. However, I also find that the offspring did not differentiate between orders based on whether parental training was aversive or appetitive. Disruption of both olfactory-receptor and dorsal-paired-medial neuron input into the mushroom bodies abolished this change in offspring response, but disrupting synaptic output from α/β neurons of the mushroom body themselves had little effect on behavior even though they remained necessary for enacting newly trained conditioned responses. This study provides a circuit-based understanding of how specific sensory experiences in Drosophila may bias the behavior of subsequent generations, and identifies a transgenerational dissociation between the effects of conditioned and unconditioned sensory stimuli. PMID:27044678

  15. Neural basis of decision-making and assessment: Issues on testability and philosophical relevance

    Directory of Open Access Journals (Sweden)

    Mograbi Gabriel José

    2011-01-01

    Full Text Available Decision-making is an intricate subject in neuroscience. It is often argued that laboratorial research is not capable of dealing with the necessary complexity to study the issue. Whereas philosophers in general neglect the physiological features that constitute the main aspects of thought and behaviour, I advocate that cutting-edge neuroscientific experiments can offer us a framework to explain human behaviour in its relationship with will, self-control, inhibition, emotion and reasoning. It is my contention that self-control mechanisms can modulate more basic stimuli. Assuming the aforementioned standpoints, I show the physiological mechanisms underlying social assessment and decision-making. I also establish a difference between veridical and adaptive decision-making, useful to create experimental designs that can better mimic the complexity of our day-by-day decisions in more ecologically relevant laboratorial research. Moreover, I analyse some experiments in order to develop an epistemological reflection about the necessary neural mechanisms to social assessment and decision-making.

  16. Boiling detection on the basis neutron noise analysis by neural network

    International Nuclear Information System (INIS)

    Accident nature which is ruling the nuclear fission and the influence of different phenomena on the cross section and multiplication factor, cause perturbations in the neutron flux, which is, named neutron noise. Neutron noise analysis gives some useful information about the sources of produced noise. One of the important sources of neutron noise in BWR is changing the level of local moderator via production of steam bubbles due of boiling. Here, by designing an appropriate neural network, it has been tried to simulate the mapping between neutron noise and local void fraction. Auto power spectral density and cross power spectral density of the neutron noise that are provided by neutron detectors in reactor core used as input data. By testing several different the network which have 2, 3, 4, or 5 layers with 3, 5 and 10 neurons in their hidden layers, the optimum network was found as feed forward multi layer with 4 layers and 6 neurons in it's hidden layers. The results show that the network gives acceptable rate of void fraction when the local void fraction is more than 30%

  17. RADIAL BASIS FUNCTION NETWORK DEPENDENT EXCLUSIVE MUTUAL INTERPOLATION FOR MISSING VALUE IMPUTATION

    Directory of Open Access Journals (Sweden)

    R. S. Somasundaram

    2013-01-01

    Full Text Available The success of data mining relies on the purity of the data set. Before performing the data mining, the data has to be cleaned. An unprocessed data set may contain noisy or missing values which is a critical research issue in the pre-processing stage. Imputation methods are being used to solve the missing value problems. In this proposed work, a machine learning based imputation method is proposed by using the mutual information by exclusively interpolating two different section of the same dataset. For designing the proposed model, a radial basis function based neural network has been used. The performance of the proposed algorithm has been measured with respect to different rate or percentage of missing values in the data set and the results has been compared with existing simple and efficient imputation methods also. To evaluate the performance, the standard WDBC data set has been used. The proposed algorithm performs well and was able to impute the missing values even in the worst cases with more than 50% of missing values. Instead of using simple quality measure such as Mean Square Error (MSE to evaluate the imputed data quality, in this study, the quality is measured in terms of classification performance. The results arrived were more significant and comparable.

  18. Physiological basis, use and abuse of functional imaging

    International Nuclear Information System (INIS)

    Functional imaging in the contrast to conventional methods of nuclear medicine is defined. The importance of an isomorphic physiological model as a link between the physics of the test and the clinical problem is discussed. Clinical, physiological and mathematical criteria for use of computer assisted functional images are developed. (WU)

  19. Functional neuroanatomy of anxiety: a neural circuit perspective.

    Science.gov (United States)

    Etkin, Amit

    2010-01-01

    Anxiety is a commonly experienced subjective state that can have both adaptive and maladaptive properties. Clinical disorders of anxiety are likewise also common, and range widely in their symptomatology and consequences for the individual. Cognitive neuroscience has provided an increasingly sophisticated understanding of the processes underlying normal human emotion, and its disruption or dysregulation in clinical anxiety disorders. In this chapter, I review functional neuroimaging studies of emotion in healthy and anxiety-disordered populations. A limbic-medial prefrontal circuit is emphasized and an information processing model is proposed for the processing of negative emotion. Data on negative emotion processing in a variety of anxiety disorders are presented and integrated within an understanding of the functions of elements within the limbic-medial prefrontal circuit. These data suggest that anxiety disorders may be usefully conceptualized as differentially affecting emotional reactivity and regulatory processes--functions that involve different neurobiological mechanisms. While the neural bases of several anxiety disorders are increasingly better understood, advances have lagged significantly behind in others. Nonetheless, the conceptual framework provided by convergent findings in studies of emotional processing in normative and anxiety-disordered populations promises to yield continued insights and nuances, and will likely provide useful information in the search for etiology and novel treatments. PMID:21309113

  20. Neurally augmented sexual function in human females: a preliminary investigation.

    Science.gov (United States)

    Meloy, T Stuart; Southern, Joan P

    2006-01-01

    Objective.  Neurally augmented sexual function (NASF) is the production of pleasurable genital stimulation and subsequent orgasm through the application of electrical energy to provide stimulation of the spinal cord or peripheral nerves. The purpose of this paper is to demonstrate the reproducibility of this phenomenon. Materials and Methods.  Eleven otherwise healthy women, ages 32-60 years, were selected for this study. Through standard techniques, quadripolar (octopolar in the final patient) leads were placed in the epidural space percutaneuously. The lead was maneuvered initially to an L1-L2 position and then repositioned based on feedback from the patient. The patients were allowed to utilize the device ad libitum for up to 9 days. Results.  Successful stimulation was achieved in 91% (10/11) of patients. These women described a greater frequency in sexual activity, increased lubrication, and overall satisfaction. A smaller subset had substantial improvement in sexual function as measured by orgasmic capacity. This subset consisted of women with secondary anorgasmia. A return of orgasmic capacity was found in 80% (4/5) of patients having secondary anorgasmia with an average intensity of ≥ 3/5 while using the device. Once the device was removed, the patients returned to their previous anorgasmic status. Conclusions.  Pleasurable genital stimulation of the spinal cord is a consistently reproducible phenomenon. In a subset of the population studied, improvement in orgasmic function was noted. This was noted in the group with secondary orgasmic dysfunction. PMID:22151591

  1. An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function

    OpenAIRE

    Onursal Çetin; Feyzullah Temurtaş; Şenol Gülgönül

    2015-01-01

    Objective: Implementation of multilayer neural network (MLNN) with sigmoid activation function for the diagnosis of hepatitis disease.Methods: Artificial neural networks (ANNs) are efficient tools currently in common use for medical diagnosis. In hardware based architectures activation functions play an important role in ANN behavior. Sigmoid function is the most frequently used activation function because of its smooth response. Thus, sigmoid function and its close approximations were implem...

  2. Neurogenic and non neurogenic functions of endogenous neural stem cells.

    Directory of Open Access Journals (Sweden)

    Erica eButti

    2014-04-01

    Full Text Available Adult neurogenesis is a lifelong process that occurs in two main neurogenic niches of the brain, namely in the subventricular zone (SVZ of the lateral ventricles and in the subgranular zone (SGZ of the dentate gyrus (DG in the hippocampus. In the 1960s, studies on adult neurogenesis have been hampered by the lack of established phenotypic markers. The precise tracing of neural stem/progenitor cells (NPCs was therefore, not properly feasible. After the (partial identification of those markers, it was the lack of specific tools that hindered a proper experimental elimination and tracing of those cells to demonstrate their terminal fate and commitment. Nowadays, irradia-tion, cytotoxic drugs as well as genetic tracing/ablation procedures have moved the field forward and increased our understanding of neurogenesis processes in both physiological and pathological conditions. Newly formed NPC progeny from the SVZ can replace granule cells in the olfactory bulbs of rodents, thus contributing to orchestrate sophisticated odour behaviour. SGZ-derived new granule cells, instead, integrate within the DG where they play an essential role in memory functions. Furthermore, converging evidence claim that endogenous NPCs not only exert neurogenic functions, but might also have non-neurogenic homeostatic functions by the release of different types of neuroprotective molecules. Remarkably, these non-neurogenic homeostatic functions seem to be necessary, both in healthy and diseased conditions, for example for preventing or limiting tissue damage. In this review, we will discuss the neurogenic and the non-neurogenic functions of adult NPCs both in physiological and pathological conditions.

  3. Determination of Activation Functions in A Feedforward Neural Network by using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Oğuz ÜSTÜN

    2009-03-01

    Full Text Available In this study, activation functions of all layers of the multilayered feedforward neural network have been determined by using genetic algorithm. The main criteria that show the efficiency of the neural network is to approximate to the desired output with the same number nodes and connection weights. One of the important parameter to determine this performance is to choose a proper activation function. In the classical neural network designing, a network is designed by choosing one of the generally known activation function. In the presented study, a table has been generated for the activation functions. The ideal activation function for each node has been chosen from this table by using the genetic algorithm. Two dimensional regression problem clusters has been used to compare the performance of the classical static neural network and the genetic algorithm based neural network. Test results reveal that the proposed method has a high level approximation capacity.

  4. Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

    Directory of Open Access Journals (Sweden)

    Yunfeng Wu

    2014-01-01

    Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

  5. Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization

    International Nuclear Information System (INIS)

    An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting

  6. Functional 3D Neural Mini-Tissues from Printed Gel-Based Bioink and Human Neural Stem Cells.

    Science.gov (United States)

    Gu, Qi; Tomaskovic-Crook, Eva; Lozano, Rodrigo; Chen, Yu; Kapsa, Robert M; Zhou, Qi; Wallace, Gordon G; Crook, Jeremy M

    2016-06-01

    Direct-write printing of stem cells within biomaterials presents an opportunity to engineer tissue for in vitro modeling and regenerative medicine. Here, a first example of constructing neural tissue by printing human neural stem cells that are differentiated in situ to functional neurons and supporting neuroglia is reported. The supporting biomaterial incorporates a novel clinically relevant polysaccharide-based bioink comprising alginate, carboxymethyl-chitosan, and agarose. The printed bioink rapidly gels by stable cross-linking to form a porous 3D scaffold encapsulating stem cells for in situ expansion and differentiation. Differentiated neurons form synaptic contacts, establish networks, are spontaneously active, show a bicuculline-induced increased calcium response, and are predominantly gamma-aminobutyric acid expressing. The 3D tissues will facilitate investigation of human neural development, function, and disease, and may be adaptable for engineering other 3D tissues from different stem cell types. PMID:27028356

  7. The neural basis of impaired self-awareness after traumatic brain injury

    OpenAIRE

    Ham, Timothy E.; Bonnelle, Valerie; Hellyer, Peter; Jilka, Sagar; Ian H Robertson; Leech, Robert; Sharp, David J.

    2013-01-01

    Impaired self-awareness is a disabling consequence of many neurological diseases. Ham et al. use structural and functional MRI to compare patients with high and low levels of performance monitoring after traumatic brain injury. Dysfunction of the insulae and anterior cingulate cortices within the salience network contributes to deficits in self-awareness.

  8. Functional integration of human neural precursor cells in mouse cortex.

    Directory of Open Access Journals (Sweden)

    Fu-Wen Zhou

    Full Text Available This study investigates the electrophysiological properties and functional integration of different phenotypes of transplanted human neural precursor cells (hNPCs in immunodeficient NSG mice. Postnatal day 2 mice received unilateral injections of 100,000 GFP+ hNPCs into the right parietal cortex. Eight weeks after transplantation, 1.21% of transplanted hNPCs survived. In these hNPCs, parvalbumin (PV-, calretinin (CR-, somatostatin (SS-positive inhibitory interneurons and excitatory pyramidal neurons were confirmed electrophysiologically and histologically. All GFP+ hNPCs were immunoreactive with anti-human specific nuclear protein. The proportions of PV-, CR-, and SS-positive cells among GFP+ cells were 35.5%, 15.7%, and 17.1%, respectively; around 15% of GFP+ cells were identified as pyramidal neurons. Those electrophysiologically and histological identified GFP+ hNPCs were shown to fire action potentials with the appropriate firing patterns for different classes of neurons and to display spontaneous excitatory and inhibitory postsynaptic currents (sEPSCs and sIPSCs. The amplitude, frequency and kinetic properties of sEPSCs and sIPSCs in different types of hNPCs were comparable to host cells of the same type. In conclusion, GFP+ hNPCs produce neurons that are competent to integrate functionally into host neocortical neuronal networks. This provides promising data on the potential for hNPCs to serve as therapeutic agents in neurological diseases with abnormal neuronal circuitry such as epilepsy.

  9. Physiologic Basis for Improved Pulmonary Function after Lung Volume Reduction

    OpenAIRE

    Fessler, Henry E.; Scharf, Steven M; Ingenito, Edward P.; McKenna, Robert J.; Sharafkhaneh, Amir

    2008-01-01

    It is not readily apparent how pulmonary function could be improved by resecting portions of the lung in patients with emphysema. In emphysema, elevation in residual volume relative to total lung capacity reduces forced expiratory volumes, increases inspiratory effort, and impairs inspiratory muscle mechanics. Lung volume reduction surgery (LVRS) better matches the size of the lungs to the size of the thorax containing them. This restores forced expiratory volumes and the mechanical advantage...

  10. Design Optimization of Centrifugal Pump Using Radial Basis Function Metamodels

    OpenAIRE

    Yu Zhang; Jinglai Wu; Yunqing Zhang; Liping Chen

    2014-01-01

    Optimization design of centrifugal pump is a typical multiobjective optimization (MOO) problem. This paper presents an MOO design of centrifugal pump with five decision variables and three objective functions, and a set of centrifugal pumps with various impeller shroud shapes are studied by CFD numerical simulations. The important performance indexes for centrifugal pump such as head, efficiency, and required net positive suction head (NPSHr) are investigated, and the results indicate that th...

  11. The neural basis of novelty and appropriateness in processing of creative chunk decomposition.

    Science.gov (United States)

    Huang, Furong; Fan, Jin; Luo, Jing

    2015-06-01

    Novelty and appropriateness have been recognized as the fundamental features of creative thinking. However, the brain mechanisms underlying these features remain largely unknown. In this study, we used event-related functional magnetic resonance imaging (fMRI) to dissociate these mechanisms in a revised creative chunk decomposition task in which participants were required to perform different types of chunk decomposition that systematically varied in novelty and appropriateness. We found that novelty processing involved functional areas for procedural memory (caudate), mental rewarding (substantia nigra, SN), and visual-spatial processing, whereas appropriateness processing was mediated by areas for declarative memory (hippocampus), emotional arousal (amygdala), and orthography recognition. These results indicate that non-declarative and declarative memory systems may jointly contribute to the two fundamental features of creative thinking. PMID:25797834

  12. Toward the neural basis of processing structure in music: Comparative results of different neurophysiological investigation methods

    OpenAIRE

    Koelsch, S.; Friederici, A

    2003-01-01

    In major-minor tonal music, chord functions are arranged according to certain regularities. The dominant-tonic progression, known as an authentic cadence, is often used as a marker of the end of a harmonic progression and has been considered a basic syntactic structure of major-minor tonal music by several music theorists and music psychologists. We review data from studies in which brain responses to an authentic cadence were compared to those elicited by music-syntactically inappropriate en...

  13. Near and long-term load prediction using radial basis function networks

    Energy Technology Data Exchange (ETDEWEB)

    Hancock, M.F. [Rollins College, Winter Park, FL (United States)

    1995-12-31

    A number of researchers have investigated the application of multi-layer perceptrons (MLP`s), a variety of neural network, to the problem of short-term load forecasting for electric utilities (e.g., Rahman & Hazin, IEEE Trans. Power Systems, May 1993). {open_quotes}Short-term{close_quotes} in this context typically means {open_quotes}next day{close_quotes}. These forecasts have been based upon previous day actual loads and meteorological factors (e.g., max-min temperature, relative humidity). We describe the application of radial basis function networks (RBF`s) to the {open_quotes}long-term{close_quotes} (next year) load forecasting problem. The RBF network performs a two-stage classification based upon annual average loads and meteorological data. During stage 1, discrete classification is performed using radius-limited elements. During stage 2, a multi-layer perceptron may be applied. The quantized output is used to correct a prediction template. The stage 1 classifier is trained by maximizing an objective function (the {open_quotes}disambiguity{close_quotes}). The stage 2 MLP`s are trained by standard back-propagation. This work uses 12 months of hourly meteorological data, and the corresponding hourly load data for both commercial and residential feeders. At the current stage of development, the RBF machine can train on 20% of the weather/load data (selected by simple linear sampling), and estimate the hourly load for an entire year (8,760 data points) with 9.1% error (RMS, relative to daily peak load). (By comparison, monthly mean profiles perform at c. 12% error.) The best short-term load forecasters operate in the 2% error range. The current system is an engineering prototype, and development is continuing.

  14. Assessing the utility of phase-space-localized basis functions: Exploiting direct product structure and a new basis function selection procedure

    Science.gov (United States)

    Brown, James; Carrington, Tucker

    2016-06-01

    In this paper we show that it is possible to use an iterative eigensolver in conjunction with Halverson and Poirier's symmetrized Gaussian (SG) basis [T. Halverson and B. Poirier, J. Chem. Phys. 137, 224101 (2012)] to compute accurate vibrational energy levels of molecules with as many as five atoms. This is done, without storing and manipulating large matrices, by solving a regular eigenvalue problem that makes it possible to exploit direct-product structure. These ideas are combined with a new procedure for selecting which basis functions to use. The SG basis we work with is orders of magnitude smaller than the basis made by using a classical energy criterion. We find significant convergence errors in previous calculations with SG bases. For sum-of-product Hamiltonians, SG bases large enough to compute accurate levels are orders of magnitude larger than even simple pruned bases composed of products of harmonic oscillator functions.

  15. The molecular basis for centromere identity and function.

    Science.gov (United States)

    McKinley, Kara L; Cheeseman, Iain M

    2016-01-01

    The centromere is the region of the chromosome that directs its segregation in mitosis and meiosis. Although the functional importance of the centromere has been appreciated for more than 130 years, elucidating the molecular features and properties that enable centromeres to orchestrate chromosome segregation is an ongoing challenge. Most eukaryotic centromeres are defined epigenetically and require the presence of nucleosomes containing the histone H3 variant centromere protein A (CENP-A; also known as CENH3). Ongoing work is providing important molecular insights into the central requirements for centromere identity and propagation, and the mechanisms by which centromeres recruit kinetochores to connect to spindle microtubules. PMID:26601620

  16. Shared neural basis of social and non-social reward deficits in chronic cocaine users

    DEFF Research Database (Denmark)

    Tobler, Philippe N; Preller, Katrin H; Campbell-Meiklejohn, Daniel K;

    2016-01-01

    -social reinforcements. We used functional neuroimaging in cocaine users to investigate explicit social reward as modeled by agreement of music preferences with music experts. In addition, we investigated non-social reward as modeled by winning desired music pieces. The study included 17 chronic cocaine users and 17...... the processing of non-drug rewards. Interestingly, in the posterior lateral orbitofrontal cortex, social reward responses of cocaine users decreased with the degree to which they were influenced by social feedback from the experts, a response pattern that was opposite to that observed in healthy...

  17. Avian magnetic compass: Its functional properties and physical basis

    Directory of Open Access Journals (Sweden)

    Roswitha WILTSCHKO, Wolfgang WILTSCHKO

    2010-06-01

    Full Text Available The avian magnetic compass was analyzed in bird species of three different orders – Passeriforms, Columbiforms and Galliforms – and in three different behavioral contexts, namely migratory orientation, homing and directional conditioning. The respective findings indicate similar functional properties: it is an inclination compass that works only within a functional window around the ambient magnetic field intensity; it tends to be lateralized in favor of the right eye, and it is wavelength-dependent, requiring light from the short-wavelength range of the spectrum. The underlying physical mechanisms have been identified as radical pair processes, spin-chemical reactions in specialized photopigments. The iron-based receptors in the upper beak do not seem to be involved. The existence of the same type of magnetic compass in only very distantly related bird species suggests that it may have been present already in the common ancestors of all modern birds, where it evolved as an all-purpose compass mechanism for orientation within the home range [Current Zoology 56 (3: 265–276, 2010].

  18. Generating functions for q-Bernstein, q-Meyer-Konig-Zeller and q-Beta basis

    OpenAIRE

    Gupta, Vijay; Kim, Taekyun; Choi, Jongsung; Kim, Young-Hee

    2010-01-01

    The present paper deals with the q-analogue of Bernstein, Meyer-Konig-Zeller and Beta operators. Here we estimate the generating functions for q-Bernstein, q-Meyer-Konig-Zeller and q-Beta basis functions.

  19. Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2014-06-01

    Full Text Available In present study we used self-organizing map (SOM neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of neurons in neural network. We may chose these functions and results based on comparison with the practical situation.

  20. [Synapse elimination and functional neural circuit formation in the cerebellum].

    Science.gov (United States)

    Kano, Masanobu

    2013-06-01

    Neuronal connections are initially redundant, but unnecessary connections are eliminated subsequently during postnatal development. This process, known as 'synapse elimination', is thought to be crucial for establishing functionally mature neural circuits. The climbing fiber (CF) to the Purkinje cell (PC) synapse in the cerebellum is a representative model of synapse elimination. We disclose that one-to-one connection from CF to PC is established through four distinct phases: (1) strengthening of a single CF among multiple CFs in each PC at P3-P7, (2) translocation of a single strengthened CF to PC dendrites from around P9, and (3) early phase (P7 to around P11) and (4) late phase (around P12 to P17) of elimination of weak CF synapses from PC somata. Mice with PC-selective deletion of P/Q-type voltage-dependent Ca2+ channel (VDCC) exhibit severe defects in strengthening of single CFs, dendritic translocation of single CFs and CF elimination from P7. In contrast, mice with a mutation of a single allele for the GABA-synthesizing enzyme GAD67 have a selective impairment of CF elimination from P10 due to reduced inhibition and elevated Ca2+ influx to PC somata. Thus, regulation of Ca2+ influx to PCs is crucial for the four phases of CF synapse elimination. PMID:25069248

  1. Localized basis functions and other computational improvements in variational nonorthogonal basis function methods for quantum mechanical scattering problems involving chemical reactions

    Science.gov (United States)

    Schwenke, David W.; Truhlar, Donald G.

    1990-01-01

    The Generalized Newton Variational Principle for 3D quantum mechanical reactive scattering is briefly reviewed. Then three techniques are described which improve the efficiency of the computations. First, the fact that the Hamiltonian is Hermitian is used to reduce the number of integrals computed, and then the properties of localized basis functions are exploited in order to eliminate redundant work in the integral evaluation. A new type of localized basis function with desirable properties is suggested. It is shown how partitioned matrices can be used with localized basis functions to reduce the amount of work required to handle the complex boundary conditions. The new techniques do not introduce any approximations into the calculations, so they may be used to obtain converged solutions of the Schroedinger equation.

  2. The transsexual brain--A review of findings on the neural basis of transsexualism.

    Science.gov (United States)

    Smith, Elke Stefanie; Junger, Jessica; Derntl, Birgit; Habel, Ute

    2015-12-01

    Transsexualism describes the condition when a person's psychological gender differs from his or her biological sex and is commonly thought to arise from a discrepant cerebral and genital sexual differentiation. This review intends to give an extensive overview of structural and functional neurobiological correlates of transsexualism and their course under cross-sex hormonal treatment. Research in this field enables insight into the stability or variability of gender differences and their relation to hormonal status. For a number of sexually dimorphic brain structures or processes, signs of feminisation or masculinisation are observable in transsexual individuals, which, during hormonal treatment, partly seem to further adjust to characteristics of the desired sex. Still, it appears the data are quite inhomogeneous, mostly not replicated and in many cases available for male-to-female transsexuals only. As the prevalence of homosexuality is markedly higher among transsexuals than among the general population, disentangling correlates of sexual orientation and gender identity is a major problem. To resolve such deficiencies, the implementation of specific research standards is proposed. PMID:26429593

  3. Vulnerability to paroxysmal oscillations in delayed neural networks: A basis for nocturnal frontal lobe epilepsy?

    Science.gov (United States)

    Quan, Austin; Osorio, Ivan; Ohira, Toru; Milton, John

    2011-12-01

    Resonance can occur in bistable dynamical systems due to the interplay between noise and delay (τ) in the absence of a periodic input. We investigate resonance in a two-neuron model with mutual time-delayed inhibitory feedback. For appropriate choices of the parameters and inputs three fixed-point attractors co-exist: two are stable and one is unstable. In the absence of noise, delay-induced transient oscillations (referred to herein as DITOs) arise whenever the initial function is tuned sufficiently close to the unstable fixed-point. In the presence of noisy perturbations, DITOs arise spontaneously. Since the correlation time for the stationary dynamics is ˜τ, we approximated a higher order Markov process by a three-state Markov chain model by rescaling time as t → 2sτ, identifying the states based on whether the sub-intervals were completely confined to one basin of attraction (the two stable attractors) or straddled the separatrix, and then determining the transition probability matrix empirically. The resultant Markov chain model captured the switching behaviors including the statistical properties of the DITOs. Our observations indicate that time-delayed and noisy bistable dynamical systems are prone to generate DITOs as switches between the two attractors occur. Bistable systems arise transiently in situations when one attractor is gradually replaced by another. This may explain, for example, why seizures in certain epileptic syndromes tend to occur as sleep stages change.

