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Sample records for multiple neural functions

  1. Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks

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    Thierry Moudiki

    2018-03-01

    Full Text Available We are interested in obtaining forecasts for multiple time series, by taking into account the potential nonlinear relationships between their observations. For this purpose, we use a specific type of regression model on an augmented dataset of lagged time series. Our model is inspired by dynamic regression models (Pankratz 2012, with the response variable’s lags included as predictors, and is known as Random Vector Functional Link (RVFL neural networks. The RVFL neural networks have been successfully applied in the past, to solving regression and classification problems. The novelty of our approach is to apply an RVFL model to multivariate time series, under two separate regularization constraints on the regression parameters.

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

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    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.

  3. Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws.

    Science.gov (United States)

    Hsu, Ching-Chi; Lin, Jinn; Chao, Ching-Kong

    2011-12-01

    Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  4. Effects of Time-Compressed Speech Training on Multiple Functional and Structural Neural Mechanisms Involving the Left Superior Temporal Gyrus.

    Science.gov (United States)

    Maruyama, Tsukasa; Takeuchi, Hikaru; Taki, Yasuyuki; Motoki, Kosuke; Jeong, Hyeonjeong; Kotozaki, Yuka; Nakagawa, Seishu; Nouchi, Rui; Iizuka, Kunio; Yokoyama, Ryoichi; Yamamoto, Yuki; Hanawa, Sugiko; Araki, Tsuyoshi; Sakaki, Kohei; Sasaki, Yukako; Magistro, Daniele; Kawashima, Ryuta

    2018-01-01

    Time-compressed speech is an artificial form of rapidly presented speech. Training with time-compressed speech (TCSSL) in a second language leads to adaptation toward TCSSL. Here, we newly investigated the effects of 4 weeks of training with TCSSL on diverse cognitive functions and neural systems using the fractional amplitude of spontaneous low-frequency fluctuations (fALFF), resting-state functional connectivity (RSFC) with the left superior temporal gyrus (STG), fractional anisotropy (FA), and regional gray matter volume (rGMV) of young adults by magnetic resonance imaging. There were no significant differences in change of performance of measures of cognitive functions or second language skills after training with TCSSL compared with that of the active control group. However, compared with the active control group, training with TCSSL was associated with increased fALFF, RSFC, and FA and decreased rGMV involving areas in the left STG. These results lacked evidence of a far transfer effect of time-compressed speech training on a wide range of cognitive functions and second language skills in young adults. However, these results demonstrated effects of time-compressed speech training on gray and white matter structures as well as on resting-state intrinsic activity and connectivity involving the left STG, which plays a key role in listening comprehension.

  5. 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.

  6. Spiking neural P systems with multiple channels.

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    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Podocalyxin Is a Novel Polysialylated Neural Adhesion Protein with Multiple Roles in Neural Development and Synapse Formation

    Science.gov (United States)

    Vitureira, Nathalia; Andrés, Rosa; Pérez-Martínez, Esther; Martínez, Albert; Bribián, Ana; Blasi, Juan; Chelliah, Shierley; López-Doménech, Guillermo; De Castro, Fernando; Burgaya, Ferran; McNagny, Kelly; Soriano, Eduardo

    2010-01-01

    Neural development and plasticity are regulated by neural adhesion proteins, including the polysialylated form of NCAM (PSA-NCAM). Podocalyxin (PC) is a renal PSA-containing protein that has been reported to function as an anti-adhesin in kidney podocytes. Here we show that PC is widely expressed in neurons during neural development. Neural PC interacts with the ERM protein family, and with NHERF1/2 and RhoA/G. Experiments in vitro and phenotypic analyses of podxl-deficient mice indicate that PC is involved in neurite growth, branching and axonal fasciculation, and that PC loss-of-function reduces the number of synapses in the CNS and in the neuromuscular system. We also show that whereas some of the brain PC functions require PSA, others depend on PC per se. Our results show that PC, the second highly sialylated neural adhesion protein, plays multiple roles in neural development. PMID:20706633

  8. Neural Computations in a Dynamical System with Multiple Time Scales.

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

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

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    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.

  10. Neural Computations in a Dynamical System with Multiple Time Scales

    Directory of Open Access Journals (Sweden)

    Yuanyuan Mi

    2016-09-01

    Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

  11. Efficient Cancer Detection Using Multiple Neural Networks.

    Science.gov (United States)

    Shell, John; Gregory, William D

    2017-01-01

    The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

  12. Global exponential stability for reaction-diffusion recurrent neural networks with multiple time varying delays

    International Nuclear Information System (INIS)

    Lou, X.; Cui, B.

    2008-01-01

    In this paper we consider the problem of exponential stability for recurrent neural networks with multiple time varying delays and reaction-diffusion terms. The activation functions are supposed to be bounded and globally Lipschitz continuous. By means of Lyapunov functional, sufficient conditions are derived, which guarantee global exponential stability of the delayed neural network. Finally, a numerical example is given to show the correctness of our analysis. (author)

  13. Robustness analysis of uncertain dynamical neural networks with multiple time delays.

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    Senan, Sibel

    2015-10-01

    This paper studies the problem of global robust asymptotic stability of the equilibrium point for the class of dynamical neural networks with multiple time delays with respect to the class of slope-bounded activation functions and in the presence of the uncertainties of system parameters of the considered neural network model. By using an appropriate Lyapunov functional and exploiting the properties of the homeomorphism mapping theorem, we derive a new sufficient condition for the existence, uniqueness and global robust asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays. The obtained stability condition basically relies on testing some relationships imposed on the interconnection matrices of the neural system, which can be easily verified by using some certain properties of matrices. An instructive numerical example is also given to illustrate the applicability of our result and show the advantages of this new condition over the previously reported corresponding results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Neural modeling of prefrontal executive function

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    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.

