Radial basis function neural networks applied to NASA SSME data
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.
Implementation of Radial Basis Function Neural Network for Image Steganalysis
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 fisherÃ¢â‚¬â„¢s linear discriminant (FLD) function followed by the radial basis function (RBF). Experiments show promising resu...
Implementation of Radial Basis Function Neural Network for Image Steganalysis
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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 fisherÃ¢â‚¬â„¢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.
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.
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.
Synchronization of chaos using radial basis functions neural networks
Institute of Scientific and Technical Information of China (English)
Ren Haipeng; Liu Ding
2007-01-01
The Radial Basis Functions Neural Network (RBFNN) is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.
Neuronal spike sorting based on radial basis function neural networks
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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.
Estimation of spatiotemporal neural activity using radial basis function networks.
Anderson, R W; Das, S; Keller, E L
1998-12-01
We report a method using radial basis function (RBF) networks to estimate the time evolution of population activity in topologically organized neural structures from single-neuron recordings. This is an important problem in neuroscience research, as such estimates may provide insights into systems-level function of these structures. Since single-unit neural data tends to be unevenly sampled and highly variable under similar behavioral conditions, obtaining such estimates is a difficult task. In particular, a class of cells in the superior colliculus called buildup neurons can have very narrow regions of saccade vectors for which they discharge at high rates but very large surround regions over which they discharge at low, but not zero, levels. Estimating the dynamic movement fields for these cells for two spatial dimensions at closely spaced timed intervals is a difficult problem, and no general method has been described that can be applied to all buildup cells. Estimation of individual collicular cells' spatiotemporal movement fields is a prerequisite for obtaining reliable two-dimensional estimates of the population activity on the collicular motor map during saccades. Therefore, we have developed several computational-geometry-based algorithms that regularize the data before computing a surface estimation using RBF networks. The method is then expanded to the problem of estimating simultaneous spatiotemporal activity occurring across the superior colliculus during a single movement (the inverse problem). In principle, this methodology could be applied to any neural structure with a regular, two-dimensional organization, provided a sufficient spatial distribution of sampled neurons is available.
An Efficient Weather Forecasting System using Radial Basis Function Neural Network
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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.
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.
Satisfiability of logic programming based on radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong
2014-07-01
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.
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...
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.
Radial basis function neural network for power system load-flow
Energy Technology Data Exchange (ETDEWEB)
Karami, A.; Mohammadi, M.S. [Faculty of Engineering, The University of Guilan, P.O. Box 41635-3756, Rasht (Iran)
2008-01-15
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)
Upset Prediction in Friction Welding Using Radial Basis Function Neural Network
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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.
[Hyperspectral remote sensing image classification based on radical basis function neural network].
Tan, Kun; Du, Pei-jun
2008-09-01
Based on the radial basis function neural network (RBFNN) theory and the specialty of hyperspectral remote sensing data, the effective feature extraction model was designed, and those extracted features were connected to the input layer of RBFNN, finally the classifier based on radial basis function neural network was constructed. The hyperspectral image with 64 bands of OMIS II made by Chinese was experimented, and the case study area was zhongguancun in Beijing. Minimum noise fraction (MNF) was conducted, and the former 20 components were extracted for further processing. The original data (20 dimension) of extraction by MNF, the texture transformation data (20 dimension) extracted from the former 20 components after MNF, and the principal component analysis data (20 dimension) of extraction were combined to 60 dimension. For classification by RBFNN, the sizes of training samples were less than 6.13% of the whole image. That classifier has a simple structure and fast convergence capacity, and can be easily trained. The classification precision of radial basis function neural network classifier is up to 69.27% in contrast with the 51.20% of back propagation neural network (BPNN) and 40. 88% of traditional minimum distance classification (MDC), so RBFNN classifier performs better than the other three classifiers. It proves that RBFNN is of validity in hyperspectral remote sensing classification.
Filtered-X Radial Basis Function Neural Networks for Active Noise Control
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Bambang Riyanto
2004-05-01
Full Text Available This paper presents active control of acoustic noise using radial basis function (RBF networks and its digital signal processor (DSP real-time implementation. The neural control system consists of two stages: first, identification (modeling of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural model obtained in the first stage. A tapped delay line is introduced in front of controller neural, and another tapped delay line is inserted between controller neural networks and model neural networks. A new algorithm referred to as Filtered X-RBF is proposed to account for secondary path effects of the control system arising in active noise control. The resulting algorithm turns out to be the filtered-X version of the standard RBF learning algorithm. We address centralized and decentralized controller configurations and their DSP implementation is carried out. Effectiveness of the neural controller is demonstrated by applying the algorithm to active noise control within a 3 dimension enclosure to generate quiet zones around error microphones. Results of the real-time experiments show that 10-23 dB noise attenuation is produced with moderate transient response.
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.
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
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-07-01
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.
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.
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.
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.
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.
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.
On-line Cutting Quality Recognition in Milling Using a Radical Basis Function Neural Network
Institute of Scientific and Technical Information of China (English)
无
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.
Radial basis function neural networks with sequential learning MRAN and its applications
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
Design of Radial Basis Function Neural Networks for Software Effort Estimation
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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.
An improved method using radial basis function neural networks to speed up optimization algorithms
Bazan, M; Russenschuck, Stephan
2002-01-01
The paper presents a method using radial basis function (RBF) neural networks to speed up deterministic search algorithms used for the optimization of superconducting magnets for the LHC accelerator project at CERN. The optimization of the iron yoke of the main LHC dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation and local iron saturation in the yoke. This results in computation times of about 30 min for each objective function evaluation (on DEC-Alpha 600 /333). In this paper, we present a method for constructing an RBF neural network for a local approximation of the objective function. The computational time required for such a construction is negligible compared to the deterministic function evaluation, and, thus, yields a speed-up of the overall search process. The effectiveness of this method is demonstrated by means of two- and three-parametric optimization examples. The achieved speed-up of the search routine is up to 30%. (12 r...
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.
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.
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.
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.
Institute of Scientific and Technical Information of China (English)
YANG Xiao-hua; HUANG Jing-feng; WANG Jian-wen; WANG Xiu-zhen; LIU Zhan-yu
2007-01-01
Hyperspectral reflectance (350-2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD,mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980's. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used.Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.
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 ...
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
Radial Basis Function Based Neural Network for Motion Detection in Dynamic Scenes.
Huang, Shih-Chia; Do, Ben-Hsiang
2014-01-01
Motion detection, the process which segments moving objects in video streams, is the first critical process and plays an important role in video surveillance systems. Dynamic scenes are commonly encountered in both indoor and outdoor situations and contain objects such as swaying trees, spouting fountains, rippling water, moving curtains, and so on. However, complete and accurate motion detection in dynamic scenes is often a challenging task. This paper presents a novel motion detection approach based on radial basis function artificial neural networks to accurately detect moving objects not only in dynamic scenes but also in static scenes. The proposed method involves two important modules: a multibackground generation module and a moving object detection module. The multibackground generation module effectively generates a flexible probabilistic model through an unsupervised learning process to fulfill the property of either dynamic background or static background. Next, the moving object detection module achieves complete and accurate detection of moving objects by only processing blocks that are highly likely to contain moving objects. This is accomplished by two procedures: the block alarm procedure and the object extraction procedure. The detection results of our method were evaluated by qualitative and quantitative comparisons with other state-of-the-art methods based on a wide range of natural video sequences. The overall results show that the proposed method substantially outperforms existing methods with Similarity and F1 accuracy rates of 69.37% and 65.50%, respectively. PMID:24108721
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M. Safish Mary
2012-04-01
Full Text Available Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making.
Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction
Venkatesan, R.
2016-01-01
Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.
Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks
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Ali Reza Zirak
2016-06-01
Full Text Available A radial basis function (RBF artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power as the output variable. The presented RBF network models the designed class-F PA as a block, which could be applied in circuit design. The presented model could be used to model any RF power amplifier. The obtained results show a good agreement between real data and predicted values by RBF model. The results clearly show that the presented RBF network is more precise than multilayer perceptron (MLP model. According to the results, better than 84% and 92% improvement is achieved in MAE and RMSE, respectively.
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
Evolutionary Basis of Human Running and Its Impact on Neural Function.
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
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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.
Analysis of CT Brain Images using Radial Basis Function Neural Network
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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
The neural basis of bounded rational behavior
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. ...
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…
Institute of Scientific and Technical Information of China (English)
HUANG He; BAI Ji-cheng; LU Ze-sheng; GUO Yong-feng
2009-01-01
Milling electrical discharge machining (EDM) enables the machining of complex cavities using cylindrical or tubular electrodes. To ensure acceptable machining accuracy the process requires some methods of compensating for electrode wear. Due to the complexity and random nature of the process, existing methods of compensating for such wear usually involve off-line prediction. This paper discusses an innovative model of electrode wear prediction for milling EDM based upon a radial basis function (RBF) network. Data gained from an orthogonal experiment were used to provide training samples for the RBF network. The model established was used to forecast the electrode wear, making it possible to calculate the real-time tool wear in the milling EDM process and, to lay the foundations for dynamic compensation of the electrode wear on-line. This paper demonstrates that by using this model prediction errors can be controlled within 8%.
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.
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2014-12-01
In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.
Li, Meina; Kwak, Keun-Chang; Kim, Youn Tae
2016-01-01
Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model. PMID:27669249
Li, Meina; Kwak, Keun-Chang; Kim, Youn Tae
2016-09-22
Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model.
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Meina Li
2016-09-01
Full Text Available Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR and movement index (MI monitoring. The embedded incremental network includes linear regression (LR and RBFNN based on context-based fuzzy c-means (CFCM clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model.
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Peng Zhang
Full Text Available Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP. Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm.
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.
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.
Kuo, R J.; Cohen, P H.
1999-03-01
On-line tool wear estimation plays a very critical role in industry automation for higher productivity and product quality. In addition, appropriate and timely decision for tool change is significantly required in the machining systems. Thus, this paper is dedicated to develop an estimation system through integration of two promising technologies, artificial neural networks (ANN) and fuzzy logic. An on-line estimation system consisting of five components: (1) data collection; (2) feature extraction; (3) pattern recognition; (4) multi-sensor integration; and (5) tool/work distance compensation for tool flank wear, is proposed herein. For each sensor, a radial basis function (RBF) network is employed to recognize the extracted features. Thereafter, the decisions from multiple sensors are integrated through a proposed fuzzy neural network (FNN) model. Such a model is self-organizing and self-adjusting, and is able to learn from the experience. Physical experiments for the metal cutting process are implemented to evaluate the proposed system. The results show that the proposed system can significantly increase the accuracy of the product profile.
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Xiaodong Mao
2014-06-01
Full Text Available In this study, near-infrared reflectance spectroscopy and radial basis function (RBF neural network algorithm were used to measure the protein content of wheat owing to their nondestructiveness and quick speed as well as better performance compared to the traditional measuring method (semimicro-Kjeldahl in actual practice. To simplify the complex structure of the RBF network caused by the excessive wave points of samples obtained by near-infrared reflectance spectroscopy, we proposed the particle swarm optimization (PSO algorithm to optimize the cluster center in the hidden layers of the RBF neural network. In addition, a series of improvements for the PSO algorithm was also made to deal with its drawbacks in premature convergence and mechanical inertia weight setting. The experimental analysis demonstrated that the improved PSO algorithm greatly reduced the complexity of the network structure and improved the training speed of the RBF network. Meanwhile, the research result also proved the high performance of the model with its root-mean-square error of prediction (RMSEP and prediction correlation coefficient (R at 0.26576 and 0.975, respectively, thereby fulfilling the modern agricultural testing requirements featuring nondestructiveness, real-timing, and abundance in the number of samples.
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Chun-Cheng Lin
2016-09-01
Full Text Available Abnormal intra-QRS potentials (AIQPs are commonly observed in patients at high risk for ventricular tachycardia. We present a method for approximating a measured QRS complex using a non-linear neural network with all radial basis functions having the same smoothness. We extracted the high frequency, but low amplitude intra-QRS potentials using the approximation error to identify possible ventricular tachycardia. With a specified number of neurons, we performed an orthogonal least squares algorithm to determine the center of each Gaussian radial basis function. We found that the AIQP estimation error arising from part of the normal QRS complex could cause clinicians to misjudge patients with ventricular tachycardia. Our results also show that it is possible to correct this misjudgment by combining multiple AIQP parameters estimated using various spread parameters and numbers of neurons. Clinical trials demonstrate that higher AIQP-to-QRS ratios in the X, Y and Z leads are visible in patients with ventricular tachycardia than in normal subjects. A linear combination of 60 AIQP-to-QRS ratios can achieve 100% specificity, 90% sensitivity, and 95.8% total prediction accuracy for diagnosing ventricular tachycardia.
Institute of Scientific and Technical Information of China (English)
DONG Li-xin; XIAO Deng-ming Xiao; LIU Yi-lu
2007-01-01
Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose "confidence" and "support" is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose "confidence and support" is lower than requirement,are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e. , as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.
A Novel Carbon Steel Pipe Protection Based on Radial Basis Function Neural Network
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Sami A. Ajeel
2010-01-01
Full Text Available Problem statement: The cost due to corrosion Damage have estimated to be 3-4% of their gross national product which significantly Countries problem around the world. Approach: In this study, a novel carbon steel pipe protection based on RBFNN was proposed. The RBFNN used to predict the minimum current density required in impressed current cathodic protection to protect low carbon steel pipe. Learning data was performed by using a 30 samples test with different concentration C%, temperature T, distance D and pH. The RBFNN model has four input nodes representing the (concentration C%, temperature T, distance D and pH, eight nodes at hidden layer and one output node representing the min. current density. Results: Generalization test used 5 data samples taken from the experimental results other than those data samples used in the learning process to check the performance of the neural network on these data. Conclusion: In addition, the experimental results indicate that proposed system can be used successfully to obtain minimum cathodic protection current density to protect low carbon steel pipes.
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.
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.
[Neural basis of maternal behavior].
Noriuchi, Madoka; Kikuchi, Yoshiaki
2013-01-01
Maternal love, which may be the core of maternal behavior, is essential for the mother-infant attachment relationship and is important for the infant's development and mental health. However, little has been known about these neural mechanisms in human mothers. We examined patterns of maternal brain activation in response to infant cues using video clips. We performed functional magnetic resonance imaging (fMRI) measurements while 13 mothers viewed video clips, with no sound, of their own infant and other infants of approximately 16 months of age who demonstrated two different attachment behaviors (smiling at the infant's mother and crying for her). We found that a limited number of the mother's brain areas were specifically involved in recognition of the mother's own infant, namely orbitofrontal cortex (OFC). and periaqueductal gray, anterior insula, and dorsal and ventrolateral parts of putamen. Additionally, we found the strong and specific mother's brain response for the mother's own infant's distress. The differential neural activation pattern was found in the dorsal region of OFC, caudate nucleus, right inferior frontal gyrus, dorsomedial prefrontal cortex (PFC), anterior cingulate, posterior cingulate, posterior superior temporal sulcus, and dorsolateral PFC. Our results showed the highly elaborate neural mechanism mediating maternal love and diverse and complex maternal behaviors for vigilant protectiveness.
[Neural basis of procedural memory].
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
Institute of Scientific and Technical Information of China (English)
Junfei Liu; Shiwen Feng; Wei Song; Liang Yu; Haijiang Cui; Yiming Yang
2011-01-01
Studies concerning phonological processing mainly use written stimuli. Functional magnetic resonance imaging was used to investigate the brain regions related to the phonological processing under the picture stimulus in the rhyme task of Chinese language. Results of the test in 13 healthy college students whose native language is Chinese showed the extensive activation in the frontal lobe, parietal lobe and the occipitotemporal cortex, including the inferior frontal gyrus, middle frontal gyrus, supramarginal gyrus and medial occipitotemporal gyrus under the picture stimuli. Moreover, phonological processing induced activation in the superior temporal gyrus (BA 22) under the picture stimuli, but activation was not found in the middle temporal gyrus.
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.
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
Dodell-Feder, David; Tully, Laura M; Lincoln, Sarah Hope; Hooker, Christine I
2014-01-01
Theory of mind (ToM), the ability to attribute and reason about the mental states of others, is a strong determinant of social functioning among individuals with schizophrenia. Identifying the neural bases of ToM and their relationship to social functioning may elucidate functionally relevant neurobiological targets for intervention. ToM ability may additionally account for other social phenomena that affect social functioning, such as social anhedonia (SocAnh). Given recent research in schizophrenia demonstrating improved neural functioning in response to increased use of cognitive skills, it is possible that SocAnh, which decreases one's opportunity to engage in ToM, could compromise social functioning through its deleterious effect on ToM-related neural circuitry. Here, twenty individuals with schizophrenia and 18 healthy controls underwent fMRI while performing the False-Belief Task. Aspects of social functioning were assessed using multiple methods including self-report (Interpersonal Reactivity Index, Social Adjustment Scale), clinician-ratings (Global Functioning Social Scale), and performance-based tasks (MSCEIT-Managing Emotions). SocAnh was measured with the Revised Social Anhedonia Scale. Region-of-interest and whole-brain analyses revealed reduced recruitment of medial prefrontal cortex (MPFC) for ToM in individuals with schizophrenia. Across all participants, activity in this region correlated with most social variables. Mediation analysis revealed that neural activity for ToM in MPFC accounted for the relationship between SocAnh and social functioning. These findings demonstrate that reduced recruitment of MPFC for ToM is an important neurobiological determinant of social functioning. Furthermore, SocAhn may affect social functioning through its impact on ToM-related neural circuitry. Together, these findings suggest ToM ability as an important locus for intervention.
Directory of Open Access Journals (Sweden)
David Dodell-Feder
2014-01-01
Full Text Available Theory of mind (ToM, the ability to attribute and reason about the mental states of others, is a strong determinant of social functioning among individuals with schizophrenia. Identifying the neural bases of ToM and their relationship to social functioning may elucidate functionally relevant neurobiological targets for intervention. ToM ability may additionally account for other social phenomena that affect social functioning, such as social anhedonia (SocAnh. Given recent research in schizophrenia demonstrating improved neural functioning in response to increased use of cognitive skills, it is possible that SocAnh, which decreases one's opportunity to engage in ToM, could compromise social functioning through its deleterious effect on ToM-related neural circuitry. Here, twenty individuals with schizophrenia and 18 healthy controls underwent fMRI while performing the False-Belief Task. Aspects of social functioning were assessed using multiple methods including self-report (Interpersonal Reactivity Index, Social Adjustment Scale, clinician-ratings (Global Functioning Social Scale, and performance-based tasks (MSCEIT—Managing Emotions. SocAnh was measured with the Revised Social Anhedonia Scale. Region-of-interest and whole-brain analyses revealed reduced recruitment of medial prefrontal cortex (MPFC for ToM in individuals with schizophrenia. Across all participants, activity in this region correlated with most social variables. Mediation analysis revealed that neural activity for ToM in MPFC accounted for the relationship between SocAnh and social functioning. These findings demonstrate that reduced recruitment of MPFC for ToM is an important neurobiological determinant of social functioning. Furthermore, SocAhn may affect social functioning through its impact on ToM-related neural circuitry. Together, these findings suggest ToM ability as an important locus for intervention.
Prediction of fMRI time series of a single voxel using radial basis function neural network
Song, Sutao; Zhang, Jiacai; Yao, Li
2011-03-01
A great deal of current literature regarding functional neuroimaging has elucidated the relationships of neurons distributed all over the brain. Modern neuroimaging techniques, such as the functional MRI (fMRI), provide a convenient tool for people to study the correlation among different voxels as well as the spatio-temporal patterns of brain activity. In this study, we present a computational model using radial basis function neural network (RBF-NN) to predict the fMRI voxel activation with the activation of other voxels acquired at the same time. The fMRI data from a visual images stimuli presentation experiment was separated into two sets; one was used to train the model, and the other to validate the accuracy or generalizability of the model. In the visual stimuli presentation experiment, the subject did simple one-back-repetition tasks when four categories of stimuli (houses, faces, cars, and cats) were presented. Voxel sets A and B were selected from fMRI data by two different voxel selection criterion: (1) Voxel set A are those activated for any kind of object stronger than the other three objects in regions of interest (ROIs) without correction (P=0.001); (2) Voxel set B are those activated for at least one of the categories of stimuli within the ROIs (FWE correction, P=0.05). RBF-NN regression models construct the nonlinear relationship between the activation of voxels in A and B. Our test results showed that RBF-NN can capture the nonlinear relationship existing in neurons and reveal the relationship between voxel's activation from different brain regions.
Neural Basis of Visual Distraction
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…
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.
Baraldi, Andrea; Parmiggiani, Flavio
1997-10-01
In the first part of this paper a new on-line fully self- organizing artificial neural network model (FSONN), pursuing dynamic generation and removal of neurons and synaptic links, is proposed. The model combines properties of the self- organizing map (SOM), fuzzy c-means (FCM), growing neural gas (GNG) and fuzzy simplified adaptive resonance theory (Fuzzy SART) algorithms. In the second part of the paper experimental results are provided and discussed. Our conclusion is that the proposed connectionist model features several interesting properties, such as the following: (1) the system requires no a priori knowledge of the dimension, size and/or adjacency structure of the network; (2) with respect to other connectionist models found in the literature, the system can be employed successfully in: (a) a vector quantization; (b) density function estimation; and (c) structure detection in input data to be mapped topologically correctly onto an output lattice pursuing dimensionality reduction; and (3) the system is computationally efficient, its processing time increasing linearly with the number of neurons and synaptic links.
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.
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
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
The neural basis of tactile motion perception
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...
Afkhami, Abbas; Abbasi-Tarighat, Maryam
2008-06-01
In the present study, chemometric analysis of visible spectral data of phospho-and silico-molybdenum blue complexes was used to develop artificial neural networks (ANNs) for the simultaneous determination of the phosphate and silicate. Combinations of principal component analysis (PCA) with feed-forward neural networks (FFNNs) and radial basis function networks (RBFNs) were built and investigated. The structures of the models were simplified by using the corresponding important principal components as input instead of the original spectra. Number of inputs and hidden nodes, learning rate, transfer functions and number of epochs and SPREAD values were optimized. Performances of methods were tested with root mean square errors prediction (RMSEP, %), using synthetic solutions. The obtained satisfactory results indicate the applicability of this ANN approach based on PCA input selection for determination in highly spectral overlapping. The results obtained by FFNNs and by RBF networks were compared. The applicability of methods was investigated for synthetic samples, for detergent formulations, and for a river water sample.