  4. Assessment of Various Density Functionals and Basis Sets for the Calculation of Molecular Anharmonic Force Fields

    CERN Document Server

    Boese, A D; Martin, J M L; Klopper, Wim; Martin, Jan M. L.

    2005-01-01

    In a previous contribution (Mol. Phys. {\\bf 103}, xxxx, 2005), we established the suitability of density functional theory (DFT) for the calculation of molecular anharmonic force fields. In the present work, we have assessed a wide variety of basis sets and exchange-correlation functionals for harmonic and fundamental frequencies, equilibrium and ground-state rotational constants, and thermodynamic functions beyond the RRHO (rigid rotor-harmonic oscillator) approximation. The fairly good performance of double-zeta plus polarization basis sets for frequencies results from an error compensation between basis set incompleteness and the intrinsic error of exchange-correlation functionals. Triple-zeta plus polarization basis sets are recommended, with an additional high-exponent $d$ function on second-row atoms. All conventional hybrid GGA functionals perform about equally well: high-exchange hybrid GGA and meta-GGA functionals designed for kinetics yield poor results, with the exception of of the very recently de...

  5. The neural basis of integrating pre- and post-response information for goal-directed actions.

    Science.gov (United States)

    Frimmel, Steffi; Wolfensteller, Uta; Mohr, Holger; Ruge, Hannes

    2016-01-01

    A fundamental prerequisite for goal-directed action is to encode the contingencies between responses (R) producing specific outcomes (O) in specific stimulus conditions (S). The present study aimed to characterize the functional neuroanatomy of different associational sub-components of such S-R-O contingencies during the first few trials of exposure. We devised a novel paradigm that was suited to distinguish BOLD activation patterns related to S-R, R-O, and the full S-R-O contingency. Different from previous studies our experimental design ensured that stimulus-related processes and outcome-related processes were maximally comparable, as both were learned incidentally and lacked intrinsic incentive value, and different from trial-and-error learning situations, outcomes did not serve a special role as performance feedback. We observed contingency-related dissociations between SMA, lateral OFC, and large parts of the reward system including central OFC, anterior striatum and midbrain areas. While the lateral OFC was involved in processing differential outcomes irrespective of a predictive stimulus context, the SMA was specifically engaged when differential outcomes could be predicted by the stimulus. By contrast, the activation pattern of reward system areas suggested that these regions serve a role in integrating non-incentive differential outcome information and incentive common outcome information. Together, these results support the notion that striatal and orbitofrontal regions are involved in outcome-related processes beyond trial-and-error S-R learning, that is, when outcomes are non-incentive and do not serve as reinforcing feedback that drives learning. Furthermore, our results clarify the role of the SMA in outcome-related processes thereby supporting current versions of ideomotor theory. PMID:26522619

  6. Models of Hopfield-type quaternion neural networks and their energy functions.

    Science.gov (United States)

    Yoshida, Mitsuo; Kuroe, Yasuaki; Mori, Takehiro

    2005-01-01

    Recently models of neural networks that can directly deal with complex numbers, complex-valued neural networks, have been proposed and several studies on their abilities of information processing have been done. Furthermore models of neural networks that can deal with quaternion numbers, which is the extension of complex numbers, have also been proposed. However they are all multilayer quaternion neural networks. This paper proposes models of fully connected recurrent quaternion neural networks, Hopfield-type quaternion neural networks. Since quaternion numbers are non-commutative on multiplication, some different models can be considered. We investigate dynamics of these proposed models from the point of view of the existence of an energy function and derive their conditions for existence. PMID:15912590

  7. Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

    OpenAIRE

    WenJun Zhang; QuHuan Li

    2014-01-01

    In present study we used self-organizing map (SOM) neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of ...

  8. Modeling the Flux-Charge Relation of Memristor with Neural Network of Smooth Hinge Functions

    OpenAIRE

    X. Mu; Yu, J.; Wang, S.

    2014-01-01

    The memristor was proposed to characterize the flux-charge relation. We propose the generalized flux-charge relation model of memristor with neural network of smooth hinge functions. There is effective identification algorithm for the neural network of smooth hinge functions. The representation capability of this model is theoretically guaranteed. Any functional flux-charge relation of a memristor can be approximated by the model. We also give application examples to show that the given model...

  9. Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders.

    Science.gov (United States)

    Zhang, Jie; Cheng, Wei; Liu, Zhaowen; Zhang, Kai; Lei, Xu; Yao, Ye; Becker, Benjamin; Liu, Yicen; Kendrick, Keith M; Lu, Guangming; Feng, Jianfeng

    2016-08-01

    SEE MATTAR ET AL DOI101093/AWW151 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the

  10. Application of functional-link neural network in evaluation of sublayer suspension based on FWD test

    Institute of Scientific and Technical Information of China (English)

    陈瑜; 张起森

    2004-01-01

    Several methods for evaluating the sublayer suspension beneath old pavement with falling weight deflectormeter(FWD), were summarized and the respective advantages and disadvantages were analyzed. Based on these methods, the evaluation principles were improved and a new type of the neural network, functional-link neural network was proposed to evaluate the sublayer suspension with FWD test results. The concept of function link, learning method of functional-link neural network and the establishment process of neural network model were studied in detail. Based on the old pavement over-repairing engineering of Kaiping section, Guangdong Province in G325 National Highway, the application of functional-link neural network in evaluation of sublayer suspension beneath old pavement based on FWD test data on the spot was investigated. When learning rate is 0.1 and training cycles are 405, the functional-link network error is less than 0.0001, while the optimum chosen 4-8-1 BP needs over 10000 training cycles to reach the same accuracy with less precise evaluation results. Therefore, in contrast to common BP neural network,the functional-link neural network adopts single layer structure to learn and calculate, which simplifies the network, accelerates the convergence speed and improves the accuracy. Moreover the trained functional-link neural network can be adopted to directly evaluate the sublayer suspension based on FWD test data on the site. Engineering practice indicates that the functional-link neural model gains very excellent results and effectively guides the pavement over-repairing construction.

  11. Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

    Science.gov (United States)

    Zhang, WenJun

    2007-07-01

    Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance

  12. Computational Exploration of the Biological Basis of Black-Scholes Expected Utility Function

    OpenAIRE

    Sukanto Bhattacharya; Kuldeep Kumar

    2007-01-01

    It has often been argued that there exists an underlying biological basis of utility functions. Taking this line of argument a step further in this paper, we have aimed to computationally demonstrate the biological basis of the Black-Scholes functional form as applied to classical option pricing and hedging theory. The evolutionary optimality of the classical Black-Scholes function has been computationally established by means of a haploid genetic algorithm model. The objective was to minimiz...

  13. Computational Exploration of the Biological Basis of Black-Scholes Expected Utility Function

    OpenAIRE

    Kuldeep Kumar; Sukanto Bhattacharya

    2007-01-01

    It has often been argued that there exists an underlying biological basis of utility functions. Taking this line of argument a step further in this paper, we have aimed to computationally demonstrate the biological basis of the Black-Scholes functional form as applied to classical option pricing and hedging theory. The evolutionary optimality of the classical Black-Scholes function has been computationally established by means of a haploid genetic algorithm model. The objective was to mi...

  14. Free vibrations and buckling analysis of laminated plates by oscillatory radial basis functions

    Science.gov (United States)

    Neves, A. M. A.; Ferreira, A. J. M.

    2015-12-01

    In this paper the free vibrations and buckling analysis of laminated plates is performed using a global meshless method. A refined version of Kant's theorie which accounts for transverse normal stress and through-the-thickness deformation is used. The innovation is the use of oscillatory radial basis functions. Numerical examples are performed and results are presented and compared to available references. Such functions proved to be an alternative to the tradicional nonoscillatory radial basis functions.

  15. Use of unsupervised and supervised artificial neural networks for the identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins.

    Science.gov (United States)

    Piraino, P; Ricciardi, A; Salzano, G; Zotta, T; Parente, E

    2006-08-01

    Conventional multivariate statistical techniques (hierarchical cluster analysis, linear discriminant analysis) and unsupervised (Kohonen Self Organizing Map) and supervised (Bayesian network) artificial neural networks were compared for as tools for the classification and identification of 352 SDS-PAGE patterns of whole cell proteins of lactic acid bacteria belonging to 22 species of the genera Lactobacillus, Leuconostoc, Enterococcus, Lactococcus and Streptococcus including 47 reference strains. Electrophoretic data were pre-treated using the logistic weighting function described by Piraino et al. [Piraino, P., Ricciardi, A., Lanorte, M. T., Malkhazova, I., Parente, E., 2002. A new procedure for data reduction in electrophoretic fingerprints of whole-cell proteins. Biotechnol. Lett. 24, 1477-1482]. Hierarchical cluster analysis provided a satisfactory classification of the patterns but was unable to discriminate some species (Leuconostoc, Lb. sakei/Lb. curvatus, Lb. acidophilus/Lb. helveticus, Lb. plantarum/Lb. paraplantarum, Lc. lactis/Lc. raffinolactis). A 7x7 Kohonen self-organizing map (KSOM), trained with the patterns of the reference strains, provided a satisfactory classification of the patterns and was able to discriminate more species than hierarchical cluster analysis. The map was used in predictive mode to identify unknown strains and provided results which in 85.5% of cases matched the classification obtained by hierarchical cluster analysis. Two supervised tools, linear discriminant analysis and a 23:5:2 Bayesian network were proven to be highly effective in the discrimination of SDS-PAGE patterns of Lc. lactis from those of other species. We conclude that data reduction by logistic weighting coupled to traditional multivariate statistical analysis or artificial neural networks provide an effective tool for the classification and identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins. PMID:16480784

  16. The Neural Correlates of Long-Term Carryover following Functional Electrical Stimulation for Stroke.

    Science.gov (United States)

    Gandolla, Marta; Ward, Nick S; Molteni, Franco; Guanziroli, Eleonora; Ferrigno, Giancarlo; Pedrocchi, Alessandra

    2016-01-01

    Neurorehabilitation effective delivery for stroke is likely to be improved by establishing a mechanistic understanding of how to enhance adaptive plasticity. Functional electrical stimulation is effective at reducing poststroke foot drop; in some patients, the effect persists after therapy has finished with an unknown mechanism. We used fMRI to examine neural correlates of functional electrical stimulation key elements, volitional intent to move and concurrent stimulation, in a group of chronic stroke patients receiving functional electrical stimulation for foot-drop correction. Patients exhibited task-related activation in a complex network, sharing bilateral sensorimotor and supplementary motor activation with age-matched controls. We observed consistent separation of patients with and without carryover effect on the basis of brain responses. Patients who experienced the carryover effect had responses in supplementary motor area that correspond to healthy controls; the interaction between experimental factors in contralateral angular gyrus was seen only in those without carryover. We suggest that the functional electrical stimulation carryover mechanism of action is based on movement prediction and sense of agency/body ownership-the ability of a patient to plan the movement and to perceive the stimulation as a part of his/her own control loop is important for carryover effect to take place. PMID:27073701

  17. The Neural Correlates of Long-Term Carryover following Functional Electrical Stimulation for Stroke

    Directory of Open Access Journals (Sweden)

    Marta Gandolla

    2016-01-01

    Full Text Available Neurorehabilitation effective delivery for stroke is likely to be improved by establishing a mechanistic understanding of how to enhance adaptive plasticity. Functional electrical stimulation is effective at reducing poststroke foot drop; in some patients, the effect persists after therapy has finished with an unknown mechanism. We used fMRI to examine neural correlates of functional electrical stimulation key elements, volitional intent to move and concurrent stimulation, in a group of chronic stroke patients receiving functional electrical stimulation for foot-drop correction. Patients exhibited task-related activation in a complex network, sharing bilateral sensorimotor and supplementary motor activation with age-matched controls. We observed consistent separation of patients with and without carryover effect on the basis of brain responses. Patients who experienced the carryover effect had responses in supplementary motor area that correspond to healthy controls; the interaction between experimental factors in contralateral angular gyrus was seen only in those without carryover. We suggest that the functional electrical stimulation carryover mechanism of action is based on movement prediction and sense of agency/body ownership—the ability of a patient to plan the movement and to perceive the stimulation as a part of his/her own control loop is important for carryover effect to take place.

  18. Motor nerve conduction velocity and function in carpal tunnel syndrome following neural mobilization: A randomized clinical trial

    Directory of Open Access Journals (Sweden)

    Manu Goyal

    2016-01-01

    Full Text Available Introduction: Carpal tunnel syndrome (CTS is the most common nerve entrapment syndrome in the upper extremity leading to the functional disability. The consequence of the entrapment is the poor health of the nerve (conduction, mobility, and blood flow. Purpose of the Study: The aim of the study is to evaluate the effect of neural mobilization on the motor nerve conduction velocity and function in the CTS patients. Methods: Thirty CTS patients (only females were scrutinized on the basis of the inclusion and exclusion criteria. They were randomized into two groups A (n = 15 and B (n = 15 using simple random sampling. Group A patients were treated with the conventional physiotherapy regimen and Group B were provided neural mobilization. Results: The data analysis was done using SPSS version 22. The t-test reveals that there was statistically significant improvement in posttreatment values of Group B for numeric pain rating scale, symptom severity scale, function status scale, motor nerve conduction latency, and velocity at P≤ 0.05. Conclusions: Neural mobilization in the CTS patients improves the motor nerve conduction and functional status. It may be incorporated in the physiotherapy treatment protocol of CTS patients.

  19. Mechanical and neural function of triceps surae in elite racewalking.

    Science.gov (United States)

    Cronin, Neil J; Hanley, Brian; Bissas, Athanassios

    2016-07-01

    Racewalking is a unique event combining mechanical elements of walking with speeds associated with running. It is currently unclear how racewalking technique impacts lower limb muscle-tendon function despite the relevance of this to muscle economy and overall performance. The present study examined triceps surae neuromechanics in 11 internationally competitive racewalkers (age 25 ± 11 yr) walking and running on a treadmill at speeds between 4.5 and 13.8 km/h while triceps surae fascicle lengths, electromyography, and kinematic data were recorded. Cumulative muscle activity required to traverse a unit distance (CMAPD) was calculated for each muscle. Medial gastrocnemius (MG) and soleus fascicle lengths/velocities were determined using an automated tracking algorithm, and muscle-tendon unit lengths were determined. Running was associated with net shortening of muscle fascicles during stance, combined with substantial lengthening of the muscle-tendon unit, implying energy storage in the Achilles tendon. When the same participants racewalked at the same speed, the fascicles shortened (soleus) or lengthened (MG), coinciding with rapid shortening followed by a relatively small increase in muscle-tendon length during stance. Consequently, compared with running at the same speed, racewalking decreased the energy-saving role of the Achilles tendon. Moreover, CMAPD was generally highest in racewalking, implying that in individual muscles, the energy cost of racewalking was higher than running. Together these results suggest that racewalking is neurally and mechanically costly relative to running at a given speed. As racewalking events are typically between 10 and 50 km, neuromechanical inefficiencies that occur with each stride likely result in substantial energetic penalties. PMID:27255524

  20. Efficient basis for the Dicke model: II. Wave function convergence and excited states

    International Nuclear Information System (INIS)

    An extended bosonic coherent basis has been shown by Chen et al (2008 Phys. Rev. A 78 051801) and Liu T et al (2009 Phys. Rev. A 80 165308) to provide numerically exact solutions of the finite-size Dicke model. The advantages in employing this basis, as compared with the photon number (Fock) basis, are exhibited to be valid for a large region of the Hamiltonian parameter space and many excited states by analyzing the convergence in the wave functions. (paper)

  1. The Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks

    CERN Document Server

    Yao, Kun

    2015-01-01

    We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from electron density. The output of the network is used as a non-local correction to the conventional local and semi-local kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. Numerical noise inherited from the non-linearity of the neural network is identified as the major challenge for the model. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.

  2. A fast learning algorithm of neural network with tunable activation function

    Institute of Scientific and Technical Information of China (English)

    SHEN Yanjun; WANG Bingwen

    2004-01-01

    This paper presents a modified structure of a neural network with tunable activation function and provides a new learning algorithm for the neural network training. Simulation results of XOR problem, Feigenbaum function, and Henon map show that the new algorithm has better performance than BP (back propagation) algorithm in terms of shorter convergence time and higher convergence accuracy. Further modifications of the structure of the neural network with the faster learning algorithm demonstrate simpler structure with even faster convergence speed and better convergence accuracy.

  3. New results for global exponential synchronization in neural networks via functional differential inclusions

    Science.gov (United States)

    Wang, Dongshu; Huang, Lihong; Tang, Longkun

    2015-08-01

    This paper is concerned with the synchronization dynamical behaviors for a class of delayed neural networks with discontinuous neuron activations. Continuous and discontinuous state feedback controller are designed such that the neural networks model can realize exponential complete synchronization in view of functional differential inclusions theory, Lyapunov functional method and inequality technique. The new proposed results here are very easy to verify and also applicable to neural networks with continuous activations. Finally, some numerical examples show the applicability and effectiveness of our main results.

  4. Fusion prediction based on the attribute clustering net-work and the radial basis function

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    A fusion prediction method is introduced on the basis of attribute clustering network and radial basis functions. An algorithm of quasi-self organization for developing the model for the fusion prediction is introduced. Some simulation results for chaotic time series are presented to show the performance of the method.

  5. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  6. Sensitive periods for the functional specialization of the neural system for human face processing

    OpenAIRE

    Röder, Brigitte; Ley, Pia; Shenoy, Bhamy H.; Kekunnaya, Ramesh; Bottari, Davide

    2013-01-01

    Sensitive periods in human functional brain development were tested in humans who had been blind from birth and whose sight was restored as long as 14 y later. In investigating this rare population, our data demonstrate a general principle of brain development: rather than being born with highly specialized neural systems (e.g., for specific object categories such as faces), the functional differentiation of neural circuits seems to depend on early (visual) experience involving a decrease in ...

  7. Shaping Early Reorganization of Neural Networks Promotes Motor Function after Stroke.

    OpenAIRE

    Volz, L. J.; Rehme, A K; Michely, J.; Nettekoven, C.; Eickhoff, Simon; Fink, G R; Grefkes, Christian

    2016-01-01

    Neural plasticity is a major factor driving cortical reorganization after stroke. We here tested whether repetitively enhancing motor cortex plasticity by means of intermittent theta-burst stimulation (iTBS) prior to physiotherapy might promote recovery of function early after stroke. Functional magnetic resonance imaging (fMRI) was used to elucidate underlying neural mechanisms. Twenty-six hospitalized, first-ever stroke patients (time since stroke: 1-16 days) with hand motor deficits were e...

  8. Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets

    OpenAIRE

    Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

    2015-01-01

    Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work,...

  9. Multiscale finite element methods for high-contrast problems using local spectral basis functions

    KAUST Repository

    Efendiev, Yalchin

    2011-02-01

    In this paper we study multiscale finite element methods (MsFEMs) using spectral multiscale basis functions that are designed for high-contrast problems. Multiscale basis functions are constructed using eigenvectors of a carefully selected local spectral problem. This local spectral problem strongly depends on the choice of initial partition of unity functions. The resulting space enriches the initial multiscale space using eigenvectors of local spectral problem. The eigenvectors corresponding to small, asymptotically vanishing, eigenvalues detect important features of the solutions that are not captured by initial multiscale basis functions. Multiscale basis functions are constructed such that they span these eigenfunctions that correspond to small, asymptotically vanishing, eigenvalues. We present a convergence study that shows that the convergence rate (in energy norm) is proportional to (H/Λ*)1/2, where Λ* is proportional to the minimum of the eigenvalues that the corresponding eigenvectors are not included in the coarse space. Thus, we would like to reach to a larger eigenvalue with a smaller coarse space. This is accomplished with a careful choice of initial multiscale basis functions and the setup of the eigenvalue problems. Numerical results are presented to back-up our theoretical results and to show higher accuracy of MsFEMs with spectral multiscale basis functions. We also present a hierarchical construction of the eigenvectors that provides CPU savings. © 2010.

  10. Gaussian continuum basis functions for calculating high-harmonic generation spectra

    CERN Document Server

    Coccia, Emanuele; Labeye, Marie; Caillat, Jérémie; Taieb, Richard; Toulouse, Julien; Luppi, Eleonora

    2016-01-01

    We explore the computation of high-harmonic generation spectra by means of Gaussian basis sets in approaches propagating the time-dependent Schr{\\"o}dinger equation. We investigate the efficiency of Gaussian functions specifically designed for the description of the continuum proposed by Kaufmann et al. [ J. Phys. B 22 , 2223 (1989) ]. We assess the range of applicability of this approach by studying the hydrogen atom , i. e. the simplest atom for which "exact" calculations on a grid can be performed. We notably study the effect of increasing the basis set cardinal number , the number of diffuse basis functions , and the number of Gaussian pseudo-continuum basis functions for various laser parameters. Our results show that the latter significantly improve the description of the low-lying continuum states , and provide a satisfactory agreement with grid calculations for laser wavelengths $\\lambda$0 = 800 and 1064 nm. The Kaufmann continuum functions therefore appear as a promising way of constructing Gaussian ...

  11. Estimated Quality of Multistage Process on the Basis of Probabilistic Approach with Continuous Response Functions

    OpenAIRE

    Olga V. Avseeva; Yuri B. Tebekin

    2011-01-01

    The article is devoted to the problem of the quality management for multiphase processes on the basis of the probabilistic approach. Method with continuous response functions is offered from the application of the method of Lagrange multipliers.

  12. Estimated Quality of Multistage Process on the Basis of Probabilistic Approach with Continuous Response Functions

    Directory of Open Access Journals (Sweden)

    Yuri B. Tebekin

    2011-11-01

    Full Text Available The article is devoted to the problem of the quality management for multiphase processes on the basis of the probabilistic approach. Method with continuous response functions is offered from the application of the method of Lagrange multipliers.

  13. Identification of genes required for neural-specific glycosylation using functional genomics.

    Directory of Open Access Journals (Sweden)

    Miki Yamamoto-Hino

    Full Text Available Glycosylation plays crucial regulatory roles in various biological processes such as development, immunity, and neural functions. For example, α1,3-fucosylation, the addition of a fucose moiety abundant in Drosophila neural cells, is essential for neural development, function, and behavior. However, it remains largely unknown how neural-specific α1,3-fucosylation is regulated. In the present study, we searched for genes involved in the glycosylation of a neural-specific protein using a Drosophila RNAi library. We obtained 109 genes affecting glycosylation that clustered into nine functional groups. Among them, members of the RNA regulation group were enriched by a secondary screen that identified genes specifically regulating α1,3-fucosylation. Further analyses revealed that an RNA-binding protein, second mitotic wave missing (Swm, upregulates expression of the neural-specific glycosyltransferase FucTA and facilitates its mRNA export from the nucleus. This first large-scale genetic screen for glycosylation-related genes has revealed novel regulation of fucTA mRNA in neural cells.

  14. Efficient wave function simulations in nonlinear quantum optics using an adaptive coherent state basis

    International Nuclear Information System (INIS)

    Full text: We show that a suitable set of coherent basis states placed on a discrete hexagonal grid can be used to numerically very accurately represent general quantum states in a memory efficient way. Adding an algorithm for dynamic basis adaptation allows highly accurate Quantum Monte Carlo wave function simulations with small basis sets. At the example of the intricate nonlinear dynamics of an optical parametric oscillator around threshold, we demonstrate that this approach yields accurate time dependent solutions with a substantially smaller basis sets than required for a photon number basis. Above threshold the adaptive basis splits into localized subsets allowing efficient representation of bimodal or even more complex phase space distributions and directly yields an intuitive physical picture of the ongoing dynamics. (author)

  15. Computationally efficient double hybrid density functional theory using dual basis methods

    CERN Document Server

    Byrd, Jason N

    2015-01-01

    We examine the application of the recently developed dual basis methods of Head-Gordon and co-workers to double hybrid density functional computations. Using the B2-PLYP, B2GP-PLYP, DSD-BLYP and DSD-PBEP86 density functionals, we assess the performance of dual basis methods for the calculation of conformational energy changes in C$_4$-C$_7$ alkanes and for the S22 set of noncovalent interaction energies. The dual basis methods, combined with resolution-of-the-identity second-order M{\\o}ller-Plesset theory, are shown to give results in excellent agreement with conventional methods at a much reduced computational cost.