  15. 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 )

  16. Exploiting deep neural networks and head movements for binaural localisation of multiple speakers in reverberant conditions

    DEFF Research Database (Denmark)

    Ma, Ning; Brown, Guy J.; May, Tobias

    2015-01-01

    This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for binaural localisation of multiple speakers in reverberant conditions. DNNs are used to map binaural features, consisting of the complete crosscorrelation function (CCF) and interaural...

  17. 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.

  18. 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

  19. Matrix regulators in neural stem cell functions.

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    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.

  20. Gender-related differences in functional connectivity in multiple sclerosis

    NARCIS (Netherlands)

    Schoonheim, M.M.; Hulst, H.E.; Landi, D.; Ciccarelli, O.; Roosendaal, S.D.; Sanz-Arigita, E.J.; Vrenken, H.; Polman, C.H.; Stam, C.J.; Barkhof, F.; Geurts, J.J.G.

    2012-01-01

    Background: Gender effects are strong in multiple sclerosis (MS), with male patients showing a worse clinical outcome than female patients. Functional reorganization of neural activity may contribute to limit disability, and possible gender differences in this process may have important clinical

  1. 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.

  2. New results for global robust stability of bidirectional associative memory neural networks with multiple time delays

    International Nuclear Information System (INIS)

    Senan, Sibel; Arik, Sabri

    2009-01-01

    This paper presents some new sufficient conditions for the global robust asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with multiple time delays. The results we obtain impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. We also give some numerical examples to demonstrate the applicability and effectiveness of our results, and compare the results with the previous robust stability results derived in the literature.

  3. Emotion and decision making: multiple modulatory neural circuits.

    Science.gov (United States)

    Phelps, Elizabeth A; Lempert, Karolina M; Sokol-Hessner, Peter

    2014-01-01

    Although the prevalent view of emotion and decision making is derived from the notion that there are dual systems of emotion and reason, a modulatory relationship more accurately reflects the current research in affective neuroscience and neuroeconomics. Studies show two potential mechanisms for affect's modulation of the computation of subjective value and decisions. Incidental affective states may carry over to the assessment of subjective value and the decision, and emotional reactions to the choice may be incorporated into the value calculation. In addition, this modulatory relationship is reciprocal: Changing emotion can change choices. This research suggests that the neural mechanisms mediating the relation between affect and choice vary depending on which affective component is engaged and which decision variables are assessed. We suggest that a detailed and nuanced understanding of emotion and decision making requires characterizing the multiple modulatory neural circuits underlying the different means by which emotion and affect can influence choices.

  4. 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

  5. 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.

  6. Time functions function best as functions of multiple times

    NARCIS (Netherlands)

    Desain, P.; Honing, H.

    1992-01-01

    This article presents an elegant way of representing control functions at an abstractlevel. It introduces time functions that have multiple times as arguments. In this waythe generalized concept of a time function can support absolute and relative kinds of time behavior. Furthermore the

  7. Functional Multiple-Set Canonical Correlation Analysis

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    Hwang, Heungsun; Jung, Kwanghee; Takane, Yoshio; Woodward, Todd S.

    2012-01-01

    We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the…

  8. 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

  9. LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

    Science.gov (United States)

    Smith, James A.

    1992-01-01

    The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI 1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

  10. AIR POLLUITON INDEX PREDICTION USING MULTIPLE NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Zainal Ahmad

    2017-05-01

    Full Text Available Air quality monitoring and forecasting tools are necessary for the purpose of taking precautionary measures against air pollution, such as reducing the effect of a predicted air pollution peak on the surrounding population and ecosystem. In this study a single Feed-forward Artificial Neural Network (FANN is shown to be able to predict the Air Pollution Index (API with a Mean Squared Error (MSE and coefficient determination, R2, of 0.1856 and 0.7950 respectively. However, due to the non-robust nature of single FANN, a selective combination of Multiple Neural Networks (MNN is introduced using backward elimination and a forward selection method. The results show that both selective combination methods can improve the robustness and performance of the API prediction with the MSE and R2 of 0.1614 and 0.8210 respectively. This clearly shows that it is possible to reduce the number of networks combined in MNN for API prediction, without losses of any information in terms of the performance of the final API prediction model.

  11. Novel global robust stability criteria for interval neural networks with multiple time-varying delays

    International Nuclear Information System (INIS)

    Xu Shengyuan; Lam, James; Ho, Daniel W.C.

    2005-01-01

    This Letter is concerned with the problem of robust stability analysis for interval neural networks with multiple time-varying delays and parameter uncertainties. The parameter uncertainties are assumed to be bounded in given compact sets and the activation functions are supposed to be bounded and globally Lipschitz continuous. A sufficient condition is obtained by means of Lyapunov functionals, which guarantees the existence, uniqueness and global asymptotic stability of the delayed neural network for all admissible uncertainties. This condition is in terms of a linear matrix inequality (LMI), which can be easily checked by using recently developed algorithms in solving LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method

  12. Psychedelics Promote Structural and Functional Neural Plasticity

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    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

  13. Deep convolutional neural network based antenna selection in multiple-input multiple-output system

    Science.gov (United States)

    Cai, Jiaxin; Li, Yan; Hu, Ying

    2018-03-01

    Antenna selection of wireless communication system has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity in large-scale Multiple-Input MultipleOutput antenna systems. Recently, deep learning based methods have achieved promising performance for large-scale data processing and analysis in many application fields. This paper is the first attempt to introduce the deep learning technique into the field of Multiple-Input Multiple-Output antenna selection in wireless communications. First, the label of attenuation coefficients channel matrix is generated by minimizing the key performance indicator of training antenna systems. Then, a deep convolutional neural network that explicitly exploits the massive latent cues of attenuation coefficients is learned on the training antenna systems. Finally, we use the adopted deep convolutional neural network to classify the channel matrix labels of test antennas and select the optimal antenna subset. Simulation experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based wireless antenna selection.

