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Sample records for basis function neural

  1. Radial basis function neural network in fault detection of automotive ...

    African Journals Online (AJOL)

    Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection

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

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

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

  3. The Neural Basis of Typewriting: A Functional MRI Study.

    Science.gov (United States)

    Higashiyama, Yuichi; Takeda, Katsuhiko; Someya, Yoshiaki; Kuroiwa, Yoshiyuki; Tanaka, Fumiaki

    2015-01-01

    To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI) study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.

  4. The Neural Basis of Typewriting: A Functional MRI Study.

    Directory of Open Access Journals (Sweden)

    Yuichi Higashiyama

    Full Text Available To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.

  5. Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

    Science.gov (United States)

    Burken, John J.

    2005-01-01

    This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

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

    International Nuclear Information System (INIS)

    Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong

    2014-01-01

    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

  7. 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.

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

    CERN Document Server

    Liu, Jinkun

    2013-01-01

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

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

    International Nuclear Information System (INIS)

    Karami, A.; Mohammadi, M.S.

    2008-01-01

    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)

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

    Directory of Open Access Journals (Sweden)

    Wei Liu

    2013-01-01

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

  11. A prediction method for the wax deposition rate based on a radial basis function neural network

    Directory of Open Access Journals (Sweden)

    Ying Xie

    2017-06-01

    Full Text Available The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power, a decreased flow rate or even to the total blockage of the line, with losses of production and capital investment, so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline. This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors, the pipe wall temperature gradient, pipe wall wax crystal solubility coefficient, pipe wall shear stress and crude oil viscosity, by the gray correlational analysis method. MATLAB software is employed to establish the RBF neural network. Compared with the previous literature, favorable consistency exists between the predicted outcomes and the experimental results, with a relative error of 1.5%. It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.

  12. A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

    Science.gov (United States)

    Hong, Xia

    2006-07-01

    In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

  13. 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.

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

    Science.gov (United States)

    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.

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

    International Nuclear Information System (INIS)

    Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

    2014-01-01

    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 (T p :I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks

  16. Machine learning of radial basis function neural network based on Kalman filter: Introduction

    Directory of Open Access Journals (Sweden)

    Vuković Najdan L.

    2014-01-01

    Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.

  17. Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors.

    Science.gov (United States)

    Sabour, Mohammad Reza; Moftakhari Anasori Movahed, Saman

    2017-02-01

    The soil sorption partition coefficient logK oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    CERN Document Server

    Sundararajan, N; Wei Lu Ying

    1999-01-01

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

  19. Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks

    OpenAIRE

    Ali Reza Zirak; Sobhan Roshani

    2016-01-01

    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 a...

  20. Neural Basis of Enhanced Executive Function in Older Video Game Players: An fMRI Study.

    Science.gov (United States)

    Wang, Ping; Zhu, Xing-Ting; Qi, Zhigang; Huang, Silin; Li, Hui-Jie

    2017-01-01

    Video games have been found to have positive influences on executive function in older adults; however, the underlying neural basis of the benefits from video games has been unclear. Adopting a task-based functional magnetic resonance imaging (fMRI) study targeted at the flanker task, the present study aims to explore the neural basis of the improved executive function in older adults with video game experiences. Twenty video game players (VGPs) and twenty non-video game players (NVGPs) of 60 years of age or older participated in the present study, and there are no significant differences in age ( t = 0.62, p = 0.536), gender ratio ( t = 1.29, p = 0.206) and years of education ( t = 1.92, p = 0.062) between VGPs and NVGPs. The results show that older VGPs present significantly better behavioral performance than NVGPs. Older VGPs activate greater than NVGPs in brain regions, mainly in frontal-parietal areas, including the right dorsolateral prefrontal cortex, the left supramarginal gyrus, the right angular gyrus, the right precuneus and the left paracentral lobule. The present study reveals that video game experiences may have positive influences on older adults in behavioral performance and the underlying brain activation. These results imply the potential role that video games can play as an effective tool to improve cognitive ability in older adults.

  1. Neural Basis of Enhanced Executive Function in Older Video Game Players: An fMRI Study

    Directory of Open Access Journals (Sweden)

    Ping Wang

    2017-11-01

    Full Text Available Video games have been found to have positive influences on executive function in older adults; however, the underlying neural basis of the benefits from video games has been unclear. Adopting a task-based functional magnetic resonance imaging (fMRI study targeted at the flanker task, the present study aims to explore the neural basis of the improved executive function in older adults with video game experiences. Twenty video game players (VGPs and twenty non-video game players (NVGPs of 60 years of age or older participated in the present study, and there are no significant differences in age (t = 0.62, p = 0.536, gender ratio (t = 1.29, p = 0.206 and years of education (t = 1.92, p = 0.062 between VGPs and NVGPs. The results show that older VGPs present significantly better behavioral performance than NVGPs. Older VGPs activate greater than NVGPs in brain regions, mainly in frontal-parietal areas, including the right dorsolateral prefrontal cortex, the left supramarginal gyrus, the right angular gyrus, the right precuneus and the left paracentral lobule. The present study reveals that video game experiences may have positive influences on older adults in behavioral performance and the underlying brain activation. These results imply the potential role that video games can play as an effective tool to improve cognitive ability in older adults.

  2. The neural basis of human social values: evidence from functional MRI.

    Science.gov (United States)

    Zahn, Roland; Moll, Jorge; Paiva, Mirella; Garrido, Griselda; Krueger, Frank; Huey, Edward D; Grafman, Jordan

    2009-02-01

    Social values are composed of social concepts (e.g., "generosity") and context-dependent moral sentiments (e.g., "pride"). The neural basis of this intricate cognitive architecture has not been investigated thus far. Here, we used functional magnetic resonance imaging while subjects imagined their own actions toward another person (self-agency) which either conformed or were counter to a social value and were associated with pride or guilt, respectively. Imagined actions of another person toward the subjects (other-agency) in accordance with or counter to a value were associated with gratitude or indignation/anger. As hypothesized, superior anterior temporal lobe (aTL) activity increased with conceptual detail in all conditions. During self-agency, activity in the anterior ventromedial prefrontal cortex correlated with pride and guilt, whereas activity in the subgenual cingulate solely correlated with guilt. In contrast, indignation/anger activated lateral orbitofrontal-insular cortices. Pride and gratitude additionally evoked mesolimbic and basal forebrain activations. Our results demonstrate that social values emerge from coactivation of stable abstract social conceptual representations in the superior aTL and context-dependent moral sentiments encoded in fronto-mesolimbic regions. This neural architecture may provide the basis of our ability to communicate about the meaning of social values across cultural contexts without limiting our flexibility to adapt their emotional interpretation.

  3. Fault diagnosis and performance evaluation for high current LIA based on radial basis function neural network

    International Nuclear Information System (INIS)

    Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin

    2006-01-01

    High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)

  4. Vibration control of uncertain multiple launch rocket system using radial basis function neural network

    Science.gov (United States)

    Li, Bo; Rui, Xiaoting

    2018-01-01

    Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.

  5. Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm. (On-Line Harmonics Estimation Application

    Directory of Open Access Journals (Sweden)

    Eyad K Almaita

    2017-03-01

    Keywords: Energy efficiency, Power quality, Radial basis function, neural networks, adaptive, harmonic. Article History: Received Dec 15, 2016; Received in revised form Feb 2nd 2017; Accepted 13rd 2017; Available online How to Cite This Article: Almaita, E.K and Shawawreh J.Al (2017 Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm (On-Line Harmonics Estimation Application.  International Journal of Renewable Energy Develeopment, 6(1, 9-17. http://dx.doi.org/10.14710/ijred.6.1.9-17

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

    Science.gov (United States)

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

    2014-09-18

    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.

  7. DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING

    Directory of Open Access Journals (Sweden)

    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.

  8. Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction.

    Science.gov (United States)

    Kumudha, P; Venkatesan, R

    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.

  9. Prediction of water formation temperature in natural gas dehydrators using radial basis function (RBF neural networks

    Directory of Open Access Journals (Sweden)

    Tatar Afshin

    2016-03-01

    Full Text Available Raw natural gases usually contain water. It is very important to remove the water from these gases through dehydration processes due to economic reasons and safety considerations. One of the most important methods for water removal from these gases is using dehydration units which use Triethylene glycol (TEG. The TEG concentration at which all water is removed and dew point characteristics of mixture are two important parameters, which should be taken into account in TEG dehydration system. Hence, developing a reliable and accurate model to predict the performance of such a system seems to be very important in gas engineering operations. This study highlights the use of intelligent modeling techniques such as Multilayer perceptron (MLP and Radial Basis Function Neural Network (RBF-ANN to predict the equilibrium water dew point in a stream of natural gas based on the TEG concentration of stream and contractor temperature. Literature data set used in this study covers temperatures from 10 °C to 80 °C and TEG concentrations from 90.000% to 99.999%. Results showed that both models are accurate in prediction of experimental data and the MLP model gives more accurate predictions compared to RBF model.

  10. Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network.

    Science.gov (United States)

    Mohamed Yacin, S; Srinivasa Chakravarthy, V; Manivannan, M

    2011-11-01

    Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.

  11. An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks

    Science.gov (United States)

    Kayri, Murat

    2015-01-01

    The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…

  12. The functional and structural neural basis of individual differences in loss aversion.

    Science.gov (United States)

    Canessa, Nicola; Crespi, Chiara; Motterlini, Matteo; Baud-Bovy, Gabriel; Chierchia, Gabriele; Pantaleo, Giuseppe; Tettamanti, Marco; Cappa, Stefano F

    2013-09-04

    Decision making under risk entails the anticipation of prospective outcomes, typically leading to the greater sensitivity to losses than gains known as loss aversion. Previous studies on the neural bases of choice-outcome anticipation and loss aversion provided inconsistent results, showing either bidirectional mesolimbic responses of activation for gains and deactivation for losses, or a specific amygdala involvement in processing losses. Here we focused on loss aversion with the aim to address interindividual differences in the neural bases of choice-outcome anticipation. Fifty-six healthy human participants accepted or rejected 104 mixed gambles offering equal (50%) chances of gaining or losing different amounts of money while their brain activity was measured with functional magnetic resonance imaging (fMRI). We report both bidirectional and gain/loss-specific responses while evaluating risky gambles, with amygdala and posterior insula specifically tracking the magnitude of potential losses. At the individual level, loss aversion was reflected both in limbic fMRI responses and in gray matter volume in a structural amygdala-thalamus-striatum network, in which the volume of the "output" centromedial amygdala nuclei mediating avoidance behavior was negatively correlated with monetary performance. We conclude that outcome anticipation and ensuing loss aversion involve multiple neural systems, showing functional and structural individual variability directly related to the actual financial outcomes of choices. By supporting the simultaneous involvement of both appetitive and aversive processing in economic decision making, these results contribute to the interpretation of existing inconsistencies on the neural bases of anticipating choice outcomes.

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

    International Nuclear Information System (INIS)

    Chen Shaoqi; Zhang Yanzhen; Xiao Zhuangwei; Zhang Xuexin

    2007-01-01

    Objective: To address the neural basis of Chinese idioms processing with different kinds of stimuli using an event-related fMRI design. Methods: Sixteen native Chinese speakers were asked to perform a semantic decision task during fMRI scanning. Three kinds of stimuli were used: Real idioms (Real-idiom condition); Literally plausible phrases (Pseudo-idiom condition, the last character of a real idiom was replaced by a character with similar meaning); Literally implausible strings (Non-idiom condition, the last character of a real idiom was replaced by a character with unrelated meaning). Reaction time and correct rate were recorded at the same time. Results: The error rate was 2.6%, 5.2% and 0.9% (F=3.51, P 0.05) for real idioms, pseudo-idioms and wrong idioms, respectively. Similar neural network was activated in all of the three conditions. However, the right hippocampus was only activated in the real idiom condition, and significant activations were found in anterior portion of left inferior frontal gyms (BA47) in real-and pseudo-idiom conditions, but not in non-idiom condition. Conclusion: The right hippocampus plays a specific role in the particular wording of the Chinese idioms. And the left anterior inferior frontal gyms (BA47) may be engaged in the semantic processing of Chinese idioms. The results support the notion that there were specific neural bases for Chinese idioms processing. (authors)

  14. Dynamic Fault Diagnosis for Semi-Batch Reactor under Closed-Loop Control via Independent Radial Basis Function Neural Network

    OpenAIRE

    Abdelkarim M. Ertiame; D. W. Yu; D. L. Yu; J. B. Gomm

    2015-01-01

    In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is emplo...

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

    International Nuclear Information System (INIS)

    Vaziri, Nima; Hojabri, Alireza; Erfani, Ali; Monsefi, Mehrdad; Nilforooshan, Behnam

    2007-01-01

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

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

    Science.gov (United States)

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

    2010-02-01

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

  17. Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads

    Directory of Open Access Journals (Sweden)

    Ali Mansourkhaki

    2018-01-01

    Full Text Available Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effectson humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to findnew approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LA eq by measuring the traffivolume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficienof determination (R 2 and the Mean Squared Error (MSE. Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.

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

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Vidnerová, Petra

    2009-01-01

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

  19. The neural basis of love as a subliminal prime: an event-related functional magnetic resonance imaging study.

    Science.gov (United States)

    Ortigue, S; Bianchi-Demicheli, F; Hamilton, A F de C; Grafton, S T

    2007-07-01

    Throughout the ages, love has been defined as a motivated and goal-directed mechanism with explicit and implicit mechanisms. Recent evidence demonstrated that the explicit representation of love recruits subcorticocortical pathways mediating reward, emotion, and motivation systems. However, the neural basis of the implicit (unconscious) representation of love remains unknown. To assess this question, we combined event-related functional magnetic resonance imaging (fMRI) with a behavioral subliminal priming paradigm embedded in a lexical decision task. In this task, the name of either a beloved partner, a neutral friend, or a passionate hobby was subliminally presented before a target stimulus (word, nonword, or blank), and participants were required to decide if the target was a word or not. Behavioral results showed that subliminal presentation of either a beloved's name (love prime) or a passion descriptor (passion prime) enhanced reaction times in a similar fashion. Subliminal presentation of a friend's name (friend prime) did not show any beneficial effects. Functional results showed that subliminal priming with a beloved's name (as opposed to either a friend's name or a passion descriptor) specifically recruited brain areas involved in abstract representations of others and the self, in addition to motivation circuits shared with other sources of passion. More precisely, love primes recruited the fusiform and angular gyri. Our findings suggest that love, as a subliminal prime, involves a specific neural network that surpasses a dopaminergic-motivation system.

  20. Decoupling control of a five-phase fault-tolerant permanent magnet motor by radial basis function neural network inverse

    Science.gov (United States)

    Chen, Qian; Liu, Guohai; Xu, Dezhi; Xu, Liang; Xu, Gaohong; Aamir, Nazir

    2018-05-01

    This paper proposes a new decoupled control for a five-phase in-wheel fault-tolerant permanent magnet (IW-FTPM) motor drive, in which radial basis function neural network inverse (RBF-NNI) and internal model control (IMC) are combined. The RBF-NNI system is introduced into original system to construct a pseudo-linear system, and IMC is used as a robust controller. Hence, the newly proposed control system incorporates the merits of the IMC and RBF-NNI methods. In order to verify the proposed strategy, an IW-FTPM motor drive is designed based on dSPACE real-time control platform. Then, the experimental results are offered to verify that the d-axis current and the rotor speed are successfully decoupled. Besides, the proposed motor drive exhibits strong robustness even under load torque disturbance.

  1. Decoupling Research on Flexible Tactile Sensors Interfered by White Gaussian Noise Using Improved Radical Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Feilu Wang

    2014-04-01

    Full Text Available Research on tactile sensors to enhance their flexibility and ability of multi- dimensional information detection is a key issue to develop humanoid robots. In view of that the tactile sensor is often affected by noise, this paper adds different white Gaussian noises (WGN into the ideal model of flexible tactile sensors based on conductive rubber purposely, then improves the standard radial basis function neural network (RNFNN to deal with the noises. The modified RBFNN is applied to approximate and decouple the mapping relationship between row-column resistance with WGNs and three-dimensional deformation. Numerical experiments demonstrate that the decoupling result of the deformation for the sensor is quite good. The results show that the improved RBFNN which doesn’t rely on the mathematical model of the system has good anti-noise ability and robustness.

  2. Simulation and prediction of the thuringiensin abiotic degradation processes in aqueous solution by a radius basis function neural network model.

    Science.gov (United States)

    Zhou, Jingwen; Xu, Zhenghong; Chen, Shouwen

    2013-04-01

    The thuringiensin abiotic degradation processes in aqueous solution under different conditions, with a pH range of 5.0-9.0 and a temperature range of 10-40°C, were systematically investigated by an exponential decay model and a radius basis function (RBF) neural network model, respectively. The half-lives of thuringiensin calculated by the exponential decay model ranged from 2.72 d to 16.19 d under the different conditions mentioned above. Furthermore, an RBF model with accuracy of 0.1 and SPREAD value 5 was employed to model the degradation processes. The results showed that the model could simulate and predict the degradation processes well. Both the half-lives and the prediction data showed that thuringiensin was an easily degradable antibiotic, which could be an important factor in the evaluation of its safety. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. Influence of the Training Methods in the Diagnosis of Multiple Sclerosis Using Radial Basis Functions Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ángel Gutiérrez

    2015-04-01

    Full Text Available The data available in the average clinical study of a disease is very often small. This is one of the main obstacles in the application of neural networks to the classification of biological signals used for diagnosing diseases. A rule of thumb states that the number of parameters (weights that can be used for training a neural network should be around 15% of the available data, to avoid overlearning. This condition puts a limit on the dimension of the input space. Different authors have used different approaches to solve this problem, like eliminating redundancy in the data, preprocessing the data to find centers for the radial basis functions, or extracting a small number of features that were used as inputs. It is clear that the classification would be better the more features we could feed into the network. The approach utilized in this paper is incrementing the number of training elements with randomly expanding training sets. This way the number of original signals does not constraint the dimension of the input set in the radial basis network. Then we train the network using the method that minimizes the error function using the gradient descent algorithm and the method that uses the particle swarm optimization technique. A comparison between the two methods showed that for the same number of iterations on both methods, the particle swarm optimization was faster, it was learning to recognize only the sick people. On the other hand, the gradient method was not as good in general better at identifying those people.

  4. Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Seng-Chi Chen

    2014-01-01

    Full Text Available Studies on active magnetic bearing (AMB systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC. The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC, the parameters of which are adjusted using a radial basis function neural network (RBFNN, is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

  5. Functional mapping of the neural basis for the encoding and retrieval of human episodic memory using H215O PET

    International Nuclear Information System (INIS)

    Lee, Jae Sung; Nam, Hyun Woo; Lee, Dong Soo; Lee, Sang Kun; Jang, Myoung Jin; Ahn, Ji Young; Park, Kwang Suk; Chung, June Key; Lee, Myung Chul

    2000-01-01

    Episodic memory is described as an 'autobiographical' memory responsible for storing a record of the events in our lives. We performed functional brain activation study using H 2 1 5O PET to reveal the neural basis of the encoding and the retrieval of episodic memory in human normal volunteers. Four repeated H 2 1 5O PET scans with two reference and two activation tasks were performed on 6 normal volunteers to activate brain areas engaged in encoding and retrieval with verbal materials. Images from the same subject were spatially registered and normalized using linear and nonlinear transformation. Using the means and variances for every condition which were adjusted with analysis of covariance, t-statistic analysis were performed voxel-wise. Encoding of episodic memory activated the opercular and triangular parts of left inferior frontal gyrus, right prefrontal cortex, medial frontal area, cingulate gyrus, posterior middle and inferior temporal gyri, and cerebellum, and both primary visual and visual association areas. Retrieval of episodic memory activated the triangular part of left inferior frontal gyrus and inferior temporal gyrus, right prefrontal cortex and medial temporal ares, and both cerebellum and primary visual and visual association areas. The activations in the opercular part of left inferior frontal gyrus and the right prefrontal cortex meant the essential role of these areas in the encoding and retrieval of episodic memeory. We could localize the neural basis of the encoding and retrieval of episodic memory using H 2 1 5O PET, which was partly consistent with the hypothesis of hemispheric encoding/retrieval asymmetry.=20

  6. Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional Connectivity Network Recovery in Resting-State Functional MRI.

    Science.gov (United States)

    DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel

    2016-03-29

    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.

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

    Science.gov (United States)

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

    2013-03-01

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

  8. Application of radial basis neural network for state estimation of ...

    African Journals Online (AJOL)

    An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...

  9. [Application of wavelet transform-radial basis function neural network in NIRS for determination of rifampicin and isoniazide tablets].

    Science.gov (United States)

    Lu, Jia-hui; Zhang, Yi-bo; Zhang, Zhuo-yong; Meng, Qing-fan; Guo, Wei-liang; Teng, Li-rong

    2008-06-01

    A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.

  10. Neural Basis of Visual Distraction

    Science.gov (United States)

    Kim, So-Yeon; Hopfinger, Joseph B.

    2010-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Xiao-Bing eGao

    2015-10-01

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

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

    Science.gov (United States)

    Gao, Xiao-Bing; Hermes, Gretchen

    2015-01-01

    The neuronal system that resides in the perifornical and lateral hypothalamus (Pf/LH) and synthesizes the neuropeptide hypocretin/orexin participates in critical brain functions across species from fish to human. The hypocretin system regulates neural activity responsible for daily functions (such as sleep/wake homeostasis, energy balance, appetite, etc.) and long-term behavioral changes (such as reward seeking and addiction, stress response, etc.) in animals. The most recent evidence 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. PMID:26539086

  13. The neural basis of the imitation drive.

    Science.gov (United States)

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

    2016-01-01

    Spontaneous imitation is assumed to underlie the acquisition of important skills by infants, including language and social interaction. In this study, functional magnetic resonance imaging (fMRI) was used to examine the neural basis of 'spontaneously' driven imitation, which has not yet been fully investigated. Healthy participants were presented with movie clips of meaningless bimanual actions and instructed to observe and imitate them during an fMRI scan. The participants were subsequently shown the movie clips again and asked to evaluate the strength of their 'urge to imitate' (Urge) for each action. We searched for cortical areas where the degree of activation positively correlated with Urge scores; significant positive correlations were observed in the right supplementary motor area (SMA) and bilateral midcingulate cortex (MCC) under the imitation condition. These areas were not explained by explicit reasons for imitation or the kinematic characteristics of the actions. Previous studies performed in monkeys and humans have implicated the SMA and MCC/caudal cingulate zone in voluntary actions. This study also confirmed the functional connectivity between Urge and imitation performance using a psychophysiological interaction analysis. Thus, our findings reveal the critical neural components that underlie spontaneous imitation and provide possible reasons why infants imitate spontaneously. © The Author (2015). Published by Oxford University Press.

  14. 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

  15. Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

    Science.gov (United States)

    Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar

    2018-06-01

    The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20-70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction( v/v%) 0.05.

  16. Framing effects: behavioral dynamics and neural basis.

    Science.gov (United States)

    Zheng, Hongming; Wang, X T; Zhu, Liqi

    2010-09-01

    This study examined the neural basis of framing effects using life-death decision problems framed either positively in terms of lives saved or negatively in terms of lives lost in large group and small group contexts. Using functional MRI we found differential brain activations to the verbal and social cues embedded in the choice problems. In large group contexts, framing effects were significant where participants were more risk seeking under the negative (loss) framing than under the positive (gain) framing. This behavioral difference in risk preference was mainly regulated by the activation in the right inferior frontal gyrus, including the homologue of the Broca's area. In contrast, framing effects diminished in small group contexts while the insula and parietal lobe in the right hemisphere were distinctively activated, suggesting an important role of emotion in switching choice preference from an indecisive mode to a more consistent risk-taking inclination, governed by a kith-and-kin decision rationality. Copyright 2010 Elsevier Ltd. All rights reserved.

  17. Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.

    Science.gov (United States)

    Fei, Yang; Hu, Jian; Gao, Kun; Tu, Jianfeng; Li, Wei-Qin; Wang, Wei

    2017-06-01

    To construct a radical basis function (RBF) artificial neural networks (ANNs) model to predict the incidence of acute pancreatitis (AP)-induced portal vein thrombosis. The analysis included 353 patients with AP who had admitted between January 2011 and December 2015. RBF ANNs model and logistic regression model were constructed based on eleven factors relevant to AP respectively. Statistical indexes were used to evaluate the value of the prediction in two models. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by RBF ANNs model for PVT were 73.3%, 91.4%, 68.8%, 93.0% and 87.7%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (Plogistic regression model. D-dimer, AMY, Hct and PT were important prediction factors of approval for AP-induced PVT. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    International Nuclear Information System (INIS)

    Gomes, Carla Regina; Canedo Medeiros, Jose Antonio Carlos

    2015-01-01

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

  19. Functional mapping of the neural basis for the encoding and retrieval of human episodic memory using H{sub 2}{sup 15}O PET

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jae Sung; Nam, Hyun Woo; Lee, Dong Soo; Lee, Sang Kun; Jang, Myoung Jin; Ahn, Ji Young; Park, Kwang Suk; Chung, June Key; Lee, Myung Chul [Seoul National Univ., Seoul (Korea, Republic of)

    2000-02-01

    Episodic memory is described as an 'autobiographical' memory responsible for storing a record of the events in our lives. We performed functional brain activation study using H{sub 2}{sup 1}5O PET to reveal the neural basis of the encoding and the retrieval of episodic memory in human normal volunteers. Four repeated H{sub 2}{sup 1}5O PET scans with two reference and two activation tasks were performed on 6 normal volunteers to activate brain areas engaged in encoding and retrieval with verbal materials. Images from the same subject were spatially registered and normalized using linear and nonlinear transformation. Using the means and variances for every condition which were adjusted with analysis of covariance, t-statistic analysis were performed voxel-wise. Encoding of episodic memory activated the opercular and triangular parts of left inferior frontal gyrus, right prefrontal cortex, medial frontal area, cingulate gyrus, posterior middle and inferior temporal gyri, and cerebellum, and both primary visual and visual association areas. Retrieval of episodic memory activated the triangular part of left inferior frontal gyrus and inferior temporal gyrus, right prefrontal cortex and medial temporal ares, and both cerebellum and primary visual and visual association areas. The activations in the opercular part of left inferior frontal gyrus and the right prefrontal cortex meant the essential role of these areas in the encoding and retrieval of episodic memeory. We could localize the neural basis of the encoding and retrieval of episodic memory using H{sub 2}{sup 1}5O PET, which was partly consistent with the hypothesis of hemispheric encoding/retrieval asymmetry.

  20. The neural basis of monitoring goal progress

    Directory of Open Access Journals (Sweden)

    Yael eBenn

    2014-09-01

    Full Text Available The neural basis of progress monitoring has received relatively little attention compared to other sub-processes that are involved in goal directed behavior such as motor control and response inhibition. Studies of error-monitoring have identified the dorsal anterior cingulate cortex (dACC as a structure that is sensitive to conflict detection, and triggers corrective action. However, monitoring goal progress involves monitoring correct as well as erroneous events over a period of time. In the present research, 20 healthy participants underwent fMRI while playing a game that involved monitoring progress towards either a numerical or a visuo-spatial target. The findings confirmed the role of the dACC in detecting situations in which the current state may conflict with the desired state, but also revealed activations in the frontal and parietal regions, pointing to the involvement of processes such as attention and working memory in monitoring progress over time. In addition, activation of the cuneus was associated with monitoring progress towards a specific target presented in the visual modality. This is the first time that activation in this region has been linked to higher-order processing of goal-relevant information, rather than low-level anticipation of visual stimuli. Taken together, these findings identify the neural substrates involved in monitoring progress over time, and how these extend beyond activations observed in conflict and error monitoring.

  1. Molecular basis of neural function

    International Nuclear Information System (INIS)

    Tucek, S.; Stipek, S.; Stastny, F.; Krivanek, J.

    1986-01-01

    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.)

  2. The neural basis of unconditional love.

    Science.gov (United States)

    Beauregard, Mario; Courtemanche, Jérôme; Paquette, Vincent; St-Pierre, Evelyne Landry

    2009-05-15

    Functional neuroimaging studies have shown that romantic love and maternal love are mediated by regions specific to each, as well as overlapping regions in the brain's reward system. Nothing is known yet regarding the neural underpinnings of unconditional love. The main goal of this functional magnetic resonance imaging study was to identify the brain regions supporting this form of love. Participants were scanned during a control condition and an experimental condition. In the control condition, participants were instructed to simply look at a series of pictures depicting individuals with intellectual disabilities. In the experimental condition, participants were instructed to feel unconditional love towards the individuals depicted in a series of similar pictures. Significant loci of activation were found, in the experimental condition compared with the control condition, in the middle insula, superior parietal lobule, right periaqueductal gray, right globus pallidus (medial), right caudate nucleus (dorsal head), left ventral tegmental area and left rostro-dorsal anterior cingulate cortex. These results suggest that unconditional love is mediated by a distinct neural network relative to that mediating other emotions. This network contains cerebral structures known to be involved in romantic love or maternal love. Some of these structures represent key components of the brain's reward system.

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

    Science.gov (United States)

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

    2015-01-01

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

  4. Study on Magneto-Hydro-Dynamics Disturbance Signal Feature Classification Using Improved S-Transform Algorithm and Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Nan YU

    2014-09-01

    Full Text Available The interference signal in magneto-hydro-dynamics (MHD may be the disturbance from the power supply, the equipment itself, or the electromagnetic radiation. Interference signal mixed in normal signal, brings difficulties for signal analysis and processing. Recently proposed S-Transform algorithm combines advantages of short time Fourier transform and wavelet transform. It uses Fourier kernel and wavelet like Gauss window whose width is inversely proportional to the frequency. Therefore, S-Transform algorithm not only preserves the phase information of the signals but also has variable resolution like wavelet transform. This paper proposes a new method to establish a MHD signal classifier using S-transform algorithm and radial basis function neural network (RBFNN. Because RBFNN centers ascertained by k-means clustering algorithm probably are the local optimum, this paper analyzes the characteristics of k-means clustering algorithm and proposes an improved k-means clustering algorithm called GCW (Group-cluster-weight k-means clustering algorithm to improve the centers distribution. The experiment results show that the improvement greatly enhances the RBFNN performance.

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

    International Nuclear Information System (INIS)

    Wu Xue-Dong; Liu Wei-Ting; Zhu Zhi-Yu; Wang Yao-Nan

    2011-01-01

    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)

  6. The neural basis of academic achievement motivation.

    Science.gov (United States)

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

    2008-08-01

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

  7. Towards a neural basis of music perception.

    Science.gov (United States)

    Koelsch, Stefan; Siebel, Walter A

    2005-12-01

    Music perception involves complex brain functions underlying acoustic analysis, auditory memory, auditory scene analysis, and processing of musical syntax and semantics. Moreover, music perception potentially affects emotion, influences the autonomic nervous system, the hormonal and immune systems, and activates (pre)motor representations. During the past few years, research activities on different aspects of music processing and their neural correlates have rapidly progressed. This article provides an overview of recent developments and a framework for the perceptual side of music processing. This framework lays out a model of the cognitive modules involved in music perception, and incorporates information about the time course of activity of some of these modules, as well as research findings about where in the brain these modules might be located.

  8. Tensor Basis Neural Network v. 1.0 (beta)

    Energy Technology Data Exchange (ETDEWEB)

    2017-03-28

    This software package can be used to build, train, and test a neural network machine learning model. The neural network architecture is specifically designed to embed tensor invariance properties by enforcing that the model predictions sit on an invariant tensor basis. This neural network architecture can be used in developing constitutive models for applications such as turbulence modeling, materials science, and electromagnetism.

  9. Neural basis of preference for human social hierarchy versus egalitarianism.

    Science.gov (United States)

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

    2009-06-01

    A fundamental way that individuals differ is in the degree to which they prefer social dominance hierarchy over egalitarianism as a guiding principle of societal structure, a phenomenon known as social dominance orientation. Here we show that preference for hierarchical rather than egalitarian social relations varies as a function of neural responses within left anterior insula and anterior cingulate cortices. Our findings provide novel evidence that preference for social dominance hierarchy is associated with neural functioning within brain regions that are associated with the ability to share and feel concern for the pain of others; this suggests a neurobiological basis for social and political attitudes. Implications of these findings for research on the social neuroscience of fairness, justice, and intergroup relations are discussed.

  10. 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.

  11. Sex differences in the neural basis of emotional memories.

    Science.gov (United States)

    Canli, Turhan; Desmond, John E; Zhao, Zuo; Gabrieli, John D E

    2002-08-06

    Psychological studies have found better memory in women than men for emotional events, but the neural basis for this difference is unknown. We used event-related functional MRI to assess whether sex differences in memory for emotional stimuli is associated with activation of different neural systems in men and women. Brain activation in 12 men and 12 women was recorded while they rated their experience of emotional arousal in response to neutral and emotionally negative pictures. In a recognition memory test 3 weeks after scanning, highly emotional pictures were remembered best, and remembered better by women than by men. Men and women activated different neural circuits to encode stimuli effectively into memory even when the analysis was restricted to pictures rated equally arousing by both groups. Men activated significantly more structures than women in a network that included the right amygdala, whereas women activated significantly fewer structures in a network that included the left amygdala. Women had significantly more brain regions where activation correlated with both ongoing evaluation of emotional experience and with subsequent memory for the most emotionally arousing pictures. Greater overlap in brain regions sensitive to current emotion and contributing to subsequent memory may be a neural mechanism for emotions to enhance memory more powerfully in women than in men.

  12. The neural circuit basis of learning

    Science.gov (United States)

    Patrick, Kaifosh William John

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

  13. The neural basis of financial risk taking.

    Science.gov (United States)

    Kuhnen, Camelia M; Knutson, Brian

    2005-09-01

    Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.

  14. The neural basis of task switching changes with skill acquisition

    Directory of Open Access Journals (Sweden)

    Koji eJimura

    2014-05-01

    Full Text Available Learning novel skills involves reorganization and optimization of cognitive processing involving a broad network of brain regions. Previous work has shown asymmetric costs of switching to a well-trained task versus a poorly-trained task, but the neural basis of these differential switch costs is unclear. The current study examined the neural signature of task switching in the context of acquisition of new skill. Human participants alternated randomly between a novel visual task (mirror-reversed word reading and a highly practiced one (plain word reading, allowing the isolation of task switching and skill set maintenance. Two scan sessions were separated by two weeks, with behavioral training on the mirror reading task in between the two sessions. Broad cortical regions, including bilateral prefrontal, parietal, and extrastriate cortices, showed decreased activity associated with learning of the mirror reading skill. In contrast, learning to switch to the novel skill was associated with decreased activity in a focal subcortical region in the dorsal striatum. Switching to the highly practiced task was associated with a non-overlapping set of regions, suggesting substantial differences in the neural substrates of switching as a function of task skill. Searchlight multivariate pattern analysis also revealed that learning was associated with decreased pattern information for mirror versus plain reading tasks in fronto-parietal regions. Inferior frontal junction and posterior parietal cortex showed a joint effect of univariate activation and pattern information. These results suggest distinct learning mechanisms task performance and executive control as a function of learning.

  15. On supertaskers and the neural basis of efficient multitasking.

    Science.gov (United States)

    Medeiros-Ward, Nathan; Watson, Jason M; Strayer, David L

    2015-06-01

    The present study used brain imaging to determine the neural basis of individual differences in multitasking, the ability to successfully perform at least two attention-demanding tasks at once. Multitasking is mentally taxing and, therefore, should recruit the prefrontal cortex to maintain task goals when coordinating attentional control and managing the cognitive load. To investigate this possibility, we used functional neuroimaging to assess neural activity in both extraordinary multitaskers (Supertaskers) and control subjects who were matched on working memory capacity. Participants performed a challenging dual N-back task in which auditory and visual stimuli were presented simultaneously, requiring independent and continuous maintenance, updating, and verification of the contents of verbal and spatial working memory. With the task requirements and considerable cognitive load that accompanied increasing N-back, relative to the controls, the multitasking of Supertaskers was characterized by more efficient recruitment of anterior cingulate and posterior frontopolar prefrontal cortices. Results are interpreted using neuropsychological and evolutionary perspectives on individual differences in multitasking ability and the neural correlates of attentional control.

  16. 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 t...

  17. Neural basis for generalized quantifier comprehension.

    Science.gov (United States)

    McMillan, Corey T; Clark, Robin; Moore, Peachie; Devita, Christian; Grossman, Murray

    2005-01-01

    Generalized quantifiers like "all cars" are semantically well understood, yet we know little about their neural representation. Our model of quantifier processing includes a numerosity device, operations that combine number elements and working memory. Semantic theory posits two types of quantifiers: first-order quantifiers identify a number state (e.g. "at least 3") and higher-order quantifiers additionally require maintaining a number state actively in working memory for comparison with another state (e.g. "less than half"). We used BOLD fMRI to test the hypothesis that all quantifiers recruit inferior parietal cortex associated with numerosity, while only higher-order quantifiers recruit prefrontal cortex associated with executive resources like working memory. Our findings showed that first-order and higher-order quantifiers both recruit right inferior parietal cortex, suggesting that a numerosity component contributes to quantifier comprehension. Moreover, only probes of higher-order quantifiers recruited right dorsolateral prefrontal cortex, suggesting involvement of executive resources like working memory. We also observed activation of thalamus and anterior cingulate that may be associated with selective attention. Our findings are consistent with a large-scale neural network centered in frontal and parietal cortex that supports comprehension of generalized quantifiers.

  18. Neural basis of disgust perception in racial prejudice.

    Science.gov (United States)

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

    2015-12-01

    Worldwide racial prejudice is originated from in-group/out-group discrimination. This prejudice can bias face perception at the very beginning of social interaction. However, little is known about the neurocognitive mechanism underlying the influence of racial prejudice on facial emotion perception. Here, we examined the neural basis of disgust perception in racial prejudice using a passive viewing task and functional magnetic resonance imaging. We found that compared with the disgusted faces of in-groups, the disgusted faces of out-groups result in increased amygdala and insular engagement, positive coupling of the insula with amygdala-based emotional system, and negative coupling of the insula with anterior cingulate cortex (ACC)-based regulatory system. Furthermore, machine-learning algorithms revealed that the level of implicit racial prejudice could be predicted by functional couplings of the insula with both the amygdala and the ACC, which suggests that the insula is largely involved in racially biased disgust perception through two distinct neural circuits. In addition, individual difference in disgust sensitivity was found to be predictive of implicit racial prejudice. Taken together, our results suggest a crucial role of insula-centered circuits for disgust perception in racial prejudice. © 2015 Wiley Periodicals, Inc.

  19. The neural basis of trait self-esteem revealed by the amplitude of low-frequency fluctuations and resting state functional connectivity.

    Science.gov (United States)

    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. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  20. Behavior and neural basis of near-optimal visual search

    Science.gov (United States)

    Ma, Wei Ji; Navalpakkam, Vidhya; Beck, Jeffrey M; van den Berg, Ronald; Pouget, Alexandre

    2013-01-01

    The ability to search efficiently for a target in a cluttered environment is one of the most remarkable functions of the nervous system. This task is difficult under natural circumstances, as the reliability of sensory information can vary greatly across space and time and is typically a priori unknown to the observer. In contrast, visual-search experiments commonly use stimuli of equal and known reliability. In a target detection task, we randomly assigned high or low reliability to each item on a trial-by-trial basis. An optimal observer would weight the observations by their trial-to-trial reliability and combine them using a specific nonlinear integration rule. We found that humans were near-optimal, regardless of whether distractors were homogeneous or heterogeneous and whether reliability was manipulated through contrast or shape. We present a neural-network implementation of near-optimal visual search based on probabilistic population coding. The network matched human performance. PMID:21552276

  1. Using imagination to understand the neural basis of episodic memory

    Science.gov (United States)

    Hassabis, Demis; Kumaran, Dharshan; Maguire, Eleanor A.

    2008-01-01

    Functional MRI (fMRI) studies investigating the neural basis of episodic memory recall, and the related task of thinking about plausible personal future events, have revealed a consistent network of associated brain regions. Surprisingly little, however, is understood about the contributions individual brain areas make to the overall recollective experience. In order to examine this, we employed a novel fMRI paradigm where subjects had to imagine fictitious experiences. In contrast to future thinking, this results in experiences that are not explicitly temporal in nature or as reliant on self-processing. By using previously imagined fictitious experiences as a comparison for episodic memories, we identified the neural basis of a key process engaged in common, namely scene construction, involving the generation, maintenance and visualisation of complex spatial contexts. This was associated with activations in a distributed network, including hippocampus, parahippocampal gyrus, and retrosplenial cortex. Importantly, we disambiguated these common effects from episodic memory-specific responses in anterior medial prefrontal cortex, posterior cingulate cortex and precuneus. These latter regions may support self-schema and familiarity processes, and contribute to the brain's ability to distinguish real from imaginary memories. We conclude that scene construction constitutes a common process underlying episodic memory and imagination of fictitious experiences, and suggest it may partially account for the similar brain networks implicated in navigation, episodic future thinking, and the default mode. We suggest that further brain regions are co-opted into this core network in a task-specific manner to support functions such as episodic memory that may have additional requirements. PMID:18160644

  2. The Neural Basis of Aversive Pavlovian Guidance during Planning.

    Science.gov (United States)

    Lally, Níall; Huys, Quentin J M; Eshel, Neir; Faulkner, Paul; Dayan, Peter; Roiser, Jonathan P

    2017-10-18

    Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic and what underlies individual differences have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy male and female volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning was associated with the responses of insula and dorsolateral prefrontal cortices to the receipt of large monetary losses, and also with subclinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian process that shapes goal-directed evaluations and thereby determines the outcome of high-level sequential cognitive processes. SIGNIFICANCE STATEMENT Multistep decisions are complex because initial choices constrain future options. Evaluating every path for long decision sequences

  3. The Neural Basis of Aversive Pavlovian Guidance during Planning

    Science.gov (United States)

    Faulkner, Paul

    2017-01-01

    Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic and what underlies individual differences have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy male and female volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning was associated with the responses of insula and dorsolateral prefrontal cortices to the receipt of large monetary losses, and also with subclinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian process that shapes goal-directed evaluations and thereby determines the outcome of high-level sequential cognitive processes. SIGNIFICANCE STATEMENT Multistep decisions are complex because initial choices constrain future options. Evaluating every path for long decision sequences

  4. Identifying Emotions on the Basis of Neural Activation.

    Science.gov (United States)

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

    2013-01-01

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

  5. Identifying Emotions on the Basis of Neural Activation.

    Directory of Open Access Journals (Sweden)

    Karim S Kassam

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

  6. 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

  7. Neural basis of imprinting behavior in chicks.

    Science.gov (United States)

    Nakamori, Tomoharu; Maekawa, Fumihiko; Sato, Katsushige; Tanaka, Kohichi; Ohki-Hamazaki, Hiroko

    2013-01-01

    Newly hatched chicks memorize the characteristics of the first moving object they encounter, and subsequently show a preference for it. This "imprinting" behavior is an example of infant learning and is elicited by visual and/or auditory cues. Visual information of imprinting stimuli in chicks is first processed in the visual Wulst (VW), a telencephalic area corresponding to the mammalian visual cortex, congregates in the core region of the hyperpallium densocellulare (HDCo) cells, and transmitted to the intermediate medial mesopallium (IMM), a region similar to the mammalian association cortex. The imprinting memory is stored in the IMM, and activities of IMM neurons are altered by imprinting. Imprinting also induces functional and structural plastic changes of neurons in the circuit that links the VW and the IMM. Of these neurons, the activity of the HDCo cells is strongly influenced by imprinting. Expression and modulation of NR2B subunit-containing N-methyl-D-aspartate (NMDA) receptors in the HDCo cells are crucial for plastic changes in this circuit as well as the process of visual imprinting. Thus, elucidation of cellular and molecular mechanisms underlying the plastic changes that occurred in the HDCo cells may provide useful knowledge about infant learning. © 2012 The Authors Development, Growth & Differentiation © 2012 Japanese Society of Developmental Biologists.