  16. Functional recordings from awake, behaving rodents through a microchannel based regenerative neural interface

    Science.gov (United States)

    Gore, Russell K.; Choi, Yoonsu; Bellamkonda, Ravi; English, Arthur

    2015-02-01

    Objective. Neural interface technologies could provide controlling connections between the nervous system and external technologies, such as limb prosthetics. The recording of efferent, motor potentials is a critical requirement for a peripheral neural interface, as these signals represent the user-generated neural output intended to drive external devices. Our objective was to evaluate structural and functional neural regeneration through a microchannel neural interface and to characterize potentials recorded from electrodes placed within the microchannels in awake and behaving animals. Approach. Female rats were implanted with muscle EMG electrodes and, following unilateral sciatic nerve transection, the cut nerve was repaired either across a microchannel neural interface or with end-to-end surgical repair. During a 13 week recovery period, direct muscle responses to nerve stimulation proximal to the transection were monitored weekly. In two rats repaired with the neural interface, four wire electrodes were embedded in the microchannels and recordings were obtained within microchannels during proximal stimulation experiments and treadmill locomotion. Main results. In these proof-of-principle experiments, we found that axons from cut nerves were capable of functional reinnervation of distal muscle targets, whether regenerating through a microchannel device or after direct end-to-end repair. Discrete stimulation-evoked and volitional potentials were recorded within interface microchannels in a small group of awake and behaving animals and their firing patterns correlated directly with intramuscular recordings during locomotion. Of 38 potentials extracted, 19 were identified as motor axons reinnervating tibialis anterior or soleus muscles using spike triggered averaging. Significance. These results are evidence for motor axon regeneration through microchannels and are the first report of in vivo recordings from regenerated motor axons within microchannels in a small

  17. Microelectronic neural bridge for signal regeneration and function rebuilding over two separate nerves

    International Nuclear Information System (INIS)

    According to the feature of neural signals, a micro-electronic neural bridge (MENB) has been designed. It consists of two electrode arrays for neural signal detection and functional electrical stimulation (FES), and a microelectronic circuit for signal amplifying, processing, and FES driving. The core of the system is realized in 0.5-μm CMOS technology and used in animal experiments. A special experimental strategy has been designed to demonstrate the feasibility of the system. With the help of the MENB, the withdrawal reflex function of the left/right leg of one spinal toad has been rebuilt in the corresponding leg of another spinal toad. According to the coherence analysis between the source and regenerated neural signals, the controlled spinal toad's sciatic nerve signal is delayed by 0.72 ms in relation to the sciatic nerve signal of the source spinal toad and the cross-correlation function reaches a value of 0.73. This shows that the regenerated signal is correlated with the source sciatic signal significantly and the neural activities involved in reflex function have been regenerated. The experiment demonstrates that the MENB is useful in rebuilding the neural function between nerves of different bodies. (semiconductor integrated circuits)

  18. Microelectronic neural bridge for signal regeneration and function rebuilding over two separate nerves

    Science.gov (United States)

    Xiaoyan, Shen; Zhigong, Wang; Xiaoying, Lü; Shushan, Xie; Zonghao, Huang

    2011-06-01

    According to the feature of neural signals, a micro-electronic neural bridge (MENB) has been designed. It consists of two electrode arrays for neural signal detection and functional electrical stimulation (FES), and a microelectronic circuit for signal amplifying, processing, and FES driving. The core of the system is realized in 0.5-μm CMOS technology and used in animal experiments. A special experimental strategy has been designed to demonstrate the feasibility of the system. With the help of the MENB, the withdrawal reflex function of the left/right leg of one spinal toad has been rebuilt in the corresponding leg of another spinal toad. According to the coherence analysis between the source and regenerated neural signals, the controlled spinal toad's sciatic nerve signal is delayed by 0.72 ms in relation to the sciatic nerve signal of the source spinal toad and the cross-correlation function reaches a value of 0.73. This shows that the regenerated signal is correlated with the source sciatic signal significantly and the neural activities involved in reflex function have been regenerated. The experiment demonstrates that the MENB is useful in rebuilding the neural function between nerves of different bodies.

  19. Microelectronic neural bridge for signal regeneration and function rebuilding over two separate nerves*

    Institute of Scientific and Technical Information of China (English)

    Shen Xiaoyan; Wang Zhigong; Lü Xiaoying; Xie Shushan; Huang Zonghao

    2011-01-01

    According to the feature of neural signals, a micro-electronic neural bridge (MENB) has been designed.It consists of two electrode arrays for neural signal detection and functional electrical stimulation (FES), and a microelectronic circuit for signal amplifying, processing, and FES driving. The core of the system is realized in 0.5-μm CMOS technology and used in animal experiments. A special experimental strategy has been designed to demonstrate the feasibility of the system. With the help of the MENB, the withdrawal reflex function of the left/right leg of one spinal toad has been rebuilt in the corresponding leg of another spinal toad. According to the coherence analysis between the source and regenerated neural signals, the controlled spinal toad's sciatic nerve signal is delayed by 0.72 ms in relation to the sciatic nerve signal of the source spinal toad and the cross-correlation function reaches a value of 0.73. This shows that the regenerated signal is correlated with the source sciatic signal significantly and the neural activities involved in reflex function have been regenerated. The experiment demonstrates that the MENB is useful in rebuilding the neural function between nerves of different bodies.

  20. Microelectronic neural bridge for signal regeneration and function rebuilding over two separate nerves

    Energy Technology Data Exchange (ETDEWEB)

    Shen Xiaoyan; Wang Zhigong; Xie Shushan; Huang Zonghao [Institute of RF- and OE-ICs, Southeast University, Nanjing 210096 (China); Lue Xiaoying, E-mail: zgwang@seu.edu.cn [Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096 (China)

    2011-06-15

    According to the feature of neural signals, a micro-electronic neural bridge (MENB) has been designed. It consists of two electrode arrays for neural signal detection and functional electrical stimulation (FES), and a microelectronic circuit for signal amplifying, processing, and FES driving. The core of the system is realized in 0.5-{mu}m CMOS technology and used in animal experiments. A special experimental strategy has been designed to demonstrate the feasibility of the system. With the help of the MENB, the withdrawal reflex function of the left/right leg of one spinal toad has been rebuilt in the corresponding leg of another spinal toad. According to the coherence analysis between the source and regenerated neural signals, the controlled spinal toad's sciatic nerve signal is delayed by 0.72 ms in relation to the sciatic nerve signal of the source spinal toad and the cross-correlation function reaches a value of 0.73. This shows that the regenerated signal is correlated with the source sciatic signal significantly and the neural activities involved in reflex function have been regenerated. The experiment demonstrates that the MENB is useful in rebuilding the neural function between nerves of different bodies. (semiconductor integrated circuits)

  1. Exponential-trigonometric basis functions in the Coulomb four-body problem

    International Nuclear Information System (INIS)

    Basis functions of a new type--specifically, exponential-trigonometric functions depending on all six interparticle distances--have been proposed for the Coulomb four-body problem. A method has been developed for computing nine-dimensional integrals determining the matrix elements of the Hamiltonian for a four-body system and featuring these functions. The efficiency of the approach that relies on the proposed basis functions has been tested by calculating the e+e-e+e-, p+μ-p+μ-, μ+e-μ+e-, and p+e-p+e- molecules

  2. Experimental - trigonometric basis functions for the Coulomb-four-body problem

    International Nuclear Information System (INIS)

    Basis functions of a new type are proposed for the Coulomb four-body problem: the exponential-trigonometric functions depending on all the six interparticle separations. The method of computation of the nine-dimensional integrals determining the matrix elements of the four-body system energy operator with these functions is outlined. The efficiency of new basis functions is verified by computations of the e+e-e+e-, p+μ-p+μ-, μ+e-μ+e-, and p+e-p+e- molecules

  3. Neural Tuning Functions Underlie Both Generalization and Interference.

    Directory of Open Access Journals (Sweden)

    Ian S Howard

    Full Text Available In sports, the role of backswing is considered critical for generating a good shot, even though it plays no direct role in hitting the ball. We recently demonstrated the scientific basis of this phenomenon by showing that immediate past movement affects the learning and recall of motor memories. This effect occurred regardless of whether the past contextual movement was performed actively, passively, or shown visually. In force field studies, it has been shown that motor memories generalize locally and that the level of compensation decays as a function of movement angle away from the trained movement. Here we examine if the contextual effect of past movement exhibits similar patterns of generalization and whether it can explain behavior seen in interference studies. Using a single force-field learning task, the directional tuning curves of both the prior contextual movement and the subsequent force field adaptive movements were measured. The adaptation movement direction showed strong directional tuning, decaying to zero by 90° relative to the training direction. The contextual movement direction exhibited a similar directional tuning, although the effect was always above 60%. We then investigated the directional tuning of the passive contextual movement using interference tasks, where the contextual movements that uniquely specified the force field direction were separated by ±15° or ±45°. Both groups showed a pronounced tuning effect, which could be well explained by the directional tuning functions for single force fields. Our results show that contextual effect of past movement influences predictive force compensation, even when adaptation does not require contextual information. However, when such past movement contextual information is crucial to the task, such as in an interference study, it plays a strong role in motor memory learning and recall. This work demonstrates that similar tuning responses underlie both generalization of

  4. Neural Tuning Functions Underlie Both Generalization and Interference.

    Science.gov (United States)

    Howard, Ian S; Franklin, David W

    2015-01-01

    In sports, the role of backswing is considered critical for generating a good shot, even though it plays no direct role in hitting the ball. We recently demonstrated the scientific basis of this phenomenon by showing that immediate past movement affects the learning and recall of motor memories. This effect occurred regardless of whether the past contextual movement was performed actively, passively, or shown visually. In force field studies, it has been shown that motor memories generalize locally and that the level of compensation decays as a function of movement angle away from the trained movement. Here we examine if the contextual effect of past movement exhibits similar patterns of generalization and whether it can explain behavior seen in interference studies. Using a single force-field learning task, the directional tuning curves of both the prior contextual movement and the subsequent force field adaptive movements were measured. The adaptation movement direction showed strong directional tuning, decaying to zero by 90° relative to the training direction. The contextual movement direction exhibited a similar directional tuning, although the effect was always above 60%. We then investigated the directional tuning of the passive contextual movement using interference tasks, where the contextual movements that uniquely specified the force field direction were separated by ±15° or ±45°. Both groups showed a pronounced tuning effect, which could be well explained by the directional tuning functions for single force fields. Our results show that contextual effect of past movement influences predictive force compensation, even when adaptation does not require contextual information. However, when such past movement contextual information is crucial to the task, such as in an interference study, it plays a strong role in motor memory learning and recall. This work demonstrates that similar tuning responses underlie both generalization of movement direction

  5. Functional analysis of the notch signalling cascade in neural progenitors

    OpenAIRE

    Vilas-Boas, Filipe

    2010-01-01

    Tese de doutoramento, Ciências Biomédicas (Biologia do Desenvolvimento), Universidade de Lisboa, Faculdade de Medicina, 2011 With the ageing of world population, the number of people suffering from neuronal degeneration is drastically increasing and new strategies to prevent or cure neurodegenerative diseases are urgently needed. One exciting avenue is the use of stem cells to replace damaged neural tissues, but this requires a more comprehensive understanding of the molecular events regul...

  6. Modeling the Flux-Charge Relation of Memristor with Neural Network of Smooth Hinge Functions

    Directory of Open Access Journals (Sweden)

    X. Mu

    2014-09-01

    Full Text Available The memristor was proposed to characterize the flux-charge relation. We propose the generalized flux-charge relation model of memristor with neural network of smooth hinge functions. There is effective identification algorithm for the neural network of smooth hinge functions. The representation capability of this model is theoretically guaranteed. Any functional flux-charge relation of a memristor can be approximated by the model. We also give application examples to show that the given model can approximate the flux-charge relation of existing piecewise linear memristor model, window function memristor model, and a physical memristor device.

  7. The neural development and organization of letter recognition: Evidence from functional neuroimaging, computational modeling, and behavioral studies

    OpenAIRE

    Polk, Thad A.; Farah, Martha J.

    1998-01-01

    Although much of the brain’s functional organization is genetically predetermined, it appears that some noninnate functions can come to depend on dedicated and segregated neural tissue. In this paper, we describe a series of experiments that have investigated the neural development and organization of one such noninnate function: letter recognition. Functional neuroimaging demonstrates that letter and digit recognition depend on different neural substrates in some ...

  8. Id4 knockdown during zebrafish development revealed its functional role in neural stem cell survival

    OpenAIRE

    Patlola, Santosh

    2010-01-01

    Id4 (Inhibitor of DNA binding 4 / Inhibitor of Differentiation 4) is one of the four members of Id protein family that antagonise the function of basic helix-loop-helix (bHLH) transcriptional regulators. In the mouse it has been shown that Id4 plays an important role in the timing of neural stem and progenitor cell differentiation and knockout mice exhibited premature neural stem cell differentiation resulting in significantly smaller brains. To further establish the molecular mechanism under...

  9. UNIVERSAL APPROXIMATION WITH NON-SIGMOID HIDDEN LAYER ACTIVATION FUNCTIONS BY USING ARTIFICIAL NEURAL NETWORK MODELING

    OpenAIRE

    R. Murugadoss; Dr. M.Ramakrishnan

    2014-01-01

    Neural networks are modeled on the way the human brain. They are capable of learning and can automatically recognize by skillfully training and design complex relationships and hidden dependencies based on historical example patterns and use this information for forecasting. The main difference, and at the same time is biggest advantage of the model of neural networks over statistical techniques seen that the forecaster the exact functional structure between input and Output variables need no...

  10. Electromyography function, disability degree, and pain in leprosy patients undergoing neural mobilization treatment

    OpenAIRE

    Larissa Sales Téles Véras; Rodrigo Gomes de Souza Vale; Danielli Braga de Mello; José Adail Fonseca de Castro; Vicente Lima; Alexis Trott; Estélio Henrique Martin Dantas

    2012-01-01

    INTRODUCTION: This study aimed to evaluate the effect of the neural mobilization technique on electromyography function, disability degree, and pain in patients with leprosy. METHODS: A sample of 56 individuals with leprosy was randomized into an experimental group, composed of 29 individuals undergoing treatment with neural mobilization, and a control group of 27 individuals who underwent conventional treatment. In both groups, the lesions in the lower limbs were treated. In the treatment wi...

  11. Solution of volume-surface integral equations using higher-order hierarchical Legendre basis functions

    DEFF Research Database (Denmark)

    Kim, Oleksiy S.; Meincke, Peter; Breinbjerg, Olav;

    2007-01-01

    applied to transform the VSIE into a system of linear equations. The higher-order MoM provides significant reduction in the number of unknowns in comparison with standard MoM formulations using low-order basis functions, such as RWG functions. Due to the orthogonal nature of the higher-order Legendre......The problem of electromagnetic scattering by composite metallic and dielectric objects is solved using the coupled volume-surface integral equation (VSIE). The method of moments (MoM) based on higher-order hierarchical Legendre basis functions and higher-order curvilinear geometrical elements is...

  12. MESHLESS METHOD BASED ON COLLOCATION WITH CONSISTENT COMPACTLY SUPPORTED RADIAL BASIS FUNCTIONS

    Institute of Scientific and Technical Information of China (English)

    SONG Kangzu; ZHANG Xiong; LU Mingwan

    2004-01-01

    Based on our previous study, the accuracy of derivatives of interpolating functions are usually very poor near the boundary of domain when Compactly Supported Radial Basis Functions (CSRBFs) are used, so that it could result in significant error in solving partial differential equations with Neumann boundary conditions. To overcome this drawback, the Consistent Compactly Supported Radial Basis Functions (CCSRBFs) are developed, which satisfy the predetermined consistency conditions. Meshless method based on point collocation with CCSRBFs is developed for solving partial differential equations. Numerical studies show that the proposed method improves the accuracy of approximation significantly.

  13. Nonlinear System Identification via Basis Functions Based Time Domain Volterra Model

    Directory of Open Access Journals (Sweden)

    Yazid Edwar

    2014-07-01

    Full Text Available This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA. The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement.

  14. Dynamical Behaviors of Multiple Equilibria in Competitive Neural Networks With Discontinuous Nonmonotonic Piecewise Linear Activation Functions.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing

    2016-03-01

    This paper addresses the problem of coexistence and dynamical behaviors of multiple equilibria for competitive neural networks. First, a general class of discontinuous nonmonotonic piecewise linear activation functions is introduced for competitive neural networks. Then based on the fixed point theorem and theory of strict diagonal dominance matrix, it is shown that under some conditions, such n -neuron competitive neural networks can have 5(n) equilibria, among which 3(n) equilibria are locally stable and the others are unstable. More importantly, it is revealed that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibria and locally stable equilibria than the ones with other activation functions, such as the continuous Mexican-hat-type activation function and discontinuous two-level activation function. Furthermore, the 3(n) locally stable equilibria given in this paper are located in not only saturated regions, but also unsaturated regions, which is different from the existing results on multistability of neural networks with multiple level activation functions. A simulation example is provided to illustrate and validate the theoretical findings. PMID:25826814

  15. Is there evidence for neural compensation in attention deficit hyperactivity disorder? A review of the functional neuroimaging literature

    OpenAIRE

    Fassbender, Catherine; Schweitzer, Julie B.

    2006-01-01

    This article reviews evidence for the presence of a compensatory, alternative, neural system and its possible link to associated processing strategies in children and adults with attention deficit hyperactivity disorder (ADHD). The article presents findings on a region by region basis that suggests ADHD should be characterized not only by neural hypo-activity, as it is commonly thought but neural hyperactivity as well, in regions of the brain that may relate to compensatory brain and behavior...

  16. The neural basis of the abnormal self-referential processing and its impact on cognitive control in depressed patients.

    Science.gov (United States)

    Wagner, Gerd; Schachtzabel, Claudia; Peikert, Gregor; Bär, Karl-Jürgen

    2015-07-01

    Persistent pondering over negative self-related thoughts is a central feature of depressive psychopathology. In this study, we sought to investigate the neural correlates of abnormal negative self-referential processing (SRP) in patients with Major Depressive Disorder and its impact on subsequent cognitive control-related neuronal activation. We hypothesized aberrant activation dynamics during the period of negative and neutral SRP in the rostral anterior cingulate cortex (rACC) and in the amygdala in patients with major depressive disorder. Additionally, we assumed abnormal activation in the fronto-cingulate network during Stroop task execution. 19 depressed patients and 20 healthy controls participated in the study. Using an event-related functional magnetic resonance imaging (fMRI) design, negative, positive and neutral self-referential statements were displayed for 6.5 s and followed by incongruent or congruent Stroop conditions. The data were analyzed with SPM8. In contrast to controls, patients exhibited no significant valence-dependent rACC activation differences during SRP. A novel finding was the significant activation of the amygdala and the reward-processing network during presentation of neutral self-referential stimuli relative to baseline and to affective stimuli in patients. The fMRI analysis of the Stroop task revealed a reduced BOLD activation in the right fronto-parietal network of patients in the incongruent condition after negative SRP only. Thus, the inflexible activation in the rACC may correspond to the inability of depressed patients to shift their attention away from negative self-related stimuli. The accompanying negative affect and task-irrelevant emotional processing may compete for neuronal resources with cognitive control processes and lead thereby to deficient cognitive performance associated with decreased fronto-parietal activation. PMID:25872899

  17. Using an iterative eigensolver to compute vibrational energies with phase-spaced localized basis functions

    International Nuclear Information System (INIS)

    Although phase-space localized Gaussians are themselves poor basis functions, they can be used to effectively contract a discrete variable representation basis [A. Shimshovitz and D. J. Tannor, Phys. Rev. Lett. 109, 070402 (2012)]. This works despite the fact that elements of the Hamiltonian and overlap matrices labelled by discarded Gaussians are not small. By formulating the matrix problem as a regular (i.e., not a generalized) matrix eigenvalue problem, we show that it is possible to use an iterative eigensolver to compute vibrational energy levels in the Gaussian basis

  18. Using an iterative eigensolver to compute vibrational energies with phase-spaced localized basis functions

    Science.gov (United States)

    Brown, James; Carrington, Tucker

    2015-07-01

    Although phase-space localized Gaussians are themselves poor basis functions, they can be used to effectively contract a discrete variable representation basis [A. Shimshovitz and D. J. Tannor, Phys. Rev. Lett. 109, 070402 (2012)]. This works despite the fact that elements of the Hamiltonian and overlap matrices labelled by discarded Gaussians are not small. By formulating the matrix problem as a regular (i.e., not a generalized) matrix eigenvalue problem, we show that it is possible to use an iterative eigensolver to compute vibrational energy levels in the Gaussian basis.

  19. Niche-dependent development of functional neuronal networks from embryonic stem cell-derived neural populations

    Directory of Open Access Journals (Sweden)

    Siebler Mario

    2009-08-01

    Full Text Available Abstract Background The present work was performed to investigate the ability of two different embryonic stem (ES cell-derived neural precursor populations to generate functional neuronal networks in vitro. The first ES cell-derived neural precursor population was cultivated as free-floating neural aggregates which are known to form a developmental niche comprising different types of neural cells, including neural precursor cells (NPCs, progenitor cells and even further matured cells. This niche provides by itself a variety of different growth factors and extracellular matrix proteins that influence the proliferation and differentiation of neural precursor and progenitor cells. The second population was cultivated adherently in monolayer cultures to control most stringently the extracellular environment. This population comprises highly homogeneous NPCs which are supposed to represent an attractive way to provide well-defined neuronal progeny. However, the ability of these different ES cell-derived immature neural cell populations to generate functional neuronal networks has not been assessed so far. Results While both precursor populations were shown to differentiate into sufficient quantities of mature NeuN+ neurons that also express GABA or vesicular-glutamate-transporter-2 (vGlut2, only aggregate-derived neuronal populations exhibited a synchronously oscillating network activity 2–4 weeks after initiating the differentiation as detected by the microelectrode array technology. Neurons derived from homogeneous NPCs within monolayer cultures did merely show uncorrelated spiking activity even when differentiated for up to 12 weeks. We demonstrated that these neurons exhibited sparsely ramified neurites and an embryonic vGlut2 distribution suggesting an inhibited terminal neuronal maturation. In comparison, neurons derived from heterogeneous populations within neural aggregates appeared as fully mature with a dense neurite network and punctuated

  20. Radial basis function simulation and metamodelling of surface roughness in centreless grinding

    Directory of Open Access Journals (Sweden)

    P. Krajnik

    2005-12-01

    Full Text Available Purpose: The purpose of this study was to investigate the efficiency of artificial neural networks and the related metamodels to simulate and identify complex centreless grinding process.Design/methodology/approach: The modelling is founded on the system approach, which is efficiently dealing with the complexity of the grinding process. The unknown process transfer function is identified via artificial neural network that requires fewer assumptions and less precise information about the process modelled than other conventional modelling techniques. The developed metamodel is a response surface (polynomial fit of the simulated process that is achieved by the computer model.Findings: The metamodel quality is strongly related to the prediction accuracy of the underlying simulation model. The generalisation capability of an artificial neural network is sensitive to the training samples (design of experiments. The predictive ability of a metamodel is comparable to the accuracy of the response surface regression model.Research limitations/implications: Improved simulation model and application of unconventional metamodels (Gaussian process regression will significantly improve the presented preliminary results.Originality/value: Metamodelling of computer experiments is an expansion of response surface methodology and the classical designs of experiments and represents a new paradigm in empirical modelling of machining operations.

  1. Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome

    Directory of Open Access Journals (Sweden)

    Katherine E. Manning

    2015-12-01

    Full Text Available Prader-Willi syndrome (PWS is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study.

  2. Assessment of Various Density Functionals and Basis Sets for the Calculation of Molecular Anharmonic Force Fields

    OpenAIRE

    Boese, A. Daniel; Klopper, Wim; Martin, Jan M. L.

    2005-01-01

    In a previous contribution (Mol. Phys. {\\bf 103}, xxxx, 2005), we established the suitability of density functional theory (DFT) for the calculation of molecular anharmonic force fields. In the present work, we have assessed a wide variety of basis sets and exchange-correlation functionals for harmonic and fundamental frequencies, equilibrium and ground-state rotational constants, and thermodynamic functions beyond the RRHO (rigid rotor-harmonic oscillator) approximation. The fairly good perf...

  3. Multi nodal load forecasting in electric power systems using a radial basis neural network; Previsao de carga multinodal em sistemas eletricos de potencia usando uma rede neural de base radial

    Energy Technology Data Exchange (ETDEWEB)

    Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br

    2009-07-01

    This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)

  4. Plant geography upon the basis of functional traits: an example from eastern North American trees.

    Science.gov (United States)

    Swenson, Nathan G; Weiser, Michael D

    2010-08-01

    Plant geographers have sought for decades to describe and predict the geographic distribution of vegetation types on the basis of plant function and its relationship with the abiotic environment. Traditionally this has been accomplished using categorical representations such as plant functional types. Increasingly, plant functional ecologists have sought to refine categorical functional types via quantitative functional traits in order to understand the ecological implications of trade-offs in plant form and function. Fewer works have focused upon testing whether commonly measured functional traits enhance our understanding of plant biogeography broadly and the geographic distribution of vegetation types in particular. Here we combine a continental-scale forest inventory data set containing 18 111 plots with a plant functional trait data set to ask: (1) Is there a strong relationship between the abiotic environment and the distribution of functional trait values in forest inventory plots? And (2) can different Holdridge life zones be distinguished upon the basis of their functional trait distributions? The results show geographic patterns of functional trait distributions that are often strongly correlated with climate and also show that the Holdridge life zones in the study area can be differentiated using a combination of functional traits. PMID:20836445

  5. Human neural progenitors express functional lysophospholipid receptors that regulate cell growth and morphology

    Directory of Open Access Journals (Sweden)

    Callihan Phillip

    2008-12-01

    Full Text Available Abstract Background Lysophospholipids regulate the morphology and growth of neurons, neural cell lines, and neural progenitors. A stable human neural progenitor cell line is not currently available in which to study the role of lysophospholipids in human neural development. We recently established a stable, adherent human embryonic stem cell-derived neuroepithelial (hES-NEP cell line which recapitulates morphological and phenotypic features of neural progenitor cells isolated from fetal tissue. The goal of this study was to determine if hES-NEP cells express functional lysophospholipid receptors, and if activation of these receptors mediates cellular responses critical for neural development. Results Our results demonstrate that Lysophosphatidic Acid (LPA and Sphingosine-1-phosphate (S1P receptors are functionally expressed in hES-NEP cells and are coupled to multiple cellular signaling pathways. We have shown that transcript levels for S1P1 receptor increased significantly in the transition from embryonic stem cell to hES-NEP. hES-NEP cells express LPA and S1P receptors coupled to Gi/o G-proteins that inhibit adenylyl cyclase and to Gq-like phospholipase C activity. LPA and S1P also induce p44/42 ERK MAP kinase phosphorylation in these cells and stimulate cell proliferation via Gi/o coupled receptors in an Epidermal Growth Factor Receptor (EGFR- and ERK-dependent pathway. In contrast, LPA and S1P stimulate transient cell rounding and aggregation that is independent of EGFR and ERK, but dependent on the Rho effector p160 ROCK. Conclusion Thus, lysophospholipids regulate neural progenitor growth and morphology through distinct mechanisms. These findings establish human ES cell-derived NEP cells as a model system for studying the role of lysophospholipids in neural progenitors.