  14. Anti-synchronization control of BAM memristive neural networks with multiple proportional delays and stochastic perturbations

    Science.gov (United States)

    Wang, Weiping; Yuan, Manman; Luo, Xiong; Liu, Linlin; Zhang, Yao

    2018-01-01

    Proportional delay is a class of unbounded time-varying delay. A class of bidirectional associative memory (BAM) memristive neural networks with multiple proportional delays is concerned in this paper. First, we propose the model of BAM memristive neural networks with multiple proportional delays and stochastic perturbations. Furthermore, by choosing suitable nonlinear variable transformations, the BAM memristive neural networks with multiple proportional delays can be transformed into the BAM memristive neural networks with constant delays. Based on the drive-response system concept, differential inclusions theory and Lyapunov stability theory, some anti-synchronization criteria are obtained. Finally, the effectiveness of proposed criteria are demonstrated through numerical examples.

  15. Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Chang Soon Heung; Chung, Hak Yeong

    1992-01-01

    This paper presents feasibility studies of multiple alarm processing and diagnosis using neural networks. The back-propagation neural network model is applied to the training of multiple alarm patterns for the identification of failure in a reactor coolant pump (RCP) system. The general mapping capability of the neural network enables to identify a fault easily. The case studies are performed with emphasis on the applicability of the neural network to pattern recognition problems. It is revealed that the neural network model can identify the cause of multiple alarms properly, even when untrained or sensor-failed alarm symptoms are given. It is also shown that multiple failures are easily identified using the symptoms of multiple alarms

  16. 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

  17. Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems.

    Directory of Open Access Journals (Sweden)

    Yuhan Chen

    Full Text Available The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter α, and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of α, resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of α values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real

  18. Speech comprehension aided by multiple modalities: behavioural and neural interactions

    Science.gov (United States)

    McGettigan, Carolyn; Faulkner, Andrew; Altarelli, Irene; Obleser, Jonas; Baverstock, Harriet; Scott, Sophie K.

    2014-01-01

    Speech comprehension is a complex human skill, the performance of which requires the perceiver to combine information from several sources – e.g. voice, face, gesture, linguistic context – to achieve an intelligible and interpretable percept. We describe a functional imaging investigation of how auditory, visual and linguistic information interact to facilitate comprehension. Our specific aims were to investigate the neural responses to these different information sources, alone and in interaction, and further to use behavioural speech comprehension scores to address sites of intelligibility-related activation in multifactorial speech comprehension. In fMRI, participants passively watched videos of spoken sentences, in which we varied Auditory Clarity (with noise-vocoding), Visual Clarity (with Gaussian blurring) and Linguistic Predictability. Main effects of enhanced signal with increased auditory and visual clarity were observed in overlapping regions of posterior STS. Two-way interactions of the factors (auditory × visual, auditory × predictability) in the neural data were observed outside temporal cortex, where positive signal change in response to clearer facial information and greater semantic predictability was greatest at intermediate levels of auditory clarity. Overall changes in stimulus intelligibility by condition (as determined using an independent behavioural experiment) were reflected in the neural data by increased activation predominantly in bilateral dorsolateral temporal cortex, as well as inferior frontal cortex and left fusiform gyrus. Specific investigation of intelligibility changes at intermediate auditory clarity revealed a set of regions, including posterior STS and fusiform gyrus, showing enhanced responses to both visual and linguistic information. Finally, an individual differences analysis showed that greater comprehension performance in the scanning participants (measured in a post-scan behavioural test) were associated with

  19. Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

    Science.gov (United States)

    Rai, Man Mohan

    2006-01-01

    Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more

  20. 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...

  1. 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.

  2. Representations of Multiple-Valued Logic Functions

    CERN Document Server

    Stankovic, Radomir S

    2012-01-01

    Compared to binary switching functions, multiple-valued functions offer more compact representations of the information content of signals modeled by logic functions and, therefore, their use fits very well in the general settings of data compression attempts and approaches. The first task in dealing with such signals is to provide mathematical methods for their representation in a way that will make their application in practice feasible.Representation of Multiple-Valued Logic Functions is aimed at providing an accessible introduction to these mathematical techniques that are necessary for ap

  3. 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

  4. Impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach.

    Science.gov (United States)

    Chandrasekar, A; Rakkiyappan, R; Cao, Jinde

    2015-10-01

    This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. 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)

  6. 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.

  7. Modified-hybrid optical neural network filter for multiple object recognition within cluttered scenes

    Science.gov (United States)

    Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.

    2009-08-01

    Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network's filter input. The mask was built with the constant weight connections of a randomly chosen image included in the training set. The resulted design of the modified-hybrid optical neural network filter is optimized for performing best in cluttered scenes of the true-class object. Due to the shift invariance properties inherited by its correlator unit the filter can accommodate multiple objects of the same class to be detected within an input cluttered image. Additionally, the architecture of the neural network unit of the general hybrid optical neural network filter allows the recognition of multiple objects of different classes within the input cluttered image by modifying the output layer of the unit. We test the modified-hybrid optical neural network filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. The filter is shown to exhibit with a single pass over the input data simultaneously out-of-plane rotation, shift invariance and good clutter tolerance. It is able to successfully detect and classify correctly the true-class objects within background clutter for which there has been no previous training.

  8. 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.

  9. 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.

  10. 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.

  11. A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

    Science.gov (United States)

    Tsumori, Kenji; Ozawa, Seiichi

    When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

  12. Global robust stability of neural networks with multiple discrete delays and distributed delays

    International Nuclear Information System (INIS)

    Gao Ming; Cui Baotong

    2009-01-01

    The problem of global robust stability is investigated for a class of uncertain neural networks with both multiple discrete time-varying delays and distributed time-varying delays. The uncertainties are assumed to be of norm-bounded form and the activation functions are supposed to be bounded and globally Lipschitz continuous. Based on the Lyapunov stability theory and linear matrix inequality technique, some robust stability conditions guaranteeing the global robust convergence of the equilibrium point are derived. The proposed LMI-based criteria are computationally efficient as they can be easily checked by using recently developed algorithms in solving LMIs. Two examples are given to show the effectiveness of the proposed results.