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.
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
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
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.
Neurons with radial basis like rate functions.
Kovács, Zsolt László
2005-01-01
Artificial neural networks constructed with "locally tuned processing units" and more generally referred to as "radial basis function networks" have been proposed by a number of workers. In this communication, I submit a conjecture, based on indirect experimental and direct computational evidence of the Hodgkin-Huxley model, that there may be biological neurons in nervous systems for which the rate function is locally tuned. If proved to be valid, this conjecture may simplify neurodynamic models of some functions of nervous systems.
Neural basis of interpersonal traits in neurodegenerative diseases.
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
2009-11-01
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 (dominance, arrogance, coldness, introversion, submissiveness, ingenuousness, warmth, and extraversion) in 257 subjects: 214 patients with neurodegenerative diseases such as FTD, SD, progressive nonfluent aphasia, Alzheimer's disease, amnestic mild cognitive impairment, corticobasal degeneration, and progressive supranuclear palsy and 43 healthy elderly people. Measures of interpersonal traits were correlated with regional atrophy pattern using voxel-based morphometry (VBM) analysis of structural MR images. Interpersonal traits mapped onto distinct brain regions depending on the degree to which they involved agency and affiliation. Interpersonal traits high in agency related to left dorsolateral prefrontal and left lateral frontopolar regions, whereas interpersonal traits high in affiliation related to right ventromedial prefrontal and right anteromedial temporal regions. Consistent with the existing literature on neural networks underlying social cognition, these results indicate that brain regions related to externally focused, executive control-related processes underlie agentic interpersonal traits such as dominance, whereas brain regions related to internally focused, emotion- and reward-related processes underlie affiliative interpersonal traits such as warmth. In addition, these findings indicate that interpersonal traits are subserved by complex neural networks rather than discrete anatomic areas.
Institute of Scientific and Technical Information of China (English)
刘朝晖; 黄榕波; 陈庆强; 温预关; 李明亚
2011-01-01
目的:评价用径向基(RBF)神经网络所建立的预测氯氮平稳态血药浓度模型的预测性能.方法:将数据分为训练集、校验集和测试集来建立获取输入、输出变量两者间关系的RBF网络模型,其中以患者的性别、年龄、体重、剂量、血压、多项生理生化指标等37项参数为输入变量,氯氮平稳态血药浓度为输出变量.用训练集和校验集的网络计算输出值与目标输出值之间的均方差(MSE)和相关系数(R)来综合评价网络模型的学习效果,用测试集的网络计算输出值与目标输出值之间的MSE和R来评价网络模型的预测性能.结果:当扩展系数(SP)值为3.0时,训练集的MSE为1.33 ×10(-5),R值为0.99985,校验集的MSE为0.002 833,R值为0.971 86,测试集的MSE为0.005 439,R值为0.93676,网络模型的预测效果和泛化能力较好.结论:RBF网络用于预测氯氮平稳态血药浓度的研究是可行和有效的.%OBJECTIVE: To evaluate the performance of a model for predicting the steady-state plasma concentration of clozapine established by radial basis function (RBF) neural network. METHODS: The data was divided into training set, validation set and test set to establish the RBF neural network model which had obtained the relationships between input variables and output variable. Input variables included 37 parameters, such as patients' gender, age, body weight, dosage, blood pressure and multiple physiological and biochemical indexes. Output variable was steady-state plasma concentration of clozapine. The effect of RBF neural network model was evaluated comprehensively using mean square (MSE) and coefficient correlation (R) between the computed output value and objective output value of training set and validation set. And predictive performance of the model was evaluated by MSE and R between the computed output value and objective output value of test set. RESULTS: When the value of SP was 3.0, the MSE and R values of the
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.)
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
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)
Institute of Scientific and Technical Information of China (English)
Bi Jun; Shao Sai; Guan Wei; Wang Lu
2012-01-01
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 LiFePO4Li-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.
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.
The neural basis of human tool use.
Orban, Guy A; Caruana, Fausto
2014-01-01
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 anterior intraparietal (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. PMID
The neural basis of human tool use
Directory of Open Access Journals (Sweden)
Guy A Orban
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.
Neurally augmented sexual function.
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
The neural basis of event simulation: an FMRI study.
Directory of Open Access Journals (Sweden)
Yukihito Yomogida
Full Text Available Event simulation (ES is the situational inference process in which perceived event features such as objects, agents, and actions are associated in the brain to represent the whole situation. ES provides a common basis for various cognitive processes, such as perceptual prediction, situational understanding/prediction, and social cognition (such as mentalizing/trait inference. Here, functional magnetic resonance imaging was used to elucidate the neural substrates underlying important subdivisions within ES. First, the study investigated whether ES depends on different neural substrates when it is conducted explicitly and implicitly. Second, the existence of neural substrates specific to the future-prediction component of ES was assessed. Subjects were shown contextually related object pictures implying a situation and performed several picture-word-matching tasks. By varying task goals, subjects were made to infer the implied situation implicitly/explicitly or predict the future consequence of that situation. The results indicate that, whereas implicit ES activated the lateral prefrontal cortex and medial/lateral parietal cortex, explicit ES activated the medial prefrontal cortex, posterior cingulate cortex, and medial/lateral temporal cortex. Additionally, the left temporoparietal junction plays an important role in the future-prediction component of ES. These findings enrich our understanding of the neural substrates of the implicit/explicit/predictive aspects of ES-related cognitive processes.
The neural circuit basis of learning
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
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.
On the classification enhancement of radial basis function networks
Ciftcioglu, O.; Durmisevic, S.; Sariyildiz, I.S.
2001-01-01
Artificial neural networks are powerfultools for analysing information expressed as data sets, which contain complex nonlinear relationships to be identified and classified. In particular radial basis function (RBF) neural networks have outstanding features for this. However, due to far reaching imp
The neural basis of stereotypic impact on multiple social categorization.
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
Afkhami, Abbas; Abbasi-Tarighat, Maryam; Bahram, Morteza
2008-03-15
In this work feed-forward neural networks and radial basis function networks were used for the determination of enantiomeric composition of alpha-phenylglycine using UV spectra of cyclodextrin host-guest complexes and the data provided by two techniques were compared. Wavelet transformation (WT) and principal component analysis (PCA) were used for data compression prior to neural network construction and their efficiencies were compared. The structures of the wavelet transformation-radial basis function networks (WT-RBFNs) and wavelet transformation-feed-forward neural networks (WT-FFNNs), were simplified by using the corresponding wavelet coefficients of three mother wavelets (Mexican hat, daubechies and symlets). Dilation parameters, number of inputs, hidden nodes, learning rate, transfer functions, number of epochs and SPREAD values were optimized. Performances of the proposed methods were tested with regard to root mean square errors of prediction (RMSE%), using synthetic solutions containing a fixed concentration of beta-cyclodextrin (beta-CD) and fixed concentration of alpha-phenylglycine (alpha-Gly) with different enantiomeric compositions. Although satisfactory results with regard to some statistical parameters were obtained for all the investigated methods but the best results were achieved by WT-RBFNs.
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..
Pan, Weigang; Liu, Congcong; Yang, Qian; Gu, Yan; Yin, Shouhang; Chen, Antao
2016-03-01
Self-esteem is an affective, self-evaluation of oneself and has a significant effect on mental and behavioral health. Although research has focused on the neural substrates of self-esteem, little is known about the spontaneous brain activity that is associated with trait self-esteem (TSE) during the resting state. In this study, we used the resting-state functional magnetic resonance imaging (fMRI) signal of the amplitude of low-frequency fluctuations (ALFFs) and resting state functional connectivity (RSFC) to identify TSE-related regions and networks. We found that a higher level of TSE was associated with higher ALFFs in the left ventral medial prefrontal cortex (vmPFC) and lower ALFFs in the left cuneus/lingual gyrus and right lingual gyrus. RSFC analyses revealed that the strengths of functional connectivity between the left vmPFC and bilateral hippocampus were positively correlated with TSE; however, the connections between the left vmPFC and right inferior frontal gyrus and posterior superior temporal sulcus were negatively associated with TSE. Furthermore, the strengths of functional connectivity between the left cuneus/lingual gyrus and right dorsolateral prefrontal cortex and anterior cingulate cortex were positively related to TSE. These findings indicate that TSE is linked to core regions in the default mode network and social cognition network, which is involved in self-referential processing, autobiographical memory and social cognition.
Neural basis of disgust perception in racial prejudice.
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
Neural basis of disgust perception in racial prejudice.
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.
Genetic influences on the neural basis of social cognition
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...
Mixtures of truncated basis functions
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael;
2012-01-01
In this paper we propose a framework, called mixtures of truncated basis functions (MoTBFs), for representing general hybrid Bayesian networks. The proposed framework generalizes both the mixture of truncated exponentials (MTEs) framework and the mixture of polynomials (MoPs) framework. Similar...... for efficiently approximating an arbitrary density function using the MoTBF framework. The transla- tion method is more flexible than existing MTE or MoP-based methods, and it supports an online/anytime tradeoff between the accuracy and the complexity of the approxima- tion. Experimental results show...
The neural basis of testable and non-testable beliefs.
Directory of Open Access Journals (Sweden)
Jonathon R Howlett
Full Text Available Beliefs about the state of the world are an important influence on both normal behavior and psychopathology. However, understanding of the neural basis of belief processing remains incomplete, and several aspects of belief processing have only recently been explored. Specifically, different types of beliefs may involve fundamentally different inferential processes and thus recruit distinct brain regions. Additionally, neural processing of truth and falsity may differ from processing of certainty and uncertainty. The purpose of this study was to investigate the neural underpinnings of assessment of testable and non-testable propositions in terms of truth or falsity and the level of certainty in a belief. Functional magnetic resonance imaging (fMRI was used to study 14 adults while they rated propositions as true or false and also rated the level of certainty in their judgments. Each proposition was classified as testable or non-testable. Testable propositions activated the DLPFC and posterior cingulate cortex, while non-testable statements activated areas including inferior frontal gyrus, superior temporal gyrus, and an anterior region of the superior frontal gyrus. No areas were more active when a proposition was accepted, while the dorsal anterior cingulate was activated when a proposition was rejected. Regardless of whether a proposition was testable or not, certainty that the proposition was true or false activated a common network of regions including the medial prefrontal cortex, caudate, posterior cingulate, and a region of middle temporal gyrus near the temporo-parietal junction. Certainty in the truth or falsity of a non-testable proposition (a strong belief without empirical evidence activated the insula. The results suggest that different brain regions contribute to the assessment of propositions based on the type of content, while a common network may mediate the influence of beliefs on motivation and behavior based on the level of
Universal approximation by radial basis function networks of Delsarte translates.
Arteaga, Cristian; Marrero, Isabel
2013-10-01
We prove that, under certain mild conditions on the kernel function (or activation function), the family of radial basis function neural networks obtained by replacing the usual translation with the Delsarte one, and taking the same smoothing factor in all kernel nodes, has the universal approximation property.
The neural circuit basis of Rett syndrome
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...
改进RBF神经网络用于降雨量预测%PRECIPITATION PREDICTING BASED ON IMPROVED RADIAL BASIS FUNCTION NEURAL NETWORKS
Institute of Scientific and Technical Information of China (English)
周佩玲; 陶小丽; 傅忠谦; 彭虎; 王新跃
2001-01-01
利用基于GA的改进RBF网络建立了为时间序列为对象的预测模型，并提出了基于模型的数据处理方法，在此基础上，对安徽省蚌埠地区42年来6—8月份的降水量进行预测，结果表明了该模型在时间序列预测中有良好的推广和应用能力.%The paper suggests the forecasting model about objects that havetime sequence by using improved Radial Basis Function network model based on GA and data proceding method based on the model,and verifies it through precipitation pre dicting . A good product is obtained and proved its well spread and application.
The Neural Basis for Learning of Simple Motor Skills.
Lisberger, Stephen G.
1988-01-01
Discusses the vestibulo-ocular reflex (VOR) which is used to investigate the neural basis for motor learning in monkeys. Suggests organizing principles that may apply in forms of motor learning as a result of similarities among VOR and other motor systems. (Author/RT)
Radial basis function networks and complexity regularization in function learning.
Krzyzak, A; Linder, T
1998-01-01
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function network. Our approach differs from previous complexity regularization neural-network function learning schemes in that we operate with random covering numbers and l(1) metric entropy, making it possible to consider much broader families of activation functions, namely functions of bounded variation. Some constraints previously imposed on the network parameters are also eliminated this way. The network is trained by means of complexity regularization involving empirical risk minimization. Bounds on the expected risk in terms of the sample size are obtained for a large class of loss functions. Rates of convergence to the optimal loss are also derived.
Urban Ecosystem Pressure Based on Radial Basis Function Neural Network%基于径向基函数神经网络方法的城市生态压力预测
Institute of Scientific and Technical Information of China (English)
吴明; 姚尧; 贾冯睿; 王雷; 高艳波
2013-01-01
针对城市生态压力影响因素复杂,难以对城市未来可持续发展状况做出准确判断的问题,提出了城市生态压力的径向基函数神经网络预测模型,分析了影响城市生态系统的主要因素.以抚顺市1995-2009年数据为基础,验证了模型的准确性并预测了该市2010-2015年城市生态系统的压力情况.研究结果表明:能源消耗指标是影响城市生态系统压力的主要因素；运用径向基函数神经网络模型对训练样本的拟合精度以及对测试样本的仿真精度分别达97.91％和94.16％；抚顺市2015年的人均生态足迹、生态承载力和生态赤字分别达到7.013、0.523和6.49 hm2/人.%Considering the difficulty in the estimation of sustainable development due to the complex ecological pressure influencing factors, this paper proposed the radial basis function neural network model for predicting urban ecological system pressure and analyzed the primary influencing factors on urban ecological system pressure. The model was studied on the basis of Fushun' s data during 1995 -2009 , the accuracy of the radial basis function neural network prediction model is validated and then the situation of urban ecological system pressure was predicted from 2010 to 2015. The results of the study showed that the energy consumption indicator was the primary influencing factor for urban ecological system pressure; the fitting and simulation precision for training and testing samples were 97. 91% and 94. 16% by using radial basis function neural network model, respectively; the ecological footprint, the ecological carrying capacity and the ecological deficit would be 7. 013 , 0. 523 and 6. 49 hmVcap for Fushun in 2015 , respectively.
Neural Basis of Brain Dysfunction Produced by Early Sleep Problems.
Kohyama, Jun
2016-01-01
There is a wealth of evidence that disrupted sleep and circadian rhythms, which are common in modern society even during the early stages of life, have unfavorable effects on brain function. Altered brain function can cause problem behaviors later in life, such as truancy from or dropping out of school, quitting employment, and committing suicide. In this review, we discuss findings from several large cohort studies together with recent results of a cohort study using the marshmallow test, which was first introduced in the 1960s. This test assessed the ability of four-year-olds to delay gratification and showed how this ability correlated with success later in life. The role of the serotonergic system in sleep and how this role changes with age are also discussed. The serotonergic system is involved in reward processing and interactions with the dorsal striatum, ventral striatum, and the prefrontal cortex are thought to comprise the neural basis for behavioral patterns that are affected by the quantity, quality, and timing of sleep early in life. PMID:26840337
Neural Basis of Brain Dysfunction Produced by Early Sleep Problems
Directory of Open Access Journals (Sweden)
Jun Kohyama
2016-01-01
Full Text Available There is a wealth of evidence that disrupted sleep and circadian rhythms, which are common in modern society even during the early stages of life, have unfavorable effects on brain function. Altered brain function can cause problem behaviors later in life, such as truancy from or dropping out of school, quitting employment, and committing suicide. In this review, we discuss findings from several large cohort studies together with recent results of a cohort study using the marshmallow test, which was first introduced in the 1960s. This test assessed the ability of four-year-olds to delay gratification and showed how this ability correlated with success later in life. The role of the serotonergic system in sleep and how this role changes with age are also discussed. The serotonergic system is involved in reward processing and interactions with the dorsal striatum, ventral striatum, and the prefrontal cortex are thought to comprise the neural basis for behavioral patterns that are affected by the quantity, quality, and timing of sleep early in life.
The neural basis of unwanted thoughts during resting state
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...
Institute of Scientific and Technical Information of China (English)
刘朝晖; 黄榕波; 陈庆强; 温预关; 李明亚
2012-01-01
目的 评价用径向基(RBF)神经网络所建立的预测氯丙嗪稳态血药浓度模型的预测性能.方法 将数据分为训练集、校验集和测试集,来建立获取输出变量(37项参数)与输出变量(氯丙嗪稳态血药浓度)两者间关系的RBF网络模型,并评价其预测性能.结果 当扩展速度(SP)值为2.8时,所建立的RBF网络模型,预测奋乃静稳态血药浓度的效果和泛化能力较好.结论 RBF网络用于预测氯丙嗪稳态血药浓度是可行的和有效的.%Objective To evaluate the performance of a model for predicting the steady - state plasma concentration of chlorpromazine established by using radial basis function (RBF) neural network. Methods The data was divided into training set, validation set and test set to establish the RBF neural network model which had captured the relationships between the input variables (37 parametes) and the output variable ( steady - state plasma concentration of chlorpromazine) and evaluate predictive performance of the model. Results When the SPREAD (SP) value was 2. 8, the RBF neural network model had the better effect on predicting the steady - state plasma concentration of chlorpromazine and better generalization. Conclusion It is practical and valid for RBF neural network model to be applied to the study of steady - state plasma concentration prediction of chlorpromazine.
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.
The neural basis of responsibility attribution in decision-making.
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
Institute of Scientific and Technical Information of China (English)
徐圆; 冯晶; 朱群雄
2011-01-01
针对径向基函数（RBF）神经网络构造时其结构和参数难以确定的问题,结合可拓理论对输入样本和基函数的中心向量建立物元模型,并借鉴第2类型可拓神经网络（ENN2）的聚类思想,根据样本分布,采用可拓分析及可拓变换动态调整隐节点数目和基函数中心,从而提出基于可拓理论的RBF（ERBF）神经网络.同时,通过UCI标准数据集进行了测试,并通过应用实例进行了验证,结果表明,ERBF结构和参数的确定方法简单、收敛速度快,且泛化精度、鲁棒性和稳定性均显著提高.%During the construction process of radical basis function（RBF） neural network,the structure and parameters are hard to be determined.Therefore,combining with the extension theory,an extension theory-based RBF（ERBF） neural network is proposed,in which the matter-element models including input samples and center vectors of the basis function are established,the clustering method of extension neural network type 2（ENN2） is introduced,and the hidden layer nodes number and center vectors of the basis function are dynamically adjusted by using extension analysis and extension transformation according to the sample distribution.Meanwhile,UCI standard data sets are tested,and application object is validated.Through the verification and comparison,the proposed ERBF algorithm has the advantages of simple calculation and fast convergence,which significantly enhances the generalization accuracy,robustness and stability.
Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts
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…
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.
The neural basis of implicit perceptual sequence learning
Directory of Open Access Journals (Sweden)
Freja eGheysen
2011-11-01
Full Text Available The present fMRI study investigated the neural areas involved in implicit perceptual sequence learning. To obtain more insight in the functional contributions of the brain areas, we tracked both the behavioral and neural time course of the learning process, using a perceptual serial color matching task. Next, to investigate whether the neural time course was specific for perceptual information, imaging results were compared to the results of implicit motor sequence learning, previously investigated using an identical serial color matching task. Results indicated that implicit sequences can be acquired by at least two neural systems: the caudate nucleus and the hippocampus, having different operating principles. The caudate nucleus contributed to the implicit sequence learning process for perceptual as well as motor information in a similar and gradual way. The hippocampus, on the other hand, was engaged in a much faster learning process which was more pronounced for the motor compared to the perceptual task. Interestingly, the perceptual and motor learning process occurred on a comparable implicit level, suggesting that consciousness is not the main determinant factor dissociating the hippocampal from the caudate learning system. This study is not only the first to successfully and unambiguously compare brain activation between perceptual and motor levels of implicit sequence learning, it also provides new insights into the specific hippocampal and caudate learning function.
Conventional modeling of the multilayer perceptron using polynomial basis functions
Chen, Mu-Song; Manry, Michael T.
1993-01-01
A technique for modeling the multilayer perceptron (MLP) neural network, in which input and hidden units are represented by polynomial basis functions (PBFs), is presented. The MLP output is expressed as a linear combination of the PBFs and can therefore be expressed as a polynomial function of its inputs. Thus, the MLP is isomorphic to conventional polynomial discriminant classifiers or Volterra filters. The modeling technique was successfully applied to several trained MLP networks.
Exploring the Neural Basis of Cognitive Reserve in Aging
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...
Functional Basis of Microorganism Classification.
Directory of Open Access Journals (Sweden)
Chengsheng Zhu
2015-08-01
Full Text Available Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion. Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1 the inconsistency of functional diversity levels among different taxa and (2 an (unsurprising bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less
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.
Institute of Scientific and Technical Information of China (English)
张智; 胡晓辉; 邹志荣
2014-01-01
Summary Tomato is one of the main vegetables cultivated in greenhouse for its rich nutrition and good taste. In recent years,the occurrence of tomato disease brought a big threat to the yield and quality of tomato. Therefore,it is becoming more important to forecast and prevent the disease occurrence. In greenhouse, environmental factors,such as temperature and humidity,have great effect on occurrence of grey mould disease. Environmental factors are interactional in greenhouse,and the relationships between these factors and disease occurrence are complex,nonlinear and not easy to articulate.The traditional methods of mathematical statistics have some limitations in modeling the effects of environmental factors on the disease occurrence.Radical basis function (RBF) neural network is an ideal tool that could be applied to predict the grey mould disease from greenhouse tomato.It has fine characteristics of approximation performance and the global optimum which can overcome the limitations. It is difficult to build up the prediction model with all the involved factors,so the most correlated factors with the disease occurrence should be determined as predictors. A good prediction can strengthen foresight for preventing and treating diseases,which will provide scientific basis to formulate the most reasonable scheme for control diseases.In this paper,we used a neural network toolbox provided by Matlab to establish the RBF neural networks.The data of grey mould disease from greenhouse tomato and corresponding environmental data used in the experiments had been collected from one of the greenhouses in Xintiandi demonstration garden (Yangling, shaanxi Province) during the dates from 2009 04 01 to 2010 05 18.There were 102 sets of sample data in all,among which 90 sets were used to train the neural network models,and the other 12 sets were used to test. The RBF network consisted of input layer,hidden layer and output layer.Grey mould disease grade of tomato was target
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.