  8. The shared neural basis of music and language.

    Science.gov (United States)

    Yu, Mengxia; Xu, Miao; Li, Xueting; Chen, Zhencai; Song, Yiying; Liu, Jia

    2017-08-15

    Human musical ability is proposed to play a key phylogenetical role in the evolution of language, and the similarity of hierarchical structure in music and language has led to considerable speculation about their shared mechanisms. While behavioral and electrophysioglocial studies have revealed associations between music and linguistic abilities, results from functional magnetic resonance imaging (fMRI) studies on their relations are contradictory, possibly because these studies usually treat music or language as single entities without breaking down to their components. Here, we examined the relations between different components of music (i.e., melodic and rhythmic analysis) and language (i.e., semantic and phonological processing) using both behavioral tests and resting-state fMRI. Behaviorally, we found that individuals with music training experiences were better at semantic processing, but not at phonological processing, than those without training. Further correlation analyses showed that semantic processing of language was related to melodic, but not rhythmic, analysis of music. Neurally, we found that performances in both semantic processing and melodic analysis were correlated with spontaneous brain activities in the bilateral precentral gyrus (PCG) and superior temporal plane at the regional level, and with the resting-state functional connectivity of the left PCG with the left supramarginal gyrus and left superior temporal gyrus at the network level. Together, our study revealed the shared spontaneous neural basis of music and language based on the behavioral link between melodic analysis and semantic processing, which possibly relied on a common mechanism of automatic auditory-motor integration. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2014-01-01

    Empathy involves experiencing emotion vicariously, and understanding the reasons for those emotions. It may be served partly by a motor simulation function, and therefore share a neural basis with imitation (as opposed to mimicry), as both involve sensorimotor representations of intentions based on perceptions of others' actions. We recently showed a correlation between imitation accuracy and Empathy Quotient (EQ) using a facial imitation task and hypothesised that this relationship would be mediated by the human mirror neuron system. During functional Magnetic Resonance Imaging (fMRI), 20 adults observed novel 'blends' of facial emotional expressions. According to instruction, they either imitated (i.e. matched) the expressions or executed alternative, pre-prescribed mismatched actions as control. Outside the scanner we replicated the association between imitation accuracy and EQ. During fMRI, activity was greater during mismatch compared to imitation, particularly in the bilateral insula. Activity during imitation correlated with EQ in somatosensory cortex, intraparietal sulcus and premotor cortex. Imitation accuracy correlated with activity in insula and areas serving motor control. Overlapping voxels for the accuracy and EQ correlations occurred in premotor cortex. We suggest that both empathy and facial imitation rely on formation of action plans (or a simulation of others' intentions) in the premotor cortex, in connection with representations of emotional expressions based in the somatosensory cortex. In addition, the insula may play a key role in the social regulation of facial expression. © 2013.

  10. 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.

  11. Neural Basis of Brain Dysfunction Produced by Early Sleep Problems.

    Science.gov (United States)

    Kohyama, Jun

    2016-01-29

    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.

  12. Neural Basis of Action Understanding: Evidence from Sign Language Aphasia.

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    Rogalsky, Corianne; Raphel, Kristin; Tomkovicz, Vivian; O'Grady, Lucinda; Damasio, Hanna; Bellugi, Ursula; Hickok, Gregory

    2013-01-01

    The neural basis of action understanding is a hotly debated issue. The mirror neuron account holds that motor simulation in fronto-parietal circuits is critical to action understanding including speech comprehension, while others emphasize the ventral stream in the temporal lobe. Evidence from speech strongly supports the ventral stream account, but on the other hand, evidence from manual gesture comprehension (e.g., in limb apraxia) has led to contradictory findings. Here we present a lesion analysis of sign language comprehension. Sign language is an excellent model for studying mirror system function in that it bridges the gap between the visual-manual system in which mirror neurons are best characterized and language systems which have represented a theoretical target of mirror neuron research. Twenty-one life long deaf signers with focal cortical lesions performed two tasks: one involving the comprehension of individual signs and the other involving comprehension of signed sentences (commands). Participants' lesions, as indicated on MRI or CT scans, were mapped onto a template brain to explore the relationship between lesion location and sign comprehension measures. Single sign comprehension was not significantly affected by left hemisphere damage. Sentence sign comprehension impairments were associated with left temporal-parietal damage. We found that damage to mirror system related regions in the left frontal lobe were not associated with deficits on either of these comprehension tasks. We conclude that the mirror system is not critically involved in action understanding.

  13. 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.

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

    Science.gov (United States)

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

    2013-01-01

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

  15. Neural basis of acquired amusia and its recovery after stroke

    OpenAIRE

    Sihvonen, A.J.; Ripollés, P.; Leo, V.; Rodríguez-Fornells, Antoni; Soinila, S.; Särkämö, T.

    2016-01-01

    Although acquired amusia is a relatively common disorder after stroke, its precise neuroanatomical basis is still unknown. To evaluate which brain regions form the neural substrate for acquired amusia and its recovery, we performed a voxel-based lesion-symptom mapping (VLSM) and morphometry (VBM) study with 77 human stroke subjects. Structural MRIs were acquired at acute and 6 month poststroke stages. Amusia and aphasia were behaviorally assessed at acute and 3 month poststroke stages using t...

  16. The Neural Basis of Changing Social Norms through Persuasion

    OpenAIRE

    Yomogida, Yukihito; Matsumoto, Madoka; Aoki, Ryuta; Sugiura, Ayaka; Phillips, Adam N.; Matsumoto, Kenji

    2017-01-01

    Social norms regulate behavior, and changes in norms have a great impact on society. In most modern societies, norms change through interpersonal communication and persuasive messages found in media. Here, we examined the neural basis of persuasion-induced changes in attitude toward and away from norms using fMRI. We measured brain activity while human participants were exposed to persuasive messages directed toward specific norms. Persuasion directed toward social norms specifically activate...

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

    Science.gov (United States)

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

    2010-01-01

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

  18. The Neural Basis of Vocal Pitch Imitation in Humans.

    Science.gov (United States)

    Belyk, Michel; Pfordresher, Peter Q; Liotti, Mario; Brown, Steven

    2016-04-01

    Vocal imitation is a phenotype that is unique to humans among all primate species, and so an understanding of its neural basis is critical in explaining the emergence of both speech and song in human evolution. Two principal neural models of vocal imitation have emerged from a consideration of nonhuman animals. One hypothesis suggests that putative mirror neurons in the inferior frontal gyrus pars opercularis of Broca's area may be important for imitation. An alternative hypothesis derived from the study of songbirds suggests that the corticostriate motor pathway performs sensorimotor processes that are specific to vocal imitation. Using fMRI with a sparse event-related sampling design, we investigated the neural basis of vocal imitation in humans by comparing imitative vocal production of pitch sequences with both nonimitative vocal production and pitch discrimination. The strongest difference between these tasks was found in the putamen bilaterally, providing a striking parallel to the role of the analogous region in songbirds. Other areas preferentially activated during imitation included the orofacial motor cortex, Rolandic operculum, and SMA, which together outline the corticostriate motor loop. No differences were seen in the inferior frontal gyrus. The corticostriate system thus appears to be the central pathway for vocal imitation in humans, as predicted from an analogy with songbirds.

  19. Separation and Determination of Honokiol and Magnolol in Chinese Traditional Medicines by Capillary Electrophoresis with the Application of Response Surface Methodology and Radial Basis Function Neural Network

    Science.gov (United States)

    Han, Ping; Luan, Feng; Yan, Xizu; Gao, Yuan; Liu, Huitao

    2012-01-01

    A method for the separation and determination of honokiol and magnolol in Magnolia officinalis and its medicinal preparation is developed by capillary zone electrophoresis and response surface methodology. The concentration of borate, content of organic modifier, and applied voltage are selected as variables. The optimized conditions (i.e., 16 mmol/L sodium tetraborate at pH 10.0, 11% methanol, applied voltage of 25 kV and UV detection at 210 nm) are obtained and successfully applied to the analysis of honokiol and magnolol in Magnolia officinalis and Huoxiang Zhengqi Liquid. Good separation is achieved within 6 min. The limits of detection are 1.67 µg/mL for honokiol and 0.83 µg/mL for magnolol, respectively. In addition, an artificial neural network with “3-7-1” structure based on the ratio of peak resolution to the migration time of the later component (Rs/t) given by Box-Behnken design is also reported, and the predicted results are in good agreement with the values given by the mathematic software and the experimental results. PMID:22291059

  20. 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.

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

    Science.gov (United States)

    Luo, Junlong; Li, Wenfu; Qiu, Jiang; Wei, Dongtao; Liu, Yijun; Zhang, Qinlin

    2013-01-01

    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.

  2. The neural basis of attaining conscious awareness of sad mood.

    Science.gov (United States)

    Smith, Ryan; Braden, B Blair; Chen, Kewei; Ponce, Francisco A; Lane, Richard D; Baxter, Leslie C

    2015-09-01

    The neural processes associated with becoming aware of sad mood are not fully understood. We examined the dynamic process of becoming aware of sad mood and recovery from sad mood. Sixteen healthy subjects underwent fMRI while participating in a sadness induction task designed to allow for variable mood induction times. Individualized regressors linearly modeled the time periods during the attainment of self-reported sad and baseline "neutral" mood states, and the validity of the linearity assumption was further tested using independent component analysis. During sadness induction the dorsomedial and ventrolateral prefrontal cortices, and anterior insula exhibited a linear increase in the blood oxygen level-dependent (BOLD) signal until subjects became aware of a sad mood and then a subsequent linear decrease as subjects transitioned from sadness back to the non-sadness baseline condition. These findings extend understanding of the neural basis of conscious emotional experience.

  3. Neural basis of individualistic and collectivistic views of self.

    Science.gov (United States)

    Chiao, Joan Y; Harada, Tokiko; Komeda, Hidetsugu; Li, Zhang; Mano, Yoko; Saito, Daisuke; Parrish, Todd B; Sadato, Norihiro; Iidaka, Tetsuya

    2009-09-01

    Individualism and collectivism refer to cultural values that influence how people construe themselves and their relation to the world. Individualists perceive themselves as stable entities, autonomous from other people and their environment, while collectivists view themselves as dynamic entities, continually defined by their social context and relationships. Despite rich understanding of how individualism and collectivism influence social cognition at a behavioral level, little is known about how these cultural values modulate neural representations underlying social cognition. Using cross-cultural functional magnetic resonance imaging (fMRI), we examined whether the cultural values of individualism and collectivism modulate neural activity within medial prefrontal cortex (MPFC) during processing of general and contextual self judgments. Here, we show that neural activity within the anterior rostral portion of the MPFC during processing of general and contextual self judgments positively predicts how individualistic or collectivistic a person is across cultures. These results reveal two kinds of neural representations of self (eg, a general self and a contextual self) within MPFC and demonstrate how cultural values of individualism and collectivism shape these neural representations. 2008 Wiley-Liss, Inc.

  4. Functional Basis of Microorganism Classification.

    Science.gov (United States)

    Zhu, Chengsheng; Delmont, Tom O; Vogel, Timothy M; Bromberg, Yana

    2015-08-01

    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 concerned with

  5. Functional Basis of Microorganism Classification

    Science.gov (United States)

    Zhu, Chengsheng; Delmont, Tom O.; Vogel, Timothy M.; Bromberg, Yana

    2015-01-01

    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 concerned

  6. Investigating the Influence of Biological Sex on the Behavioral and Neural Basis of Face Recognition.

    Science.gov (United States)

    Scherf, K Suzanne; Elbich, Daniel B; Motta-Mena, Natalie V

    2017-01-01

    There is interest in understanding the influence of biological factors, like sex, on the organization of brain function. We investigated the influence of biological sex on the behavioral and neural basis of face recognition in healthy, young adults. In behavior, there were no sex differences on the male Cambridge Face Memory Test (CFMT)+ or the female CFMT+ (that we created) and no own-gender bias (OGB) in either group. We evaluated the functional topography of ventral stream organization by measuring the magnitude and functional neural size of 16 individually defined face-, two object-, and two place-related regions bilaterally. There were no sex differences in any of these measures of neural function in any of the regions of interest (ROIs) or in group level comparisons. These findings reveal that men and women have similar category-selective topographic organization in the ventral visual pathway. Next, in a separate task, we measured activation within the 16 face-processing ROIs specifically during recognition of target male and female faces. There were no sex differences in the magnitude of the neural responses in any face-processing region. Furthermore, there was no OGB in the neural responses of either the male or female participants. Our findings suggest that face recognition behavior, including the OGB, is not inherently sexually dimorphic. Face recognition is an essential skill for navigating human social interactions, which is reflected equally in the behavior and neural architecture of men and women.

  7. Investigating the Influence of Biological Sex on the Behavioral and Neural Basis of Face Recognition

    Science.gov (United States)

    2017-01-01

    Abstract There is interest in understanding the influence of biological factors, like sex, on the organization of brain function. We investigated the influence of biological sex on the behavioral and neural basis of face recognition in healthy, young adults. In behavior, there were no sex differences on the male Cambridge Face Memory Test (CFMT)+ or the female CFMT+ (that we created) and no own-gender bias (OGB) in either group. We evaluated the functional topography of ventral stream organization by measuring the magnitude and functional neural size of 16 individually defined face-, two object-, and two place-related regions bilaterally. There were no sex differences in any of these measures of neural function in any of the regions of interest (ROIs) or in group level comparisons. These findings reveal that men and women have similar category-selective topographic organization in the ventral visual pathway. Next, in a separate task, we measured activation within the 16 face-processing ROIs specifically during recognition of target male and female faces. There were no sex differences in the magnitude of the neural responses in any face-processing region. Furthermore, there was no OGB in the neural responses of either the male or female participants. Our findings suggest that face recognition behavior, including the OGB, is not inherently sexually dimorphic. Face recognition is an essential skill for navigating human social interactions, which is reflected equally in the behavior and neural architecture of men and women. PMID:28497111

  8. Exploring the spatio-temporal neural basis of face learning

    Science.gov (United States)

    Yang, Ying; Xu, Yang; Jew, Carol A.; Pyles, John A.; Kass, Robert E.; Tarr, Michael J.

    2017-01-01

    Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150–250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150–250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces. PMID:28570739

  9. Neural Basis of Video Gaming: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Marc Palaus

    2017-05-01

    Full Text Available Background: Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study. Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades. There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video games.Objectives: We aim to understand the relationship between the use of video games and their neural correlates, taking into account the whole variety of cognitive factors that they encompass.Methods: A systematic review was conducted using standardized search operators that included the presence of video games and neuro-imaging techniques or references to structural or functional brain changes. Separate categories were made for studies featuring Internet Gaming Disorder and studies focused on the violent content of video games.Results: A total of 116 articles were considered for the final selection. One hundred provided functional data and 22 measured structural brain changes. One-third of the studies covered video game addiction, and 14% focused on video game related violence.Conclusions: Despite the innate heterogeneity of the field of study, it has been possible to establish a series of links between the neural and cognitive aspects, particularly regarding attention, cognitive control, visuospatial skills, cognitive workload, and reward processing. However, many aspects could be improved. The lack of standardization in the different aspects of video game related research, such as the participants' characteristics, the features of each video game genre and the diverse study goals could contribute to discrepancies in many related studies.

  10. Neural Basis of Video Gaming: A Systematic Review

    Science.gov (United States)

    Palaus, Marc; Marron, Elena M.; Viejo-Sobera, Raquel; Redolar-Ripoll, Diego

    2017-01-01

    Background: Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study. Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades. There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video games. Objectives: We aim to understand the relationship between the use of video games and their neural correlates, taking into account the whole variety of cognitive factors that they encompass. Methods: A systematic review was conducted using standardized search operators that included the presence of video games and neuro-imaging techniques or references to structural or functional brain changes. Separate categories were made for studies featuring Internet Gaming Disorder and studies focused on the violent content of video games. Results: A total of 116 articles were considered for the final selection. One hundred provided functional data and 22 measured structural brain changes. One-third of the studies covered video game addiction, and 14% focused on video game related violence. Conclusions: Despite the innate heterogeneity of the field of study, it has been possible to establish a series of links between the neural and cognitive aspects, particularly regarding attention, cognitive control, visuospatial skills, cognitive workload, and reward processing. However, many aspects could be improved. The lack of standardization in the different aspects of video game related research, such as the participants' characteristics, the features of each video game genre and the diverse study goals could contribute to discrepancies in many related studies. PMID:28588464

  11. Neural Basis of Video Gaming: A Systematic Review.

    Science.gov (United States)

    Palaus, Marc; Marron, Elena M; Viejo-Sobera, Raquel; Redolar-Ripoll, Diego

    2017-01-01

    Background: Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study. Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades. There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video games. Objectives: We aim to understand the relationship between the use of video games and their neural correlates, taking into account the whole variety of cognitive factors that they encompass. Methods: A systematic review was conducted using standardized search operators that included the presence of video games and neuro-imaging techniques or references to structural or functional brain changes. Separate categories were made for studies featuring Internet Gaming Disorder and studies focused on the violent content of video games. Results: A total of 116 articles were considered for the final selection. One hundred provided functional data and 22 measured structural brain changes. One-third of the studies covered video game addiction, and 14% focused on video game related violence. Conclusions: Despite the innate heterogeneity of the field of study, it has been possible to establish a series of links between the neural and cognitive aspects, particularly regarding attention, cognitive control, visuospatial skills, cognitive workload, and reward processing. However, many aspects could be improved. The lack of standardization in the different aspects of video game related research, such as the participants' characteristics, the features of each video game genre and the diverse study goals could contribute to discrepancies in many related studies.

  12. The neural basis of intuitive and counterintuitive moral judgment

    Science.gov (United States)

    Wiech, Katja; Shackel, Nicholas; Farias, Miguel; Savulescu, Julian; Tracey, Irene

    2012-01-01

    Neuroimaging studies on moral decision-making have thus far largely focused on differences between moral judgments with opposing utilitarian (well-being maximizing) and deontological (duty-based) content. However, these studies have investigated moral dilemmas involving extreme situations, and did not control for two distinct dimensions of moral judgment: whether or not it is intuitive (immediately compelling to most people) and whether it is utilitarian or deontological in content. By contrasting dilemmas where utilitarian judgments are counterintuitive with dilemmas in which they are intuitive, we were able to use functional magnetic resonance imaging to identify the neural correlates of intuitive and counterintuitive judgments across a range of moral situations. Irrespective of content (utilitarian/deontological), counterintuitive moral judgments were associated with greater difficulty and with activation in the rostral anterior cingulate cortex, suggesting that such judgments may involve emotional conflict; intuitive judgments were linked to activation in the visual and premotor cortex. In addition, we obtained evidence that neural differences in moral judgment in such dilemmas are largely due to whether they are intuitive and not, as previously assumed, to differences between utilitarian and deontological judgments. Our findings therefore do not support theories that have generally associated utilitarian and deontological judgments with distinct neural systems. PMID:21421730

  13. Cultural influences on neural basis of intergroup empathy.

    Science.gov (United States)

    Cheon, Bobby K; Im, Dong-Mi; Harada, Tokiko; Kim, Ji-Sook; Mathur, Vani A; Scimeca, Jason M; Parrish, Todd B; Park, Hyun Wook; Chiao, Joan Y

    2011-07-15

    Cultures vary in the extent to which people prefer social hierarchical or egalitarian relations between individuals and groups. Here we examined the effect of cultural variation in preference for social hierarchy on the neural basis of intergroup empathy. Using cross-cultural neuroimaging, we measured neural responses while Korean and American participants observed scenes of racial ingroup and outgroup members in emotional pain. Compared to Caucasian-American participants, Korean participants reported experiencing greater empathy and elicited stronger activity in the left temporo-parietal junction (L-TPJ), a region previously associated with mental state inference, for ingroup compared to outgroup members. Furthermore, preferential reactivity within this region to the pain of ingroup relative to outgroup members was associated with greater preference for social hierarchy and ingroup biases in empathy. Together, these results suggest that cultural variation in preference for social hierarchy leads to cultural variation in ingroup-preferences in empathy, due to increased engagement of brain regions associated with representing and inferring the mental states of others. Copyright © 2011 Elsevier Inc. All rights reserved.

  14. The neural basis of deception in strategic interactions.

    Science.gov (United States)

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

    2015-01-01

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

  15. 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.

  16. The Neural Basis of Changing Social Norms through Persuasion.

    Science.gov (United States)

    Yomogida, Yukihito; Matsumoto, Madoka; Aoki, Ryuta; Sugiura, Ayaka; Phillips, Adam N; Matsumoto, Kenji

    2017-11-24

    Social norms regulate behavior, and changes in norms have a great impact on society. In most modern societies, norms change through interpersonal communication and persuasive messages found in media. Here, we examined the neural basis of persuasion-induced changes in attitude toward and away from norms using fMRI. We measured brain activity while human participants were exposed to persuasive messages directed toward specific norms. Persuasion directed toward social norms specifically activated a set of brain regions including temporal poles, temporo-parietal junction, and medial prefrontal cortex. Beyond these regions, when successful, persuasion away from an accepted norm specifically recruited the left middle temporal and supramarginal gyri. Furthermore, in combination with data from a separate attitude-rating task, we found that left supramarginal gyrus activity represented participant attitude toward norms and tracked the persuasion-induced attitude changes that were away from agreement.

  17. The neural basis of visual behaviors in the larval zebrafish.

    Science.gov (United States)

    Portugues, Ruben; Engert, Florian

    2009-12-01

    We review visually guided behaviors in larval zebrafish and summarise what is known about the neural processing that results in these behaviors, paying particular attention to the progress made in the last 2 years. Using the examples of the optokinetic reflex, the optomotor response, prey tracking and the visual startle response, we illustrate how the larval zebrafish presents us with a very promising model vertebrate system that allows neurocientists to integrate functional and behavioral studies and from which we can expect illuminating insights in the near future. Copyright 2009 Elsevier Ltd. All rights reserved.

  18. Bounds on Rates of Variable-Basis and Neural-Network Approximation

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra; Sanguineti, M.

    2001-01-01

    Roč. 47, č. 6 (2001), s. 2659-2665 ISSN 0018-9448 R&D Projects: GA ČR GA201/00/1482 Institutional research plan: AV0Z1030915 Keywords : approximation by variable-basis functions * bounds on rates of approximation * complexity of neural networks * high-dimensional optimal decision problems Subject RIV: BA - General Mathematics Impact factor: 2.077, year: 2001

  19. Neural Basis of Reinforcement Learning and Decision Making

    Science.gov (United States)

    Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan

    2012-01-01

    Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain. PMID:22462543

  20. The Neural Basis of Social Influence in a Dictator Decision

    Directory of Open Access Journals (Sweden)

    Zhenyu Wei

    2017-12-01

    Full Text Available Humans tend to reduce inequitable distributions. Previous neuroimaging studies have shown that inequitable decisions are related to brain regions that associated with negative emotion and signaling conflict. In the highly complex human social environment, our opinions and behaviors can be affected by social information. In current study, we used a modified dictator game to investigate the effect of social influence on making an equitable decision. We found that the choices of participants in present task was influenced by the choices of peers. However, participants’ decisions were influenced by equitable rather than inequitable group choices. fMRI results showed that brain regions that related to norm violation and social conflict were related to the inequitable social influence. The neural responses in the dorsomedial prefrontal cortex, rostral cingulate zone, and insula predicted subsequent conforming behavior in individuals. Additionally, psychophysiological interaction analysis revealed that the interconnectivity between the dorsal striatum and insula was elevated in advantageous inequity influence versus no-social influence conditions. We found decreased functional connectivity between the medial prefrontal cortex and insula, supplementary motor area, posterior cingulate gyrus and dorsal anterior cingulate cortex in the disadvantageous inequity influence versus no-social influence conditions. This suggests that a disadvantageous inequity influence may decrease the functional connectivity among brain regions that are related to reward processes. Thus, the neural mechanisms underlying social influence in an equitable decision may be similar to those implicated in social norms and reward processing.

  1. The Neural Basis of Social Influence in a Dictator Decision.

    Science.gov (United States)

    Wei, Zhenyu; Zhao, Zhiying; Zheng, Yong

    2017-01-01

    Humans tend to reduce inequitable distributions. Previous neuroimaging studies have shown that inequitable decisions are related to brain regions that associated with negative emotion and signaling conflict. In the highly complex human social environment, our opinions and behaviors can be affected by social information. In current study, we used a modified dictator game to investigate the effect of social influence on making an equitable decision. We found that the choices of participants in present task was influenced by the choices of peers. However, participants' decisions were influenced by equitable rather than inequitable group choices. fMRI results showed that brain regions that related to norm violation and social conflict were related to the inequitable social influence. The neural responses in the dorsomedial prefrontal cortex, rostral cingulate zone, and insula predicted subsequent conforming behavior in individuals. Additionally, psychophysiological interaction analysis revealed that the interconnectivity between the dorsal striatum and insula was elevated in advantageous inequity influence versus no-social influence conditions. We found decreased functional connectivity between the medial prefrontal cortex and insula, supplementary motor area, posterior cingulate gyrus and dorsal anterior cingulate cortex in the disadvantageous inequity influence versus no-social influence conditions. This suggests that a disadvantageous inequity influence may decrease the functional connectivity among brain regions that are related to reward processes. Thus, the neural mechanisms underlying social influence in an equitable decision may be similar to those implicated in social norms and reward processing.

  2. Evolvable synthetic neural system

    Science.gov (United States)

    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.

  3. 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

  4. Towards a neural basis of music-evoked emotions.

    Science.gov (United States)

    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. Copyright 2010 Elsevier Ltd. All rights reserved.

  5. 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.

  6. Modeling Marine Electromagnetic Survey with Radial Basis Function Networks

    Directory of Open Access Journals (Sweden)

    Agus Arif

    2014-11-01

    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[1].[1] This manuscript is an extended version of our previous paper, entitled Radial Basis Function Networks for Modeling Marine Electromagnetic Survey, which had been presented on 2011 International Conference on Electrical Engineering and Informatics, 17-19 July 2011, Bandung, Indonesia.

  7. The Matlab Radial Basis Function Toolbox

    Directory of Open Access Journals (Sweden)

    Scott A. Sarra

    2017-03-01

    Full Text Available Radial Basis Function (RBF methods are important tools for scattered data interpolation and for the solution of Partial Differential Equations in complexly shaped domains. The most straight forward approach used to evaluate the methods involves solving a linear system which is typically poorly conditioned. The Matlab Radial Basis Function toolbox features a regularization method for the ill-conditioned system, extended precision floating point arithmetic, and symmetry exploitation for the purpose of reducing flop counts of the associated numerical linear algebra algorithms.

  8. Neural Basis of Acquired Amusia and Its Recovery after Stroke.

    Science.gov (United States)

    Sihvonen, Aleksi J; Ripollés, Pablo; Leo, Vera; Rodríguez-Fornells, Antoni; Soinila, Seppo; Särkämö, Teppo

    2016-08-24

    Although acquired amusia is a relatively common disorder after stroke, its precise neuroanatomical basis is still unknown. To evaluate which brain regions form the neural substrate for acquired amusia and its recovery, we performed a voxel-based lesion-symptom mapping (VLSM) and morphometry (VBM) study with 77 human stroke subjects. Structural MRIs were acquired at acute and 6 month poststroke stages. Amusia and aphasia were behaviorally assessed at acute and 3 month poststroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA) and language tests. VLSM analyses indicated that amusia was associated with a lesion area comprising the superior temporal gyrus, Heschl's gyrus, insula, and striatum in the right hemisphere, clearly different from the lesion pattern associated with aphasia. Parametric analyses of MBEA Pitch and Rhythm scores showed extensive lesion overlap in the right striatum, as well as in the right Heschl's gyrus and superior temporal gyrus. Lesions associated with Rhythm scores extended more superiorly and posterolaterally. VBM analysis of volume changes from the acute to the 6 month stage showed a clear decrease in gray matter volume in the right superior and middle temporal gyri in nonrecovered amusic patients compared with nonamusic patients. This increased atrophy was more evident in anterior temporal areas in rhythm amusia and in posterior temporal and temporoparietal areas in pitch amusia. Overall, the results implicate right temporal and subcortical regions as the crucial neural substrate for acquired amusia and highlight the importance of different temporal lobe regions for the recovery of amusia after stroke. Lesion studies are essential in uncovering the brain regions causally linked to a given behavior or skill. For music perception ability, previous lesion studies of amusia have been methodologically limited in both spatial accuracy and time domain as well as by small sample sizes, providing

  9. Doubly stochastic radial basis function methods

    Science.gov (United States)

    Yang, Fenglian; Yan, Liang; Ling, Leevan

    2018-06-01

    We propose a doubly stochastic radial basis function (DSRBF) method for function recoveries. Instead of a constant, we treat the RBF shape parameters as stochastic variables whose distribution were determined by a stochastic leave-one-out cross validation (LOOCV) estimation. A careful operation count is provided in order to determine the ranges of all the parameters in our methods. The overhead cost for setting up the proposed DSRBF method is O (n2) for function recovery problems with n basis. Numerical experiments confirm that the proposed method not only outperforms constant shape parameter formulation (in terms of accuracy with comparable computational cost) but also the optimal LOOCV formulation (in terms of both accuracy and computational cost).

  10. The neural basis for simulated drawing and the semantic implications.

    Science.gov (United States)

    Harrington, Greg S; Farias, Dana; Davis, Christine H

    2009-03-01

    This functional magnetic resonance imaging (fMRI) study of the mental simulation of drawing (1) investigated the neural substrates of drawing and (2) delineated the semantic aspects of drawing. The goal was to advance our understanding of how drawing a familiar object is linked to lexical semantics and therefore a viable method to use to rehabilitate aphasia. We hypothesized that the semantic aspects of drawing familiar objects compared to drawing non-objects would yield greater activation in the inferior temporal cortex and the inferior frontal cortex of the left hemisphere. To test this hypothesis, eight right-handed subjects performed an fMRI experiment that directly contrasted drawing familiar objects to non-objects using mental imagery. Simulated drawing recruited a large, distributed network of frontal, parietal, and temporal structures. In the contrast comparing drawing familiar objects to non-objects there was stronger activation in the left hemisphere within the inferior temporal, anterior inferior frontal, inferior parietal and superior frontal cortices. The activation within the inferior temporal cortex was associated with visual semantic processing and semantic mediated naming. We suggest that the anterior inferior frontal activation is linked to the inferior temporal cortex and is involved in the selection of specific semantic features of the object as well as retrieval of information regarding the perceptual aspects of the object.

  11. Incidental regulation of attraction: The neural basis of the derogation of attractive alternatives in romantic relationships

    Science.gov (United States)

    Meyer, Meghan L.; Berkman, Elliot T.; Karremans, Johan C.; Lieberman, Matthew 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 implicitly derogate the attractiveness of alternative partners, and the present study sought to examine the neural basis of this effect. Romantically committed participants in the present study were scanned with functional magnetic resonance imaging (fMRI) while indicating whether they would consider each of a series of attractive (or unattractive) opposite-sex others as a hypothetical dating partner both while under cognitive load and no cognitive load. Successful derogation of attractive others during the no cognitive load compared to the cognitive load trials corresponded with increased activation in the ventrolateral prefrontal cortex (VLPFC) and posterior dorsomedial prefrontal cortex (pDMPFC), and decreased activation in the ventral striatum, a pattern similar to those reported in deliberate emotion-regulation studies. Activation in the VLPFC and pDMPFC was not significant in the cognitive load condition, indicating that while the derogation effect may be implicit, it nonetheless requires cognitive resources. Additionally, activation in the right VLPFC correlated with participants’ level of relationship investment. These findings suggest that the RVLPFC may play a particularly important role in implicitly regulating the emotions that threaten the stability of a romantic relationship. PMID:21432689

  12. Incidental regulation of attraction: the neural basis of the derogation of attractive alternatives in romantic relationships.

    Science.gov (United States)

    Meyer, Meghan L; Berkman, Elliot T; Karremans, Johan C; Lieberman, Matthew D

    2011-04-01

    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 implicitly derogate the attractiveness of alternative partners, and the present study sought to examine the neural basis of this effect. Romantically committed participants in the present study were scanned with functional magnetic resonance imaging (fMRI) while indicating whether they would consider each of a series of attractive (or unattractive) opposite-sex others as a hypothetical dating partner both while under cognitive load and no cognitive load. Successful derogation of attractive others during the no cognitive load compared to the cognitive load trials corresponded with increased activation in the ventrolateral prefrontal cortex (VLPFC) and posterior dorsomedial prefrontal cortex (pDMPFC), and decreased activation in the ventral striatum, a pattern similar to those reported in deliberate emotion-regulation studies. Activation in the VLPFC and pDMPFC was not significant in the cognitive load condition, indicating that while the derogation effect may be implicit, it nonetheless requires cognitive resources. Additionally, activation in the right VLPFC correlated with participants' level of relationship investment. These findings suggest that the RVLPFC may play a particularly important role in implicitly regulating the emotions that threaten the stability of a romantic relationship. © 2011 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business

  13. 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...

  14. Neural Basis of Video Gaming: A Systematic Review

    OpenAIRE

    Marc Palaus; Elena M. Marron; Raquel Viejo-Sobera; Raquel Viejo-Sobera; Diego Redolar-Ripoll

    2017-01-01

    Background: Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study. Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades. There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video ga...

  15. Neural Basis of Video Gaming: A Systematic Review

    OpenAIRE

    Palaus, Marc; Marron, Elena M.; Viejo-Sobera, Raquel; Redolar-Ripoll, Diego

    2017-01-01

    Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study. Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades. There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video games. We aim ...

  16. Learning Methods for Radial Basis Functions Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Kudová, Petra

    2005-01-01

    Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005

  17. The Molecular Basis of Neural Memory. Part 7: Neural Intelligence (NI versus Artificial Intelligence (AI

    Directory of Open Access Journals (Sweden)

    Gerard Marx

    2017-07-01

    Full Text Available The link of memory to intelligence is incontestable, though the development of electronic artifacts with memory has confounded cognitive and computer scientists’ conception of memory and its relevance to “intelligence”. We propose two categories of “Intelligence”: (1 Logical (objective — mathematics, numbers, pattern recognition, games, programmable in binary format. (2 Emotive (subjective — sensations, feelings, perceptions, goals desires, sociability, sex, food, love. The 1st has been reduced to computational algorithms of which we are well versed, witness global technology and the internet. The 2nd relates to the mysterious process whereby (psychic emotive states are achieved by neural beings sensing, comprehending, remembering and dealing with their surroundings. Many theories and philosophies have been forwarded to rationalize this process, but as neuroscientists, we remain dissatisfied. Our own musings on universal neural memory, suggest a tripartite mechanism involving neurons interacting with their surroundings, notably the neural extracellular matrix (nECM with dopants [trace metals and neurotransmitters (NTs]. In particular, the NTs are the molecular encoders of emotive states. We have developed a chemographic representation of such a molecular code.To quote Longuet-Higgins, “Perhaps it is time for the term ‘artificial intelligence’ to be replaced by something more modest and less provisional”. We suggest “artifact intelligence” (ARTI or “machine intelligence” (MI, neither of which imply emulation of emotive neural processes, but simply refer to the ‘demotive’ (lacking emotive quality capability of electronic artifacts that employ a recall function, to calculate algorithms.

  18. Neural basis of decision making guided by emotional outcomes.

    Science.gov (United States)

    Katahira, Kentaro; Matsuda, Yoshi-Taka; Fujimura, Tomomi; Ueno, Kenichi; Asamizuya, Takeshi; Suzuki, Chisato; Cheng, Kang; Okanoya, Kazuo; Okada, Masato

    2015-05-01

    Emotional events resulting from a choice influence an individual's subsequent decision making. Although the relationship between emotion and decision making has been widely discussed, previous studies have mainly investigated decision outcomes that can easily be mapped to reward and punishment, including monetary gain/loss, gustatory stimuli, and pain. These studies regard emotion as a modulator of decision making that can be made rationally in the absence of emotions. In our daily lives, however, we often encounter various emotional events that affect decisions by themselves, and mapping the events to a reward or punishment is often not straightforward. In this study, we investigated the neural substrates of how such emotional decision outcomes affect subsequent decision making. By using functional magnetic resonance imaging (fMRI), we measured brain activities of humans during a stochastic decision-making task in which various emotional pictures were presented as decision outcomes. We found that pleasant pictures differentially activated the midbrain, fusiform gyrus, and parahippocampal gyrus, whereas unpleasant pictures differentially activated the ventral striatum, compared with neutral pictures. We assumed that the emotional decision outcomes affect the subsequent decision by updating the value of the options, a process modeled by reinforcement learning models, and that the brain regions representing the prediction error that drives the reinforcement learning are involved in guiding subsequent decisions. We found that some regions of the striatum and the insula were separately correlated with the prediction error for either pleasant pictures or unpleasant pictures, whereas the precuneus was correlated with prediction errors for both pleasant and unpleasant pictures. Copyright © 2015 the American Physiological Society.

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

    Science.gov (United States)

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

    2016-02-01

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

  20. Neural basis of decision making guided by emotional outcomes

    Science.gov (United States)

    Matsuda, Yoshi-Taka; Fujimura, Tomomi; Ueno, Kenichi; Asamizuya, Takeshi; Suzuki, Chisato; Cheng, Kang; Okanoya, Kazuo; Okada, Masato

    2015-01-01

    Emotional events resulting from a choice influence an individual's subsequent decision making. Although the relationship between emotion and decision making has been widely discussed, previous studies have mainly investigated decision outcomes that can easily be mapped to reward and punishment, including monetary gain/loss, gustatory stimuli, and pain. These studies regard emotion as a modulator of decision making that can be made rationally in the absence of emotions. In our daily lives, however, we often encounter various emotional events that affect decisions by themselves, and mapping the events to a reward or punishment is often not straightforward. In this study, we investigated the neural substrates of how such emotional decision outcomes affect subsequent decision making. By using functional magnetic resonance imaging (fMRI), we measured brain activities of humans during a stochastic decision-making task in which various emotional pictures were presented as decision outcomes. We found that pleasant pictures differentially activated the midbrain, fusiform gyrus, and parahippocampal gyrus, whereas unpleasant pictures differentially activated the ventral striatum, compared with neutral pictures. We assumed that the emotional decision outcomes affect the subsequent decision by updating the value of the options, a process modeled by reinforcement learning models, and that the brain regions representing the prediction error that drives the reinforcement learning are involved in guiding subsequent decisions. We found that some regions of the striatum and the insula were separately correlated with the prediction error for either pleasant pictures or unpleasant pictures, whereas the precuneus was correlated with prediction errors for both pleasant and unpleasant pictures. PMID:25695644

  1. 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

  2. The neural basis of body form and body action agnosia.

    Science.gov (United States)

    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.

  3. Neural basis of social status hierarchy across species.

    Science.gov (United States)

    Chiao, Joan Y

    2010-12-01

    Social status hierarchy is a ubiquitous principle of social organization across the animal kingdom. Recent findings in social neuroscience reveal distinct neural networks associated with the recognition and experience of social hierarchy in humans, as well as modulation of these networks by personality and culture. Additionally, allelic variation in the serotonin transporter gene is associated with prevalence of social hierarchy across species and cultures, suggesting the importance of the study of genetic factors underlying social hierarchy. Future studies are needed to determine how genetic and environmental factors shape neural systems involved in the production and maintenance of social hierarchy across ontogeny and phylogeny. Copyright © 2010 Elsevier Ltd. All rights reserved.

  4. Fast radial basis functions for engineering applications

    CERN Document Server

    Biancolini, Marco Evangelos

    2017-01-01

    This book presents the first “How To” guide to the use of radial basis functions (RBF). It provides a clear vision of their potential, an overview of ready-for-use computational tools and precise guidelines to implement new engineering applications of RBF. Radial basis functions (RBF) are a mathematical tool mature enough for useful engineering applications. Their mathematical foundation is well established and the tool has proven to be effective in many fields, as the mathematical framework can be adapted in several ways. A candidate application can be faced considering the features of RBF:  multidimensional space (including 2D and 3D), numerous radial functions available, global and compact support, interpolation/regression. This great flexibility makes RBF attractive – and their great potential has only been partially discovered. This is because of the difficulty in taking a first step toward RBF as they are not commonly part of engineers’ cultural background, but also due to the numerical complex...

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

    Science.gov (United States)

    Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

    2015-04-01

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

  6. Incidental regulation of attraction: The neural basis of the derogation of attractive alternatives in romantic relationships

    NARCIS (Netherlands)

    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

  7. Spherical radial basis functions, theory and applications

    CERN Document Server

    Hubbert, Simon; Morton, Tanya M

    2015-01-01

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

  8. Shindigs, brunches, and rodeos: the neural basis of event words.

    Science.gov (United States)

    Bedny, Marina; Dravida, Swethasri; Saxe, Rebecca

    2014-09-01

    Events (e.g., "running" or "eating") constitute a basic type within human cognition and human language. We asked whether thinking about events, as compared to other conceptual categories, depends on partially independent neural circuits. Indirect evidence for this hypothesis comes from previous studies showing elevated posterior temporal responses to verbs, which typically label events. Neural responses to verbs could, however, be driven either by their grammatical or by their semantic properties. In the present experiment, we separated the effects of grammatical class (verb vs. noun) and semantic category (event vs. object) by measuring neural responses to event nouns (e.g., "the hurricane"). Participants rated the semantic relatedness of event nouns, as well as of two categories of object nouns-animals (e.g., "the alligator") and plants (e.g., "the acorn")-and three categories of verbs-manner of motion (e.g., "to roll"), emission (e.g., "to sparkle"), and perception (e.g., "to gaze"). As has previously been observed, we found larger responses to verbs than to object nouns in the left posterior middle (LMTG) and superior (LSTG) temporal gyri. Crucially, we also found that the LMTG responds more to event than to object nouns. These data suggest that part of the posterior lateral temporal response to verbs is driven by their semantic properties. By contrast, a more superior region, at the junction of the temporal and parietal cortices, responded more to verbs than to all nouns, irrespective of their semantic category. We concluded that the neural mechanisms engaged when thinking about event and object categories are partially dissociable.

  9. The neural basis of suppression and amblyopia in strabismus.

    Science.gov (United States)

    Sengpiel, F; Blakemore, C

    1996-01-01

    The neurophysiological consequences of artificial strabismus in cats and monkeys have been studied for 30 years. However, until very recently no clear picture has emerged of neural deficits that might account for the powerful interocular suppression that strabismic humans experience, nor for the severe amblyopia that is often associated with convergent strabismus. Here we review the effects of squint on the integrative capacities of the primary visual cortex and propose a hypothesis about the relationship between suppression and amblyopia. Most neurons in the visual cortex of normal cats and monkeys can be excited through either eye and show strong facilitation during binocular stimulation with contours of similar orientation in the two eyes. But in strabismic animals, cortical neurons tend to fall into two populations of monocularly excitable cells and exhibit suppressive binocular interactions that share key properties with perceptual suppression in strabismic humans. Such interocular suppression, if prolonged and asymmetric (with input from the squinting eye habitually suppressed by that from the fixating eye), might lead to neural defects in the representation of the deviating eye and hence to amblyopia.

  10. Neural basis of quasi-rational decision making.

    Science.gov (United States)

    Lee, Daeyeol

    2006-04-01

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

  11. The neural basis of implicit learning and memory: a review of neuropsychological and neuroimaging research.

    Science.gov (United States)

    Reber, Paul J

    2013-08-01

    Memory systems research has typically described the different types of long-term memory in the brain as either declarative versus non-declarative or implicit versus explicit. These descriptions reflect the difference between declarative, conscious, and explicit memory that is dependent on the medial temporal lobe (MTL) memory system, and all other expressions of learning and memory. The other type of memory is generally defined by an absence: either the lack of dependence on the MTL memory system (nondeclarative) or the lack of conscious awareness of the information acquired (implicit). However, definition by absence is inherently underspecified and leaves open questions of how this type of memory operates, its neural basis, and how it differs from explicit, declarative memory. Drawing on a variety of studies of implicit learning that have attempted to identify the neural correlates of implicit learning using functional neuroimaging and neuropsychology, a theory of implicit memory is presented that describes it as a form of general plasticity within processing networks that adaptively improve function via experience. Under this model, implicit memory will not appear as a single, coherent, alternative memory system but will instead be manifested as a principle of improvement from experience based on widespread mechanisms of cortical plasticity. The implications of this characterization for understanding the role of implicit learning in complex cognitive processes and the effects of interactions between types of memory will be discussed for examples within and outside the psychology laboratory. Copyright © 2013 Elsevier Ltd. All rights reserved.