  13. 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.

  14. Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

    International Nuclear Information System (INIS)

    Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohammad; Balakrishnan, Jaydeep

    2015-01-01

    This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. - Highlights: • Integrated Markovian and back propagation neural network approach to compute reliability. • Markovian based reliability assessment method. • Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks

  15. 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

  16. 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/

  17. 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....

  18. Optimal multiple-information integration inherent in a ring neural network

    International Nuclear Information System (INIS)

    Takiyama, Ken

    2017-01-01

    Although several behavioral experiments have suggested that our neural system integrates multiple sources of information based on the certainty of each type of information in the manner of maximum-likelihood estimation, it is unclear how the maximum-likelihood estimation is implemented in our neural system. Here, I investigate the relationship between maximum-likelihood estimation and a widely used ring-type neural network model that is used as a model of visual, motor, or prefrontal cortices. Without any approximation or ansatz, I analytically demonstrate that the equilibrium of an order parameter in the neural network model exactly corresponds to the maximum-likelihood estimation when the strength of the symmetrical recurrent synaptic connectivity within a neural population is appropriately stronger than that of asymmetrical connectivity, that of local and external inputs, and that of symmetrical or asymmetrical connectivity between different neural populations. In this case, strengths of local and external inputs or those of symmetrical connectivity between different neural populations exactly correspond to the input certainty in maximum-likelihood estimation. Thus, my analysis suggests appropriately strong symmetrical recurrent connectivity as a possible candidate for implementing the maximum-likelihood estimation within our neural system. (paper)

  19. Building a functional multiple intelligences theory to advance educational neuroscience.

    Science.gov (United States)

    Cerruti, Carlo

    2013-01-01

    A key goal of educational neuroscience is to conduct constrained experimental research that is theory-driven and yet also clearly related to educators' complex set of questions and concerns. However, the fields of education, cognitive psychology, and neuroscience use different levels of description to characterize human ability. An important advance in research in educational neuroscience would be the identification of a cognitive and neurocognitive framework at a level of description relatively intuitive to educators. I argue that the theory of multiple intelligences (MI; Gardner, 1983), a conception of the mind that motivated a past generation of teachers, may provide such an opportunity. I criticize MI for doing little to clarify for teachers a core misunderstanding, specifically that MI was only an anatomical map of the mind but not a functional theory that detailed how the mind actually processes information. In an attempt to build a "functional MI" theory, I integrate into MI basic principles of cognitive and neural functioning, namely interregional neural facilitation and inhibition. In so doing I hope to forge a path toward constrained experimental research that bears upon teachers' concerns about teaching and learning.

  20. 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...

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Multistability of delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions

    International Nuclear Information System (INIS)

    Huang Yu-Jiao; Hu Hai-Gen

    2015-01-01

    In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. One numerical example is provided to show the effectiveness and superiority of the presented results. (paper)

  11. Neural tube closure depends on expression of Grainyhead-like 3 in multiple tissues.

    Science.gov (United States)

    De Castro, Sandra C P; Hirst, Caroline S; Savery, Dawn; Rolo, Ana; Lickert, Heiko; Andersen, Bogi; Copp, Andrew J; Greene, Nicholas D E

    2018-03-15

    Failure of neural tube closure leads to neural tube defects (NTDs), common congenital abnormalities in humans. Among the genes whose loss of function causes NTDs in mice, Grainyhead-like3 (Grhl3) is essential for spinal neural tube closure, with null mutants exhibiting fully penetrant spina bifida. During spinal neurulation Grhl3 is initially expressed in the surface (non-neural) ectoderm, subsequently in the neuroepithelial component of the neural folds and at the node-streak border, and finally in the hindgut endoderm. Here, we show that endoderm-specific knockout of Grhl3 causes late-arising spinal NTDs, preceded by increased ventral curvature of the caudal region which was shown previously to suppress closure of the spinal neural folds. This finding supports the hypothesis that diminished Grhl3 expression in the hindgut is the cause of spinal NTDs in the curly tail, carrying a hypomorphic Grhl3 allele. Complete loss of Grhl3 function produces a more severe phenotype in which closure fails earlier in neurulation, before the stage of onset of expression in the hindgut of wild-type embryos. This implicates additional tissues and NTD mechanisms in Grhl3 null embryos. Conditional knockout of Grhl3 in the neural plate and node-streak border has minimal effect on closure, suggesting that abnormal function of surface ectoderm, where Grhl3 transcripts are first detected, is primarily responsible for early failure of spinal neurulation in Grhl3 null embryos. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  12. 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.

  13. Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

    Science.gov (United States)

    Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

    2018-01-01

    Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

  14. Activation of endogenous neural stem cells for multiple sclerosis therapy

    NARCIS (Netherlands)

    Michailidou, I.; de Vries, H.E.; Hol, E.M.; van Strien, M.E.

    2015-01-01

    Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system, leading to severe neurological deficits. Current MS treatment regimens, consist of immunomodulatory agents aiming to reduce the rate of relapses. However, these agents are usually insufficient to treat chronic

  15. Quantitative differentiation of multiple virus in blood using nanoporous silicon oxide immunosensor and artificial neural network.

    Science.gov (United States)

    Chakraborty, W; Ray, R; Samanta, N; RoyChaudhuri, C

    2017-12-15

    In spite of the rapid developments in various nanosensor technologies, it still remains challenging to realize a reliable ultrasensitive electrical biosensing platform which will be able to detect multiple viruses in blood simultaneously with a fairly high reproducibility without using secondary labels. In this paper, we have reported quantitative differentiation of Hep-B and Hep-C viruses in blood using nanoporous silicon oxide immunosensor array and artificial neural network (ANN). The peak frequency output (f p ) from the steady state sensitivity characteristics and the first cut off frequency (f c ) from the transient characteristics have been considered as inputs to the multilayer ANN. Implementation of several classifier blocks in the ANN architecture and coupling them with both the sensor chips, functionalized with Hep-B and Hep-C antibodies have enabled the quantification of the viruses with an accuracy of around 95% in the range of 0.04fM-1pM and with an accuracy of around 90% beyond 1pM and within 25nM in blood serum. This is the most sensitive report on multiple virus quantification using label free method. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. 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.