Evolvable synthetic neural system
Curtis, Steven A. (Inventor)
2009-01-01
An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.
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.
Emotional moments across time: a possible neural basis for time perception in the anterior insula
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...
The neural basis of unwanted thoughts during resting state.
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
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.
Generalized multiscale radial basis function networks.
Billings, Stephen A; Wei, Hua-Liang; Balikhin, Michael A
2007-12-01
A novel modelling framework is proposed for constructing parsimonious and flexible multiscale radial basis function networks (RBF). Unlike a conventional standard single scale RBF network, where all the basis functions have a common kernel width, the new network structure adopts multiscale Gaussian functions as the bases, where each selected centre has multiple kernel widths, to provide more flexible representations with better generalization properties for general nonlinear dynamical systems. As a direct extension of the traditional single scale Gaussian networks, the new multiscale network is easy to implement and is quick to learn using standard learning algorithms. A k-means clustering algorithm and an improved orthogonal least squares (OLS) algorithm are used to determine the unknown parameters in the network model including the centres and widths of the basis functions, and the weights between the basis functions. It is demonstrated that the new network can lead to a parsimonious model with much better generalization property compared with the traditional single width RBF networks.
Institute of Scientific and Technical Information of China (English)
连丽婷; 肖昌汉; 杨明明
2012-01-01
在解决闭环消磁绕组电流优化计算问题时,会面临将外部磁场推算误差带入电流反演计算或完备的基函数难以确定等问题.为了降低这些因素对舰船最终补偿效果的影响,从智能优化的角度出发,在讨论散布常数对模型预测误差的影响后,确定了适宜的散布常数,建立了内部磁场与补偿电流之间的径向基函数神经网络预报模型.该方法通过样本对网络进行训练,无须推算内外磁场,就能直接得到使绕组磁场与目标磁场拟合误差最小的补偿电流向量.对比其他数值建模方法,其换算精度有所提高,且选择不同的同维向量作为基函数对补偿结果影响较小.船模实验验证了该方法的有效性.%As the errors from off-board magnetic field evaluation and difficulties in determining basis functions tend to affect the result of calculating the degaussing currents, an intelligent control method was introduced. After discussing the influence from spread coefficient, a radial basis function (RBF) neural network model was established for predicting optimal currents from onboard measurements directly. The magnetic field produced by degaussing coils is very similar to ship' s object field. The method can avoid many problems from the numerical model. Its high accuracy and effectiveness were verified by mockup experiments.
A Neural Basis for the Acquired Capability for Suicide
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
A Neural Basis for the Acquired Capability for Suicide.
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 the
A Neural Basis for the Acquired Capability for Suicide
Directory of Open Access Journals (Sweden)
Gopikrishna Deshpande
2016-08-01
Full Text Available The high rate of fatal suicidal behavior 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 suicidal behavior. The Interpersonal-Psychological Theory of Suicide (IPTS has proposed an explanation for the seemingly paradoxical relationship between gender and suicidal behavior, 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 acquired the 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
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.
Institute of Scientific and Technical Information of China (English)
李翔; 朱全银; 王尊
2013-01-01
针对传统小波神经网络（ wavelet neural network， WNN）受隐含层节点数影响大、网络误差易陷入局部极小、预测结果不稳定的问题，提出使用GentleAdaBoost和小波神经网络相结合的方法，提高网络预测精度和泛化能力。该方法首先对样本数据进行预处理并初始化测试数据分布权值；然后通过选取不同的隐含层节点数、小波基函数构造出不同类型的小波神经网络弱预测器序列并对样本数据进行反复训练；最后使用GentleAdaBoost算法将得到的多个小波神经网络弱预测器组成新的强预测器并进行回归预测。对UCI数据库中数据集进行仿真实验，结果表明，本方法比传统小波神经网络预测平均误差减少40％以上，有效地提高了神经网络预测精度，为小波神经网络应用提供借鉴。%In view that the traditional wavelet neural network ( WNN) was affected largely by the number of hidden lay-er nodes, easy to fall into local minimum and had unstable forecast results, a method of combining the GentleAdaBoost algorithm with WNN was put forward to improve the forecasting accuracy and generalization ability.First, this method performed the pretreatment for the historical data and initialized the distribution weights of test data.Second, different hidden layer nodes and wavelet basis functions were selected randomly to construct weak predictors of WNN and trained the sample data repeatedly.Finally, the multiple weak predictors of WNN were used to form a new strong predictor by GentleAdaBoost algorithm for regression forecasting.A simulation experiment using datasets from the UCI database was carried out.The results showed that this method had reduced the average error value by more than 40%compared to the traditional WNN, improved the forecasting accuracy of neural network, and could provide references for the WNN fore-casting.
Towards a neural basis of music-evoked emotions.
Koelsch, Stefan
2010-03-01
Music is capable of evoking exceptionally strong emotions and of reliably affecting the mood of individuals. Functional neuroimaging and lesion studies show that music-evoked emotions can modulate activity in virtually all limbic and paralimbic brain structures. These structures are crucially involved in the initiation, generation, detection, maintenance, regulation and termination of emotions that have survival value for the individual and the species. Therefore, at least some music-evoked emotions involve the very core of evolutionarily adaptive neuroaffective mechanisms. Because dysfunctions in these structures are related to emotional disorders, a better understanding of music-evoked emotions and their neural correlates can lead to a more systematic and effective use of music in therapy.
Using Radial-basis Function Network for CLV
Institute of Scientific and Technical Information of China (English)
李纯青; 郑建国
2002-01-01
Analysis and comparing with three existing and popularly used forcasting customer lifetime value (CLV) methods, which are the Dwyer method, customer event-method and fitting method, and using performances of the existent artificial neural network, we apply the Radial-basis Function(RBF) network to forecast the CLV, the RBF network can approach the objective function partially. To every input/output pairs, it only needs adjust the weight a little and learn quickly which is very important to the forecast precision. Simulation and experimental results on the customers' data of a company in Shaanxi Province reveal the effectiveness and feasibility of the RBF network.
Self-organization: the basic principle of neural functions.
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
Orthogonal least squares learning algorithm for radial basis function networks
Energy Technology Data Exchange (ETDEWEB)
Chen, S.; Cowan, C.F.N.; Grant, P.M. (Dept. of Electrical Engineering, Univ. of Edinburgh, Mayfield Road, Edinburgh EH9 3JL, Scotland (GB))
1991-03-01
The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular value decomposition to solve for the weights of the network. Such a procedure has several drawbacks and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The paper proposes an alternative learning procedure based on the orthogonal least squares method. The procedure choose radial basis function centers one by one in a rational way until an adequate network has been constructed. The algorithm has the property that each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least squares learning strategy provides a simple and efficient means for fitting radial basis function networks, and this is illustrated using examples taken from two different signal processing applications.
Orthogonal least squares learning algorithm for radial basis function networks.
Chen, S; Cowan, C N; Grant, P M
1991-01-01
The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular-value decomposition to solve for the weights of the network. Such a procedure has several drawbacks, and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The authors propose an alternative learning procedure based on the orthogonal least-squares method. The procedure chooses radial basis function centers one by one in a rational way until an adequate network has been constructed. In the algorithm, each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least-squares learning strategy provides a simple and efficient means for fitting radial basis function networks. This is illustrated using examples taken from two different signal processing applications.
The neural basis of predicting the outcomes of planned actions
Directory of Open Access Journals (Sweden)
Andrew eJahn
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.
Dynamic programming using radial basis functions
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.
Five Lectures on Radial Basis Functions
DEFF Research Database (Denmark)
Powell, Mike J.D.
2005-01-01
Professor Mike J. D. Powell spent three weeks at IMM in November - December 2004. During the visit he gave five lectures on radial basis functions. These notes are a TeXified version of his hand-outs, made by Hans Bruun Nielsen, IMM.......Professor Mike J. D. Powell spent three weeks at IMM in November - December 2004. During the visit he gave five lectures on radial basis functions. These notes are a TeXified version of his hand-outs, made by Hans Bruun Nielsen, IMM....
Institute of Scientific and Technical Information of China (English)
2016-01-01
采用脉冲涡流技术进行检测时,为准确得到被测缺陷的轮廓,提出了一种基于径向基神经网络的缺陷轮廓重构方法.该方法为降低网络结构对重构结果的影响,采用主成分分析法对网络隐层应选择的最少节点数进行了计算,进而确定了较合理的网络结构;而后采用混合学习算法求得了网络参数,并通过引入梯度信息衰减系数对求解过程进行了优化;最后将其应用于脉冲涡流检测的缺陷轮廓重构实验,结果表明:基于径向基神经网络的缺陷轮廓重构方法不仅具有较高的重构精度而且具有较强的抗噪声干扰能力,是一种有效可行的脉冲涡流缺陷轮廓重构方法.%In order to obtain the defect profile when the pulsed eddy current testing was used, an approach of defect profile reconstruction from pulsed eddy current signals based on radial basis function neural network( RBFNN) was proposed in this paper. To reduce the effect of network structure on reconstruction result, principal components analysis was used to determine the least number of hidden nodes, then the appropriate network structure was determined. The hybrid learning algorithm was used to solve the parameter of RBFNN, and the solving process was optimized by attenuation coefficient of gradient information. Then the pro-posed approach was utilized in the experiment of defect profile reconstruction, the results indicate that the defect profile can be re-constructed accurately and the performance of noise interference suppression of the method is high, thus it is an effective and feasi-ble approach for pulsed eddy current defect profile reconstruction.
Wideband Characteristic Basis Functions in Radiation Problems
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A. A. Kucharski
2012-06-01
Full Text Available In this paper, the use of characteristic basis function (CBF method, augmented by the application of asymptotic waveform evaluation (AWE technique is analyzed in the context of the application to radiation problems. Both conventional and wideband CBFs are applied to the analysis of wire and planar antennas.
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.
Creating metaphors: the neural basis of figurative language production.
Benedek, Mathias; Beaty, Roger; Jauk, Emanuel; Koschutnig, Karl; Fink, Andreas; Silvia, Paul J; Dunst, Beate; Neubauer, Aljoscha C
2014-04-15
Neuroscience research has thoroughly studied how nonliteral language is processed during metaphor comprehension. However, it is not clear how the brain actually creates nonliteral language. Therefore, the present study for the first time investigates the neural correlates of metaphor production. Participants completed sentences by generating novel metaphors or literal synonyms during functional imaging. Responses were spoken aloud in the scanner, recorded, and subsequently rated for their creative quality. We found that metaphor production was associated with focal activity in predominantly left-hemispheric brain regions, specifically the left angular gyrus, the left middle and superior frontal gyri-corresponding to the left dorsomedial prefrontal (DMPFC) cortex-and the posterior cingulate cortex. Moreover, brain activation in the left anterior DMPFC and the right middle temporal gyrus was found to linearly increase with the creative quality of metaphor responses. These findings are related to neuroscientific evidence on metaphor comprehension, creative idea generation and episodic future thought, suggesting that creating metaphors involves the flexible adaptation of semantic memory to imagine and construct novel figures of speech. Furthermore, the left DMPFC may exert executive control to maintain strategic search and selection, thus facilitating creativity of thought.
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...
Institute of Scientific and Technical Information of China (English)
江虹; 杨彦超; 伍春
2012-01-01
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 (Qos). The core technology for CR is the design of cognitive engine, which can introduce reasoning and learning methods to achieve the perception, adaptation and learning. Considering the dynamical environment and demands, a scheme of cognitive engine was proposed based on the radial basis function (RBF) neural network. The scheme could study from experience and environment to reconfigure communication parameters and improve system performance. The cognitive engine was composed of two RBF_NN layers to solve the learning configurations of routing protocol and local parameters. The outer layer learned the global properties, while the inner layer learned the local attributes. After training, the learning model performance was evaluated according to two defined benchmark functions. The simulation results show that the learning model is effective and the cognitive engine can effectively achieve the study and reconfiguration function.%认知无线电（CR）是一种智能无线通信系统，它能根据环境变化、业务需求动态调整参数，提高系统性能，其核心技术是认知引擎的设计。认知引擎可引入人工智能领域的推理与学习方法来实现CR的感知、自适应与学习能力。为适应变化的无线环境和用户需求，提曲基于径向基神经网络（RBF）的CR认知引擎设计方法，该法通过对经验知识和环境的学习，重配置通信参数，以达到资源合理分配，提高系统性能。该引擎由两层RBF神经网络组成，外层神经网络学习全局属性，内层神经网络学习局部属性，以解决路由协议及局部参数的学习配置。在训练RBF神经网络学习模型后，根据定义的两个测试基准函数，评估模型性能，仿真验证了该学习模型
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
Modeling Marine Electromagnetic Survey with Radial Basis Function Networks
Directory of Open Access Journals (Sweden)
Agus Arif
2011-08-01
Full Text Available A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded in receivers must be processed and further analysed to estimate the hydrocarbon location and dimension. To conduct those analyses successfuly, a radial basis function (RBF network could be employed to become a forward model of the input-output relationship of the data from a marine electromagnetic survey. This type of neural networks is working based on distances between its inputs and predetermined centres of some basis functions. A previous research had been conducted to model the same marine electromagnetic survey using another type of neural networks, which is a multi layer perceptron (MLP network. By comparing their validation and training performances (mean-squared errors and correlation coefficients, it is concluded that, in this case, the MLP network is comparatively better than the RBF network
The neural basis of body form and body action agnosia.
Moro, Valentina; Urgesi, Cosimo; Pernigo, Simone; Lanteri, Paola; Pazzaglia, Mariella; Aglioti, Salvatore Maria
2008-10-23
Visual analysis of faces and nonfacial body stimuli brings about neural activity in different cortical areas. Moreover, processing body form and body action relies on distinct neural substrates. Although brain lesion studies show specific face processing deficits, neuropsychological evidence for defective recognition of nonfacial body parts is lacking. By combining psychophysics studies with lesion-mapping techniques, we found that lesions of ventromedial, occipitotemporal areas induce face and body recognition deficits while lesions involving extrastriate body area seem causatively associated with impaired recognition of body but not of face and object stimuli. We also found that body form and body action recognition deficits can be double dissociated and are causatively associated with lesions to extrastriate body area and ventral premotor cortex, respectively. Our study reports two category-specific visual deficits, called body form and body action agnosia, and highlights their neural underpinnings.
On the nature, modeling, and neural basis of social ties
F. van Winden; M. Stallen; K.R. Ridderinkhof
2009-01-01
Purpose This paper addresses the nature, formalization, and neural bases of (affective) social ties and discusses the relevance of ties for health economics. A social tie is defined as an affective weight attached by an individual to the well-being of another individual (‘utility interdependence’).
The neural basis of the speed-accuracy tradeoff
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
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.
Meyer, M.L.; Berkman, E.T.; Karremans, J.C.T.M.; Lieberman, M.D.
2011-01-01
Although a great deal of research addresses the neural basis of deliberate and intentional emotion-regulation strategies, less attention has been paid to the neural mechanisms involved in implicit forms of emotion regulation. Behavioural research suggests that romantically involved participants impl
Dynamic social power modulates neural basis of math calculation
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...
Learning without local minima in radial basis function networks.
Bianchini, M; Frasconi, P; Gori, M
1995-01-01
Learning from examples plays a central role in artificial neural networks. The success of many learning schemes is not guaranteed, however, since algorithms like backpropagation may get stuck in local minima, thus providing suboptimal solutions. For feedforward networks, optimal learning can be achieved provided that certain conditions on the network and the learning environment are met. This principle is investigated for the case of networks using radial basis functions (RBF). It is assumed that the patterns of the learning environment are separable by hyperspheres. In that case, we prove that the attached cost function is local minima free with respect to all the weights. This provides us with some theoretical foundations for a massive application of RBF in pattern recognition.
Neural modeling of prefrontal executive function
Energy Technology Data Exchange (ETDEWEB)
Levine, D.S. [Univ. of Texas, Arlington, TX (United States)
1996-12-31
Brain executive function is based in a distributed system whereby prefrontal cortex is interconnected with other cortical. and subcortical loci. Executive function is divided roughly into three interacting parts: affective guidance of responses; linkage among working memory representations; and forming complex behavioral schemata. Neural network models of each of these parts are reviewed and fit into a preliminary theoretical framework.
Generalization performance of radial basis function networks.
Lei, Yunwen; Ding, Lixin; Zhang, Wensheng
2015-03-01
This paper studies the generalization performance of radial basis function (RBF) networks using local Rademacher complexities. We propose a general result on controlling local Rademacher complexities with the L1 -metric capacity. We then apply this result to estimate the RBF networks' complexities, based on which a novel estimation error bound is obtained. An effective approximation error bound is also derived by carefully investigating the Hölder continuity of the lp loss function's derivative. Furthermore, it is demonstrated that the RBF network minimizing an appropriately constructed structural risk admits a significantly better learning rate when compared with the existing results. An empirical study is also performed to justify the application of our structural risk in model selection.
Spherical radial basis functions, theory and applications
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...
Neural and neurochemical basis of reinforcement-guided decision making.
Khani, Abbas; Rainer, Gregor
2016-08-01
Decision making is an adaptive behavior that takes into account several internal and external input variables and leads to the choice of a course of action over other available and often competing alternatives. While it has been studied in diverse fields ranging from mathematics, economics, ecology, and ethology to psychology and neuroscience, recent cross talk among perspectives from different fields has yielded novel descriptions of decision processes. Reinforcement-guided decision making models are based on economic and reinforcement learning theories, and their focus is on the maximization of acquired benefit over a defined period of time. Studies based on reinforcement-guided decision making have implicated a large network of neural circuits across the brain. This network includes a wide range of cortical (e.g., orbitofrontal cortex and anterior cingulate cortex) and subcortical (e.g., nucleus accumbens and subthalamic nucleus) brain areas and uses several neurotransmitter systems (e.g., dopaminergic and serotonergic systems) to communicate and process decision-related information. This review discusses distinct as well as overlapping contributions of these networks and neurotransmitter systems to the processing of decision making. We end the review by touching on neural circuitry and neuromodulatory regulation of exploratory decision making.
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.
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…
Basis Function Sampling for Material Property Computations
Whitmer, Jonathan K.; Chiu, Chi-Cheng; Joshi, Abhijeet A.; de Pablo, Juan J.
2014-03-01
Wang-Landau sampling, and the associated class of flat histogram simulation methods, have been particularly successful for free energy calculations in a wide array of physical systems. Practically, the convergence of these calculations to a target free energy surface is hampered by reliance on parameters which are unknown a priori. We derive and implement a method based on orthogonal (basis) functions which is fast, parameter-free, and geometrically robust. An important feature of this method is its ability to achieve arbitrary levels of description for the free energy. It is thus ideally suited to in silico measurement of elastic moduli and other quantities related to free energy perturbations. We demonstrate the utility of such applications by applying our method to calculation of the Frank elastic constants of the Lebwohl-Lasher model.
Neural basis of quasi-rational decision making.
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
Neural basis of quasi-rational decision making.
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.
The neural basis of predicate-argument structure.
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
Dynamics Model Abstraction Scheme Using Radial Basis Functions
Directory of Open Access Journals (Sweden)
Silvia Tolu
2012-01-01
Full Text Available This paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the motor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot manipulator. The dynamics of a robot arm change as it manipulates objects with different physical properties, for example, the mass, shape, or mass distribution. We address active sensing strategies to acquire object dynamical models with a radial basis function neural network (RBF. Experiments are done using a real robot’s arm, and trajectory data are gathered during various trials manipulating different objects. Biped robots do not have high force joint servos and the control system hardly compensates all the inertia variation of the adjacent joints and disturbance torque on dynamic gait control. In order to achieve smoother control and lead to more reliable sensorimotor complexes, we evaluate and compare a sparse velocity-driven versus a dense position-driven control scheme.
Radial basis function networks for fast contingency ranking
Energy Technology Data Exchange (ETDEWEB)
Devaraj, D.; Ramar, K. [Indian Inst. of Technology, Madras (India). Dept. of Electrical Engineering; Yegnanarayana, B. [Indian Inst. of Technology, Madras (India). Dept. of Computer Science and Engineering
2002-06-01
This paper presents an artificial neural network-based approach for static-security assessment. The proposed approach uses radial basis function (RBF) networks to predict the system severity level following a given list of contingencies. The RBF networks are trained off-line to capture the nonlinear relationship between the pre-contingency line flows and the post-contingency severity index. A method based on mutual information is proposed for selecting the input features of the networks. Mutual information has the advantage of measuring the general relationship between the independent variables and the dependent variables as against the linear relationship measured by the correlation-based methods. The performance of the proposed approach is demonstrated through contingency ranking in IEEE 30-bus test system. (author)
Artificial neural network modeling of fixed bed biosorption using radial basis approach
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%.
The Role of the Basis Set: Assessing Density Functional Theory
Boese, A D; Handy, N C; Martin, Jan M. L.; Handy, Nicholas C.
2003-01-01
When developing and assessing density functional theory methods, a finite basis set is usually employed. In most cases, however, the issue of basis set dependency is neglected. Here, we assess several basis sets and functionals. In addition, the dependency of the semiempirical fits to a given basis set for a generalised gradient approximation and a hybrid functional is investigated. The resulting functionals are then tested for other basis sets, evaluating their errors and transferability.
fMRI of Simultaneous Interpretation Reveals the Neural Basis of Extreme Language Control.
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
Neural basis of music knowledge: evidence from the dementias.
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
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.
Institute of Scientific and Technical Information of China (English)
杨晓丽; 何琼
2016-01-01
Consensus strategy was applied to radial basis function neural network and used in NIRS determination of total flavonoids in panax notoginseng.Firstly,the spectra were preprocessed using discrete wavelet transform for noise filtering and data compression.Then,consensus radial basis function neural network was used for establishing the calibration model.It was shown by the results that the consensus models were more stable and accurate than the conventional regression models.%将共识策略结合径向基神经网络用于近红外光谱法测定三七中总黄酮的含量中。首先采用离散小波变换对近红外光谱进行预处理，去除噪声并压缩数据。继而采用共识径向基神经网络建立校正模型。结果表明：共识策略可以使模型更稳定、更准确。
Neural basis of reward anticipation and its genetic determinants.