  6. A New Training Method for Feedforward Neural Networks Based on Geometric Contraction Property of Activation Functions

    OpenAIRE

    Birtea, Petre; Cernazanu-Glavan, Cosmin; Sisu, Alexandru

    2016-01-01

    We propose a new training method for a feedforward neural network having the activation functions with the geometric contraction property. The method consists of constructing a new functional that is less nonlinear in comparison with the classical functional by removing the nonlinearity of the activation functions from the output layer. We validate this new method by a series of experiments that show an improved learning speed and also a better classification error.

  7. Neural networks with periodic and monotonic activation functions: a comparative study in classification problems

    OpenAIRE

    Romero Merino, Enrique; Sopena, Josep Maria; Alquézar Mancho, René; Moliner, Joan L.

    2000-01-01

    This article discusses a number of reasons why the use of non-monotonic functions as activation functions can lead to a marked improvement in the performance of a neural network. Using a wide range of benchmarks we show that a multilayer feed-forward network using sine activation functions (and an appropriate choice of initial parameters) learns much faster than one incorporating sigmoid functions - as much as 150-500 times faster - when both types are trained with backpr...

  8. Specific features of modelling rules of monetary policy on the basis of hybrid regression models with a neural component

    Directory of Open Access Journals (Sweden)

    Lukianenko Iryna H.

    2014-01-01

    Full Text Available The article considers possibilities and specific features of modelling economic phenomena with the help of the category of models that unite elements of econometric regressions and artificial neural networks. This category of models contains auto-regression neural networks (AR-NN, regressions of smooth transition (STR/STAR, multi-mode regressions of smooth transition (MRSTR/MRSTAR and smooth transition regressions with neural coefficients (NCSTR/NCSTAR. Availability of the neural network component allows models of this category achievement of a high empirical authenticity, including reproduction of complex non-linear interrelations. On the other hand, the regression mechanism expands possibilities of interpretation of the obtained results. An example of multi-mode monetary rule is used to show one of the cases of specification and interpretation of this model. In particular, the article models and interprets principles of management of the UAH exchange rate that come into force when economy passes from a relatively stable into a crisis state.

  9. An Investigation into Food Preferences and the Neural Basis of Food-Related Incentive Motivation in Prader-Willi Syndrome

    Science.gov (United States)

    Hinton, E. C.; Holland, A. J.; Gellatly, M. S. N.; Soni, S.; Owen, A. M.

    2006-01-01

    Background: Research into the excessive eating behaviour associated with Prader-Willi syndrome (PWS) to date has focused on homeostatic and behavioural investigations. The aim of this study was to examine the role of the reward system in such eating behaviour, in terms of both the pattern of food preferences and the neural substrates of incentive…

  10. The neural basis of learning to spell again: An fMRI study of spelling training in acquired dysgraphia.

    Directory of Open Access Journals (Sweden)

    Jeremy Purcell

    2015-05-01

    1 For all participants we identified brain areas associated with a normalized response for the TRAINING words at the post-training time point. 2 For all participants we identified an up-regulation of the TRAINING response (i.e., the TRAINING neural response was initially low and then increased post-training; whereas in only one participant did we also observe a down-regulation of the training response (i.e., the TRAINING neural response was initially high, but then decreased post-training. 3 Although the areas associated with the normalized TRAINING response were different in each individual, they all include areas typically associated with the spelling system (Purcell et al. 2011, including the right homologues of typically left hemisphere spelling regions. Across the participants, the following areas of normalization were observed: bilateral superior temporal gyrus, inferior frontal gyrus, and the bilateral inferior temporal/fusiform gyrus. Discussion: We found that the predominant BOLD response to training involved an up-regulation of the neural response to spelling the TRAINING items. In addition, we found individual differences in the neurotopography of the normalization response patterns although all were with within brain areas that form a part of the spelling network(Purcell et al. 2011. This work provides evidence regarding one aspect of the multiplicity of neural responses associated with recovery of spelling in individuals with acquired dysgraphia.

  11. Prediction of the functional properties of ceramic materials from composition using artificial neural networks

    OpenAIRE

    Scott, D. J.; Coveney, P. V.; Kilner, J. A.; Rossiny, J. C. H.; Alford, N. Mc N.

    2007-01-01

    We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric materials are of interest in telecommunication applications where they are used in tuning and filtering equipment. Ionic and mixed conductors are the subjects of a concerted effort in the search for new...

  12. Artificial Neural Network for Transfer Function Placental Development: DCT and DWT Approach

    Directory of Open Access Journals (Sweden)

    Mohammad Ayache

    2011-09-01

    Full Text Available The aim of our study is to propose an approach for transfer function placental development using ultrasound images. This approach is based to the selection of tissues, feature extraction by discrete cosine transform DCT, discrete wavelet transform DWT and classification of different grades of placenta by artificial neural network and especially the multi layer perceptron MLP. The proposed approach is tested for ultrasound images of placenta, resulting in 75% success rate of classification using DCT and 92% using DWT. The method based on multi resolution decomposition analysis and on supervised neural network technique MLP, seems a good method to study the transfer function of placental development in ultrasound.

  13. Many-Electron Integrals over Gaussian Basis Functions. I. Recurrence Relations for Three-Electron Integrals.

    Science.gov (United States)

    Barca, Giuseppe M J; Loos, Pierre-François; Gill, Peter M W

    2016-04-12

    Explicitly correlated F12 methods are becoming the first choice for high-accuracy molecular orbital calculations and can often achieve chemical accuracy with relatively small Gaussian basis sets. In most calculations, the many three- and four-electron integrals that formally appear in the theory are avoided through judicious use of resolutions of the identity (RI). However, for the intrinsic accuracy of the F12 wave function to not be jeopardized, the associated RI auxiliary basis set must be large. Here, inspired by the Head-Gordon-Pople and PRISM algorithms for two-electron integrals, we present an algorithm to directly compute three-electron integrals over Gaussian basis functions and a very general class of three-electron operators without invoking RI approximations. A general methodology to derive vertical, transfer, and horizontal recurrence relations is also presented. PMID:26981747

  14. Many-electron integrals over gaussian basis functions. I. Recurrence relations for three-electron integrals

    CERN Document Server

    Barca, Giuseppe M J; Gill, Peter M W

    2016-01-01

    Explicitly-correlated F12 methods are becoming the first choice for high-accuracy molecular orbital calculations, and can often achieve chemical accuracy with relatively small gaussian basis sets. In most calculations, the many three- and four-electron integrals that formally appear in the theory are avoided through judicious use of resolutions of the identity (RI). However, in order not to jeopardize the intrinsic accuracy of the F12 wave function, the associated RI auxiliary basis set must be large. Here, inspired by the Head-Gordon-Pople (HGP) and PRISM algorithms for two-electron integrals, we present an algorithm to compute directly three-electron integrals over gaussian basis functions and a very general class of three-electron operators, without invoking RI approximations. A general methodology to derive vertical, transfer and horizontal recurrence relations is also presented.

  15. Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

    Science.gov (United States)

    Meng, Qinggang; Lee, M. H.

    2007-03-01

    Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.

  16. Reconfiguration of face expressions based on the discrete capture data of radial basis function interpolation

    Institute of Scientific and Technical Information of China (English)

    ZHENG Guangguo; ZHOU Dongsheng; WEI Xiaopeng; ZHANG Qiang

    2012-01-01

    Compactly supported radial basis function can enable the coefficient matrix of solving weigh linear system to have a sparse banded structure, thereby reducing the complexity of the algorithm. Firstly, based on the compactly supported radial basis function, the paper makes the complex quadratic function (Multiquadric, MQ for short) to be transformed and proposes a class of compactly supported MQ function. Secondly, the paper describes a method that interpolates discrete motion capture data to solve the motion vectors of the interpolation points and they are used in facial expression reconstruction. Finally, according to this characteris- tic of the uneven distribution of the face markers, the markers are numbered and grouped in accordance with the density level, and then be interpolated in line with each group. The approach not only ensures the accuracy of the deformation of face local area and smoothness, but also reduces the time complexity of computing.

  17. Application of backpropagation neural architectures to the realization of control transfer functions and compensators

    Science.gov (United States)

    Diaz-Robainas, Regino R.; Pandya, Abhijit S.; Huang, Ming Z.

    1994-03-01

    A method is developed to design simulations of neural-network based transfer functions, applicable to both linear and nonlinear structures. The algorithm used to implement the trainable neural mechanism is backpropagation. Using the trained structures as building blocks, a neural architecture is constructed in order to drive systems from expected inputs to satisfactory transient and steady-state output performance, in effect, the scope of control compensation; this method results in the design of neural-net control compensators. The algorithms are coded in a PC-based prolog, traditionally used for rule-based logic and Artificial Intelligence, rather than for Neural or Fuzzy models. Given a sequence representing the time-sample of a desired control input trajectory that will drive the plant to a desired output response, such a control input will be modelled as the desired output layer of an antecedent network driven by an error vector consistent with the closed-loop system's commanded behavior. This Controller network is trained to provide such an output profile for all expected inputs, in accordance with arbitrary specifications of rise-time, permitted overshoot, settling time, etc. The control vectors are generated as a by-product of this training. Additionally, a correlation is investigated between classical control parameters and the characteristics of the weight matrices, threshold vectors, and representation traits of the converged neural nets.

  18. IMAGE COMPRESSION USING DISCRETE ORTHOGONAL TRANSFORMS WITH THE «NOISE-LIKE» BASIS FUNCTIONS

    OpenAIRE

    Chernov, V.; Dmitriyev, A.

    1999-01-01

    The generalization of the discrete orthogonal transforms with the basis functions generated in a pseudorandom way is the subject of the article. The examples of such transforms application in the field of videoinformation coding in the channels with the high level of «seldom» noise are also given.

  19. Basis for Functional Performance Requirements for a Spent Nuclear Fuel Treatment and Storage Facility

    International Nuclear Information System (INIS)

    The US Department of Energy has selected the Savannah River Site (SRS) as the location to consolidate and store aluminum-based spent nuclear fuel (Al-SNF) from domestic and foreign research reactors. This report presents the technical basis for the functional performance requirements

  20. The near-equivalence of five species of spectrally-accurate radial basis functions (RBFs): Asymptotic approximations to the RBF cardinal functions on a uniform, unbounded grid

    Science.gov (United States)

    Boyd, John P.

    2011-02-01

    Radial basis function (RBF) interpolants have become popular in computer graphics, neural networks and for solving partial differential equations in many fields of science and engineering. In this article, we compare five different species of RBFs: Gaussians, hyperbolic secant (sech's), inverse quadratics, multiquadrics and inverse multiquadrics. We show that the corresponding cardinal functions for a uniform, unbounded grid are all approximated by the same function: C(X) ∼ (1/(ρ)) sin (πX)/sinh (πX/ρ) for some constant ρ(α) which depends on the inverse width parameter (“shape parameter”) α of the RBF and also on the RBF species. The error in this approximation is exponentially small in 1/α for sech's and inverse quadratics and exponentially small in 1/α2 for Gaussians; the error is proportional to α4 for multiquadrics and inverse multiquadrics. The error in all cases is small even for α ∼ O(1). These results generalize to higher dimensions. The Gaussian RBF cardinal functions in any number of dimensions d are, without approximation, the tensor product of one dimensional Gaussian cardinal functions: Cd(x1,x2…,xd)=∏j=1dC(xj). For other RBF species, we show that the two-dimensional cardinal functions are well approximated by the products of one-dimensional cardinal functions; again the error goes to zero as α → 0. The near-identity of the cardinal functions implies that all five species of RBF interpolants are (almost) the same, despite the great differences in the RBF ϕ's themselves.

  1. MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions.

    Science.gov (United States)

    Novosad, Philip; Reader, Andrew J

    2016-06-21

    Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [(18)F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral

  2. Alternative basis functions for L2 calculations on the molecular continuum. I. The basic prototype integrals

    International Nuclear Information System (INIS)

    Alternative square-integrable (L2) basis functions, the oscillating Hermite Gaussian functions (OHGF's), are proposed for describing the continuum orbitals in L2 calculations on molecules. Each function is the product of a Hermite Gaussian function (HGF), which gives the proper dumping and angular factor, and a radial trigonometric function, cos(kr), which describes the oscillating asymptotic behavior of a continuum orbital. Analytic expressions for the one- and two-electron integrals involving s-type OHGF's and many-center s-type HGF's are derived and their numerical implementation is discussed in detail. The present proposal of adopting a mixed basis set of OHGF's and many-center HGF's for the L2 description of bound and continuum molecular states is compared with the other types of basis functions currently employed. With respect to these, it requires a greater computational effort in the integral evaluation, but it also allows an accurate description of the electronic continuum in general polyatomic systems

  3. A mixed basis density functional approach for one-dimensional systems with B-splines

    Science.gov (United States)

    Ren, Chung-Yuan; Chang, Yia-Chung; Hsue, Chen-Shiung

    2016-05-01

    A mixed basis approach based on density functional theory is extended to one-dimensional (1D) systems. The basis functions here are taken to be the localized B-splines for the two finite non-periodic dimensions and the plane waves for the third periodic direction. This approach will significantly reduce the number of the basis and therefore is computationally efficient for the diagonalization of the Kohn-Sham Hamiltonian. For 1D systems, B-spline polynomials are particularly useful and efficient in two-dimensional spatial integrations involved in the calculations because of their absolute localization. Moreover, B-splines are not associated with atomic positions when the geometry structure is optimized, making the geometry optimization easy to implement. With such a basis set we can directly calculate the total energy of the isolated system instead of using the conventional supercell model with artificial vacuum regions among the replicas along the two non-periodic directions. The spurious Coulomb interaction between the charged defect and its repeated images by the supercell approach for charged systems can also be avoided. A rigorous formalism for the long-range Coulomb potential of both neutral and charged 1D systems under the mixed basis scheme will be derived. To test the present method, we apply it to study the infinite carbon-dimer chain, graphene nanoribbon, carbon nanotube and positively-charged carbon-dimer chain. The resulting electronic structures are presented and discussed in detail.

  4. The neural basis of non-verbal communication – enhanced processing of perceived give-me gestures in 9-month-old girls.

    Directory of Open Access Journals (Sweden)

    Marta eBakker

    2015-02-01

    Full Text Available This study investigated the neural basis of non-verbal communication. Event-related potentials were recorded while 29 nine-month-old infants were presented with a give-me gesture (experimental condition and the same hand shape but rotated 90 degrees, resulting in a non-communicative hand configuration (control condition. We found different responses in amplitude between the two conditions, captured in the P400 ERP component. Moreover, the size of this effect was modulated by participants’ sex, with girls generally demonstrating a larger relative difference between the two conditions than boys.

  5. An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory

    OpenAIRE

    Naiqi Song; Jin-Tun Zhang

    2013-01-01

    Functional diversity in plant communities is a key driver of ecosystem processes. The effective methods for measuring functional diversity are important in ecological studies. A new method based on neural network, self-organizing feature map (SOFM index), was put forward and described. A case application to the study of functional diversity of Phellodendron amurense communities in Xiaolongmen Forest Park of Beijing was carried out in this paper. The results showed that SOFM index was an effec...

  6. The neural basis of mark making: a functional MRI study of drawing.

    Directory of Open Access Journals (Sweden)

    Ye Yuan

    Full Text Available Compared to most other forms of visually-guided motor activity, drawing is unique in that it "leaves a trail behind" in the form of the emanating image. We took advantage of an MRI-compatible drawing tablet in order to examine both the motor production and perceptual emanation of images. Subjects participated in a series of mark making tasks in which they were cued to draw geometric patterns on the tablet's surface. The critical comparison was between when visual feedback was displayed (image generation versus when it was not (no image generation. This contrast revealed an occipito-parietal stream involved in motion-based perception of the emerging image, including areas V5/MT+, LO, V3A, and the posterior part of the intraparietal sulcus. Interestingly, when subjects passively viewed animations of visual patterns emerging on the projected surface, all of the sensorimotor network involved in drawing was strongly activated, with the exception of the primary motor cortex. These results argue that the origin of the human capacity to draw and write involves not only motor skills for tool use but also motor-sensory links between drawing movements and the visual images that emanate from them in real time.

  7. The Neural Basis of Mark Making: A Functional MRI Study of Drawing

    Science.gov (United States)

    Yuan, Ye; Brown, Steven

    2014-01-01

    Compared to most other forms of visually-guided motor activity, drawing is unique in that it “leaves a trail behind” in the form of the emanating image. We took advantage of an MRI-compatible drawing tablet in order to examine both the motor production and perceptual emanation of images. Subjects participated in a series of mark making tasks in which they were cued to draw geometric patterns on the tablet's surface. The critical comparison was between when visual feedback was displayed (image generation) versus when it was not (no image generation). This contrast revealed an occipito-parietal stream involved in motion-based perception of the emerging image, including areas V5/MT+, LO, V3A, and the posterior part of the intraparietal sulcus. Interestingly, when subjects passively viewed animations of visual patterns emerging on the projected surface, all of the sensorimotor network involved in drawing was strongly activated, with the exception of the primary motor cortex. These results argue that the origin of the human capacity to draw and write involves not only motor skills for tool use but also motor-sensory links between drawing movements and the visual images that emanate from them in real time. PMID:25271440

  8. An on-line training radial basis function neural network for optimum operation of the UPFC

    OpenAIRE

    Farrag, Mohamed; Putrus, Ghanim

    2011-01-01

    The concept of Flexible A.C. Transmission Systems (FACTS) technology was developed to enhance the performance of electric power networks (both in steady-state and transient-state) and to make better utilization of existing power transmission facilities. The continuous improvement in power ratings and switching performance of power electronic devices together with advances in circuit design and control techniques are making this concept and devices employed in FACTS more commercially attractiv...

  9. RCS Computation by Parallel MoM Using Higher-Order Basis Functions

    Directory of Open Access Journals (Sweden)

    Ying Yan

    2012-01-01

    Full Text Available A Message-Passing Interface (MPI parallel implementation of an integral equation solver that uses the Method of Moments (MoM with higher-order basis functions has been proposed to compute the Radar Cross-Section (RCS of various targets. The block-partitioned scheme for the large dense MoM matrix is designed to achieve excellent load balance and high parallel efficiency. Some numerical results demonstrate that higher-order basis in this parallelized scheme is more efficient than the conventional RWG method and able to efficiently analyze RCS of various electrically large platforms.

  10. RCS Computation by Parallel MoM Using Higher-Order Basis Functions

    OpenAIRE

    Ying Yan; Yu Zhang; Chang-Hong Liang; Hui Zhao; D. García-Doñoro

    2012-01-01

    A Message-Passing Interface (MPI) parallel implementation of an integral equation solver that uses the Method of Moments (MoM) with higher-order basis functions has been proposed to compute the Radar Cross-Section (RCS) of various targets. The block-partitioned scheme for the large dense MoM matrix is designed to achieve excellent load balance and high parallel efficiency. Some numerical results demonstrate that higher-order basis in this parallelized scheme is more efficient than the convent...

  11. A tomographic reconstruction method with generalized natural basis functions and 'a priori' information

    International Nuclear Information System (INIS)

    Series-expansion tomography methods that use natural basis functions (NBFs), also called natural pixels, often use iterative solution techniques or solution by truncated singular value decomposition (TSVD). Here, solution by constrained optimization is proposed. It is shown that significant improvements in the tomographic reconstructions can be obtained, in particular when the coverage by the imaging system is irregular. The analogy between regular NBFs and the filtered backprojection or convolution-backprojection tomography method suggests maximum smoothness in projection space as object function (i.e. a priori information) in the constrained optimization. A further improvement was found by employing NBFs that correspond to a bilinear interpolation in projection space. The new NBF method is compared with various tomography methods: constrained optimization with local basis functions, NBFs with TSVD, and an iterative projection-space reconstruction method. (author)

  12. An efficient ensemble of radial basis functions method based on quadratic programming

    Science.gov (United States)

    Shi, Renhe; Liu, Li; Long, Teng; Liu, Jian

    2016-07-01

    Radial basis function (RBF) surrogate models have been widely applied in engineering design optimization problems to approximate computationally expensive simulations. Ensemble of radial basis functions (ERBF) using the weighted sum of stand-alone RBFs improves the approximation performance. To achieve a good trade-off between the accuracy and efficiency of the modelling process, this article presents a novel efficient ERBF method to determine the weights through solving a quadratic programming subproblem, denoted ERBF-QP. Several numerical benchmark functions are utilized to test the performance of the proposed ERBF-QP method. The results show that ERBF-QP can significantly improve the modelling efficiency compared with several existing ERBF methods. Moreover, ERBF-QP also provides satisfactory performance in terms of approximation accuracy. Finally, the ERBF-QP method is applied to a satellite multidisciplinary design optimization problem to illustrate its practicality and effectiveness for real-world engineering applications.

  13. Approximation properties of basis functions in variational three-body problem

    CERN Document Server

    Vanyashin, V S

    2000-01-01

    A new variational basis with well-behaved local approximation properties and multiple output is proposed for Coulomb systems. The trial function has proper behaviour at all Coulomb centres. Nonlinear asymptotic parameters are introduced softly: they do not destroy the self-optimized local behaviour of the wave function at vanishing interparticle distances. The diagonalization of the Hamiltonian on a finite Hilbert subspace gives a number of meaningful eigenvalues. Thus together with the ground state some excited states are also reliably approximated. For three-body systems all matrix elements are analytically obtainable up to rational functions of asymptotic parameters. The feasibility of the new basis usage has been proved by a pilot computer algebra calculation. The negative sign of an electron pair local energy at their Coulomb centre has been revealed. PACS number: 31.15.Pf

  14. Functional dissociations in top-down control dependent neural repetition priming.

    NARCIS (Netherlands)

    Klaver, P.; Schnaidt, M.; Fell, J.; Ruhlmann, J.; Elger, C.E.; Fernandez, G.

    2007-01-01

    Little is known about the neural mechanisms underlying top-down control of repetition priming. Here, we use functional brain imaging to investigate these mechanisms. Study and repetition tasks used a natural/man-made forced choice task. In the study phase subjects were required to respond to either

  15. Zero Cost Function Training Algorithms for Three-Layered Feedforward Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper, two theorems are proved for zero cost function (or precise I/O map ping) training algorithms about three-layered feedforward neural networks. Two training algorithms based on Moore-Penrose pseudoinverse (MPPI) matrix together with corresponding structure design guidelines are also proposed.

  16. Neuralized1 Activates CPEB3: A Novel Function of Ubiquitination in Synaptic Plasticity and Memory Storage

    OpenAIRE

    Pavlopoulos, Elias; Trifilieff, Pierre; Chevaleyre, Vivien; Fioriti, Luana; Zairis, Sakellarios; Pagano, Andrew; Malleret, Gaël; Kandel, Eric R.

    2011-01-01

    The cytoplasmic polyadenylation element-binding protein 3 (CPEB3), a regulator of local protein synthesis, is the mouse homologue of ApCPEB, a functional prion protein in Aplysia. Here, we provide evidence that CPEB3 is activated by Neuralized1, an E3 ubiquitin ligase. In hippocampal cultures, CPEB3 activated by Neuralized1-mediated ubiquitination leads both to the growth of new dendritic spines and to an increase of the GluA1 and GluA2 subunits of AMPA receptors, two CPEB3 targets essential ...

  17. An Improved Scheme for Digital Watermarking Using Functional Link Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Banshidhar Majhi

    2005-01-01

    Full Text Available The present study proposes a novel technique for copyright protection by utilizing digital watermarking of Images. The watermark is embedded and detected by using Functional Link Artificial Neural Network (FLANN and Discrete Cosine Transform (DCT. The exhaustive simulation results of the proposed scheme show improved performance over the existing methods in all cases, i.e. when the watermarked image is subjected to compression, cropping, sharpening, blurring and noise. Comparative analysis with an existing neural approach shows the superiority of the proposed scheme of computational complexity and performance.

  18. Williams Syndrome Transcription Factor is critical for neural crest cell function in Xenopus laevis

    Science.gov (United States)

    Barnett, Chris; Yazgan, Oya; Kuo, Hui-Ching; Malakar, Sreepurna; Thomas, Trevor; Fitzgerald, Amanda; Harbour, Billy; Henry, Jonathan J.; Krebs, Jocelyn E.