  12. Cognitive processes and neural basis of language switching: proposal of a new model.

    Science.gov (United States)

    Moritz-Gasser, Sylvie; Duffau, Hugues

    2009-12-09

    Although studies on bilingualism are abundant, cognitive processes and neural foundations of language switching received less attention. The aim of our study is to provide new insights to this still open question: do dedicated region(s) for language switching exist or is this function underlain by a distributed circuit of interconnected brain areas, part of a more general cognitive system? On the basis of recent behavioral, neuroimaging, and brain stimulation studies, we propose an original 'hodological' model of language switching. This process might be subserved by a large-scale cortico-subcortical network, with an executive system (prefrontal cortex, anterior cingulum, caudate nucleus) controlling a more dedicated language subcircuit, which involves postero-temporal areas, supramarginal and angular gyri, Broca's area, and the superior longitudinal fasciculus.

  13. 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.

  14. Neural basis of moral verdict and moral deliberation

    Science.gov (United States)

    Borg, Jana Schaich; Sinnott-Armstrong, Walter; Calhoun, Vince D.; Kiehl, Kent A.

    2011-01-01

    How people judge something to be morally right or wrong is a fundamental question of both the sciences and the humanities. Here we aim to identify the neural processes that underlie the specific conclusion that something is morally wrong. To do this, we introduce a novel distinction between “moral deliberation,” or the weighing of moral considerations, and the formation of a “moral verdict,” or the commitment to one moral conclusion. We predict and identify hemodynamic activity in the bilateral anterior insula and basal ganglia that correlates with committing to the moral verdict “this is morally wrong” as opposed to “this is morally not wrong,” a finding that is consistent with research from economic decision-making. Using comparisons of deliberation-locked vs. verdict-locked analyses, we also demonstrate that hemodynamic activity in high-level cortical regions previously implicated in morality—including the ventromedial prefrontal cortex, posterior cingulate, and temporoparietal junction—correlates primarily with moral deliberation as opposed to moral verdicts. These findings provide new insights into what types of processes comprise the enterprise of moral judgment, and in doing so point to a framework for resolving why some clinical patients, including psychopaths, may have intact moral judgment but impaired moral behavior. PMID:21590588

  15. Feeling form: the neural basis of haptic shape perception.

    Science.gov (United States)

    Yau, Jeffrey M; Kim, Sung Soo; Thakur, Pramodsingh H; Bensmaia, Sliman J

    2016-02-01

    The tactile perception of the shape of objects critically guides our ability to interact with them. In this review, we describe how shape information is processed as it ascends the somatosensory neuraxis of primates. At the somatosensory periphery, spatial form is represented in the spatial patterns of activation evoked across populations of mechanoreceptive afferents. In the cerebral cortex, neurons respond selectively to particular spatial features, like orientation and curvature. While feature selectivity of neurons in the earlier processing stages can be understood in terms of linear receptive field models, higher order somatosensory neurons exhibit nonlinear response properties that result in tuning for more complex geometrical features. In fact, tactile shape processing bears remarkable analogies to its visual counterpart and the two may rely on shared neural circuitry. Furthermore, one of the unique aspects of primate somatosensation is that it contains a deformable sensory sheet. Because the relative positions of cutaneous mechanoreceptors depend on the conformation of the hand, the haptic perception of three-dimensional objects requires the integration of cutaneous and proprioceptive signals, an integration that is observed throughout somatosensory cortex. Copyright © 2016 the American Physiological Society.

  16. Towards a neural basis of processing musical semantics

    Science.gov (United States)

    Koelsch, Stefan

    2011-06-01

    Processing of meaning is critical for language perception, and therefore the majority of research on meaning processing has focused on the semantic, lexical, conceptual, and propositional processing of language. However, music is another a means of communication, and meaning also emerges from the interpretation of musical information. This article provides a framework for the investigation of the processing of musical meaning, and reviews neuroscience studies investigating this issue. These studies reveal two neural correlates of meaning processing, the N400 and the N5 (which are both components of the event-related electric brain potential). Here I argue that the N400 can be elicited by musical stimuli due to the processing of extra-musical meaning, whereas the N5 can be elicited due to the processing of intra-musical meaning. Notably, whereas the N400 can be elicited by both linguistic and musical stimuli, the N5 has so far only been observed for the processing of meaning in music. Thus, knowledge about both the N400 and the N5 can advance our understanding of how the human brain processes meaning information.

  17. [Neural basis of self-face recognition: social aspects].

    Science.gov (United States)

    Sugiura, Motoaki

    2012-07-01

    Considering the importance of the face in social survival and evidence from evolutionary psychology of visual self-recognition, it is reasonable that we expect neural mechanisms for higher social-cognitive processes to underlie self-face recognition. A decade of neuroimaging studies so far has, however, not provided an encouraging finding in this respect. Self-face specific activation has typically been reported in the areas for sensory-motor integration in the right lateral cortices. This observation appears to reflect the physical nature of the self-face which representation is developed via the detection of contingency between one's own action and sensory feedback. We have recently revealed that the medial prefrontal cortex, implicated in socially nuanced self-referential process, is activated during self-face recognition under a rich social context where multiple other faces are available for reference. The posterior cingulate cortex has also exhibited this activation modulation, and in the separate experiment showed a response to attractively manipulated self-face suggesting its relevance to positive self-value. Furthermore, the regions in the right lateral cortices typically showing self-face-specific activation have responded also to the face of one's close friend under the rich social context. This observation is potentially explained by the fact that the contingency detection for physical self-recognition also plays a role in physical social interaction, which characterizes the representation of personally familiar people. These findings demonstrate that neuroscientific exploration reveals multiple facets of the relationship between self-face recognition and social-cognitive process, and that technically the manipulation of social context is key to its success.

  18. Tracting the neural basis of music: Deficient structural connectivity underlying acquired amusia.

    Science.gov (United States)

    Sihvonen, Aleksi J; Ripollés, Pablo; Särkämö, Teppo; Leo, Vera; Rodríguez-Fornells, Antoni; Saunavaara, Jani; Parkkola, Riitta; Soinila, Seppo

    2017-12-01

    Acquired amusia provides a unique opportunity to investigate the fundamental neural architectures of musical processing due to the transition from a functioning to defective music processing system. Yet, the white matter (WM) deficits in amusia remain systematically unexplored. To evaluate which WM structures form the neural basis for acquired amusia and its recovery, we studied 42 stroke patients longitudinally at acute, 3-month, and 6-month post-stroke stages using DTI [tract-based spatial statistics (TBSS) and deterministic tractography (DT)] and the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Non-recovered amusia was associated with structural damage and subsequent degeneration in multiple WM tracts including the right inferior fronto-occipital fasciculus (IFOF), arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and frontal aslant tract (FAT), as well as in the corpus callosum (CC) and its posterior part (tapetum). In a linear regression analysis, the volume of the right IFOF was the main predictor of MBEA performance across time. Overall, our results provide a comprehensive picture of the large-scale deficits in intra- and interhemispheric structural connectivity underlying amusia, and conversely highlight which pathways are crucial for normal music perception. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Neural basis of the undermining effect of monetary reward on intrinsic motivation.

    Science.gov (United States)

    Murayama, Kou; Matsumoto, Madoka; Izuma, Keise; Matsumoto, Kenji

    2010-12-07

    Contrary to the widespread belief that people are positively motivated by reward incentives, some studies have shown that performance-based extrinsic reward can actually undermine a person's intrinsic motivation to engage in a task. This "undermining effect" has timely practical implications, given the burgeoning of performance-based incentive systems in contemporary society. It also presents a theoretical challenge for economic and reinforcement learning theories, which tend to assume that monetary incentives monotonically increase motivation. Despite the practical and theoretical importance of this provocative phenomenon, however, little is known about its neural basis. Herein we induced the behavioral undermining effect using a newly developed task, and we tracked its neural correlates using functional MRI. Our results show that performance-based monetary reward indeed undermines intrinsic motivation, as assessed by the number of voluntary engagements in the task. We found that activity in the anterior striatum and the prefrontal areas decreased along with this behavioral undermining effect. These findings suggest that the corticobasal ganglia valuation system underlies the undermining effect through the integration of extrinsic reward value and intrinsic task value.

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

    Science.gov (United States)

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

    2015-12-01

    We used functional magnetic resonance imaging (fMRI) to examine the neural basis of extreme multilingual language control in a group of 50 multilingual participants. Comparing brain responses arising during simultaneous interpretation (SI) with those arising during simultaneous repetition revealed activation of regions known to be involved in speech perception and production, alongside a network incorporating the caudate nucleus that is known to be implicated in domain-general cognitive control. The similarity between the networks underlying bilingual language control and general executive control supports the notion that the frequently reported bilingual advantage on executive tasks stems from the day-to-day demands of language control in the multilingual brain. We examined neural correlates of the management of simultaneity by correlating brain activity during interpretation with the duration of simultaneous speaking and hearing. This analysis showed significant modulation of the putamen by the duration of simultaneity. Our findings suggest that, during SI, the caudate nucleus is implicated in the overarching selection and control of the lexico-semantic system, while the putamen is implicated in ongoing control of language output. These findings provide the first clear dissociation of specific dorsal striatum structures in polyglot language control, roles that are consistent with previously described involvement of these regions in nonlinguistic executive control. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  1. The Neural Basis of Cognitive Control: Response Selection and Inhibition

    Science.gov (United States)

    Goghari, Vina M.; MacDonald, Angus W., III

    2009-01-01

    The functional neuroanatomy of tasks that recruit different forms of response selection and inhibition has to our knowledge, never been directly addressed in a single fMRI study using similar stimulus-response paradigms where differences between scanning time and sequence, stimuli, and experimenter instructions were minimized. Twelve right-handed…

  2. Polarized DIS Structure Functions from Neural Networks

    International Nuclear Information System (INIS)

    Del Debbio, L.; Guffanti, A.; Piccione, A.

    2007-01-01

    We present a parametrization of polarized Deep-Inelastic-Scattering (DIS) structure functions based on Neural Networks. The parametrization provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. As an example we discuss the application of this method to the study of the structure function g 1 p (x,Q 2 )

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

    Science.gov (United States)

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

    2012-10-01

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

  4. Comparison of the neural basis for imagined writing and drawing.

    Science.gov (United States)

    Harrington, Greg S; Farias, Dana; Davis, Christine H; Buonocore, Michael H

    2007-05-01

    Drawing and writing are complex processes that require the synchronization of cognition, language, and perceptual-motor skills. Drawing and writing have both been utilized in the treatment of aphasia to improve communication. Recent research suggests that the act of drawing an object facilitated naming, whereas writing the word diminished accurate naming in individuals with aphasia. However, the relationship between object drawing and subsequent phonological output is unclear. Although the right hemisphere is characteristically mute, there is evidence from split-brain research that the right hemisphere can integrate pictures and words, likely via a semantic network. We hypothesized that drawing activates right hemispheric and left perilesional regions that are spared in aphasic individuals and may contribute to semantic activation that supports naming. Eleven right-handed subjects participated in a functional MRI (fMRI) experiment involving imagined drawing and writing and 6 of the 11 subjects participated in a second fMRI experiment involving actual writing and drawing. Drawing and writing produced very similar group activation maps including activation bilaterally in the premotor, inferior frontal, posterior inferior temporal, and parietal areas. The comparison of drawing vs. writing revealed significant differences between the conditions in areas of the brain known for language processing. The direct comparison between drawing and writing revealed greater right hemisphere activation for drawing in language areas such as Brodmann area (BA) 46 and BA 37.

  5. Neural Basis of Emotional Decision Making in Trait Anxiety

    Science.gov (United States)

    Xu, Pengfei; Gu, Ruolei; Broster, Lucas S.; Wu, Runguo; Van Dam, Nicholas T.; Jiang, Yang; Fan, Jin

    2013-01-01

    Although trait anxiety has been associated with risk decision making, whether it is related to risk per se or to the feeling of the risk, as well as the underlying neurocognitive mechanisms, remains unclear. Using a decision-making task with a manipulation of frame (i.e., written description of options as a potential gain or loss) and functional magnetic resonance imaging, we investigated the neurocognitive relationship between trait anxiety and decision making. The classic framing effect was observed: participants chose the safe option when it was described as a potential gain, but they avoided the same option when it was described as a potential loss. Most importantly, trait anxiety was positively correlated with this behavioral bias. Trait anxiety was also positively correlated with amygdala-based “emotional” system activation and its coupling with the ventromedial prefrontal cortex (vmPFC) when decisions were consistent with the framing effect, but negatively correlated with the dorsal anterior cingulate cortex (dACC)-based “analytic” system activation and its connectivity to the vmPFC when decisions ran counter to the framing effect. Our findings suggest that trait anxiety is not associated with subjective risk preference but an evaluative bias of emotional information in decision making, underpinned by a hyperactive emotional system and a hypoactive analytic system in the brain. PMID:24259585

  6. Neural basis of emotional decision making in trait anxiety.

    Science.gov (United States)

    Xu, Pengfei; Gu, Ruolei; Broster, Lucas S; Wu, Runguo; Van Dam, Nicholas T; Jiang, Yang; Fan, Jin; Luo, Yue-jia

    2013-11-20

    Although trait anxiety has been associated with risk decision making, whether it is related to risk per se or to the feeling of the risk, as well as the underlying neurocognitive mechanisms, remains unclear. Using a decision-making task with a manipulation of frame (i.e., written description of options as a potential gain or loss) and functional magnetic resonance imaging, we investigated the neurocognitive relationship between trait anxiety and decision making. The classic framing effect was observed: participants chose the safe option when it was described as a potential gain, but they avoided the same option when it was described as a potential loss. Most importantly, trait anxiety was positively correlated with this behavioral bias. Trait anxiety was also positively correlated with amygdala-based "emotional" system activation and its coupling with the ventromedial prefrontal cortex (vmPFC) when decisions were consistent with the framing effect, but negatively correlated with the dorsal anterior cingulate cortex (dACC)-based "analytic" system activation and its connectivity to the vmPFC when decisions ran counter to the framing effect. Our findings suggest that trait anxiety is not associated with subjective risk preference but an evaluative bias of emotional information in decision making, underpinned by a hyperactive emotional system and a hypoactive analytic system in the brain.

  7. The Neural Basis of Risky Choice with Affective Outcomes

    Science.gov (United States)

    Suter, Renata S.; Pachur, Thorsten; Hertwig, Ralph; Endestad, Tor; Biele, Guido

    2015-01-01

    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 of the decision

  8. 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

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

    Science.gov (United States)

    Suter, Renata S; Pachur, Thorsten; Hertwig, Ralph; Endestad, Tor; Biele, Guido

    2015-01-01

    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 of the decision to

  10. Absence of visual experience modifies the neural basis of numerical thinking.

    Science.gov (United States)

    Kanjlia, Shipra; Lane, Connor; Feigenson, Lisa; Bedny, Marina

    2016-10-04

    In humans, the ability to reason about mathematical quantities depends on a frontoparietal network that includes the intraparietal sulcus (IPS). How do nature and nurture give rise to the neurobiology of numerical cognition? We asked how visual experience shapes the neural basis of numerical thinking by studying numerical cognition in congenitally blind individuals. Blind (n = 17) and blindfolded sighted (n = 19) participants solved math equations that varied in difficulty (e.g., 27 - 12 = x vs. 7 - 2 = x), and performed a control sentence comprehension task while undergoing fMRI. Whole-cortex analyses revealed that in both blind and sighted participants, the IPS and dorsolateral prefrontal cortices were more active during the math task than the language task, and activity in the IPS increased parametrically with equation difficulty. Thus, the classic frontoparietal number network is preserved in the total absence of visual experience. However, surprisingly, blind but not sighted individuals additionally recruited a subset of early visual areas during symbolic math calculation. The functional profile of these "visual" regions was identical to that of the IPS in blind but not sighted individuals. Furthermore, in blindness, number-responsive visual cortices exhibited increased functional connectivity with prefrontal and IPS regions that process numbers. We conclude that the frontoparietal number network develops independently of visual experience. In blindness, this number network colonizes parts of deafferented visual cortex. These results suggest that human cortex is highly functionally flexible early in life, and point to frontoparietal input as a mechanism of cross-modal plasticity in blindness.

  11. Identification and integration of sensory modalities: Neural basis and relation to consciousness

    NARCIS (Netherlands)

    Pennartz, C.M.A.

    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

  12. Neural basis of limb ownership in individuals with body integrity identity disorder

    NARCIS (Netherlands)

    van Dijk, Milenna T.; van Wingen, Guido A.; van Lammeren, Anouk; Blom, Rianne M.; de Kwaasteniet, Bart P.; Scholte, H. Steven; Denys, Damiaan

    2013-01-01

    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

  13. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  14. The Neural Basis of and a Common Neural Circuitry in Different Types of Pro-social Behavior

    Directory of Open Access Journals (Sweden)

    Jun Luo

    2018-06-01

    Full Text Available Pro-social behaviors are voluntary behaviors that benefit other people or society as a whole, such as charitable donations, cooperation, trust, altruistic punishment, and fairness. These behaviors have been widely described through non self-interest decision-making in behavioral experimental studies and are thought to be increased by social preference motives. Importantly, recent studies using a combination of neuroimaging and brain stimulation, designed to reveal the neural mechanisms of pro-social behaviors, have found that a wide range of brain areas, specifically the prefrontal cortex, anterior insula, anterior cingulate cortex, and amygdala, are correlated or causally related with pro-social behaviors. In this review, we summarize the research on the neural basis of various kinds of pro-social behaviors and describe a common shared neural circuitry of these pro-social behaviors. We introduce several general ways in which experimental economics and neuroscience can be combined to develop important contributions to understanding social decision-making and pro-social behaviors. Future research should attempt to explore the neural circuitry between the frontal lobes and deeper brain areas.

  15. Neural basis of exertional fatigue in the heat: A review of magnetic resonance imaging methods.

    Science.gov (United States)

    Tan, X R; Low, I C C; Stephenson, M C; Soong, T W; Lee, J K W

    2018-03-01

    The central nervous system, specifically the brain, is implicated in the development of exertional fatigue under a hot environment. Diverse neuroimaging techniques have been used to visualize the brain activity during or after exercise. Notably, the use of magnetic resonance imaging (MRI) has become prevalent due to its excellent spatial resolution and versatility. This review evaluates the significance and limitations of various brain MRI techniques in exercise studies-brain volumetric analysis, functional MRI, functional connectivity MRI, and arterial spin labeling. The review aims to provide a summary on the neural basis of exertional fatigue and proposes future directions for brain MRI studies. A systematic literature search was performed where a total of thirty-seven brain MRI studies associated with exercise, fatigue, or related physiological factors were reviewed. The findings suggest that with moderate dehydration, there is a decrease in total brain volume accompanied with expansion of ventricular volume. With exercise fatigue, there is increased activation of sensorimotor and cognitive brain areas, increased thalamo-insular activation and decreased interhemispheric connectivity in motor cortex. Under passive hyperthermia, there are regional changes in cerebral perfusion, a reduction in local connectivity in functional brain networks and an impairment to executive function. Current literature suggests that the brain structure and function are influenced by exercise, fatigue, and related physiological perturbations. However, there is still a dearth of knowledge and it is hoped that through understanding of MRI advantages and limitations, future studies will shed light on the central origin of exertional fatigue in the heat. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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

    Science.gov (United States)

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

    2016-04-01

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

  17. Function approximation of tasks by neural networks

    International Nuclear Information System (INIS)

    Gougam, L.A.; Chikhi, A.; Mekideche-Chafa, F.

    2008-01-01

    For several years now, neural network models have enjoyed wide popularity, being applied to problems of regression, classification and time series analysis. Neural networks have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. The latter is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. In a previous contribution, we have used a well known simplified architecture to show that it provides a reasonably efficient, practical and robust, multi-frequency analysis. We have investigated the universal approximation theory of neural networks whose transfer functions are: sigmoid (because of biological relevance), Gaussian and two specified families of wavelets. The latter have been found to be more appropriate to use. The aim of the present contribution is therefore to use a m exican hat wavelet a s transfer function to approximate different tasks relevant and inherent to various applications in physics. The results complement and provide new insights into previously published results on this problem

  18. The neural basis of the bystander effect--the influence of group size on neural activity when witnessing an emergency.

    Science.gov (United States)

    Hortensius, Ruud; de Gelder, Beatrice

    2014-06-01

    Naturalistic observation and experimental studies in humans and other primates show that observing an individual in need automatically triggers helping behavior. The aim of the present study is to clarify the neurofunctional basis of social influences on individual helping behavior. We investigate whether when participants witness an emergency, while performing an unrelated color-naming task in an fMRI scanner, the number of bystanders present at the emergency influences neural activity in regions related to action preparation. The results show a decrease in activity with the increase in group size in the left pre- and postcentral gyri and left medial frontal gyrus. In contrast, regions related to visual perception and attention show an increase in activity. These results demonstrate the neural mechanisms of social influence on automatic action preparation that is at the core of helping behavior when witnessing an emergency. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. 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.

  20. A genetic basis for functional hypothalamic amenorrhea.

    OpenAIRE

    Caronia, L.M.; Martin, C.; Welt, C.K.; Sykiotis, G.P.; Quinton, R.; Thambundit, A.; Avbelj, M.; Dhruvakumar, S.; Plummer, L.; Hughes, V.A.; Seminara, S.B.; Boepple, P.A.; Sidis, Y.; Crowley, W.F.; Martin, K.A.

    2011-01-01

    Background: Functional hypothalamic amenorrhea is a reversible form of gonadotropin-releasing hormone (GnRH) deficiency commonly triggered by stressors such as excessive exercise, nutritional deficits, or psychological distress. Women vary in their susceptibility to inhibition of the reproductive axis by such stressors, but it is unknown whether this variability reflects a genetic predisposition to hypothalamic amenorrhea. We hypothesized that mutations in genes involved in idiopathic hypogon...

  1. A Genetic Basis for Functional Hypothalamic Amenorrhea

    Science.gov (United States)

    Caronia, Lisa M.; Martin, Cecilia; Welt, Corrine K.; Sykiotis, Gerasimos P.; Quinton, Richard; Thambundit, Apisadaporn; Avbelj, Magdalena; Dhruvakumar, Sadhana; Plummer, Lacey; Hughes, Virginia A.; Seminara, Stephanie B.; Boepple, Paul A.; Sidis, Yisrael; Crowley, William F.; Martin, Kathryn A.; Hall, Janet E.; Pitteloud, Nelly

    2011-01-01

    BACKGROUND Functional hypothalamic amenorrhea is a reversible form of gonadotropin-releasing hormone (GnRH) deficiency commonly triggered by stressors such as excessive exercise, nutritional deficits, or psychological distress. Women vary in their susceptibility to inhibition of the reproductive axis by such stressors, but it is unknown whether this variability reflects a genetic predisposition to hypothalamic amenorrhea. We hypothesized that mutations in genes involved in idiopathic hypogonadotropic hypogonadism, a congenital form of GnRH deficiency, are associated with hypothalamic amenorrhea. METHODS We analyzed the coding sequence of genes associated with idiopathic hypogonadotropic hypogonadism in 55 women with hypothalamic amenorrhea and performed in vitro studies of the identified mutations. RESULTS Six heterozygous mutations were identified in 7 of the 55 patients with hypothalamic amenorrhea: two variants in the fibroblast growth factor receptor 1 gene FGFR1 (G260E and R756H), two in the prokineticin receptor 2 gene PROKR2 (R85H and L173R), one in the GnRH receptor gene GNRHR (R262Q), and one in the Kall-mann syndrome 1 sequence gene KAL1 (V371I). No mutations were found in a cohort of 422 controls with normal menstrual cycles. In vitro studies showed that FGFR1 G260E, FGFR1 R756H, and PROKR2 R85H are loss-of-function mutations, as has been previously shown for PROKR2 L173R and GNRHR R262Q. CONCLUSIONS Rare variants in genes associated with idiopathic hypogonadotropic hypogonadism are found in women with hypothalamic amenorrhea, suggesting that these mutations may contribute to the variable susceptibility of women to the functional changes in GnRH secretion that characterize hypothalamic amenorrhea. Our observations provide evidence for the role of rare variants in common multifactorial disease. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and others; ClinicalTrials.gov number, NCT00494169.) PMID:21247312

  2. A genetic basis for functional hypothalamic amenorrhea.

    Science.gov (United States)

    Caronia, Lisa M; Martin, Cecilia; Welt, Corrine K; Sykiotis, Gerasimos P; Quinton, Richard; Thambundit, Apisadaporn; Avbelj, Magdalena; Dhruvakumar, Sadhana; Plummer, Lacey; Hughes, Virginia A; Seminara, Stephanie B; Boepple, Paul A; Sidis, Yisrael; Crowley, William F; Martin, Kathryn A; Hall, Janet E; Pitteloud, Nelly

    2011-01-20

    Functional hypothalamic amenorrhea is a reversible form of gonadotropin-releasing hormone (GnRH) deficiency commonly triggered by stressors such as excessive exercise, nutritional deficits, or psychological distress. Women vary in their susceptibility to inhibition of the reproductive axis by such stressors, but it is unknown whether this variability reflects a genetic predisposition to hypothalamic amenorrhea. We hypothesized that mutations in genes involved in idiopathic hypogonadotropic hypogonadism, a congenital form of GnRH deficiency, are associated with hypothalamic amenorrhea. We analyzed the coding sequence of genes associated with idiopathic hypogonadotropic hypogonadism in 55 women with hypothalamic amenorrhea and performed in vitro studies of the identified mutations. Six heterozygous mutations were identified in 7 of the 55 patients with hypothalamic amenorrhea: two variants in the fibroblast growth factor receptor 1 gene FGFR1 (G260E and R756H), two in the prokineticin receptor 2 gene PROKR2 (R85H and L173R), one in the GnRH receptor gene GNRHR (R262Q), and one in the Kallmann syndrome 1 sequence gene KAL1 (V371I). No mutations were found in a cohort of 422 controls with normal menstrual cycles. In vitro studies showed that FGFR1 G260E, FGFR1 R756H, and PROKR2 R85H are loss-of-function mutations, as has been previously shown for PROKR2 L173R and GNRHR R262Q. Rare variants in genes associated with idiopathic hypogonadotropic hypogonadism are found in women with hypothalamic amenorrhea, suggesting that these mutations may contribute to the variable susceptibility of women to the functional changes in GnRH secretion that characterize hypothalamic amenorrhea. Our observations provide evidence for the role of rare variants in common multifactorial disease. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and others; ClinicalTrials.gov number, NCT00494169.).

  3. 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.

  4. Neural basis of limb ownership in individuals with body integrity identity disorder.

    Science.gov (United States)

    van Dijk, Milenna T; van Wingen, Guido A; van Lammeren, Anouk; Blom, Rianne M; de Kwaasteniet, Bart P; Scholte, H Steven; Denys, Damiaan

    2013-01-01

    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.

  5. New Computer Simulations of Macular Neural Functioning

    Science.gov (United States)

    Ross, Muriel D.; Doshay, D.; Linton, S.; Parnas, B.; Montgomery, K.; Chimento, T.

    1994-01-01

    We use high performance graphics workstations and supercomputers to study the functional significance of the three-dimensional (3-D) organization of gravity sensors. These sensors have a prototypic architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scaled-up, 3-D versions run on a Cray Y-MP supercomputer. A semi-automated method of reconstruction of neural tissue from serial sections studied in a transmission electron microscope has been developed to eliminate tedious conventional photography. The reconstructions use a mesh as a step in generating a neural surface for visualization. Two meshes are required to model calyx surfaces. The meshes are connected and the resulting prisms represent the cytoplasm and the bounding membranes. A finite volume analysis method is employed to simulate voltage changes along the calyx in response to synapse activation on the calyx or on calyceal processes. The finite volume method insures that charge is conserved at the calyx-process junction. These and other models indicate that efferent processes act as voltage followers, and that the morphology of some afferent processes affects their functioning. In a final application, morphological information is symbolically represented in three dimensions in a computer. The possible functioning of the connectivities is tested using mathematical interpretations of physiological parameters taken from the literature. Symbolic, 3-D simulations are in progress to probe the functional significance of the connectivities. This research is expected to advance computer-based studies of macular functioning and of synaptic plasticity.

  6. Using piecewise sinusoidal basis functions to blanket multiple wire segments

    CSIR Research Space (South Africa)

    Lysko, AA

    2009-06-01

    Full Text Available This paper discusses application of the piecewise sinusoidal (PWS) basis function (BF) over a chain of several wire segments, for example as a multiple domain basis function. The usage of PWS BF is compared to results based on the piecewise linear...

  7. Neural Basis of Limb Ownership in Individuals with Body Integrity Identity Disorder

    OpenAIRE

    van Dijk, Milenna T.; van Wingen, Guido A.; van Lammeren, Anouk; Blom, Rianne M.; de Kwaasteniet, Bart P.; Scholte, H. Steven; Denys, Damiaan

    2013-01-01

    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 ...

  8. METHODS OF TEXT INFORMATION CLASSIFICATION ON THE BASIS OF ARTIFICIAL NEURAL AND SEMANTIC NETWORKS

    Directory of Open Access Journals (Sweden)

    L. V. Serebryanaya

    2016-01-01

    Full Text Available The article covers the use of perseptron, Hopfild artificial neural network and semantic network for classification of text information. Network training algorithms are studied. An algorithm of inverse mistake spreading for perceptron network and convergence algorithm for Hopfild network are implemented. On the basis of the offered models and algorithms automatic text classification software is developed and its operation results are evaluated.

  9. An enhanced radial basis function network for short-term electricity price forecasting

    International Nuclear Information System (INIS)

    Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

    2010-01-01

    This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)

  10. Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

    Science.gov (United States)

    Xue, Gui; Love, Bradley C.; Preston, Alison R.; Poldrack, Russell A.

    2014-01-01

    Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. PMID:24872552

  11. Numerical analysis of different neural transfer functions used for best approximation

    International Nuclear Information System (INIS)

    Gougam, L.A.; Chikhi, A.; Biskri, S.; Chafa, F.

    2006-01-01

    It is widely recognised that the choice of transfer functions in neural networks is of en importance to their performance. In this paper, different neural transfer functions usec approximation are discussed. We begin with sigmoi'dal functions used most often by diffi authors . At a second step, we use Gaussian functions as previously suggested in refere Finally, we deal with a specified wavelet family. A comparison between the three cases < above is made exhibiting therefore the advantages of each transfer function. The approa< function improves as the dimension N of the elementary task basis increases

  12. The neural basis of economic decision-making in the ultimatum game

    NARCIS (Netherlands)

    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.

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

    Science.gov (United States)

    Jin, Jia; Yu, Liping; Ma, Qingguo

    2015-01-01

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

  14. Smooth function approximation using neural networks.

    Science.gov (United States)

    Ferrari, Silvia; Stengel, Robert F

    2005-01-01

    An algebraic approach for representing multidimensional nonlinear functions by feedforward neural networks is presented. In this paper, the approach is implemented for the approximation of smooth batch data containing the function's input, output, and possibly, gradient information. The training set is associated to the network adjustable parameters by nonlinear weight equations. The cascade structure of these equations reveals that they can be treated as sets of linear systems. Hence, the training process and the network approximation properties can be investigated via linear algebra. Four algorithms are developed to achieve exact or approximate matching of input-output and/or gradient-based training sets. Their application to the design of forward and feedback neurocontrollers shows that algebraic training is characterized by faster execution speeds and better generalization properties than contemporary optimization techniques.

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

    Science.gov (United States)

    Marko, Mollie K; Crocetti, Deana; Hulst, Thomas; Donchin, Opher; Shadmehr, Reza; Mostofsky, Stewart H

    2015-03-01

    Autism spectrum disorder is a developmental disorder characterized by deficits in social and communication skills and repetitive and stereotyped interests and behaviours. Although not part of the diagnostic criteria, individuals with autism experience a host of motor impairments, potentially due to abnormalities in how they learn motor control throughout development. Here, we used behavioural techniques to quantify motor learning in autism spectrum disorder, and structural brain imaging to investigate the neural basis of that learning in the cerebellum. Twenty children with autism spectrum disorder and 20 typically developing control subjects, aged 8-12, made reaching movements while holding the handle of a robotic manipulandum. In random trials the reach was perturbed, resulting in errors that were sensed through vision and proprioception. The brain learned from these errors and altered the motor commands on the subsequent reach. We measured learning from error as a function of the sensory modality of that error, and found that children with autism spectrum disorder outperformed typically developing children when learning from errors that were sensed through proprioception, but underperformed typically developing children when learning from errors that were sensed through vision. Previous work had shown that this learning depends on the integrity of a region in the anterior cerebellum. Here we found that the anterior cerebellum, extending into lobule VI, and parts of lobule VIII were smaller than normal in children with autism spectrum disorder, with a volume that was predicted by the pattern of learning from visual and proprioceptive errors. We suggest that the abnormal patterns of motor learning in children with autism spectrum disorder, showing an increased sensitivity to proprioceptive error and a decreased sensitivity to visual error, may be associated with abnormalities in the cerebellum. © The Author (2015). Published by Oxford University Press on behalf

  16. The neural basis of kinesthetic and visual imagery in sports: an ALE meta - analysis.

    Science.gov (United States)

    Filgueiras, Alberto; Quintas Conde, Erick Francisco; Hall, Craig R

    2017-12-19

    Imagery is a widely spread technique in the sport sciences that entails the mental rehearsal of a given situation to improve an athlete's learning, performance and motivation. Two modalities of imagery are reported to tap into distinct brain structures, but sharing common components: kinesthetic and visual imagery. This study aimed to investigate the neural basis of those types of imagery with Activation Likelihood Estimation algorithm to perform a meta - analysis. A systematic search was used to retrieve only experimental studies with athletes or sportspersons. Altogether, nine studies were selected and an ALE meta - analysis was performed. Results indicated significant activation of the premotor, somatosensory cortex, supplementary motor areas, inferior and superior parietal lobule, caudate, cingulate and cerebellum in both imagery tasks. It was concluded that visual and kinesthetic imagery share similar neural networks which suggests that combined interventions are beneficial to athletes whereas separate use of those two modalities of imagery may seem less efficient from a neuropsychological approach.

  17. 基于径向基神经网络-遗传算法的海流能水轮机叶片翼型优化%Optimization of hydrofoil for marine current turbine based on radial basis function neural network and genetic algorithm

    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

  18. Analysis of neural networks through base functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.

    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

  19. Neural Basis of Anhedonia and Amotivation in Patients with Schizophrenia: The Role of Reward System.

    Science.gov (United States)

    Lee, Jung Suk; Jung, Suwon; Park, Il Ho; Kim, Jae-Jin

    2015-01-01

    Anhedonia, the inability to feel pleasure, and amotivation, the lack of motivation, are two prominent negative symptoms of schizophrenia, which contribute to the poor social and occupational behaviors in the patients. Recently growing evidence shows that anhedonia and amotivation are tied together, but have distinct neural correlates. It is important to note that both of these symptoms may derive from deficient functioning of the reward network. A further analysis into the neuroimaging findings of schizophrenia shows that the neural correlates overlap in the reward network including the ventral striatum, anterior cingulate cortex and orbitofrontal cortex. Other neuroimaging studies have demonstrated the involvement of the default mode network in anhedonia. The identification of aspecific deficit in hedonic and motivational capacity may help to elucidate the mechanisms behind social functioning deficits in schizophrenia, and may also lead to more targeted treatment of negative symptoms.

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

    OpenAIRE

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

    2007-01-01

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

  1. Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA

    Directory of Open Access Journals (Sweden)

    Alisson C. D. de Souza

    2014-09-01

    Full Text Available This paper proposes a parallel fixed point radial basis function (RBF artificial neural network (ANN, implemented in a field programmable gate array (FPGA trained online with a least mean square (LMS algorithm. The processing time and occupied area were analyzed for various fixed point formats. The problems of precision of the ANN response for nonlinear classification using the XOR gate and interpolation using the sine function were also analyzed in a hardware implementation. The entire project was developed using the System Generator platform (Xilinx, with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.

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

    Directory of Open Access Journals (Sweden)

    Khang Jie Liew

    Full Text Available 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.

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

    Science.gov (United States)

    Liew, Khang Jie; 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.

  4. Closed fringe demodulation using phase decomposition by Fourier basis functions.

    Science.gov (United States)

    Kulkarni, Rishikesh; Rastogi, Pramod

    2016-06-01

    We report a new technique for the demodulation of a closed fringe pattern by representing the phase as a weighted linear combination of a certain number of linearly independent Fourier basis functions in a given row/column at a time. A state space model is developed with the weights of the basis functions as the elements of the state vector. The iterative extended Kalman filter is effectively utilized for the robust estimation of the weights. A coarse estimate of the fringe density based on the fringe frequency map is used to determine the initial row/column to start with and subsequently the optimal number of basis functions. The performance of the proposed method is evaluated with several noisy fringe patterns. Experimental results are also reported to support the practical applicability of the proposed method.

  5. Speech/Nonspeech Detection Using Minimal Walsh Basis Functions

    Directory of Open Access Journals (Sweden)

    Pwint Moe

    2007-01-01

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

  6. Psychedelics Promote Structural and Functional Neural Plasticity

    Directory of Open Access Journals (Sweden)

    Calvin Ly

    2018-06-01

    Full Text Available Summary: Atrophy of neurons in the prefrontal cortex (PFC plays a key role in the pathophysiology of depression and related disorders. The ability to promote both structural and functional plasticity in the PFC has been hypothesized to underlie the fast-acting antidepressant properties of the dissociative anesthetic ketamine. Here, we report that, like ketamine, serotonergic psychedelics are capable of robustly increasing neuritogenesis and/or spinogenesis both in vitro and in vivo. These changes in neuronal structure are accompanied by increased synapse number and function, as measured by fluorescence microscopy and electrophysiology. The structural changes induced by psychedelics appear to result from stimulation of the TrkB, mTOR, and 5-HT2A signaling pathways and could possibly explain the clinical effectiveness of these compounds. Our results underscore the therapeutic potential of psychedelics and, importantly, identify several lead scaffolds for medicinal chemistry efforts focused on developing plasticity-promoting compounds as safe, effective, and fast-acting treatments for depression and related disorders. : Ly et al. demonstrate that psychedelic compounds such as LSD, DMT, and DOI increase dendritic arbor complexity, promote dendritic spine growth, and stimulate synapse formation. These cellular effects are similar to those produced by the fast-acting antidepressant ketamine and highlight the potential of psychedelics for treating depression and related disorders. Keywords: neural plasticity, psychedelic, spinogenesis, synaptogenesis, depression, LSD, DMT, ketamine, noribogaine, MDMA

  7. Gaussian basis functions for highly oscillatory scattering wavefunctions

    Science.gov (United States)

    Mant, B. P.; Law, M. M.

    2018-04-01

    We have applied a basis set of distributed Gaussian functions within the S-matrix version of the Kohn variational method to scattering problems involving deep potential energy wells. The Gaussian positions and widths are tailored to the potential using the procedure of Bačić and Light (1986 J. Chem. Phys. 85 4594) which has previously been applied to bound-state problems. The placement procedure is shown to be very efficient and gives scattering wavefunctions and observables in agreement with direct numerical solutions. We demonstrate the basis function placement method with applications to hydrogen atom–hydrogen atom scattering and antihydrogen atom–hydrogen atom scattering.

  8. 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.

  9. Optimal choice of basis functions in the linear regression analysis

    International Nuclear Information System (INIS)

    Khotinskij, A.M.

    1988-01-01

    Problem of optimal choice of basis functions in the linear regression analysis is investigated. Step algorithm with estimation of its efficiency, which holds true at finite number of measurements, is suggested. Conditions, providing the probability of correct choice close to 1 are formulated. Application of the step algorithm to analysis of decay curves is substantiated. 8 refs

  10. Radial Basis Function Based Quadrature over Smooth Surfaces

    Science.gov (United States)

    2016-03-24

    Radial Basis Functions φ(r) Piecewise Smooth (Conditionally Positive Definite) MN Monomial |r|2m+1 TPS thin plate spline |r|2mln|r| Infinitely Smooth...smooth surfaces using polynomial interpolants, while [27] couples Thin - Plate Spline interpolation (see table 1) with Green’s integral formula [29

  11. Large-Eddy Simulation Using Projection onto Local Basis Functions

    Science.gov (United States)

    Pope, S. B.

    In the traditional approach to LES for inhomogeneous flows, the resolved fields are obtained by a filtering operation (with filter width Delta). The equations governing the resolved fields are then partial differential equations, which are solved numerically (on a grid of spacing h). For an LES computation of a given magnitude (i.e., given h), there are conflicting considerations in the choice of Delta: to resolve a large range of turbulent motions, Delta should be small; to solve the equations with numerical accuracy, Delta should be large. In the alternative approach advanced here, this conflict is avoided. The resolved fields are defined by projection onto local basis functions, so that the governing equations are ordinary differential equations for the evolution of the basis-function coefficients. There is no issue of numerical spatial discretization errors. A general methodology for modelling the effects of the residual motions is developed. The model is based directly on the basis-function coefficients, and its effect is to smooth the fields where their rates of change are not well resolved by the basis functions. Demonstration calculations are performed for Burgers' equation.

  12. Higher-Order Hierarchical Legendre Basis Functions in Applications

    DEFF Research Database (Denmark)

    Kim, Oleksiy S.; Jørgensen, Erik; Meincke, Peter

    2007-01-01

    The higher-order hierarchical Legendre basis functions have been developed for effective solution of integral equations with the method of moments. They are derived from orthogonal Legendre polynomials modified to enforce normal continuity between neighboring mesh elements, while preserving a high...

  13. Performance-oriented asymptotic tracking control of hydraulic systems with radial basis function network disturbance observer

    Directory of Open Access Journals (Sweden)

    Jian Hu

    2016-05-01

    Full Text Available Uncertainties, including parametric uncertainties and uncertain nonlinearities, always exist in positioning servo systems driven by a hydraulic actuator, which would degrade their tracking accuracy. In this article, an integrated control scheme, which combines adaptive robust control together with radial basis function neural network–based disturbance observer, is proposed for high-accuracy motion control of hydraulic systems. Not only parametric uncertainties but also uncertain nonlinearities (i.e. nonlinear friction, external disturbances, and/or unmodeled dynamics are taken into consideration in the proposed controller. The above uncertainties are compensated, respectively, by adaptive control and radial basis function neural network, which are ultimately integrated together by applying feedforward compensation technique, in which the global stabilization of the controller is ensured via a robust feedback path. A new kind of parameter and weight adaptation law is designed on the basis of Lyapunov stability theory. Furthermore, the proposed controller obtains an expected steady performance even if modeling uncertainties exist, and extensive simulation results in various working conditions have proven the high performance of the proposed control scheme.

  14. Neural basis for dynamic updating of object representation in visual working memory.

    Science.gov (United States)

    Takahama, Sachiko; Miyauchi, Satoru; Saiki, Jun

    2010-02-15

    In real world, objects have multiple features and change dynamically. Thus, object representations must satisfy dynamic updating and feature binding. Previous studies have investigated the neural activity of dynamic updating or feature binding alone, but not both simultaneously. We investigated the neural basis of feature-bound object representation in a dynamically updating situation by conducting a multiple object permanence tracking task, which required observers to simultaneously process both the maintenance and dynamic updating of feature-bound objects. Using an event-related design, we separated activities during memory maintenance and change detection. In the search for regions showing selective activation in dynamic updating of feature-bound objects, we identified a network during memory maintenance that was comprised of the inferior precentral sulcus, superior parietal lobule, and middle frontal gyrus. In the change detection period, various prefrontal regions, including the anterior prefrontal cortex, were activated. In updating object representation of dynamically moving objects, the inferior precentral sulcus closely cooperates with a so-called "frontoparietal network", and subregions of the frontoparietal network can be decomposed into those sensitive to spatial updating and feature binding. The anterior prefrontal cortex identifies changes in object representation by comparing memory and perceptual representations rather than maintaining object representations per se, as previously suggested. Copyright 2009 Elsevier Inc. All rights reserved.