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
Neural basis of reward anticipation and its genetic determinants.
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.
Neural basis of irony comprehension in children with autism: the role of prosody and context
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...
Institute of Scientific and Technical Information of China (English)
李洁; 彭昱忠; 吴建生
2014-01-01
构造了一种带多个核函数的结构自适应径向基函数神经网络（RBF NN）降水预报集成模型。该网络结构同时使用6个核函数，采用基于输入输出全部信息的模糊相似矩阵的平均矩阵元法自动确定隐节点数，自适应地生成RBF神经网络集成个体，最后建立多元回归模型集成。对日本的细网格资料数据建立平均日降水量预报模型，利用MATLAB进行仿真实验，结果表明，该模型预报性能明显优于同期中国气象局的T213（中国气象局的全球中期天气数值预报产品预报值）降水预报，可为气象预报研究提供新思路，为降水预报决策的制定提供重要的参考。%This paper constructs an integrated model of radial basis function neural network (RBF NN)with self-a-daptive structure of multi-kernel function on precipitation forecast. The network structure using six nuclear functions at the same time adapts the method of mean matrix element based on the fuzzy similarity matrix of the input and output information and automatically ensures the number of hidden nodes. The generated RBF neural network ensembles indi-vidual and finally establishes integrated model of multiple regression. It also establishes the mean daily precipitation forecast model on Japanese fine grid data and does simulation experiment by using the method of MATLAB. The result shows that the predictability of this model is obviously superior to the T213 (the forecast value tested by the global me-dium-range numerical forecasting product of China Meteorological Bureau),which can provide new ideas for the study of weather forecast and important references for the decision making of the precipitation forecast.
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.
Neural basis of an inherited speech and language disorder
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
Identification and integration of sensory modalities: Neural basis and relation to consciousness
C.M.A. Pennartz
2009-01-01
A key question in studying consciousness is how neural operations in the brain can identify streams of sensory input as belonging to distinct modalities, which contributes to the representation of qualitatively different experiences. The basis for identification of modalities is proposed to be const
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
Institute of Scientific and Technical Information of China (English)
胡焱弟; 李新欣; 白志鹏; 冯银厂; 赵玉杰; 吴建会
2006-01-01
将径向基函数网络(Radial Basis Function Network,RBFN)应用于城市环境颗粒物来源解析工作.模拟数据计算的解析结果表明:RBFN可以实现对多源(14个可能源,其中13个为有效源)的解析,在5%～15%的源和受体测量误差的情况下,对于分担率大于15%的主要源,其解析结果与真实值的相对误差均不高于5%;对于分担率大于5%的源,其解析相对误差均低于15%.RBF网络可以很好地识别无效源.因此,在充分掌握可能污染源成分谱信息的基础上,该方法具有源解析应用潜力.
A growing and pruning method for radial basis function networks.
Bortman, M; Aladjem, M
2009-06-01
A recently published generalized growing and pruning (GGAP) training algorithm for radial basis function (RBF) neural networks is studied and modified. GGAP is a resource-allocating network (RAN) algorithm, which means that a created network unit that consistently makes little contribution to the network's performance can be removed during the training. GGAP states a formula for computing the significance of the network units, which requires a d-fold numerical integration for arbitrary probability density function p(x) of the input data x (x in R(d)) . In this work, the GGAP formula is approximated using a Gaussian mixture model (GMM) for p(x) and an analytical solution of the approximated unit significance is derived. This makes it possible to employ the modified GGAP for input data having complex and high-dimensional p(x), which was not possible in the original GGAP. The results of an extensive experimental study show that the modified algorithm outperforms the original GGAP achieving both a lower prediction error and reduced complexity of the trained network.
Representation of Functional Data in Neural Networks
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...
Institute of Scientific and Technical Information of China (English)
王孔森; 盛戈皞; 孙旭日; 王威; 王世强; 江秀臣
2013-01-01
在线预测输电线路的动态热容量，合理安排负荷高峰时期运行方式和调度管理，对输电线路的安全和经济运行具有重要意义，同时也对确定风电等间歇式可再生能源的接入容量提供技术支持。为此，提出了利用径向基神经网络实现线路动态容量的在线预测方法。该方法首先利用径向基神经网络进行风速和日照辐射温度的在线学习和预测，基于IEEE 738标准进行输电线路动态容量的在线短期预测。利用典型的夏季和冬季实测数据进行动态容量预测后发现，预测未来1、2、4 h的动态容量的最大相对误差分别在10%、20%、40%以内。将短期的负荷预测与该方法结合起来，可为电力紧张地区和负荷高峰时期的智能调度提供决策支持。%It is significant for secure and economic operation of transmission lines to predict dynamic line rating (DLR) in realtime mode and reasonably arrange the operation mode during peak load period and scheduling management, meanwhile it can provide technical support for determining grid-connected capacity of intermittent renewable energy resources such as wind power and so on. For this reason, using redial base function neural network (RBFNN) an online prediction method to implement the prediction of DLR is proposed. Firstly, the online learning and prediction of wind and sunshine radiation temperature is performed by RBFNN, then based on IEEE 738 standard the online short-term DLR is predicted. In the prediction of DLR by use of typical measured data in summer season and winter season, it is found that the maximum relative error in the prediction of dynamic capacity for next one, two and four hours are less than 10%, 20% and 40% respectively. Combining the short-term load forecasting method with the proposed method, the decision-making can be provided to intelligent dispatching during the peak load period in the region lacking of power supply.
Vector control of wind turbine on the basis of the fuzzy selective neural net*
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.
Ultrasonic flaw detection using radial basis function networks (RBFNs).
Gil Pita, R; Vicen, R; Rosa, M; Jarabo, M P; Vera, P; Curpian, J
2004-04-01
Ultrasonic flaw detection has been studied many times in the literature. Schemes based on thresholding after a previous matched filter use to be the best solution, but results obtained with this method are only satisfactory when scattering and attenuation are not considered. In this paper, we propose an alternative solution to thresholding detection method. We deal with the usage of different flaw detection methods comparing them with the proposed one. The experiment tries to determinate whether a given ultrasonic signal contains a flaw echo or not. Starting with a set of 24,000 patterns with 750 samples each one, two subsets are defined for the experiments. The first one, the training set, is used to obtain the detection parameters of the different methods, and the second one is used to test the performance of them. The proposed method is based on radial basis functions networks, one of the most powerful neural network techniques. This signal processing technique tries to find the optimal decision criterion. Comparing this method with thresholding based ones, an improvement over 25-30% is obtained, depending on the probability of false alarm. So our new method is a good alternative to flaw detection problem.
Radial basis function networks GPU-based implementation.
Brandstetter, Andreas; Artusi, Alessandro
2008-12-01
Neural networks (NNs) have been used in several areas, showing their potential but also their limitations. One of the main limitations is the long time required for the training process; this is not useful in the case of a fast training process being required to respond to changes in the application domain. A possible way to accelerate the learning process of an NN is to implement it in hardware, but due to the high cost and the reduced flexibility of the original central processing unit (CPU) implementation, this solution is often not chosen. Recently, the power of the graphic processing unit (GPU), on the market, has increased and it has started to be used in many applications. In particular, a kind of NN named radial basis function network (RBFN) has been used extensively, proving its power. However, their limiting time performances reduce their application in many areas. In this brief paper, we describe a GPU implementation of the entire learning process of an RBFN showing the ability to reduce the computational cost by about two orders of magnitude with respect to its CPU implementation.
Institute of Scientific and Technical Information of China (English)
周泰; 王亚玲
2011-01-01
To effectively improve regional logistics capability and promote regional economic growth, the authors established investment structural optimization model of regional logistics capability. First of all, the authors analyzed the reasons why optimization of investment structure of regional industries can enhance regional logistics capability detailedly, and revealed the complex nonlinear relationship between regional logistics capability and investment structural from the perspective of the industrial structure; Then the authors implemented the nonlinear mapping by using radial basis function (RBF) network, and set up a nonlinear programming (NLP) optimization model with constraint conditions; Finally, based on the true data of industry's investment of Sichuan province in 2005, the authors solved the model by improved genetic algorithm(IGA), and obtained the approximate optimal solution of the optimization problem as well as the optimal direction of investment structural. The optimization results indicate that the model is effective and reasonable for optimization of industry's investment structure; it is a new practical and operable method for improving regional logistics capability.%针对如何有效地提高区域物流能力,以推动区域经济增长的问题,构建了区域物流能力的投资结构优化模型.首先详细分析了优化区域产业投资结构能增强区域物流能力的原因,从产业结构的角度揭示了区域物流能力与产业投资分配之间复杂的非线性关系;然后采用径向基函数神经网络实现了它们之间的非线性映射,进而建立了有约束条件限制的非线性规划投资结构优化模型;最后以四川省2005年的产业投资实际数据为基础,采用改进遗传算法对该模型进行求解,获得了优化问题的近似最优解以及投资结构的优化方向.优化结果表明:建立的模型对产业投资结构的优化是合理、有效的,从而提供了一个能提高区域
Molecular basis of whey protein functionality
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...
Side effects of normalising radial basis function networks.
Shorten, R; Murray-Smith, R
1996-05-01
Normalisation of the basis function activations in a Radial Basis Function (RBF) network is a common way of achieving the partition of unity often desired for modelling applications. It results in the basis functions covering the whole of the input space to the same degree. However, normalisation of the basis functions can lead to other effects which are sometimes less desirable for modelling applications. This paper describes some side effects of normalisation which fundamentally alter properties of the basis functions, e.g. the shape is no longer uniform, maxima of basis functions can be shifted from their centres, and the basis functions are no longer guaranteed to decrease monotonically as distance from their centre increases--in many cases basis functions can 'reactivate', i.e. re-appear far from the basis function centre. This paper examines how these phenomena occur, discusses their relevance for non-linear function approximation and examines the effect of normalisation on the network condition number and weights.
Neural basis of limb ownership in individuals with body integrity identity disorder.
Directory of Open Access Journals (Sweden)
Milenna T van Dijk
Full Text Available Our body feels like it is ours. However, individuals with body integrity identity disorder (BIID lack this feeling of ownership for distinct limbs and desire amputation of perfectly healthy body parts. This extremely rare condition provides us with an opportunity to study the neural basis underlying the feeling of limb ownership, since these individuals have a feeling of disownership for a limb in the absence of apparent brain damage. Here we directly compared brain activation between limbs that do and do not feel as part of the body using functional MRI during separate tactile stimulation and motor execution experiments. In comparison to matched controls, individuals with BIID showed heightened responsivity of a large somatosensory network including the parietal cortex and right insula during tactile stimulation, regardless of whether the stimulated leg felt owned or alienated. Importantly, activity in the ventral premotor cortex depended on the feeling of ownership and was reduced during stimulation of the alienated compared to the owned leg. In contrast, no significant differences between groups were observed during the performance of motor actions. These results suggest that altered somatosensory processing in the premotor cortex is associated with the feeling of disownership in BIID, which may be related to altered integration of somatosensory and proprioceptive information.
Differential Forms Basis Functions for Better Conditioned Integral Equations
Energy Technology Data Exchange (ETDEWEB)
Fasenfest, B; White, D; Stowell, M; Rieben, R; Sharpe, R; Madsen, N; Rockway, J D; Champagne, N J; Jandhyala, V; Pingenot, J
2005-01-13
Differential forms offer a convenient way to classify physical quantities and set up computational problems. By observing the dimensionality and type of derivatives (divergence,curl,gradient) applied to a quantity, an appropriate differential form can be chosen for that quantity. To use these differential forms in a simulation, the forms must be discretized using basis functions. The 0-form through 2-form basis functions are formed for surfaces. Twisted 1-form and 2-form bases will be presented in this paper. Twisted 1-form (1-forms) basis functions ({Lambda}) are divergence-conforming edge basis functions with units m{sup -1}. They are appropriate for representing vector quantities with continuous normal components, and they belong to the same function space as the commonly used RWG bases [1]. They are used here to formulate the frequency-domain EFIE with Galerkin testing. The 2-form basis functions (f) are scalar basis functions with units m{sup -2} and with no enforced continuity between elements. At lowest order, the 2-form basis functions are similar to pulse basis functions. They are used here to formulate an electrostatic integral equation. It should be noted that the derivative of an n-form differential form basis function is an (n+1)-form, i.e. the derivative of a 1-form basis function is a 2-form. Because the basis functions are constructed such that they have spatial units, the spatial units are removed from the degrees of freedom, leading to a better-conditioned system matrix. In this conference paper, we look at the performance of these differential forms and bases by examining the conditioning of matrix systems for electrostatics and the EFIE. The meshes used were refined across the object to consider the behavior of these basis transforms for elements of different sizes.
Organisms modeling: The question of radial basis function networks
Directory of Open Access Journals (Sweden)
Muzy Alexandre
2014-01-01
Full Text Available There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evalution of an usual machine learning technique (radial basis funtion(RBF networks in the context of systems and biological reactive organisms.
Analysis of Neural Networks through Base Functions
Zwaag, van der B.J.; Slump, C.H.; Spaanenburg, L.
2002-01-01
Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more
Higher-Order Hierarchical Legendre Basis Functions in Applications
DEFF Research Database (Denmark)
Kim, Oleksiy S.; Jørgensen, Erik; Meincke, Peter;
2007-01-01
degree of orthogonality. The basis functions are well-suited for solution of complex electromagnetic problems involving multiple homogeneous or inhomogeneous dielectric regions, metallic surfaces, layered media, etc. This paper presents real-life complex antenna radiation problems modeled...... with electromagnetic simulation tools based on the higher-order hierarchical Legendre basis functions....
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...
Radial basis function network design for chaotic time series prediction
Energy Technology Data Exchange (ETDEWEB)
Shin, Chang Yong; Kim, Taek Soo; Park, Sang Hui [Yonsei University, Seoul (Korea, Republic of); Choi, Yoon Ho [Kyonggi University, Suwon (Korea, Republic of)
1996-04-01
In this paper, radial basis function networks with two hidden layers, which employ the K-means clustering method and the hierarchical training, are proposed for improving the short-term predictability of chaotic time series. Furthermore the recursive training method of radial basis function network using the recursive modified Gram-Schmidt algorithm is proposed for the purpose. In addition, the radial basis function networks trained by the proposed training methods are compared with the X.D. He A Lapedes`s model and the radial basis function network by non-recursive training method. Through this comparison, an improved radial basis function network for predicting chaotic time series is presented. (author). 17 refs., 8 figs., 3 tabs.
Neural origins of psychosocial functioning impairments in major depression.
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
The neural basis of economic decision-making in the ultimatum game
Sanfey, A.G.; Rilling, J.K.; Aronson, J.A.; Nystrom, L.E.; Cohen, J.D.
2003-01-01
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. I
Institute of Scientific and Technical Information of China (English)
朱国俊; 冯建军; 郭鹏程; 罗兴锜
2014-01-01
, the Bezier curve was used to parameterize the hydrofoil. The Latin Hypercube experiment design method was used to select the sample points in the design space which were used for training the Radial Basis Function neural network. The hydrodynamic performance for each sample was calculated by the computational fluid dynamic method, and then the Radial Basis Function neural network would be trained by these sample points. After the neural network had been trained, the multi-point optimization method of hydrofoil was solved by combining the NSGA-II method and the Radial Basis Function neural network. The method mentioned above was applied to the optimal design of NACA63-815 hydrofoil, and the optimization problems of the hydrofoil in three typical conditions in which the attack angle is 0, 6º and 12º were mainly studied in this paper. After optimization, two optimized hydrofoils were selected in the Pareto solution to compare with initial, which were named Optimal A and Optimal B. According to the CFD simulation, the optimized hydrofoil’s performance was gotten and compared with the initial hydrofoil. By comparison, it was found that the drag coefficient of the optimized hydrofoil in the three conditions are less than or equal to the initial. Moreover, the lift-drag ratios of the Optimal A hydrofoil in which the attack angles is 0, 6° and 12° have been improve by 4.6%, 4.4%and 22.8%respectively. And the lift-drag ratios of the Optimal B hydrofoils have also been improved by 6.6%, 3.8%and 16.6%respectively. In addition, according to the comparison of the optimized and initial hydrofoil’s pressure coefficient at the 12°attack angle, it can be found that the optimized hydrofoil can effectively suppress the stall phenomenon. Finally, two conclusions can be drawn from the optimal results. Firstly, use the Radial Basis Function neural network to replace the CFD simulation can effectively decrease the time that the optimization cycle spent. Secondly, the hydrofoil
Integration of macromolecular diffraction data using radial basis function networks.
Pokrić, B; Allinson, N M; Helliwell, J R
2000-11-01
This paper presents a novel approach for intensity calculation of X-ray diffraction spots based on a two-stage radial basis function (RBF) network. The first stage uses pre-determined reference profiles from a database as basis functions in order to locate the diffraction spots and identify any overlapping regions. The second-stage RBF network employs narrow basis functions capable of local modifications of the reference profiles leading to a more accurate observed diffraction spot approximation and therefore accurate determination of spot positions and integrated intensities.
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.
Dynamic Analysis of Wind Turbine Blades Using Radial Basis Functions
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...
Higher Order Hierarchical Legendre Basis Functions for Electromagnetic Modeling
DEFF Research Database (Denmark)
Jørgensen, Erik; Volakis, John L.; Meincke, Peter;
2004-01-01
This paper presents a new hierarchical basis of arbitrary order for integral equations solved with the Method of Moments (MoM). The basis is derived from orthogonal Legendre polynomials which are modified to impose continuity of vector quantities between neighboring elements while maintaining most....... In addition, all higher-order terms in the expansion have two vanishing moments.In contrast to existing formulations, these properties allow the use of very high-order basis functions without introducing ill-conditioning of the resulting MoM matrix. Numerical results confirm that the condition number...... of the MoM matrix obtained with this new basis is much lower than existing higher-order interpolatory and hierarchical basis functions. As a consequence of the excellent condition numbers, we demonstrate that even very high-order MoM systems, e.g. 10th order, can be solved efficiently with an iterative...
Brain–immune interactions and the neural basis of disease-avoidant ingestive behaviour
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...
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
Neural network model for the efficient calculation of Green's functions in layered media
Soliman, E A; El-Gamal, M A; 10.1002/mmce.10066
2003-01-01
In this paper, neural networks are employed for fast and efficient calculation of Green's functions in a layered medium. Radial basis function networks (RBFNs) are effectively trained to estimate the coefficients and the exponents that represent a Green's function in the discrete complex image method (DCIM). Results show very good agreement with the DCIM, and the trained RBFNs are very fast compared with the corresponding DCIM. (23 refs).
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....
Point Set Denoising Using Bootstrap-Based Radial Basis Function
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
Direction-dependent learning approach for radial basis function networks.
Singla, Puneet; Subbarao, Kamesh; Junkins, John L
2007-01-01
Direction-dependent scaling, shaping, and rotation of Gaussian basis functions are introduced for maximal trend sensing with minimal parameter representations for input output approximation. It is shown that shaping and rotation of the radial basis functions helps in reducing the total number of function units required to approximate any given input-output data, while improving accuracy. Several alternate formulations that enforce minimal parameterization of the most general radial basis functions are presented. A novel "directed graph" based algorithm is introduced to facilitate intelligent direction based learning and adaptation of the parameters appearing in the radial basis function network. Further, a parameter estimation algorithm is incorporated to establish starting estimates for the model parameters using multiple windows of the input-output data. The efficacy of direction-dependent shaping and rotation in function approximation is evaluated by modifying the minimal resource allocating network and considering different test examples. The examples are drawn from recent literature to benchmark the new algorithm versus existing methods.
Gonzalez, J; Rojas, I; Ortega, J; Pomares, H; Fernandez, F J; Diaz, A F
2003-01-01
This paper presents a multiobjective evolutionary algorithm to optimize radial basis function neural networks (RBFNNs) in order to approach target functions from a set of input-output pairs. The procedure allows the application of heuristics to improve the solution of the problem at hand by including some new genetic operators in the evolutionary process. These new operators are based on two well-known matrix transformations: singular value decomposition (SVD) and orthogonal least squares (OLS), which have been used to define new mutation operators that produce local or global modifications in the radial basis functions (RBFs) of the networks (the individuals in the population in the evolutionary procedure). After analyzing the efficiency of the different operators, we have shown that the global mutation operators yield an improved procedure to adjust the parameters of the RBFNNs.
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......-validation this approach is more automated than competing approaches such as Multiple Sparse Priors (Friston et al., 2008) or Champagne (Wipf et al., 2010) that require manual selection of noise level and auxiliary signal free data, respectively. Finally, we propose an unbiased estimator of the reproducibility...
Neural basis of first and second language processing of sentence-level linguistic prosody.
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
The neural basis of regret and relief during a sequential risk-taking task.
Liu, Zhiyuan; Li, Lin; Zheng, Li; Hu, Zengxi; Roberts, Ian D; Guo, Xiuyan; Yang, Guang
2016-07-01
Regret and relief are associated with counterfactual thinking and are sensitive to various social contexts. In the present fMRI study, we investigated the neural basis for regret and relief and how social context (following vs. not following advice) modulates them by employing a sequential risk-taking task. Participants were asked to open a series of boxes consecutively until they decided to stop. Each box contained a reward (gold), except for one that contained an adverse stimulus (devil), which caused the participant to lose all the gold collected in that trial. Before each trial, participants received advice about when to stop, which they then chose to follow or not. Behaviorally, subjective regret and relief were primarily dependent on the number of missed chances and the trade-off between obtained gains and missed chances, respectively. Participants felt less regret when they chose not to follow the advice than when they did. At the neural level, striatum, vmPFC/mOFC, and vACC activations were associated with greater relief. Meanwhile, dmPFC and left superior temporal gyrus were associated with greater regret. Additionally, dACC showed stronger activation in the Not-Follow context than the Follow context. PMID:27102420
Beyond laterality: a critical assessment of research on the neural basis of metaphor.