    2012-01-01

    Williams Syndrome Transcription Factor (WSTF) is one of ~25 haplodeficient genes in patients with the complex developmental disorder Williams Syndrome (WS). WS results in visual/spatial processing defects, cognitive impairment, unique behavioral phenotypes, characteristic “elfin” facial features, low muscle tone and heart defects. WSTF exists in several chromatin remodeling complexes and has roles in transcription, replication, and repair. Chromatin remodeling is essential during embryogenesis, but WSTF’s role in vertebrate development is poorly characterized. To investigate the developmental role of WSTF, we knocked down WSTF in Xenopus laevis embryos using a morpholino that targets WSTF mRNA. BMP4 shows markedly increased and spatially aberrant expression in WSTF-deficient embryos, while SHH, MRF4, PAX2, EPHA4 and SOX2 expression are severely reduced, coupled with defects in a number of developing embryonic structures and organs. WSTF-deficient embryos display defects in anterior neural development. Induction of the neural crest, measured by expression of the neural crest-specific genes SNAIL and SLUG, is unaffected by WSTF depletion. However, at subsequent stages WSTF knockdown results in a severe defect in neural crest migration and/or maintenance. Consistent with a maintenance defect, WSTF knockdowns display a specific pattern of increased apoptosis at the tailbud stage in regions corresponding to the path of cranial neural crest migration. Our work is the first to describe a role for WSTF in proper neural crest function, and suggests that neural crest defects resulting from WSTF haploinsufficiency may be a major contributor to the pathoembryology of WS. PMID:22691402

  19. Immunomodulation of enteric neural function in irritable bowel syndrome

    OpenAIRE

    O’Malley, Dervla

    2015-01-01

    Irritable bowel syndrome (IBS) is a common functional gastrointestinal disorder which is characterised by symptoms such as bloating, altered bowel habit and visceral pain. It’s generally accepted that miscommunication between the brain and gut underlies the changes in motility, absorpto-secretory function and pain sensitivity associated with IBS. However, partly due to the lack of disease-defining biomarkers, understanding the aetiology of this complex and multifactorial disease remains elusi...

  20. Intelligent Control of Welding Gun Pose for Pipeline Welding Robot Based on Improved Radial Basis Function Network and Expert System

    Directory of Open Access Journals (Sweden)

    Jingwen Tian

    2013-02-01

    Full Text Available Since the control system of the welding gun pose in whole‐position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN and expert system (ES is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN and the ES. The ADXRS300 micro‐mechanical gyro is used as the welding gun position sensor in this system. When the welding gun position is obtained, an appropriate pitch angle can be obtained through expert knowledge and the numeric reasoning capacity of the IRBFNN. ARM is used as the controller to drive the welding gun pitch angle step motor in order to adjust the pitch angle of the welding gun in real‐time. The experiment results show that the intelligent control system of the welding gun pose using the IRBFNN and expert system is feasible and it enhances the welding quality. This system has wide prospects for application.

  1. Stem Cell Bioprinting: Functional 3D Neural Mini-Tissues from Printed Gel-Based Bioink and Human Neural Stem Cells (Adv. Healthcare Mater. 12/2016).

    Science.gov (United States)

    Gu, Qi; Tomaskovic-Crook, Eva; Lozano, Rodrigo; Chen, Yu; Kapsa, Robert M; Zhou, Qi; Wallace, Gordon G; Crook, Jeremy M

    2016-06-01

    On page 1429 G. G. Wallace, J. M. Crook, and co-workers report the first example of fabricating neural tissue by 3D bioprinting human neural stem cells. A novel polysaccharide based bioink preserves stem cell viability and function within the printed construct, enabling self-renewal and differentiation to neurons and supporting neuroglia. Neurons are predominantly GABAergic, establish networks, are spontaneously active, and show a bicuculline induced increased calcium response. PMID:27333401

  2. Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points

    Directory of Open Access Journals (Sweden)

    K. K. Aggarwal

    2005-01-01

    Full Text Available It is a well known fact that at the beginning of any project, the software industry needs to know, how much will it cost to develop and what would be the time required ? . This paper examines the potential of using a neural network model for estimating the lines of code, once the functional requirements are known. Using the International Software Benchmarking Standards Group (ISBSG Repository Data (release 9 for the experiment, this paper examines the performance of back propagation feed forward neural network to estimate the Source Lines of Code. Multiple training algorithms are used in the experiments. Results demonstrate that the neural network models trained using Bayesian Regularization provide the best results and are suitable for this purpose.

  3. Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image

    Directory of Open Access Journals (Sweden)

    Qi Dawei

    2010-01-01

    Full Text Available A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods.

  4. Polychromatic sparse image reconstruction and mass attenuation spectrum estimation via B-spline basis function expansion

    Energy Technology Data Exchange (ETDEWEB)

    Gu, Renliang, E-mail: Venliang@iastate.edu, E-mail: ald@iastate.edu; Dogandžić, Aleksandar, E-mail: Venliang@iastate.edu, E-mail: ald@iastate.edu [Iowa State University, Center for Nondestructive Evaluation, 1915 Scholl Road, Ames, IA 50011 (United States)

    2015-03-31

    We develop a sparse image reconstruction method for polychromatic computed tomography (CT) measurements under the blind scenario where the material of the inspected object and the incident energy spectrum are unknown. To obtain a parsimonious measurement model parameterization, we first rewrite the measurement equation using our mass-attenuation parameterization, which has the Laplace integral form. The unknown mass-attenuation spectrum is expanded into basis functions using a B-spline basis of order one. We develop a block coordinate-descent algorithm for constrained minimization of a penalized negative log-likelihood function, where constraints and penalty terms ensure nonnegativity of the spline coefficients and sparsity of the density map image in the wavelet domain. This algorithm alternates between a Nesterov’s proximal-gradient step for estimating the density map image and an active-set step for estimating the incident spectrum parameters. Numerical simulations demonstrate the performance of the proposed scheme.

  5. Phase estimation from noisy phase fringe patterns using linearly independent basis functions

    International Nuclear Information System (INIS)

    A novel technique is proposed for obtaining unwrapped phase estimation from a highly noisy exponential phase field. In this technique, the interference phase is represented as a linear combination of linearly independent and pre-defined basis functions along each row/column of the phase field at a time. Consequently, the problem of phase estimation is converted into the problem of the estimation of the weights of the basis functions. The extended Kalman filter formulation allows for the accurate estimation of these weights. The simulation results indicate that the formulation offers a strong noise robustness in the phase estimation. Experimental results obtained using digital holographic interferometry and digital speckle pattern interferometry set-ups are provided to demonstrate the practical applicability of the proposed method. (paper)

  6. Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration

    Science.gov (United States)

    Petković, Dalibor; Gocic, Milan; Shamshirband, Shahaboddin; Qasem, Sultan Noman; Trajkovic, Slavisa

    2016-08-01

    Accurate estimation of the reference evapotranspiration (ET0) is important for the water resource planning and scheduling of irrigation systems. For this purpose, the radial basis function network with particle swarm optimization (RBFN-PSO) and radial basis function network with back propagation (RBFN-BP) were used in this investigation. The FAO-56 Penman-Monteith equation was used as reference equation to estimate ET0 for Serbia during the period of 1980-2010. The obtained simulation results confirmed the proposed models and were analyzed using the root mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination ( R 2). The analysis showed that the RBFN-PSO had better statistical characteristics than RBFN-BP and can be helpful for the ET0 estimation.

  7. Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization

    Institute of Scientific and Technical Information of China (English)

    ZHAO Min; CUI Wei-cheng

    2007-01-01

    Improving the efficiency of ship optimization is crucial for modern ship design. Compared with traditional methods, multidisciplinary design optimization (MDO) is a more promising approach. For this reason, Collaborative Optimization (CO) is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.

  8. Polychromatic sparse image reconstruction and mass attenuation spectrum estimation via B-spline basis function expansion

    International Nuclear Information System (INIS)

    We develop a sparse image reconstruction method for polychromatic computed tomography (CT) measurements under the blind scenario where the material of the inspected object and the incident energy spectrum are unknown. To obtain a parsimonious measurement model parameterization, we first rewrite the measurement equation using our mass-attenuation parameterization, which has the Laplace integral form. The unknown mass-attenuation spectrum is expanded into basis functions using a B-spline basis of order one. We develop a block coordinate-descent algorithm for constrained minimization of a penalized negative log-likelihood function, where constraints and penalty terms ensure nonnegativity of the spline coefficients and sparsity of the density map image in the wavelet domain. This algorithm alternates between a Nesterov’s proximal-gradient step for estimating the density map image and an active-set step for estimating the incident spectrum parameters. Numerical simulations demonstrate the performance of the proposed scheme

  9. Real-space Kerker method for self-consistent calculation using non-orthogonal basis functions

    International Nuclear Information System (INIS)

    We have proposed the real-space Kerker method for fast self-consistent-field calculations in real-space approaches using non-orthogonal basis functions. In large-scale systems with many atoms, the Kerker method is a very efficient way to prevent charge sloshing, which induces numerical instability during the self-consistent iterations. We construct the Kerker preconditioning matrix with non-orthogonal basis functions and the preconditioning is performed by solving linear equations. The proposed real-space Kerker method is identical to the method in reciprocal space, with the following two advantages: (i) the method is suitable for massively parallel computation since it does not use the fast Fourier transform. (ii) The preconditioning is performed in an acceptable computational time since time-consuming integration, including the exponential kernel, need not be performed, unlike the method used by Manninen et al (1975 Phys. Rev. B 12 4012)

  10. Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration

    Science.gov (United States)

    Petković, Dalibor; Gocic, Milan; Shamshirband, Shahaboddin; Qasem, Sultan Noman; Trajkovic, Slavisa

    2015-06-01

    Accurate estimation of the reference evapotranspiration (ET0) is important for the water resource planning and scheduling of irrigation systems. For this purpose, the radial basis function network with particle swarm optimization (RBFN-PSO) and radial basis function network with back propagation (RBFN-BP) were used in this investigation. The FAO-56 Penman-Monteith equation was used as reference equation to estimate ET0 for Serbia during the period of 1980-2010. The obtained simulation results confirmed the proposed models and were analyzed using the root mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R 2). The analysis showed that the RBFN-PSO had better statistical characteristics than RBFN-BP and can be helpful for the ET0 estimation.

  11. The Neural Correlates of Long-Term Carryover following Functional Electrical Stimulation for Stroke

    OpenAIRE

    Marta Gandolla; Ward, Nick S; Franco Molteni; Eleonora Guanziroli; Giancarlo Ferrigno; Alessandra Pedrocchi

    2016-01-01

    Neurorehabilitation effective delivery for stroke is likely to be improved by establishing a mechanistic understanding of how to enhance adaptive plasticity. Functional electrical stimulation is effective at reducing poststroke foot drop; in some patients, the effect persists after therapy has finished with an unknown mechanism. We used fMRI to examine neural correlates of functional electrical stimulation key elements, volitional intent to move and concurrent stimulation, in a group of chron...

  12. Neural interface of mirror therapy in chronic stroke patients: A functional magnetic resonance imaging study

    OpenAIRE

    Ashu Bhasin; M V Padma Srivastava; Kumaran, Senthil S; Rohit Bhatia; Sujata Mohanty

    2012-01-01

    Background: Recovery in stroke is mediated by neural plasticity. Neuro-restorative therapies improve recovery after stroke by promoting repair and function. Mirror neuron system (MNS) has been studied widely in humans in stroke and phantom sensations. Materials and Methods: Study subjects included 20 patients with chronic stroke and 10 healthy controls. Patients had clinical disease-severity scores, functional magnetic resonance imaging (fMRI) and diffuse tensor imaging (DTI) at baseline, 8 a...

  13. Assigning Function to Adult-Born Neurons: A Theoretical Framework for Characterizing Neural Manipulation of Learning

    OpenAIRE

    Hersman, Sarah; Rodriguez Barrera, Vanessa; Fanselow, Michael

    2016-01-01

    Neuroscientists are concerned with neural processes or computations, but these may not be directly observable. In the field of learning, a behavioral procedure is observed to lead to performance outcomes, but differing inferences on underlying internal processes can lead to difficulties in interpreting conflicting results. An example of this challenge is how many functions have been attributed to adult-born granule cells in the dentate gyrus. Some of these functions were suggested by computat...

  14. Multi-dimensional option pricing using radial basis functions and the generalized Fourier transform

    Science.gov (United States)

    Larsson, Elisabeth; Ahlander, Krister; Hall, Andreas

    2008-12-01

    We show that the generalized Fourier transform can be used for reducing the computational cost and memory requirements of radial basis function methods for multi-dimensional option pricing. We derive a general algorithm, including a transformation of the Black-Scholes equation into the heat equation, that can be used in any number of dimensions. Numerical experiments in two and three dimensions show that the gain is substantial even for small problem sizes. Furthermore, the gain increases with the number of dimensions.

  15. Genetic basis of cytokinin and auxin functions during root nodule development

    OpenAIRE

    Suzaki, Takuya; Ito, Momoyo; Kawaguchi, Masayoshi

    2013-01-01

    The phytohormones cytokinin and auxin are essential for the control of diverse aspects of cell proliferation and differentiation processes in plants. Although both phytohormones have been suggested to play key roles in the regulation of root nodule development, only recently, significant progress has been made in the elucidation of the molecular genetic basis of cytokinin action in the model leguminous species, Lotus japonicus and Medicago truncatula. Identification and functional analyses of...

  16. Multiuser detection using soft particle swarm optimization along with radial basis function

    OpenAIRE

    Zubair, Muhammad; CHOUDHRY, Muhammad Aamer Saleem; Qureshi, Ijaz Mansoor

    2014-01-01

    The multiuser detection (MUD) problem was addressed as a pattern classification problem. Due to their strength in solving nonlinear separable problems, radial basis functions, aided by soft particle swarm optimization, were proposed to perform MUD for a synchronous direct sequence code division multiple access system. The proposed solution was shown to exhibit performance better than a number of other suboptimum detectors including the genetic algorithm and the classical particle swarm optimi...

  17. Construction of Canonical Polynomial Basis Functions for Solving Special Nth -Order Linear Integro-Differential Equations

    OpenAIRE

    1 Taiwo O. A

    2013-01-01

    The problem of solving special nth-order linear integro-differential equations has special importance in engineering and sciences that constitutes a good model for many systems in various fields. In this paper, we construct canonical polynomial from the differential parts of special nth-order integro-differential equations and use it as our basis function for the numerical solutions of special nth-order integro-differential equations. The results obtained by this method are compared with thos...

  18. Analytic eigenenergies of Dirac equation under a confining linear potential using basis functions localized in spacetime

    CERN Document Server

    Fukushima, Kimichika

    2015-01-01

    This paper presents analytical eigenenergies for a pair of confined fundamental fermion and antifermion under a linear potential derived from the Wilson loop for the non-Abelian Yang-Mills field. We use basis functions localized in spacetime, and the Hamiltonian matrix of the Dirac equation is analytically diagonalized. The squared system eigenenergies are proportional to the string tension and the absolute value of the Dirac's relativistic quantum number related to the total angular momentum, consistent with the expectation.

  19. Calculation of integrals for the LCAO MO method in a basis of hydrogen functions

    International Nuclear Information System (INIS)

    The authors give an algorithm for the analytical calculation of multicenter quantum chemical integrals in a basis of hydrogenic AO's. A characteristic feature of the algorithm involves expansion of two-center distributions with respect to one-center functions, which leads to a more compact expression for the multicenter integral than does the use of an expansion of AO's relative to another center. They obtain recurrence relations for the expansion coefficients

  20. Analysis of the diffracted current basis functions used in the hybrid MoM-PO method

    Institute of Scientific and Technical Information of China (English)

    GONG Zhuqian; XIAO Boxun; ZHU Guoqiang; GUO Jianyan

    2007-01-01

    The combined moment method(MoM)-physical optics (PO)approach proposed by Bilow fails in some cases.Based on the theory of diffraction and the fundamental theory of electromagnetism,Bilow's diffracted current basis function was modified both within and outside the transition regions.The improved MoM-PO technique is validated by comparison with exact solutions for a right-angled perfectly conducting wedge at normal incidence.

  1. Human Brain Basis of Musical Rhythm Perception: Common and Distinct Neural Substrates for Meter, Tempo, and Pattern

    Directory of Open Access Journals (Sweden)

    Michael H. Thaut

    2014-06-01

    Full Text Available Rhythm as the time structure of music is composed of distinct temporal components such as pattern, meter, and tempo. Each feature requires different computational processes: meter involves representing repeating cycles of strong and weak beats; pattern involves representing intervals at each local time point which vary in length across segments and are linked hierarchically; and tempo requires representing frequency rates of underlying pulse structures. We explored whether distinct rhythmic elements engage different neural mechanisms by recording brain activity of adult musicians and non-musicians with positron emission tomography (PET as they made covert same-different discriminations of (a pairs of rhythmic, monotonic tone sequences representing changes in pattern, tempo, and meter, and (b pairs of isochronous melodies. Common to pattern, meter, and tempo tasks were focal activities in right, or bilateral, areas of frontal, cingulate, parietal, prefrontal, temporal, and cerebellar cortices. Meter processing alone activated areas in right prefrontal and inferior frontal cortex associated with more cognitive and abstract representations. Pattern processing alone recruited right cortical areas involved in different kinds of auditory processing. Tempo processing alone engaged mechanisms subserving somatosensory and premotor information (e.g., posterior insula, postcentral gyrus. Melody produced activity different from the rhythm conditions (e.g., right anterior insula and various cerebellar areas. These exploratory findings suggest the outlines of some distinct neural components underlying the components of rhythmic structure.

  2. Human brain basis of musical rhythm perception: common and distinct neural substrates for meter, tempo, and pattern.

    Science.gov (United States)

    Thaut, Michael H; Trimarchi, Pietro Davide; Parsons, Lawrence M

    2014-01-01

    Rhythm as the time structure of music is composed of distinct temporal components such as pattern, meter, and tempo. Each feature requires different computational processes: meter involves representing repeating cycles of strong and weak beats; pattern involves representing intervals at each local time point which vary in length across segments and are linked hierarchically; and tempo requires representing frequency rates of underlying pulse structures. We explored whether distinct rhythmic elements engage different neural mechanisms by recording brain activity of adult musicians and non-musicians with positron emission tomography (PET) as they made covert same-different discriminations of (a) pairs of rhythmic, monotonic tone sequences representing changes in pattern, tempo, and meter, and (b) pairs of isochronous melodies. Common to pattern, meter, and tempo tasks were focal activities in right, or bilateral, areas of frontal, cingulate, parietal, prefrontal, temporal, and cerebellar cortices. Meter processing alone activated areas in right prefrontal and inferior frontal cortex associated with more cognitive and abstract representations. Pattern processing alone recruited right cortical areas involved in different kinds of auditory processing. Tempo processing alone engaged mechanisms subserving somatosensory and premotor information (e.g., posterior insula, postcentral gyrus). Melody produced activity different from the rhythm conditions (e.g., right anterior insula and various cerebellar areas). These exploratory findings suggest the outlines of some distinct neural components underlying the components of rhythmic structure. PMID:24961770

  3. Numerical Investigation of Electromagnetic Scattering Problems Based on the Compactly Supported Radial Basis Functions

    Science.gov (United States)

    Roohani Ghehsareh, Hadi; Kamal Etesami, Seyed; Hajisadeghi Esfahani, Maryam

    2016-08-01

    In the current work, the electromagnetic (EM) scattering from infinite perfectly conducting cylinders with arbitrary cross sections in both transverse magnetic (TM) and transverse electric (TE) modes is numerically investigated. The problems of TE and TM EM scattering can be mathematically modelled via the magnetic field integral equation (MFIE) and the electric field integral equation (EFIE), respectively. An efficient technique is performed to approximate the solution of these surface integral equations. In the proposed numerical method, compactly supported radial basis functions (RBFs) are employed as the basis functions. The radial and compactly supported properties of these basis functions substantially reduce the computational cost and improve the efficiency of the method. To show the accuracy of the proposed technique, it has been applied to solve three interesting test problems. Moreover, the method is well used to compute the electric current density and also the radar cross section (RCS) for some practical scatterers with different cross section geometries. The reported numerical results through the tables and figures demonstrate the efficiency and accuracy of the proposed technique.

  4. Application of natural basis functions to soft x-ray tomography

    International Nuclear Information System (INIS)

    Natural basis functions (NBFs), also known as natural pixels in the literature, have been applied in tomographic reconstructions of simulated measurements for the JET soft x-ray system, which has a total of about 200 detectors spread over 6 directions. Various types of NBFs, i.e. normal, generalized and orthonormal NBFs, are reviewed. The number of basis functions is roughly equal to the number of measurements. Therefore, little a priori information is required as regularization and truncated singular-value decomposition can be used for the tomographic inversion. The results of NBFs are compared with reconstructions by the same solution technique using local basis functions (LBFs), and with the reconstructions of a conventional constrained-optimization tomography method with many more LBFs that requires more a priori information. Although the results of the conventional method are superior due to the a priori information, the results of the NBF and other LBF methods are reasonable and show the main features. Therefore, NBFs are a promising way to assess whether features in reconstructions are real or artefacts resulting from the a priori information. Of the NBFs, regular triangular (generalized) NBFs give the most acceptable reconstructions, much better than traditional square pixels, although the reconstructions with pyramid-shaped LBFs are also reasonable and have slightly smaller reconstruction errors. A more-regular (virtual) viewing geometry improves the reconstructions. However, simulations with a viewing geometry with a total of 480 channels spread over 12 directions clearly show that a priori information still improves the reconstructions considerably. (author)

  5. Configuring radial basis function network using fractal scaling process with application to chaotic time series prediction

    International Nuclear Information System (INIS)

    This paper introduces a novel algorithm for determining the structure of a radial basis function (RBF) network (the number of hidden units) while it is used for dynamic modeling of chaotic time series. It can be seen that the hidden units in the RBF network can form hyperplanes to partition the input space into various regions in each of which it is possible to approximate the dynamics with a basis function. The number of regions corresponds to the number of hidden units. The basic idea of the proposed algorithm is to partition the input space by fractal scaling of the chaotic time series being modeled. By fractal scaling process, the number of basis functions (hidden units) as well as the number of input variables can be specified. Accordingly, the network topology is efficiently determined based on the complexity of the underlying dynamics as reflected in the observed time series. The feasibility of the proposed scheme is examined through dynamic modeling of the well-known chaotic time series. The results show that the new method can improve the predictability of chaotic time series with a suitable number of hidden units compared to that of reported in the literature

  6. The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication

    Science.gov (United States)

    Symons, Ashley E.; El-Deredy, Wael; Schwartze, Michael; Kotz, Sonja A.

    2016-01-01

    Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples

  7. The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication.

    Science.gov (United States)

    Symons, Ashley E; El-Deredy, Wael; Schwartze, Michael; Kotz, Sonja A

    2016-01-01

    Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples

  8. The functional role of neural oscillations in non-verbal emotional communication

    Directory of Open Access Journals (Sweden)

    Ashley E Symons

    2016-05-01

    Full Text Available Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS, and orbitofrontal cortex (OFC. However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterise the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronisation appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronisation may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronisation reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities, presence or absence of predictive information, and attentional or task demands. Thus, the synchronisation of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity

  9. Functional neural substrates of posterior cortical atrophy patients.

    Science.gov (United States)

    Shames, H; Raz, N; Levin, Netta

    2015-07-01

    Posterior cortical atrophy (PCA) is a neurodegenerative syndrome in which the most pronounced pathologic involvement is in the occipito-parietal visual regions. Herein, we aimed to better define the cortical reflection of this unique syndrome using a thorough battery of behavioral and functional MRI (fMRI) tests. Eight PCA patients underwent extensive testing to map their visual deficits. Assessments included visual functions associated with lower and higher components of the cortical hierarchy, as well as dorsal- and ventral-related cortical functions. fMRI was performed on five patients to examine the neuronal substrate of their visual functions. The PCA patient cohort exhibited stereopsis, saccadic eye movements and higher dorsal stream-related functional impairments, including simultant perception, image orientation, figure-from-ground segregation, closure and spatial orientation. In accordance with the behavioral findings, fMRI revealed intact activation in the ventral visual regions of face and object perception while more dorsal aspects of perception, including motion and gestalt perception, revealed impaired patterns of activity. In most of the patients, there was a lack of activity in the word form area, which is known to be linked to reading disorders. Finally, there was evidence of reduced cortical representation of the peripheral visual field, corresponding to the behaviorally assessed peripheral visual deficit. The findings are discussed in the context of networks extending from parietal regions, which mediate navigationally related processing, visually guided actions, eye movement control and working memory, suggesting that damage to these networks might explain the wide range of deficits in PCA patients. PMID:25976028

  10. Aberrant regional neural fluctuations and functional connectivity in generalized anxiety disorder revealed by resting-state functional magnetic resonance imaging.