  17. Global robust stability of bidirectional associative memory neural networks with multiple time delays.

    Science.gov (United States)

    Senan, Sibel; Arik, Sabri

    2007-10-01

    This correspondence presents a sufficient condition for the existence, uniqueness, and global robust asymptotic stability of the equilibrium point for bidirectional associative memory neural networks with discrete time delays. The results impose constraint conditions on the network parameters of the neural system independently of the delay parameter, and they are applicable to all bounded continuous nonmonotonic neuron activation functions. Some numerical examples are given to compare our results with the previous robust stability results derived in the literature.

  18. 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.

  19. 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

  20. Pulmonary function and dysfunction in multiple sclerosis.

    Science.gov (United States)

    Smeltzer, S C; Utell, M J; Rudick, R A; Herndon, R M

    1988-11-01

    Pulmonary function was studied in 25 patients with clinically definite multiple sclerosis with a range of motor impairment. Forced vital capacity (FVC), maximal voluntary ventilation (MVV), and maximal expiratory pressure (MEP) were normal in the ambulatory patients (mean greater than or equal to 80% predicted) but reduced in bedridden patients (mean, 38.5%, 31.6%, and 36.3% predicted; FCV, MVV, and MEP, respectively) and wheelchair-bound patients with upper extremity involvement (mean, 69.4%, 50.4%, and 62.6% predicted; FVC, MVV, and MEP, respectively). Forced vital capacity, MVV, and MEP correlated with Kurtzke Expanded Disability Status scores (tau = -0.72, -0.70, and -0.65) and expiratory muscle weakness occurred most frequently. These findings demonstrate that marked expiratory weakness develops in severely paraparetic patients with multiple sclerosis and the weakness increases as the upper extremities become increasingly involved.

  1. [Multiple sclerosis, loss of functionality and gender].

    Science.gov (United States)

    Bravo-González, Félix; Álvarez-Roldán, Arturo

    2017-12-01

    To identify the type of support and assistance that patients with multiple sclerosis need in order to cope with the loss of functionality, and to show how gender affects the perception of these needs. Interpretative-phenomenological qualitative study. Granada (Spain). Year: 2014. Intentional sample: 30 patients and 20 family caregivers. Data were gathered from 26 interviews and 4 focus groups. The data were coded and analysed with the NVivo programme. The multiple sclerosis patients and family caregivers had different perceptions of the loss of capacity to undertake activities of daily living. Being able to self care was considered the last vestige of autonomy. The women with multiple sclerosis tried to take on the responsibility of housework, but the male caregivers became gradually involved in these tasks. Gender roles were redefined with respect to housekeeping. The multiple sclerosis patients showed a need for emotional support. Some of the men had abandoned the stereotype of the strong male as a result of the decline in their health. Adaptations in the home took place without planning them in advance. The use of mobility devices started on an occasional basis. A fear of stigma was an obstacle for regular use of assistive technology. Health care for people with multiple sclerosis should include family caregivers. Gender influences the perception that caregivers and patients have of the assistance they require to maximise their quality of life. This flags up several intervention areas for the follow-up and long-term care of these patients by the healthcare system. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  2. Exploiting Deep Neural Networks and Head Movements for Robust Binaural Localization of Multiple Sources in Reverberant Environments

    DEFF Research Database (Denmark)

    Ma, Ning; May, Tobias; Brown, Guy J.

    2017-01-01

    This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for robust binaural localization of multiple sources in reverberant environments. DNNs are used to learn the relationship between the source azimuth and binaural cues, consisting...... of the complete cross-correlation function (CCF) and interaural level differences (ILDs). In contrast to many previous binaural hearing systems, the proposed approach is not restricted to localization of sound sources in the frontal hemifield. Due to the similarity of binaural cues in the frontal and rear...

  3. 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

  4. Rock property estimates using multiple seismic attributes and neural networks; Pegasus Field, West Texas

    Energy Technology Data Exchange (ETDEWEB)

    Schuelke, J.S.; Quirein, J.A.; Sarg, J.F.

    1998-12-31

    This case study shows the benefit of using multiple seismic trace attributes and the pattern recognition capabilities of neural networks to predict reservoir architecture and porosity distribution in the Pegasus Field, West Texas. The study used the power of neural networks to integrate geologic, borehole and seismic data. Illustrated are the improvements between the new neural network approach and the more traditional method of seismic trace inversion for porosity estimation. Comprehensive statistical methods and interpretational/subjective measures are used in the prediction of porosity from seismic attributes. A 3-D volume of seismic derived porosity estimates for the Devonian reservoir provide a very detailed estimate of porosity, both spatially and vertically, for the field. The additional reservoir porosity detail provided, between the well control, allows for optimal placement of horizontal wells and improved field development. 6 refs., 2 figs.

  5. 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.

  6. 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)

  7. A Unified Approach to Functional Principal Component Analysis and Functional Multiple-Set Canonical Correlation.

    Science.gov (United States)

    Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S

    2017-06-01

    Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

  8. 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.

  9. Fatigue in multiple sclerosis: neural correlates and the role of non-invasive brain stimulation

    Directory of Open Access Journals (Sweden)

    Moussa A. Chalah

    2015-11-01

    Full Text Available Multiple sclerosis (MS is a chronic progressive inflammatory disease of the central nervous system and the major cause of non-traumatic disability in young adults. Fatigue is a frequent symptom reported by the majority of MS patients during their disease course and drastically af-fects their quality of life. Despite its significant prevalence and impact, the underlying patho-physiological mechanisms are not well elucidated. MS fatigue is still considered the result of multifactorial and complex constellations, and is commonly classified into primary fatigue related to the pathological changes of the disease itself, and secondary fatigue attributed to mimicking symptoms, comorbid sleep and mood disorders, and medications side effects. Data from neuroimaging, neurophysiology, neuroendocrine and neuroimmune studies have raised hypotheses regarding the origin of this symptom, some of which have succeeded in identifying an association between MS fatigue and structural or functional abnormalities within various brain networks. Hence, the aim of this work is to reappraise the neural correlates of MS fatigue and to discuss the rationale for the emergent use of noninvasive brain stimulation (NIBS techniques as potential treatments. This will include a presentation of the various NIBS modalities and a proposition of their potential mechanisms of action in this context. Specific issues related to the value of transcranial direct current stimulation will be addressed.