Schmidt, Gwenda L; Kranjec, Alexander; Cardillo, Eileen R; Chatterjee, Anjan
2010-01-01
Metaphors are a fundamental aspect of human cognition. The major neuropsychological hypothesis that metaphoric processing relies primarily on the right hemisphere is not confirmed consistently. We propose ways to advance our understanding of the neuropsychology of metaphor that go beyond simple laterality. Neuropsychological studies need to more carefully control confounding lexical and sentential factors, and consider the role of different parts of speech as they are extended metaphorically. They need to incorporate recent theoretical frameworks such as the career of metaphor theory, and address factors such as novelty. We also advocate the use of new methods such as voxel-based lesion-symptom mapping, which permits precise and formal tests of hypotheses correlating behavior with lesions sites. Finally, we outline a plausible model for the neural basis of metaphor. (JINS, 2009, 16, 1-5.).
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.
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...
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...
Institute of Scientific and Technical Information of China (English)
张超; 彭道黎
2012-01-01
Aiming at the problem of multicollinearity and low precision predictions by the regression prediction model of carbon storage, this study used forest resource inventory data and SPOT5 image to retrieve the aboveground forest carbon storage of Populus forests in Yanqing County. Firstly, 10 factors were analyzed by principal components analysis. Then this paper introduced a method based on PCA and radial basis function （RBF） neural network for predicting forest carbon storage. The research results show that forest resource inventory data combined SPOT5 image is very useful for retrieving study of carbon storage of Populus forests~ the fitting precision of the PCA-RBF neural network model was 99.90% ,and the average prediction reached 96.71%. The model has a good retrieval accuracy, which can be well used for retrieval of regional aboveground forest carbon storage.%针对碳储量回归预测模型存在共线性和精度较低的问题,利用森林资源二类调查数据和SPOT5影像数据对北京市延庆县的杨树林进行碳储量反演研究。先对选取的10个指标进行主成分分析,在此基础上采用径向基函数（RBF）神经网络方法构建碳储量反演模型,用预留测试样本验证,并与实测值进行比较。研究结果表明：SPOT5数据和二类数据可以很好地结合起来用于森林地上碳储量反演研究;PCA-RBF神经网络森林碳储量遥感反演模型拟合精度为99.90%,平均预测精度达到96.71%,预估效果较理想;模型训练完成后,可以应用于延庆县森林地上碳储量反演。
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.
Shared neural basis of social and non-social reward deficits in chronic cocaine users.
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
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.
AN IMPROVED RADIAL BASIS FUNCTION BASED METHOD FOR IMAGE WARPING
Institute of Scientific and Technical Information of China (English)
Nie Xuan; Zhao Rongchun; Zhang Cheng; Zhang Xiaoyan
2005-01-01
A new image warping method is proposed in this letter, which can warp a given image by some manual defined features. Based on the radial basis interpolation function algorithm, the proposed method can transform the original optimized problem into nonsingular linear problem by adding one-order term and affine differentiable condition. This linear system can get the steady unique solution by choosing suitable kernel function. Furthermore, the proposed method demonstrates how to set up the radial basis function in the target image so as to achieve supports to adopt the backward re-sampling technology accordingly which could gain the very slippery warping image. Theexperimental result shows that the proposed method can implement smooth and gradual image warping with multi-anchor points' accurate interpolation.
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.
Algebraic evaluation of matrix elements in the Laguerre function basis
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.
Neural progenitor cells regulate microglia functions and activity.
Mosher, Kira I; Andres, Robert H; Fukuhara, Takeshi; Bieri, Gregor; Hasegawa-Moriyama, Maiko; He, Yingbo; Guzman, Raphael; Wyss-Coray, Tony
2012-11-01
We found mouse neural progenitor cells (NPCs) to have a secretory protein profile distinct from other brain cells and to modulate microglial activation, proliferation and phagocytosis. NPC-derived vascular endothelial growth factor was necessary and sufficient to exert at least some of these effects in mice. Thus, neural precursor cells may not only be shaped by microglia, but also regulate microglia functions and activity.
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.
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.
Learning and generalization in radial basis function networks.
Freeman, J A; Saad, D
1995-09-01
The two-layer radial basis function network, with fixed centers of the basis functions, is analyzed within a stochastic training paradigm. Various definitions of generalization error are considered, and two such definitions are employed in deriving generic learning curves and generalization properties, both with and without a weight decay term. The generalization error is shown analytically to be related to the evidence and, via the evidence, to the prediction error and free energy. The generalization behavior is explored; the generic learning curve is found to be inversely proportional to the number of training pairs presented. Optimization of training is considered by minimizing the generalization error with respect to the free parameters of the training algorithms. Finally, the effect of the joint activations between hidden-layer units is examined and shown to speed training.
Combining regression trees and radial basis function networks.
Orr, M; Hallam, J; Takezawa, K; Murra, A; Ninomiya, S; Oide, M; Leonard, T
2000-12-01
We describe a method for non-parametric regression which combines regression trees with radial basis function networks. The method is similar to that of Kubat, who was first to suggest such a combination, but has some significant improvements. We demonstrate the features of the new method, compare its performance with other methods on DELVE data sets and apply it to a real world problem involving the classification of soybean plants from digital images.
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.
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…
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…
Husband, E. Matthew; Kelly, Lisa A.; Zhu, David C.
2011-01-01
Previous research regarding the neural basis of semantic composition has relied heavily on violation paradigms, which often compare implausible sentences that violate world knowledge to plausible sentences that do not violate world knowledge. This comparison is problematic as it may involve extralinguistic operations such as contextual repair and…
Pulver, Stefan R.; Hornstein, Nicholas J.; Land, Bruce L.; Johnson, Bruce R.
2011-01-01
Here we incorporate recent advances in "Drosophila" neurogenetics and "optogenetics" into neuroscience laboratory exercises. We used the light-activated ion channel channelrhodopsin-2 (ChR2) and tissue-specific genetic expression techniques to study the neural basis of behavior in "Drosophila" larvae. We designed and implemented exercises using…
The Neural Basis of Economic Decision-Making in the Ultimatum Game
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.
Radial Basis Function Networks for Conversion of Sound Spectra
Directory of Open Access Journals (Sweden)
Carlo Drioli
2001-03-01
Full Text Available In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN is proposed for the modeling of the spectral changes (or conversions related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.
Neural basis of music imagery and the effect of musical expertise.
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
Degenhardt, Karl R; Milewski, Rita C; Padmanabhan, Arun; Miller, Mayumi; Singh, Manvendra K; Lang, Deborah; Engleka, Kurt A; Wu, Meilin; Li, Jun; Zhou, Diane; Antonucci, Nicole; Li, Li; Epstein, Jonathan A
2010-03-15
Pax3 is a transcription factor expressed in somitic mesoderm, dorsal neural tube and pre-migratory neural crest during embryonic development. We have previously identified cis-acting enhancer elements within the proximal upstream genomic region of Pax3 that are sufficient to direct functional expression of Pax3 in neural crest. These elements direct expression of a reporter gene to pre-migratory neural crest in transgenic mice, and transgenic expression of a Pax3 cDNA using these elements is sufficient to rescue neural crest development in mice otherwise lacking endogenous Pax3. We show here that deletion of these enhancer sequences by homologous recombination is insufficient to abrogate neural crest expression of Pax3 and results in viable mice. We identify a distinct enhancer in the fourth intron that is also capable of mediating neural crest expression in transgenic mice and zebrafish. Our analysis suggests the existence of functionally redundant neural crest enhancer modules for Pax3.
Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A
2008-12-01
It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.
Directory of Open Access Journals (Sweden)
Bhuvana R.
2015-02-01
Full Text Available Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA and Radial Basis Function (RBF are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN and one desired category of depression as the output variable for training and testing proposed BPA/RBF algorithms. Using the data collected, the training patterns, and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of BPA/RBF. The optimum number of nodes required in the hidden layer of BPA/RBF is obtained, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to RBF. Through the combined topology, the work proves to be an efficient system for diagnosis of depression.
Characteristic functions and process identification by neural networks
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
Libcint: An efficient general integral library for Gaussian basis functions
Sun, Qiming
2014-01-01
An efficient integral library Libcint was designed to automatically implement general integrals for Gaussian-type scalar and spinor basis functions. The library can handle arbitrary integral expressions on top of $\\mathbf{p}$, $\\mathbf{r}$ and $\\sigma$ operators with one-electron overlap and nuclear attraction, two-electron Coulomb and Gaunt operators. Using a symbolic algebra tool, new integrals are derived and translated to C code programmatically. The generated integrals can be used in various types of molecular properties. In the present work, we computed the analytical gradients and NMR shielding constants at both non-relativistic and four-component relativistic Hartree-Fock level to demonstrate the capability of the integral library. Due to the use of kinetically balanced basis and gauge including atomic orbitals, the relativistic analytical gradients and shielding constants requires the integral library to handle the fifth-order electron repulsion integral derivatives. The generality of the integral li...
Institute of Scientific and Technical Information of China (English)
叶林; 陈政; 赵永宁; 朱倩雯
2015-01-01
To deal with the problem of fluctuating photovoltaic power,a photovoltaic power forecasting model based on genetic algorithm (GA) and fuzzy radial basis function (RBF) neural network is proposed and the output power is applied to the battery energy storage system to mitigate the electric shock on the power system.A historical day of identical weather type,a close date and minimum temperature Euclidean distance is chosen as the similar day.The solar radiation intensity and temperature closely correlated with photovoltaic (PV) power are chosen as input variables of the model,a GA and fuzzy RBF neural network is built as the final prediction model based on the parameter optimization method of K-means clustering and genetic algorithm.Furthermore,a smooth control strategy considering PV power forecasting is used to control the grid-connected PV power,so as to smooth the PV power fluctuation.The experimental results show that the proposed forecasting model has high accuracy and the smooth control strategy for power fluctuation based on photovoltaic power forecasting is effective.%针对光伏发电系统出力波动问题，提出遗传算法(GA)—模糊径向基(RBF)神经网络的光伏发电功率预测模型，将功率预测值应用于光伏发电的蓄电池储能功率调节系统，以降低对电网的冲击。选择与待预测日天气类型相同、日期相近、温度欧氏距离最小的历史日作为相似日，把与光伏发电功率相关性大的太阳辐射强度和温度作为模型输入变量，提出 K 均值聚类和遗传算法的参数优化方法，建立基于 GA—模糊 RBF 神经网络的最终预测模型。在光伏功率预测的基础上，提出一种平滑控制策略，对光伏并网功率进行有效调节，从而达到平滑光伏功率波动的目的。实例证明，所述预测模型具有较高精度，并验证了平滑功率波动控制策略的有效性。
Product design on the basis of fuzzy quality function deployment
Institute of Scientific and Technical Information of China (English)
无
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.
Snow cover thickness estimation using radial basis function networks
Directory of Open Access Journals (Sweden)
E. Binaghi
2013-05-01
Full Text Available This paper reports an experimental study designed for the in-depth investigation of how the radial basis function network (RBFN estimates snow cover thickness as a function of climate and topographic parameters. The estimation problem is modeled in terms of both function regression and classification, obtaining continuous and discrete thickness values, respectively. The model is based on a minimal set of climatic and topographic data collected from a limited number of stations located in the Italian Central Alps. Several experiments have been conceived and conducted adopting different evaluation indexes. A comparison analysis was also developed for a quantitative evaluation of the advantages of the RBFN method over to conventional widely used spatial interpolation techniques when dealing with critical situations originated by lack of data and limited n-homogeneously distributed instrumented sites. The RBFN model proved competitive behavior and a valuable tool in critical situations in which conventional techniques suffer from a lack of representative data.
Glial Contributions to Neural Function and Disease.
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
The neural basis of temporal individuation and its capacity limits in the human brain.
Naughtin, Claire K; Tamber-Rosenau, Benjamin J; Dux, Paul E
2014-02-01
Individuation refers to individuals' use of spatial and temporal properties to register an object as a distinct perceptual event relative to other stimuli. Although behavioral studies have examined both spatial and temporal individuation, neuroimaging investigations of individuation have been restricted to the spatial domain and at relatively late stages of information processing. In this study we used univariate and multivoxel pattern analyses of functional magnetic resonance imaging data to identify brain regions involved in individuating temporally distinct visual items and the neural consequences that arise when this process reaches its capacity limit (repetition blindness, RB). First, we found that regional patterns of blood oxygen level-dependent activity in a large group of brain regions involved in "lower-level" perceptual and "higher-level" attentional/executive processing discriminated between instances where repeated and nonrepeated stimuli were successfully individuated, conditions that placed differential demands on temporal individuation. These results could not be attributed to repetition suppression, stimulus or response factors, task difficulty, regional activation differences, other capacity-limited processes, or artifacts in the data or analyses. Consistent with the global workplace model of consciousness, this finding suggests that temporal individuation is supported by a distributed set of brain regions, rather than a single neural correlate. Second, conditions that reflect the capacity limit of individuation (instances of RB) modulated the amplitude, rather than spatial pattern, of activity in the left hemisphere premotor cortex. This finding could not be attributed to response conflict/ambiguity and likely reflects a candidate brain region underlying the capacity-limited process that gives rise to RB.
Foraging under competition: the neural basis of input-matching in humans.
Mobbs, Dean; Hassabis, Demis; Yu, Rongjun; Chu, Carlton; Rushworth, Matthew; Boorman, Erie; Dalgleish, Tim
2013-06-01
Input-matching is a key mechanism by which animals optimally distribute themselves across habitats to maximize net gains based on the changing input values of food supply rate and competition. To examine the neural systems that underlie this rule in humans, we created a continuous-input foraging task where subjects had to decide to stay or switch between two habitats presented on the left and right of the screen. The subject's decision to stay or switch was based on changing input values of reward-token supply rate and competition density. High density of competition or low-reward token rate was associated with decreased chance of winning. Therefore, subjects attempted to maximize their gains by switching to habitats that possessed low competition density and higher token rate. When it was increasingly disadvantageous to be in a habitat, we observed increased activity in brain regions that underlie preparatory motor actions, including the dorsal anterior cingulate cortex and the supplementary motor area, as well as the insula, which we speculate may be involved in the conscious urge to switch habitats. Conversely, being in an advantageous habitat is associated with activity in the reward systems, namely the striatum and medial prefrontal cortex. Moreover, amygdala and dorsal putamen activity steered interindividual preferences in competition avoidance and pursuing reward. Our results suggest that input-matching decisions are made as a net function of activity in a distributed set of neural systems. Furthermore, we speculate that switching behaviors are related to individual differences in competition avoidance and reward drive.
Dynamics of learning near singularities in radial basis function networks.
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.
Snow cover thickness estimation by using radial basis function networks
Directory of Open Access Journals (Sweden)
A. Guidali
2012-07-01
Full Text Available This work investigates learning and generalisation capabilities of radial basis function networks (RBFN used to solve snow cover thickness estimation model as regression and classification. The model is based on a minimal set of climatic and topographic data collected from a limited number of stations located in the Italian Central Alps. Several experiments have been conceived and conducted adopting different evaluation indexes in both regression and classification tasks. The snow cover thickness estimation by RBFN has been proved a valuable tool able to deal with several critical aspects arising from the specific experimental context.
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...
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...
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…
Learning Mixtures of Truncated Basis Functions from Data
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Pérez-Bernabé, Inmaculada;
2014-01-01
-likelihood), they are 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...... 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...
The Gaussian Radial Basis Function Method for Plasma Kinetic Theory
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...
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....
A functional clustering algorithm for the analysis of neural relationships
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.
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.
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
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.
Age and experience shape developmental changes in the neural basis of language-related learning.
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
Age and experience shape developmental changes in the neural basis of language-related learning.
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.
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.
Response variance in functional maps: neural darwinism revisited.
Takahashi, Hirokazu; Yokota, Ryo; Kanzaki, Ryohei
2013-01-01
The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.
Response variance in functional maps: neural darwinism revisited.
Directory of Open Access Journals (Sweden)
Hirokazu Takahashi
Full Text Available The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.
An incremental design of radial basis function networks.
Yu, Hao; Reiner, Philip D; Xie, Tiantian; Bartczak, Tomasz; Wilamowski, Bogdan M
2014-10-01
This paper proposes an offline algorithm for incrementally constructing and training radial basis function (RBF) networks. In each iteration of the error correction (ErrCor) algorithm, one RBF unit is added to fit and then eliminate the highest peak (or lowest valley) in the error surface. This process is repeated until a desired error level is reached. Experimental results on real world data sets show that the ErrCor algorithm designs very compact RBF networks compared with the other investigated algorithms. Several benchmark tests such as the duplicate patterns test and the two spiral problem were applied to show the robustness of the ErrCor algorithm. The proposed ErrCor algorithm generates very compact networks. This compactness leads to greatly reduced computation times of trained networks.
Efficient VLSI Architecture for Training Radial Basis Function Networks
Directory of Open Access Journals (Sweden)
Wen-Jyi Hwang
2013-03-01
Full Text Available This paper presents a novel VLSI architecture for the training of radial basis function (RBF networks. The architecture contains the circuits for fuzzy C-means (FCM and the recursive Least Mean Square (LMS operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA. It is used as a hardware accelerator in a system on programmable chip (SOPC for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired.
Efficient VLSI architecture for training radial basis function networks.
Fan, Zhe-Cheng; Hwang, Wen-Jyi
2013-03-19
This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired.
Relativistic Kinetic-Balance Condition for Explicitly Correlated Basis Functions
Simmen, Benjamin; Reiher, Markus
2015-01-01
This paper presents the derivation of a kinetic-balance condition for explicitly correlated basis functions employed in semi-classical relativistic calculations. Such a condition is important to ensure variational stability in algorithms based on the first-quantized Dirac theory of 1/2-fermions. We demonstrate that the kinetic-balance condition can be obtained from the row reduction process commonly applied to solve systems of linear equations. The resulting form of kinetic balance establishes a relation for the $4^N$ components of the spinor of an $N$-fermion system to the non-relativistic limit, which is in accordance with recent developments in the field of exact decoupling in relativistic orbital-based many-electron theory.
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.
Recent advances in radial basis function collocation methods
Chen, Wen; Chen, C S
2014-01-01
This book surveys the latest advances in radial basis function (RBF) meshless collocation methods which emphasis on recent novel kernel RBFs and new numerical schemes for solving partial differential equations. The RBF collocation methods are inherently free of integration and mesh, and avoid tedious mesh generation involved in standard finite element and boundary element methods. This book focuses primarily on the numerical algorithms, engineering applications, and highlights a large class of novel boundary-type RBF meshless collocation methods. These methods have shown a clear edge over the traditional numerical techniques especially for problems involving infinite domain, moving boundary, thin-walled structures, and inverse problems. Due to the rapid development in RBF meshless collocation methods, there is a need to summarize all these new materials so that they are available to scientists, engineers, and graduate students who are interest to apply these newly developed methods for solving real world’s ...
Binary higher order neural networks for realizing Boolean functions.
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
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.
Neural Substrate Expansion for the Restoration of Brain Function.
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
Dual-orthogonal radial basis function networks for nonlinear time series prediction.
Hong, X; Billings, Steve A.
1998-04-01
A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.
Rotation Invariant Face Detection Using Wavelet, PCA and Radial Basis Function Networks
Kamruzzaman, S M; Islam, Md Saiful; Haque, Md Emdadul; Alam, Mohammad Shamsul
2010-01-01
This paper introduces a novel method for human face detection with its orientation by using wavelet, principle component analysis (PCA) and redial basis networks. The input image is analyzed by two-dimensional wavelet and a two-dimensional stationary wavelet. The common goals concern are the image clearance and simplification, which are parts of de-noising or compression. We applied an effective procedure to reduce the dimension of the input vectors using PCA. Radial Basis Function (RBF) neural network is then used as a function approximation network to detect where either the input image is contained a face or not and if there is a face exists then tell about its orientation. We will show how RBF can perform well then back-propagation algorithm and give some solution for better regularization of the RBF (GRNN) network. Compared with traditional RBF networks, the proposed network demonstrates better capability of approximation to underlying functions, faster learning speed, better size of network, and high ro...
Ma, Shu-min; Liu, Si-dong; Zhang, Zhuo-yong; Fan, Guo-qiang
2005-06-01
The Fourier transform infrared spectrometry (FTIRS) and radial basis function neural network (RBF-NN) have been applied to develop classification models for identifying official and unofficial rhubarb samples. The original data were compressed from 775 variables to 49 variables by using wavelet transformation method. The compressed spectra with reduced variables maintain the characteristics of the IR spectra and speed up the network training process. The effects of network parameters including error goal and spread constant, were investigated. The rate of correct classification is up to 97.78% at optimized conditions. Results show that the combination of IRS and ANN can be used as fast and convenient tool for identification of Chinese herbal samples.
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.
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.
Losing Neutrality: The Neural Basis of Impaired Emotional Control without Sleep.
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
Tools for Resolving Functional Activity and Connectivity within Intact Neural Circuits
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...
Adaptive radial basis function mesh deformation using data reduction
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
Institute of Scientific and Technical Information of China (English)
尤文坚; 梁兵; 李荫军
2013-01-01
In view of problem that eddy-current sensor cannot reflect measured physical quantity accurately caused by higher nonlinear of output characteristic parameter, the paper proposed a scheme of using RBF neural network to fit output characteristic parameter of eddy-current sensor. The scheme uses newrb function to create RBF neural network, and takes measured physical quantity as input matrix and output of eddy-current sensor as output matrix to train the RBF neural network, so as to obtain low root-mean-square error and smooth output characteristic fitting curve of eddy-current sensor. The simulation result showed that RBF neural network can effectively realize fitting of output characteristic of eddy-current sensor by selecting proper creating function and expanding coefficient.%针对电涡流传感器的输出特性参数非线性较大,不能精确地反映被测物理量的问题,提出了一种采用径向基神经网络对电涡流传感器的输出特性参数进行拟合的方案.该方案采用newrb函数创建一个径向基神经网络,以被测物理量作为输入矩阵、电涡流传感器输出电压作为输出矩阵,对该径向基神经网络进行训练,从而可得到均方根误差小且光滑的电涡流传感器输出特性拟合曲线.实验结果表明,只要选择合适的创建函数和扩展系数,径向基神经网络能有效地实现电涡流传感器输出特性的拟合.