    Science.gov (United States)

    Wang, Wei; Hou, Jingming; Qian, Shaowen; Liu, Kai; Li, Bo; Li, Min; Peng, Zhaohui; Xin, Kuolin; Sun, Gang

    2016-06-15

    The purpose of this study was to investigate the neural activity and functional connectivity in generalized anxiety disorder (GAD) during resting state, and how these alterations correlate to patients' symptoms. Twenty-eight GAD patients and 28 matched healthy controls underwent resting-state functional magnetic resonance (fMRI) scans. Amplitude of low-frequency fluctuation (ALFF) and seed-based resting-state functional connectivity (RSFC) were computed to explore regional activity and functional integration, and were compared between the two groups using the voxel-based two-sample t test. Pearson's correlation analyses were performed to examine the neural relationships with demographics and clinical symptoms scores. Compared to controls, GAD patients showed functional abnormalities: higher ALFF in the bilateral dorsomedial prefrontal cortex, bilateral dorsolateral prefrontal cortex and left precuneus/posterior cingulate cortex; lower connectivity in prefrontal gyrus; lower in prefrontal-limbic and cingulate RSFC and higher prefrontal-hippocampus RSFC were correlated with clinical symptoms severity, but these associations were unable to withstand correction for multiple testing. These findings may help facilitate further understanding of the potential neural substrate of GAD. PMID:27163197

  11. Functional Optimisation of Online Algorithms in Multilayer Neural Networks

    OpenAIRE

    Vicente, Renato; Caticha, Nestor

    1997-01-01

    We study the online dynamics of learning in fully connected soft committee machines in the student-teacher scenario. The locally optimal modulation function, which determines the learning algorithm, is obtained from a variational argument in such a manner as to maximise the average generalisation error decay per example. Simulations results for the resulting algorithm are presented for a few cases. The symmetric phase plateaux are found to be vastly reduced in comparison to those found when o...

  12. Functional neural correlates of reduced physiological falls risk

    Directory of Open Access Journals (Sweden)

    Hsu Chun

    2011-08-01

    Full Text Available Abstract Background It is currently unclear whether the function of brain regions associated with executive cognitive processing are independently associated with reduced physiological falls risk. If these are related, it would suggest that the development of interventions targeted at improving executive neurocognitive function would be an effective new approach for reducing physiological falls risk in seniors. Methods We performed a secondary analysis of 73 community-dwelling senior women aged 65 to 75 years old who participated in a 12-month randomized controlled trial of resistance training. Functional MRI data were acquired while participants performed a modified Eriksen Flanker Task - a task of selective attention and conflict resolution. Brain volumes were obtained using MRI. Falls risk was assessed using the Physiological Profile Assessment (PPA. Results After accounting for baseline age, experimental group, baseline PPA score, and total baseline white matter brain volume, baseline activation in the left frontal orbital cortex extending towards the insula was negatively associated with reduced physiological falls risk over the 12-month period. In contrast, baseline activation in the paracingulate gyrus extending towards the anterior cingulate gyrus was positively associated with reduced physiological falls risk. Conclusions Baseline activation levels of brain regions underlying response inhibition and selective attention were independently associated with reduced physiological falls risk. This suggests that falls prevention strategies may be facilitated by incorporating intervention components - such as aerobic exercise - that are specifically designed to induce neurocognitive plasticity. Trial Registration ClinicalTrials.gov Identifier: NCT00426881

  13. Stability analysis of memristor-based fractional-order neural networks with different memductance functions.

    Science.gov (United States)

    Rakkiyappan, R; Velmurugan, G; Cao, Jinde

    2015-04-01

    In this paper, the problem of the existence, uniqueness and uniform stability of memristor-based fractional-order neural networks (MFNNs) with two different types of memductance functions is extensively investigated. Moreover, we formulate the complex-valued memristor-based fractional-order neural networks (CVMFNNs) with two different types of memductance functions and analyze the existence, uniqueness and uniform stability of such networks. By using Banach contraction principle and analysis technique, some sufficient conditions are obtained to ensure the existence, uniqueness and uniform stability of the considered MFNNs and CVMFNNs with two different types of memductance functions. The analysis results establish from the theory of fractional-order differential equations with discontinuous right-hand sides. Finally, four numerical examples are presented to show the effectiveness of our theoretical results. PMID:25861402

  14. A nonlinear neural fir filter with an adaptive activation function

    Directory of Open Access Journals (Sweden)

    Lee Su Goh

    2003-01-01

    Full Text Available An adaptive amplitude normalized nonlinear gradient descent (AANNGD algorithm for the class of nonlinear finite impulse response (FIR adaptive filters (dynamical perception is introduced. This is achieved by making the amplitude of the nonlinear activation function gradient adaptive. The proposed learning algorithm is suitable for processing of nonlinear and nonstationary signals with a large dynamical range, and removes the unwanted effect of saturation nonlinearities. For rigor, sensitivity analysis is performed and the improved performance of the AANNGD algorithm over the standard LMS, NGD, NNGD, the fully adaptive NNGD (FANNGD and the sign algorithm is verified by simulations on nonlinear and nonstationary inputs with large dynamics.

  15. The Rule of Four, Executive Function and Neural Exercises

    Directory of Open Access Journals (Sweden)

    Russell Jay Hendel

    2015-08-01

    Full Text Available Deborah Hughes-Hallet has made many significant contributions to Calculus pedagogy. Among the tools she has introduced is the rule of four, which requires successful pedagogy to simultaneously address four approaches to each course concept, verbal, graphical, algebraic and numeric. We explore examples of this rule of X approach in other disciplines: i Literary analysis is enhanced through the rule of two, a simultaneous approach of grammar and literary analysis; ii Actuarial mathematics requires a rule of six, a simultaneous approach of verbal, graphical, algebraic, calculator, modules, and English conventions; (iii Masters of Tic-Tac-Toe and Chess use a rule of two, simultaneously approaching the game positionally and combinatorically. We offer a unified and deep analysis of the rule of X approach by relating it to executive function, the area of the brain responsible for organizing and synthesizing multiple brain areas. We conclude the paper with an illustration of classroom activities that strengthen executive function and improve pedagogy. Our results are content independent, depending exclusively on paths of information flow, and consequently, our analysis is cybernetic in flavor [1]. [1] American Society of Cybernetics, www.asc-cybernetics.org/

  16. A Mixed Basis Density Functional Approach for Low Dimensional Systems with B-splines

    CERN Document Server

    Ren, Chung-Yuan; Chang, Yia-Chung

    2014-01-01

    A mixed basis approach based on density functional theory is employed for low dimensional systems. The basis functions are taken to be plane waves for the periodic direction multiplied by B-spline polynomials in the non-periodic direction. B-splines have the following advantages:(1) the associated matrix elements are sparse, (2) B-splines possess a superior treatment of derivatives, (3) B-splines are not associated with atomic positions when the geometry structure is optimized, making the geometry optimization easy to implement. With this mixed basis set we can directly calculate the total energy of the system instead of using the conventional supercell model with a slab sandwiched between vacuum regions. A generalized Lanczos-Krylov iterative method is implemented for the diagonalization of the Hamiltonian matrix. To demonstrate the present approach, we apply it to study the C(001)-(2x1) surface with the norm-conserving pseudopotential, the n-type delta-doped graphene, and graphene nanoribbon with Vanderbilt...

  17. Bayesian Smoothing with Gaussian Processes Using Fourier Basis Functions in the spectralGP Package

    Directory of Open Access Journals (Sweden)

    Christopher J. Paciorek

    2007-04-01

    Full Text Available The spectral representation of stationary Gaussian processes via the Fourier basis provides a computationally efficient specification of spatial surfaces and nonparametric regression functions for use in various statistical models. I describe the representation in detail and introduce the spectralGP package in R for computations. Because of the large number of basis coefficients, some form of shrinkage is necessary; I focus on a natural Bayesian approach via a particular parameterized prior structure that approximates stationary Gaussian processes on a regular grid. I review several models from the literature for data that do not lie on a grid, suggest a simple model modification, and provide example code demonstrating MCMC sampling using the spectralGP package. I describe reasons that mixing can be slow in certain situations and provide some suggestions for MCMC techniques to improve mixing, also with example code, and some general recommendations grounded in experience.

  18. Integrated Neural and Endocrine Control of Gastrointestinal Function.

    Science.gov (United States)

    Furness, John B

    2016-01-01

    The activity of the digestive system is dynamically regulated by external factors, including body nutritional and activity states, emotions and the contents of the digestive tube. The gut must adjust its activity to assimilate a hugely variable mixture that is ingested, particularly in an omnivore such as human for which a wide range of food choices exist. It must also guard against toxins and pathogens. These nutritive and non-nutritive components of the gut contents interact with the largest and most vulnerable surface in the body, the lining of the gastrointestinal tract. This requires a gut sensory system that can detect many classes of nutrients, non-nutrient components of food, physicochemical conditions, toxins, pathogens and symbionts (Furness et al., Nat Rev Gastroenterol Hepatol 10:729-740, 2013). The gut sensors are in turn coupled to effector systems that can respond to the sensory information. The responses are exerted through enteroendocrine cells (EEC), the enteric nervous system (ENS), the central nervous system (CNS) and the gut immune and tissue defence systems. It is apparent that the control of the digestive organs is an integrated function of these effectors. The peripheral components of the EEC, ENS and CNS triumvirate are extensive. EEC cells have traditionally been classified into about 12 types (disputed in this review), releasing about 20 hormones, together making the gut endocrine system the largest endocrine organ in the body. Likewise, in human the ENS contains about 500 million neurons, far more than the number of neurons in the remainder of the peripheral autonomic nervous system. Together gut hormones, the ENS and the CNS control or influence functions including satiety, mixing and propulsive activity, release of digestive enzymes, induction of nutrient transporters, fluid transport, local blood flow, gastric acid secretion, evacuation and immune responses. Gut content receptors, including taste, free fatty acid, peptide and

  19. Challenges to normal neural functioning provide insights into separability of motion processing mechanisms.

    Science.gov (United States)

    Billino, Jutta; Braun, Doris I; Bremmer, Frank; Gegenfurtner, Karl R

    2011-10-01

    There is a long history of attempts to disentangle different visual processing mechanisms for physically different motion cues. However, underlying neural correlates and separability of networks are still under debate. We aimed to refine the current understanding by studying differential vulnerabilities when normal neural functioning is challenged. We investigated effects of ageing and extrastriate brain lesions on detection thresholds for motion defined by either luminance- or contrast modulations, known as first- and second-order motion. Both approaches focus on extrastriate processing changes and combine distributed as well as more focal constraints. Our ageing sample comprised 102 subjects covering an age range from 20 to 82 years. Threshold signal-to-noise ratios for detection approximately doubled across the age range for both motion types. Results suggest that ageing affects perception of both motion types to an equivalent degree and thus support overlapping processing resources. Underlying neural substrates were further qualified by testing perceptual performance of 18 patients with focal cortical brain lesions. We determined selective first-order motion deficits in three patients, selective second-order motion deficits in only one patient, and deficits for both motion types in three patients. Lesion analysis yielded support for common functional substrates in higher cortical regions. Functionally specific substrates remained ambiguous, but tended to cover earlier visual areas. We conclude that observed vulnerabilities of first- and second-order motion perception provide limited evidence for functional specialization at early extrastriate stages, but emphasize shared processing pathways at higher cortical levels. PMID:21807009

  20. Statistical characterization of an ensemble of functional neural networks

    Science.gov (United States)

    Silva, B. B. M.; Miranda, J. G. V.; Corso, G.; Copelli, M.; Vasconcelos, N.; Ribeiro, S.; Andrade, R. F. S.

    2012-10-01

    This work uses a complex network approach to analyze temporal sequences of electrophysiological signals of brain activity from freely behaving rats. A network node represents a neuron and a network link is included between a pair of nodes whenever their firing rates are correlated. The framework of time varying graph (TVG) is used to deal with a very large number (>30 000) of time dependent networks, which are set up by taking into account correlations between neuron firing rates in a moving time lag window of suitable width. Statistical distributions for the following network measures are obtained: size of the largest connected cluster, number of edges, average node degree, and average minimal path. We find that the number of networks with highly correlated activity in distinct brain areas has a fat-tailed distribution, irrespective of the behavioral state of the animal. This contrasts with short-tailed distributions for surrogates obtained by shuffling the original data, and reflects the fact that neurons in the neocortex and hippocampus often act in precise temporal coordination. Our results also suggest that functional neuronal networks at the millimeter scale undergo statistically nontrivial rearrangements over time, thus delimitating an empirical constraint for models of brain activity.

  1. Impairments of neural circuit function in Alzheimer's disease.

    Science.gov (United States)

    Busche, Marc Aurel; Konnerth, Arthur

    2016-08-01

    An essential feature of Alzheimer's disease (AD) is the accumulation of amyloid-β (Aβ) peptides in the brain, many years to decades before the onset of overt cognitive symptoms. We suggest that during this very extended early phase of the disease, soluble Aβ oligomers and amyloid plaques alter the function of local neuronal circuits and large-scale networks by disrupting the balance of synaptic excitation and inhibition (E/I balance) in the brain. The analysis of mouse models of AD revealed that an Aβ-induced change of the E/I balance caused hyperactivity in cortical and hippocampal neurons, a breakdown of slow-wave oscillations, as well as network hypersynchrony. Remarkably, hyperactivity of hippocampal neurons precedes amyloid plaque formation, suggesting that hyperactivity is one of the earliest dysfunctions in the pathophysiological cascade initiated by abnormal Aβ accumulation. Therapeutics that correct the E/I balance in early AD may prevent neuronal dysfunction, widespread cell loss and cognitive impairments associated with later stages of the disease.This article is part of the themed issue 'Evolution brings Ca(2+) and ATP together to control life and death'. PMID:27377723

  2. Brain-Controlled Neuromuscular Stimulation to Drive Neural Plasticity and Functional Recovery

    Science.gov (United States)

    Ethier, C.; Gallego, J.A.; Miller, L.E.

    2015-01-01

    There is mounting evidence that appropriately timed neuromuscular stimulation can induce neural plasticity and generate functional recovery from motor disorders. This review addresses the idea that coordinating stimulation with a patient’s voluntary effort might further enhance neurorehabilitation. Studies in cell cultures and behaving animals have delineated the rules underlying neural plasticity when single neurons are used as triggers. However, the rules governing more complex stimuli and larger networks are less well understood. We argue that functional recovery might be optimized if stimulation were modulated by a brain machine interface, to matched the details of the patient’s voluntary intent. The potential of this novel approach highlights the need for a better understanding of the complex rules underlying this form of plasticity. PMID:25827275

  3. Neural mechanism of lmplicit and explicit memory retrieval: functional MR imaging

    International Nuclear Information System (INIS)

    To identify, using functional MR imaging, distinct cerebral centers and to evaluate the neural mechanism associated with implicit and explicit retrieval of words during conceptual processing. Seven healthy volunteers aged 21-25 (mean, 22) years underwent BOLD-based fMR imaging using a 1.5T signa horizon echospeed MR system. To activate the cerebral cortices, a series of tasks was performed as follows: the encoding of two-syllable words, and implicit and explicit retrieval of previously learned words during conceptual processing. The activation paradigm consisted of a cycle of alternating periods of 30 seconds of stimulation and 30 seconds of rest. Stimulation was accomplished by encoding eight two-syllable words and the retrieval of previously presented words, while the control condition was a white screen with a small fixed cross. During the tasks we acquired ten slices (6 mm slice thickness, 1 mm gap) parallel to the AC-PC line, and the resulting functional activation maps were reconstructed using a statistical parametric mapping program (SPM99). A comparison of activation ratios (percentages), based on the number of volunteers, showed that activation of Rhs-35, PoCiG-23 and ICiG-26·30 was associated with explicit retrieval only; other brain areas were activated during the performance of both implicit and explicit retrieval tasks. Activation ratios were higher for explicit tasks than for implicit; in the cingulate gyrus and temporal lobe they were 30% and 10% greater, respectively. During explicit retrieval, a distinct brain activation index (percentage) was seen in the temporal, parietal, and occipital lobe and cingulate gyrus, and PrCeG-4, Pr/ PoCeG-43 in the frontal lobe. During implicit retrieval, on the other hand, activity was greater in the frontal lobe, including the areas of SCA-25, SFG/MFG-10, IFG-44·45, OrbG-11·47, SFG-6·8 and MFG-9·46. Overall, activation was lateralized mainly in the left hemisphere during both implicit and explicit retrieval

  4. Artificial Neural Network for Transfer Function Placental Development: DCT and DWT Approach

    OpenAIRE

    Mohammad Ayache; Mohamad Khalil; Francois Tranquart

    2011-01-01

    The aim of our study is to propose an approach for transfer function placental development using ultrasound images. This approach is based to the selection of tissues, feature extraction by discrete cosine transform DCT, discrete wavelet transform DWT and classification of different grades of placenta by artificial neural network and especially the multi layer perceptron MLP. The proposed approach is tested for ultrasound images of placenta, resulting in 75% success rate of classification usi...

  5. 3D Bioprinting of functionalized graphene nanoplatelet-doped hydrogel for neural regeneration

    OpenAIRE

    O’Brien, Christopher

    2014-01-01

    Each year more than 20 million Americans are affected by peripheral nervous system damage, leaving them in pain and with restricted mobility. Various cell therapies and implants have been investigated, yet a comprehensive treatment to that grants full functional recovery has still not been elucidated. Traditional neural guidance constructs lack the ability to incorporate biomimetic nanofeatures, well-controlled 3D geometry, and appropriate electrical conductivity in the same scaffold, limitin...

  6. Magnesium regulates neural stem cell proliferation in the mouse hippocampus by altering mitochondrial function.

    Science.gov (United States)

    Jia, Shanshan; Mou, Chengzhi; Ma, Yihe; Han, Ruijie; Li, Xue

    2016-04-01

    In the adult brain, neural stem cells from the subgranular zone (SGZ) of the hippocampus and the subventricular zone (SVZ) of the cortex progress through the following five developmental stages: radial glia-like cells, neural progenitor cells, neuroblasts, immature neurons, and mature neurons. These developmental stages are linked to both neuronal microenvironments and energy metabolism. Neurogenesis is restricted and has been demonstrated to arise from tissue microenvironments. We determined that magnesium, a key nutrient in cellular energy metabolism, affects neural stem cell (NSC) proliferation in cells derived from the embryonic hippocampus by influencing mitochondrial function. Densities of proliferating cells and NSCs both showed their highest values at 0.8 mM [Mg(2+) ]o , whereas lower proliferation rates were observed at 0.4 and 1.4 mM [Mg(2+) ]o . The numbers and sizes of the neurospheres reached the maximum at 0.8 mM [Mg(2+) ]o and were weaker under both low (0.4 mM) and high (1.4 mM) concentrations of magnesium. In vitro experimental evidence demonstrates that extracellular magnesium regulates the number of cultured hippocampal NSCs, affecting both magnesium homeostasis and mitochondrial function. Our findings indicate that the effect of [Mg(2+) ]o on NSC proliferation may lie downstream of alterations in mitochondrial function because mitochondrial membrane potential was highest in the NSCs in the moderate [Mg(2+) ]o (0.8 mM) group and lower in both the low (0.4 mM) and high (1.4 mM) [Mg(2+) ]o groups. Overall, these findings demonstrate a new function for magnesium in the brain in the regulation of hippocampal neural stem cells: affecting their cellular energy metabolism. PMID:26634890

  7. Neural correlates of perceptual learning: A functional MRI study of visual texture discrimination

    OpenAIRE

    Schwartz, Sophie; Maquet, Pierre; Frith, Chris

    2002-01-01

    Visual texture discrimination has been shown to induce long-lasting behavioral improvement restricted to the trained eye and trained location in visual field [Karni, A. & Sagi, D. (1991) Proc. Natl. Acad. Sci. USA 88, 4966–4970]. We tested the hypothesis that such learning involves durable neural modifications at the earliest cortical stages of the visual system, where eye specificity, orientation, and location information are mapped with highest resolution. Using functional magnetic resonanc...

  8. Common and distinct neural targets of treatment: changing brain function in substance addiction

    OpenAIRE

    Konova, Anna B.; Moeller, Scott J.; Goldstein, Rita Z.

    2013-01-01

    Neuroimaging offers an opportunity to examine the neurobiological effects of therapeutic interventions for human drug addiction. Using activation likelihood estimation, the aim of the current meta-analysis was to quantitatively summarize functional neuroimaging studies of pharmacological and cognitive-based interventions for drug addiction, with an emphasis on their common and distinct neural targets. More exploratory analyses also contrasted subgroups of studies based on specific study and s...

  9. Neural correlates of improved executive function following erythropoietin treatment in mood disorders

    DEFF Research Database (Denmark)

    Miskowiak, K W; Vinberg, M; Glerup, L;

    2016-01-01

    magnetic resonance imaging (fMRI) study investigated the effects of EPO on neural circuitry activity during working memory (WM) performance. METHOD: Patients with treatment-resistant major depression, who were moderately depressed, or with BD in partial remission, were randomized to eight weekly infusions......BACKGROUND: Cognitive dysfunction in depression and bipolar disorder (BD) is insufficiently targeted by available treatments. Erythropoietin (EPO) increases neuroplasticity and may improve cognition in mood disorders, but the neuronal mechanisms of these effects are unknown. This functional...

  10. Neural Mechanism of Cognitive Control Impairment in Patients with Hepatic Cirrhosis: A Functional Magnetic Resonance Imaging Study

    Energy Technology Data Exchange (ETDEWEB)

    Long Jiang Zhang; Guifen Yang; Jianzhong Yin; Yawu Liu; Ji Qi [Dept. of Radiology, Tianjin First Central Hospital of Tianjin Medical Univ, Tianjin (China)

    2007-07-15

    Background: Many studies have claimed the existence of attention alterations in cirrhotic patients without overt hepatic encephalopathy (HE). No functional magnetic resonance imaging (fMRI) study in this respect has been published. Purpose: To investigate the neural basis of cognitive control deficiency in cirrhotic patients using fMRI. Material and Methods: 14 patients with hepatic cirrhosis and 14 healthy volunteers were included in the study. A modified Stroop task with Chinese characters was used as the target stimulus, and block-design fMRI was used to acquire resource data, including four stimulus blocks and five control blocks, each presented alternatively. Image analysis was performed using statistical parametric mapping 99. After fMRI examinations were complete, behavior tests of Stroop interference were performed for all subjects. Overall reaction time and error numbers were recorded. Results: Both healthy volunteers and patients with hepatic cirrhosis had Stroop interference effects. Patients with hepatic cirrhosis had more errors and longer reaction time in performing an incongruous color-naming task than healthy volunteers (P<0.001); there was no significant difference in performing an incongruous word-reading task (P 0.066). Compared with controls, patients with hepatic cirrhosis had greater activation of the bilateral prefrontal cortex and parietal cortex when performing the incongruous word-reading task. With increased conflict, activation of the anterior cingulate cortex (ACC), bilateral prefrontal cortex (PFC), parietal lobe, and temporal fusiform gyrus (TFG) was decreased when patients with hepatic cirrhosis performed the incongruous color-naming task. Conclusion: This study demonstrates that patients with hepatic cirrhostic have cognitive control deficiency. The abnormal brain network of the ACC-PFC-parietal lobe-TFG is the neural basis of cognitive control impairment in cirrhotic patients.

  11. Neural Mechanism of Cognitive Control Impairment in Patients with Hepatic Cirrhosis: A Functional Magnetic Resonance Imaging Study

    International Nuclear Information System (INIS)

    Background: Many studies have claimed the existence of attention alterations in cirrhotic patients without overt hepatic encephalopathy (HE). No functional magnetic resonance imaging (fMRI) study in this respect has been published. Purpose: To investigate the neural basis of cognitive control deficiency in cirrhotic patients using fMRI. Material and Methods: 14 patients with hepatic cirrhosis and 14 healthy volunteers were included in the study. A modified Stroop task with Chinese characters was used as the target stimulus, and block-design fMRI was used to acquire resource data, including four stimulus blocks and five control blocks, each presented alternatively. Image analysis was performed using statistical parametric mapping 99. After fMRI examinations were complete, behavior tests of Stroop interference were performed for all subjects. Overall reaction time and error numbers were recorded. Results: Both healthy volunteers and patients with hepatic cirrhosis had Stroop interference effects. Patients with hepatic cirrhosis had more errors and longer reaction time in performing an incongruous color-naming task than healthy volunteers (P<0.001); there was no significant difference in performing an incongruous word-reading task (P 0.066). Compared with controls, patients with hepatic cirrhosis had greater activation of the bilateral prefrontal cortex and parietal cortex when performing the incongruous word-reading task. With increased conflict, activation of the anterior cingulate cortex (ACC), bilateral prefrontal cortex (PFC), parietal lobe, and temporal fusiform gyrus (TFG) was decreased when patients with hepatic cirrhosis performed the incongruous color-naming task. Conclusion: This study demonstrates that patients with hepatic cirrhostic have cognitive control deficiency. The abnormal brain network of the ACC-PFC-parietal lobe-TFG is the neural basis of cognitive control impairment in cirrhotic patients

  12. Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks.

    Directory of Open Access Journals (Sweden)

    Shengqiao Ni

    Full Text Available This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.

  13. Construction of Canonical Polynomial Basis Functions for Solving Special Nth -Order Linear Integro-Differential Equations

    Directory of Open Access Journals (Sweden)

    1 Taiwo O. A

    2013-01-01

    Full Text Available The problem of solving special nth-order linear integro-differential equations has special importance in engineering and sciences that constitutes a good model for many systems in various fields. In this paper, we construct canonical polynomial from the differential parts of special nth-order integro-differential equations and use it as our basis function for the numerical solutions of special nth-order integro-differential equations. The results obtained by this method are compared with those obtained by Adomian Decomposition method. It is also observed that the new method is an effective method with high accuracy. Some examples are given to illustrate the method.

  14. A method of weighted residuals with trigonometric basis functions for sound transmission in circular ducts

    Science.gov (United States)

    Vo, P. T.; Eversman, W.