  10. Small leucine rich proteoglycan family regulates multiple signalling pathways in neural development and maintenance.

    Science.gov (United States)

    Dellett, Margaret; Hu, Wanzhou; Papadaki, Vasiliki; Ohnuma, Shin-ichi

    2012-04-01

    The small leucine-rich repeat proteoglycan (SLRPs) family of proteins currently consists of five classes, based on their structural composition and chromosomal location. As biologically active components of the extracellular matrix (ECM), SLRPs were known to bind to various collagens, having a role in regulating fibril assembly, organization and degradation. More recently, as a function of their diverse proteins cores and glycosaminoglycan side chains, SLRPs have been shown to be able to bind various cell surface receptors, growth factors, cytokines and other ECM components resulting in the ability to influence various cellular functions. Their involvement in several signaling pathways such as Wnt, transforming growth factor-β and epidermal growth factor receptor also highlights their role as matricellular proteins. SLRP family members are expressed during neural development and in adult neural tissues, including ocular tissues. This review focuses on describing SLRP family members involvement in neural development with a brief summary of their role in non-neural ocular tissues and in response to neural injury. © 2012 The Authors Development, Growth & Differentiation © 2012 Japanese Society of Developmental Biologists.

  11. Altered thalamic functional connectivity in multiple sclerosis

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Yaou; Liang, Peipeng; Duan, Yunyun; Huang, Jing; Ren, Zhuoqiong; Jia, Xiuqin [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Dong, Huiqing; Ye, Jing [Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Shi, Fu-Dong [Department of Neurology and Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin 300052 (China); Butzkueven, Helmut [Department of Medicine, University of Melbourne, Parkville 3010 (Australia); Li, Kuncheng, E-mail: kunchengli55@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China)

    2015-04-15

    Highlights: •We demonstrated decreased connectivity between thalamus and cortical regions in MS. •Increased intra- and inter-thalamic connectivity was also observed in MS. •The increased functional connectivity is attenuated by increasing disease duration. -- Abstract: Objective: To compare thalamic functional connectivity (FC) in patients with multiple sclerosis (MS) and healthy controls (HC), and correlate these connectivity measures with other MRI and clinical variables. Methods: We employed resting-state functional MRI (fMRI) to examine changes in thalamic connectivity by comparing thirty-five patients with MS and 35 age- and sex-matched HC. Thalamic FC was investigated by correlating low frequency fMRI signal fluctuations in thalamic voxels with voxels in all other brain regions. Additionally thalamic volume fraction (TF), T2 lesion volume (T2LV), EDSS and disease duration were recorded and correlated with the FC changes. Results: MS patients were found to have a significantly lower TF than HC in bilateral thalami. Compared to HC, the MS group showed significantly decreased FC between thalamus and several brain regions including right middle frontal and parahippocampal gyri, and the left inferior parietal lobule. Increased intra- and inter-thalamic FC was observed in the MS group compared to HC. These FC alterations were not correlated with T2LV, thalamic volume or lesions. In the MS group, however, there was a negative correlation between disease duration and inter-thalamic connectivity (r = −0.59, p < 0.001). Conclusion: We demonstrated decreased FC between thalamus and several cortical regions, while increased intra- and inter-thalamic connectivity in MS patients. These complex functional changes reflect impairments and/or adaptations that are independent of T2LV, thalamic volume or presence of thalamic lesions. The negative correlation between disease duration and inter-thalamic connectivity could indicate an adaptive role of thalamus that is

  12. Multiple Linear Regression and Artificial Neural Network to Predict Blood Glucose in Overweight Patients.

    Science.gov (United States)

    Wang, J; Wang, F; Liu, Y; Xu, J; Lin, H; Jia, B; Zuo, W; Jiang, Y; Hu, L; Lin, F

    2016-01-01

    Overweight individuals are at higher risk for developing type II diabetes than the general population. We conducted this study to analyze the correlation between blood glucose and biochemical parameters, and developed a blood glucose prediction model tailored to overweight patients. A total of 346 overweight Chinese people patients ages 18-81 years were involved in this study. Their levels of fasting glucose (fs-GLU), blood lipids, and hepatic and renal functions were measured and analyzed by multiple linear regression (MLR). Based the MLR results, we developed a back propagation artificial neural network (BP-ANN) model by selecting tansig as the transfer function of the hidden layers nodes, and purelin for the output layer nodes, with training goal of 0.5×10(-5). There was significant correlation between fs-GLU with age, BMI, and blood biochemical indexes (P<0.05). The results of MLR analysis indicated that age, fasting alanine transaminase (fs-ALT), blood urea nitrogen (fs-BUN), total protein (fs-TP), uric acid (fs-BUN), and BMI are 6 independent variables related to fs-GLU. Based on these parameters, the BP-ANN model was performed well and reached high prediction accuracy when training 1 000 epoch (R=0.9987). The level of fs-GLU was predictable using the proposed BP-ANN model based on 6 related parameters (age, fs-ALT, fs-BUN, fs-TP, fs-UA and BMI) in overweight patients. © Georg Thieme Verlag KG Stuttgart · New York.

  13. Strategy over operation: neural activation in subtraction and multiplication during fact retrieval and procedural strategy use in children.