  15. Diffusion Forecasting Model with Basis Functions from QR-Decomposition

    Science.gov (United States)

    Harlim, John; Yang, Haizhao

    2017-12-01

    The diffusion forecasting is a nonparametric approach that provably solves the Fokker-Planck PDE corresponding to Itô diffusion without knowing the underlying equation. The key idea of this method is to approximate the solution of the Fokker-Planck equation with a discrete representation of the shift (Koopman) operator on a set of basis functions generated via the diffusion maps algorithm. While the choice of these basis functions is provably optimal under appropriate conditions, computing these basis functions is quite expensive since it requires the eigendecomposition of an N× N diffusion matrix, where N denotes the data size and could be very large. For large-scale forecasting problems, only a few leading eigenvectors are computationally achievable. To overcome this computational bottleneck, a new set of basis functions constructed by orthonormalizing selected columns of the diffusion matrix and its leading eigenvectors is proposed. This computation can be carried out efficiently via the unpivoted Householder QR factorization. The efficiency and effectiveness of the proposed algorithm will be shown in both deterministically chaotic and stochastic dynamical systems; in the former case, the superiority of the proposed basis functions over purely eigenvectors is significant, while in the latter case forecasting accuracy is improved relative to using a purely small number of eigenvectors. Supporting arguments will be provided on three- and six-dimensional chaotic ODEs, a three-dimensional SDE that mimics turbulent systems, and also on the two spatial modes associated with the boreal winter Madden-Julian Oscillation obtained from applying the Nonlinear Laplacian Spectral Analysis on the measured Outgoing Longwave Radiation.

  16. Systematic review of the neural basis of social cognition in patients with mood disorders.

    Science.gov (United States)

    Cusi, Andrée M; Nazarov, Anthony; Holshausen, Katherine; Macqueen, Glenda M; McKinnon, Margaret C

    2012-05-01

    This review integrates neuroimaging studies of 2 domains of social cognition--emotion comprehension and theory of mind (ToM)--in patients with major depressive disorder and bipolar disorder. The influence of key clinical and method variables on patterns of neural activation during social cognitive processing is also examined. Studies were identified using PsycINFO and PubMed (January 1967 to May 2011). The search terms were "fMRI," "emotion comprehension," "emotion perception," "affect comprehension," "affect perception," "facial expression," "prosody," "theory of mind," "mentalizing" and "empathy" in combination with "major depressive disorder," "bipolar disorder," "major depression," "unipolar depression," "clinical depression" and "mania." Taken together, neuroimaging studies of social cognition in patients with mood disorders reveal enhanced activation in limbic and emotion-related structures and attenuated activity within frontal regions associated with emotion regulation and higher cognitive functions. These results reveal an overall lack of inhibition by higher-order cognitive structures on limbic and emotion-related structures during social cognitive processing in patients with mood disorders. Critically, key variables, including illness burden, symptom severity, comorbidity, medication status and cognitive load may moderate this pattern of neural activation. Studies that did not include control tasks or a comparator group were included in this review. Further work is needed to examine the contribution of key moderator variables and to further elucidate the neural networks underlying altered social cognition in patients with mood disorders. The neural networks under lying higher-order social cognitive processes, including empathy, remain unexplored in patients with mood disorders.

  17. Extent and neural basis of semantic memory impairment in mild cognitive impairment.

    Science.gov (United States)

    Barbeau, Emmanuel J; Didic, Mira; Joubert, Sven; Guedj, Eric; Koric, Lejla; Felician, Olivier; Ranjeva, Jean-Philippe; Cozzone, Patrick; Ceccaldi, Mathieu

    2012-01-01

    An increasing number of studies indicate that semantic memory is impaired in mild cognitive impairment (MCI). However, the extent and the neural basis of this impairment remain unknown. The aim of the present study was: 1) to evaluate whether all or only a subset of semantic domains are impaired in MCI patients; and 2) to assess the neural substrate of the semantic impairment in MCI patients using voxel-based analysis of MR grey matter density and SPECT perfusion. 29 predominantly amnestic MCI patients and 29 matched control subjects participated in this study. All subjects underwent a full neuropsychological assessment, along with a battery of five tests evaluating different domains of semantic memory. A semantic memory composite Z-score was established on the basis of this battery and was correlated with MRI grey matter density and SPECT perfusion measures. MCI patients were found to have significantly impaired performance across all semantic tasks, in addition to their anterograde memory deficit. Moreover, no temporal gradient was found for famous faces or famous public events and knowledge for the most remote decades was also impaired. Neuroimaging analyses revealed correlations between semantic knowledge and perirhinal/entorhinal areas as well as the anterior hippocampus. Therefore, the deficits in the realm of semantic memory in patients with MCI is more widespread than previously thought and related to dysfunction of brain areas beyond the limbic-diencephalic system involved in episodic memory. The severity of the semantic impairment may indicate a decline of semantic memory that began many years before the patients first consulted.

  18. Surface interpolation with radial basis functions for medical imaging

    International Nuclear Information System (INIS)

    Carr, J.C.; Beatson, R.K.; Fright, W.R.

    1997-01-01

    Radial basis functions are presented as a practical solution to the problem of interpolating incomplete surfaces derived from three-dimensional (3-D) medical graphics. The specific application considered is the design of cranial implants for the repair of defects, usually holes, in the skull. Radial basis functions impose few restrictions on the geometry of the interpolation centers and are suited to problems where interpolation centers do not form a regular grid. However, their high computational requirements have previously limited their use to problems where the number of interpolation centers is small (<300). Recently developed fast evaluation techniques have overcome these limitations and made radial basis interpolation a practical approach for larger data sets. In this paper radial basis functions are fitted to depth-maps of the skull's surface, obtained from X-ray computed tomography (CT) data using ray-tracing techniques. They are used to smoothly interpolate the surface of the skull across defect regions. The resulting mathematical description of the skull's surface can be evaluated at any desired resolution to be rendered on a graphics workstation or to generate instructions for operating a computer numerically controlled (CNC) mill

  19. Quantum phase space with a basis of Wannier functions

    Science.gov (United States)

    Fang, Yuan; Wu, Fan; Wu, Biao

    2018-02-01

    A quantum phase space with Wannier basis is constructed: (i) classical phase space is divided into Planck cells; (ii) a complete set of Wannier functions are constructed with the combination of Kohn’s method and Löwdin method such that each Wannier function is localized at a Planck cell. With these Wannier functions one can map a wave function unitarily onto phase space. Various examples are used to illustrate our method and compare it to Wigner function. The advantage of our method is that it can smooth out the oscillations in wave functions without losing any information and is potentially a better tool in studying quantum-classical correspondence. In addition, we point out that our method can be used for time-frequency analysis of signals.

  20. When a loved one feels unfamiliar: a case study on the neural basis of Capgras delusion.

    Science.gov (United States)

    Thiel, Christiane M; Studte, Sara; Hildebrandt, Helmut; Huster, Rene; Weerda, Riklef

    2014-03-01

    Perception of familiar faces depends on a core system analysing visual appearance and an extended system dealing with inference of mental states and emotional responses. Damage to the core system impairs face perception as seen in prosopagnosia. In contrast, patients with Capgras delusion show intact face perception but believe that closely related persons are impostors. It has been suggested that two deficits are necessary for the delusion, an aberrant perceptual or affective experience that leads to a bizarre belief as well as an impaired ability to evaluate beliefs. Using functional magnetic resonance imaging, we compared neural activity to familiar and unfamiliar faces in a patient with Capgras delusion and an age matched control group. We provide evidence that Capgras delusion is related to dysfunctional activity in the extended face processing system. The patient, who developed the delusion for the partner after a large right prefrontal lesion sparing the ventromedial and medial orbitofrontal cortex, lacked neural activity to the partner's face in left posterior cingulate cortex and left posterior superior temporal sulcus. Further, we found impaired functional connectivity of the latter region with the left superior frontal gyrus and to a lesser extent with the right superior frontal sulcus/middle frontal gyrus. The findings of this case study suggest that the first factor in Capgras delusion may be reduced neural activity in the extended face processing system that deals with inference of mental states while the second factor may be due to a lesion in the right middle frontal gyrus. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. AKT signaling displays multifaceted functions in neural crest development.

    Science.gov (United States)

    Sittewelle, Méghane; Monsoro-Burq, Anne H

    2018-05-31

    AKT signaling is an essential intracellular pathway controlling cell homeostasis, cell proliferation and survival, as well as cell migration and differentiation in adults. Alterations impacting the AKT pathway are involved in many pathological conditions in human disease. Similarly, during development, multiple transmembrane molecules, such as FGF receptors, PDGF receptors or integrins, activate AKT to control embryonic cell proliferation, migration, differentiation, and also cell fate decisions. While many studies in mouse embryos have clearly implicated AKT signaling in the differentiation of several neural crest derivatives, information on AKT functions during the earliest steps of neural crest development had remained relatively scarce until recently. However, recent studies on known and novel regulators of AKT signaling demonstrate that this pathway plays critical roles throughout the development of neural crest progenitors. Non-mammalian models such as fish and frog embryos have been instrumental to our understanding of AKT functions in neural crest development, both in neural crest progenitors and in the neighboring tissues. This review combines current knowledge acquired from all these different vertebrate animal models to describe the various roles of AKT signaling related to neural crest development in vivo. We first describe the importance of AKT signaling in patterning the tissues involved in neural crest induction, namely the dorsal mesoderm and the ectoderm. We then focus on AKT signaling functions in neural crest migration and differentiation. Copyright © 2018 Elsevier Inc. All rights reserved.

  2. Compactly Supported Basis Functions as Support Vector Kernels for Classification.

    Science.gov (United States)

    Wittek, Peter; Tan, Chew Lim

    2011-10-01

    Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.

  3. Complexity of Gaussian-Radial-Basis Networks Approximating Smooth Functions

    Czech Academy of Sciences Publication Activity Database

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

    2009-01-01

    Roč. 25, č. 1 (2009), s. 63-74 ISSN 0885-064X R&D Projects: GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : Gaussian-radial-basis-function networks * rates of approximation * model complexity * variation norms * Bessel and Sobolev norms * tractability of approximation Subject RIV: IN - Informatics, Computer Science Impact factor: 1.227, year: 2009

  4. 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.

  5. On-line learning in radial basis functions networks

    OpenAIRE

    Freeman, Jason; Saad, David

    1997-01-01

    An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...

  6. Radial basis function and its application in tourism management

    Science.gov (United States)

    Hu, Shan-Feng; Zhu, Hong-Bin; Zhao, Lei

    2018-05-01

    In this work, several applications and the performances of the radial basis function (RBF) are briefly reviewed at first. After that, the binomial function combined with three different RBFs including the multiquadric (MQ), inverse quadric (IQ) and inverse multiquadric (IMQ) distributions are adopted to model the tourism data of Huangshan in China. Simulation results showed that all the models match very well with the sample data. It is found that among the three models, the IMQ-RBF model is more suitable for forecasting the tourist flow.

  7. 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.

  8. 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.

  9. Application of RBF neural network improved by peak density function in intelligent color matching of wood dyeing

    International Nuclear Information System (INIS)

    Guan, Xuemei; Zhu, Yuren; Song, Wenlong

    2016-01-01

    According to the characteristics of wood dyeing, we propose a predictive model of pigment formula for wood dyeing based on Radial Basis Function (RBF) neural network. In practical application, however, it is found that the number of neurons in the hidden layer of RBF neural network is difficult to determine. In general, we need to test several times according to experience and prior knowledge, which is lack of a strict design procedure on theoretical basis. And we also don’t know whether the RBF neural network is convergent. This paper proposes a peak density function to determine the number of neurons in the hidden layer. In contrast to existing approaches, the centers and the widths of the radial basis function are initialized by extracting the features of samples. So the uncertainty caused by random number when initializing the training parameters and the topology of RBF neural network is eliminated. The average relative error of the original RBF neural network is 1.55% in 158 epochs. However, the average relative error of the RBF neural network which is improved by peak density function is only 0.62% in 50 epochs. Therefore, the convergence rate and approximation precision of the RBF neural network are improved significantly.

  10. DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORS

    Directory of Open Access Journals (Sweden)

    V. A. Lazarenko

    2017-01-01

    Full Text Available Purpose. To develop an artificial neural network for diagnosing and predicting the development of cholecystitis based on an analysis of data on risk factors, and to explore the possibilities of its application in real clinical practice.Materials and methods. The collection of materials was held in at the hospitals of the city of Kursk and included a survey of 488 patients with hepatopancreatoduodenal diseases. 203 patients were suffering from cholecystitis, in 285 patients the diagnosis of cholecystitis was excluded. Analysis of risk factors’ data (such as sex, age, bad habits, profession, family relationships, etc. was carried out using an internally developed artificial neural network (multilayer perceptron with hyperbolic tangent as the activation function. The computer program “System of Intellectual Analysis and Diagnosis of Diseases” was registered in accordance with established procedure (Certificate No. 2017613090.Results. The use of neural network analysis of data on risk factors in comparison with the processing of information that forms a clinical picture allows the diagnosis of a potential disease with cholecystitis before the onset of symptoms. The training of the artificial neural network with a quantitative output coding the age of probable hospitalization made it possible to generate an array of values, signifficantly (α ≤ 0.001 not differing from the empirical data. The difference between the mean calculated and mean empirical values was 0.45 for the training set and 1.75 for the clinical approbation group. The mean absolute error was within the range of 1.87–2.07 years.Conclusion. 1. The proposed new approach to the diagnosis and prognosis of cholecystitis has demonstrated its effectiveness, which is confirmed in clinical approbation by the levels of sensitivity (94.44%, m = 2.26 and specificity (80.6%, m = 3.9.2. The error in predicting the age of probable hospitalization of patients with cholecystitis did not

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

    Science.gov (United States)

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

    2007-03-01

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

  12. Optogenetics in the Teaching Laboratory: Using Channelrhodopsin-2 to Study the Neural Basis of Behavior and Synaptic Physiology in "Drosophila"

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    Pulcu, Erdem; Elliott, Rebecca

    2015-09-01

    Major depressive disorder, a complex neuropsychiatric condition, is associated with psychosocial functioning impairments that could become chronic even after symptoms remit. Social functioning impairments in patients could also pose coping difficulties to individuals around them. In this Personal View, we trace the potential neurobiological origins of these impairments down to three candidate domains-namely, social perception and emotion processing, motivation and reward value processing, and social decision making. We argue that the neural basis of abnormalities in these domains could be detectable at different temporal stages during social interactions (eg, before and after decision stages), particularly within frontomesolimbic networks (ie, frontostriatal and amygdala-striatal circuitries). We review some of the experimental designs used to probe these circuits and suggest novel, integrative approaches. We propose that an understanding of the interactions between these domains could provide valuable insights for the clinical stratification of major depressive disorder subtypes and might inform future developments of novel treatment options in return. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2003-06-01

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

  15. Practical auxiliary basis implementation of Rung 3.5 functionals

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2008-12-01

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

  17. Matrix regulators in neural stem cell functions.

    Science.gov (United States)

    Wade, Anna; McKinney, Andrew; Phillips, Joanna J

    2014-08-01

    Neural stem/progenitor cells (NSPCs) reside within a complex and dynamic extracellular microenvironment, or niche. This niche regulates fundamental aspects of their behavior during normal neural development and repair. Precise yet dynamic regulation of NSPC self-renewal, migration, and differentiation is critical and must persist over the life of an organism. In this review, we summarize some of the major components of the NSPC niche and provide examples of how cues from the extracellular matrix regulate NSPC behaviors. We use proteoglycans to illustrate the many diverse roles of the niche in providing temporal and spatial regulation of cellular behavior. The NSPC niche is comprised of multiple components that include; soluble ligands, such as growth factors, morphogens, chemokines, and neurotransmitters, the extracellular matrix, and cellular components. As illustrated by proteoglycans, a major component of the extracellular matrix, the NSPC, niche provides temporal and spatial regulation of NSPC behaviors. The factors that control NSPC behavior are vital to understand as we attempt to modulate normal neural development and repair. Furthermore, an improved understanding of how these factors regulate cell proliferation, migration, and differentiation, crucial for malignancy, may reveal novel anti-tumor strategies. This article is part of a Special Issue entitled Matrix-mediated cell behaviour and properties. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Learning Mixtures of Truncated Basis Functions from Data

    DEFF Research Database (Denmark)

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

    2014-01-01

    In this paper we investigate methods for learning hybrid Bayesian networks from data. First we utilize a kernel density estimate of the data in order to translate the data into a mixture of truncated basis functions (MoTBF) representation using a convex optimization technique. When utilizing a ke...... 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......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......-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....

  19. A Simple Quantum Neural Net with a Periodic Activation Function

    OpenAIRE

    Daskin, Ammar

    2018-01-01

    In this paper, we propose a simple neural net that requires only $O(nlog_2k)$ number of qubits and $O(nk)$ quantum gates: Here, $n$ is the number of input parameters, and $k$ is the number of weights applied to these parameters in the proposed neural net. We describe the network in terms of a quantum circuit, and then draw its equivalent classical neural net which involves $O(k^n)$ nodes in the hidden layer. Then, we show that the network uses a periodic activation function of cosine values o...

  20. Functional and Anatomic Correlates of Neural Aging in Birds.

    Science.gov (United States)

    Ottinger, Mary Ann

    2018-01-01

    Avian species show variation in longevity, habitat, physiologic characteristics, and lifetime endocrine patterns. Lifetime reproductive and metabolic function vary. Much is known about the neurobiology of the song system in many altricial birds. Little is known about aging in neural systems in birds. Captive birds often survive beyond the age they would in the wild, providing an opportunity to gain an understanding of the physiologic and neural changes. This paper reviews the available information with the goal of capturing areas of potential investigation into gaps in our understanding of neural aging as reflected in physiologic, endocrine, and cognitive aging. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. The Neural Basis of Postural Instability Gait Disorder Subtype of Parkinson's Disease: A PET and fMRI Study.

    Science.gov (United States)

    Zhang, Li; Li, Tian-Nv; Yuan, Yong-Sheng; Jiang, Si-Ming; Tong, Qing; Wang, Min; Wang, Jian-Wei; Chen, Hua-Jun; Ding, Jian; Xu, Qin-Rong; Zhang, Ke-Zhong

    2016-05-01

    The aim of this study is to further uncover the neural basis of postural instability gait disorder (PIGD) subtype of Parkinson's disease. With F-18 fluorodeoxyglucose PET (FDG-PET), brain glucose metabolism of patients with PIGD (n = 15) was compared with healthy controls (n = 17) and tremor-dominant (TD) patients (n = 15), and the correlation between metabolism and PIGD symptoms was also assessed. Within PIGD symptom-correlated hypometabolic areas, the relationship of functional connectivity (FC) with motor and cognitive symptoms was examined by using functional MRI. Compared with controls, patients with PIGD displayed a distributed pattern of brain hypometabolism including striatal, frontal, and parietal areas. Relative to the pattern of TD patients, the pattern of patients with PIGD had additional metabolic decreases in caudate and inferior parietal lobule (IPL, Brodmann area [BA] 40). In PIGD group, the metabolic reductions in IPL (BA 40), middle frontal gyrus (MFG, BA 9) and fusiform gyrus (FG, BA 20) were associated with severe PIGD symptoms. Regions showing such correlation were chosen for further seed-based FC analysis. Decreased FC within the prefrontal-parietal network (between the MFG and IPL) was associated with severe PIGD symptoms. The involvement of the caudate, FG, and prefrontal-parietal network may be associated with the prominent gait impairments of PIGD subtype. Our findings expand the pathophysiological knowledge of PIGD subtype and provide valuable information for potential neuromodulation therapies alleviating gait disorders. © 2016 John Wiley & Sons Ltd.

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

    International Nuclear Information System (INIS)

    Ohgami, Yuko; Uchida, Nobuko; Matsuo, Kayako; Nakai, Toshiharu

    2007-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Koichi eToida

    2016-01-01

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

  4. Median nerve fascicular anatomy as a basis for distal neural prostheses.

    Science.gov (United States)

    Planitzer, Uwe; Steinke, Hanno; Meixensberger, Jürgen; Bechmann, Ingo; Hammer, Niels; Winkler, Dirk

    2014-05-01

    Functional electrical stimulation (FES) serves as a possible therapy to restore missing motor functions of peripheral nerves by means of cuff electrodes. FES is established for improving lower limb function. Transferring this method to the upper extremity is complex, due to a lack of anatomical data on the physiological configuration of nerve fascicles. Our study's aim was to provide an anatomical basis for FES of the median nerve in the distal forearm and hand. We investigated 21 distal median nerves from 12 body donors. The peripheral fascicles were traced back by removing the external and interfascicular epineurium and then assigned to 4 quadrants. A distinct motor and sensory distribution was observed. The fascicles innervating the thenar eminence and the first lumbrical muscle originated from the nerves' radial parts in 82%. The fascicle supplying the second lumbrical muscle originated from the ulnar side in 78%. No macroscopically visible plexus formation was observed for the distal median nerve in the forearm. The findings on the distribution of the motor branches of the median nerve and the missing plexus formation may likely serve as an anatomical basis for FES of the distal forearm. However, due to the considerable variability of the motor branches, cuff electrodes will need to be adapted individually in FES. Taking into account the sensory distribution of the median nerve, FES may also possibly be applied in the treatment of regional pain syndromes. Copyright © 2013 Elsevier GmbH. All rights reserved.

  5. Brains creating stories of selves: the neural basis of autobiographical reasoning

    Science.gov (United States)

    Cassol, Helena; Phillips, Christophe; Balteau, Evelyne; Salmon, Eric; Van der Linden, Martial

    2014-01-01

    Personal identity critically depends on the creation of stories about the self and one’s life. The present study investigates the neural substrates of autobiographical reasoning, a process central to the construction of such narratives. During functional magnetic resonance imaging scanning, participants approached a set of personally significant memories in two different ways: in some trials, they remembered the concrete content of the events (autobiographical remembering), whereas in other trials they reflected on the broader meaning and implications of their memories (autobiographical reasoning). Relative to remembering, autobiographical reasoning recruited a left-lateralized network involved in conceptual processing [including the dorsal medial prefrontal cortex (MPFC), inferior frontal gyrus, middle temporal gyrus and angular gyrus]. The ventral MPFC—an area that may function to generate personal/affective meaning—was not consistently engaged during autobiographical reasoning across participants but, interestingly, the activity of this region was modulated by individual differences in interest and willingness to engage in self-reflection. These findings support the notion that autobiographical reasoning and the construction of personal narratives go beyond mere remembering in that they require deriving meaning and value from past experiences. PMID:23482628

  6. Comparing the neural basis of monetary reward and cognitive feedback during information-integration category learning.

    Science.gov (United States)

    Daniel, Reka; Pollmann, Stefan

    2010-01-06

    The dopaminergic system is known to play a central role in reward-based learning (Schultz, 2006), yet it was also observed to be involved when only cognitive feedback is given (Aron et al., 2004). Within the domain of information-integration category learning, in which information from several stimulus dimensions has to be integrated predecisionally (Ashby and Maddox, 2005), the importance of contingent feedback is well established (Maddox et al., 2003). We examined the common neural correlates of reward anticipation and prediction error in this task. Sixteen subjects performed two parallel information-integration tasks within a single event-related functional magnetic resonance imaging session but received a monetary reward only for one of them. Similar functional areas including basal ganglia structures were activated in both task versions. In contrast, a single structure, the nucleus accumbens, showed higher activation during monetary reward anticipation compared with the anticipation of cognitive feedback in information-integration learning. Additionally, this activation was predicted by measures of intrinsic motivation in the cognitive feedback task and by measures of extrinsic motivation in the rewarded task. Our results indicate that, although all other structures implicated in category learning are not significantly affected by altering the type of reward, the nucleus accumbens responds to the positive incentive properties of an expected reward depending on the specific type of the reward.

  7. Neural basis for hand muscle synergies in the primate spinal cord.

    Science.gov (United States)

    Takei, Tomohiko; Confais, Joachim; Tomatsu, Saeka; Oya, Tomomichi; Seki, Kazuhiko

    2017-08-08

    Grasping is a highly complex movement that requires the coordination of multiple hand joints and muscles. Muscle synergies have been proposed to be the functional building blocks that coordinate such complex motor behaviors, but little is known about how they are implemented in the central nervous system. Here we demonstrate that premotor interneurons (PreM-INs) in the primate cervical spinal cord underlie the spatiotemporal patterns of hand muscle synergies during a voluntary grasping task. Using spike-triggered averaging of hand muscle activity, we found that the muscle fields of PreM-INs were not uniformly distributed across hand muscles but rather distributed as clusters corresponding to muscle synergies. Moreover, although individual PreM-INs have divergent activation patterns, the population activity of PreM-INs reflects the temporal activation of muscle synergies. These findings demonstrate that spinal PreM-INs underlie the muscle coordination required for voluntary hand movements in primates. Given the evolution of neural control of primate hand functions, we suggest that spinal premotor circuits provide the fundamental coordination of multiple joints and muscles upon which more fractionated control is achieved by superimposed, phylogenetically newer, pathways.

  8. Effects of sleep deprivation on neural functioning: an integrative review

    NARCIS (Netherlands)

    Boonstra, T.W.; Stins, J.F.; Daffertshofer, A.; Beek, P.J.

    2007-01-01

    Sleep deprivation has a broad variety of effects on human performance and neural functioning that manifest themselves at different levels of description. On a macroscopic level, sleep deprivation mainly affects executive functions, especially in novel tasks. Macroscopic and mesoscopic effects of

  9. Neural basis of distorted self-face recognition in social anxiety disorder.

    Science.gov (United States)

    Kim, Min-Kyeong; Yoon, Hyung-Jun; Shin, Yu-Bin; Lee, Seung-Koo; Kim, Jae-Jin

    2016-01-01

    The observer perspective causes patients with social anxiety disorder (SAD) to excessively inspect their performance and appearance. This study aimed to investigate the neural basis of distorted self-face recognition in non-social situations in patients with SAD. Twenty patients with SAD and 20 age- and gender-matched healthy controls participated in this fMRI study. Data were acquired while participants performed a Composite Face Evaluation Task, during which they had to press a button indicating how much they liked a series of self-faces, attractively transformed self-faces, and attractive others' faces. Patients had a tendency to show more favorable responses to the self-face and unfavorable responses to the others' faces compared with controls, but the two groups' responses to the attractively transformed self-faces did not differ. Significant group differences in regional activity were observed in the middle frontal and supramarginal gyri in the self-face condition (patients self-face condition (patients > controls); and the middle frontal, supramarginal, and angular gyri in the attractive others' face condition (patients > controls). Most fronto-parietal activities during observation of the self-face were negatively correlated with preference scores in patients but not in controls. Patients with SAD have a positive point of view of their own face and experience self-relevance for the attractively transformed self-faces. This distorted cognition may be based on dysfunctions in the frontal and inferior parietal regions. The abnormal engagement of the fronto-parietal attentional network during processing face stimuli in non-social situations may be linked to distorted self-recognition in SAD.

  10. Revisiting the Neural Basis of Acquired Amusia: Lesion Patterns and Structural Changes Underlying Amusia Recovery.

    Science.gov (United States)

    Sihvonen, Aleksi J; Ripollés, Pablo; Rodríguez-Fornells, Antoni; Soinila, Seppo; Särkämö, Teppo

    2017-01-01

    Although, acquired amusia is a common deficit following stroke, relatively little is still known about its precise neural basis, let alone to its recovery. Recently, we performed a voxel-based lesion-symptom mapping (VLSM) and morphometry (VBM) study which revealed a right lateralized lesion pattern, and longitudinal gray matter volume (GMV) and white matter volume (WMV) changes that were specifically associated with acquired amusia after stroke. In the present study, using a larger sample of stroke patients ( N = 90), we aimed to replicate and extend the previous structural findings as well as to determine the lesion patterns and volumetric changes associated with amusia recovery. Structural MRIs were acquired at acute and 6-month post-stroke stages. Music perception was behaviorally assessed at acute and 3-month post-stroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Using these scores, the patients were classified as non-amusic, recovered amusic, and non-recovered amusic. The results of the acute stage VLSM analyses and the longitudinal VBM analyses converged to show that more severe and persistent (non-recovered) amusia was associated with an extensive pattern of lesions and GMV/WMV decrease in right temporal, frontal, parietal, striatal, and limbic areas. In contrast, less severe and transient (recovered) amusia was linked to lesions specifically in left inferior frontal gyrus as well as to a GMV decrease in right parietal areas. Separate continuous analyses of MBEA Scale and Rhythm scores showed extensively overlapping lesion pattern in right temporal, frontal, and subcortical structures as well as in the right insula. Interestingly, the recovered pitch amusia was related to smaller GMV decreases in the temporoparietal junction whereas the recovered rhythm amusia was associated to smaller GMV decreases in the inferior temporal pole. Overall, the results provide a more comprehensive picture of the lesions

  11. Revisiting the Neural Basis of Acquired Amusia: Lesion Patterns and Structural Changes Underlying Amusia Recovery

    Science.gov (United States)

    Sihvonen, Aleksi J.; Ripollés, Pablo; Rodríguez-Fornells, Antoni; Soinila, Seppo; Särkämö, Teppo

    2017-01-01

    Although, acquired amusia is a common deficit following stroke, relatively little is still known about its precise neural basis, let alone to its recovery. Recently, we performed a voxel-based lesion-symptom mapping (VLSM) and morphometry (VBM) study which revealed a right lateralized lesion pattern, and longitudinal gray matter volume (GMV) and white matter volume (WMV) changes that were specifically associated with acquired amusia after stroke. In the present study, using a larger sample of stroke patients (N = 90), we aimed to replicate and extend the previous structural findings as well as to determine the lesion patterns and volumetric changes associated with amusia recovery. Structural MRIs were acquired at acute and 6-month post-stroke stages. Music perception was behaviorally assessed at acute and 3-month post-stroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Using these scores, the patients were classified as non-amusic, recovered amusic, and non-recovered amusic. The results of the acute stage VLSM analyses and the longitudinal VBM analyses converged to show that more severe and persistent (non-recovered) amusia was associated with an extensive pattern of lesions and GMV/WMV decrease in right temporal, frontal, parietal, striatal, and limbic areas. In contrast, less severe and transient (recovered) amusia was linked to lesions specifically in left inferior frontal gyrus as well as to a GMV decrease in right parietal areas. Separate continuous analyses of MBEA Scale and Rhythm scores showed extensively overlapping lesion pattern in right temporal, frontal, and subcortical structures as well as in the right insula. Interestingly, the recovered pitch amusia was related to smaller GMV decreases in the temporoparietal junction whereas the recovered rhythm amusia was associated to smaller GMV decreases in the inferior temporal pole. Overall, the results provide a more comprehensive picture of the lesions

  12. Revisiting the Neural Basis of Acquired Amusia: Lesion Patterns and Structural Changes Underlying Amusia Recovery

    Directory of Open Access Journals (Sweden)

    Aleksi J. Sihvonen

    2017-07-01

    Full Text Available Although, acquired amusia is a common deficit following stroke, relatively little is still known about its precise neural basis, let alone to its recovery. Recently, we performed a voxel-based lesion-symptom mapping (VLSM and morphometry (VBM study which revealed a right lateralized lesion pattern, and longitudinal gray matter volume (GMV and white matter volume (WMV changes that were specifically associated with acquired amusia after stroke. In the present study, using a larger sample of stroke patients (N = 90, we aimed to replicate and extend the previous structural findings as well as to determine the lesion patterns and volumetric changes associated with amusia recovery. Structural MRIs were acquired at acute and 6-month post-stroke stages. Music perception was behaviorally assessed at acute and 3-month post-stroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA. Using these scores, the patients were classified as non-amusic, recovered amusic, and non-recovered amusic. The results of the acute stage VLSM analyses and the longitudinal VBM analyses converged to show that more severe and persistent (non-recovered amusia was associated with an extensive pattern of lesions and GMV/WMV decrease in right temporal, frontal, parietal, striatal, and limbic areas. In contrast, less severe and transient (recovered amusia was linked to lesions specifically in left inferior frontal gyrus as well as to a GMV decrease in right parietal areas. Separate continuous analyses of MBEA Scale and Rhythm scores showed extensively overlapping lesion pattern in right temporal, frontal, and subcortical structures as well as in the right insula. Interestingly, the recovered pitch amusia was related to smaller GMV decreases in the temporoparietal junction whereas the recovered rhythm amusia was associated to smaller GMV decreases in the inferior temporal pole. Overall, the results provide a more comprehensive picture of

  13. Using function approximation to determine neural network accuracy

    International Nuclear Information System (INIS)

    Wichman, R.F.; Alexander, J.

    2013-01-01

    Many, if not most, control processes demonstrate nonlinear behavior in some portion of their operating range and the ability of neural networks to model non-linear dynamics makes them very appealing for control. Control of high reliability safety systems, and autonomous control in process or robotic applications, however, require accurate and consistent control and neural networks are only approximators of various functions so their degree of approximation becomes important. In this paper, the factors affecting the ability of a feed-forward back-propagation neural network to accurately approximate a non-linear function are explored. Compared to pattern recognition using a neural network for function approximation provides an easy and accurate method for determining the network's accuracy. In contrast to other techniques, we show that errors arising in function approximation or curve fitting are caused by the neural network itself rather than scatter in the data. A method is proposed that provides improvements in the accuracy achieved during training and resulting ability of the network to generalize after training. Binary input vectors provided a more accurate model than with scalar inputs and retraining using a small number of the outlier x,y pairs improved generalization. (author)

  14. Functional neural networks underlying response inhibition in adolescents and adults.

    Science.gov (United States)

    Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

    2007-07-19

    This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.

  15. The neural basis for writing from dictation in the temporoparietal cortex.

    Science.gov (United States)

    Roux, Franck-Emmanuel; Durand, Jean-Baptiste; Réhault, Emilie; Planton, Samuel; Draper, Louisa; Démonet, Jean-François

    2014-01-01

    Cortical electrical stimulation mapping was used to study neural substrates of the function of writing in the temporoparietal cortex. We identified the sites involved in oral language (sentence reading and naming) and writing from dictation, in order to spare these areas during removal of brain tumours in 30 patients (23 in the left, and 7 in the right hemisphere). Electrostimulation of the cortex impaired writing ability in 62 restricted cortical areas (.25 cm2). These were found in left temporoparietal lobes and were mostly located along the superior temporal gyrus (Brodmann's areas 22 and 42). Stimulation of right temporoparietal lobes in right-handed patients produced no writing impairments. However there was a high variability of location between individuals. Stimulation resulted in combined symptoms (affecting oral language and writing) in fourteen patients, whereas in eight other patients, stimulation-induced pure agraphia symptoms with no oral language disturbance in twelve of the identified areas. Each detected area affected writing in a different way. We detected the various different stages of the auditory-to-motor pathway of writing from dictation: either through comprehension of the dictated sentences (word deafness areas), lexico-semantic retrieval, or phonologic processing. In group analysis, barycentres of all different types of writing interferences reveal a hierarchical functional organization along the superior temporal gyrus from initial word recognition to lexico-semantic and phonologic processes along the ventral and the dorsal comprehension pathways, supporting the previously described auditory-to-motor process. The left posterior Sylvian region supports different aspects of writing function that are extremely specialized and localized, sometimes being segregated in a way that could account for the occurrence of pure agraphia that has long-been described in cases of damage to this region. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. How the Brain Wants What the Body Needs: The Neural Basis of Positive Alliesthesia.

    Science.gov (United States)

    Avery, Jason A; Burrows, Kaiping; Kerr, Kara L; Bodurka, Jerzy; Khalsa, Sahib S; Paulus, Martin P; Simmons, W Kyle

    2017-03-01

    Discontinuing unhealthy behaviors, such as overeating or drug use, depends upon an individual's ability to overcome the influence of environmental reward cues. The strength of that influence, however, varies greatly depending upon the internal state of the body. Characterizing the relationship between interoceptive signaling and shifting drug cue valuation provides an opportunity for understanding the neural bases of how changing internal states alter reward processing more generally. A total of 17 cigarette smokers rated the pleasantness of cigarette pictures when they were nicotine sated or nicotine abstinent. On both occasions, smokers also underwent functional magnetic resonance imaging (fMRI) scanning while performing a visceral interoceptive attention task and a resting-state functional connectivity scan. Hemodynamic, physiological, and behavioral parameters were compared between sated and abstinent scans. The relationships between changes in these parameters across scan sessions were also examined. Smokers rated cigarette pictures as significantly more pleasant while nicotine abstinent than while nicotine sated. Comparing abstinent with sated scans, smokers also exhibited significantly decreased mid-insula, amygdala, and orbitofrontal activity while attending to interoceptive signals from the body. Change in interoceptive activity within the left mid-insula predicted the increase in smoker's pleasantness ratings of cigarette cues. This increase in pleasantness ratings was also correlated with an increase in resting-state functional connectivity between the mid-insula and the ventral striatum and ventral pallidum. These findings support a model wherein interoceptive processing in the mid-insula of withdrawal signals from the body potentiates the motivational salience of reward cues through the recruitment of hedonic 'hot spots' within the brain's reward circuitry.

  17. Recent advances in radial basis function collocation methods

    CERN Document Server

    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 ...

  18. The Gaussian radial basis function method for plasma kinetic theory

    Energy Technology Data Exchange (ETDEWEB)

    Hirvijoki, E., E-mail: eero.hirvijoki@chalmers.se [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden); Candy, J.; Belli, E. [General Atomics, PO Box 85608, San Diego, CA 92186-5608 (United States); Embréus, O. [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden)

    2015-10-30

    Description of a magnetized plasma involves the Vlasov equation supplemented with the non-linear Fokker–Planck collision operator. For non-Maxwellian distributions, the collision operator, however, is difficult to compute. In this Letter, we introduce Gaussian Radial Basis Functions (RBFs) to discretize the velocity space of the entire kinetic system, and give the corresponding analytical expressions for the Vlasov and collision operator. Outlining the general theory, we also highlight the connection to plasma fluid theories, and give 2D and 3D numerical solutions of the non-linear Fokker–Planck equation. Applications are anticipated in both astrophysical and laboratory plasmas. - Highlights: • A radically new method to address the velocity space discretization of the non-linear kinetic equation of plasmas. • Elegant and physically intuitive, flexible and mesh-free. • Demonstration of numerical solution of both 2-D and 3-D non-linear Fokker–Planck relaxation problem.

  19. The neural basis of precise visual short-term memory for complex recognisable objects.

    Science.gov (United States)

    Veldsman, Michele; Mitchell, Daniel J; Cusack, Rhodri

    2017-10-01

    Recent evidence suggests that visual short-term memory (VSTM) capacity estimated using simple objects, such as colours and oriented bars, may not generalise well to more naturalistic stimuli. More visual detail can be stored in VSTM when complex, recognisable objects are maintained compared to simple objects. It is not yet known if it is recognisability that enhances memory precision, nor whether maintenance of recognisable objects is achieved with the same network of brain regions supporting maintenance of simple objects. We used a novel stimulus generation method to parametrically warp photographic images along a continuum, allowing separate estimation of the precision of memory representations and the number of items retained. The stimulus generation method was also designed to create unrecognisable, though perceptually matched, stimuli, to investigate the impact of recognisability on VSTM. We adapted the widely-used change detection and continuous report paradigms for use with complex, photographic images. Across three functional magnetic resonance imaging (fMRI) experiments, we demonstrated greater precision for recognisable objects in VSTM compared to unrecognisable objects. This clear behavioural advantage was not the result of recruitment of additional brain regions, or of stronger mean activity within the core network. Representational similarity analysis revealed greater variability across item repetitions in the representations of recognisable, compared to unrecognisable complex objects. We therefore propose that a richer range of neural representations support VSTM for complex recognisable objects. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Potential Mechanisms and Functions of Intermittent Neural Synchronization

    Directory of Open Access Journals (Sweden)

    Sungwoo Ahn

    2017-05-01

    Full Text Available Neural synchronization is believed to play an important role in different brain functions. Synchrony in cortical and subcortical circuits is frequently variable in time and not perfect. Few long intervals of desynchronized dynamics may be functionally different from many short desynchronized intervals although the average synchrony may be the same. Recent analysis of imperfect synchrony in different neural systems reported one common feature: neural oscillations may go out of synchrony frequently, but primarily for a short time interval. This study explores potential mechanisms and functional advantages of this short desynchronizations dynamics using computational neuroscience techniques. We show that short desynchronizations are exhibited in coupled neurons if their delayed rectifier potassium current has relatively large values of the voltage-dependent activation time-constant. The delayed activation of potassium current is associated with generation of quickly-rising action potential. This “spikiness” is a very general property of neurons. This may explain why very different neural systems exhibit short desynchronization dynamics. We also show how the distribution of desynchronization durations may be independent of the synchronization strength. Finally, we show that short desynchronization dynamics requires weaker synaptic input to reach a pre-set synchrony level. Thus, this dynamics allows for efficient regulation of synchrony and may promote efficient formation of synchronous neural assemblies.

  1. Adaptive radial basis function mesh deformation using data reduction

    Science.gov (United States)

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

    2016-09-01

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

  2. Neural Basis of Cognitive Assessment in Alzheimer Disease, Amnestic Mild Cognitive Impairment, and Subjective Memory Complaints.

    Science.gov (United States)

    Matías-Guiu, Jordi A; Cabrera-Martín, María Nieves; Valles-Salgado, María; Pérez-Pérez, Alicia; Rognoni, Teresa; Moreno-Ramos, Teresa; Carreras, José Luis; Matías-Guiu, Jorge

    2017-07-01

    Interpreting cognitive tests is often challenging. The same test frequently examines multiple cognitive functions, and the functional and anatomical basis underlying test performance is unknown in many cases. This study analyses the correlation of different neuropsychological test results with brain metabolism in a series of patients evaluated for suspected Alzheimer disease. 20 healthy controls and 80 patients consulting for memory loss were included, in which cognitive study and 18 F-fluorodeoxyglucose PET were performed. Patients were categorized according to Reisberg's Global Deterioration Scale. Voxel-based analysis was used to determine correlations between brain metabolism and performance on the following tests: Free and Cued Selective Reminding Test (FCSRT), Boston Naming Test (BNT), Trail Making Test, Rey-Osterrieth Complex Figure test, Visual Object and Space Perception Battery (VOSP), and Tower of London (ToL) test. Mean age in the patient group was 73.9 ± 10.6 years, and 47 patients were women (58.7%). FCSRT findings were positively correlated with metabolism in the medial and anterior temporal region bilaterally, the left precuneus, and posterior cingulate. BNT results were correlated with metabolism in the middle temporal, superior, fusiform, and frontal medial gyri bilaterally. VOSP results were related to the occipital and parietotemporal regions bilaterally. ToL scores were correlated to metabolism in the right temporoparietal and frontal regions. These results suggest that different areas of the brain are involved in the processes required to complete different cognitive tests. Ascertaining the functional basis underlying these tests may prove helpful for understanding and interpreting them. Copyright © 2017 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

  3. 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.

  4. The neural basis of social risky decision making in females with major depressive disorder.

    Science.gov (United States)

    Shao, Robin; Zhang, Hui-jun; Lee, Tatia M C

    2015-01-01

    Recent evidence indicates that Major Depressive Disorder (MDD) may be associated with reduced tendency of committing noncompliant actions during social decision-making even when the risk of being punished is low. The neural underpinnings of this behavioral pattern are unknown, although it likely relates to compromised functioning of the lateral prefrontal-striatal/limbic networks implicated in executive control, emotion regulation and risk/value-based instrumental behaviors. We employed a modified trust game (TG) that provided explicit information on the risk levels of cheating behaviors being detected and punished. Behavioral and neuro-image data were acquired and analyzed from 14 first-episode female MDD patients and 15 age- and gender-matched controls performing the role of trustee in the TG. Relative to controls, MDD patients exhibited less behavioral switching to making cheating choices under low risk, and reduced activity in the dorsal putamen, anterior insula and dorsolateral prefrontal cortex (DLPFC) during making low-risk cheating versus benevolent choices, with limited evidence indicating abnormal bilateral inferior frontal gyrus activities of patients when making high-risk cheating versus benevolent choices. Patients' left dorsal putamen/anterior insular signals correlated positively with their frequency of low-risk cheating. MDD patients' symptom severity correlated positively with their signals in the lateral prefrontal networks during decision-making. A psycho-physiological interaction analysis provided tentative evidence for the recruitment of IFG-striatal/limbic circuitry among the control participants, but greater frontopolar-striatal/limbic connectivity among the MDD patients, during low-risk decision-making. We propose that making risky social decisions based on the balancing of self-gain and other's welfare relies on the functioning of the integrated lateral prefrontal-striatal/limbic networks, which are less efficient and dysregulated among MDD

  5. Music perception and cognition: development, neural basis, and rehabilitative use of music.

    Science.gov (United States)

    Särkämö, Teppo; Tervaniemi, Mari; Huotilainen, Minna

    2013-07-01

    Music is a highly versatile form of art and communication that has been an essential part of human society since its early days. Neuroimaging studies indicate that music is a powerful stimulus also for the human brain, engaging not just the auditory cortex but also a vast, bilateral network of temporal, frontal, parietal, cerebellar, and limbic brain areas that govern auditory perception, syntactic and semantic processing, attention and memory, emotion and mood control, and motor skills. Studies of amusia, a severe form of musical impairment, highlight the right temporal and frontal cortices as the core neural substrates for adequate perception and production of music. Many of the basic auditory and musical skills, such as pitch and timbre perception, start developing already in utero, and babies are born with a natural preference for music and singing. Music has many important roles and functions throughout life, ranging from emotional self-regulation, mood enhancement, and identity formation to promoting the development of verbal, motor, cognitive, and social skills and maintaining their healthy functioning in old age. Music is also used clinically as a part of treatment in many illnesses, which involve affective, attention, memory, communication, or motor deficits. Although more research is still needed, current evidence suggests that music-based rehabilitation can be effective in many developmental, psychiatric, and neurological disorders, such as autism, depression, schizophrenia, and stroke, as well as in many chronic somatic illnesses that cause pain and anxiety. WIREs Cogn Sci 2013, 4:441-451. doi: 10.1002/wcs.1237 The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. Copyright © 2013 John Wiley & Sons, Ltd.