    1978-01-01

    The method of weighted residuals (MWR) in the form of a modified Galerkin method with trigonometric basis functions is used to compute the transmission of sound in an axisymmetric duct. The method is used to generate the axial wave number for uniform ducts. These are compared with exact solutions generated by a formal eigenvalue routine in the hard-wall case and a Runge-Kutta integration eigenvalue scheme in the soft-wall case. The method is applicable to both flow and no-flow cases.

  15. Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

    Science.gov (United States)

    Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

    1991-01-01

    Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

  16. Geometric neural computing.

    Science.gov (United States)

    Bayro-Corrochano, E J

    2001-01-01

    This paper shows the analysis and design of feedforward neural networks using the coordinate-free system of Clifford or geometric algebra. It is shown that real-, complex-, and quaternion-valued neural networks are simply particular cases of the geometric algebra multidimensional neural networks and that some of them can also be generated using support multivector machines (SMVMs). Particularly, the generation of radial basis function for neurocomputing in geometric algebra is easier using the SMVM, which allows one to find automatically the optimal parameters. The use of support vector machines in the geometric algebra framework expands its sphere of applicability for multidimensional learning. Interesting examples of nonlinear problems show the effect of the use of an adequate Clifford geometric algebra which alleviate the training of neural networks and that of SMVMs. PMID:18249926

  17. Differentiation of Spiral Ganglion-Derived Neural Stem Cells into Functional Synaptogenetic Neurons.

    Science.gov (United States)

    Li, Xiaoyang; Aleardi, Alicia; Wang, Jue; Zhou, Yang; Andrade, Rodrigo; Hu, Zhengqing

    2016-05-15

    Spiral ganglion neurons (SGNs) are usually damaged in sensorineural hearing loss. SGN-derived neural stem cells (NSCs) have been identified and proposed to differentiate into neurons to replace damaged SGNs. However, it remains obscure whether SGN-NSC-derived neurons (ScNs) are electrophysiologically functional and possess the capability to form neural connections. Here, we found that SGN-derived cells demonstrated NSC characteristics and differentiated into SGN-like glutamatergic neurons. Neurotrophins significantly increased neuronal differentiation and neurite length of ScNs. Patch clamp recording revealed that ScNs possessed SGN-like NaV and HCN channels, suggesting electrophysiological function. FM1-43 staining and synaptic protein immunofluorescence showed ScNs possess the ability to form neural connections. Astrocyte-conditioned medium was able to stimulate ScNs to express synaptic proteins. These data suggested that neurotrophins are able to stimulate postnatal SGN-NSCs to differentiate into functional glutamatergic ScNs with the capability to form synaptic connections in vitro. PMID:27021700

  18. Relating functional connectivity in V1 neural circuits and 3D natural scenes using Boltzmann machines.

    Science.gov (United States)

    Zhang, Yimeng; Li, Xiong; Samonds, Jason M; Lee, Tai Sing

    2016-03-01

    Bayesian theory has provided a compelling conceptualization for perceptual inference in the brain. Central to Bayesian inference is the notion of statistical priors. To understand the neural mechanisms of Bayesian inference, we need to understand the neural representation of statistical regularities in the natural environment. In this paper, we investigated empirically how statistical regularities in natural 3D scenes are represented in the functional connectivity of disparity-tuned neurons in the primary visual cortex of primates. We applied a Boltzmann machine model to learn from 3D natural scenes, and found that the units in the model exhibited cooperative and competitive interactions, forming a "disparity association field", analogous to the contour association field. The cooperative and competitive interactions in the disparity association field are consistent with constraints of computational models for stereo matching. In addition, we simulated neurophysiological experiments on the model, and found the results to be consistent with neurophysiological data in terms of the functional connectivity measurements between disparity-tuned neurons in the macaque primary visual cortex. These findings demonstrate that there is a relationship between the functional connectivity observed in the visual cortex and the statistics of natural scenes. They also suggest that the Boltzmann machine can be a viable model for conceptualizing computations in the visual cortex and, as such, can be used to predict neural circuits in the visual cortex from natural scene statistics. PMID:26712581

  19. Co-opting functions of cholinesterases in neural, limb and stem cell development.

    Science.gov (United States)

    Vogel-Hopker, Astrid; Sperling, Laura E; Layer, Paul G

    2012-02-01

    Acetylcholinesterase (AChE) is a most remarkable protein, not only because it is one of the fastest enzymes in nature, but also since it appears in many molecular forms and is regulated by elaborate genetic networks. As revealed by sensitive histochemical procedures, AChE is expressed specifically in many tissues during development and in many mature organisms, as well as in healthy and diseased states. Therefore it is not surprising that there has been a long-standing search for additional, "non-classical" functions of cholinesterases (ChEs). In principle, AChE could either act nonenzymatically, e.g. exerting cell adhesive roles, or, alternatively, it could work within the frame of classic cholinergic systems, but in non-neural tissues. AChE might be considered a highly co-opting protein, since possibly it combines such various functions within one molecule. By presenting four different developmental cases, we here review i) the expression of ChEs in the neural tube and their close relation to cell proliferation and differentiation, ii) that AChE expression reflects a polycentric brain development, iii) the retina as a model for AChE functioning in neural network formation, and iv) nonneural ChEs in limb development and mature bones. Also, possible roles of AChE in neuritic growth and of cholinergic regulations in stem cells are briefly outlined. PMID:21933123

  20. GRACE L1b inversion through a self-consistent modified radial basis function approach

    Science.gov (United States)

    Yang, Fan; Kusche, Juergen; Rietbroek, Roelof; Eicker, Annette

    2016-04-01

    Implementing a regional geopotential representation such as mascons or, more general, RBFs (radial basis functions) has been widely accepted as an efficient and flexible approach to recover the gravity field from GRACE (Gravity Recovery and Climate Experiment), especially at higher latitude region like Greenland. This is since RBFs allow for regionally specific regularizations over areas which have sufficient and dense GRACE observations. Although existing RBF solutions show a better resolution than classical spherical harmonic solutions, the applied regularizations cause spatial leakage which should be carefully dealt with. It has been shown that leakage is a main error source which leads to an evident underestimation of yearly trend of ice-melting over Greenland. Unlike some popular post-processing techniques to mitigate leakage signals, this study, for the first time, attempts to reduce the leakage directly in the GRACE L1b inversion by constructing an innovative modified (MRBF) basis in place of the standard RBFs to retrieve a more realistic temporal gravity signal along the coastline. Our point of departure is that the surface mass loading associated with standard RBF is smooth but disregards physical consistency between continental mass and passive ocean response. In this contribution, based on earlier work by Clarke et al.(2007), a physically self-consistent MRBF representation is constructed from standard RBFs, with the help of the sea level equation: for a given standard RBF basis, the corresponding MRBF basis is first obtained by keeping the surface load over the continent unchanged, but imposing global mass conservation and equilibrium response of the oceans. Then, the updated set of MRBFs as well as standard RBFs are individually employed as the basis function to determine the temporal gravity field from GRACE L1b data. In this way, in the MRBF GRACE solution, the passive (e.g. ice melting and land hydrology response) sea level is automatically

  1. Projections from the posterolateral olfactory amygdala to the ventral striatum: neural basis for reinforcing properties of chemical stimuli

    Directory of Open Access Journals (Sweden)

    Lanuza Enrique

    2007-11-01

    Full Text Available Abstract Background Vertebrates sense chemical stimuli through the olfactory receptor neurons whose axons project to the main olfactory bulb. The main projections of the olfactory bulb are directed to the olfactory cortex and olfactory amygdala (the anterior and posterolateral cortical amygdalae. The posterolateral cortical amygdaloid nucleus mainly projects to other amygdaloid nuclei; other seemingly minor outputs are directed to the ventral striatum, in particular to the olfactory tubercle and the islands of Calleja. Results Although the olfactory projections have been previously described in the literature, injection of dextran-amines into the rat main olfactory bulb was performed with the aim of delimiting the olfactory tubercle and posterolateral cortical amygdaloid nucleus in our own material. Injection of dextran-amines into the posterolateral cortical amygdaloid nucleus of rats resulted in anterograde labeling in the ventral striatum, in particular in the core of the nucleus accumbens, and in the medial olfactory tubercle including some islands of Calleja and the cell bridges across the ventral pallidum. Injections of Fluoro-Gold into the ventral striatum were performed to allow retrograde confirmation of these projections. Conclusion The present results extend previous descriptions of the posterolateral cortical amygdaloid nucleus efferent projections, which are mainly directed to the core of the nucleus accumbens and the medial olfactory tubercle. Our data indicate that the projection to the core of the nucleus accumbens arises from layer III; the projection to the olfactory tubercle arises from layer II and is much more robust than previously thought. This latter projection is directed to the medial olfactory tubercle including the corresponding islands of Calleja, an area recently described as critical node for the neural circuit of addiction to some stimulant drugs of abuse.

  2. Neural basis of phonological awareness in beginning readers with familial risk of dyslexia-Results from shallow orthography.

    Science.gov (United States)

    Dębska, Agnieszka; Łuniewska, Magdalena; Chyl, Katarzyna; Banaszkiewicz, Anna; Żelechowska, Agata; Wypych, Marek; Marchewka, Artur; Pugh, Kenneth R; Jednoróg, Katarzyna

    2016-05-15

    Phonological processing ability is a key factor in reading acquisition, predicting its later success or causing reading problems when it is weakened. Our aim here was to establish the neural correlates of auditory word rhyming (a standard phonological measure) in 102 young children with (FHD+) and without familial history of dyslexia (FHD-) in a shallow orthography (i.e. Polish). Secondly, in order to gain a deeper understanding on how schooling shapes brain activity to phonological awareness, a comparison was made of children who had had formal literacy instruction for several months (in first grade) and those who had not yet had any formal instruction in literacy (in kindergarten). FHD+ children compared to FHD- children in the first grade scored lower in an early print task and showed longer reaction times in the in-scanner rhyme task. No behavioral differences between FHD+ and FHD- were found in the kindergarten group. On the neuronal level, overall familial risk was associated with reduced activation in the bilateral temporal, tempo-parietal and inferior temporal-occipital regions, as well as the bilateral inferior and middle frontal gyri. Subcortically, hypoactivation was found in the bilateral thalami, caudate, and right putamen in FHD+. A main effect of the children's grade was present only in the left inferior frontal gyrus, where reduced activation for rhyming was shown in first-graders. Several regions in the ventral occipital cortex, including the fusiform gyrus, and in the right middle frontal and postcentral gyri, displayed an interaction between familial risk and grade. The present results show strong influence of familial risk that may actually increase with formal literacy instruction. PMID:26931814

  3. Comparative performance of some popular artificial neural network algorithms on benchmark and function approximation problems

    Indian Academy of Sciences (India)

    V K Dhar; A K Tickoo; R Koul; B P Dubey

    2010-02-01

    We report an inter-comparison of some popular algorithms within the artificial neural network domain (viz., local search algorithms, global search algorithms, higher-order algorithms and the hybrid algorithms) by applying them to the standard benchmarking problems like the IRIS data, XOR/N-bit parity and two-spiral problems. Apart from giving a brief description of these algorithms, the results obtained for the above benchmark problems are presented in the paper. The results suggest that while Levenberg–Marquardt algorithm yields the lowest RMS error for the N-bit parity and the two-spiral problems, higher-order neuron algorithm gives the best results for the IRIS data problem. The best results for the XOR problem are obtained with the neuro-fuzzy algo- rithm. The above algorithms were also applied for solving several regression problems such as cos() and a few special functions like the gamma function, the complimentary error function and the upper tail cumulative 2-distribution function. The results of these regression problems indicate that, among all the ANN algorithms used in the present study, Levenberg–Marquardt algorithm yields the best results. Keeping in view the highly non-linear behaviour and the wide dynamic range of these functions, it is suggested that these functions can also be considered as standard benchmark problems for function approximation using artificial neural networks.

  4. Stock market index prediction using neural networks

    Science.gov (United States)

    Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok

    1994-03-01

    A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

  5. A model for integrating elementary neural functions into delayed-response behavior.

    Directory of Open Access Journals (Sweden)

    Thomas Gisiger

    2006-04-01

    Full Text Available It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning, and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task, or recalling from this image another one that has been associated with it during training (delayed-pair association task. The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

  6. Electromyography function, disability degree, and pain in leprosy patients undergoing neural mobilization treatment

    Directory of Open Access Journals (Sweden)

    Larissa Sales Téles Véras

    2012-02-01

    Full Text Available INTRODUCTION: This study aimed to evaluate the effect of the neural mobilization technique on electromyography function, disability degree, and pain in patients with leprosy. METHODS: A sample of 56 individuals with leprosy was randomized into an experimental group, composed of 29 individuals undergoing treatment with neural mobilization, and a control group of 27 individuals who underwent conventional treatment. In both groups, the lesions in the lower limbs were treated. In the treatment with neural mobilization, the procedure used was mobilization of the lumbosacral roots and sciatic nerve biased to the peroneal nerve that innervates the anterior tibial muscle, which was evaluated in the electromyography. RESULTS: Analysis of the electromyography function showed a significant increase (p<0.05 in the experimental group in both the right (Δ%=22.1, p=0.013 and the left anterior tibial muscles (Δ%=27.7, p=0.009, compared with the control group pre- and post-test. Analysis of the strength both in the movement of horizontal extension (Δ%right=11.7, p=0.003/Δ%left=27.4, p=0.002 and in the movement of back flexion (Δ%right=31.1; p=0.000/Δ%left=34.7, p=0.000 showed a significant increase (p<0.05 in both the right and the left segments when comparing the experimental group pre- and post-test. The experimental group showed a significant reduction (p=0.000 in pain perception and disability degree when the pre- and post-test were compared and when compared with the control group in the post-test. CONCLUSIONS: Leprosy patients undergoing the technique of neural mobilization had an improvement in electromyography function and muscle strength, reducing disability degree and pain.

  7. Functional imaging in oncology. Biophysical basis and technical approaches. Vol. 1

    Energy Technology Data Exchange (ETDEWEB)

    Luna, Antonio [Health Time Group, Jaen (Spain); University Hospitals, Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Radiology; Vilanova, Joan C. [Clinica Girona - Hospital Sta. Caterina, Girona (Spain); Hygino da Cruz, L. Celso Jr. [CDPI and IRM, Rio de Janeiro, RJ (Brazil). Dept. of Radiology; Rossi, Santiago E. (ed.) [Centro de Diagnostico, Buenos Aires (Argentina)

    2014-07-01

    Easy-to-read manual on new functional imaging techniques in oncology. Explains current clinical applications and outlines future avenues. Includes numerous high-quality illustrations to highlight the major teaching points. In the new era of functional and molecular imaging, both currently available imaging biomarkers and biomarkers under development are expected to lead to major changes in the management of oncological patients. This well-illustrated two-volume book is a practical manual on the various imaging techniques capable of delivering functional information on cancer, including preclinical and clinical imaging techniques, based on US, CT, MRI, PET and hybrid modalities. This first volume explains the biophysical basis for these functional imaging techniques and describes the techniques themselves. Detailed information is provided on the imaging of cancer hallmarks, including angiogenesis, tumor metabolism, and hypoxia. The techniques and their roles are then discussed individually, covering the full range of modalities in clinical use as well as new molecular and functional techniques. The value of a multiparametric approach is also carefully considered.

  8. Tests of Maxwellian-Weighted Basis Functions in a Discontinuous Galerkin Kinetic Code

    Science.gov (United States)

    Hammett, G. W.; Hakim, A.; Shi, E. L.

    2013-10-01

    Discontinuous Galerkin (DG) algorithms have been very actively studied and used in the applied math and computational fluid dynamics communities in the past decade. They combine certain attractive properties of finite element methods (like high accuracy per interpolation point) and finite volume methods (like locality of calculation for parallel computers and flexibility for limiters). Higher-order methods also have more floating point operations per data point, and so can be more efficient on modern computers that are often bandwidth limited. The flexibility of DG allows one to consider various types of Maxwellian-weighted basis functions while preserving important conservation properties of the underlying system. One can think of this either as a modified inner-product norm or a Petrov-Galerkin approach. Here we explore some ways of using Maxwellian-Weighted Basis functions and test them on paradigm problems using the Gkeyll code, which is being developed for edge gyrokinetic simulations. In addition to the formal order of accuracy in the asymptotic limit as a grid is refined, we are also interested in robust reasonable solutions on coarser grids. This work was supported by the Max-Planck/Princeton Center for Plasma Physics and DOE Contract DE-AC02-09CH11466.

  9. Functional assessment and structural basis of antibody binding to human papillomavirus capsid.

    Science.gov (United States)

    Zhang, Xiao; Li, Shaowei; Modis, Yorgo; Li, Zhihai; Zhang, Jun; Xia, Ningshao; Zhao, Qinjian

    2016-03-01

    Persistent high-risk human papillomavirus (HPV) infection is linked to cervical cancer. Two prophylactic virus-like particle (VLP)-based vaccines have been marketed globally for nearly a decade. Here, we review the HPV pseudovirion (PsV)-based assays for the functional assessment of the HPV neutralizing antibodies and the structural basis for these clinically relevant epitopes. The PsV-based neutralization assay was developed to evaluate the efficacy of neutralization antibodies in sera elicited by vaccination or natural infection or to assess the functional characteristics of monoclonal antibodies. Different antibody binding modes were observed when an antibody was complexed with virions, PsVs or VLPs. The neutralizing epitopes are localized on surface loops of the L1 capsid protein, at various locations on the capsomere. Different neutralization antibodies exert their neutralizing function via different mechanisms. Some antibodies neutralize the virions by inducing conformational changes in the viral capsid, which can result in concealing the binding site for a cellular receptor like 1A1D-2 against dengue virus, or inducing premature genome release like E18 against enterovirus 71. Higher-resolution details on the epitope composition of HPV neutralizing antibodies would shed light on the structural basis of the highly efficacious vaccines and aid the design of next generation vaccines. In-depth understanding of epitope composition would ensure the development of function-indicating assays for the comparability exercise to support process improvement or process scale up. Elucidation of the structural elements of the type-specific epitopes would enable rational design of cross-type neutralization via epitope re-engineering or epitope grafting in hybrid VLPs. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26676802

  10. Green's function multiple-scattering theory with a truncated basis set: An augmented-KKR formalism

    Science.gov (United States)

    Alam, Aftab; Khan, Suffian N.; Smirnov, A. V.; Nicholson, D. M.; Johnson, Duane D.

    2014-11-01

    The Korringa-Kohn-Rostoker (KKR) Green's function, multiple-scattering theory is an efficient site-centered, electronic-structure technique for addressing an assembly of N scatterers. Wave functions are expanded in a spherical-wave basis on each scattering center and indexed up to a maximum orbital and azimuthal number Lmax=(l,mmax), while scattering matrices, which determine spectral properties, are truncated at Lt r=(l,mt r) where phase shifts δl >ltr are negligible. Historically, Lmax is set equal to Lt r, which is correct for large enough Lmax but not computationally expedient; a better procedure retains higher-order (free-electron and single-site) contributions for Lmax>Lt r with δl >ltr set to zero [X.-G. Zhang and W. H. Butler, Phys. Rev. B 46, 7433 (1992), 10.1103/PhysRevB.46.7433]. We present a numerically efficient and accurate augmented-KKR Green's function formalism that solves the KKR equations by exact matrix inversion [R3 process with rank N (ltr+1 ) 2 ] and includes higher-L contributions via linear algebra [R2 process with rank N (lmax+1) 2 ]. The augmented-KKR approach yields properly normalized wave functions, numerically cheaper basis-set convergence, and a total charge density and electron count that agrees with Lloyd's formula. We apply our formalism to fcc Cu, bcc Fe, and L 1 0 CoPt and present the numerical results for accuracy and for the convergence of the total energies, Fermi energies, and magnetic moments versus Lmax for a given Lt r.

  11. Age related-changes in the neural basis of self-generation in verbal paired associate learning

    Directory of Open Access Journals (Sweden)

    Jennifer Vannest

    2015-01-01

    Full Text Available Verbal information is better retained when it is self-generated rather than when it is received passively. The application of self-generation procedures has been found to improve memory in healthy elderly and in individuals with impaired cognition. Overall, the available studies support the notion that active participation in verbal encoding engages memory mechanisms that supplement those used during passive observation. Thus, the objective of this study was to investigate the age-related changes in the neural mechanisms involved in the encoding of paired-associates using a self-generation method that has been shown to improve memory performance across the lifespan. Subjects were 113 healthy right-handed adults (Edinburgh Handedness Inventory >50; 67 females ages 18–76, native speakers of English with no history of neurological or psychiatric disorders. Subjects underwent fMRI at 3 T while performing didactic learning (“read” or self-generation learning (“generate” of 30 word pairs per condition. After fMRI, recognition memory for the second word in each pair was evaluated outside of the scanner. On the post-fMRI testing more “generate” words were correctly recognized than “read” words (p < 0.001 with older adults recognizing the “generated” words less accurately (p < 0.05. Independent component analysis of fMRI data identified task-related brain networks. Several components were positively correlated with the task reflecting multiple cognitive processes involved in self-generated encoding; other components correlated negatively with the task, including components of the default-mode network. Overall, memory performance on generated words decreased with age, but the benefit from self-generation remained consistently significant across ages. Independent component analysis of the neuroimaging data revealed an extensive set of components engaged in self-generation learning compared with didactic learning, and identified

  12. Neural Correlates of Symptom Dimensions in Pediatric Obsessive-Compulsive Disorder: A Functional Magnetic Resonance Imaging Study

    Science.gov (United States)

    Gilbert, Andrew R.; Akkal, Dalila; Almeida, Jorge R. C.; Mataix-Cols, David; Kalas, Catherine; Devlin, Bernie; Birmaher, Boris; Phillips, Mary L.

    2009-01-01

    The use of functional magnetic resonance imaging on a group of pediatric subjects with obsessive compulsive disorder reveals that this group has reduced activity in neural regions underlying emotional processing, cognitive processing, and motor performance as compared to control subjects.

  13. Human-derived neural progenitors functionally replace astrocytes in adult mice

    Science.gov (United States)

    Chen, Hong; Qian, Kun; Chen, Wei; Hu, Baoyang; Blackbourn, Lisle W.; Du, Zhongwei; Ma, Lixiang; Liu, Huisheng; Knobel, Karla M.; Ayala, Melvin; Zhang, Su-Chun

    2015-01-01

    Astrocytes are integral components of the homeostatic neural network as well as active participants in pathogenesis of and recovery from nearly all neurological conditions. Evolutionarily, compared with lower vertebrates and nonhuman primates, humans have an increased astrocyte-to-neuron ratio; however, a lack of effective models has hindered the study of the complex roles of human astrocytes in intact adult animals. Here, we demonstrated that after transplantation into the cervical spinal cords of adult mice with severe combined immunodeficiency (SCID), human pluripotent stem cell–derived (PSC-derived) neural progenitors migrate a long distance and differentiate to astrocytes that nearly replace their mouse counterparts over a 9-month period. The human PSC-derived astrocytes formed networks through their processes, encircled endogenous neurons, and extended end feet that wrapped around blood vessels without altering locomotion behaviors, suggesting structural, and potentially functional, integration into the adult mouse spinal cord. Furthermore, in SCID mice transplanted with neural progenitors derived from induced PSCs from patients with ALS, astrocytes were generated and distributed to a similar degree as that seen in mice transplanted with healthy progenitors; however, these mice exhibited motor deficit, highlighting functional integration of the human-derived astrocytes. Together, these results indicate that this chimeric animal model has potential for further investigating the roles of human astrocytes in disease pathogenesis and repair. PMID:25642771

  14. 基于改进教学优化算法的Hermite正交基神经网络混沌时间序列预测∗%Hermite orthogonal basis neural network based on improved teaching-learning-based optimization al-gorithm for chaotic time series prediction

    Institute of Scientific and Technical Information of China (English)

    李瑞国; 张宏立; 范文慧; 王雅

    2015-01-01

    Chaos phenomenon which exists widely in nature and society affects people’s production and life. It has great important significance to find out the regularity of chaotic time series from a chaotic system. Since chaotic system has extremely complex dynamic characteristics and unpredictability, and chaotic time series prediction through traditional methods has low prediction precision, slow convergence speed and complex model structure, a prediction model about Hermite orthogonal basis neural network based on improved teaching-learning-based optimization algorithm is proposed. Firstly, according to the chaotic time series, autocorrelation method and Cao method are used to determine the best delay time and the minimum embedding dimension respectively, then a phase space is reconstructed to obtain the refactoring delay time vector. Secondly, on the basis of phase space reconstruction and best square approximation theory, combined with the neural network topology, a prediction model about Hermite orthogonal basis neural network with excitation functions based on the Hermite orthogonal basis functions is put forward. Thirdly, in order to optimize the parameters of the prediction model, an improved teaching-learning-based optimization algorithm is proposed, where a feedback stage is introduced at the end of the learning stage based on the teaching-learning-based optimization algorithm. Finally, the parameter optimization problem is transformed into a function optimization problem in the multidimensional space, then the improved teaching-learning-based optimization algorithm is used for parameter optimization of the prediction model so as to establish it and analyze it. Lorenz and Liu chaotic systems are taken as models respectively, then the chaotic time series which will be used as simulation object is produced by the fourth order Runge-Kutta method. The comparison experiments with other prediction models are conducted on single-step and multi-step prediction for the

  15. fMRI Study Revealing Neural Mechanisms of the Functions of SOA in Spatial Orienting

    Institute of Scientific and Technical Information of China (English)

    Yin Tian; Qian Zhang; De-Zhong Yao

    2009-01-01

    It is well documented that orienting attention plays an important role in visual search. However, it remains unclear how the executive brain regions will act when two different stimulus onset asynchrony (SOA) are used in visual search. In this work, event-related fMRI was used to investigate neural mechanisms on the functions of SOA in endogenous and exogenous orienting. The results showed that in the endogenous orienting, long SOA versus short SOA resulted in widespread cortical activation mainly including right medial frontal gyrus and bilateral middle frontal gyri. Conversely, in exogenous orienting, long SOA compared to short SOA resulted in only activations in bilateral middle frontal gyri. These findings indicated that these two spatial orienting involved different brain areas and neural mechanisms.