Institute of Scientific and Technical Information of China (English)
陈红梅; 刘兴高
2015-01-01
In view of the problems that not highlighted predominant data and variables affect the prediction accuracy and not enough data smoothness influences the generalization performance in the prediction of PolyPropylene ( PP ) Melt Index ( MI) , this paper proposed a forecasting model of MI based on Radial Basis Function Neural Network ( RBFNN) with weighting and smoothing of multi-technology integration. The proposed model integratedly applied two data weighting schemes: weighting based on space Euclidean distance in the time scale, weighting based on grey correlation and linear autoregression error in the variable dimension, and also applied two data smoothing methods: smoothing based upon the Euclidean distance of process variable differential sequence and partial linear smoothing, to solve the problems of low prediction precision and generalization ability. To further improve the forecasting capability of the model, based on the Nonlinear Autoregressive Moving Average ( NARMA) model framework with error compensation and the RBFNN, the paper used the self-tuning predictive control algorithm and the piecewise-linear alterable learning rate algorithm to optimize this model. The presented model is validated by the real data from a plant and the prediction results on the generalization database are as follows: Mean Relative Error ( MRE) is 1. 32%, Root Mean Square Error ( RMSE) is 0. 045 9. Compared and analysed detailedly with the report in the literature, the results show that the proposed model in the paper has an excellent forecasting accuracy and generalization ability, and has a certain application value in the industrial process with large time delay.%针对在对聚丙烯熔融指数进行预测时优势数据和优势变量不突出影响预测精度、数据平滑度不够影响泛化性能的问题，提出了基于多技术融合加权平滑的径向基函数神经网络预报模型。综合运用了在时间尺度基于空间欧氏距离加权、在变量
Institute of Scientific and Technical Information of China (English)
Ping Jiang; Hongyi Wu; Jieqing Tan
2006-01-01
The generalized Ball curves of Wang-Said type with a position parameter L not only unify the Wang-Ball curves and the Said-Ball curves, but also include several useful intermediate curves. This paper presents the dual functionals for the generalized Ball basis of Wang-Said type. The relevant basis transformation formulae are also worked out.
Circadian gating of neuronal functionality: a basis for iterative metaplasticity.
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
Functional equivalence between radial basis function networks and fuzzy inference systems.
Jang, J R; Sun, C T
1993-01-01
It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functionally equivalent.
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.
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
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.
Nuclear charge radii: Density functional theory meets Bayesian neural networks
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...
An efficient method for ectopic beats cancellation based on radial basis function.
Mateo, Jorge; Torres, Ana; Rieta, José J
2011-01-01
The analysis of the surface Electrocardiogram (ECG) is the most extended noninvasive technique in cardiological diagnosis. In order to properly use the ECG, we need to cancel out ectopic beats. These beats may occur in both normal subjects and patients with heart disease, and their presence represents an important source of error which must be handled before any other analysis. This paper presents a method for electrocardiogram ectopic beat cancellation based on Radial Basis Function Neural Network (RBFNN). A train-able neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care is presented. Six types of beats including: Normal Beats (NB); Premature Ventricular Contractions (PVC); Left Bundle Branch Blocks (LBBB); Right Bundle Branch Blocks (RBBB); Paced Beats (PB) and Ectopic Beats (EB) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. Average Results for the RBFNN based method provided an ectopic beat reduction (EBR) of (mean ± std) EBR = 7, 23 ± 2.18 in contrast to traditional compared methods that, for the best case, yielded EBR = 4.05 ± 2.13. The results prove that RBFNN based methods are able to obtain a very accurate reduction of ectopic beats together with low distortion of the QRST complex.
Heidari, Mohammad; Heidari, Ali; Homaei, Hadi
2014-01-01
The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS.
Scott, Sophie K.; Rosen, Stuart; Wickham, Lindsay; Wise, Richard J. S.
2004-02-01
Positron emission tomography (PET) was used to investigate the neural basis of the comprehension of speech in unmodulated noise (``energetic'' masking, dominated by effects at the auditory periphery), and when presented with another speaker (``informational'' masking, dominated by more central effects). Each type of signal was presented at four different signal-to-noise ratios (SNRs) (+3, 0, -3, -6 dB for the speech-in-speech, +6, +3, 0, -3 dB for the speech-in-noise), with listeners instructed to listen for meaning to the target speaker. Consistent with behavioral studies, there was SNR-dependent activation associated with the comprehension of speech in noise, with no SNR-dependent activity for the comprehension of speech-in-speech (at low or negative SNRs). There was, in addition, activation in bilateral superior temporal gyri which was associated with the informational masking condition. The extent to which this activation of classical ``speech'' areas of the temporal lobes might delineate the neural basis of the informational masking is considered, as is the relationship of these findings to the interfering effects of unattended speech and sound on more explicit working memory tasks. This study is a novel demonstration of candidate neural systems involved in the perception of speech in noisy environments, and of the processing of multiple speakers in the dorso-lateral temporal lobes.
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.
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...
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)
Piecewise nonlinear image registration using DCT basis functions
Gan, Lin; Agam, Gady
2015-03-01
The deformation field in nonlinear image registration is usually modeled by a global model. Such models are often faced with the problem that a locally complex deformation cannot be accurately modeled by simply increasing degrees of freedom (DOF). In addition, highly complex models require additional regularization which is usually ineffective when applied globally. Registering locally corresponding regions addresses this problem in a divide and conquer strategy. In this paper we propose a piecewise image registration approach using Discrete Cosine Transform (DCT) basis functions for a nonlinear model. The contributions of this paper are three-folds. First, we develop a multi-level piecewise registration framework that extends the concept of piecewise linear registration and works with any nonlinear deformation model. This framework is then applied to nonlinear DCT registration. Second, we show how adaptive model complexity and regularization could be applied for local piece registration, thus accounting for higher variability. Third, we show how the proposed piecewise DCT can overcome the fundamental problem of a large curvature matrix inversion in global DCT when using high degrees of freedoms. The proposed approach can be viewed as an extension of global DCT registration where the overall model complexity is increased while achieving effective local regularization. Experimental evaluation results provide comparison of the proposed approach to piecewise linear registration using an affine transformation model and a global nonlinear registration using DCT model. Preliminary results show that the proposed approach achieves improved performance.
Both of us disgusted in My Insula : The common neural basis of seeing and feeling disgust
Wicker, B; Keysers, C; Plailly, J; Royet, JP; Gallese, [No Value; Rizzolatti, G
2003-01-01
What neural mechanism underlies the capacity to understand the emotions of others? Does this mechanism involve brain areas normally involved in experiencing the same emotion? We performed an fMRI study in which participants inhaled odorants producing a strong feeling of disgust. The same participant
Neural basis for brain responses to TV commercials: a high-resolution EEG study.
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
Neural basis for brain responses to TV commercials: a high-resolution EEG study.
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
Anderson, H C; Lotfi, A; Westphal, L C; Jang, J R
1998-01-01
The above paper claims that under a set of minor restrictions radial basis function networks and fuzzy inference systems are functionally equivalent. The purpose of this letter is to show that this set of restrictions is incomplete and that, when it is completed, the said functional equivalence applies only to a small range of fuzzy inference systems. In addition, a modified set of restrictions is proposed which is applicable for a much wider range of fuzzy inference systems.
Possible scenarios of the influence of low-dose ionizing radiation on neural functioning.
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
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.
Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function
Energy Technology Data Exchange (ETDEWEB)
Ericson, Milton Nance [ORNL; McKnight, Timothy E [ORNL; Melechko, Anatoli Vasilievich [ORNL; Simpson, Michael L [ORNL; Morrison, Barclay [ORNL; Yu, Zhe [Columbia University
2012-01-01
Neural chips, which are capable of simultaneous, multi-site neural recording and stimulation, have been used to detect and modulate neural activity for almost 30 years. As a neural interface, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface can potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single cell level and even inside the cell.
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
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.
Numerical solution of differential equations using multiquadric radial basis functions networks.
Mai-Duy, N; Tran-Cong, T
2001-03-01
This paper presents mesh-free procedures for solving linear differential equations (ODEs and elliptic PDEs) based on multiquadric (MQ) radial basis function networks (RBFNs). Based on our study of approximation of function and its derivatives using RBFNs that was reported in an earlier paper (Mai-Duy, N. & Tran-Cong, T. (1999). Approximation of function and its derivatives using radial basis function networks. Neural networks, submitted), new RBFN approximation procedures are developed in this paper for solving DEs, which can also be classified into two types: a direct (DRBFN) and an indirect (IRBFN) RBFN procedure. In the present procedures, the width of the RBFs is the only adjustable parameter according to a(i) = betad(i), where d(i) is the distance from the ith centre to the nearest centre. The IRBFN method is more accurate than the DRBFN one and experience so far shows that beta can be chosen in the range 7 < or = beta 10 for the former. Different combinations of RBF centres and collocation points (uniformly and randomly distributed) are tested on both regularly and irregularly shaped domains. The results for a 1D Poisson's equation show that the DRBFN and the IRBFN procedures achieve a norm of error of at least O(1.0 x 10(-4)) and O(1.0 x 10(-8)), respectively, with a centre density of 50. Similarly, the results for a 2D Poisson's equation show that the DRBFN and the IRBFN procedures achieve a norm of error of at least O(1.0 x 10(-3)) and O(1.0 x10(-6)) respectively, with a centre density of 12 X 12.
A generalized exchange-correlation functional: the Neural-Networks approach
Zheng, X; Wang, X J; Chen, G H; Zheng, Xiao; Hu, LiHong; Wang, XiuJun; Chen, GuanHua
2003-01-01
A Neural-Networks-based approach is proposed to construct a new type of exchange-correlation functional for density functional theory. It is applied to improve B3LYP functional by taking into account of high-order contributions to the exchange-correlation functional. The improved B3LYP functional is based on a neural network whose structure and synaptic weights are determined from 116 known experimental atomization energies, ionization potentials, proton affinities or total atomic energies which were used by Becke in his pioneer work on the hybrid functionals [J. Chem. Phys. ${\\bf 98}$, 5648 (1993)]. It leads to better agreement between the first-principles calculation results and these 116 experimental data. The new B3LYP functional is further tested by applying it to calculate the ionization potentials of 24 molecules of the G2 test set. The 6-311+G(3{\\it df},2{\\it p}) basis set is employed in the calculation, and the resulting root-mean-square error is reduced to 2.2 kcal$\\cdot$mol$^{-1}$ in comparison to ...
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)
Estimation of State of Charge of Lead Acid Battery using Radial Basis Function
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.
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.
Bruni, S.; Llombart Juan, N.; Neto, A.; Gerini, G.; Maci, S.
2004-01-01
A general algorithm for the analysis of microstrip coupled leaky wave slot antennas was discussed. The method was based on the construction of physically appealing entire domain Methods of Moments (MoM) basis function that allowed a consistent reduction of the number of unknowns and of total computa
Extending the functional equivalence of radial basis function networks and fuzzy inference systems.
Hunt, K J; Haas, R; Murray-Smith, R
1996-01-01
We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditions of the previous result.
Novel Sequential Neural Network Learning Algorithm for Function Approximation
Institute of Scientific and Technical Information of China (English)
KANG Huai-qi; SHI Cai-cheng; HE Pei-kun; LI Xiao-qiong
2007-01-01
A novel sequential neural network learning algorithm for function approximation is presented.The multi-step-ahead output predictor of the stochastic time series is introduced to the growing and pruning network for constructing network structure.And the network parameters are adjusted by the proportional differential filter (PDF) rather than EKF when the network growing criteria are not met.Experimental results show that the proposed algorithm can obtain a more compact network along with a smaller error in mean square sense than other typical sequential learning algorithms.
Neural basis of three dimensions of agitated behaviors in patients with Alzheimer disease
Directory of Open Access Journals (Sweden)
Banno K
2014-02-01
Full Text Available Koichi Banno,1 Shutaro Nakaaki,2 Junko Sato,1 Katsuyoshi Torii,1 Jin Narumoto,3 Jun Miyata,4 Nobutsugu Hirono,5 Toshi A Furukawa,6 Masaru Mimura,2 Tatsuo Akechi1 1Department of Psychiatry and Cognitive-Behavioral Medicine, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan; 2Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; 3Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 4Department of Neuropsychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan; 5Department of Psychology, Kobe Gakuin University; Hyogo, Japan; 6Departments of Health Promotion and Human Behavior and of Clinical Epidemiology, Graduate School of Medicine/School of Public Health, Kyoto University, Kyoto, Japan Background: Agitated behaviors are frequently observed in patients with Alzheimer disease (AD. The neural substrate underlying the agitated behaviors in dementia is unclear. We hypothesized that different dimensions of agitated behaviors are mediated by distinct neural systems. Methods: All the patients (n=32 underwent single photon emission computed tomography (SPECT. Using the Agitated Behavior in Dementia scale, we identified the relationships between regional cerebral blood flow (rCBF patterns and the presence of each of three dimensions of agitated behavior (physically agitated behavior, verbally agitated behavior, and psychosis symptoms in AD patients. Statistical parametric mapping (SPM software was used to explore these neural correlations. Results: Physically agitated behavior was significantly correlated with lower rCBF values in the right superior temporal gyrus (Brodmann 22 and the right inferior frontal gyrus (Brodmann 47. Verbally agitated behavior was significantly associated with lower rCBF values in the left inferior frontal gyrus (Brodmann 46, 44 and the left insula (Brodmann 13. The psychosis symptoms were significantly correlated
Directory of Open Access Journals (Sweden)
Zoë A Englander
Full Text Available BACKGROUND: Most research investigating the neural basis of social emotions has examined emotions that give rise to negative evaluations of others (e.g. anger, disgust. Emotions triggered by the virtues and excellences of others have been largely ignored. Using fMRI, we investigated the neural basis of two "other-praising" emotions--Moral Elevation (a response to witnessing acts of moral beauty, and Admiration (which we restricted to admiration for physical skill. METHODOLOGY/PRINCIPAL FINDINGS: Ten participants viewed the same nine video clips. Three clips elicited moral elevation, three elicited admiration, and three were emotionally neutral. We then performed pair-wise voxel-by-voxel correlations of the BOLD signal between individuals for each video clip and a separate resting-state run. We observed a high degree of inter-subject synchronization, regardless of stimulus type, across several brain regions during free-viewing of videos. Videos in the elevation condition evoked significant inter-subject synchronization in brain regions previously implicated in self-referential and interoceptive processes, including the medial prefrontal cortex, precuneus, and insula. The degree of synchronization was highly variable over the course of the videos, with the strongest synchrony occurring during portions of the videos that were independently rated as most emotionally arousing. Synchrony in these same brain regions was not consistently observed during the admiration videos, and was absent for the neutral videos. CONCLUSIONS/SIGNIFICANCE: Results suggest that the neural systems supporting moral elevation are remarkably consistent across subjects viewing the same emotional content. We demonstrate that model-free techniques such as inter-subject synchronization may be a useful tool for studying complex, context dependent emotions such as self-transcendent emotion.
Englander, Zoë A.; Haidt, Jonathan; Morris, James P.
2012-01-01
Background Most research investigating the neural basis of social emotions has examined emotions that give rise to negative evaluations of others (e.g. anger, disgust). Emotions triggered by the virtues and excellences of others have been largely ignored. Using fMRI, we investigated the neural basis of two “other-praising" emotions – Moral Elevation (a response to witnessing acts of moral beauty), and Admiration (which we restricted to admiration for physical skill). Methodology/Principal Findings Ten participants viewed the same nine video clips. Three clips elicited moral elevation, three elicited admiration, and three were emotionally neutral. We then performed pair-wise voxel-by-voxel correlations of the BOLD signal between individuals for each video clip and a separate resting-state run. We observed a high degree of inter-subject synchronization, regardless of stimulus type, across several brain regions during free-viewing of videos. Videos in the elevation condition evoked significant inter-subject synchronization in brain regions previously implicated in self-referential and interoceptive processes, including the medial prefrontal cortex, precuneus, and insula. The degree of synchronization was highly variable over the course of the videos, with the strongest synchrony occurring during portions of the videos that were independently rated as most emotionally arousing. Synchrony in these same brain regions was not consistently observed during the admiration videos, and was absent for the neutral videos. Conclusions/Significance Results suggest that the neural systems supporting moral elevation are remarkably consistent across subjects viewing the same emotional content. We demonstrate that model-free techniques such as inter-subject synchronization may be a useful tool for studying complex, context dependent emotions such as self-transcendent emotion. PMID:22745745
Mayorga, René V; Carrera, Jonathan
2007-06-01
This Paper presents an efficient approach for the fast computation of inverse continuous time variant functions with the proper use of Radial Basis Function Networks (RBFNs). The approach is based on implementing RBFNs for computing inverse continuous time variant functions via an overall damped least squares solution that includes a novel null space vector for singularities prevention. The singularities avoidance null space vector is derived from developing a sufficiency condition for singularities prevention that conduces to establish some characterizing matrices and an associated performance index.
Optimized basis functions for slot antennas excited by coplanar waveguides
Neto, A.; Maagt, P.de; Maci, S.
2003-01-01
A method is proposed for the analysis of slot antennas excited by coplanar waveguides. First, a standard integral equation for the continuity of the magnetic field is formulated. Then the appropriate equivalent magnetic currents of the method of moments are represented in terms of entire-domain basi
Kanungo, Bikash
2016-01-01
We present a computationally efficient approach to perform large-scale all-electron density functional theory calculations by enriching the classical finite element basis with compactly supported atom-centered numerical basis functions that are constructed from the solution of the Kohn-Sham (KS) problem for single atoms. We term these numerical basis functions as enrichment functions, and the resultant basis as the enriched finite element basis. The enrichment functions are compactly supported through the use of smooth cutoff functions, which enhances the conditioning and maintains the locality of the basis. The integrals involved in the evaluation of the discrete KS Hamiltonian and overlap matrix in the enriched finite element basis are computed using an adaptive quadrature grid based on the characteristics of enrichment functions. Further, we propose an efficient scheme to invert the overlap matrix by using a block-wise matrix inversion in conjunction with special reduced-order quadrature rules to transform...
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.
Neural basis of attachment-caregiving systems interaction: insights from neuroimaging studies
Lenzi, Delia; Trentini, Cristina; Tambelli, Renata; Pantano, Patrizia
2015-01-01
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. PMID:26379578
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.
Fink, G R; Driver, J; Rorden, C; Baldeweg, T; Dolan, R J
2000-04-01
We used positron emission tomography in healthy volunteers to test hemispheric rivalry theories for normal and pathological spatial attention, which provide an influential account of contralesional extinction on bilateral stimulation after unilateral brain injury. Subjects reported visual characters presented either unilaterally or bilaterally. An extinction-like pattern was found behaviorally, with characters in one hemifield reported less accurately when competing characters appeared in the other hemifield. Differences in neural activity for unilateral minus bilateral conditions revealed greater activation of striate and extrastriate areas for stimuli presented without competing stimuli in the other hemifield. Thus, simultaneous bilateral stimulation led to a significant reduction in response by spatiotopic visual cortex contralateral to a particular stimulus. These data provide physiological support for interhemispheric rivalry in the intact human brain, and demonstrate that such competition impacts at early levels of perceptual processing.
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.
Defining the neural basis of appetite and obesity: from genes to behaviour.
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
A Quasi-Interpolation Satisfying Quadratic Polynomial Reproduction with Radial Basis Functions
Institute of Scientific and Technical Information of China (English)
Li Zha; Renzhong Feng
2007-01-01
In this paper, a new quasi-interpolation with radial basis functions which satisfies quadratic polynomial reproduction is constructed on the infinite set of equally spaced data. A new basis function is constructed by making convolution integral with a constructed spline and a given radial basis function. In particular, for twicely differentiable function the proposed method provides better approximation and also takes care of derivatives approximation.
The neural basis of the interaction between theory of mind and moral judgment
Young, Liane; Cushman, Fiery; Hauser, Marc; Saxe, Rebecca
2007-01-01
Is the basis of criminality an act that causes harm, or an act undertaken with the belief that one will cause harm? The present study takes a cognitive neuroscience approach to investigating how information about an agent's beliefs and an action's consequences contribute to moral judgment. We build on prior developmental evidence showing that these factors contribute differentially to the young child's moral judgments coupled with neurobiological evidence suggesting a role for the right tempo...
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)
Analysis of Neural Networks in Terms of Domain Functions
Zwaag, van der Berend Jan; Slump, Cees; Spaanenburg, Lambert
2002-01-01
Despite their success-story, artificial neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more as a my
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.
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.
Neural Basis of Two Kinds of Social Influence: Obedience and Conformity.
Xie, Ying; Chen, Mingliang; Lai, Hongxia; Zhang, Wuke; Zhao, Zhen; Anwar, Ch Mahmood
2016-01-01
Event-related potentials (ERPs) were used in this study to explore the neural mechanism of obedience and conformity on the model of online book purchasing. Participants were asked to decide as quickly as possible whether to buy a book based on limited information including its title, keywords and number of positive and negative reviews. Obedience was induced by forcing participants to buy books which received mostly negative reviews. In contrast, conformity was aroused by majority influence (caused by positive and negative comments). P3 and N2, two kinds of ERP components related to social cognitive process, were measured and recorded with electroencephalogram (EEG) test. The results show that compared with conformity decisions, obedience decisions induced greater cognitive conflicts. In ERP measurements, greater amplitudes of N2 component were observed in the context of obedience. However, consistency level did not make a difference on P3 peak latency for both conformity and obedience. This shows that classification process is implicit in both conformity and obedience decision-making. In addition, for both conformity and obedience decisions, augmented P3 was observed when the reviews consistency (either negative or positive) was higher.
Zhang, Zhuoyong; Wang, Dan; Harrington, Peter de B; Voorhees, Kent J; Rees, Jon
2004-06-17
Forward selection improved radial basis function (RBF) network was applied to bacterial classification based on the data obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The classification of each bacterium cultured at different time was discussed and the effect of parameters of the RBF network was investigated. The new method involves forward selection to prevent overfitting and generalized cross-validation (GCV) was used as model selection criterion (MSC). The original data was compressed by using wavelet transformation to speed up the network training and reduce the number of variables of the original MS data. The data was normalized prior training and testing a network to define the area the neural network to be trained in, accelerate the training rate, and reduce the range the parameters to be selected in. The one-out-of-n method was used to split the data set of p samples into a training set of size p-1 and a test set of size 1. With the improved method, the classification correctness for the five bacteria discussed in the present paper are 87.5, 69.2, 80, 92.3, and 92.8%, respectively.
RADIAL BASIS FUNCTION NETWORK DEPENDENT EXCLUSIVE MUTUAL INTERPOLATION FOR MISSING VALUE IMPUTATION
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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.