  6. The specialization of function: cognitive and neural perspectives.

    Science.gov (United States)

    Mahon, Bradford Z; Cantlon, Jessica F

    2011-05-01

    A unifying theme that cuts across all research areas and techniques in the cognitive and brain sciences is whether there is specialization of function at levels of processing that are "abstracted away" from sensory inputs and motor outputs. Any theory that articulates claims about specialization of function in the mind/brain confronts the following types of interrelated questions, each of which carries with it certain theoretical commitments. What methods are appropriate for decomposing complex cognitive and neural processes into their constituent parts? How do cognitive processes map onto neural processes, and at what resolution are they related? What types of conclusions can be drawn about the structure of mind from dissociations observed at the neural level, and vice versa? The contributions that form this Special Issue of Cognitive Neuropsychology represent recent reflections on these and other issues from leading researchers in different areas of the cognitive and brain sciences.

  7. Global sensitivity analysis using a Gaussian Radial Basis Function metamodel

    International Nuclear Information System (INIS)

    Wu, Zeping; Wang, Donghui; Okolo N, Patrick; Hu, Fan; Zhang, Weihua

    2016-01-01

    Sensitivity analysis plays an important role in exploring the actual impact of adjustable parameters on response variables. Amongst the wide range of documented studies on sensitivity measures and analysis, Sobol' indices have received greater portion of attention due to the fact that they can provide accurate information for most models. In this paper, a novel analytical expression to compute the Sobol' indices is derived by introducing a method which uses the Gaussian Radial Basis Function to build metamodels of computationally expensive computer codes. Performance of the proposed method is validated against various analytical functions and also a structural simulation scenario. Results demonstrate that the proposed method is an efficient approach, requiring a computational cost of one to two orders of magnitude less when compared to the traditional Quasi Monte Carlo-based evaluation of Sobol' indices. - Highlights: • RBF based sensitivity analysis method is proposed. • Sobol' decomposition of Gaussian RBF metamodel is obtained. • Sobol' indices of Gaussian RBF metamodel are derived based on the decomposition. • The efficiency of proposed method is validated by some numerical examples.

  8. Generation of Optimal Basis Functions for Reconstruction of Power Distribution

    Energy Technology Data Exchange (ETDEWEB)

    Park, Moonghu [Sejong Univ., Seoul (Korea, Republic of)

    2014-05-15

    This study proposes GMDH to find not only the best functional form but also the optimal parameters those describe the power distribution most accurately. A total of 1,060 cases of axially 1-dimensional core power distributions of 20-nodes are generated by 3-dimensional core analysis code covering BOL to EOL core burnup histories to validate the method. Axially five-point box powers at in-core detectors are considered as measurements. The reconstructed axial power shapes using GMDH method are compared to the reference power shapes. The results show that the proposed method is very robust and accurate compared with spline fitting method. It is shown that the GMDH analysis can give optimal basis functions for core power shape reconstruction. The in-core measurements are the 5 detector snapshots and the 20-node power distribution is successfully reconstructed. The effectiveness of the method is demonstrated by comparing the results of spline fitting for BOL, saddle and top-skewed power shapes.

  9. The neural basis of speech sound discrimination from infancy to adulthood

    OpenAIRE

    Partanen, Eino

    2013-01-01

    Rapid processing of speech is facilitated by neural representations of native language phonemes. However, some disorders and developmental conditions, such as developmental dyslexia, can hamper the development of these neural memory traces, leading to language delays and poor academic achievement. While the early identification of such deficits is paramount so that interventions can be started as early as possible, there is currently no systematically used ecologically valid paradigm for the ...

  10. Development of the disable software reporting system on the basis of the neural network

    Science.gov (United States)

    Gavrylenko, S.; Babenko, O.; Ignatova, E.

    2018-04-01

    The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

  11. Neural correlates of executive functions in patients with obesity.

    Science.gov (United States)

    Ho, Ming-Chou; Chen, Vincent Chin-Hung; Chao, Seh-Huang; Fang, Ching-Tzu; Liu, Yi-Chun; Weng, Jun-Cheng

    2018-01-01

    Obesity is one of the most challenging problems in human health and is recognized as an important risk factor for many chronic diseases. It remains unclear how the neural systems (e.g., the mesolimbic "reward" and the prefrontal "control" neural systems) are correlated with patients' executive function (EF), conceptualized as the integration of "cool" EF and "hot" EF. "Cool" EF refers to relatively abstract, non-affective operations such as inhibitory control and mental flexibility. "Hot" EF refers to motivationally significant affective operations such as affective decision-making. We tried to find the correlation between structural and functional neuroimaging indices and EF in obese patients. The study population comprised seventeen patients with obesity (seven males and 10 females, BMI = 37.99 ± 5.40, age = 31.82 ± 8.75 year-old) preparing to undergo bariatric surgery. We used noninvasive diffusion tensor imaging, generalized q-sampling imaging, and resting-state functional magnetic resonance imaging to examine the neural correlations between structural and functional neuroimaging indices and EF performances in patients with obesity. We reported that many brain areas are correlated to the patients' EF performances. More interestingly, some correlations may implicate the possible associations of EF and the incentive motivational effects of food. The neural correlation between the left precuneus and middle occipital gyrus and inhibitory control may suggest that patients with a better ability to detect appetitive food may have worse inhibitory control. Also, the neural correlation between the superior frontal blade and affective decision-making may suggest that patients' affective decision-making may be associated with the incentive motivational effects of food. Our results provide evidence suggesting neural correlates of EF in patients with obesity.

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

    Science.gov (United States)

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

    2015-09-23

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

  13. Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

    2018-06-13

    Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks. Copyright © 2018 Elsevier Inc. All rights reserved.

  14. Apraxia: neural mechanisms and functional recovery.

    Science.gov (United States)

    Foundas, Anne L

    2013-01-01

    Apraxia is a cognitive-motor disorder that impacts the performance of learned, skilled movements. Limb apraxia, which is the topic of this chapter, is specific to disordered movements of the upper limb that cannot be explained by weakness, sensory loss, abnormalities of posture/tone/movement, or a lack of understanding/cooperation. Patients with limb apraxia have deficits in the control or programming of the spatial-temporal organization and sequencing of goal-directed movements. People with limb apraxia can have difficulty manipulating and using tools including cutting with scissors or making a cup of coffee. Two praxis systems have been identified including a production system (action plan and production) and a conceptual system (action knowledge). Dysfunction of the former produces ideomotor apraxia (e.g., difficulty using scissors), and dysfunction of the latter induces ideational apraxia (e.g., difficulty making a cup of coffee). Neural mechanisms, including how to evaluate apraxia, will be presented in the context of these two praxis systems. Information about these praxis systems, including the nature of the disordered limb movement, is important for rehabilitation clinicians to understand for several reasons. First, limb apraxia is a common disorder. It is common in patients who have had a stroke, in neurodegenerative disorders like Alzheimer disease, in traumatic brain injury, and in developmental disorders. Second, limb apraxia has real world consequences. Patients with limb apraxia have difficulty managing activities of daily living. This factor impacts healthcare costs and contributes to increased caregiver burden. Unfortunately, very few treatments have been systematically studied in large numbers of patients with limb apraxia. This overview of limb apraxia should help rehabilitation clinicians to educate patients and caregivers about this debilitating problem, and should facilitate the development of better treatments that could benefit many people in

  15. Neural Activations of Guided Imagery and Music in Negative Emotional Processing: A Functional MRI Study.

    Science.gov (United States)

    Lee, Sang Eun; Han, Yeji; Park, HyunWook

    2016-01-01

    The Bonny Method of Guided Imagery and Music uses music and imagery to access and explore personal emotions associated with episodic memories. Understanding the neural mechanism of guided imagery and music (GIM) as combined stimuli for emotional processing informs clinical application. We performed functional magnetic resonance imaging (fMRI) to demonstrate neural mechanisms of GIM for negative emotional processing when personal episodic memory is recalled and re-experienced through GIM processes. Twenty-four healthy volunteers participated in the study, which used classical music and verbal instruction stimuli to evoke negative emotions. To analyze the neural mechanism, activated regions associated with negative emotional and episodic memory processing were extracted by conducting volume analyses for the contrast between GIM and guided imagery (GI) or music (M). The GIM stimuli showed increased activation over the M-only stimuli in five neural regions associated with negative emotional and episodic memory processing, including the left amygdala, left anterior cingulate gyrus, left insula, bilateral culmen, and left angular gyrus (AG). Compared with GI alone, GIM showed increased activation in three regions associated with episodic memory processing in the emotional context, including the right posterior cingulate gyrus, bilateral parahippocampal gyrus, and AG. No neural regions related to negative emotional and episodic memory processing showed more activation for M and GI than for GIM. As a combined multimodal stimulus, GIM may increase neural activations related to negative emotions and episodic memory processing. Findings suggest a neural basis for GIM with personal episodic memories affecting cortical and subcortical structures and functions. © the American Music Therapy Association 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  16. Modelling and prediction for chaotic fir laser attractor using rational function neural network.

    Science.gov (United States)

    Cho, S

    2001-02-01

    Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.

  17. Kernel Function Tuning for Single-Layer Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Vidnerová, Petra; Neruda, Roman

    -, accepted 28.11. 2017 (2018) ISSN 2278-0149 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : single-layer neural networks * kernel methods * kernel function * optimisation Subject RIV: IN - Informatics, Computer Science http://www.ijmerr.com/

  18. Working Memory after Traumatic Brain Injury: The Neural Basis of Improved Performance with Methylphenidate.

    Science.gov (United States)

    Manktelow, Anne E; Menon, David K; Sahakian, Barbara J; Stamatakis, Emmanuel A

    2017-01-01

    Traumatic brain injury (TBI) often results in cognitive impairments for patients. The aim of this proof of concept study was to establish the nature of abnormalities, in terms of activity and connectivity, in the working memory network of TBI patients and how these relate to compromised behavioral outcomes. Further, this study examined the neural correlates of working memory improvement following the administration of methylphenidate. We report behavioral, functional and structural MRI data from a group of 15 Healthy Controls (HC) and a group of 15 TBI patients, acquired during the execution of the N-back task. The patients were studied on two occasions after the administration of either placebo or 30 mg of methylphenidate. Between group tests revealed a significant difference in performance when HCs were compared to TBI patients on placebo [ F (1, 28) = 4.426, p performance demonstrated the most benefit from methylphenidate. Changes in the TBI patient activation levels in the Left Cerebellum significantly and positively correlated with changes in performance ( r = 0.509, df = 13, p = 0.05). Whole-brain connectivity analysis using the Left Cerebellum as a seed revealed widespread negative interactions between the Left Cerebellum and parietal and frontal cortices as well as subcortical areas. Neither the TBI group on methylphenidate nor the HC group demonstrated any significant negative interactions. Our findings indicate that (a) TBI significantly reduces the levels of activation and connectivity strength between key areas of the working memory network and (b) Methylphenidate improves the cognitive outcomes on a working memory task. Therefore, we conclude that methylphenidate may render the working memory network in a TBI group more consistent with that of an intact working memory network.

  19. 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....

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

    Science.gov (United States)

    Falat, Lukas; Marcek, Dusan; Durisova, Maria

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Lukas Falat

    2016-01-01

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

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

    Science.gov (United States)

    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

  3. Comparative quantification of dietary supplemented neural creatine concentrations with (1)H-MRS peak fitting and basis spectrum methods.

    Science.gov (United States)

    Turner, Clare E; Russell, Bruce R; Gant, Nicholas

    2015-11-01

    Magnetic resonance spectroscopy (MRS) is an analytical procedure that can be used to non-invasively measure the concentration of a range of neural metabolites. Creatine is an important neurometabolite with dietary supplementation offering therapeutic potential for neurological disorders with dysfunctional energetic processes. Neural creatine concentrations can be probed using proton MRS and quantified using a range of software packages based on different analytical methods. This experiment examines the differences in quantification performance of two commonly used analysis packages following a creatine supplementation strategy with potential therapeutic application. Human participants followed a seven day dietary supplementation regime in a placebo-controlled, cross-over design interspersed with a five week wash-out period. Spectroscopy data were acquired the day immediately following supplementation and analyzed with two commonly-used software packages which employ vastly different quantification methods. Results demonstrate that neural creatine concentration was augmented following creatine supplementation when analyzed using the peak fitting method of quantification (105.9%±10.1). In contrast, no change in neural creatine levels were detected with supplementation when analysis was conducted using the basis spectrum method of quantification (102.6%±8.6). Results suggest that software packages that employ the peak fitting procedure for spectral quantification are possibly more sensitive to subtle changes in neural creatine concentrations. The relative simplicity of the spectroscopy sequence and the data analysis procedure suggest that peak fitting procedures may be the most effective means of metabolite quantification when detection of subtle alterations in neural metabolites is necessary. The straightforward technique can be used on a clinical magnetic resonance imaging system. Copyright © 2015 Elsevier Inc. All rights reserved.

  4. Meshfree Local Radial Basis Function Collocation Method with Image Nodes

    Energy Technology Data Exchange (ETDEWEB)

    Baek, Seung Ki; Kim, Minjae [Pukyong National University, Busan (Korea, Republic of)

    2017-07-15

    We numerically solve two-dimensional heat diffusion problems by using a simple variant of the meshfree local radial-basis function (RBF) collocation method. The main idea is to include an additional set of sample nodes outside the problem domain, similarly to the method of images in electrostatics, to perform collocation on the domain boundaries. We can thereby take into account the temperature profile as well as its gradients specified by boundary conditions at the same time, which holds true even for a node where two or more boundaries meet with different boundary conditions. We argue that the image method is computationally efficient when combined with the local RBF collocation method, whereas the addition of image nodes becomes very costly in case of the global collocation. We apply our modified method to a benchmark test of a boundary value problem, and find that this simple modification reduces the maximum error from the analytic solution significantly. The reduction is small for an initial value problem with simpler boundary conditions. We observe increased numerical instability, which has to be compensated for by a sufficient number of sample nodes and/or more careful parameter choices for time integration.

  5. Complex basis functions for molecular resonances: Methodology and applications

    Science.gov (United States)

    White, Alec; McCurdy, C. William; Head-Gordon, Martin

    The computation of positions and widths of metastable electronic states is a challenge for molecular electronic structure theory because, in addition to the difficulty of the many-body problem, such states obey scattering boundary conditions. These resonances cannot be addressed with naïve application of traditional bound state electronic structure theory. Non-Hermitian electronic structure methods employing complex basis functions is one way that we may rigorously treat resonances within the framework of traditional electronic structure theory. In this talk, I will discuss our recent work in this area including the methodological extension from single determinant SCF-based approaches to highly correlated levels of wavefunction-based theory such as equation of motion coupled cluster and many-body perturbation theory. These approaches provide a hierarchy of theoretical methods for the computation of positions and widths of molecular resonances. Within this framework, we may also examine properties of resonances including the dependence of these parameters on molecular geometry. Some applications of these methods to temporary anions and dianions will also be discussed.

  6. Wavelets as basis functions in electronic structure calculations

    International Nuclear Information System (INIS)

    Chauvin, C.

    2005-11-01

    This thesis is devoted to the definition and the implementation of a multi-resolution method to determine the fundamental state of a system composed of nuclei and electrons. In this work, we are interested in the Density Functional Theory (DFT), which allows to express the Hamiltonian operator with the electronic density only, by a Coulomb potential and a non-linear potential. This operator acts on orbitals, which are solutions of the so-called Kohn-Sham equations. Their resolution needs to express orbitals and density on a set of functions owing both physical and numerical properties, as explained in the second chapter. One can hardly satisfy these two properties simultaneously, that is why we are interested in orthogonal and bi-orthogonal wavelets basis, whose properties of interpolation are presented in the third chapter. We present in the fourth chapter three dimensional solvers for the Coulomb's potential, using not only the preconditioning property of wavelets, but also a multigrid algorithm. Determining this potential allows us to solve the self-consistent Kohn-Sham equations, by an algorithm presented in chapter five. The originality of our method consists in the construction of the stiffness matrix, combining a Galerkin formulation and a collocation scheme. We analyse the approximation properties of this method in case of linear Hamiltonian, such as harmonic oscillator and hydrogen, and present convergence results of the DFT for small electrons. Finally we show how orbital compression reduces considerably the number of coefficients to keep, while preserving a good accuracy of the fundamental energy. (author)

  7. Density functional and neural network analysis

    DEFF Research Database (Denmark)

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

    1997-01-01

    Density functional theory (DFT) calculations have been carried out for hydrated L-alanine, L-alanyl-L-alanine and N-acetyl L-alanine N'-methylamide and examined with respect to the effect of water on the structure, the vibrational frequencies, vibrational absorption (VA) and vibrational circular...

  8. Functional neural circuits that underlie developmental stuttering.

    Directory of Open Access Journals (Sweden)

    Jianping Qiao

    Full Text Available The aim of this study was to identify differences in functional and effective brain connectivity between persons who stutter (PWS and typically developing (TD fluent speakers, and to assess whether those differences can serve as biomarkers to distinguish PWS from TD controls. We acquired resting-state functional magnetic resonance imaging data in 44 PWS and 50 TD controls. We then used Independent Component Analysis (ICA together with Hierarchical Partner Matching (HPM to identify networks of robust, functionally connected brain regions that were highly reproducible across participants, and we assessed whether connectivity differed significantly across diagnostic groups. We then used Granger Causality (GC to study the causal interactions (effective connectivity between the regions that ICA and HPM identified. Finally, we used a kernel support vector machine to assess how well these measures of functional connectivity and granger causality discriminate PWS from TD controls. Functional connectivity was stronger in PWS compared with TD controls in the supplementary motor area (SMA and primary motor cortices, but weaker in inferior frontal cortex (IFG, Broca's area, caudate, putamen, and thalamus. Additionally, causal influences were significantly weaker in PWS from the IFG to SMA, and from the basal ganglia to IFG through the thalamus, compared to TD controls. ICA and GC indices together yielded an accuracy of 92.7% in classifying PWS from TD controls. Our findings suggest the presence of dysfunctional circuits that support speech planning and timing cues for the initiation and execution of motor sequences in PWS. Our high accuracy of classification further suggests that these aberrant brain features may serve as robust biomarkers for PWS.

  9. Functional neural circuits that underlie developmental stuttering.

    Science.gov (United States)

    Qiao, Jianping; Wang, Zhishun; Zhao, Guihu; Huo, Yuankai; Herder, Carl L; Sikora, Chamonix O; Peterson, Bradley S

    2017-01-01

    The aim of this study was to identify differences in functional and effective brain connectivity between persons who stutter (PWS) and typically developing (TD) fluent speakers, and to assess whether those differences can serve as biomarkers to distinguish PWS from TD controls. We acquired resting-state functional magnetic resonance imaging data in 44 PWS and 50 TD controls. We then used Independent Component Analysis (ICA) together with Hierarchical Partner Matching (HPM) to identify networks of robust, functionally connected brain regions that were highly reproducible across participants, and we assessed whether connectivity differed significantly across diagnostic groups. We then used Granger Causality (GC) to study the causal interactions (effective connectivity) between the regions that ICA and HPM identified. Finally, we used a kernel support vector machine to assess how well these measures of functional connectivity and granger causality discriminate PWS from TD controls. Functional connectivity was stronger in PWS compared with TD controls in the supplementary motor area (SMA) and primary motor cortices, but weaker in inferior frontal cortex (IFG, Broca's area), caudate, putamen, and thalamus. Additionally, causal influences were significantly weaker in PWS from the IFG to SMA, and from the basal ganglia to IFG through the thalamus, compared to TD controls. ICA and GC indices together yielded an accuracy of 92.7% in classifying PWS from TD controls. Our findings suggest the presence of dysfunctional circuits that support speech planning and timing cues for the initiation and execution of motor sequences in PWS. Our high accuracy of classification further suggests that these aberrant brain features may serve as robust biomarkers for PWS.

  10. Functional neural circuits that underlie developmental stuttering

    Science.gov (United States)

    Zhao, Guihu; Huo, Yuankai; Herder, Carl L.; Sikora, Chamonix O.; Peterson, Bradley S.

    2017-01-01

    The aim of this study was to identify differences in functional and effective brain connectivity between persons who stutter (PWS) and typically developing (TD) fluent speakers, and to assess whether those differences can serve as biomarkers to distinguish PWS from TD controls. We acquired resting-state functional magnetic resonance imaging data in 44 PWS and 50 TD controls. We then used Independent Component Analysis (ICA) together with Hierarchical Partner Matching (HPM) to identify networks of robust, functionally connected brain regions that were highly reproducible across participants, and we assessed whether connectivity differed significantly across diagnostic groups. We then used Granger Causality (GC) to study the causal interactions (effective connectivity) between the regions that ICA and HPM identified. Finally, we used a kernel support vector machine to assess how well these measures of functional connectivity and granger causality discriminate PWS from TD controls. Functional connectivity was stronger in PWS compared with TD controls in the supplementary motor area (SMA) and primary motor cortices, but weaker in inferior frontal cortex (IFG, Broca’s area), caudate, putamen, and thalamus. Additionally, causal influences were significantly weaker in PWS from the IFG to SMA, and from the basal ganglia to IFG through the thalamus, compared to TD controls. ICA and GC indices together yielded an accuracy of 92.7% in classifying PWS from TD controls. Our findings suggest the presence of dysfunctional circuits that support speech planning and timing cues for the initiation and execution of motor sequences in PWS. Our high accuracy of classification further suggests that these aberrant brain features may serve as robust biomarkers for PWS. PMID:28759567

  11. The neural basis of loss aversion in decision-making under risk.

    Science.gov (United States)

    Tom, Sabrina M; Fox, Craig R; Trepel, Christopher; Poldrack, Russell A

    2007-01-26

    People typically exhibit greater sensitivity to losses than to equivalent gains when making decisions. We investigated neural correlates of loss aversion while individuals decided whether to accept or reject gambles that offered a 50/50 chance of gaining or losing money. A broad set of areas (including midbrain dopaminergic regions and their targets) showed increasing activity as potential gains increased. Potential losses were represented by decreasing activity in several of these same gain-sensitive areas. Finally, individual differences in behavioral loss aversion were predicted by a measure of neural loss aversion in several regions, including the ventral striatum and prefrontal cortex.

  12. Intranasal oxytocin modulates neural functional connectivity during human social interaction.

    Science.gov (United States)

    Rilling, James K; Chen, Xiangchuan; Chen, Xu; Haroon, Ebrahim

    2018-02-10

    Oxytocin (OT) modulates social behavior in primates and many other vertebrate species. Studies in non-primate animals have demonstrated that, in addition to influencing activity within individual brain areas, OT influences functional connectivity across networks of areas involved in social behavior. Previously, we used fMRI to image brain function in human subjects during a dyadic social interaction task following administration of either intranasal oxytocin (INOT) or placebo, and analyzed the data with a standard general linear model. Here, we conduct an extensive re-analysis of these data to explore how OT modulates functional connectivity across a neural network that animal studies implicate in social behavior. OT induced widespread increases in functional connectivity in response to positive social interactions among men and widespread decreases in functional connectivity in response to negative social interactions among women. Nucleus basalis of Meynert, an important regulator of selective attention and motivation with a particularly high density of OT receptors, had the largest number of OT-modulated connections. Regions known to receive mesolimbic dopamine projections such as the nucleus accumbens and lateral septum were also hubs for OT effects on functional connectivity. Our results suggest that the neural mechanism by which OT influences primate social cognition may include changes in patterns of activity across neural networks that regulate social behavior in other animals. © 2018 Wiley Periodicals, Inc.

  13. A positron emission tomography study of the neural basis of informational and energetic masking effects in speech perception

    Science.gov (United States)

    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.

  14. Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes.

    Science.gov (United States)

    Yang, Jingjing; Cox, Dennis D; Lee, Jong Soo; Ren, Peng; Choi, Taeryon

    2017-12-01

    Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo methods. Compared to the standard Bayesian inference that suffers serious computational burden and instability in analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results to those obtainable by the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids when the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes. © 2017, The International Biometric Society.

  15. Dynamic neural networking as a basis for plasticity in the control of heart rate.

    Science.gov (United States)

    Kember, G; Armour, J A; Zamir, M

    2013-01-21

    A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2008-12-01

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

  17. 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.

  18. Response variance in functional maps: neural darwinism revisited.

    Science.gov (United States)

    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.

  19. Psychedelics Promote Structural and Functional Neural Plasticity.

    Science.gov (United States)

    Ly, Calvin; Greb, Alexandra C; Cameron, Lindsay P; Wong, Jonathan M; Barragan, Eden V; Wilson, Paige C; Burbach, Kyle F; Soltanzadeh Zarandi, Sina; Sood, Alexander; Paddy, Michael R; Duim, Whitney C; Dennis, Megan Y; McAllister, A Kimberley; Ori-McKenney, Kassandra M; Gray, John A; Olson, David E

    2018-06-12

    Atrophy of neurons in the prefrontal cortex (PFC) plays a key role in the pathophysiology of depression and related disorders. The ability to promote both structural and functional plasticity in the PFC has been hypothesized to underlie the fast-acting antidepressant properties of the dissociative anesthetic ketamine. Here, we report that, like ketamine, serotonergic psychedelics are capable of robustly increasing neuritogenesis and/or spinogenesis both in vitro and in vivo. These changes in neuronal structure are accompanied by increased synapse number and function, as measured by fluorescence microscopy and electrophysiology. The structural changes induced by psychedelics appear to result from stimulation of the TrkB, mTOR, and 5-HT2A signaling pathways and could possibly explain the clinical effectiveness of these compounds. Our results underscore the therapeutic potential of psychedelics and, importantly, identify several lead scaffolds for medicinal chemistry efforts focused on developing plasticity-promoting compounds as safe, effective, and fast-acting treatments for depression and related disorders. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  20. 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.

  1. 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.

  2. The neural basis of sex differences in sexual behavior: A quantitative meta-analysis

    Science.gov (United States)

    Poeppl, Timm B.; Langguth, Berthold; Rupprecht, Rainer; Safron, Adam; Bzdok, Danilo; Laird, Angela R.; Eickhoff, Simon B.

    2016-01-01

    Sexuality as to its etymology presupposes the duality of sexes. Using quantitative neuroimaging meta-analyses, we demonstrate robust sex differences in the neural processing of sexual stimuli in thalamus, hypothalamus, and basal ganglia. In a narrative review, we show how these relate to the well-established sex differences on the behavioral level. More specifically, we describe the neural bases of known poor agreement between self-reported and genital measures of female sexual arousal, of previously proposed male proneness to affective sexual conditioning, as well as hints of unconscious activation of bonding mechanisms during sexual stimulation in women. In summary, our meta-analytic review demonstrates that neurofunctional sex differences during sexual stimulation can account for well-established sex differences in sexual behavior. PMID:27742561

  3. PROCESSING THE INFORMATION CONTENT ON THE BASIS OF FUZZY NEURAL MODEL OF DECISION MAKING

    Directory of Open Access Journals (Sweden)

    Nina V. Komleva

    2013-01-01

    Full Text Available The article is devoted to the issues of mathematical modeling of the decision-making process of information content processing based on the fuzzy neural network TSK. Integral rating assessment of the content, which is necessary for taking a decision about its further usage, is made depended on varying characteristics. Mechanism for building individual trajectory and forming individual competence is provided to make the intellectual content search.

  4. Neural Basis of Empathy and Its Dysfunction in Autism Spectrum Disorders (ASD)

    Science.gov (United States)

    2014-10-01

    our work tests the idea that empathy derives from the activation of neural circuits that process primary emotions or feelings , such as reward or...ticipated. For all monkeys, a sterile surgery was performed to implant a head- restraint prosthesis (Crist Instruments) using standard techniques11...participated in the study. All animals underwent standard surgical procedures for implanting a head-restraint prosthesis at least 6 mo before the present study

  5. Problem-Matched Basis Functions for Microstrip Coupled Slot Antennas based on Transmission Line Greens Functions

    NARCIS (Netherlands)

    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

  6. Transiently chaotic neural networks with piecewise linear output functions

    Energy Technology Data Exchange (ETDEWEB)

    Chen, S.-S. [Department of Mathematics, National Taiwan Normal University, Taipei, Taiwan (China); Shih, C.-W. [Department of Applied Mathematics, National Chiao Tung University, 1001 Ta-Hsueh Road, Hsinchu, Taiwan (China)], E-mail: cwshih@math.nctu.edu.tw

    2009-01-30

    Admitting both transient chaotic phase and convergent phase, the transiently chaotic neural network (TCNN) provides superior performance than the classical networks in solving combinatorial optimization problems. We derive concrete parameter conditions for these two essential dynamic phases of the TCNN with piecewise linear output function. The confirmation for chaotic dynamics of the system results from a successful application of the Marotto theorem which was recently clarified. Numerical simulation on applying the TCNN with piecewise linear output function is carried out to find the optimal solution of a travelling salesman problem. It is demonstrated that the performance is even better than the previous TCNN model with logistic output function.

  7. Sex, Lies and fMRI—Gender Differences in Neural Basis of Deception

    Science.gov (United States)

    Falkiewicz, Marcel; Szeszkowski, Wojciech; Grabowska, Anna; Szatkowska, Iwona

    2012-01-01

    Deception has always been a part of human communication as it helps to promote self-presentation. Although both men and women are equally prone to try to manage their appearance, their strategies, motivation and eagerness may be different. Here, we asked if lying could be influenced by gender on both the behavioral and neural levels. To test whether the hypothesized gender differences in brain activity related to deceptive responses were caused by differential socialization in men and women, we administered the Gender Identity Inventory probing the participants’ subjective social sex role. In an fMRI session, participants were instructed either to lie or to tell the truth while answering a questionnaire focusing on general and personal information. Only for personal information, we found differences in neural responses during instructed deception in men and women. The women vs. men direct contrast revealed no significant differences in areas of activation, but men showed higher BOLD signal compared to women in the left middle frontal gyrus (MFG). Moreover, this effect remained unchanged when self-reported psychological gender was controlled for. Thus, our study showed that gender differences in the neural processes engaged during falsifying personal information might be independent from socialization. PMID:22952631

  8. Sex, lies and fMRI--gender differences in neural basis of deception.

    Directory of Open Access Journals (Sweden)

    Artur Marchewka

    Full Text Available Deception has always been a part of human communication as it helps to promote self-presentation. Although both men and women are equally prone to try to manage their appearance, their strategies, motivation and eagerness may be different. Here, we asked if lying could be influenced by gender on both the behavioral and neural levels. To test whether the hypothesized gender differences in brain activity related to deceptive responses were caused by differential socialization in men and women, we administered the Gender Identity Inventory probing the participants' subjective social sex role. In an fMRI session, participants were instructed either to lie or to tell the truth while answering a questionnaire focusing on general and personal information. Only for personal information, we found differences in neural responses during instructed deception in men and women. The women vs. men direct contrast revealed no significant differences in areas of activation, but men showed higher BOLD signal compared to women in the left middle frontal gyrus (MFG. Moreover, this effect remained unchanged when self-reported psychological gender was controlled for. Thus, our study showed that gender differences in the neural processes engaged during falsifying personal information might be independent from socialization.

  9. 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

  10. Detection of an inhibitory cortical gradient underlying peak shift in learning: a neural basis for a false memory.

    Science.gov (United States)

    Miasnikov, Alexandre A; Weinberger, Norman M

    2012-11-01

    Experience often does not produce veridical memory. Understanding false attribution of events constitutes an important problem in memory research. "Peak shift" is a well-characterized, controllable phenomenon in which human and animal subjects that receive reinforcement associated with one sensory stimulus later respond maximally to another stimulus in post-training stimulus generalization tests. Peak shift ordinarily develops in discrimination learning (reinforced CS+, unreinforced CS-) and has long been attributed to the interaction of an excitatory gradient centered on the CS+ and an inhibitory gradient centered on the CS-; the shift is away from the CS-. In contrast, we have obtained peak shifts during single tone frequency training, using stimulation of the cholinergic nucleus basalis (NB) to implant behavioral memory into the rat. As we also recorded cortical activity, we took the opportunity to investigate the possible existence of a neural frequency gradient that could account for behavioral peak shift. Behavioral frequency generalization gradients (FGGs, interruption of ongoing respiration) were determined twice before training while evoked potentials were recorded from the primary auditory cortex (A1), to obtain a baseline gradient of "habituatory" neural decrement. A post-training behavioral FGG obtained 24h after three daily sessions of a single tone paired with NB stimulation (200 trials/day) revealed a peak shift. The peak of the FGG was at a frequency lower than the CS while the cortical inhibitory gradient was at a frequency higher than the CS frequency. Further analysis indicated that the frequency location and magnitude of the gradient could account for the behavioral peak shift. These results provide a neural basis for a systematic case of memory misattribution and may provide an animal model for the study of the neural bases of a type of "false memory". Published by Elsevier Inc.

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

    Directory of Open Access Journals (Sweden)

    Zoë A Englander

    Full Text Available 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.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.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.

  12. Variability of Neuronal Responses: Types and Functional Significance in Neuroplasticity and Neural Darwinism.

    Science.gov (United States)

    Chervyakov, Alexander V; Sinitsyn, Dmitry O; Piradov, Michael A

    2016-01-01

    HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), "genuine harmful" (noise), "genuine neutral" (synonyms, repeats), and "genuine useful" (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory. In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection.

  13. The neural basis of sublexical speech and corresponding nonspeech processing: a combined EEG-MEG study.

    Science.gov (United States)

    Kuuluvainen, Soila; Nevalainen, Päivi; Sorokin, Alexander; Mittag, Maria; Partanen, Eino; Putkinen, Vesa; Seppänen, Miia; Kähkönen, Seppo; Kujala, Teija

    2014-03-01

    We addressed the neural organization of speech versus nonspeech sound processing by investigating preattentive cortical auditory processing of changes in five features of a consonant-vowel syllable (consonant, vowel, sound duration, frequency, and intensity) and their acoustically matched nonspeech counterparts in a simultaneous EEG-MEG recording of mismatch negativity (MMN/MMNm). Overall, speech-sound processing was enhanced compared to nonspeech sound processing. This effect was strongest for changes which affect word meaning (consonant, vowel, and vowel duration) in the left and for the vowel identity change in the right hemisphere also. Furthermore, in the right hemisphere, speech-sound frequency and intensity changes were processed faster than their nonspeech counterparts, and there was a trend for speech-enhancement in frequency processing. In summary, the results support the proposed existence of long-term memory traces for speech sounds in the auditory cortices, and indicate at least partly distinct neural substrates for speech and nonspeech sound processing. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. Study of the neural basis of striatal modulation of the jaw-opening reflex.

    Science.gov (United States)

    Barceló, Ana C; Fillipini, B; Pazo, Jorge Horacio

    2010-02-01

    Previous experimental data from this laboratory demonstrated the participation of the striatum and dopaminergic pathways in central nociceptive processing. The objective of this study was to examine the possible pathways and neural structures associated with the analgesic action of the striatum. The experiments were carried out in rats anesthetized with urethane. The jaw-opening reflex (JOR) was evoked by electrical stimulation of the tooth pulp of lower incisors and recorded in the anterior belly of the digastric muscles. Intrastriatal microinjection of apomorphine, a nonspecific dopamine agonist, reduced or abolished the JOR amplitude. Electrolytic or kainic acid lesions, unilateral to the apomorphine-injected striatum, of the globus pallidus, substantia nigra pars reticulata, subthalamic nucleus and bilateral lesion the rostroventromedial medulla (RVM), blocked the inhibition of the JOR by striatal stimulation. These findings suggest that the main output nuclei of the striatum and the RVM may be critical elements in the neural pathways mediating the inhibition of the reflex response, evoked in jaw muscles by noxious stimulation of dental pulp.

  15. The relationship between structural and functional connectivity: graph theoretical analysis of an EEG neural mass model

    NARCIS (Netherlands)

    Ponten, S.C.; Daffertshofer, A.; Hillebrand, A.; Stam, C.J.

    2010-01-01

    We investigated the relationship between structural network properties and both synchronization strength and functional characteristics in a combined neural mass and graph theoretical model of the electroencephalogram (EEG). Thirty-two neural mass models (NMMs), each representing the lump activity

  16. Memory and neural networks on the basis of color centers in solids.

    Science.gov (United States)

    Winnacker, Albrecht; Osvet, Andres

    2009-11-01

    Optical data recording is one of the most widely used and efficient systems of memory in the non-living world. The application of color centers in this context offers not only systems of high speed in writing and read-out due to a high degree of parallelism in data handling but also a possibility to set up models of neural networks. In this way, systems with a high potential for image processing, pattern recognition and logical operations can be constructed. A limitation to storage density is given by the diffraction limit of optical data recording. It is shown that this limitation can at least in principle be overcome by the principle of spectral hole burning, which results in systems of storage capacities close to the human brain system.

  17. 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.

  18. Immunomodulation of enteric neural function in irritable bowel syndrome.

    Science.gov (United States)

    O'Malley, Dervla

    2015-06-28

    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 elusive. Anecdotally, IBS patients have noted that periods of stress can result in symptom flares and many patients exhibit co-morbid stress-related mood disorders such as anxiety and depression. However, in addition to psychosocial stressors, infection-related stress has also been linked with the initiation, persistence and severity of symptom flares. Indeed, prior gastrointestinal infection is one of the strongest predictors of developing IBS. Despite a lack of overt morphological inflammation, the importance of immune factors in the pathophysiology of IBS is gaining acceptance. Subtle changes in the numbers of mucosal immune cell infiltrates and elevated levels of circulating pro-inflammatory cytokines have been reproducibly demonstrated in IBS populations. Moreover, these immune mediators directly affect neural signalling. An exciting new area of research is the role of luminal microbiota in the modulation of neuro-immune signalling, resulting in local changes in gastrointestinal function and alterations in central neural functioning. Progress in this area has begun to unravel some of the complexities of neuroimmune and neuroendocrine interactions and how these molecular exchanges contribute to GI dysfunction.

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

    International Nuclear Information System (INIS)

    Mueller, Jerel K; Tyler, William J

    2014-01-01

    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)

  20. 52 Sociocultural Competence as a Basis for Functional Education: A ...

    African Journals Online (AJOL)

    User

    2010-10-17

    Oct 17, 2010 ... This paper examines the relevance of socio-cultural competence in functional ... non-material expressions of the people as well as the processes with which .... themselves in all kinds of immorality, smoking, stealing, drug ...

  1. Folate status in women of reproductive age as basis of neural tube defect risk assessment.

    Science.gov (United States)

    Bailey, Lynn B; Hausman, Dorothy B

    2018-02-01

    Reliable folate status data for women of reproductive age (WRA) to assess global risk for neural tube defects (NTDs) are needed. We focus on a recent recommendation by the World Health Organization that a specific "optimal" red blood cell (RBC) folate concentration be used as the sole indicator of NTD risk within a population and discuss how to best apply this guidance to reach the goal of assessing NTD risk globally. We also emphasize the importance of using the microbiologic assay (MBA) as the most reliable assay for obtaining comparable results for RBC folate concentration across time and countries, the need for harmonization of the MBA through use of consistent key reagents and procedures within laboratories, and the requirement to apply assay-matched cutoffs for folate deficiency and insufficiency. To estimate NTD risk globally, the ideal scenario would be to have country-specific population-based surveys of RBC folate in WRA determined utilizing a harmonized MBA, as was done in recent studies in Guatemala and Belize. We conclude with guidance on next steps to best navigate the road map toward the goal of generating reliable folate status data on which to assess NTD risk in WRA in low- and middle-income countries. © 2017 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

  2. Neural Basis of Two Kinds of Social Influence: Obedience and Conformity

    Directory of Open Access Journals (Sweden)

    Ying eXie

    2016-02-01

    Full Text Available 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.

  3. The neural basis of temporal order processing in past and future thought.

    Science.gov (United States)

    D'Argembeau, Arnaud; Jeunehomme, Olivier; Majerus, Steve; Bastin, Christine; Salmon, Eric

    2015-01-01

    Although growing evidence has shown that remembering the past and imagining the future recruit a common core network of frontal-parietal-temporal regions, the extent to which these regions contribute to the temporal dimension of autobiographical thought remains unclear. In this fMRI study, we focused on the event-sequencing aspect of time and examined whether ordering past and future events involve common neural substrates. Participants had to determine which of two past (or future) events occurred (or would occur) before the other, and these order judgments were compared with a task requiring to think about the content of the same past or future events. For both past and future events, we found that the left posterior hippocampus was more activated when establishing the order of events, whereas the anterior hippocampus was more activated when representing their content. Aside from the hippocampus, most of the brain regions that were activated when thinking about temporal order (notably the intraparietal sulcus, dorsolateral pFC, dorsal anterior cingulate, and visual cortex) lied outside the core network and may reflect the involvement of controlled processes and visuospatial imagery to locate events in time. Collectively, these findings suggest (a) that the same processing operations are engaged for ordering past events and planned future events in time, (b) that anterior and posterior portions of the hippocampus are involved in processing different aspects of autobiographical thought, and (c) that temporal order is not necessarily an intrinsic property of memory or future thought but instead requires additional, controlled processes.

  4. Neural Basis of Two Kinds of Social Influence: Obedience and Conformity.

    Science.gov (United States)

    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.

  5. The Neural Basis of Speech Perception through Lipreading and Manual Cues: Evidence from Deaf Native Users of Cued Speech

    Science.gov (United States)

    Aparicio, Mario; Peigneux, Philippe; Charlier, Brigitte; Balériaux, Danielle; Kavec, Martin; Leybaert, Jacqueline

    2017-01-01

    We present here the first neuroimaging data for perception of Cued Speech (CS) by deaf adults who are native users of CS. CS is a visual mode of communicating a spoken language through a set of manual cues which accompany lipreading and disambiguate it. With CS, sublexical units of the oral language are conveyed clearly and completely through the visual modality without requiring hearing. The comparison of neural processing of CS in deaf individuals with processing of audiovisual (AV) speech in normally hearing individuals represents a unique opportunity to explore the similarities and differences in neural processing of an oral language delivered in a visuo-manual vs. an AV modality. The study included deaf adult participants who were early CS users and native hearing users of French who process speech audiovisually. Words were presented in an event-related fMRI design. Three conditions were presented to each group of participants. The deaf participants saw CS words (manual + lipread), words presented as manual cues alone, and words presented to be lipread without manual cues. The hearing group saw AV spoken words, audio-alone and lipread-alone. Three findings are highlighted. First, the middle and superior temporal gyrus (excluding Heschl’s gyrus) and left inferior frontal gyrus pars triangularis constituted a common, amodal neural basis for AV and CS perception. Second, integration was inferred in posterior parts of superior temporal sulcus for audio and lipread information in AV speech, but in the occipito-temporal junction, including MT/V5, for the manual cues and lipreading in CS. Third, the perception of manual cues showed a much greater overlap with the regions activated by CS (manual + lipreading) than lipreading alone did. This supports the notion that manual cues play a larger role than lipreading for CS processing. The present study contributes to a better understanding of the role of manual cues as support of visual speech perception in the framework

  6. Neural basis of self and other representation in autism: an FMRI study of self-face recognition.

    Directory of Open Access Journals (Sweden)

    Lucina Q Uddin

    Full Text Available Autism is a developmental disorder characterized by decreased interest and engagement in social interactions and by enhanced self-focus. While previous theoretical approaches to understanding autism have emphasized social impairments and altered interpersonal interactions, there is a recent shift towards understanding the nature of the representation of the self in individuals with autism spectrum disorders (ASD. Still, the neural mechanisms subserving self-representations in ASD are relatively unexplored.We used event-related fMRI to investigate brain responsiveness to images of the subjects' own face and to faces of others. Children with ASD and typically developing (TD children viewed randomly presented digital morphs between their own face and a gender-matched other face, and made "self/other" judgments. Both groups of children activated a right premotor/prefrontal system when identifying images containing a greater percentage of the self face. However, while TD children showed activation of this system during both self- and other-processing, children with ASD only recruited this system while viewing images containing mostly their own face.This functional dissociation between the representation of self versus others points to a potential neural substrate for the characteristic self-focus and decreased social understanding exhibited by these individuals, and suggests that individuals with ASD lack the shared neural representations for self and others that TD children and adults possess and may use to understand others.