    Science.gov (United States)

    Polspoel, Brecht; Peters, Lien; Vandermosten, Maaike; De Smedt, Bert

    2017-09-01

    Arithmetic development is characterized by strategy shifts between procedural strategy use and fact retrieval. This study is the first to explicitly investigate children's neural activation associated with the use of these different strategies. Participants were 26 typically developing 4th graders (9- to 10-year-olds), who, in a behavioral session, were asked to verbally report on a trial-by-trial basis how they had solved 100 subtraction and multiplication items. These items were subsequently presented during functional magnetic resonance imaging. An event-related design allowed us to analyze the brain responses during retrieval and procedural trials, based on the children's verbal reports. During procedural strategy use, and more specifically for the decomposition of operands strategy, activation increases were observed in the inferior and superior parietal lobes (intraparietal sulci), inferior to superior frontal gyri, bilateral areas in the occipital lobe, and insular cortex. For retrieval, in comparison to procedural strategy use, we observed increased activity in the bilateral angular and supramarginal gyri, left middle to inferior temporal gyrus, right superior temporal gyrus, and superior medial frontal gyrus. No neural differences were found between the two operations under study. These results are the first in children to provide direct evidence for alternate neural activation when different arithmetic strategies are used and further unravel that previously found effects of operation on brain activity reflect differences in arithmetic strategy use. Hum Brain Mapp 38:4657-4670, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

    Science.gov (United States)

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-05-01

    Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. 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.

  1. Multiple-failure signal validation in nuclear power plants using artificial neural networks

    International Nuclear Information System (INIS)

    Fantoni, P.F.; Mazzola, A.

    1996-01-01

    The possibility of using a neural network to validate process signals during normal and abnormal plant conditions is explored. In boiling water reactor plants, signal validation is used to generate reliable thermal limits calculation and to supply reliable inputs to other computerized systems that support the operator during accident scenarios. The way that autoassociative neural networks can promptly detect faulty process signal measurements and produce a best estimate of the actual process values even in multifailure situations is shown. A method was developed to train the network for multiple sensor-failure detection, based on a random failure simulation algorithm. Noise was artificially added to the input to evaluate the network's ability to respond in a very low signal-to-noise ratio environment. Training and test data sets were simulated by the real-time transient simulator code APROS

  2. Functional Specificity and Sex Differences in the Neural Circuits Supporting the Inhibition of Automatic Imitation.

    Science.gov (United States)

    Darda, Kohinoor M; Butler, Emily E; Ramsey, Richard

    2018-06-01

    Humans show an involuntary tendency to copy other people's actions. Although automatic imitation builds rapport and affiliation between individuals, we do not copy actions indiscriminately. Instead, copying behaviors are guided by a selection mechanism, which inhibits some actions and prioritizes others. To date, the neural underpinnings of the inhibition of automatic imitation and differences between the sexes in imitation control are not well understood. Previous studies involved small sample sizes and low statistical power, which produced mixed findings regarding the involvement of domain-general and domain-specific neural architectures. Here, we used data from Experiment 1 ( N = 28) to perform a power analysis to determine the sample size required for Experiment 2 ( N = 50; 80% power). Using independent functional localizers and an analysis pipeline that bolsters sensitivity, during imitation control we show clear engagement of the multiple-demand network (domain-general), but no sensitivity in the theory-of-mind network (domain-specific). Weaker effects were observed with regard to sex differences, suggesting that there are more similarities than differences between the sexes in terms of the neural systems engaged during imitation control. In summary, neurocognitive models of imitation require revision to reflect that the inhibition of imitation relies to a greater extent on a domain-general selection system rather than a domain-specific system that supports social cognition.

  3. Study of functional viability of SU-8-based microneedles for neural applications

    International Nuclear Information System (INIS)

    Fernández, Luis J; Altuna, Ane; Tijero, Maria; Vilares, Roman; Berganzo, Javier; Blanco, F J; Gabriel, Gemma; Villa, Rosa; Rodríguez, Manuel J; Batlle, Montse

    2009-01-01

    This paper presents the design, fabrication, packaging and first test results of SU-8-based microneedles for neural applications. By the use of photolithography, sputtering and bonding techniques, polymer needles with integrated microchannels and electrodes have been successfully fabricated. The use of photolithography for the patterning of the fluidic channel integrated in the needle allows the design of multiple outlet ports at the needle tip, minimizing the possibility of being blocked by the tissue. Furthermore, the flexibility of the polymer reduces the risk of fracture and tissue damage once the needle is inserted, while it is still rigid enough to allow a perfect insertion into the neural tissue. Fluidic and electric characterization of the microneedles has shown their viability for drug delivery and monitoring in neural applications. First drug delivery tests in ex vivo tissue demonstrated the functional viability of the needle to deliver drugs to precise points. Furthermore, in vivo experiments have demonstrated lower associated damages during insertion than those by stereotaxic standard needles

  4. Asymptotic functions and multiplication of distributions

    International Nuclear Information System (INIS)

    Todorov, T.D.

    1979-01-01

    Considered is a new type of generalized asymptotic functions, which are not functionals on some space of test functions as the Schwartz distributions. The definition of the generalized asymptotic functions is given. It is pointed out that in future the particular asymptotic functions will be used for solving some topics of quantum mechanics and quantum theory

  5. 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.

  6. 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.

  7. Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and Unknown Deadzone

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2012-01-01

    Full Text Available This paper deals with the problem of trajectory tracking for a broad class of uncertain nonlinear systems with multiple inputs each one subject to an unknown symmetric deadzone. On the basis of a model of the deadzone as a combination of a linear term and a disturbance-like term, a continuous-time recurrent neural network is directly employed in order to identify the uncertain dynamics. By using a Lyapunov analysis, the exponential convergence of the identification error to a bounded zone is demonstrated. Subsequently, by a proper control law, the state of the neural network is compelled to follow a bounded reference trajectory. This control law is designed in such a way that the singularity problem is conveniently avoided and the exponential convergence to a bounded zone of the difference between the state of the neural identifier and the reference trajectory can be proven. Thus, the exponential convergence of the tracking error to a bounded zone and the boundedness of all closed-loop signals can be guaranteed. One of the main advantages of the proposed strategy is that the controller can work satisfactorily without any specific knowledge of an upper bound for the unmodeled dynamics and/or the disturbance term.