Generalized profile function model based on neural networks
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Radonja Pero
2009-01-01
Full Text Available Generalized profile function model (GPFM provides approximations of the individual models (individual stem profile models of the objects using only two basic measurements. In this paper it is shown that this GPFM can be successfully derived by using artificial computational intelligence, that is, neural networks. GPFM is obtained as a mean value of all the available normalized individual models. Generation of GPFM is performed by using the basic dataset, and verification is done by using the validation data set. Statistical properties of the original, measured data and estimated data based on the generalized model are presented and compared. Testing of the obtained GPFM is performed also by the regression analysis. The obtained correlation coefficients between the real data and the estimated data are very high, 0.9946 for the basic data set, and 0.9933 for the validation dataset. .
[Structure and function of neural plasticity-related gene products].
Yamagata, K; Sugiura, H; Suzuki, K
1998-08-01
We have isolated novel immediate early genes (IEGs) from the hippocampus by differential cloning techniques. These mRNAs are induced by synaptic activity and translated into proteins that may affect neural function. We have analyzed a variety of "effector" immediate early genes. These mRNAs encode: 1) cytoplasmic proteins, such as cyclooxygenase-2, a small G protein, Rheb, and a cytoskeleton-associated protein, Arc; 2) membrane-bound proteins, such as the cell adhesion protein Arcadlin, and a neurite-outgrowth protein, Neuritin; and 3) a secreted protein, Narp. We hypothesize that physiological stimulation induces "effector" proteins that might strengthen synaptic connections of activated synapses. In contrast, pathological conditions such as epilepsy or drug addiction may accelerate overproduction of these gene products, which cause abnormal synapse formation. Gene targeting and in vivo gene transfer techniques are required to prove this hypothesis. PMID:9866829
Hydrogen sulfide improves neural function in rats following cardiopulmonary resuscitation
LIN, JI-YAN; ZHANG, MIN-WEI; WANG, JIN-GAO; LI, HUI; WEI, HONG-YAN; LIU, RONG; DAI, GANG; LIAO, XIAO-XING
2016-01-01
The alleviation of brain injury is a key issue following cardiopulmonary resuscitation (CPR). Hydrogen sulfide (H2S) is hypothesized to be involved in the pathophysiological process of ischemia-reperfusion injury, and exerts a protective effect on neurons. The aim of the present study was to investigate the effects of H2S on neural functions following cardiac arrest (CA) in rats. A total of 60 rats were allocated at random into three groups. CA was induced to establish the model and CPR was performed after 6 min. Subsequently, sodium hydrosulfide (NaHS), hydroxylamine or saline was administered to the rats. Serum levels of H2S, neuron-specific enolase (NSE) and S100β were determined following CPR. In addition, neurological deficit scoring (NDS), the beam walking test (BWT), prehensile traction test and Morris water maze experiment were conducted. Neuronal apoptosis rates were detected in the hippocampal region following sacrifice. After CPR, as the H2S levels increased or decreased, the serum NSE and S100β concentrations decreased or increased, respectively (P<0.0w. The NDS results of the NaHS group were improved compared with those of the hydroxylamine group at 24 h after CPR (P<0.05). In the Morris water maze experiment, BWT and prehensile traction test the animals in the NaHS group performed best and rats in the hydroxylamine group performed worst. At day 7, the apoptotic index and the expression of caspase-3 were reduced in the hippocampal CA1 region, while the expression of Bcl-2 increased in the NaHS group; and results of the hydroxylamine group were in contrast. Therefore, the results of the present study indicate that H2S is able to improve neural function in rats following CPR. PMID:26893650
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
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.
Using symmetry-adapted optimized sum-of-products basis functions to calculate vibrational spectra
Leclerc, Arnaud
2016-01-01
Vibrational spectra can be computed without storing full-dimensional vectors by using low-rank sum-of-products (SOP) basis functions. We introduce symmetry constraints in the SOP basis functions to make it possible to separately calculate states in different symmetry subgroups. This is done using a power method to compute eigenvalues and an alternating least squares method to optimize basis functions. Owing to the fact that the power method favours the convergence of the lowest states, one must be careful not to exclude basis functions of some symmetries. Exploiting symmetry facilitates making assignments and improves the accuracy. The method is applied to the acetonitrile molecule.
Investigating the neural basis of empathy by EEG hyperscanning during a Third Party Punishment.
Astolfi, Laura; Toppi, Jlenia; Casper, Chantal; Freitag, Christine; Mattia, Donatella; Babiloni, Fabio; Ciaramidaro, Angela; Siniatchkin, Michael
2015-01-01
The recently developed technique of hyperscanning consists of the simultaneous recording of brain activity from multiple subjects involved in social interaction. The multivariate analysis of data coming from different subjects allows to model a system made of multiple brains interacting, and to characterize it in relation with different processes at the basis of social cognition. In this study, we investigate the empathy established between two subjects during a Third Party Punishment paradigm, in terms of the properties of the multiple-brain network obtained from EEG hyperscanning. Preliminary results show that significantly different multiple-brain network structures characterize a social situation operated by a human agent with respect to a computer based condition, and that the different levels of empathy induced by a fair or unfair treatment received by one of the subjects are characterized by denser inter-subjects connectivity and lower divisibility in the two single brain networks.
Gerini, G.; Maci, S.; Bruni, S.; Llombart, N.; Neto, A.
2005-01-01
Problem matched basis functions are proposed for the method of moments analysis of printed slot coupled microstrips. The appropriate equivalent currents of the integral equation kernel are represented in terms of two sets of entire domain basis functions. These functions synthesize on one hand the r
Selver, M Alper; Güzeliş, Cüneyt
2009-01-01
As being a tool that assigns optical parameters used in interactive visualization, Transfer Functions (TF) have important effects on the quality of volume rendered medical images. Unfortunately, finding accurate TFs is a tedious and time consuming task because of the trade off between using extensive search spaces and fulfilling the physician's expectations with interactive data exploration tools and interfaces. By addressing this problem, we introduce a semi-automatic method for initial generation of TFs. The proposed method uses a Self Generating Hierarchical Radial Basis Function Network to determine the lobes of a Volume Histogram Stack (VHS) which is introduced as a new domain by aligning the histograms of slices of a image series. The new self generating hierarchical design strategy allows the recognition of suppressed lobes corresponding to suppressed tissues and representation of the overlapping regions which are parts of the lobes but can not be represented by the Gaussian bases in VHS. Moreover, approximation with a minimum set of basis functions provides the possibility of selecting and adjusting suitable units to optimize the TF. Applications on different CT and MR data sets show enhanced rendering quality and reduced optimization time in abdominal studies.
Energy Technology Data Exchange (ETDEWEB)
Casadio, R.; Fariselli, P.; Vivarelli, F. [Univ. of Bologna (Italy); Compiani, M. [Univ. of Camerino (Italy)
1995-12-31
Radial basis function neural networks are trained on a data base comprising 38 globular proteins of well resolved crystallographic structure and the corresponding free energy contributions to the overall protein stability (as computed partially from crystallographic analysis and partially with multiple regression from experimental thermodynamic data by Ponnuswamy and Gromiha (1994)). Starting from the residue sequence and using as input code the percentage of each residue and the total residue number of the protein, it is found with a cross-validation method that neural networks can optimally predict the free energy contributions due to hydrogen bonds, hydrophobic interactions and the unfolded state. Terms due to electrostatic and disulfide bonding free energies are poorly predicted. This is so also when other input codes, including the percentage of secondary structure type of the protein and/or residue-pair information are used. Furthermore, trained on the computed and/or experimental {Delta}G values of the data base, neural networks predict a conformational stability ranging from about 10 to 20 kcal mol{sup -1} rather independently of the residue sequence, with an average error per protein of about 9 kcal mol{sup -1}.
Casadio, R; Compiani, M; Fariselli, P; Vivarelli, F
1995-01-01
Radial basis function neural networks are trained on a data base comprising 38 globular proteins of well resolved crystallographic structure and the corresponding free energy contributions to the overall protein stability (as computed partially from chrystallographic analysis and partially with multiple regression from experimental thermodynamic data by Ponnuswamy and Gromiha (1994)). Starting from the residue sequence and using as input code the percentage of each residue and the total residue number of the protein, it is found with a cross-validation method that neural networks can optimally predict the free energy contributions due to hydrogen bonds, hydrophobic interactions and the unfolded state. Terms due to electrostatic and disulfide bonding free energies are poorly predicted. This is so also when other input codes, including the percentage of secondary structure type of the protein and/or residue-pair information are used. Furthermore, trained on the computed and/or experimental delta G values of the data base, neural networks predict a conformational stability ranging from about 10 to 20 kcal mol-1 rather independently of the residue sequence, with an average error per protein of about 9 kcal mol-1.
Physiologic Basis for Improved Pulmonary Function after Lung Volume Reduction
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...
Design Optimization of Centrifugal Pump Using Radial Basis Function Metamodels
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...
Neurally augmented sexual function in human females: a preliminary investigation.
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
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 neural basis of impaired self-awareness after traumatic brain injury
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.
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.
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].
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.
The neural basis of novelty and appropriateness in processing of creative chunk decomposition.
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
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.
Basis Function Sampling: A New Paradigm for Material Property Computation
Whitmer, Jonathan K.; Chiu, Chi-cheng; Joshi, Abhijeet A.; de Pablo, Juan J.
2014-11-01
Wang-Landau sampling, and the associated class of flat histogram simulation methods have been remarkably helpful for calculations of the free energy in a wide variety of physical systems. Practically, convergence of these calculations to a target free energy surface is hampered by reliance on parameters which are unknown a priori. Here, we derive and implement a method built upon orthogonal functions which is fast, parameter-free, and (importantly) geometrically robust. The method is shown to be highly effective in achieving convergence. An important feature of this method is its ability to attain arbitrary levels of description for the free energy. It is thus ideally suited to in silico measurement of elastic moduli and other material properties related to free energy perturbations. We demonstrate the utility of such applications by applying our method to calculate the Frank elastic constants of the Lebwohl-Lasher model of liquid crystals.
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.
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.
Functional 3D Neural Mini-Tissues from Printed Gel-Based Bioink and Human Neural Stem Cells.
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
Institute of Scientific and Technical Information of China (English)
林涛; 葛玉敏; 安玳宁
2015-01-01
提出一种基于 RBF 神经网络的数据挖掘方法，将 RBF 神经网络应用于数据挖掘的分类和预测中，解决钢构件过程中的性能预测问题。其中用黄金分割法确定基于 RBF 神经网络的隐层节点数，减少该算法的计算复杂度，最终将其应用于某钢铁企业质量控制系统。构建对钢构件质量检测的数据挖掘及质量追溯平台，该平台是基于 RBF 神经网络的数据挖掘技术的。实际应用证明，产品的质量合格率可达到96.27%，符合国家相关的标准和技术指标。%To solve the performance prediction problem in the steel production process,this paper presentsed an ap-proach which is based on RBF neural network data mining method and uses RBF neural network in classification and predic-tion of data mining.The hidden layer nodes of the RBF neural network were determined by the golden section method to re-duce the computational complexity of the algorithm,which were applied to a steel enterprise quality control system.Finally, a platform of data mining and quality retrospective,which is based on RBF neural network data mining technology,was con-structed in product quality testing in steel companies.Practical application shows that the qualified rate of products can reach 96.27%,in line with national standards and technical specifications.
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.
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.
Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.
2012-04-01
Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.
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.
Gastrointestinal parasites and the neural control of gut functions
Directory of Open Access Journals (Sweden)
Marie Christiane Halliez
2015-11-01
Full Text Available Gastrointestinal motility and transport of water and electrolytes play key roles in the pathophysiology of diarrhea upon exposure to enteric parasites. These processes are actively modulated by the enteric nervous system (ENS, which includes efferent, and afferent neurons, as well as interneurons. ENS integrity is essential to the maintenance of homeostatic gut responses. A number of gastrointestinal parasites are known to cause disease by altering the enteric nervous system. The mechanisms remain incompletely understood. Cryptosporidium parvum, Giardia duodenalis (syn. G. intestinalis, G. lamblia, Trypanosoma cruzi, Schistosoma sp and others alter gastrointestinal motility, absorption, or secretion at least in part via effects on the ENS. Recent findings also implicate enteric parasites such as Cryptosporidium parvum and Giardia duodenalis in the development of post-infectious complications such as irritable bowel syndrome, which further underscores their effects on the gut-brain axis. This article critically reviews recent advances and the current state of knowledge on the impact of enteric parasitism on the neural control of gut functions, and provides insights into mechanisms underlying these abnormalities.
[Synapse elimination and functional neural circuit formation in the cerebellum].
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
Free vibrations and buckling analysis of laminated plates by oscillatory radial basis functions
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.
Computational Exploration of the Biological Basis of Black-Scholes Expected Utility Function
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...
Computational Exploration of the Biological Basis of Black-Scholes Expected Utility Function
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...
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
Directory of Open Access Journals (Sweden)
Takashi Yamada
Full Text Available Individuals with autism spectrum condition (ASC are known to excel in some perceptual cognitive tasks, but such developed functions have been often regarded as "islets of abilities" that do not significantly contribute to broader intellectual capacities. However, recent behavioral studies have reported that individuals with ASC have advantages for performing Raven's (Standard Progressive Matrices (RPM/RSPM, a standard neuropsychological test for general fluid intelligence, raising the possibility that ASC's cognitive strength can be utilized for more general purposes like novel problem solving. Here, the brain activity of 25 adults with high-functioning ASC and 26 matched normal controls (NC was measured using functional magnetic resonance imaging (fMRI to examine neural substrates of geometric reasoning during the engagement of a modified version of the RSPM test. Among the frontal and parietal brain regions involved in fluid intelligence, ASC showed larger activation in the left lateral occipitotemporal cortex (LOTC during an analytic condition with moderate difficulty than NC. Activation in the left LOTC and ventrolateral prefrontal cortex (VLPFC increased with task difficulty in NC, whereas such modulation of activity was absent in ASC. Furthermore, functional connectivity analysis revealed a significant reduction of activation coupling between the left inferior parietal cortex and the right anterior prefrontal cortex during both figural and analytic conditions in ASC. These results indicate altered pattern of functional specialization and integration in the neural system for geometric reasoning in ASC, which may explain its atypical cognitive pattern, including performance on the Raven's Matrices test.
Duffau, Hugues; Moritz-Gasser, Sylvie; Mandonnet, Emmanuel
2014-04-01
From recent findings provided by brain stimulation mapping during picture naming, we re-examine the neural basis of language. We studied structural-functional relationships by correlating the types of language disturbances generated by stimulation in awake patients, mimicking a transient virtual lesion both at cortical and subcortical levels (white matter and deep grey nuclei), with the anatomical location of the stimulation probe. We propose a hodotopical (delocalized) and dynamic model of language processing, which challenges the traditional modular and serial view. According to this model, following the visual input, the language network is organized in parallel, segregated (even if interconnected) large-scale cortico-subcortical sub-networks underlying semantic, phonological and syntactic processing. Our model offers several advantages (i) it explains double dissociations during stimulation (comprehension versus naming disorders, semantic versus phonemic paraphasias, syntactic versus naming disturbances, plurimodal judgment versus naming disorders); (ii) it takes into account the cortical and subcortical anatomic constraints; (iii) it explains the possible recovery of aphasia following a lesion within the "classical" language areas; (iv) it establishes links with a model executive functions.
On-line Supervised Adaptive Training Using Radial Basis Function Networks.
Luo, Wan; Billings, Steve A.; Fung, Chi F.
1996-12-01
A new recursive supervised training algorithm is derived for the radial basis neural network architecture. The new algorithm combines the procedures of on-line candidate regressor selection with the conventional Givens QR based recursive parameter estimator to provide efficient adaptive supervised network training. A new concise on-line correlation based performance monitoring scheme is also introduced as an auxiliary device to detect structural changes in temporal data processing applications. Practical and simulated examples are included to demonstrate the effectiveness of the new procedures. Copyright 1996 Elsevier Science Ltd.
Sherstinsky, A; Picard, R W
1996-01-01
The efficiency of the orthogonal least squares (OLS) method for training approximation networks is examined using the criterion of energy compaction. We show that the selection of basis vectors produced by the procedure is not the most compact when the approximation is performed using a nonorthogonal basis. Hence, the algorithm does not produce the smallest possible networks for a given approximation error. Specific examples are given using the Gaussian radial basis functions type of approximation networks.
Modeling the Flux-Charge Relation of Memristor with Neural Network of Smooth Hinge Functions
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...
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 ...
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.
Energy Technology Data Exchange (ETDEWEB)
Al-Amoudi, A.; Zhang, L. [University of Leeds (United Kingdom). School of Electronic and Electrical Engineering
2000-09-01
A neural-network-based approach for solar array modelling is presented. The logic hidden unit of the proposed network consists of a set of nonlinear radial basis functions (RBFs) which are connected directly to the input vector. The links between hidden and output units are linear. The model can be trained using a random set of data collected from a real photovoltaic (PV) plant. The training procedures are fast and the accuracy of the trained models is comparable with that of the conventional model. The principle and training procedures of the RBF-network modelling when applied to emulate the I/V characteristics of PV arrays are discussed. Simulation results of the trained RBF networks for modelling a PV array and predicting the maximum power points of a real PV panel are presented. (author)
Fusion prediction based on the attribute clustering net-work and the radial basis function
Institute of Scientific and Technical Information of China (English)
无
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.
Multiscale finite element methods for high-contrast problems using local spectral basis functions
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.
The Neural Correlates of Long-Term Carryover following Functional Electrical Stimulation for Stroke.
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
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.
Mechanical and neural function of triceps surae in elite racewalking.
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
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.
Sensitivity analysis applied to the construction of radial basis function networks.
Shi, D; Yeung, D S; Gao, J
2005-09-01
Conventionally, a radial basis function (RBF) network is constructed by obtaining cluster centers of basis function by maximum likelihood learning. This paper proposes a novel learning algorithm for the construction of radial basis function using sensitivity analysis. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the output's sensitivity to the training data. In classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. Our experimental results show that our proposed sensitivity-based RBF classifier outperforms the conventional RBFs and is as accurate as support vector machine (SVM). Hence, sensitivity analysis is expected to be a new alternative way to the construction of RBF networks.
Chen, S; Mulgrew, B; Grant, P M
1993-01-01
The application of a radial basis function network to digital communications channel equalization is examined. It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer. The training of a radial basis function network to realize the Bayesian equalization solution can be achieved efficiently using a simple and robust supervised clustering algorithm. During data transmission a decision-directed version of the clustering algorithm enables the radial basis function network to track a slowly time-varying environment. Moreover, the clustering scheme provides an automatic compensation for nonlinear channel and equipment distortion. Computer simulations are included to illustrate the analytical results.
Radial Basis Function Networks: Generalization in Over-realizable and Unrealizable Scenarios.
Saad, David; Freeman, Jason A. S.
1996-12-01
Learning and generalization in a two-layer radial basis function network, with fixed centres of the basis functions, is examined within a stochastic training paradigm. Employing a Bayesian approach, expressions for generalization error are derived under the assumption that the generating mechanism (teacher) for the training data is also a radial basis function network, but one for which the basis function centres and widths need not correspond to those of the student network. The effects of regularization, via a weight decay term, are examined. The cases in which the student has greater representational power than the teacher (over-realizable), and in which the teacher has greater power than the student (unrealizable) are studied. Dependence on knowing the centres of the teacher is eliminated by introducing a single degree-of-confidence parameter. Finally, simulations are performed which validate the analytic results. Copyright 1996 Elsevier Science Ltd.
Gaussian continuum basis functions for calculating high-harmonic generation spectra
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 ...
Directory of Open Access Journals (Sweden)
Mohammad Mehdi Mazarei
2012-01-01
Full Text Available This paper presents numerical solution of elliptic partial differential equations (Poisson's equation using a combination of logarithmic and multiquadric radial basis function networks. This method uses a special combination between logarithmic and multiquadric radial basis functions with a parameter r. Further, the condition number which arises in the process is discussed, and a comparison is made between them with our earlier studies and previously known ones. It is shown that the system is stable.
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)
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.
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.
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.
The Ridge Function Representation of Polynomials and an Application to Neural Networks
Institute of Scientific and Technical Information of China (English)
Ting Fan XIE; Fei Long CAO
2011-01-01
The first goal of this paper is to establish some properties of the ridge function representation for multivariate polynomials,and the second one is to apply these results to the problem of approximation by neural networks.We find that for continuous functions,the rate of approximation obtained by a neural network with one hidden layer is no slower than that of an algebraic polynomial.
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,...
Evolvable Neural Software System
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.
Möttönen, Riikka; Farmer, Harry; Watkins, Kate E
2016-01-29
People can communicate by using hand actions, e.g., signs. Understanding communicative actions requires that the observer knows that the actor has an intention to communicate and the meanings of the actions. Here, we investigated how this prior knowledge affects processing of observed actions. We used functional MRI to determine changes in action processing when non-signers were told that the observed actions are communicative (i.e., signs) and learned the meanings of half of the actions. Processing of hand actions activated the left and right inferior frontal gyrus (IFG, BA 44 and 45) when the communicative intention of the actor was known, even when the meanings of the actions remained unknown. These regions were not active when the observers did not know about the communicative nature of the hand actions. These findings suggest that the left and right IFG play a role in understanding the intention of the actor, but do not process visuospatial features of the communicative actions. Knowing the meanings of the hand actions further enhanced activity in the anterior part of the IFG (BA 45), the inferior parietal lobule and posterior inferior and middle temporal gyri in the left hemisphere. These left-hemisphere language regions could provide a link between meanings and observed actions. In sum, the findings provide evidence for the segregation of the networks involved in the neural processing of visuospatial features of communicative hand actions and those involved in understanding the actor's intention and the meanings of the actions.
Haghdani, Shokouh; Åstrand, Per-Olof; Koch, Henrik
2016-02-01
We have calculated the electronic optical rotation of seven molecules using coupled cluster singles-doubles (CCSD) and the second-order approximation (CC2) employing the aug-cc-pVXZ (X = D, T, or Q) basis sets. We have also compared to time-dependent density functional theory (TDDFT) by utilizing two functionals B3LYP and CAM-B3LYP and the same basis sets. Using relative and absolute error schemes, our calculations demonstrate that the CAM-B3LYP functional predicts optical rotation with the minimum deviations compared to CCSD at λ = 355 and 589.3 nm. Furthermore, our results illustrate that the aug-cc-pVDZ basis set provides the optical rotation in good agreement with the larger basis sets for molecules not possessing small-angle optical rotation at λ = 589.3 nm. We have also performed several two-point inverse power extrapolations for the basis set convergence, i.e., OR(∞) + AX(-n), using the CC2 model at λ = 355 and 589.3 nm. Our results reveal that a two-point inverse power extrapolation with the aug-cc-pVTZ and aug-cc-pVQZ basis sets at n = 5 provides optical rotation deviations similar to those of aug-cc-pV5Z with respect to the basis limit.