  7. Studies in the method of correlated basis functions. Pt. 3

    International Nuclear Information System (INIS)

    Krotscheck, E.; Clark, J.W.

    1980-01-01

    A variational theory of pairing phenomena is presented for systems like neutron matter and liquid 3 He. The strong short-range correlations among the particles in these systems are incorporated into the trial states describing normal and pair-condensed phases, via a correlation operator F. The resulting theory has the same basic structure as that ordinarily applied for weak two-body interactions; in place of the pairing matrix elements of the bare interaction one finds certain effective pairing matrix elements Psub(kl), and modified single particle energies epsilon (k) appear. Detailed prescriptions are given for the construction of the Psub(kl) and epsilon (k) in terms of off-diagonal and diagonal matrix elements of the Hamiltonian and unit operators in a correlated basis of normal states. An exact criterion for instability of the assumed normal phase with respect to pair condensation is derived for general F. This criterion is investigated numerically for the special case if Jastrow correlations, the required normal-state quantities being evaluated by integral equation techniques which extend the Fermi hypernetted-chain scheme. In neutron matter, an instability with respect to 1 S 0 pairing is found in the low-density region, in concert with the predictions of Yang and Clark. In liquid 3 He, there is some indication of a 3 P 0 pairing instability in the vicinity of the experimental equilibrium density. (orig.)

  8. Neural Basis of Visual Attentional Orienting in Childhood Autism Spectrum Disorders

    Science.gov (United States)

    Murphy, Eric R.; Norr, Megan; Strang, John F.; Kenworthy, Lauren; Gaillard, William D.; Vaidya, Chandan J.

    2017-01-01

    We examined spontaneous attention orienting to visual salience in stimuli without social significance using a modified Dot-Probe task during functional magnetic resonance imaging in high-functioning preadolescent children with Autism Spectrum Disorder (ASD) and age- and IQ-matched control children. While the magnitude of attentional bias (faster…

  9. Determination of many-electron basis functions for a quantum Hall ground state using Schur polynomials

    Science.gov (United States)

    Mandal, Sudhansu S.; Mukherjee, Sutirtha; Ray, Koushik

    2018-03-01

    A method for determining the ground state of a planar interacting many-electron system in a magnetic field perpendicular to the plane is described. The ground state wave-function is expressed as a linear combination of a set of basis functions. Given only the flux and the number of electrons describing an incompressible state, we use the combinatorics of partitioning the flux among the electrons to derive the basis wave-functions as linear combinations of Schur polynomials. The procedure ensures that the basis wave-functions form representations of the angular momentum algebra. We exemplify the method by deriving the basis functions for the 5/2 quantum Hall state with a few particles. We find that one of the basis functions is precisely the Moore-Read Pfaffian wave function.

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

    Directory of Open Access Journals (Sweden)

    Yunfeng Wu

    2014-01-01

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

  11. Advantages of comparative studies in songbirds to understand the neural basis of sensorimotor integration.

    Science.gov (United States)

    Murphy, Karagh; James, Logan S; Sakata, Jon T; Prather, Jonathan F

    2017-08-01

    Sensorimotor integration is the process through which the nervous system creates a link between motor commands and associated sensory feedback. This process allows for the acquisition and refinement of many behaviors, including learned communication behaviors such as speech and birdsong. Consequently, it is important to understand fundamental mechanisms of sensorimotor integration, and comparative analyses of this process can provide vital insight. Songbirds offer a powerful comparative model system to study how the nervous system links motor and sensory information for learning and control. This is because the acquisition, maintenance, and control of birdsong critically depend on sensory feedback. Furthermore, there is an incredible diversity of song organizations across songbird species, ranging from songs with simple, stereotyped sequences to songs with complex sequencing of vocal gestures, as well as a wide diversity of song repertoire sizes. Despite this diversity, the neural circuitry for song learning, control, and maintenance remains highly similar across species. Here, we highlight the utility of songbirds for the analysis of sensorimotor integration and the insights about mechanisms of sensorimotor integration gained by comparing different songbird species. Key conclusions from this comparative analysis are that variation in song sequence complexity seems to covary with the strength of feedback signals in sensorimotor circuits and that sensorimotor circuits contain distinct representations of elements in the vocal repertoire, possibly enabling evolutionary variation in repertoire sizes. We conclude our review by highlighting important areas of research that could benefit from increased comparative focus, with particular emphasis on the integration of new technologies. Copyright © 2017 the American Physiological Society.

  12. New MoM code incorporating multiple domain basis functions

    CSIR Research Space (South Africa)

    Lysko, AA

    2011-08-01

    Full Text Available piecewise linear approximation of geometry. This often leads to an unnecessarily great number of unknowns used to model relatively small loop and spiral antennas, coils and other curved structures. This is because the program creates a dense mesh... to accelerate computation of the elements of the impedance matrix and showed acceleration factor exceeding an order of magnitude, subject to a high accuracy requirement. 3. On Code Functionality and Application Results The package of programs was written...

  13. Neural Correlates of Consumer Buying Motivations: A 7T functional Magnetic Resonance Imaging (fMRI Study

    Directory of Open Access Journals (Sweden)

    Adam M. Goodman

    2017-09-01

    Full Text Available Consumer buying motivations can be distinguished into three categories: functional, experiential, or symbolic motivations (Keller, 1993. Although prior neuroimaging studies have examined the neural substrates which enable these motivations, direct comparisons between these three types of consumer motivations have yet to be made. In the current study, we used 7 Tesla (7T functional magnetic resonance imaging (fMRI to assess the neural correlates of each motivation by instructing participants to view common consumer goods while emphasizing either functional, experiential, or symbolic values of these products. The results demonstrated mostly consistent activations between symbolic and experiential motivations. Although, these motivations differed in that symbolic motivation was associated with medial frontal gyrus (MFG activation, whereas experiential motivation was associated with posterior cingulate cortex (PCC activation. Functional motivation was associated with dorsolateral prefrontal cortex (DLPFC activation, as compared to other motivations. These findings provide a neural basis for how symbolic and experiential motivations may be similar, yet different in subtle ways. Furthermore, the dissociation of functional motivation within the DLPFC supports the notion that this motivation relies on executive function processes relatively more than hedonic motivation. These findings provide a better understanding of the underlying neural functioning which may contribute to poor self-control choices.

  14. 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].

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

    International Nuclear Information System (INIS)

    Brown, James; Carrington, Tucker

    2016-01-01

    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.

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

    Science.gov (United States)

    Brown, James; Carrington, Tucker

    2016-06-28

    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.

  17. Application of Trapezoidal-Shaped Characteristic Basis Functions to Arrays of Electrically Interconnected Antenna Elements

    NARCIS (Netherlands)

    Maaskant, R.; Mittra, R.; Tijhuis, A.G.; Graglia, R.D.

    2007-01-01

    This paper describes a novel technique for generating the characteristic basis functions (CBFs) used to represent the surface currents on finite arrays of electrically interconnected antenna elements. The CBFs are high-level basis functions, defined on subdomains in which the original problem is

  18. Factorization of products of discontinuous functions applied to Fourier-Bessel basis.

    Science.gov (United States)

    Popov, Evgeny; Nevière, Michel; Bonod, Nicolas

    2004-01-01

    The factorization rules of Li [J. Opt. Soc. Am. A 13, 1870 (1996)] are generalized to a cylindrical geometry requiring the use of a Bessel function basis. A theoretical study confirms the validity of the Laurent rule when a product of two continuous functions or of one continuous and one discontinuous function is factorized. The necessity of applying the so-called inverse rule in factorizing a continuous product of two discontinuous functions in a truncated basis is demonstrated theoretically and numerically.

  19. Neural Basis of Visual Attentional Orienting in Childhood Autism Spectrum Disorders.

    Science.gov (United States)

    Murphy, Eric R; Norr, Megan; Strang, John F; Kenworthy, Lauren; Gaillard, William D; Vaidya, Chandan J

    2017-01-01

    We examined spontaneous attention orienting to visual salience in stimuli without social significance using a modified Dot-Probe task during functional magnetic resonance imaging in high-functioning preadolescent children with Autism Spectrum Disorder (ASD) and age- and IQ-matched control children. While the magnitude of attentional bias (faster response to probes in the location of solid color patch) to visually salient stimuli was similar in the groups, activation differences in frontal and temporoparietal regions suggested hyper-sensitivity to visual salience or to sameness in ASD children. Further, activation in a subset of those regions was associated with symptoms of restricted and repetitive behavior. Thus, atypicalities in response to visual properties of stimuli may drive attentional orienting problems associated with ASD.

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

    Science.gov (United States)

    Huang, Furong; Fan, Jin; Luo, Jing

    2015-06-01

    Novelty and appropriateness have been recognized as the fundamental features of creative thinking. However, the brain mechanisms underlying these features remain largely unknown. In this study, we used event-related functional magnetic resonance imaging (fMRI) to dissociate these mechanisms in a revised creative chunk decomposition task in which participants were required to perform different types of chunk decomposition that systematically varied in novelty and appropriateness. We found that novelty processing involved functional areas for procedural memory (caudate), mental rewarding (substantia nigra, SN), and visual-spatial processing, whereas appropriateness processing was mediated by areas for declarative memory (hippocampus), emotional arousal (amygdala), and orthography recognition. These results indicate that non-declarative and declarative memory systems may jointly contribute to the two fundamental features of creative thinking. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. Tolcapone-Enhanced Neurocognition in Healthy Adults: Neural Basis and Predictors.

    Science.gov (United States)

    Bhakta, Savita G; Light, Gregory A; Talledo, Jo A; Balvaneda, Bryan; Hughes, Erica; Alvarez, Alexis; Rana, Brinda K; Young, Jared W; Swerdlow, Neal R

    2017-12-01

    Failure of procognitive drug trials in schizophrenia may reflect the clinical heterogeneity of schizophrenia, underscoring the need to identify biomarkers of treatment sensitivity. We used an experimental medicine design to test the procognitive effects of a putative procognitive agent, tolcapone, using an electroencephalogram-based cognitive control task in healthy subjects. Healthy men and women (n=27; ages 18-35 years), homozygous for either the Met/Met or Val/Val rs4680 genotype, received placebo and tolcapone 200 mg orally across 2 test days separated by 1 week in a double-blind, randomized, counterbalanced, within-subject design. On each test day, neurocognitive performance was assessed using the MATRICS Consensus Cognitive Battery and an electroencephalogram-based 5 Choice-Continuous Performance Test. Tolcapone enhanced visual learning in low-baseline MATRICS Consensus Cognitive Battery performers (d=0.35) and had an opposite effect in high performers (d=0.5), and enhanced verbal fluency across all subjects (P=.03) but had no effect on overall MATRICS Consensus Cognitive Battery performance. Tolcapone reduced false alarm rate (d=0.8) and enhanced frontal P200 amplitude during correctly identified nontarget trials (d=0.6) in low-baseline 5 Choice-Continuous Performance Test performers and had opposite effects in high performers (d=0.5 and d=0.25, respectively). Tolcapone's effect on frontal P200 amplitude and false alarm rate was correlated (rs=-0.4, P=.05). All neurocognitive effects of tolcapone were independent of rs4680 genotype. Tolcapone enhanced neurocognition and engaged electroencephalogram measures relevant to cognitive processes in specific subgroups of healthy individuals. These findings support an experimental medicine model for identifying procognitive treatments and provide a strong basis for future biomarker-informed procognitive studies in schizophrenia patients. © The Author 2017. Published by Oxford University Press on behalf of CINP.

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

    International Nuclear Information System (INIS)

    Guerra, Fabio A.; Coelho, Leandro dos S.

    2008-01-01

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

  3. Neural dichotomy of word concreteness: a view from functional neuroimaging.

    Science.gov (United States)

    Kumar, Uttam

    2016-02-01

    Our perception about the representation and processing of concrete and abstract concepts is based on the fact that concrete words are highly imagined and remembered faster than abstract words. In order to explain the processing differences between abstract and concrete concepts, various theories have been proposed, yet there is no unanimous consensus about its neural implication. The present study investigated the processing of concrete and abstract words during an orthography judgment task (implicit semantic processing) using functional magnetic resonance imaging to validate the involvement of the neural regions. Relative to non-words, both abstract and concrete words show activation in the regions of bilateral hemisphere previously associated with semantic processing. The common areas (conjunction analyses) observed for abstract and concrete words are bilateral inferior frontal gyrus (BA 44/45), left superior parietal (BA 7), left fusiform gyrus and bilateral middle occipital. The additional areas for abstract words were noticed in bilateral superior temporal and bilateral middle temporal region, whereas no distinct region was noticed for concrete words. This suggests that words with abstract concepts recruit additional language regions in the brain.

  4. Chronic stress and neural function: accounting for sex and age.

    Science.gov (United States)

    Luine, V N; Beck, K D; Bowman, R E; Frankfurt, M; Maclusky, N J

    2007-10-01

    Cognitive responses to stress follow the temporally dependent pattern originally established by Selye (1) wherein short-term stressors elicit adaptive responses whereas continued stress (chronic) results in maladaptive changes--deleterious effects on physiological systems and impaired cognition. However, this pattern for cognitive effects appears to apply to only half the population (males) and, more specifically, to young, adult males. Females show different cognitive responses to stress. In contrast to impaired cognition in males after chronic stress, female rodents show enhanced performance on the same memory tasks after the same stress. Not only cognition, but anxiety, shows sex-dependent changes following chronic stress--stress is anxiolytic in males and anxiogenic in females. Moreover, behavioral responses to chronic stress are different in developing as well as aging subjects (both sexes) as compared to adults. In aged rats, chronic stress enhances recognition memory in both sexes, does not alter spatial memory, and anxiety effects are opposite to young adults. When pregnant dams are exposed to chronic stress, at adulthood the offspring display yet different consequences of stress on anxiety and cognition, and, in contrast to adulthood when the behavioral effects of stress are reversible, prenatal stress effects appear enduring. Changing levels of estradiol in the sexes over the lifespan appear to contribute to the differences in response to stress. Thus, theories of stress dependent modulations in CNS function--developed solely in male models, focused on peripheral physiological processes and tested in adults--may require revision when applied to a more diverse population (age- and sex-wise) at least in relation to the neural functions of cognition and anxiety. Moreover, these results suggest that other stressors and neural functions should be investigated to determine whether age, sex and gonadal hormones also have an impact.

  5. Analysis of neural networks in terms of domain functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, Lambert

    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

  6. Using Neurogenetics and the Warmth-Gated Ion Channel TRPA1 to Study the Neural Basis of Behavior in Drosophila.

    Science.gov (United States)

    Berni, Jimena; Muldal, Alistair M; Pulver, Stefan R

    2010-01-01

    Here we describe a set of straightforward laboratory exercises that integrate the study of genetics, neuroanatomy, cellular physiology and animal behavior. We use genetic tools in Drosophila for visualizing and remotely activating ensembles of neurons with heat pulses. First, we show how to examine the anatomy of several neuronal populations using genetically encoded green fluorescent protein. Next we demonstrate how to use the warmth gated Drosophila TRPA1 (dTRPA1) cation channel to remotely activate neural circuits in flies. To demonstrate the cellular effects of dTRPA1 activation, we expressed dTRPA1 panneurally and recorded excitatory junctional potentials in muscles in response to warmed (29°C) saline. Finally, we present inexpensive techniques for delivering heat pulses to activate dTRPA1 in the neuronal groups we observed previously while flies are freely behaving. We suggest how to film and quantify resulting behavioral phenotypes with limited resources. Activating all neurons with dTRPA1 caused tetanic paralysis in larvae, while in adults it led to paralysis in males and continuous uncoordinated leg and wing movements in females. Activation of cholinergic neurons produced spasms and writhing in larvae while causing paralysis in adults. When a single class of nociceptive sensory neurons was activated, it caused lateral rolling in larvae, but no discernable effects in adults. Overall, these exercises illustrate principles of modern genetics, neuroanatomy, the ionic basis of neuronal excitability, and quantitative methods in neuroethology. Relatively few research studies have used dTRPA1 to activate neural circuits, so these exercises give students opportunities to test novel hypotheses and make actual contributions to the scientific record.

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

    Directory of Open Access Journals (Sweden)

    Carvalho, Bettina

    2014-04-01

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

  8. A novel method for flow pattern identification in unstable operational conditions using gamma ray and radial basis function

    International Nuclear Information System (INIS)

    Roshani, G.H.; Nazemi, E.; Roshani, M.M.

    2017-01-01

    Changes of fluid properties (especially density) strongly affect the performance of radiation-based multiphase flow meter and could cause error in recognizing the flow pattern and determining void fraction. In this work, we proposed a methodology based on combination of multi-beam gamma ray attenuation and dual modality densitometry techniques using RBF neural network in order to recognize the flow regime and determine the void fraction in gas-liquid two phase flows independent of the liquid phase changes. The proposed system is consisted of one 137 Cs source, two transmission detectors and one scattering detector. The registered counts in two transmission detectors were used as the inputs of one primary Radial Basis Function (RBF) neural network for recognizing the flow regime independent of liquid phase density. Then, after flow regime identification, three RBF neural networks were utilized for determining the void fraction independent of liquid phase density. Registered count in scattering detector and first transmission detector were used as the inputs of these three RBF neural networks. Using this simple methodology, all the flow patterns were correctly recognized and the void fraction was predicted independent of liquid phase density with mean relative error (MRE) of less than 3.28%. - Highlights: • Flow regime and void fraction were determined in two phase flows independent of the liquid phase density changes. • An experimental structure was set up and the required data was obtained. • 3 detectors and one gamma source were used in detection geometry. • RBF networks were utilized for flow regime and void fraction determination.

  9. Explicit appropriate basis function method for numerical solution of stiff systems

    International Nuclear Information System (INIS)

    Chen, Wenzhen; Xiao, Hongguang; Li, Haofeng; Chen, Ling

    2015-01-01

    Highlights: • An explicit numerical method called the appropriate basis function method is presented. • The method differs from the power series method for obtaining approximate numerical solutions. • Two cases show the method is fit for linear and nonlinear stiff systems. • The method is very simple and effective for most of differential equation systems. - Abstract: In this paper, an explicit numerical method, called the appropriate basis function method, is presented. The explicit appropriate basis function method differs from the power series method because it employs an appropriate basis function such as the exponential function, or periodic function, other than a polynomial, to obtain approximate numerical solutions. The method is successful and effective for the numerical solution of the first order ordinary differential equations. Two examples are presented to show the ability of the method for dealing with linear and nonlinear systems of differential equations

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

    Science.gov (United States)

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

    2011-12-01

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

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

    Science.gov (United States)

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

    2015-12-01

    Transsexualism describes the condition when a person's psychological gender differs from his or her biological sex and is commonly thought to arise from a discrepant cerebral and genital sexual differentiation. This review intends to give an extensive overview of structural and functional neurobiological correlates of transsexualism and their course under cross-sex hormonal treatment. Research in this field enables insight into the stability or variability of gender differences and their relation to hormonal status. For a number of sexually dimorphic brain structures or processes, signs of feminisation or masculinisation are observable in transsexual individuals, which, during hormonal treatment, partly seem to further adjust to characteristics of the desired sex. Still, it appears the data are quite inhomogeneous, mostly not replicated and in many cases available for male-to-female transsexuals only. As the prevalence of homosexuality is markedly higher among transsexuals than among the general population, disentangling correlates of sexual orientation and gender identity is a major problem. To resolve such deficiencies, the implementation of specific research standards is proposed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Seeing with the eyes shut: neural basis of enhanced imagery following Ayahuasca ingestion.

    Science.gov (United States)

    de Araujo, Draulio B; Ribeiro, Sidarta; Cecchi, Guillermo A; Carvalho, Fabiana M; Sanchez, Tiago A; Pinto, Joel P; de Martinis, Bruno S; Crippa, Jose A; Hallak, Jaime E C; Santos, Antonio C

    2012-11-01

    The hallucinogenic brew Ayahuasca, a rich source of serotonergic agonists and reuptake inhibitors, has been used for ages by Amazonian populations during religious ceremonies. Among all perceptual changes induced by Ayahuasca, the most remarkable are vivid "seeings." During such seeings, users report potent imagery. Using functional magnetic resonance imaging during a closed-eyes imagery task, we found that Ayahuasca produces a robust increase in the activation of several occipital, temporal, and frontal areas. In the primary visual area, the effect was comparable in magnitude to the activation levels of natural image with the eyes open. Importantly, this effect was specifically correlated with the occurrence of individual perceptual changes measured by psychiatric scales. The activity of cortical areas BA30 and BA37, known to be involved with episodic memory and the processing of contextual associations, was also potentiated by Ayahuasca intake during imagery. Finally, we detected a positive modulation by Ayahuasca of BA 10, a frontal area involved with intentional prospective imagination, working memory and the processing of information from internal sources. Therefore, our results indicate that Ayahuasca seeings stem from the activation of an extensive network generally involved with vision, memory, and intention. By boosting the intensity of recalled images to the same level of natural image, Ayahuasca lends a status of reality to inner experiences. It is therefore understandable why Ayahuasca was culturally selected over many centuries by rain forest shamans to facilitate mystical revelations of visual nature. Copyright © 2011 Wiley Periodicals, Inc.

  13. Statistical Downscaling of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks

    Science.gov (United States)

    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.

  14. Being in two minds: the neural basis of experiencing action crises in personal long-term goals.

    Science.gov (United States)

    Herrmann, Marcel; Baur, Volker; Brandstätter, Veronika; Hänggi, Jürgen; Jäncke, Lutz

    2014-01-01

    Although the successful pursuit of long-term goals constitutes an essential prerequisite to personal development, health, and well-being, little research has been devoted to the understanding of its underlying neural processes. A critical phase in the pursuit of long-term goals is defined as an action crisis, conceptualized as the intra-psychic conflict between further goal pursuit and disengagement from the goal. In the present research, we applied an interdisciplinary (cognitive and neural) approach to the analysis of processes underlying the experience of an action crisis. In Study 1, a longitudinal field study, action crises in personal goals gave rise to an increased and unbiased (re)evaluation of the costs and benefits (i.e., rewards) of the goal. Study 2 was a magnetic resonance imaging study examining resting-state functional connectivity. The extent of experienced action crises was associated with enhanced fronto-accumbal connectivity signifying increased reward-related impact on prefrontal action control. Action crises, furthermore, mediated the relationship between a dispositional measure of effective goal pursuit (action orientation) and fronto-accumbal connectivity. The converging and complementary results from two methodologically different approaches advance the understanding of the neurobiology of personal long-term goals, especially with respect to the role of rewards in the context of goal-related conflicts.

  15. Development of new auxiliary basis functions of the Karlsruhe segmented contracted basis sets including diffuse basis functions (def2-SVPD, def2-TZVPPD, and def2-QVPPD) for RI-MP2 and RI-CC calculations.

    Science.gov (United States)

    Hellweg, Arnim; Rappoport, Dmitrij

    2015-01-14

    We report optimized auxiliary basis sets for use with the Karlsruhe segmented contracted basis sets including moderately diffuse basis functions (Rappoport and Furche, J. Chem. Phys., 2010, 133, 134105) in resolution-of-the-identity (RI) post-self-consistent field (post-SCF) computations for the elements H-Rn (except lanthanides). The errors of the RI approximation using optimized auxiliary basis sets are analyzed on a comprehensive test set of molecules containing the most common oxidation states of each element and do not exceed those of the corresponding unaugmented basis sets. During these studies an unsatisfying performance of the def2-SVP and def2-QZVPP auxiliary basis sets for Barium was found and improved sets are provided. We establish the versatility of the def2-SVPD, def2-TZVPPD, and def2-QZVPPD basis sets for RI-MP2 and RI-CC (coupled-cluster) energy and property calculations. The influence of diffuse basis functions on correlation energy, basis set superposition error, atomic electron affinity, dipole moments, and computational timings is evaluated at different levels of theory using benchmark sets and showcase examples.

  16. Neural correlate of resting-state functional connectivity under α2 adrenergic receptor agonist, medetomidine.

    Science.gov (United States)

    Nasrallah, Fatima A; Lew, Si Kang; Low, Amanda Si-Min; Chuang, Kai-Hsiang

    2014-01-01

    Correlative fluctuations in functional MRI (fMRI) signals across the brain at rest have been taken as a measure of functional connectivity, but the neural basis of this resting-state MRI (rsMRI) signal is not clear. Previously, we found that the α2 adrenergic agonist, medetomidine, suppressed the rsMRI correlation dose-dependently but not the stimulus evoked activation. To understand the underlying electrophysiology and neurovascular coupling, which might be altered due to the vasoconstrictive nature of medetomidine, somatosensory evoked potential (SEP) and resting electroencephalography (EEG) were measured and correlated with corresponding BOLD signals in rat brains under three dosages of medetomidine. The SEP elicited by electrical stimulation to both forepaws was unchanged regardless of medetomidine dosage, which was consistent with the BOLD activation. Identical relationship between the SEP and BOLD signal under different medetomidine dosages indicates that the neurovascular coupling was not affected. Under resting state, EEG power was the same but a depression of inter-hemispheric EEG coherence in the gamma band was observed at higher medetomidine dosage. Different from medetomidine, both resting EEG power and BOLD power and coherence were significantly suppressed with increased isoflurane level. Such reduction was likely due to suppressed neural activity as shown by diminished SEP and BOLD activation under isoflurane, suggesting different mechanisms of losing synchrony at resting-state. Even though, similarity between electrophysiology and BOLD under stimulation and resting-state implicates a tight neurovascular coupling in both medetomidine and isoflurane. Our results confirm that medetomidine does not suppress neural activity but dissociates connectivity in the somatosensory cortex. The differential effect of medetomidine and its receptor specific action supports the neuronal origin of functional connectivity and implicates the mechanism of its sedative

  17. Accurate correlation energies in one-dimensional systems from small system-adapted basis functions

    Science.gov (United States)

    Baker, Thomas E.; Burke, Kieron; White, Steven R.

    2018-02-01

    We propose a general method for constructing system-dependent basis functions for correlated quantum calculations. Our construction combines features from several traditional approaches: plane waves, localized basis functions, and wavelets. In a one-dimensional mimic of Coulomb systems, it requires only 2-3 basis functions per electron to achieve high accuracy, and reproduces the natural orbitals. We illustrate its effectiveness for molecular energy curves and chains of many one-dimensional atoms. We discuss the promise and challenges for realistic quantum chemical calculations.

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

    Science.gov (United States)

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

    2016-08-01

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

  19. Regulation of adult neural progenitor cell functions by purinergic signaling.

    Science.gov (United States)

    Tang, Yong; Illes, Peter

    2017-02-01

    Extracellular purines are signaling molecules in the neurogenic niches of the brain and spinal cord, where they activate cell surface purinoceptors at embryonic neural stem cells (NSCs) and adult neural progenitor cells (NPCs). Although mRNA and protein are expressed at NSCs/NPCs for almost all subtypes of the nucleotide-sensitive P2X/P2Y, and the nucleoside-sensitive adenosine receptors, only a few of those have acquired functional significance. ATP is sequentially degraded by ecto-nucleotidases to ADP, AMP, and adenosine with agonistic properties for distinct receptor-classes. Nucleotides/nucleosides facilitate or inhibit NSC/NPC proliferation, migration and differentiation. The most ubiquitous effect of all agonists (especially of ATP and ADP) appears to be the facilitation of cell proliferation, usually through P2Y1Rs and sometimes through P2X7Rs. However, usually P2X7R activation causes necrosis/apoptosis of NPCs. Differentiation can be initiated by P2Y2R-activation or P2X7R-blockade. A key element in the transduction mechanism of either receptor is the increase of the intracellular free Ca 2+ concentration, which may arise due to its release from intracellular storage sites (G protein-coupling; P2Y) or due to its passage through the receptor-channel itself from the extracellular space (ATP-gated ion channel; P2X). Further research is needed to clarify how purinergic signaling controls NSC/NPC fate and how the balance between the quiescent and activated states is established with fine and dynamic regulation. GLIA 2017;65:213-230. © 2016 Wiley Periodicals, Inc.

  20. 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.

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

    KAUST Repository

    Efendiev, Yalchin

    2011-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Yuri B. Tebekin

    2011-11-01

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

  3. ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

    Science.gov (United States)

    Cao, Renzhi; Freitas, Colton; Chan, Leong; Sun, Miao; Jiang, Haiqing; Chen, Zhangxin

    2017-10-17

    With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.

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

    Science.gov (United States)

    Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

    2016-12-01

    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, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

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

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2014-06-01

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

  6. A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.

    Science.gov (United States)

    Huertas, Ismael; Oldehinkel, Marianne; van Oort, Erik S B; Garcia-Solis, David; Mir, Pablo; Beckmann, Christian F; Marquand, Andre F

    2017-11-01

    The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data are characterised as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state. This model is estimated using a Bayesian framework which accurately quantifies uncertainty and automatically finds the most accurate and parsimonious combination of basis functions describing the data. We demonstrate the utility of this framework by predicting quantitative SPECT images of striatal dopamine function and we compare a variety of basis sets including generic isotropic functions, anatomical representations of the striatum derived from structural MRI, and two different soft functional parcellations of the striatum derived from resting-state fMRI (rfMRI). We found that a combination of ∼50 multiscale functional basis functions accurately represented the striatal dopamine activity, and that functional basis functions derived from an advanced parcellation technique known as Instantaneous Connectivity Parcellation (ICP) provided the most parsimonious models of dopamine function. Importantly, functional basis functions derived from resting fMRI were more accurate than both structural and generic basis sets in representing dopamine function in the striatum for a fixed model order. We demonstrate the translational validity of our framework by constructing classification models for discriminating parkinsonian disorders and their subtypes. Here, we show that ICP approach is the only basis set that performs well across all comparisons and performs better overall than the classical voxel-based approach

  7. High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data.

    Science.gov (United States)

    Andras, Peter

    2018-02-01

    Approximation of high-dimensional functions is a challenge for neural networks due to the curse of dimensionality. Often the data for which the approximated function is defined resides on a low-dimensional manifold and in principle the approximation of the function over this manifold should improve the approximation performance. It has been show that projecting the data manifold into a lower dimensional space, followed by the neural network approximation of the function over this space, provides a more precise approximation of the function than the approximation of the function with neural networks in the original data space. However, if the data volume is very large, the projection into the low-dimensional space has to be based on a limited sample of the data. Here, we investigate the nature of the approximation error of neural networks trained over the projection space. We show that such neural networks should have better approximation performance than neural networks trained on high-dimensional data even if the projection is based on a relatively sparse sample of the data manifold. We also find that it is preferable to use a uniformly distributed sparse sample of the data for the purpose of the generation of the low-dimensional projection. We illustrate these results considering the practical neural network approximation of a set of functions defined on high-dimensional data including real world data as well.

  8. 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.

  9. Exponential stability of Cohen-Grossberg neural networks with a general class of activation functions

    International Nuclear Information System (INIS)

    Wan Anhua; Wang Miansen; Peng Jigen; Qiao Hong

    2006-01-01

    In this Letter, the dynamics of Cohen-Grossberg neural networks model are investigated. The activation functions are only assumed to be Lipschitz continuous, which provide a much wider application domain for neural networks than the previous results. By means of the extended nonlinear measure approach, new and relaxed sufficient conditions for the existence, uniqueness and global exponential stability of equilibrium of the neural networks are obtained. Moreover, an estimate for the exponential convergence rate of the neural networks is precisely characterized. Our results improve those existing ones

  10. Modeling multivariate time series on manifolds with skew radial basis functions.

    Science.gov (United States)

    Jamshidi, Arta A; Kirby, Michael J

    2011-01-01

    We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.

  11. The neural basis of emotions varies over time: different regions go with onset- and offset-bound processes underlying emotion intensity.

    Science.gov (United States)

    Résibois, Maxime; Verduyn, Philippe; Delaveau, Pauline; Rotgé, Jean-Yves; Kuppens, Peter; Van Mechelen, Iven; Fossati, Philippe

    2017-08-01

    According to theories of emotion dynamics, emotions unfold across two phases in which different types of processes come to the fore: emotion onset and emotion offset. Differences in onset-bound processes are reflected by the degree of explosiveness or steepness of the response at onset, and differences in offset-bound processes by the degree of accumulation or intensification of the subsequent response. Whether onset- and offset-bound processes have distinctive neural correlates and, hence, whether the neural basis of emotions varies over time, still remains unknown. In the present fMRI study, we address this question using a recently developed paradigm that allows to disentangle explosiveness and accumulation. Thirty-one participants were exposed to neutral and negative social feedback, and asked to reflect on its contents. Emotional intensity while reading and thinking about the feedback was measured with an intensity profile tracking approach. Using non-negative matrix factorization, the resulting profile data were decomposed in explosiveness and accumulation components, which were subsequently entered as continuous regressors of the BOLD response. It was found that the neural basis of emotion intensity shifts as emotions unfold over time with emotion explosiveness and accumulation having distinctive neural correlates. © The Author (2017). Published by Oxford University Press.

  12. Individual Identification Using Functional Brain Fingerprint Detected by Recurrent Neural Network.

    Science.gov (United States)

    Chen, Shiyang; Hu, Xiaoping P

    2018-03-20

    Individual identification based on brain function has gained traction in literature. Investigating individual differences in brain function can provide additional insights into the brain. In this work, we introduce a recurrent neural network based model for identifying individuals based on only a short segment of resting state functional MRI data. In addition, we demonstrate how the global signal and differences in atlases affect the individual identifiability. Furthermore, we investigate neural network features that exhibit the uniqueness of each individual. The results indicate that our model is able to identify individuals based on neural features and provides additional information regarding brain dynamics.

  13. Sleep deprivation alters functioning within the neural network underlying the covert orienting of attention.

    Science.gov (United States)

    Mander, Bryce A; Reid, Kathryn J; Davuluri, Vijay K; Small, Dana M; Parrish, Todd B; Mesulam, M-Marsel; Zee, Phyllis C; Gitelman, Darren R

    2008-06-27

    One function of spatial attention is to enable goal-directed interactions with the environment through the allocation of neural resources to motivationally relevant parts of space. Studies have shown that responses are enhanced when spatial attention is predictively biased towards locations where significant events are expected to occur. Previous studies suggest that the ability to bias attention predictively is related to posterior cingulate cortex (PCC) activation [Small, D.M., et al., 2003. The posterior cingulate and medial prefrontal cortex mediate the anticipatory allocation of spatial attention. Neuroimage 18, 633-41]. Sleep deprivation (SD) impairs selective attention and reduces PCC activity [Thomas, M., et al., 2000. Neural basis of alertness and cognitive performance impairments during sleepiness. I. Effects of 24 h of sleep deprivation on waking human regional brain activity. J. Sleep Res. 9, 335-352]. Based on these findings, we hypothesized that SD would affect PCC function and alter the ability to predictively allocate spatial attention. Seven healthy, young adults underwent functional magnetic resonance imaging (fMRI) following normal rest and 34-36 h of SD while performing a task in which attention was shifted in response to peripheral targets preceded by spatially informative (valid), misleading (invalid), or uninformative (neutral) cues. When rested, but not when sleep-deprived, subjects responded more quickly to targets that followed valid cues than those after neutral or invalid cues. Brain activity during validly cued trials with a reaction time benefit was compared to activity in trials with no benefit. PCC activation was greater during trials with a reaction time benefit following normal rest. In contrast, following SD, reaction time benefits were associated with activation in the left intraparietal sulcus, a region associated with receptivity to stimuli at unexpected locations. These changes may render sleep-deprived individuals less able

  14. multi scale analysis of a function by neural networks elementary derivatives functions

    International Nuclear Information System (INIS)

    Chikhi, A.; Gougam, A.; Chafa, F.

    2006-01-01

    Recently, the wavelet network has been introduced as a special neural network supported by the wavelet theory . Such networks constitute a tool for function approximation problems as it has been already proved in reference . Our present work deals with this model, treating a multi scale analysis of a function. We have then used a linear expansion of a given function in wavelets, neglecting the usual translation parameters. We investigate two training operations. The first one consists on an optimization of the output synaptic layer, the second one, optimizing the output function with respect to scale parameters. We notice a temporary merging of the scale parameters leading to some interesting results : new elementary derivatives units emerge, representing a new elementary task, which is the derivative of the output task

  15. Problem-Matched Basis Functions for Microstrip Coupled Slot Arrays based on Transmission Line Green+s Functions (TLGF)

    NARCIS (Netherlands)

    Bruni, S.; Llombart, N.; Neto, A.; Gerini, G.; Maci, S.

    2004-01-01

    A method is proposed for the analysis of arrays of linear printed antennas. After the formulation of pertinent set of integral equations, the appropriate equivalent currents of the Method of Moments are represented in terms of two sets of entire domain basis functions. These functions synthesize on

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

    Science.gov (United States)

    Zhang, WenJun

    2007-07-01

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

  17. Method of moments solution of volume integral equations using higher-order hierarchical Legendre basis functions

    DEFF Research Database (Denmark)

    Kim, Oleksiy S.; Jørgensen, Erik; Meincke, Peter

    2004-01-01

    An efficient higher-order method of moments (MoM) solution of volume integral equations is presented. The higher-order MoM solution is based on higher-order hierarchical Legendre basis functions and higher-order geometry modeling. An unstructured mesh composed of 8-node trilinear and/or curved 27...... of magnitude in comparison to existing higher-order hierarchical basis functions. Consequently, an iterative solver can be applied even for high expansion orders. Numerical results demonstrate excellent agreement with the analytical Mie series solution for a dielectric sphere as well as with results obtained...

  18. Exponential Convergence for Numerical Solution of Integral Equations Using Radial Basis Functions

    Directory of Open Access Journals (Sweden)

    Zakieh Avazzadeh

    2014-01-01

    Full Text Available We solve some different type of Urysohn integral equations by using the radial basis functions. These types include the linear and nonlinear Fredholm, Volterra, and mixed Volterra-Fredholm integral equations. Our main aim is to investigate the rate of convergence to solve these equations using the radial basis functions which have normic structure that utilize approximation in higher dimensions. Of course, the use of this method often leads to ill-posed systems. Thus we propose an algorithm to improve the results. Numerical results show that this method leads to the exponential convergence for solving integral equations as it was already confirmed for partial and ordinary differential equations.

  19. A neural basis for the visual sense of number and its development: A steady-state visual evoked potential study in children and adults

    Directory of Open Access Journals (Sweden)

    Joonkoo Park

    2018-04-01

    Full Text Available While recent studies in adults have demonstrated the existence of a neural mechanism for a visual sense of number, little is known about its development and whether such a mechanism exists at young ages. In the current study, I introduce a novel steady-state visual evoked potential (SSVEP technique to objectively quantify early visual cortical sensitivity to numerical and non-numerical magnitudes of a dot array. I then examine this neural sensitivity to numerical magnitude in children between three and ten years of age and in college students. Children overall exhibit strong SSVEP sensitivity to numerical magnitude in the right occipital sites with negligible SSVEP sensitivity to non-numerical magnitudes, the pattern similar to what is observed in adults. However, a closer examination of age differences reveals that this selective neural sensitivity to numerical magnitude, which is close to absent in three-year-olds, increases steadily as a function of age, while there is virtually no neural sensitivity to other non-numerical magnitudes across these ages. These results demonstrate the emergence of a neural mechanism underlying direct perception of numerosity across early and middle childhood and provide a potential neural mechanistic explanation for the development of humans’ primitive, non-verbal ability to comprehend number. Keywords: Numerosity, Steady-state visual evoked potential, Child development, Visual cortex, Approximate number system

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

    Science.gov (United States)

    Wang, Dongshu; Huang, Lihong; Tang, Longkun

    2015-08-01

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

  1. A novel approach to error function minimization for feedforward neural networks

    International Nuclear Information System (INIS)

    Sinkus, R.

    1995-01-01

    Feedforward neural networks with error backpropagation are widely applied to pattern recognition. One general problem encountered with this type of neural networks is the uncertainty, whether the minimization procedure has converged to a global minimum of the cost function. To overcome this problem a novel approach to minimize the error function is presented. It allows to monitor the approach to the global minimum and as an outcome several ambiguities related to the choice of free parameters of the minimization procedure are removed. (orig.)

  2. Neural Markers and Rehabilitation of Executive Functioning in Veterans with TBI and PTSD

    Science.gov (United States)

    2016-10-01

    1 Award Number: W81XWH-11-1-0796 TITLE: Neural Markers and Rehabilitation of Executive Functioning in Veterans with TBI and PTSD PRINCIPAL...30Sept2015 - 29Sept2016 4. TITLE AND SUBTITLE: Neural Markers and Rehabilitation of Executive Functioning in Veterans with TBI and PTSD 5a. CONTRACT... met criteria for TBI during military service, 48.8% of whom reported multiple head injuries. The most common mechanisms of injury included blast

  3. Reconfiguration of face expressions based on the discrete capture data of radial basis function interpolation

    Institute of Scientific and Technical Information of China (English)

    ZHENG Guangguo; ZHOU Dongsheng; WEI Xiaopeng; ZHANG Qiang

    2012-01-01

    Compactly supported radial basis function can enable the coefficient matrix of solving weigh linear system to have a sparse banded structure, thereby reducing the complexity of the algorithm. Firstly, based on the compactly supported radial basis function, the paper makes the complex quadratic function (Multiquadric, MQ for short) to be transformed and proposes a class of compactly supported MQ function. Secondly, the paper describes a method that interpolates discrete motion capture data to solve the motion vectors of the interpolation points and they are used in facial expression reconstruction. Finally, according to this characteris- tic of the uneven distribution of the face markers, the markers are numbered and grouped in accordance with the density level, and then be interpolated in line with each group. The approach not only ensures the accuracy of the deformation of face local area and smoothness, but also reduces the time complexity of computing.

  4. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

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

  5. Representation of linguistic form and function in recurrent neural networks

    NARCIS (Netherlands)

    Kadar, Akos; Chrupala, Grzegorz; Alishahi, Afra

    2017-01-01

    We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task gated recurrent network architecture

  6. Fast generation of macro basis functions for LEGO through the adaptive cross approximation

    NARCIS (Netherlands)

    Lancellotti, V.

    2015-01-01

    We present a method for the fast generation of macro basis functions in the context of the linear embedding via Green's operators approach (LEGO) which is a domain decomposition technique based on the combination of electromagnetic bricks in turn described by means of scattering operators. We show

  7. Method of applying single higher order polynomial basis function over multiple domains

    CSIR Research Space (South Africa)

    Lysko, AA

    2010-03-01

    Full Text Available A novel method has been devised where one set of higher order polynomial-based basis functions can be applied over several wire segments, thus permitting to decouple the number of unknowns from the number of segments, and so from the geometrical...

  8. Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter

    KAUST Repository

    Ryu, Duchwan; Liang, Faming; Mallick, Bani K.

    2013-01-01

    be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle

  9. New Method for Mesh Moving Based on Radial Basis Function Interpolation

    NARCIS (Netherlands)

    De Boer, A.; Van der Schoot, M.S.; Bijl, H.