  16. Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks.

    Science.gov (United States)

    Patra, J C; Kot, A C

    2002-01-01

    A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks, such as multilayer perceptrons (MLPs) trained with the backpropagation (BP) algorithm, is that they require a large amount of computation for learning. We propose a single-layer functional-link ANN (FLANN) in which the need for a hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. The novelty of this network is that it requires much less computation than that of a MLP. We have shown its effectiveness in the problem of nonlinear dynamic system identification. In the presence of additive Gaussian noise, the performance of the proposed network is found to be similar or superior to that of a MLP. A performance comparison in terms of computational complexity has also been carried out. PMID:18238146

  17. Stability in Switched Cohen-Grossberg Neural Networks with Mixed Time Delays and Non-Lipschitz Activation Functions

    OpenAIRE

    Qiangqiang Guo; Ning Li; Kewang Wang; Chongyang Wu; Guohua Xu; Huaiqin Wu

    2012-01-01

    The stability for the switched Cohen-Grossberg neural networks with mixed time delays and α-inverse Hölder activation functions is investigated under the switching rule with the average dwell time property. By applying multiple Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI) technique, a delay-dependent sufficient criterion is achieved to ensure such switched neural networks to be globally exponentially stable in terms of LMIs, and the exponential decay estimation i...

  18. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

    Directory of Open Access Journals (Sweden)

    Karuna Subramaniam

    2015-01-01

    Full Text Available Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life.

  19. Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation

    Science.gov (United States)

    Shankar, Praveen

    The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network

  20. Scientific basis for learning transfer from movements to urinary bladder functions for bladder repair in human patients with CNS injury.

    Science.gov (United States)

    Schalow, G

    2010-01-01

    Coordination Dynamics Therapy (CDT) has been shown to be able to partly repair CNS injury. The repair is based on a movement-based re-learning theory which requires at least three levels of description: the movement or pattern (and anamnesis) level, the collective variable level, and the neuron level. Upon CDT not only the actually performed movement pattern itself is repaired, but the entire dynamics of CNS organization is improved, which is the theoretical basis for (re-) learning transfer. The transfer of learning for repair from jumping on springboard and exercising on a special CDT and recording device to urinary bladder functions is investigated at the neuron level. At the movement or pattern level, the improvement of central nervous system (CNS) functioning in human patients can be seen (or partly measured) by the improvement of the performance of the pattern. At the collective variable level, coordination tendencies can be measured by the so-called 'coordination dynamics' before, during and after treatment. At the neuron level, re-learning can additionally be assessed by surface electromyography (sEMG) as alterations of single motor unit firings and motor programs. But to express the ongoing interaction between the numerous neural, muscular, and metabolic elements involved in perception and action, it is relevant to inquire how the individual afferent and efferent neurons adjust their phase and frequency coordination to other neurons to satisfy learning task requirements. With the single-nerve fibre action potential recording method it was possible to measure that distributed single neurons communicate by phase and frequency coordination. It is shown that this timed firing of neurons is getting impaired upon injury and has to be improved by learning The stability of phase and frequency coordination among afferent and efferent neuron firings can be related to pattern stability. The stability of phase and frequency coordination at the neuron level can

  1. Alternative basis functions for L2 calculations on the molecular continuum. II. Integrals with higher-order functions

    International Nuclear Information System (INIS)

    By differentiation of the expressions for the basic s-type integrals previously presented, analytic expressions are here derived for integrals, relevant for quantum-chemistry calculations, involving oscillating Hermite Gaussian functions (OHGF's) and many-center Hermite Gaussian functions (HGF's) of any order. The OHGF is the product of a HGF and a radial trigonometric factor cos(kr), and has been proposed for describing the continuum orbitals in L2 calculations on molecules. The resulting expressions are compact and particularly suitable for numerical implementation on a computer, while the increase in the computational effort of the integral evaluation with respect to the s-type functions is estimated to be of the same order as that found in the standard case of simple Gaussian functions. Applications of the OHGF basis to the calculation of continuum states are presented and compared to the exact results for test cases: the hydrogen atom and the H2+ molecule, in order to discuss advantages and limitations of the proposed approach

  2. Calculating vibrational spectra with sum of product basis functions without storing full-dimensional vectors or matrices

    CERN Document Server

    Leclerc, Arnaud

    2014-01-01

    We propose an iterative method for computing vibrational spectra that significantly reduces the memory cost of calculations. It uses a direct product primitive basis, but does not require storing vectors with as many components as there are product basis functions. Wavefunctions are represented in a basis each of whose functions is a sum of products (SOP) and the factorizable structure of the Hamiltonian is exploited. If the factors of the SOP basis functions are properly chosen, wavefunctions are linear combinations of a small number of SOP basis functions. The SOP basis functions are generated using a shifted block power method. The factors are refined with a rank reduction algorithm to cap the number of terms in a SOP basis function. The ideas are tested on a 20-D model Hamiltonian and a realistic CH$_3$CN (12 dimensional) potential. For the 20-D problem, to use a standard direct product iterative approach one would need to store vectors with about $10^{20}$ components and would hence require about $8 \\tim...

  3. Structural and Functional Basis for Substrate Specificity and Catalysis of Levan Fructotransferase*

    Science.gov (United States)

    Park, Jinseo; Kim, Myung-Il; Park, Young-Don; Shin, Inchul; Cha, Jaeho; Kim, Chul Ho; Rhee, Sangkee

    2012-01-01

    Levan is β-2,6-linked polymeric fructose and serves as reserve carbohydrate in some plants and microorganisms. Mobilization of fructose is usually mediated by enzymes such as glycoside hydrolase (GH), typically releasing a monosaccharide as a product. The enzyme levan fructotransferase (LFTase) of the GH32 family catalyzes an intramolecular fructosyl transfer reaction and results in production of cyclic difructose dianhydride, thus exhibiting a novel substrate specificity. The mechanism by which LFTase carries out these functions via the structural fold conserved in the GH32 family is unknown. Here, we report the crystal structure of LFTase from Arthrobacter ureafaciens in apo form, as well as in complexes with sucrose and levanbiose, a difructosacchride with a β-2,6-glycosidic linkage. Despite the similarity of its two-domain structure to members of the GH32 family, LFTase contains an active site that accommodates a difructosaccharide using the −1 and −2 subsites. This feature is unique among GH32 proteins and is facilitated by small side chain residues in the loop region of a catalytic β-propeller N-domain, which is conserved in the LFTase family. An additional oligosaccharide-binding site was also characterized in the β-sandwich C-domain, supporting its role in carbohydrate recognition. Together with functional analysis, our data provide a molecular basis for the catalytic mechanism of LFTase and suggest functional variations from other GH32 family proteins, notwithstanding the conserved structural elements. PMID:22810228

  4. Structural and functional basis for substrate specificity and catalysis of levan fructotransferase.

    Science.gov (United States)

    Park, Jinseo; Kim, Myung-Il; Park, Young-Don; Shin, Inchul; Cha, Jaeho; Kim, Chul Ho; Rhee, Sangkee

    2012-09-01

    Levan is β-2,6-linked polymeric fructose and serves as reserve carbohydrate in some plants and microorganisms. Mobilization of fructose is usually mediated by enzymes such as glycoside hydrolase (GH), typically releasing a monosaccharide as a product. The enzyme levan fructotransferase (LFTase) of the GH32 family catalyzes an intramolecular fructosyl transfer reaction and results in production of cyclic difructose dianhydride, thus exhibiting a novel substrate specificity. The mechanism by which LFTase carries out these functions via the structural fold conserved in the GH32 family is unknown. Here, we report the crystal structure of LFTase from Arthrobacter ureafaciens in apo form, as well as in complexes with sucrose and levanbiose, a difructosacchride with a β-2,6-glycosidic linkage. Despite the similarity of its two-domain structure to members of the GH32 family, LFTase contains an active site that accommodates a difructosaccharide using the -1 and -2 subsites. This feature is unique among GH32 proteins and is facilitated by small side chain residues in the loop region of a catalytic β-propeller N-domain, which is conserved in the LFTase family. An additional oligosaccharide-binding site was also characterized in the β-sandwich C-domain, supporting its role in carbohydrate recognition. Together with functional analysis, our data provide a molecular basis for the catalytic mechanism of LFTase and suggest functional variations from other GH32 family proteins, notwithstanding the conserved structural elements. PMID:22810228

  5. Improved radial basis function methods for multi-dimensional option pricing

    Science.gov (United States)

    Pettersson, Ulrika; Larsson, Elisabeth; Marcusson, Gunnar; Persson, Jonas

    2008-12-01

    In this paper, we have derived a radial basis function (RBF) based method for the pricing of financial contracts by solving the Black-Scholes partial differential equation. As an example of a financial contract that can be priced with this method we have chosen the multi-dimensional European basket call option. We have shown numerically that our scheme is second-order accurate in time and spectrally accurate in space for constant shape parameter. For other non-optimal choices of shape parameter values, the resulting convergence rate is algebraic. We propose an adapted node point placement that improves the accuracy compared with a uniform distribution. Compared with an adaptive finite difference method, the RBF method is 20-40 times faster in one and two space dimensions and has approximately the same memory requirements.

  6. On fast computation of finite-time coherent sets using radial basis functions.

    Science.gov (United States)

    Froyland, Gary; Junge, Oliver

    2015-08-01

    Finite-time coherent sets inhibit mixing over finite times. The most expensive part of the transfer operator approach to detecting coherent sets is the construction of the operator itself. We present a numerical method based on radial basis function collocation and apply it to a recent transfer operator construction [G. Froyland, "Dynamic isoperimetry and the geometry of Lagrangian coherent structures," Nonlinearity (unpublished); preprint arXiv:1411.7186] that has been designed specifically for purely advective dynamics. The construction [G. Froyland, "Dynamic isoperimetry and the geometry of Lagrangian coherent structures," Nonlinearity (unpublished); preprint arXiv:1411.7186] is based on a "dynamic" Laplace operator and minimises the boundary size of the coherent sets relative to their volume. The main advantage of our new approach is a substantial reduction in the number of Lagrangian trajectories that need to be computed, leading to large speedups in the transfer operator analysis when this computation is costly. PMID:26328580

  7. Improved radial basis function approach with the odd-even corrections

    CERN Document Server

    Niu, Z M; Liang, H Z; Niu, Y F; Guo, J Y

    2016-01-01

    The radial basis function (RBF) approach has been used to improve the mass predictions of nuclear models. However, systematic deviations exist between the improved masses and the experimental data for nuclei with different odd-even parities of ($Z$, $N$), i.e., the (even $Z$, even $N$), (even $Z$, odd $N$), (odd $Z$, even $N$), and (odd $Z$, odd $N$). By separately training the RBF for these four different groups, it is found that the systematic odd-even deviations can be cured in a large extend and the predictive power of nuclear mass models can thus be further improved. Moreover, this new approach can better reproduce the single-nucleon separation energies. Based on the latest version of Weizs\\"acker-Skyrme model WS4, the root-mean-square deviation of the improved masses with respect to known data falls to $135$ keV, approaching the chaos-related unpredictability limit ($\\sim 100$ keV).

  8. Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

    Directory of Open Access Journals (Sweden)

    Sharma Rakesh

    2004-05-01

    Full Text Available Abstract Functional magnetic resonance imaging (fMRI is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities.

  9. Basis Function Repetitive And Feedback Control With Application To A Particle Accelerator

    CERN Document Server

    Akogyeram, R A

    2002-01-01

    The thesis addresses three problem areas within repetitive control. Firstly, it addresses issues concerning the ability of repetitive control and feedback control systems to eliminate periodic disturbances occurring above the Nyquist frequency of the hardware. Methods are developed for decomposing and unfolding notch filter or comb filter feedback control so that disturbances above Nyquist frequency can be canceled. Phenomena affecting final error levels are discussed, including error in unfolding, coarseness of zero-order hold cancellation, and waterbed effects in the feedback control system frequency response for different sample rates. Secondly, matched basis function repetitive control laws are developed for batch mode and real time implementation to converge to zero tracking error in the presence of periodic disturbances. For both control methods, conditions are given that guarantee asymptotic and monotonic convergence. Stability tests are formulated to examine stability when the period of a disturbance ...

  10. Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter

    KAUST Repository

    Ryu, Duchwan

    2013-03-01

    The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.

  11. Vibration measurement based on electronic speckle pattern interferometry and radial basis function

    Science.gov (United States)

    Dai, Xiangjun; Shao, Xinxing; Geng, Zhencen; Yang, Fujun; Jiang, Yijun; He, Xiaoyuan

    2015-11-01

    A method incorporating amplitude-fluctuation electronic speckle pattern interferometry (AF-ESPI) with radial basis function (RBF) was proposed to investigate vibration characteristics of structures. The vibration patterns were obtained by AF-ESPI. A novel pre-filtering RBF method was presented to improve the quality of patterns. The out-of-plane vibration amplitude was rebuilt after fringe analysis. Ideal pre-filtering widow sizes for the presented RBF were given based on numerical experiments. For validation, an aluminum circular plate with fixed boundary was determined and compared with FEM, confirming the effectiveness of the proposed method. Finally, vibration characteristics of sandwich panels with honeycomb core were measured. The influence of presence of a pre-notch at different location was also investigated.

  12. Natural convection in porous media - radial basis function collocation method solution of the darcy model

    International Nuclear Information System (INIS)

    Full text: This paper describes the solution of a steady-state natural convection problem in porous media by the radial basis function collocation method (RBFCM). This meshless (polygon-free) numerical method is for coupled set of mass, momentum, and energy equations in two dimensions structured by the augmented scaled second order thin plate splines. The solution is formulated in primitive variables and involves iterative treatment of coupled pressure, velocity, pressure correction, velocity correction, and energy equations. Numerical examples include convergence studies with different collocation point arrangements for a two-dimensional differentially heated rectangular cavity problem at filtration Rayleigh numbers Ra* = 25, 50 and 100, and aspect ratios A = 1/2, 1, and 2. The solution is assessed by comparison with reference results of the fine-mesh finite volume method in terms of mid-plane velocity components, mid-plane and insulated surface temperatures, streamfunction minimum, and Nusselt number. Refs. 7 (author)

  13. Functional neural correlates of attentional deficits in amnestic mild cognitive impairment.

    Directory of Open Access Journals (Sweden)

    Nicholas T Van Dam

    Full Text Available Although amnestic mild cognitive impairment (aMCI; often considered a prodromal phase of Alzheimer's disease, AD is most recognized by its implications for decline in memory function, research suggests that deficits in attention are present early in aMCI and may be predictive of progression to AD. The present study used functional magnetic resonance imaging to examine differences in the brain during the attention network test between 8 individuals with aMCI and 8 neurologically healthy, demographically matched controls. While there were no significant behavioral differences between groups for the alerting and orienting functions, patients with aMCI showed more activity in neural regions typically associated with the networks subserving these functions (e.g., temporoparietal junction and posterior parietal regions, respectively. More importantly, there were both behavioral (i.e., greater conflict effect and corresponding neural deficits in executive control (e.g., less activation in the prefrontal and anterior cingulate cortices. Although based on a small number of patients, our findings suggest that deficits of attention, especially the executive control of attention, may significantly contribute to the behavioral and cognitive deficits of aMCI.

  14. Recruitment of Polysynaptic Connections Underlies Functional Recovery of a Neural Circuit after Lesion

    Science.gov (United States)

    Tamvacakis, Arianna N.

    2016-01-01

    Abstract The recruitment of additional neurons to neural circuits often occurs in accordance with changing functional demands. Here we found that synaptic recruitment plays a key role in functional recovery after neural injury. Disconnection of a brain commissure in the nudibranch mollusc, Tritonia diomedea, impairs swimming behavior by eliminating particular synapses in the central pattern generator (CPG) underlying the rhythmic swim motor pattern. However, the CPG functionally recovers within a day after the lesion. The strength of a spared inhibitory synapse within the CPG from Cerebral Neuron 2 (C2) to Ventral Swim Interneuron B (VSI) determines the level of impairment caused by the lesion, which varies among individuals. In addition to this direct synaptic connection, there are polysynaptic connections from C2 and Dorsal Swim Interneurons to VSI that provide indirect excitatory drive but play only minor roles under normal conditions. After disconnecting the pedal commissure (Pedal Nerve 6), the recruitment of polysynaptic excitation became a major source of the excitatory drive to VSI. Moreover, the amount of polysynaptic recruitment, which changed over time, differed among individuals and correlated with the degree of recovery of the swim motor pattern. Thus, functional recovery was mediated by an increase in the magnitude of polysynaptic excitatory drive, compensating for the loss of direct excitation. Since the degree of susceptibility to injury corresponds to existing individual variation in the C2 to VSI synapse, the recovery relied upon the extent to which the network reorganized to incorporate additional synapses.

  15. Consistent structures and interactions by density functional theory with small atomic orbital basis sets

    International Nuclear Information System (INIS)

    A density functional theory (DFT) based composite electronic structure approach is proposed to efficiently compute structures and interaction energies in large chemical systems. It is based on the well-known and numerically robust Perdew-Burke-Ernzerhoff (PBE) generalized-gradient-approximation in a modified global hybrid functional with a relatively large amount of non-local Fock-exchange. The orbitals are expanded in Ahlrichs-type valence-double zeta atomic orbital (AO) Gaussian basis sets, which are available for many elements. In order to correct for the basis set superposition error (BSSE) and to account for the important long-range London dispersion effects, our well-established atom-pairwise potentials are used. In the design of the new method, particular attention has been paid to an accurate description of structural parameters in various covalent and non-covalent bonding situations as well as in periodic systems. Together with the recently proposed three-fold corrected (3c) Hartree-Fock method, the new composite scheme (termed PBEh-3c) represents the next member in a hierarchy of “low-cost” electronic structure approaches. They are mainly free of BSSE and account for most interactions in a physically sound and asymptotically correct manner. PBEh-3c yields good results for thermochemical properties in the huge GMTKN30 energy database. Furthermore, the method shows excellent performance for non-covalent interaction energies in small and large complexes. For evaluating its performance on equilibrium structures, a new compilation of standard test sets is suggested. These consist of small (light) molecules, partially flexible, medium-sized organic molecules, molecules comprising heavy main group elements, larger systems with long bonds, 3d-transition metal systems, non-covalently bound complexes (S22 and S66×8 sets), and peptide conformations. For these sets, overall deviations from accurate reference data are smaller than for various other tested DFT

  16. Consistent structures and interactions by density functional theory with small atomic orbital basis sets

    Energy Technology Data Exchange (ETDEWEB)

    Grimme, Stefan, E-mail: grimme@thch.uni-bonn.de; Brandenburg, Jan Gerit; Bannwarth, Christoph; Hansen, Andreas [Mulliken Center for Theoretical Chemistry, Institut für Physikalische und Theoretische Chemie, Rheinische Friedrich-Wilhelms Universität Bonn, Beringstraße 4, 53115 Bonn (Germany)

    2015-08-07

    A density functional theory (DFT) based composite electronic structure approach is proposed to efficiently compute structures and interaction energies in large chemical systems. It is based on the well-known and numerically robust Perdew-Burke-Ernzerhoff (PBE) generalized-gradient-approximation in a modified global hybrid functional with a relatively large amount of non-local Fock-exchange. The orbitals are expanded in Ahlrichs-type valence-double zeta atomic orbital (AO) Gaussian basis sets, which are available for many elements. In order to correct for the basis set superposition error (BSSE) and to account for the important long-range London dispersion effects, our well-established atom-pairwise potentials are used. In the design of the new method, particular attention has been paid to an accurate description of structural parameters in various covalent and non-covalent bonding situations as well as in periodic systems. Together with the recently proposed three-fold corrected (3c) Hartree-Fock method, the new composite scheme (termed PBEh-3c) represents the next member in a hierarchy of “low-cost” electronic structure approaches. They are mainly free of BSSE and account for most interactions in a physically sound and asymptotically correct manner. PBEh-3c yields good results for thermochemical properties in the huge GMTKN30 energy database. Furthermore, the method shows excellent performance for non-covalent interaction energies in small and large complexes. For evaluating its performance on equilibrium structures, a new compilation of standard test sets is suggested. These consist of small (light) molecules, partially flexible, medium-sized organic molecules, molecules comprising heavy main group elements, larger systems with long bonds, 3d-transition metal systems, non-covalently bound complexes (S22 and S66×8 sets), and peptide conformations. For these sets, overall deviations from accurate reference data are smaller than for various other tested DFT

  17. Consistent structures and interactions by density functional theory with small atomic orbital basis sets

    Science.gov (United States)

    Grimme, Stefan; Brandenburg, Jan Gerit; Bannwarth, Christoph; Hansen, Andreas

    2015-08-01

    A density functional theory (DFT) based composite electronic structure approach is proposed to efficiently compute structures and interaction energies in large chemical systems. It is based on the well-known and numerically robust Perdew-Burke-Ernzerhoff (PBE) generalized-gradient-approximation in a modified global hybrid functional with a relatively large amount of non-local Fock-exchange. The orbitals are expanded in Ahlrichs-type valence-double zeta atomic orbital (AO) Gaussian basis sets, which are available for many elements. In order to correct for the basis set superposition error (BSSE) and to account for the important long-range London dispersion effects, our well-established atom-pairwise potentials are used. In the design of the new method, particular attention has been paid to an accurate description of structural parameters in various covalent and non-covalent bonding situations as well as in periodic systems. Together with the recently proposed three-fold corrected (3c) Hartree-Fock method, the new composite scheme (termed PBEh-3c) represents the next member in a hierarchy of "low-cost" electronic structure approaches. They are mainly free of BSSE and account for most interactions in a physically sound and asymptotically correct manner. PBEh-3c yields good results for thermochemical properties in the huge GMTKN30 energy database. Furthermore, the method shows excellent performance for non-covalent interaction energies in small and large complexes. For evaluating its performance on equilibrium structures, a new compilation of standard test sets is suggested. These consist of small (light) molecules, partially flexible, medium-sized organic molecules, molecules comprising heavy main group elements, larger systems with long bonds, 3d-transition metal systems, non-covalently bound complexes (S22 and S66×8 sets), and peptide conformations. For these sets, overall deviations from accurate reference data are smaller than for various other tested DFT methods

  18. Performance Comparison of Neural Networks for HRTFs Approximation

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In order to approach to head-related transfer functions (HRTFs), this paper employs and compares three kinds of one-input neural network models, namely, multi-layer perceptron (MLP) networks, radial basis function (RBF) networks and wavelet neural networks (WNN) so as to select the best network model for further HRTFs approximation. Experimental results demonstrate that wavelet neural networks are more efficient and useful.

  19. A potential neural substrate for processing functional classes of complex acoustic signals.

    Directory of Open Access Journals (Sweden)

    Isabelle George

    Full Text Available Categorization is essential to all cognitive processes, but identifying the neural substrates underlying categorization processes is a real challenge. Among animals that have been shown to be able of categorization, songbirds are particularly interesting because they provide researchers with clear examples of categories of acoustic signals allowing different levels of recognition, and they possess a system of specialized brain structures found only in birds that learn to sing: the song system. Moreover, an avian brain nucleus that is analogous to the mammalian secondary auditory cortex (the caudo-medial nidopallium, or NCM has recently emerged as a plausible site for sensory representation of birdsong, and appears as a well positioned brain region for categorization of songs. Hence, we tested responses in this non-primary, associative area to clear and distinct classes of songs with different functions and social values, and for a possible correspondence between these responses and the functional aspects of songs, in a highly social songbird species: the European starling. Our results clearly show differential neuronal responses to the ethologically defined classes of songs, both in the number of neurons responding, and in the response magnitude of these neurons. Most importantly, these differential responses corresponded to the functional classes of songs, with increasing activation from non-specific to species-specific and from species-specific to individual-specific sounds. These data therefore suggest a potential neural substrate for sorting natural communication signals into categories, and for individual vocal recognition of same-species members. Given the many parallels that exist between birdsong and speech, these results may contribute to a better understanding of the neural bases of speech.

  20. Radial-Basis-Function-Network-Based Prediction of Performance and Emission Characteristics in a Bio Diesel Engine Run on WCO Ester

    Directory of Open Access Journals (Sweden)

    Shiva Kumar

    2012-01-01

    Full Text Available Radial basis function neural networks (RBFNNs, which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel engine fuelled with waste cooking oil (WCO. The RBF networks were trained using the experimental data, where in load percentage, compression ratio, blend percentage, injection timing, and injection pressure were taken as the input parameters, and brake thermal efficiency (BTE, brake specific energy consumption (BSEC, exhaust gas temperature (, and engine emissions were used as the output parameters. The number of RBF centers was selected randomly. The network was initially trained using variable width values for the RBF units using a heuristic and then was trained by using fixed width values. Studies showed that RBFNN predicted results matched well with the experimental results over a wide range of operating conditions. Prediction accuracy for all the output parameters was above 90% in case of performance parameters and above 70% in case of emission parameters.