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.
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.
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.
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)
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.
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.
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...
Neural Tuning Functions Underlie Both Generalization and Interference.
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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
Neural Tuning Functions Underlie Both Generalization and Interference.
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
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
Functional analysis of the notch signalling cascade in neural progenitors
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...
Modeling the Flux-Charge Relation of Memristor with Neural Network of Smooth Hinge Functions
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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.
Application of neural networks with orthogonal activation functions in control of dynamical systems
Nikolić, Saša S.; Antić, Dragan S.; Milojković, Marko T.; Milovanović, Miroslav B.; Perić, Staniša Lj.; Mitić, Darko B.
2016-04-01
In this article, we present a new method for the synthesis of almost and quasi-orthogonal polynomials of arbitrary order. Filters designed on the bases of these functions are generators of generalised quasi-orthogonal signals for which we derived and presented necessary mathematical background. Based on theoretical results, we designed and practically implemented generalised first-order (k = 1) quasi-orthogonal filter and proved its quasi-orthogonality via performed experiments. Designed filters can be applied in many scientific areas. In this article, generated functions were successfully implemented in Nonlinear Auto Regressive eXogenous (NARX) neural network as activation functions. One practical application of the designed orthogonal neural network is demonstrated through the example of control of the complex technical non-linear system - laboratory magnetic levitation system. Obtained results were compared with neural networks with standard activation functions and orthogonal functions of trigonometric shape. The proposed network demonstrated superiority over existing solutions in the sense of system performances.
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
Scheler, Gabriele
2013-01-01
We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species) with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of individual transfer
Directory of Open Access Journals (Sweden)
Gabriele Scheler
Full Text Available We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of
Automatic target tracking in forward-looking infrared video sequences using tuned basis functions
Bal, Abdullah; Alam, Mohammad S.
2016-07-01
Tuned basis function (TBF) is a powerful technique for classification of two classes by transforming them into a new space, where both classes will have complementary eigenvectors. A target discrimination technique can be described based on these complementary eigenvector analyses under two classes: (1) target and (2) background clutter, where basis functions that best represent the desired targets form one class while the complementary basis functions form the second class. Since the TBF does not require pixel-based preprocessing, it provides significant advantages for target tracking applications. Furthermore, efficient eigenvector selection and subframe segmentation significantly reduce the computation burden of the target tracking algorithm. The performance of the proposed TBF-based target tracking algorithm has been tested using real-world forward looking infrared video sequences.
Symmetric multivariate polynomials as a basis for three-boson light-front wave functions.
Chabysheva, Sophia S; Elliott, Blair; Hiller, John R
2013-12-01
We develop a polynomial basis to be used in numerical calculations of light-front Fock-space wave functions. Such wave functions typically depend on longitudinal momentum fractions that sum to unity. For three particles, this constraint limits the two remaining independent momentum fractions to a triangle, for which the three momentum fractions act as barycentric coordinates. For three identical bosons, the wave function must be symmetric with respect to all three momentum fractions. Therefore, as a basis, we construct polynomials in two variables on a triangle that are symmetric with respect to the interchange of any two barycentric coordinates. We find that, through the fifth order, the polynomial is unique at each order, and, in general, these polynomials can be constructed from products of powers of the second- and third-order polynomials. The use of such a basis is illustrated in a calculation of a light-front wave function in two-dimensional ϕ(4) theory; the polynomial basis performs much better than the plane-wave basis used in discrete light-cone quantization.
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...
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.
Institute of Scientific and Technical Information of China (English)
LI An-yong
2004-01-01
A new method based on angular momentum theory was proposed to construct the basis functions of the irreducible representations(IRs) of point groups. The transformation coefficients, i. e. , coefficients S, are the components of the eigenvectors of some Hermitian matrices, and can be made as real numbers for all pure rotation point groups. The general formula for coefficient S was deduced, and applied to constructing the basis functions of single-valued irreducible representations of icosahedral group from the spherical harmonics with angular momentum j≤7.
Plant geography upon the basis of functional traits: an example from eastern North American trees.
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
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...
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 24 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
Nie, Xiaobing; Zheng, Wei Xing
2015-11-01
In this paper, we discuss the coexistence and dynamical behaviors of multiple equilibrium points for recurrent neural networks with a class of discontinuous nonmonotonic piecewise linear activation functions. It is proved that under some conditions, such n -neuron neural networks can have at least 5(n) equilibrium points, 3(n) of which are locally stable and the others are unstable, based on the contraction mapping theorem and the theory of strict diagonal dominance matrix. The investigation shows that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibrium points and more locally stable equilibrium points than the ones with continuous Mexican-hat-type activation function or discontinuous two-level activation functions. An illustrative example with computer simulations is presented to verify the theoretical analysis.
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.
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)
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.
A neural network approach to hippocampal function in classical conditioning.
Schmajuk, N A; DiCarlo, J J
1991-02-01
Hippocampal participation in classical conditioning in terms of Grossberg's (1975) attentional theory is described. According to the present rendition of this theory, pairing of a conditioned stimulus (CS) with an unconditioned stimulus (US) causes both an association of the sensory representation of the CS with the US (conditioned reinforcement learning) and an association of the sensory representation of the CS with the drive representation of the US (incentive motivation learning). Sensory representations compete among themselves for a limited-capacity short-term memory (STM) that is reflected in a long-term memory storage. The STM regulation hypothesis, which proposes that the hippocampus controls incentive motivation, self-excitation, and competition among sensory representations thereby regulating the contents of a limited capacity STM, is introduced. Under the STM regulation hypothesis, nodes and connections in Grossberg's neural network are mapped onto regional hippocampal-cerebellar circuits. The resulting neural model provides (a) a framework for understanding the dynamics of information processing and storage in the hippocampus and cerebellum during classical conditioning of the rabbit's nictitating membrane, (b) principles for understanding the effect of different hippocampal manipulations on classical conditioning, and (c) numerous novel and testable predictions.
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.
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.
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…
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.
Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl
2016-09-01
Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one's avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one's avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection. PMID:27415603
Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl
2016-09-01
Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one's avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one's avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection.
A data-driven approach to local gravity field modelling using spherical radial basis functions
Klees, R.; Tenzer, R.; Prutkin, I.; Wittwer, T.
2008-01-01
We propose a methodology for local gravity field modelling from gravity data using spherical radial basis functions. The methodology comprises two steps: in step 1, gravity data (gravity anomalies and/or gravity disturbances) are used to estimate the disturbing potential using least-squares techniqu
Sparse Linear Parametric Modeling of Room Acoustics with Orthonormal Basis Functions
DEFF Research Database (Denmark)
Vairetti, G.; von Waterschoot, T.; Moonen, M.;
2014-01-01
Orthonormal Basis Function (OBF) models provide a stable and well-conditioned representation of a linear system. When used for the modeling of room acoustics, useful information about the true dynamics of the system can be introduced by a proper selection of a set of poles, which however appear non...
IMAGE COMPRESSION USING DISCRETE ORTHOGONAL TRANSFORMS WITH THE «NOISE-LIKE» BASIS FUNCTIONS
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.
High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid
Directory of Open Access Journals (Sweden)
J. Cabello
2012-01-01
Full Text Available Statistical iterative methods are a widely used method of image reconstruction in emission tomography. Traditionally, the image space is modelled as a combination of cubic voxels as a matter of simplicity. After reconstruction, images are routinely filtered to reduce statistical noise at the cost of spatial resolution degradation. An alternative to produce lower noise during reconstruction is to model the image space with spherical basis functions. These basis functions overlap in space producing a significantly large number of non-zero elements in the system response matrix (SRM to store, which additionally leads to long reconstruction times. These two problems are partly overcome by exploiting spherical symmetries, although computation time is still slower compared to non-overlapping basis functions. In this work, we have implemented the reconstruction algorithm using Graphical Processing Unit (GPU technology for speed and a precomputed Monte-Carlo-calculated SRM for accuracy. The reconstruction time achieved using spherical basis functions on a GPU was 4.3 times faster than the Central Processing Unit (CPU and 2.5 times faster than a CPU-multi-core parallel implementation using eight cores. Overwriting hazards are minimized by combining a random line of response ordering and constrained atomic writing. Small differences in image quality were observed between implementations.
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
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.
MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions.
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
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.
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.
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.
A mixed basis density functional approach for one-dimensional systems with B-splines
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.
A canonical correlation neural network for multicollinearity and functional data.
Gou, Zhenkun; Fyfe, Colin
2004-03-01
We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing Partial Least Squares regression (at one extreme) to Canonical Correlation Analysis (at the other)and every intermediate operation between the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, we develop a second penalty term which acts on such data as a smoother in that the resulting weight vectors are much smoother and more interpretable than the weights without the robustification term. We illustrate our algorithms on both artificial and real data.
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.
RCS Computation by Parallel MoM Using Higher-Order Basis Functions
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...
An efficient ensemble of radial basis functions method based on quadratic programming
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.
Computational optical distortion correction using a radial basis function-based mapping method.
Bauer, Aaron; Vo, Sophie; Parkins, Keith; Rodriguez, Francisco; Cakmakci, Ozan; Rolland, Jannick P
2012-07-01
A distortion mapping and computational image unwarping method based on a network interpolation that uses radial basis functions is presented. The method is applied to correct distortion in an off-axis head-worn display (HWD) presenting up to 23% highly asymmetric distortion over a 27°x21° field of view. A 10(-5) mm absolute error of the mapping function over the field of view was achieved. The unwarping efficacy was assessed using the image-rendering feature of optical design software. Correlation coefficients between unwarped images seen through the HWD and the original images, as well as edge superimposition results, are presented. In an experiment, images are prewarped using radial basis functions for a recently built, off-axis HWD with a 20° diagonal field of view in a 4:3 ratio. Real-time video is generated by a custom application with 2 ms added latency and is demonstrated.
One-way hash function based on hyper-chaotic cellular neural network
Institute of Scientific and Technical Information of China (English)
Yang Qun-Ting; Gao Tie-Gang
2008-01-01
The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos characteristics is proposed. First, the chaos sequence is gotten by iterating cellular neural network with Runge-Kutta algorithm, and then the chaos sequence is iterated with the message. The hash code is obtained through the corresponding transform of the latter chaos sequence. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, especially the strong stability.
Institute of Scientific and Technical Information of China (English)
阮炯; 王军平; 郭德典
2004-01-01
In this paper, we first introduce the model of discrete-time neural networks with generalized input-output function and present a proof of the existence of a fixed point by Schauder fixed-point principle. Secondly, we study the uniformly asymptotical stability of equilibrium in non-autonomous discrete-time neural networks and give some sufficient conditions that guarantee the stability of it by using the converse theorem of Lyapunov function. Finally, several examples and numerical simulations are given to illustrate and reinforce our theories.
孤独症谱系障碍的遗传基础与神经机制%Genetic Basis and Neural Mechanism of Autism Spectrum Disorder
Institute of Scientific and Technical Information of China (English)
李晶; 林珠梅; 朱莉琪
2012-01-01
Autism spectrum disorder (ASD) is a defective mental disease and its core impairments are social function defect, communication defect, restrictive and stereotyped behavior pattern. The paper introduces the genetic basis and neural mechanism of ASD. ASD has high genetic rate, and 5-HT and testosterone of ASD individual are both higher. Neuroimaging studies find that there are some differences between ASD and normal individuals in the structure and function of amygdala, cingulate gyrus, the fusiform gyrus, mirror neurons, prefrontal lobe and other brain areas, but it is inconsistent in the discrepancy direction of some areas' activation patterns. In addition, the results of functional connectivity studies also confirm the hypothesis that the ASD individuals are under-connection. Future research should focus more on how to use the basic research outcomes to put forward effective treatment and training for clinical research.%孤独症谱系障碍(autism spectrum disorder,ASD)是一种神经精神障碍,主要表现为社会交往障碍、交流障碍以及局限性的兴趣和重复刻板的行为模式三个主要核心症状.本文介绍了ASD的遗传基础和神经机制的最新研究进展.ASD具有较高的遗传率,且ASD个体的5-羟色胺和睾丸激素都较高.神经影像学研究发现,ASD个体的杏仁核、扣带回、梭状回、镜像神经元和前额叶等大脑区域在结构和功能上都与正常发育个体存在差异,但在个别区域激活模式的差异方向上仍存在不一致的地方.此外,功能连接的研究结果也证实了ASD个体连接不良的假设.未来的研究应该更多地着眼于如何利用这些基础研究成果为临床上提出有效的治疗和训练方式.
Hoyer, Chad E; Gagliardi, Laura; Truhlar, Donald G
2015-11-01
Time-dependent Kohn-Sham density functional theory (TD-KS-DFT) is useful for calculating electronic excitation spectra of large systems, but the low-energy spectra are often complicated by artificially lowered higher-energy states. This affects even the lowest energy excited states. Here, by calculating the lowest energy spin-conserving excited state for atoms from H to K and for formaldehyde, we show that this problem does not occur in multiconfiguration pair-density functional theory (MC-PDFT). We use the tPBE on-top density functional, which is a translation of the PBE exchange-correlation functional. We compare to a robust multireference method, namely, complete active space second-order perturbation theory (CASPT2), and to TD-KS-DFT with two popular exchange-correlation functionals, PBE and PBE0. We find for atoms that the mean unsigned error (MUE) of MC-PDFT with the tPBE functional improves from 0.42 to 0.40 eV with a double set of diffuse functions, whereas the MUEs for PBE and PBE0 drastically increase from 0.74 to 2.49 eV and from 0.45 to 1.47 eV, respectively.
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.
An on-line training radial basis function neural network for optimum operation of the UPFC
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...
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...
The Neural Basis of Mark Making: A Functional MRI Study of Drawing
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
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.
Constructive feedforward neural networks using hermite polynomial activation functions.
Ma, Liying; Khorasani, K
2005-07-01
In this paper, a constructive one-hidden-layer network is introduced where each hidden unit employs a polynomial function for its activation function that is different from other units. Specifically, both a structure level as well as a function level adaptation methodologies are utilized in constructing the network. The functional level adaptation scheme ensures that the "growing" or constructive network has different activation functions for each neuron such that the network may be able to capture the underlying input-output map more effectively. The activation functions considered consist of orthonormal Hermite polynomials. It is shown through extensive simulations that the proposed network yields improved performance when compared to networks having identical sigmoidal activation functions.
Radial basis function networks with linear interval regression weights for symbolic interval data.
Su, Shun-Feng; Chuang, Chen-Chia; Tao, C W; Jeng, Jin-Tsong; Hsiao, Chih-Ching
2012-02-01
This paper introduces a new structure of radial basis function networks (RBFNs) that can successfully model symbolic interval-valued data. In the proposed structure, to handle symbolic interval data, the Gaussian functions required in the RBFNs are modified to consider interval distance measure, and the synaptic weights of the RBFNs are replaced by linear interval regression weights. In the linear interval regression weights, the lower and upper bounds of the interval-valued data as well as the center and range of the interval-valued data are considered. In addition, in the proposed approach, two stages of learning mechanisms are proposed. In stage 1, an initial structure (i.e., the number of hidden nodes and the adjustable parameters of radial basis functions) of the proposed structure is obtained by the interval competitive agglomeration clustering algorithm. In stage 2, a gradient-descent kind of learning algorithm is applied to fine-tune the parameters of the radial basis function and the coefficients of the linear interval regression weights. Various experiments are conducted, and the average behavior of the root mean square error and the square of the correlation coefficient in the framework of a Monte Carlo experiment are considered as the performance index. The results clearly show the effectiveness of the proposed structure.
Directory of Open Access Journals (Sweden)
Loic Auderset
2016-01-01
Full Text Available The central nervous system (CNS is a highly organised structure. Many signalling systems work in concert to ensure that neural stem cells are appropriately directed to generate progenitor cells, which in turn mature into functional cell types including projection neurons, interneurons, astrocytes, and oligodendrocytes. Herein we explore the role of the low density lipoprotein (LDL receptor family, in particular family members LRP1 and LRP2, in regulating the behaviour of neural stem and progenitor cells during development and adulthood. The ability of LRP1 and LRP2 to bind a diverse and extensive range of ligands, regulate ligand endocytosis, recruit nonreceptor tyrosine kinases for direct signal transduction and signal in conjunction with other receptors, enables them to modulate many crucial neural cell functions.
An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory
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...
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.
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.
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.
Radical pruning: a method to construct skeleton radial basis function networks.
Augusteijn, M F; Shaw, K A
2000-04-01
Trained radial basis function networks are well-suited for use in extracting rules and explanations because they contain a set of locally tuned units. However, for rule extraction to be useful, these networks must first be pruned to eliminate unnecessary weights. The pruning algorithm cannot search the network exhaustively because of the computational effort involved. It is shown that using multiple pruning methods with smart ordering of the pruning candidates, the number of weights in a radial basis function network can be reduced to a small fraction of the original number. The complexity of the pruning algorithm is quadratic (instead of exponential) in the number of network weights. Pruning performance is shown using a variety of benchmark problems from the University of California, Irvine machine learning database.
A Modified Beam Propagation Method Based on the Galerkin Method with Hermite-Gauss Basis Functions
Institute of Scientific and Technical Information of China (English)
Xiao Jinbiao; Liu Xu; Cai Chun; Fan Hehong; Sun Xiaohan
2006-01-01
A beam propagation method based on the Galerkin method with Hermite-Gauss basis functions for studying optical field propagation in weakly guiding dielectric structures is described. The selected basis functions naturally satisfy the required boundary conditions at infinity so that the boundary truncation is avoided. The paraxial propagation equation is converted into a set of first-order ordinary differential equations,which are solved by means of standard numerical library routines. Besides, the calculation is efficient due to its small resulted matrix. The evolution of the injected field and its normalized power along the propagation distance in an asymmetric slab waveguide and directional coupler are presented, and the solutions are good agreement with those obtained by finite difference BPM, which tests the validity of the present approach.
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)
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.
Multi-dimensional option pricing using radial basis functions and the generalized Fourier transform
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.
Chen, S; Wu, Y; Luk, B L
1999-01-01
The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.
Directory of Open Access Journals (Sweden)
Wang Pidong
2016-01-01
Full Text Available Blind source separation is a hot topic in signal processing. Most existing works focus on dealing with linear combined signals, while in practice we always encounter with nonlinear mixed signals. To address the problem of nonlinear source separation, in this paper we propose a novel algorithm using radial basis function neutral network, optimized by multi-universe parallel quantum genetic algorithm. Experiments show the efficiency of the proposed method.
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.
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.
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)
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
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.
Functional dissociations in top-down control dependent neural repetition priming.
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
Zero Cost Function Training Algorithms for Three-Layered Feedforward Neural Networks
Institute of Scientific and Technical Information of China (English)
无
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.
Hellmann-Feynman forces within the DFT + U in Wannier functions basis.
Novoselov, D; Korotin, Dm M; Anisimov, V I
2015-08-19
The most general way to describe localized atomic-like electronic states in strongly correlated compounds is to use Wannier functions. In the present paper we continue development of widely-used DFT + U method with the Wannier function basis set and propose a technique to calculate Hubbard contribution to atomic forces. The technique was implemented as a part of plane-waves pseudopotential code Quantum-ESPRESSO and tested on two compounds: charge transfer insulator NiO with cubic crystal structure and correlated metal SrVO3 with perovskite structure.
Immunomodulation of enteric neural function in irritable bowel syndrome
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...
Directory of Open Access Journals (Sweden)
ANTONELLA eMARCHETTI
2015-12-01
Full Text Available A topic of common interest to psychologists and philosophers is the spontaneous flow of thoughts when the individual is awake but not involved in cognitive demands. This argument, classically referred to as the stream of consciousness of James, is now known in the psychological literature as Mind-Wandering. Although of great interest, this construct has been scarcely investigated so far. Diaz and colleagues (2013 created the Amsterdam Resting State Questionnaire (ARSQ, composed of 27 items, distributed in seven factors: discontinuity of mind, theory of mind (ToM, self, planning, sleepiness, comfort and somatic awareness. The present study aims at: testing psychometric properties of the ARSQ in a sample of 670 Italian subjects; exploring the neural correlates of a subsample of participants (N=28 divided into two groups on the basis of the scores obtained in the ToM factor. Results show a satisfactory reliability of the original factional structure in the Italian sample. In the subjects with a high mean in the ToM factor compared to low mean subjects, functional MRI revealed: a network (48 nodes with higher functional connectivity (FC with a dominance of the left hemisphere; an increased within-lobe FC in frontal and insular lobes. In both neural and behavioral terms, our results support the idea that the mind, which does not rest even when explicitly asked to do so, has various and interesting mentalistic-like contents.
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
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.
Basis of symmetric polynomials for many-boson light-front wave functions.
Chabysheva, Sophia S; Hiller, John R
2014-12-01
We provide an algorithm for the construction of orthonormal multivariate polynomials that are symmetric with respect to the interchange of any two coordinates on the unit hypercube and are constrained to the hyperplane where the sum of the coordinates is one. These polynomials form a basis for the expansion of bosonic light-front momentum-space wave functions, as functions of longitudinal momentum, where momentum conservation guarantees that the fractions are on the interval [0,1] and sum to one. This generalizes earlier work on three-boson wave functions to wave functions for arbitrarily many identical bosons. A simple application in two-dimensional ϕ(4) theory illustrates the use of these polynomials.
Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks.
Yao, Kun; Parkhill, John
2016-03-01
We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from an input electron density. The output of the network is used as a nonlocal correction to conventional local and semilocal kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. The density which minimizes the total energy given by the functional is examined in detail. We identify several avenues to improve on this exploratory work, by reducing numerical noise and changing the structure of our functional. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.
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
Energy Technology Data Exchange (ETDEWEB)
Djukanovic, M. (Institut Nikola Tesla, Belgrade (Yugoslavia)); Sobajic, D.J.; Yohhan Pao (Case Western Reserve Univ., Cleveland, OH (United States))
1991-10-01
The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).