    2006-01-01

    A new point-by-point mesh movement algorithm is developed for the deformation of unstructured grids. The method is based on using radial basis function, RBFs, to interpolate the displacements of the boundary nodes to the whole flow mesh. A small system of equations has to be solved, only involving

  10. Least square fitting of low resolution gamma ray spectra with cubic B-spline basis functions

    International Nuclear Information System (INIS)

    Zhu Menghua; Liu Lianggang; Qi Dongxu; You Zhong; Xu Aoao

    2009-01-01

    In this paper, the least square fitting method with the cubic B-spline basis functions is derived to reduce the influence of statistical fluctuations in the gamma ray spectra. The derived procedure is simple and automatic. The results show that this method is better than the convolution method with a sufficient reduction of statistical fluctuation. (authors)

  11. A metric for the Radial Basis Function Network - Application on Real Radar Data

    NARCIS (Netherlands)

    Heiden, R. van der; Groen, F.C.A.

    1996-01-01

    A Radial Basis Functions (RBF) network for pattern recognition is considered. Classification with such a network is based on distances between patterns, so a metric is always present. Using real radar data, the Euclidean metric is shown to perform poorly - a metric based on the so called Box-Cox

  12. MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

    Science.gov (United States)

    Novosad, Philip; Reader, Andrew J.

    2016-06-01

    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 [18F]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

  13. Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Nanda

    2011-01-01

    Full Text Available Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN, polynomial perceptron network (PPN, and Legendre neural network (LeNN were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model.

  14. Neural Tuning Functions Underlie Both Generalization and Interference.

    Directory of Open Access Journals (Sweden)

    Ian S Howard

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

  15. Altered Neural Activity Associated with Mindfulness during Nociception: A Systematic Review of Functional MRI.

    Science.gov (United States)

    Bilevicius, Elena; Kolesar, Tiffany A; Kornelsen, Jennifer

    2016-04-19

    To assess the neural activity associated with mindfulness-based alterations of pain perception. The Cochrane Central, EMBASE, Ovid Medline, PsycINFO, Scopus, and Web of Science databases were searched on 2 February 2016. Titles, abstracts, and full-text articles were independently screened by two reviewers. Data were independently extracted from records that included topics of functional neuroimaging, pain, and mindfulness interventions. The literature search produced 946 total records, of which five met the inclusion criteria. Records reported pain in terms of anticipation (n = 2), unpleasantness (n = 5), and intensity (n = 5), and how mindfulness conditions altered the neural activity during noxious stimulation accordingly. Although the studies were inconsistent in relating pain components to neural activity, in general, mindfulness was able to reduce pain anticipation and unpleasantness ratings, as well as alter the corresponding neural activity. The major neural underpinnings of mindfulness-based pain reduction consisted of altered activity in the anterior cingulate cortex, insula, and dorsolateral prefrontal cortex.

  16. Neural substrates underlying balanced time perspective: A combined voxel-based morphometry and resting-state functional connectivity study.

    Science.gov (United States)

    Guo, Yiqun; Chen, Zhiyi; Feng, Tingyong

    2017-08-14

    Balanced time perspective (BTP), which is defined as a mental ability to switch flexibly among different time perspectives Zimbardo and Boyd (1999), has been suggested to be a central component of positive psychology Boniwell and Zimbardo (2004). BTP reflects individual's cognitive flexibility towards different time frames, which leads to many positive outcomes, including positive mood, subjective wellbeing, emotional intelligence, fluid intelligence, and executive control. However, the neural basis of BTP is still unclear. To address this question, we quantified individual's deviation from the BTP (DBTP), and investigated the neural substrates of DBTP using both voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods VBM analysis found that DBTP scores were positively correlated with gray matter volume (GMV) in the ventral precuneus. We further found that DBTP scores were negatively associated with RSFCs between the ventral precuneus seed region and medial prefrontal cortex (mPFC), bilateral temporoparietal junction (TPJ), parahippocampa gyrus (PHG), and middle frontal gyrus (MFG). These brain regions found in both VBM and RSFC analyses are commonly considered as core nodes of the default mode network (DMN) that is known to be involved in many functions, including episodic and autobiographical memory, self-related processing, theory of mind, and imagining the future. These functions of the DMN are also essential to individuals with BTP. Taken together, we provide the first evidence for the structural and functional neural basis of BTP, and highlight the crucial role of the DMN in cultivating an individual's BTP. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

    Science.gov (United States)

    Meng, Qinggang; Lee, M. H.

    2007-03-01

    Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.

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

    Directory of Open Access Journals (Sweden)

    Jeremy Purcell

    2015-05-01

    1 For all participants we identified brain areas associated with a normalized response for the TRAINING words at the post-training time point. 2 For all participants we identified an up-regulation of the TRAINING response (i.e., the TRAINING neural response was initially low and then increased post-training; whereas in only one participant did we also observe a down-regulation of the training response (i.e., the TRAINING neural response was initially high, but then decreased post-training. 3 Although the areas associated with the normalized TRAINING response were different in each individual, they all include areas typically associated with the spelling system (Purcell et al. 2011, including the right homologues of typically left hemisphere spelling regions. Across the participants, the following areas of normalization were observed: bilateral superior temporal gyrus, inferior frontal gyrus, and the bilateral inferior temporal/fusiform gyrus. Discussion: We found that the predominant BOLD response to training involved an up-regulation of the neural response to spelling the TRAINING items. In addition, we found individual differences in the neurotopography of the normalization response patterns although all were with within brain areas that form a part of the spelling network(Purcell et al. 2011. This work provides evidence regarding one aspect of the multiplicity of neural responses associated with recovery of spelling in individuals with acquired dysgraphia.

  19. Global convergence of periodic solution of neural networks with discontinuous activation functions

    International Nuclear Information System (INIS)

    Huang Lihong; Guo Zhenyuan

    2009-01-01

    In this paper, without assuming boundedness and monotonicity of the activation functions, we establish some sufficient conditions ensuring the existence and global asymptotic stability of periodic solution of neural networks with discontinuous activation functions by using the Yoshizawa-like theorem and constructing proper Lyapunov function. The obtained results improve and extend previous works.

  20. Exploring the Neural Basis of Avatar Identification in Pathological Internet Gamers and of Self-Reflection in Pathological Social Network Users.

    Science.gov (United States)

    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.

  1. Exploring the Neural Basis of Avatar Identification in Pathological Internet Gamers and of Self-Reflection in Pathological Social Network Users

    Science.gov (United States)

    Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl

    2016-01-01

    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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-07-01

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

  3. Programmed Cell Death and Caspase Functions During Neural Development.

    Science.gov (United States)

    Yamaguchi, Yoshifumi; Miura, Masayuki

    2015-01-01

    Programmed cell death (PCD) is a fundamental component of nervous system development. PCD serves as the mechanism for quantitative matching of the number of projecting neurons and their target cells through direct competition for neurotrophic factors in the vertebrate peripheral nervous system. In addition, PCD plays roles in regulating neural cell numbers, canceling developmental errors or noise, and tissue remodeling processes. These findings are mainly derived from genetic studies that prevent cells from dying by apoptosis, which is a major form of PCD and is executed by activation of evolutionarily conserved cysteine protease caspases. Recent studies suggest that caspase activation can be coordinated in time and space at multiple levels, which might underlie nonapoptotic roles of caspases in neural development in addition to apoptotic roles. © 2015 Elsevier Inc. All rights reserved.

  4. Transfer functions for protein signal transduction: application to a model of striatal neural plasticity.

    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

  5. A conceptual basis to encode and detect organic functional groups in XML.

    Science.gov (United States)

    Sankar, Punnaivanam; Krief, Alain; Vijayasarathi, Durairaj

    2013-06-01

    A conceptual basis to define and detect organic functional groups is developed. The basic model of a functional group is termed as a primary functional group and is characterized by a group center composed of one or more group center atoms bonded to terminal atoms and skeletal carbon atoms. The generic group center patterns are identified from the structures of known functional groups. Accordingly, a chemical ontology 'Font' is developed to organize the existing functional groups as well as the new ones to be defined by the chemists. The basic model is extended to accommodate various combinations of primary functional groups as functional group assemblies. A concept of skeletal group is proposed to define the characteristic groups composed of only carbon atoms to be regarded as equivalent to functional groups. The combination of primary functional groups with skeletal groups is categorized as skeletal group assembly. In order to make the model suitable for reaction modeling purpose, a Graphical User Interface (GUI) is developed to define the functional groups and to encode in XML format appropriate to detect them in chemical structures. The system is capable of detecting multiple instances of primary functional groups as well as the overlapping poly-functional groups as the respective assemblies. Copyright © 2013 Elsevier Inc. All rights reserved.

  6. The modulation of neural gain facilitates a transition between functional segregation and integration in the brain.

    Science.gov (United States)

    Shine, James M; Aburn, Matthew J; Breakspear, Michael; Poldrack, Russell A

    2018-01-29

    Cognitive function relies on a dynamic, context-sensitive balance between functional integration and segregation in the brain. Previous work has proposed that this balance is mediated by global fluctuations in neural gain by projections from ascending neuromodulatory nuclei. To test this hypothesis in silico, we studied the effects of neural gain on network dynamics in a model of large-scale neuronal dynamics. We found that increases in neural gain directed the network through an abrupt dynamical transition, leading to an integrated network topology that was maximal in frontoparietal 'rich club' regions. This gain-mediated transition was also associated with increased topological complexity, as well as increased variability in time-resolved topological structure, further highlighting the potential computational benefits of the gain-mediated network transition. These results support the hypothesis that neural gain modulation has the computational capacity to mediate the balance between integration and segregation in the brain. © 2018, Shine et al.

  7. Density Functional Theory and the Basis Set Truncation Problem with Correlation Consistent Basis Sets: Elephant in the Room or Mouse in the Closet?

    Science.gov (United States)

    Feller, David; Dixon, David A

    2018-03-08

    Two recent papers in this journal called into question the suitability of the correlation consistent basis sets for density functional theory (DFT) calculations, because the sets were designed for correlated methods such as configuration interaction, perturbation theory, and coupled cluster theory. These papers focused on the ability of the correlation consistent and other basis sets to reproduce total energies, atomization energies, and dipole moments obtained from "quasi-exact" multiwavelet results. Undesirably large errors were observed for the correlation consistent basis sets. One of the papers argued that basis sets specifically optimized for DFT methods were "essential" for obtaining high accuracy. In this work we re-examined the performance of the correlation consistent basis sets by resolving problems with the previous calculations and by making more appropriate basis set choices for the alkali and alkaline-earth metals and second-row elements. When this is done, the statistical errors with respect to the benchmark values and with respect to DFT optimized basis sets are greatly reduced, especially in light of the relatively large intrinsic error of the underlying DFT method. When judged with respect to high-quality Feller-Peterson-Dixon coupled cluster theory atomization energies, the PBE0 DFT method used in the previous studies exhibits a mean absolute deviation more than a factor of 50 larger than the quintuple zeta basis set truncation error.

  8. Identifying the molecular functions of electron transport proteins using radial basis function networks and biochemical properties.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Nguyen, Trinh-Trung-Duong; Ou, Yu-Yen

    2017-05-01

    The electron transport proteins have an important role in storing and transferring electrons in cellular respiration, which is the most proficient process through which cells gather energy from consumed food. According to the molecular functions, the electron transport chain components could be formed with five complexes with several different electron carriers and functions. Therefore, identifying the molecular functions in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells. This work includes two phases for discriminating electron transport proteins from transport proteins and classifying categories of five complexes in electron transport proteins. In the first phase, the performances from PSSM with AAIndex feature set were successful in identifying electron transport proteins in transport proteins with achieved sensitivity of 73.2%, specificity of 94.1%, and accuracy of 91.3%, with MCC of 0.64 for independent data set. With the second phase, our method can approach a precise model for identifying of five complexes with different molecular functions in electron transport proteins. The PSSM with AAIndex properties in five complexes achieved MCC of 0.51, 0.47, 0.42, 0.74, and 1.00 for independent data set, respectively. We suggest that our study could be a power model for determining new proteins that belongs into which molecular function of electron transport proteins. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. The neural basis of humour comprehension and humour appreciation: The roles of the temporoparietal junction and superior frontal gyrus.

    Science.gov (United States)

    Campbell, Darren W; Wallace, Marc G; Modirrousta, Mandana; Polimeni, Joseph O; McKeen, Nancy A; Reiss, Jeffrey P

    2015-12-01

    Psychological well-being and social acumen benefit from the recognition of humourous intent and its enjoyment. The enjoyment of humour requires recognition, but humour recognition is not necessarily accompanied by humour enjoyment. Humour recognition is crucial during social interactions, while the associated enjoyment is less critical. Few neuroimaging studies have explicitly differentiated between the neural foundations of humour comprehension and humour appreciation. Among such studies, design limitations have obscured the specification of neural correlates to humour comprehension or appreciation. We implemented a trichotomous response option to address these design limitations. Twenty-four participants rated 120 comics (90 unaltered with humourous intent and 30 caption-altered without humourous intent) as either funny jokes (FJ), not funny jokes but intended to be funny (NFJ), or not intended to be funny or non-jokes (NJ). We defined humour comprehension by NFJ minus NJ and humour appreciation by FJ minus NFJ. We measured localized blood oxygen level dependent (BOLD) neural responses with a 3T MRI scanner. We tested for BOLD responses in humour comprehension brain regions of interest (ROIs), humour appreciation ROIs, and across the whole-brain. We found significant NFJ-NJ BOLD responses in our humour comprehension ROIs and significant FJ-NFJ BOLD responses in select humour appreciation ROIs. One key finding is that comprehension accuracy levels correlated with humour-comprehension responses in the left temporo-parietal junction (TPJ). This finding represents a novel and precise neural linkage to humour comprehension. A second key finding is that the superior frontal gyrus (SFG) was uniquely associated with humour-appreciation. The SFG response suggests that complex cognitive processing underlies humour appreciation and that current models of humour appreciation be revised. Finally, our research design provides an operational distinction between humour

  10. The neural basis of non-verbal communication-enhanced processing of perceived give-me gestures in 9-month-old girls.

    Science.gov (United States)

    Bakker, Marta; Kaduk, Katharina; Elsner, Claudia; Juvrud, Joshua; Gustaf Gredebäck

    2015-01-01

    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°, 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.

  11. The Neural Basis of Mark Making: A Functional MRI Study of Drawing

    Science.gov (United States)

    Yuan, Ye; Brown, Steven

    2014-01-01

    Compared to most other forms of visually-guided motor activity, drawing is unique in that it “leaves a trail behind” in the form of the emanating image. We took advantage of an MRI-compatible drawing tablet in order to examine both the motor production and perceptual emanation of images. Subjects participated in a series of mark making tasks in which they were cued to draw geometric patterns on the tablet's surface. The critical comparison was between when visual feedback was displayed (image generation) versus when it was not (no image generation). This contrast revealed an occipito-parietal stream involved in motion-based perception of the emerging image, including areas V5/MT+, LO, V3A, and the posterior part of the intraparietal sulcus. Interestingly, when subjects passively viewed animations of visual patterns emerging on the projected surface, all of the sensorimotor network involved in drawing was strongly activated, with the exception of the primary motor cortex. These results argue that the origin of the human capacity to draw and write involves not only motor skills for tool use but also motor-sensory links between drawing movements and the visual images that emanate from them in real time. PMID:25271440

  12. 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.

  13. 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...

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

    DEFF Research Database (Denmark)

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

    2007-01-01

    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...... with the analytical Mie series solution. Scattering by more complex metal-dielectric objects are also considered to compare the presented technique with other numerical methods....

  15. Application of natural basis functions to soft x-ray tomography

    International Nuclear Information System (INIS)

    Ingesson, L.

    2000-03-01

    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)

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

    Directory of Open Access Journals (Sweden)

    Siebler Mario

    2009-08-01

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

  17. Application of radial basis function in densitometry of stratified regime of liquid-gas two phase flows

    International Nuclear Information System (INIS)

    Roshani, G.H.; Nazemi, E.; Roshani, M.M.

    2017-01-01

    In this paper, a novel method is proposed for predicting the density of liquid phase in stratified regime of liquid-gas two phase flows by utilizing dual modality densitometry technique and artificial neural network (ANN) model of radial basis function (RBF). The detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors for registering transmitted and scattered photons. At the first step, a Monte Carlo simulation model was utilized to obtain the optimum position for the scattering detector in dual modality densitometry configuration. At the next step, an experimental setup was designed based on obtained optimum position for detectors from simulation in order to generate the required data for training and testing the ANN. The results show that the proposed approach could be successfully applied for predicting the density of liquid phase in stratified regime of gas-liquid two phase flows with mean relative error (MRE) of less than 0.701. - Highlights: • Density of liquid phase in stratified regime of two phase flows was predicted. • Combination of dual modality densitometry technique and ANN was utilized. • Detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors. • MCNP simulation was done to obtain the optimum position for the scattering detector. • An experimental setup was designed to generate the required data for training the ANN.

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

    Directory of Open Access Journals (Sweden)

    Jingwen Tian

    2013-02-01

    Full Text Available Since the control system of the welding gun pose in whole-position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN and expert system (ES is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN and the ES. The ADXRS300 micro-mechanical gyro is used as the welding gun position sensor in this system. When the welding gun position is obtained, an appropriate pitch angle can be obtained through expert knowledge and the numeric reasoning capacity of the IRBFNN. ARM is used as the controller to drive the welding gun pitch angle step motor in order to adjust the pitch angle of the welding gun in real-time. The experiment results show that the intelligent control system of the welding gun pose using the IRBFNN and expert system is feasible and it enhances the welding quality. This system has wide prospects for application.

  19. 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.

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

    Science.gov (United States)

    Manning, Katherine E.; Holland, Anthony J.

    2015-01-01

    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. PMID:28943631

  1. The neural basis of the bystander effect : The influence of group size on neural activity when witnessing an emergency

    NARCIS (Netherlands)

    Hortensius, R.; de Gelder, B.

    2014-01-01

    Naturalistic observation and experimental studies in humans and other primates show that observing an individual in need automatically triggers helping behavior. The aim of the present study is to clarify the neurofunctional basis of social influences on individual helping behavior. We investigate

  2. Rational quadratic trigonometric Bézier curve based on new basis with exponential functions

    Directory of Open Access Journals (Sweden)

    Wu Beibei

    2017-06-01

    Full Text Available We construct a rational quadratic trigonometric Bézier curve with four shape parameters by introducing two exponential functions into the trigonometric basis functions in this paper. It has the similar properties as the rational quadratic Bézier curve. For given control points, the shape of the curve can be flexibly adjusted by changing the shape parameters and the weight. Some conics can be exactly represented when the control points, the shape parameters and the weight are chosen appropriately. The C0, C1 and C2 continuous conditions for joining two constructed curves are discussed. Some examples are given.

  3. Neural basis of postural focus effect on concurrent postural and motor tasks: phase-locked electroencephalogram responses.

    Science.gov (United States)

    Huang, Cheng-Ya; Zhao, Chen-Guang; Hwang, Ing-Shiou

    2014-11-01

    Dual-task performance is strongly affected by the direction of attentional focus. This study investigated neural control of a postural-suprapostural procedure when postural focus strategy varied. Twelve adults concurrently conducted force-matching and maintained stabilometer stance with visual feedback on ankle movement (visual internal focus, VIF) and on stabilometer movement (visual external focus, VEF). Force-matching error, dynamics of ankle and stabilometer movements, and event-related potentials (ERPs) were registered. Postural control with VEF caused superior force-matching performance, more complex ankle movement, and stronger kinematic coupling between the ankle and stabilometer movements than postural control with VIF. The postural focus strategy also altered ERP temporal-spatial patterns. Postural control with VEF resulted in later N1 with less negativity around the bilateral fronto-central and contralateral sensorimotor areas, earlier P2 deflection with more positivity around the bilateral fronto-central and ipsilateral temporal areas, and late movement-related potential commencing in the left frontal-central area, as compared with postural control with VIF. The time-frequency distribution of the ERP principal component revealed phase-locked neural oscillations in the delta (1-4Hz), theta (4-7Hz), and beta (13-35Hz) rhythms. The delta and theta rhythms were more pronounced prior to the timing of P2 positive deflection, and beta rebound was greater after the completion of force-matching in VEF condition than VIF condition. This study is the first to reveal the neural correlation of postural focusing effect on a postural-suprapostural task. Postural control with VEF takes advantage of efficient task-switching to facilitate autonomous postural response, in agreement with the "constrained-action" hypothesis. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Neural correlates of attention and arousal: insights from electrophysiology, functional neuroimaging and psychopharmacology.

    Science.gov (United States)

    Coull, J T

    1998-07-01

    Attention and arousal are multi-dimensional psychological processes, which interact closely with one another. The neural substrates of attention, as well as the interaction between arousal and attention, are discussed in this review. After a brief discussion of psychological and neuropsychological theories of attention, event-related potential correlates of attention are discussed. Essentially, attention acts to modulate stimulus-induced electrical potentials (N100/P100, P300, N400), rather than generating any unique potentials of its own. Functional neuroimaging studies of attentional orienting, selective attention, divided attention and sustained attention (and its inter-dependence on underlying levels of arousal) are then reviewed. A distinction is drawn between the brain areas which are crucially involved in the top-down modulation of attention (the 'sources' of attention) and those sensory-association areas whose activity is modulated by attention (the 'sites' of attentional expression). Frontal and parietal (usually right-lateralised) cortices and thalamus are most often associated with the source of attentional modulation. Also, the use of functional neuroimaging to test explicit hypotheses about psychological theories of attention is emphasised. These experimental paradigms form the basis for a 'new generation' of functional imaging studies which exploit the dynamic aspect of imaging and demonstrate how it can be used as more than just a 'brain mapping' device. Finally, a review of psychopharmacological studies in healthy human volunteers outlines the contributions of the noradrenergic, cholinergic and dopaminergic neurotransmitter systems to the neurochemical modulation of human attention and arousal. While, noradrenergic and cholinergic systems are involved in 'low-level' aspects of attention (e.g. attentional orienting), the dopaminergic system is associated with more 'executive' aspects of attention such as attentional set-shifting or working memory.

  5. Similar patterns of neural activity predict memory function during encoding and retrieval.

    Science.gov (United States)

    Kragel, James E; Ezzyat, Youssef; Sperling, Michael R; Gorniak, Richard; Worrell, Gregory A; Berry, Brent M; Inman, Cory; Lin, Jui-Jui; Davis, Kathryn A; Das, Sandhitsu R; Stein, Joel M; Jobst, Barbara C; Zaghloul, Kareem A; Sheth, Sameer A; Rizzuto, Daniel S; Kahana, Michael J

    2017-07-15

    Neural networks that span the medial temporal lobe (MTL), prefrontal cortex, and posterior cortical regions are essential to episodic memory function in humans. Encoding and retrieval are supported by the engagement of both distinct neural pathways across the cortex and common structures within the medial temporal lobes. However, the degree to which memory performance can be determined by neural processing that is common to encoding and retrieval remains to be determined. To identify neural signatures of successful memory function, we administered a delayed free-recall task to 187 neurosurgical patients implanted with subdural or intraparenchymal depth electrodes. We developed multivariate classifiers to identify patterns of spectral power across the brain that independently predicted successful episodic encoding and retrieval. During encoding and retrieval, patterns of increased high frequency activity in prefrontal, MTL, and inferior parietal cortices, accompanied by widespread decreases in low frequency power across the brain predicted successful memory function. Using a cross-decoding approach, we demonstrate the ability to predict memory function across distinct phases of the free-recall task. Furthermore, we demonstrate that classifiers that combine information from both encoding and retrieval states can outperform task-independent models. These findings suggest that the engagement of a core memory network during either encoding or retrieval shapes the ability to remember the past, despite distinct neural interactions that facilitate encoding and retrieval. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Callihan Phillip

    2008-12-01

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

  7. Reconstruction of tissue dynamics in the compressed breast using multiplexed measurements and temporal basis functions

    Science.gov (United States)

    Boverman, Gregory; Miller, Eric L.; Brooks, Dana H.; Fang, Qianqian; Carp, S. A.; Selb, J. J.; Boas, David A.

    2007-02-01

    In the course of our experiments imaging the compressed breast in conjunction with digital tomosynthesis, we have noted that significant changes in tissue optical properties, on the order of 5%, occur during our imaging protocol. These changes seem to consistent with changes both in total Hemoglobin concentration as well as in oxygen saturation, as was the case for our standalone breast compression study, which made use of reflectance measurements. Simulation experiments show the importance of taking into account the temporal dynamics in the image reconstruction, and demonstrate the possibility of imaging the spatio-temporal dynamics of oxygen saturation and total Hemoglobin in the breast. In the image reconstruction, we make use of spatio-temporal basis functions, specifically a voxel basis for spatial imaging, and a cubic spline basis in time, and we reconstruct the spatio-temporal images using the entire data set simultaneously, making use of both absolute and relative measurements in the cost function. We have modified the sequence of sources used in our imaging acquisition protocol to improve our temporal resolution, and preliminary results are shown for normal subjects.

  8. Symmetry-Adapted Ro-vibrational Basis Functions for Variational Nuclear Motion Calculations: TROVE Approach.

    Science.gov (United States)

    Yurchenko, Sergei N; Yachmenev, Andrey; Ovsyannikov, Roman I

    2017-09-12

    We present a general, numerically motivated approach to the construction of symmetry-adapted basis functions for solving ro-vibrational Schrödinger equations. The approach is based on the property of the Hamiltonian operator to commute with the complete set of symmetry operators and, hence, to reflect the symmetry of the system. The symmetry-adapted ro-vibrational basis set is constructed numerically by solving a set of reduced vibrational eigenvalue problems. In order to assign the irreducible representations associated with these eigenfunctions, their symmetry properties are probed on a grid of molecular geometries with the corresponding symmetry operations. The transformation matrices are reconstructed by solving overdetermined systems of linear equations related to the transformation properties of the corresponding wave functions on the grid. Our method is implemented in the variational approach TROVE and has been successfully applied to many problems covering the most important molecular symmetry groups. Several examples are used to illustrate the procedure, which can be easily applied to different types of coordinates, basis sets, and molecular systems.

  9. Group Theory of Wannier Functions Providing the Basis for a Deeper Understanding of Magnetism and Superconductivity

    Directory of Open Access Journals (Sweden)

    Ekkehard Krüger

    2015-05-01

    Full Text Available The paper presents the group theory of optimally-localized and symmetry-adapted Wannier functions in a crystal of any given space group G or magnetic group M. Provided that the calculated band structure of the considered material is given and that the symmetry of the Bloch functions at all of the points of symmetry in the Brillouin zone is known, the paper details whether or not the Bloch functions of particular energy bands can be unitarily transformed into optimally-localized Wannier functions symmetry-adapted to the space group G, to the magnetic group M or to a subgroup of G or M. In this context, the paper considers usual, as well as spin-dependent Wannier functions, the latter representing the most general definition of Wannier functions. The presented group theory is a review of the theory published by one of the authors (Ekkehard Krüger in several former papers and is independent of any physical model of magnetism or superconductivity. However, it is suggested to interpret the special symmetry of the optimally-localized Wannier functions in the framework of a nonadiabatic extension of the Heisenberg model, the nonadiabatic Heisenberg model. On the basis of the symmetry of the Wannier functions, this model of strongly-correlated localized electrons makes clear predictions of whether or not the system can possess superconducting or magnetic eigenstates.

  10. A comparison of the behavior of functional/basis set combinations for hydrogen-bonding in the water dimer with emphasis on basis set superposition error.

    Science.gov (United States)

    Plumley, Joshua A; Dannenberg, J J

    2011-06-01

    We evaluate the performance of ten functionals (B3LYP, M05, M05-2X, M06, M06-2X, B2PLYP, B2PLYPD, X3LYP, B97D, and MPWB1K) in combination with 16 basis sets ranging in complexity from 6-31G(d) to aug-cc-pV5Z for the calculation of the H-bonded water dimer with the goal of defining which combinations of functionals and basis sets provide a combination of economy and accuracy for H-bonded systems. We have compared the results to the best non-density functional theory (non-DFT) molecular orbital (MO) calculations and to experimental results. Several of the smaller basis sets lead to qualitatively incorrect geometries when optimized on a normal potential energy surface (PES). This problem disappears when the optimization is performed on a counterpoise (CP) corrected PES. The calculated interaction energies (ΔEs) with the largest basis sets vary from -4.42 (B97D) to -5.19 (B2PLYPD) kcal/mol for the different functionals. Small basis sets generally predict stronger interactions than the large ones. We found that, because of error compensation, the smaller basis sets gave the best results (in comparison to experimental and high-level non-DFT MO calculations) when combined with a functional that predicts a weak interaction with the largest basis set. As many applications are complex systems and require economical calculations, we suggest the following functional/basis set combinations in order of increasing complexity and cost: (1) D95(d,p) with B3LYP, B97D, M06, or MPWB1k; (2) 6-311G(d,p) with B3LYP; (3) D95++(d,p) with B3LYP, B97D, or MPWB1K; (4) 6-311++G(d,p) with B3LYP or B97D; and (5) aug-cc-pVDZ with M05-2X, M06-2X, or X3LYP. Copyright © 2011 Wiley Periodicals, Inc.

  11. A canonical correlation neural network for multicollinearity and functional data.

    Science.gov (United States)

    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.

  12. Beyond the neuropsychology of dreaming: Insights into the neural basis of dreaming with new techniques of sleep recording and analysis.

    Science.gov (United States)

    Cipolli, Carlo; Ferrara, Michele; De Gennaro, Luigi; Plazzi, Giuseppe

    2017-10-01

    Recent advances in electrophysiological [e.g., surface high-density electroencephalographic (hd-EEG) and intracranial recordings], video-polysomnography (video-PSG), transcranial stimulation and neuroimaging techniques allow more in-depth and more accurate investigation of the neural correlates of dreaming in healthy individuals and in patients with brain-damage, neurodegenerative diseases, sleep disorders or parasomnias. Convergent evidence provided by studies using these techniques in healthy subjects has led to a reformulation of several unresolved issues of dream generation and recall [such as the inter- and intra-individual differences in dream recall and the predictivity of specific EEG rhythms, such as theta in rapid eye movement (REM) sleep, for dream recall] within more comprehensive models of human consciousness and its variations across sleep/wake states than the traditional models, which were largely based on the neurophysiology of REM sleep in animals. These studies are casting new light on the neural bases (in particular, the activity of dorsal medial prefrontal cortex regions and hippocampus and amygdala areas) of the inter- and intra-individual differences in dream recall, the temporal location of specific contents or properties (e.g., lucidity) of dream experience and the processing of memories accessed during sleep and incorporated into dream content. Hd-EEG techniques, used on their own or in combination with neuroimaging, appear able to provide further important insights into how the brain generates not only dreaming during sleep but also some dreamlike experiences in waking. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Human brain basis of musical rhythm perception: common and distinct neural substrates for meter, tempo, and pattern.

    Science.gov (United States)

    Thaut, Michael H; Trimarchi, Pietro Davide; Parsons, Lawrence M

    2014-06-17

    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.

  14. Molecular basis of the functional heterogeneity of the muscarinic acetylcholine receptor

    International Nuclear Information System (INIS)

    Numa, S.; Fukuda, K.; Kubo, T.; Maeda, A.; Akiba, I.; Bujo, H.; Nakai, J.; Mishina, M.; Higashida, H.

    1988-01-01

    The muscarinic acetylcholine receptor (mAChR) mediates a variety of cellular responses, including inhibition of adenylate cyclase, breakdown of phosphoinositides, and modulation of potassium channels, through the action of guanine-nucleotide-binding regulatory proteins (G proteins). The question then arises as to whether multiple mAChR species exist that are responsible for the various biochemical and physiological effects. In fact, pharmacologically distinguishable forms of the mAChR occur in different tissues and have been provisionally classified into M 1 (I), M 2 cardiac (II), and M 2 glandular (III) subtypes on the basis of their difference in apparent affinity for antagonists. Here, the authors have made attempts to understand the molecular basis of the functional heterogeneity of the mAChR, using recombinant DNA technology

  15. One-way hash function based on hyper-chaotic cellular neural network

    International Nuclear Information System (INIS)

    Yang Qunting; Gao Tiegang

    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. (general)

  16. New deconvolution method for microscopic images based on the continuous Gaussian radial basis function interpolation model.

    Science.gov (United States)

    Chen, Zhaoxue; Chen, Hao

    2014-01-01

    A deconvolution method based on the Gaussian radial basis function (GRBF) interpolation is proposed. Both the original image and Gaussian point spread function are expressed as the same continuous GRBF model, thus image degradation is simplified as convolution of two continuous Gaussian functions, and image deconvolution is converted to calculate the weighted coefficients of two-dimensional control points. Compared with Wiener filter and Lucy-Richardson algorithm, the GRBF method has an obvious advantage in the quality of restored images. In order to overcome such a defect of long-time computing, the method of graphic processing unit multithreading or increasing space interval of control points is adopted, respectively, to speed up the implementation of GRBF method. The experiments show that based on the continuous GRBF model, the image deconvolution can be efficiently implemented by the method, which also has a considerable reference value for the study of three-dimensional microscopic image deconvolution.

  17. Multi-modular neural networks for the classification of e+e- hadronic events

    International Nuclear Information System (INIS)

    Proriol, J.

    1994-01-01

    Some multi-modular neural network methods of classifying e + e - hadronic events are presented. We compare the performances of the following neural networks: MLP (multilayer perceptron), MLP and LVQ (learning vector quantization) trained sequentially, and MLP and RBF (radial basis function) trained sequentially. We introduce a MLP-RBF cooperative neural network. Our last study is a multi-MLP neural network. (orig.)

  18. Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks

    Directory of Open Access Journals (Sweden)

    Zhihong Liao

    2017-11-01

    Full Text Available A radial basis function network (RBFN method is proposed to reconstruct daily Sea surface temperatures (SSTs with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the Reynolds optimum interpolation (OI v2 daily 0.25° SST (OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC algorithm is designed to search for the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then, the reconstructed SSTs from the RBFN method are compared with the results from the OI method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and that the average RMSE is 0.48 °C for the RBFN method, which is quite smaller than the value of 0.69 °C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.

  19. Amyloid-independent functional neural correlates of episodic memory in amnestic mild cognitive impairment.

    Science.gov (United States)

    Seo, Eun Hyun; Choo, I L Han

    2016-06-01

    Although amnestic mild cognitive impairment (aMCI) could have various biological characteristics, little attention has been given to the nature of episodic memory decline in aMCI with pathophysiologies other than Alzheimer's disease (AD), i.e., aMCI with low beta-amyloid (Aβ) burden. This study aimed to identify the functional neural basis of episodic memory impairment in aMCI with Aβ burden negative (aMCI-Aβ-) and to compare these results with aMCI with Aβ burden positive (aMCI-Aβ+). Individuals with aMCI (n = 498) were selected from the Alzheimer's Disease Neuroimaging Initiative database. Based on the mean florbetapir standard uptake value ratio, participants were classified as aMCI-Aβ- or aMCI-Aβ+. Correlations between memory scores and regional cerebral glucose metabolism (rCMglc) were analyzed separately for the two subgroups using a multiple regression model. For aMCI-Aβ-, significant positive correlations between memory and rCMglc were found in the bilateral claustrum, right thalamus, left anterior cingulate cortex, left insula, and right posterior cingulate. For aMCI-Aβ+, significant positive correlations between memory and rCMglc were found in the temporoparietal areas. These correlation patterns remained unchanged when clinical severity was added as a covariate Our findings indicate that memory impairment in aMCI-Aβ- is related to multimodal integrative processing and the attentional control system, whereas memory impairment in aMCI-Aβ+ is related to the typical brain memory systems and AD signature. These results suggest that although the two subgroups are clinically in the same category as aMCI, the memory impairment process depends on completely different functional brain regions according to their Aβ burden level.

  20. Modelling of coil-loaded wire antenna using composite multiple domain basis functions

    CSIR Research Space (South Africa)

    Lysko, AA

    2010-03-01

    Full Text Available - tional Electromagnetics, Artech House, 2001. 3. Rogers, S. D. and C. M. Butler, \\An e–cient curved-wire integral equation solution technique," IEEE Trans. Ant. and Propag., 70{79, Vol. 49, Jan. 2001. 4. Mosig, J. and E. Suter, \\A multilevel divide.... 8. Wan, J. X., J. Lei, and C.-H. Liang, \\An e–cient analysis of large-scale periodic microstrip antenna arrays using the characteristic basis function method," Progress In Electromagnetics Research, PIER 50, 61{81, 2005. 9. Taguchi, M., K...

  1. Interface information transfer between non-matching, nonconforming interfaces using radial basis function interpolation

    CSIR Research Space (South Africa)

    Bogaers, Alfred EJ

    2016-10-01

    Full Text Available words, gB = [ φBA PB ] [ MAA PA P TA 0 ]−1 [ gA 0 ] . (15) NAME: DEFINITION C0 compactly supported piecewise polynomial (C0): (1− (||x|| /r))2+ C2 compactly supported piecewise polynomial (C2): (1− (||x|| /r))4+ (4 (||x|| /r) + 1) Thin-plate spline (TPS... a numerical comparison to Kriging and the moving least-squares method, see Krishnamurthy [16]). RBF interpolation is based on fitting a series of splines, or basis functions to interpolate information from one point cloud to another. Let us assume we...

  2. KARHUNEN-LOÈVE Basis Functions of Kolmogorov Turbulence in the Sphere

    Science.gov (United States)

    Mathar, Richard J.

    In support of modeling atmospheric turbulence, the statistically independent Karhunen-Loève modes of refractive indices with isotropic Kolmogorov spectrum of the covariance are calculated inside a sphere of fixed radius, rendered as series of 3D Zernike functions. Many of the symmetry arguments of the well-known associated 2D problem for the circular input pupil remain valid. The technique of efficient diagonalization of the eigenvalue problem in wavenumber space is founded on the Fourier representation of the 3D Zernike basis, and extensible to the von-Kármán power spectrum.

  3. Finite element transport using Wachspress rational basis functions on quadrilaterals in diffusive regions

    International Nuclear Information System (INIS)

    Davidson, G.; Palmer, T.S.

    2005-01-01

    In 1975, Wachspress developed basis functions that can be constructed upon very general zone shapes, including convex polygons and polyhedra, as well as certain zone shapes with curved sides and faces. Additionally, Adams has recently shown that weight functions with certain properties will produce solutions with full-resolution. Wachspress rational functions possess those necessary properties. Here we present methods to construct and integrate Wachspress rational functions on quadrilaterals. We also present an asymptotic analysis of a discontinuous finite element discretization on quadrilaterals, and we present 3 numerical results that confirm the predictions of our analysis. In the first test problem, we showed that Wachspress rational functions could give robust solutions for a strongly heterogeneous problem with both orthogonal and skewed meshes. This strongly heterogenous problem contained thick, diffusive regions, and the discretization provided full-resolution solutions. In the second test problem, we confirmed our asymptotic analysis by demonstrating that the transport solution will converge to the diffusion solution as the problem is made increasingly thick and diffusive. In the third test problem, we demonstrated that bilinear discontinuous based transport and Wachspress rational function based transport converge in the one-mesh limit

  4. Lyapunov Functions to Caputo Fractional Neural Networks with Time-Varying Delays

    Directory of Open Access Journals (Sweden)

    Ravi Agarwal

    2018-05-01

    Full Text Available One of the main properties of solutions of nonlinear Caputo fractional neural networks is stability and often the direct Lyapunov method is used to study stability properties (usually these Lyapunov functions do not depend on the time variable. In connection with the Lyapunov fractional method we present a brief overview of the most popular fractional order derivatives of Lyapunov functions among Caputo fractional delay differential equations. These derivatives are applied to various types of neural networks with variable coefficients and time-varying delays. We show that quadratic Lyapunov functions and their Caputo fractional derivatives are not applicable in some cases when one studies stability properties. Some sufficient conditions for stability of equilibrium of nonlinear Caputo fractional neural networks with time dependent transmission delays, time varying self-regulating parameters of all units and time varying functions of the connection between two neurons in the network are obtained. The cases of time varying Lipschitz coefficients as well as nonLipschitz activation functions are studied. We illustrate our theory on particular nonlinear Caputo fractional neural networks.

  5. GRACE L1b inversion through a self-consistent modified radial basis function approach

    Science.gov (United States)

    Yang, Fan; Kusche, Juergen; Rietbroek, Roelof; Eicker, Annette

    2016-04-01

    Implementing a regional geopotential representation such as mascons or, more general, RBFs (radial basis functions) has been widely accepted as an efficient and flexible approach to recover the gravity field from GRACE (Gravity Recovery and Climate Experiment), especially at higher latitude region like Greenland. This is since RBFs allow for regionally specific regularizations over areas which have sufficient and dense GRACE observations. Although existing RBF solutions show a better resolution than classical spherical harmonic solutions, the applied regularizations cause spatial leakage which should be carefully dealt with. It has been shown that leakage is a main error source which leads to an evident underestimation of yearly trend of ice-melting over Greenland. Unlike some popular post-processing techniques to mitigate leakage signals, this study, for the first time, attempts to reduce the leakage directly in the GRACE L1b inversion by constructing an innovative modified (MRBF) basis in place of the standard RBFs to retrieve a more realistic temporal gravity signal along the coastline. Our point of departure is that the surface mass loading associated with standard RBF is smooth but disregards physical consistency between continental mass and passive ocean response. In this contribution, based on earlier work by Clarke et al.(2007), a physically self-consistent MRBF representation is constructed from standard RBFs, with the help of the sea level equation: for a given standard RBF basis, the corresponding MRBF basis is first obtained by keeping the surface load over the continent unchanged, but imposing global mass conservation and equilibrium response of the oceans. Then, the updated set of MRBFs as well as standard RBFs are individually employed as the basis function to determine the temporal gravity field from GRACE L1b data. In this way, in the MRBF GRACE solution, the passive (e.g. ice melting and land hydrology response) sea level is automatically

  6. Artificial Neural Network Modeling of an Inverse Fluidized Bed ...

    African Journals Online (AJOL)

    A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of pollutants in the reactor. The neural network has been trained with experimental data ...

  7. Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast

    Directory of Open Access Journals (Sweden)

    Petr Maca

    2014-01-01

    Full Text Available The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.

  8. Machine learning (ML)-guided OPC using basis functions of polar Fourier transform

    Science.gov (United States)

    Choi, Suhyeong; Shim, Seongbo; Shin, Youngsoo

    2016-03-01

    With shrinking feature size, runtime has become a limitation of model-based OPC (MB-OPC). A few machine learning-guided OPC (ML-OPC) have been studied as candidates for next-generation OPC, but they all employ too many parameters (e.g. local densities), which set their own limitations. We propose to use basis functions of polar Fourier transform (PFT) as parameters of ML-OPC. Since PFT functions are orthogonal each other and well reflect light phenomena, the number of parameters can significantly be reduced without loss of OPC accuracy. Experiments demonstrate that our new ML-OPC achieves 80% reduction in OPC time and 35% reduction in the error of predicted mask bias when compared to conventional ML-OPC.

  9. Functional imaging in oncology. Biophysical basis and technical approaches. Vol. 1

    Energy Technology Data Exchange (ETDEWEB)

    Luna, Antonio [Health Time Group, Jaen (Spain); University Hospitals, Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Radiology; Vilanova, Joan C. [Clinica Girona - Hospital Sta. Caterina, Girona (Spain); Hygino da Cruz, L. Celso Jr. [CDPI and IRM, Rio de Janeiro, RJ (Brazil). Dept. of Radiology; Rossi, Santiago E. (ed.) [Centro de Diagnostico, Buenos Aires (Argentina)

    2014-07-01

    Easy-to-read manual on new functional imaging techniques in oncology. Explains current clinical applications and outlines future avenues. Includes numerous high-quality illustrations to highlight the major teaching points. In the new era of functional and molecular imaging, both currently available imaging biomarkers and biomarkers under development are expected to lead to major changes in the management of oncological patients. This well-illustrated two-volume book is a practical manual on the various imaging techniques capable of delivering functional information on cancer, including preclinical and clinical imaging techniques, based on US, CT, MRI, PET and hybrid modalities. This first volume explains the biophysical basis for these functional imaging techniques and describes the techniques themselves. Detailed information is provided on the imaging of cancer hallmarks, including angiogenesis, tumor metabolism, and hypoxia. The techniques and their roles are then discussed individually, covering the full range of modalities in clinical use as well as new molecular and functional techniques. The value of a multiparametric approach is also carefully considered.

  10. Cellular and molecular basis of chronic constipation: taking the functional/idiopathic label out.

    Science.gov (United States)

    Bassotti, Gabrio; Villanacci, Vincenzo; Creţoiu, Dragos; Creţoiu, Sanda Maria; Becheanu, Gabriel

    2013-07-14

    In recent years, the improvement of technology and the increase in knowledge have shifted several strongly held paradigms. This is particularly true in gastroenterology, and specifically in the field of the so-called "functional" or "idiopathic" disease, where conditions thought for decades to be based mainly on alterations of visceral perception or aberrant psychosomatic mechanisms have, in fact, be reconducted to an organic basis (or, at the very least, have shown one or more demonstrable abnormalities). This is particularly true, for instance, for irritable bowel syndrome, the prototype entity of "functional" gastrointestinal disorders, where low-grade inflammation of both mucosa and myenteric plexus has been repeatedly demonstrated. Thus, researchers have also investigated other functional/idiopathic gastrointestinal disorders, and found that some organic ground is present, such as abnormal neurotransmission and myenteric plexitis in esophageal achalasia and mucosal immune activation and mild eosinophilia in functional dyspepsia. Here we show evidence, based on our own and other authors' work, that chronic constipation has several abnormalities reconductable to alterations in the enteric nervous system, abnormalities mainly characterized by a constant decrease of enteric glial cells and interstitial cells of Cajal (and, sometimes, of enteric neurons). Thus, we feel that (at least some forms of) chronic constipation should no more be considered as a functional/idiopathic gastrointestinal disorder, but instead as a true enteric neuropathic abnormality.

  11. Functional imaging in oncology. Biophysical basis and technical approaches. Vol. 1

    International Nuclear Information System (INIS)

    Luna, Antonio; Hygino da Cruz, L. Celso Jr.

    2014-01-01

    Easy-to-read manual on new functional imaging techniques in oncology. Explains current clinical applications and outlines future avenues. Includes numerous high-quality illustrations to highlight the major teaching points. In the new era of functional and molecular imaging, both currently available imaging biomarkers and biomarkers under development are expected to lead to major changes in the management of oncological patients. This well-illustrated two-volume book is a practical manual on the various imaging techniques capable of delivering functional information on cancer, including preclinical and clinical imaging techniques, based on US, CT, MRI, PET and hybrid modalities. This first volume explains the biophysical basis for these functional imaging techniques and describes the techniques themselves. Detailed information is provided on the imaging of cancer hallmarks, including angiogenesis, tumor metabolism, and hypoxia. The techniques and their roles are then discussed individually, covering the full range of modalities in clinical use as well as new molecular and functional techniques. The value of a multiparametric approach is also carefully considered.

  12. Dissecting Repulsive Guidance Molecule/Neogenin function and signaling during neural development

    NARCIS (Netherlands)

    van den Heuvel, D.M.A.

    2013-01-01

    During neural development a series of precisely ordered cellular processes acts to establish a functional brain comprising millions of neurons and many more neuronal connections. Neogenin and its repulsive guidance molecule (RGM) ligands contribute to neuronal network formation by inducing axon

  13. Functional dissociations in top-down control dependent neural repetition priming.

    NARCIS (Netherlands)

    Klaver, P.; Schnaidt, M.; Fell, J.; Ruhlmann, J.; Elger, C.E.; Fernandez, G.S.E.

    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

  14. Theory of Mind and the Whole Brain Functional Connectivity: Behavioral and Neural Evidences with the Amsterdam Resting State Questionnaire.

    Science.gov (United States)

    Marchetti, Antonella; Baglio, Francesca; Costantini, Isa; Dipasquale, Ottavia; Savazzi, Federica; Nemni, Raffaello; Sangiuliano Intra, Francesca; Tagliabue, Semira; Valle, Annalisa; Massaro, Davide; Castelli, Ilaria

    2015-01-01

    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 et al. (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.

  15. Immunomodulation of enteric neural function in irritable bowel syndrome

    OpenAIRE

    O’Malley, Dervla

    2015-01-01

    Irritable bowel syndrome (IBS) is a common functional gastrointestinal disorder which is characterised by symptoms such as bloating, altered bowel habit and visceral pain. It’s generally accepted that miscommunication between the brain and gut underlies the changes in motility, absorpto-secretory function and pain sensitivity associated with IBS. However, partly due to the lack of disease-defining biomarkers, understanding the aetiology of this complex and multifactorial disease remains elusi...

  16. A Comparison of the Behavior of Functional/Basis Set Combinations for Hydrogen-Bonding in the Water Dimer with Emphasis on Basis Set Superposition Error

    OpenAIRE

    Plumley, Joshua A.; Dannenberg, J. J.

    2011-01-01

    We evaluate the performance of nine functionals (B3LYP, M05, M05-2X, M06, M06-2X, B2PLYP, B2PLYPD, X3LYP, B97D and MPWB1K) in combination with 16 basis sets ranging in complexity from 6-31G(d) to aug-cc-pV5Z for the calculation of the H-bonded water dimer with the goal of defining which combinations of functionals and basis sets provide a combination of economy and accuracy for H-bonded systems. We have compared the results to the best non-DFT molecular orbital calculations and to experimenta...

  17. The static response function in Kohn-Sham theory: An appropriate basis for its matrix representation in case of finite AO basis sets

    International Nuclear Information System (INIS)

    Kollmar, Christian; Neese, Frank

    2014-01-01

    The role of the static Kohn-Sham (KS) response function describing the response of the electron density to a change of the local KS potential is discussed in both the theory of the optimized effective potential (OEP) and the so-called inverse Kohn-Sham problem involving the task to find the local KS potential for a given electron density. In a general discussion of the integral equation to be solved in both cases, it is argued that a unique solution of this equation can be found even in case of finite atomic orbital basis sets. It is shown how a matrix representation of the response function can be obtained if the exchange-correlation potential is expanded in terms of a Schmidt-orthogonalized basis comprising orbitals products of occupied and virtual orbitals. The viability of this approach in both OEP theory and the inverse KS problem is illustrated by numerical examples

  18. Self-tuning control of a nuclear reactor using a Gaussian function neural network

    International Nuclear Information System (INIS)

    Park, M.G.; Cho, N.Z.

    1995-01-01

    A self-tuning control method is described for a nuclear reactor system that requires only a set of input-output measurements. The use of an artificial neural network in nonlinear model-based adaptive control, both as a plant model and a controller, is investigated. A neural network called a Gaussian function network is used for one-step-ahead predictive control to track the desired plant output. The effectiveness of the controller is demonstrated by the application of the method to the power tracking control of the Korea Multipurpose Research Reactor

  19. Functional Basis for Efficient Physical Layer Classical Control in Quantum Processors

    Science.gov (United States)

    Ball, Harrison; Nguyen, Trung; Leong, Philip H. W.; Biercuk, Michael J.

    2016-12-01

    The rapid progress seen in the development of quantum-coherent devices for information processing has motivated serious consideration of quantum computer architecture and organization. One topic which remains open for investigation and optimization relates to the design of the classical-quantum interface, where control operations on individual qubits are applied according to higher-level algorithms; accommodating competing demands on performance and scalability remains a major outstanding challenge. In this work, we present a resource-efficient, scalable framework for the implementation of embedded physical layer classical controllers for quantum-information systems. Design drivers and key functionalities are introduced, leading to the selection of Walsh functions as an effective functional basis for both programing and controller hardware implementation. This approach leverages the simplicity of real-time Walsh-function generation in classical digital hardware, and the fact that a wide variety of physical layer controls, such as dynamic error suppression, are known to fall within the Walsh family. We experimentally implement a real-time field-programmable-gate-array-based Walsh controller producing Walsh timing signals and Walsh-synthesized analog waveforms appropriate for critical tasks in error-resistant quantum control and noise characterization. These demonstrations represent the first step towards a unified framework for the realization of physical layer controls compatible with large-scale quantum-information processing.

  20. Peak alpha frequency is a neural marker of cognitive function across the autism spectrum.

    Science.gov (United States)

    Dickinson, Abigail; DiStefano, Charlotte; Senturk, Damla; Jeste, Shafali Spurling

    2018-03-01

    Cognitive function varies substantially and serves as a key predictor of outcome and response to intervention in autism spectrum disorder (ASD), yet we know little about the neurobiological mechanisms that underlie cognitive function in children with ASD. The dynamics of neuronal oscillations in the alpha range (6-12 Hz) are associated with cognition in typical development. Peak alpha frequency is also highly sensitive to developmental changes in neural networks, which underlie cognitive function, and therefore, it holds promise as a developmentally sensitive neural marker of cognitive function in ASD. Here, we measured peak alpha band frequency under a task-free condition in a heterogeneous sample of children with ASD (N = 59) and age-matched typically developing (TD) children (N = 38). At a group level, peak alpha frequency was decreased in ASD compared to TD children. Moreover, within the ASD group, peak alpha frequency correlated strongly with non-verbal cognition. As peak alpha frequency reflects the integrity of neural networks, our results suggest that deviations in network development may underlie cognitive function in individuals with ASD. By shedding light on the neurobiological correlates of cognitive function in ASD, our findings lay the groundwork for considering peak alpha frequency as a useful biomarker of cognitive function within this population which, in turn, will facilitate investigations of early markers of cognitive impairment and predictors of outcome in high risk infants. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  1. Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image

    Directory of Open Access Journals (Sweden)

    Qi Dawei

    2010-01-01

    Full Text Available A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods.

  2. The strength of a remorseful heart: psychological and neural basis of how apology emolliates reactive aggression and promotes forgiveness.

    Science.gov (United States)

    Beyens, Urielle; Yu, Hongbo; Han, Ting; Zhang, Li; Zhou, Xiaolin

    2015-01-01

    Apology from the offender facilitates forgiveness and thus has the power to restore a broken relationship. Here we showed that apology from the offender not only reduces the victim's propensity to react aggressively but also alters the victim's implicit attitude and neural responses toward the offender. We adopted an interpersonal competitive game which consisted of two phases. In the first, "passive" phase, participants were punished by high or low pain stimulation chosen by the opponents when losing a trial. During the break, participants received a note from each of the opponents, one apologizing and the other not. The second, "active" phase, involved a change of roles where participants could punish the two opponents after winning. Experiment 1 included an Implicit Association Test (IAT) in between the reception of notes and the second phase. Experiment 2 recorded participants' brain potentials in the second phase. We found that participants reacted less aggressively toward the apologizing opponent than the non-apologizing opponent in the active phase. Moreover, female, but not male, participants responded faster in the IAT when positive and negative words were associated with the apologizing and the non-apologizing opponents, respectively, suggesting that female participants had enhanced implicit attitude toward the apologizing opponent. Furthermore, the late positive potential (LPP), a component in brain potentials associated with affective/motivational reactions, was larger when viewing the portrait of the apologizing than the non-apologizing opponent when participants subsequently selected low punishment. Additionally, the LPP elicited by the apologizing opponents' portrait was larger in the female than in the male participants. These findings confirm the apology's role in reducing reactive aggression and further reveal that this forgiveness process engages, at least in female, an enhancement of the victim's implicit attitude and a prosocial motivational

  3. Projections from the posterolateral olfactory amygdala to the ventral striatum: neural basis for reinforcing properties of chemical stimuli

    Directory of Open Access Journals (Sweden)

    Lanuza Enrique

    2007-11-01

    Full Text Available Abstract Background Vertebrates sense chemical stimuli through the olfactory receptor neurons whose axons project to the main olfactory bulb. The main projections of the olfactory bulb are directed to the olfactory cortex and olfactory amygdala (the anterior and posterolateral cortical amygdalae. The posterolateral cortical amygdaloid nucleus mainly projects to other amygdaloid nuclei; other seemingly minor outputs are directed to the ventral striatum, in particular to the olfactory tubercle and the islands of Calleja. Results Although the olfactory projections have been previously described in the literature, injection of dextran-amines into the rat main olfactory bulb was performed with the aim of delimiting the olfactory tubercle and posterolateral cortical amygdaloid nucleus in our own material. Injection of dextran-amines into the posterolateral cortical amygdaloid nucleus of rats resulted in anterograde labeling in the ventral striatum, in particular in the core of the nucleus accumbens, and in the medial olfactory tubercle including some islands of Calleja and the cell bridges across the ventral pallidum. Injections of Fluoro-Gold into the ventral striatum were performed to allow retrograde confirmation of these projections. Conclusion The present results extend previous descriptions of the posterolateral cortical amygdaloid nucleus efferent projections, which are mainly directed to the core of the nucleus accumbens and the medial olfactory tubercle. Our data indicate that the projection to the core of the nucleus accumbens arises from layer III; the projection to the olfactory tubercle arises from layer II and is much more robust than previously thought. This latter projection is directed to the medial olfactory tubercle including the corresponding islands of Calleja, an area recently described as critical node for the neural circuit of addiction to some stimulant drugs of abuse.

  4. Neural basis of phonological awareness in beginning readers with familial risk of dyslexia-Results from shallow orthography.

    Science.gov (United States)

    Dębska, Agnieszka; Łuniewska, Magdalena; Chyl, Katarzyna; Banaszkiewicz, Anna; Żelechowska, Agata; Wypych, Marek; Marchewka, Artur; Pugh, Kenneth R; Jednoróg, Katarzyna

    2016-05-15

    Phonological processing ability is a key factor in reading acquisition, predicting its later success or causing reading problems when it is weakened. Our aim here was to establish the neural correlates of auditory word rhyming (a standard phonological measure) in 102 young children with (FHD+) and without familial history of dyslexia (FHD-) in a shallow orthography (i.e. Polish). Secondly, in order to gain a deeper understanding on how schooling shapes brain activity to phonological awareness, a comparison was made of children who had had formal literacy instruction for several months (in first grade) and those who had not yet had any formal instruction in literacy (in kindergarten). FHD+ children compared to FHD- children in the first grade scored lower in an early print task and showed longer reaction times in the in-scanner rhyme task. No behavioral differences between FHD+ and FHD- were found in the kindergarten group. On the neuronal level, overall familial risk was associated with reduced activation in the bilateral temporal, tempo-parietal and inferior temporal-occipital regions, as well as the bilateral inferior and middle frontal gyri. Subcortically, hypoactivation was found in the bilateral thalami, caudate, and right putamen in FHD+. A main effect of the children's grade was present only in the left inferior frontal gyrus, where reduced activation for rhyming was shown in first-graders. Several regions in the ventral occipital cortex, including the fusiform gyrus, and in the right middle frontal and postcentral gyri, displayed an interaction between familial risk and grade. The present results show strong influence of familial risk that may actually increase with formal literacy instruction. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Optimization of the kernel functions in a probabilistic neural network analyzing the local pattern distribution.

    Science.gov (United States)

    Galleske, I; Castellanos, J

    2002-05-01

    This article proposes a procedure for the automatic determination of the elements of the covariance matrix of the gaussian kernel function of probabilistic neural networks. Two matrices, a rotation matrix and a matrix of variances, can be calculated by analyzing the local environment of each training pattern. The combination of them will form the covariance matrix of each training pattern. This automation has two advantages: First, it will free the neural network designer from indicating the complete covariance matrix, and second, it will result in a network with better generalization ability than the original model. A variation of the famous two-spiral problem and real-world examples from the UCI Machine Learning Repository will show a classification rate not only better than the original probabilistic neural network but also that this model can outperform other well-known classification techniques.

  6. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Institut Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D J; Pao, Yohhan [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).

  7. Nonlinear transfer function encodes synchronization in a neural network from the mammalian brain.

    Science.gov (United States)

    Menendez de la Prida, L; Sanchez-Andres, J V

    1999-09-01

    Synchronization is one of the mechanisms by which the brain encodes information. The observed synchronization of neuronal activity has, however, several levels of fluctuations, which presumably regulate local features of specific areas. This means that biological neural networks should have an intrinsic mechanism able to synchronize the neuronal activity but also to preserve the firing capability of individual cells. Here, we investigate the input-output relationship of a biological neural network from developing mammalian brain, i.e., the hippocampus. We show that the probability of occurrence of synchronous output activity (which consists in stereotyped population bursts recorded throughout the hippocampus) is encoded by a sigmoidal transfer function of the input frequency. Under this scope, low-frequency inputs will not produce any coherent output while high-frequency inputs will determine a synchronous pattern of output activity (population bursts). We analyze the effect of the network size (N) on the parameters of the transfer function (threshold and slope). We found that sigmoidal functions realistically simulate the synchronous output activity of hippocampal neural networks. This outcome is particularly important in the application of results from neural network models to neurobiology.

  8. Functional Stem Cell Integration into Neural Networks Assessed by Organotypic Slice Cultures.

    Science.gov (United States)

    Forsberg, David; Thonabulsombat, Charoensri; Jäderstad, Johan; Jäderstad, Linda Maria; Olivius, Petri; Herlenius, Eric

    2017-08-14

    Re-formation or preservation of functional, electrically active neural networks has been proffered as one of the goals of stem cell-mediated neural therapeutics. A primary issue for a cell therapy approach is the formation of functional contacts between the implanted cells and the host tissue. Therefore, it is of fundamental interest to establish protocols that allow us to delineate a detailed time course of grafted stem cell survival, migration, differentiation, integration, and functional interaction with the host. One option for in vitro studies is to examine the integration of exogenous stem cells into an existing active neural network in ex vivo organotypic cultures. Organotypic cultures leave the structural integrity essentially intact while still allowing the microenvironment to be carefully controlled. This allows detailed studies over time of cellular responses and cell-cell interactions, which are not readily performed in vivo. This unit describes procedures for using organotypic slice cultures as ex vivo model systems for studying neural stem cell and embryonic stem cell engraftment and communication with CNS host tissue. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

  9. Characterizing the Spatial Density Functions of Neural Arbors

    Science.gov (United States)

    Teeter, Corinne Michelle

    Recently, it has been proposed that a universal function describes the way in which all arbors (axons and dendrites) spread their branches over space. Data from fish retinal ganglion cells as well as cortical and hippocampal arbors from mouse, rat, cat, monkey and human provide evidence that all arbor density functions (adf) can be described by a Gaussian function truncated at approximately two standard deviations. A Gaussian density function implies that there is a minimal set of parameters needed to describe an adf: two or three standard deviations (depending on the dimensionality of the arbor) and an amplitude. However, the parameters needed to completely describe an adf could be further constrained by a scaling law found between the product of the standard deviations and the amplitude of the function. In the following document, I examine the scaling law relationship in order to determine the minimal set of parameters needed to describe an adf. First, I find that the at, two-dimensional arbors of fish retinal ganglion cells require only two out of the three fundamental parameters to completely describe their density functions. Second, the three-dimensional, volume filling, cortical arbors require four fundamental parameters: three standard deviations and the total length of an arbor (which corresponds to the amplitude of the function). Next, I characterize the shape of arbors in the context of the fundamental parameters. I show that the parameter distributions of the fish retinal ganglion cells are largely homogenous. In general, axons are bigger and less dense than dendrites; however, they are similarly shaped. The parameter distributions of these two arbor types overlap and, therefore, can only be differentiated from one another probabilistically based on their adfs. Despite artifacts in the cortical arbor data, different types of arbors (apical dendrites, non-apical dendrites, and axons) can generally be differentiated based on their adfs. In addition, within

  10. Altered Neural Activity Associated with Mindfulness during Nociception: A Systematic Review of Functional MRI

    Directory of Open Access Journals (Sweden)

    Elena Bilevicius

    2016-04-01

    Full Text Available Objective: To assess the neural activity associated with mindfulness-based alterations of pain perception. Methods: The Cochrane Central, EMBASE, Ovid Medline, PsycINFO, Scopus, and Web of Science databases were searched on 2 February 2016. Titles, abstracts, and full-text articles were independently screened by two reviewers. Data were independently extracted from records that included topics of functional neuroimaging, pain, and mindfulness interventions. Results: The literature search produced 946 total records, of which five met the inclusion criteria. Records reported pain in terms of anticipation (n = 2, unpleasantness (n = 5, and intensity (n = 5, and how mindfulness conditions altered the neural activity during noxious stimulation accordingly. Conclusions: Although the studies were inconsistent in relating pain components to neural activity, in general, mindfulness was able to reduce pain anticipation and unpleasantness ratings, as well as alter the corresponding neural activity. The major neural underpinnings of mindfulness-based pain reduction consisted of altered activity in the anterior cingulate cortex, insula, and dorsolateral prefrontal cortex.

  11. Functional neural substrates of posterior cortical atrophy patients.

    Science.gov (United States)

    Shames, H; Raz, N; Levin, Netta

    2015-07-01

    Posterior cortical atrophy (PCA) is a neurodegenerative syndrome in which the most pronounced pathologic involvement is in the occipito-parietal visual regions. Herein, we aimed to better define the cortical reflection of this unique syndrome using a thorough battery of behavioral and functional MRI (fMRI) tests. Eight PCA patients underwent extensive testing to map their visual deficits. Assessments included visual functions associated with lower and higher components of the cortical hierarchy, as well as dorsal- and ventral-related cortical functions. fMRI was performed on five patients to examine the neuronal substrate of their visual functions. The PCA patient cohort exhibited stereopsis, saccadic eye movements and higher dorsal stream-related functional impairments, including simultant perception, image orientation, figure-from-ground segregation, closure and spatial orientation. In accordance with the behavioral findings, fMRI revealed intact activation in the ventral visual regions of face and object perception while more dorsal aspects of perception, including motion and gestalt perception, revealed impaired patterns of activity. In most of the patients, there was a lack of activity in the word form area, which is known to be linked to reading disorders. Finally, there was evidence of reduced cortical representation of the peripheral visual field, corresponding to the behaviorally assessed peripheral visual deficit. The findings are discussed in the context of networks extending from parietal regions, which mediate navigationally related processing, visually guided actions, eye movement control and working memory, suggesting that damage to these networks might explain the wide range of deficits in PCA patients.

  12. Reactivating Neural Circuits with Clinically Accessible Stimulation to Restore Hand Function in Persons with Tetraplegia

    Science.gov (United States)

    2017-09-01

    AWARD NUMBER: W81XWH-16-1-0395 TITLE: Reactivating Neural Circuits with Clinically Accessible Stimulation to Restore Hand Function in...estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data...Clinically Accessible Stimulation to Restore Hand Function in Persons with Tetraplegia 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S

  13. Differential effects of erythropoietin on neural and cognitive measures of executive function 3 and 7 days post-administration

    DEFF Research Database (Denmark)

    Miskowiak, Kamilla; Inkster, Becky; O'Sullivan, Ursula

    2008-01-01

    Erythropoietin (Epo) has neuroprotective and neurotrophic effects and improves cognitive function in animal models of neurodegenerative and neuropsychiatric illness. In humans, weekly Epo administration over 3 months improves cognitive function in schizophrenia. The neural underpinnings and time...

  14. The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication.

    Science.gov (United States)

    Symons, Ashley E; El-Deredy, Wael; Schwartze, Michael; Kotz, Sonja A

    2016-01-01

    Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples

  15. Functional neural correlates of reduced physiological falls risk

    Directory of Open Access Journals (Sweden)

    Hsu Chun

    2011-08-01

    Full Text Available Abstract Background It is currently unclear whether the function of brain regions associated with executive cognitive processing are independently associated with reduced physiological falls risk. If these are related, it would suggest that the development of interventions targeted at improving executive neurocognitive function would be an effective new approach for reducing physiological falls risk in seniors. Methods We performed a secondary analysis of 73 community-dwelling senior women aged 65 to 75 years old who participated in a 12-month randomized controlled trial of resistance training. Functional MRI data were acquired while participants performed a modified Eriksen Flanker Task - a task of selective attention and conflict resolution. Brain volumes were obtained using MRI. Falls risk was assessed using the Physiological Profile Assessment (PPA. Results After accounting for baseline age, experimental group, baseline PPA score, and total baseline white matter brain volume, baseline activation in the left frontal orbital cortex extending towards the insula was negatively associated with reduced physiological falls risk over the 12-month period. In contrast, baseline activation in the paracingulate gyrus extending towards the anterior cingulate gyrus was positively associated with reduced physiological falls risk. Conclusions Baseline activation levels of brain regions underlying response inhibition and selective attention were independently associated with reduced physiological falls risk. This suggests that falls prevention strategies may be facilitated by incorporating intervention components - such as aerobic exercise - that are specifically designed to induce neurocognitive plasticity. Trial Registration ClinicalTrials.gov Identifier: NCT00426881

  16. Calculating vibrational spectra with sum of product basis functions without storing full-dimensional vectors or matrices.

    Science.gov (United States)

    Leclerc, Arnaud; Carrington, Tucker

    2014-05-07

    We propose an iterative method for computing vibrational spectra that significantly reduces the memory cost of calculations. It uses a direct product primitive basis, but does not require storing vectors with as many components as there are product basis functions. Wavefunctions are represented in a basis each of whose functions is a sum of products (SOP) and the factorizable structure of the Hamiltonian is exploited. If the factors of the SOP basis functions are properly chosen, wavefunctions are linear combinations of a small number of SOP basis functions. The SOP basis functions are generated using a shifted block power method. The factors are refined with a rank reduction algorithm to cap the number of terms in a SOP basis function. The ideas are tested on a 20-D model Hamiltonian and a realistic CH3CN (12 dimensional) potential. For the 20-D problem, to use a standard direct product iterative approach one would need to store vectors with about 10(20) components and would hence require about 8 × 10(11) GB. With the approach of this paper only 1 GB of memory is necessary. Results for CH3CN agree well with those of a previous calculation on the same potential.

  17. Radial basis functions in mathematical modelling of flow boiling in minichannels

    Directory of Open Access Journals (Sweden)

    Hożejowska Sylwia

    2017-01-01

    Full Text Available The paper addresses heat transfer processes in flow boiling in a vertical minichannel of 1.7 mm depth with a smooth heated surface contacting fluid. The heated element for FC-72 flowing in a minichannel was a 0.45 mm thick plate made of Haynes-230 alloy. An infrared camera positioned opposite the central, axially symmetric part of the channel measured the plate temperature. K-type thermocouples and pressure converters were installed at the inlet and outlet of the minichannel. In the study radial basis functions were used to solve a problem concerning heat transfer in a heated plate supplied with the controlled direct current. According to the model assumptions, the problem is treated as twodimensional and governed by the Poisson equation. The aim of the study lies in determining the temperature field and the heat transfer coefficient. The results were verified by comparing them with those obtained by the Trefftz method.

  18. Sample Data Synchronization and Harmonic Analysis Algorithm Based on Radial Basis Function Interpolation

    Directory of Open Access Journals (Sweden)

    Huaiqing Zhang

    2014-01-01

    Full Text Available The spectral leakage has a harmful effect on the accuracy of harmonic analysis for asynchronous sampling. This paper proposed a time quasi-synchronous sampling algorithm which is based on radial basis function (RBF interpolation. Firstly, a fundamental period is evaluated by a zero-crossing technique with fourth-order Newton’s interpolation, and then, the sampling sequence is reproduced by the RBF interpolation. Finally, the harmonic parameters can be calculated by FFT on the synchronization of sampling data. Simulation results showed that the proposed algorithm has high accuracy in measuring distorted and noisy signals. Compared to the local approximation schemes as linear, quadric, and fourth-order Newton interpolations, the RBF is a global approximation method which can acquire more accurate results while the time-consuming is about the same as Newton’s.

  19. Matrix elements of N-particle explicitly correlated Gaussian basis functions with complex exponential parameters.

    Science.gov (United States)

    Bubin, Sergiy; Adamowicz, Ludwik

    2006-06-14

    In this work we present analytical expressions for Hamiltonian matrix elements with spherically symmetric, explicitly correlated Gaussian basis functions with complex exponential parameters for an arbitrary number of particles. The expressions are derived using the formalism of matrix differential calculus. In addition, we present expressions for the energy gradient that includes derivatives of the Hamiltonian integrals with respect to the exponential parameters. The gradient is used in the variational optimization of the parameters. All the expressions are presented in the matrix form suitable for both numerical implementation and theoretical analysis. The energy and gradient formulas have been programmed and used to calculate ground and excited states of the He atom using an approach that does not involve the Born-Oppenheimer approximation.

  20. Matrix elements of N-particle explicitly correlated Gaussian basis functions with complex exponential parameters

    Science.gov (United States)

    Bubin, Sergiy; Adamowicz, Ludwik

    2006-06-01

    In this work we present analytical expressions for Hamiltonian matrix elements with spherically symmetric, explicitly correlated Gaussian basis functions with complex exponential parameters for an arbitrary number of particles. The expressions are derived using the formalism of matrix differential calculus. In addition, we present expressions for the energy gradient that includes derivatives of the Hamiltonian integrals with respect to the exponential parameters. The gradient is used in the variational optimization of the parameters. All the expressions are presented in the matrix form suitable for both numerical implementation and theoretical analysis. The energy and gradient formulas have been programed and used to calculate ground and excited states of the He atom using an approach that does not involve the Born-Oppenheimer approximation.

  1. Big geo data surface approximation using radial basis functions: A comparative study

    Science.gov (United States)

    Majdisova, Zuzana; Skala, Vaclav

    2017-12-01

    Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for big scattered datasets in n-dimensional space. It is a non-separable approximation, as it is based on the distance between two points. This method leads to the solution of an overdetermined linear system of equations. In this paper the RBF approximation methods are briefly described, a new approach to the RBF approximation of big datasets is presented, and a comparison for different Compactly Supported RBFs (CS-RBFs) is made with respect to the accuracy of the computation. The proposed approach uses symmetry of a matrix, partitioning the matrix into blocks and data structures for storage of the sparse matrix. The experiments are performed for synthetic and real datasets.

  2. Radial Basis Function (RBF Interpolation and Investigating its Impact on Rainfall Duration Mapping

    Directory of Open Access Journals (Sweden)

    Hassan Derakhshan

    2012-01-01

    Full Text Available The missing data in database must be reproduced primarily by appropriate interpolation techniques. Radial basis function (RBF interpolators can play a significant role in data completion of precipitation mapping. Five RBF techniques were engaged to be employed in compensating the missing data in event-wised dataset of Upper Paramatta River Catchment in the western suburbs of Sydney, Australia. The related shape parameter, C, of RBFs was optimized for first event of database during a cross-validation process. The Normalized mean square error (NMSE, percent average estimation error (PAEE and coefficient of determination (R2 were the statistics used as validation tools. Results showed that the multiquadric RBF technique with the least error, best suits compensation of the related database.

  3. THE ALGORITHM OF MESHFREE METHOD OF RADIAL BASIS FUNCTIONS IN TASKS OF UNDERGROUND HYDROMECHANICS

    Directory of Open Access Journals (Sweden)

    N. V. Medvid

    2016-01-01

    Full Text Available A Mathematical model of filtering consolidation in the body of soil dam with conduit andwashout zone in two-dimensional case is considered. The impact of such technogenic factors as temperature, salt concentration, subsidence of upper boundary and interior points of the dam with time is taken into account. The software to automate the calculation of numerical solution of the boundary problem by radial basis functions has been created, which enables to conduct numerical experiments by varying the input parameters and shape. The influence of the presence of conduit and washout zone on the pressure, temperature and concentration of salts in the dam body at different time intervals isinvestigated. A number of numerical experiments is conducted and the analysis of dam accidents is performed.

  4. Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

    Directory of Open Access Journals (Sweden)

    Sharma Rakesh

    2004-05-01

    Full Text Available Abstract Functional magnetic resonance imaging (fMRI is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities.

  5. Efficient O(N) integration for all-electron electronic structure calculation using numeric basis functions

    International Nuclear Information System (INIS)

    Havu, V.; Blum, V.; Havu, P.; Scheffler, M.

    2009-01-01

    We consider the problem of developing O(N) scaling grid-based operations needed in many central operations when performing electronic structure calculations with numeric atom-centered orbitals as basis functions. We outline the overall formulation of localized algorithms, and specifically the creation of localized grid batches. The choice of the grid partitioning scheme plays an important role in the performance and memory consumption of the grid-based operations. Three different top-down partitioning methods are investigated, and compared with formally more rigorous yet much more expensive bottom-up algorithms. We show that a conceptually simple top-down grid partitioning scheme achieves essentially the same efficiency as the more rigorous bottom-up approaches.

  6. Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter

    KAUST Repository

    Ryu, Duchwan

    2013-03-01

    The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.

  7. Improvement of the Model of Enterprise Management Process on the Basis of General Management Functions

    Directory of Open Access Journals (Sweden)

    Ruslan Skrynkovskyy

    2017-12-01

    Full Text Available The purpose of the article is to improve the model of the enterprise (institution, organization management process on the basis of general management functions. The graphic model of the process of management according to the process-structured management is presented. It has been established that in today's business environment, the model of the management process should include such general management functions as: 1 controlling the achievement of results; 2 planning based on the main goal; 3 coordination and corrective actions (in the system of organization of work and production; 4 action as a form of act (conscious, volitional, directed; 5 accounting system (accounting, statistical, operational-technical and managerial; 6 diagnosis (economic, legal with such subfunctions as: identification of the state and capabilities; analysis (economic, legal, systemic with argumentation; assessment of the state, trends and prospects of development. The prospect of further research in this direction is: 1 the formation of a system of interrelation of functions and management methods, taking into account the presented research results; 2 development of the model of effective and efficient communication business process of the enterprise.

  8. Partial information decomposition as a unified approach to the specification of neural goal functions.

    Science.gov (United States)

    Wibral, Michael; Priesemann, Viola; Kay, Jim W; Lizier, Joseph T; Phillips, William A

    2017-03-01

    In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g. sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a 'goal function', of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. 'edge filtering', 'working memory'). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon's mutual information), we argue that neural information processing crucially depends on the combination of multiple inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax and coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called 'coding with synergy', which builds on combining external input and prior knowledge in a synergistic manner. We suggest that

  9. Polarization functions for the modified m6-31G basis sets for atoms Ga through Kr.

    Science.gov (United States)

    Mitin, Alexander V

    2013-09-05

    The 2df polarization functions for the modified m6-31G basis sets of the third-row atoms Ga through Kr (Int J Quantum Chem, 2007, 107, 3028; Int J. Quantum Chem, 2009, 109, 1158) are proposed. The performances of the m6-31G, m6-31G(d,p), and m6-31G(2df,p) basis sets were examined in molecular calculations carried out by the density functional theory (DFT) method with B3LYP hybrid functional, Møller-Plesset perturbation theory of the second order (MP2), quadratic configuration interaction method with single and double substitutions and were compared with those for the known 6-31G basis sets as well as with the other similar 641 and 6-311G basis sets with and without polarization functions. Obtained results have shown that the performances of the m6-31G, m6-31G(d,p), and m6-31G(2df,p) basis sets are better in comparison with the performances of the known 6-31G, 6-31G(d,p) and 6-31G(2df,p) basis sets. These improvements are mainly reached due to better approximations of different electrons belonging to the different atomic shells in the modified basis sets. Applicability of the modified basis sets in thermochemical calculations is also discussed. © 2013 Wiley Periodicals, Inc.

  10. Consistent structures and interactions by density functional theory with small atomic orbital basis sets.

    Science.gov (United States)

    Grimme, Stefan; Brandenburg, Jan Gerit; Bannwarth, Christoph; Hansen, Andreas

    2015-08-07

    A density functional theory (DFT) based composite electronic structure approach is proposed to efficiently compute structures and interaction energies in large chemical systems. It is based on the well-known and numerically robust Perdew-Burke-Ernzerhoff (PBE) generalized-gradient-approximation in a modified global hybrid functional with a relatively large amount of non-local Fock-exchange. The orbitals are expanded in Ahlrichs-type valence-double zeta atomic orbital (AO) Gaussian basis sets, which are available for many elements. In order to correct for the basis set superposition error (BSSE) and to account for the important long-range London dispersion effects, our well-established atom-pairwise potentials are used. In the design of the new method, particular attention has been paid to an accurate description of structural parameters in various covalent and non-covalent bonding situations as well as in periodic systems. Together with the recently proposed three-fold corrected (3c) Hartree-Fock method, the new composite scheme (termed PBEh-3c) represents the next member in a hierarchy of "low-cost" electronic structure approaches. They are mainly free of BSSE and account for most interactions in a physically sound and asymptotically correct manner. PBEh-3c yields good results for thermochemical properties in the huge GMTKN30 energy database. Furthermore, the method shows excellent performance for non-covalent interaction energies in small and large complexes. For evaluating its performance on equilibrium structures, a new compilation of standard test sets is suggested. These consist of small (light) molecules, partially flexible, medium-sized organic molecules, molecules comprising heavy main group elements, larger systems with long bonds, 3d-transition metal systems, non-covalently bound complexes (S22 and S66×8 sets), and peptide conformations. For these sets, overall deviations from accurate reference data are smaller than for various other tested DFT methods

  11. Consistent structures and interactions by density functional theory with small atomic orbital basis sets

    International Nuclear Information System (INIS)

    Grimme, Stefan; Brandenburg, Jan Gerit; Bannwarth, Christoph; Hansen, Andreas

    2015-01-01

    A density functional theory (DFT) based composite electronic structure approach is proposed to efficiently compute structures and interaction energies in large chemical systems. It is based on the well-known and numerically robust Perdew-Burke-Ernzerhoff (PBE) generalized-gradient-approximation in a modified global hybrid functional with a relatively large amount of non-local Fock-exchange. The orbitals are expanded in Ahlrichs-type valence-double zeta atomic orbital (AO) Gaussian basis sets, which are available for many elements. In order to correct for the basis set superposition error (BSSE) and to account for the important long-range London dispersion effects, our well-established atom-pairwise potentials are used. In the design of the new method, particular attention has been paid to an accurate description of structural parameters in various covalent and non-covalent bonding situations as well as in periodic systems. Together with the recently proposed three-fold corrected (3c) Hartree-Fock method, the new composite scheme (termed PBEh-3c) represents the next member in a hierarchy of “low-cost” electronic structure approaches. They are mainly free of BSSE and account for most interactions in a physically sound and asymptotically correct manner. PBEh-3c yields good results for thermochemical properties in the huge GMTKN30 energy database. Furthermore, the method shows excellent performance for non-covalent interaction energies in small and large complexes. For evaluating its performance on equilibrium structures, a new compilation of standard test sets is suggested. These consist of small (light) molecules, partially flexible, medium-sized organic molecules, molecules comprising heavy main group elements, larger systems with long bonds, 3d-transition metal systems, non-covalently bound complexes (S22 and S66×8 sets), and peptide conformations. For these sets, overall deviations from accurate reference data are smaller than for various other tested DFT

  12. Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

    Science.gov (United States)

    Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

    2017-01-01

    Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

  13. Adaptive local basis set for Kohn–Sham density functional theory in a discontinuous Galerkin framework I: Total energy calculation

    International Nuclear Information System (INIS)

    Lin Lin; Lu Jianfeng; Ying Lexing; Weinan, E

    2012-01-01

    Kohn–Sham density functional theory is one of the most widely used electronic structure theories. In the pseudopotential framework, uniform discretization of the Kohn–Sham Hamiltonian generally results in a large number of basis functions per atom in order to resolve the rapid oscillations of the Kohn–Sham orbitals around the nuclei. Previous attempts to reduce the number of basis functions per atom include the usage of atomic orbitals and similar objects, but the atomic orbitals generally require fine tuning in order to reach high accuracy. We present a novel discretization scheme that adaptively and systematically builds the rapid oscillations of the Kohn–Sham orbitals around the nuclei as well as environmental effects into the basis functions. The resulting basis functions are localized in the real space, and are discontinuous in the global domain. The continuous Kohn–Sham orbitals and the electron density are evaluated from the discontinuous basis functions using the discontinuous Galerkin (DG) framework. Our method is implemented in parallel and the current implementation is able to handle systems with at least thousands of atoms. Numerical examples indicate that our method can reach very high accuracy (less than 1 meV) with a very small number (4–40) of basis functions per atom.

  14. Artificial neural networks contribution to the operational security of embedded systems. Artificial neural networks contribution to fault tolerance of on-board functions in space environment

    International Nuclear Information System (INIS)

    Vintenat, Lionel

    1999-01-01

    A good quality often attributed to artificial neural networks is fault tolerance. In general presentation works, this property is almost always introduced as 'natural', i.e. being obtained without any specific precaution during learning. Besides, space environment is known to be aggressive towards on-board hardware, inducing various abnormal operations. Particularly, digital components suffer from upset phenomenon, i.e. misplaced switches of memory flip-flops. These two observations lead to the question: would neural chips constitute an interesting and robust solution to implement some board functions of spacecrafts? First, the various aspects of the problem are detailed: artificial neural networks and their fault tolerance, neural chips, space environment and resulting failures. Further to this presentation, a particular technique to carry out neural chips is selected because of its simplicity, and especially because it requires few memory flip-flops: random pulse streams. An original method for star recognition inside a field-of-view is then proposed for the board function 'attitude computation'. This method relies on a winner-takes-all competition network, and on a Kohonen self-organized map. An hardware implementation of those two neural models is then proposed using random pulse streams. Thanks to this realization, on one hand difficulties related to that particular implementation technique can be highlighted, and on the other hand a first evaluation of its practical fault tolerance can be carried out. (author) [fr

  15. On grouping individual wire segments into equivalent wires or chains, and introduction of multiple domain basis functions

    CSIR Research Space (South Africa)

    Lysko, AA

    2009-06-01

    Full Text Available The paper introduces a method to cover several wire segments with a single basis function, describes related practical algorithms, and gives some results. The process involves three steps: identifying chains of wire segments, splitting the chains...

  16. Decision-making conflict and the neural efficiency hypothesis of intelligence: a functional near-infrared spectroscopy investigation.

    Science.gov (United States)

    Di Domenico, Stefano I; Rodrigo, Achala H; Ayaz, Hasan; Fournier, Marc A; Ruocco, Anthony C

    2015-04-01

    Research on the neural efficiency hypothesis of intelligence (NEH) has revealed that the brains of more intelligent individuals consume less energy when performing easy cognitive tasks but more energy when engaged in difficult mental operations. However, previous studies testing the NEH have relied on cognitive tasks that closely resemble psychometric tests of intelligence, potentially confounding efficiency during intelligence-test performance with neural efficiency per se. The present study sought to provide a novel test of the NEH by examining patterns of prefrontal activity while participants completed an experimental paradigm that is qualitatively distinct from the contents of psychometric tests of intelligence. Specifically, participants completed a personal decision-making task (e.g., which occupation would you prefer, dancer or chemist?) in which they made a series of forced choices according to their subjective preferences. The degree of decisional conflict (i.e., choice difficulty) between the available response options was manipulated on the basis of participants' unique preference ratings for the target stimuli, which were obtained prior to scanning. Evoked oxygenation of the prefrontal cortex was measured using 16-channel continuous-wave functional near-infrared spectroscopy. Consistent with the NEH, intelligence predicted decreased activation of the right inferior frontal gyrus (IFG) during low-conflict situations and increased activation of the right-IFG during high-conflict situations. This pattern of right-IFG activity among more intelligent individuals was complemented by faster reaction times in high-conflict situations. These results provide new support for the NEH and suggest that the neural efficiency of more intelligent individuals generalizes to the performance of cognitive tasks that are distinct from intelligence tests. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. A new diffusion nodal method based on analytic basis function expansion

    International Nuclear Information System (INIS)

    Noh, J.M.; Cho, N.Z.

    1993-01-01

    The transverse integration procedure commonly used in most advanced nodal methods results in some limitations. The first is that the transverse leakage term that appears in the transverse integration procedure must be appropriately approximated. In most advanced nodal methods, this term is expanded in a quadratic polynomial. The second arises when reconstructing the pinwise flux distribution within a node. The available one-dimensional flux shapes from nodal calculation in each spatial direction cannot be used directly in the flux reconstruction. Finally, the transverse leakage defined for a hexagonal node becomes so complicated as not to be easily handled and contains nonphysical singular terms. In this paper, a new nodal method called the analytic function expansion nodal (AFEN) method is described for both the rectangular geometry and the hexagonal geometry in order to overcome these limitations. This method does not solve the transverse-integrated one-dimensional diffusion equations but instead solves directly the original multidimensional diffusion equation within a node. This is a accomplished by expanding the solution (or the intranodal homogeneous flux distribution) in terms of nonseparable analytic basis functions satisfying the diffusion equation at any point in the node

  18. Automatic Curve Fitting Based on Radial Basis Functions and a Hierarchical Genetic Algorithm