  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. 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.

  10. COMPASS hadron multiplicity measurements and fragmentation functions

    International Nuclear Information System (INIS)

    Stolarski, M.

    2016-01-01

    COMPASS is an experiment located at CERN SPS accelerator. For the results presented in this paper a 160 GeV positive muon beam was impinging on 6 LiD target. The COMPASS spectrometer was designed to reconstruct scattered muons and charged hadrons in a wide kinematic range. COMPASS preliminary results on hadron, pion and kaon multiplicities are presented. The hadron and pion data show a good agreement with (N)LO QCD expectations and some of these preliminary data have been already successfully incorporated in the global NLO QCD fits to world data. However, the results for kaon multiplicities, are different from the expectations of the DSS fit. There is also a tension between COMPASS and HERMES results, the only other experiment which measured kaon multiplicities in Semi-Inclusive Deep Inelastic scattering

  11. 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.

  12. Functional equation for the Mordell-Tornheim multiple zeta-function

    OpenAIRE

    Okamoto, Takuya; Onozuka, Tomokazu

    2016-01-01

    We show a relation between the Mordell-Tornheim multiple zeta-function and the confluent hypergeometric function, and using it, we give the functional equation for the Mordell-Tornheim multiple zeta-function. In the double case, the functional equation includes the known functional equation for the Euler-Zagier double zeta-function.

  13. Sequential interrogation of multiple FBG sensors using LPG modulation and an artificial neural network

    International Nuclear Information System (INIS)

    Basu, Mainak; Ghorai, S K

    2015-01-01

    Interrogating multiple fiber Bragg gratings (FBG) requires highly sensitive spectrum scanning equipment such as optical spectrum analyzers, tunable filters, acousto-optic tunable filters etc, which are expensive, bulky and time consuming. In this paper, we present a new approach for multiple FBG sensor interrogation using long-period gratings and an artificial neural network. The reflection spectra of the multiplexed FBGs are modulated by two long period gratings separately and the modulated optical intensities were detected by two photodetectors. The outputs of the detectors are then used as input in a previously trained artificial neural network to interrogate the FBG sensors. Simulations have been performed to determine the strain and wavelength shift using two and four sensors. The interrogation system has also been demonstrated experimentally for two sensors using simply supported beams in the range of 0–350 μstrain. The proposed interrogation scheme has been found to identify the perturbed FBG, and to determine strain and wavelength shift with reasonable accuracy. (paper)

  14. 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.

  15. 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.

  16. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

    Science.gov (United States)

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers’ overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas. PMID:28303097

  17. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms.

    Science.gov (United States)

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-E; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs' appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers' attention from different fields and many studies have validated MMORPGs' positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers' overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas.

  18. 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.

  19. Emotional face processing and flat affect in schizophrenia: functional and structural neural correlates.

    Science.gov (United States)

    Lepage, M; Sergerie, K; Benoit, A; Czechowska, Y; Dickie, E; Armony, J L

    2011-09-01

    There is a general consensus in the literature that schizophrenia causes difficulties with facial emotion perception and discrimination. Functional brain imaging studies have observed reduced limbic activity during facial emotion perception but few studies have examined the relation to flat affect severity. A total of 26 people with schizophrenia and 26 healthy controls took part in this event-related functional magnetic resonance imaging study. Sad, happy and neutral faces were presented in a pseudo-random order and participants indicated the gender of the face presented. Manual segmentation of the amygdala was performed on a structural T1 image. Both the schizophrenia group and the healthy control group rated the emotional valence of facial expressions similarly. Both groups exhibited increased brain activity during the perception of emotional faces relative to neutral ones in multiple brain regions, including multiple prefrontal regions bilaterally, the right amygdala, right cingulate cortex and cuneus. Group comparisons, however, revealed increased activity in the healthy group in the anterior cingulate, right parahippocampal gyrus and multiple visual areas. In schizophrenia, the severity of flat affect correlated significantly with neural activity in several brain areas including the amygdala and parahippocampal region bilaterally. These results suggest that many of the brain regions involved in emotional face perception, including the amygdala, are equally recruited in both schizophrenia and controls, but flat affect can also moderate activity in some other brain regions, notably in the left amygdala and parahippocampal gyrus bilaterally. There were no significant group differences in the volume of the amygdala.

  20. Multistability of neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing

    2015-05-01

    This paper is concerned with the problem of coexistence and dynamical behaviors of multiple equilibrium points for neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays. The fixed point theorem and other analytical tools are used to develop certain sufficient conditions that ensure that the n-dimensional discontinuous neural networks with time-varying delays can have at least 5(n) equilibrium points, 3(n) of which are locally stable and the others are unstable. The importance of the derived results is that it reveals that the discontinuous neural networks can have greater storage capacity than the continuous ones. Moreover, different from the existing results on multistability of neural networks with discontinuous activation functions, the 3(n) locally stable equilibrium points obtained in this paper are located in not only saturated regions, but also unsaturated regions, due to the non-monotonic structure of discontinuous activation functions. A numerical simulation study is conducted to illustrate and support the derived theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Multiple-region directed functional connectivity based on phase delays.

    Science.gov (United States)

    Goelman, Gadi; Dan, Rotem

    2017-03-01

    Network analysis is increasingly advancing the field of neuroimaging. Neural networks are generally constructed from pairwise interactions with an assumption of linear relations between them. Here, a high-order statistical framework to calculate directed functional connectivity among multiple regions, using wavelet analysis and spectral coherence has been presented. The mathematical expression for 4 regions was derived and used to characterize a quartet of regions as a linear, combined (nonlinear), or disconnected network. Phase delays between regions were used to obtain network's temporal hierarchy and directionality. The validity of the mathematical derivation along with the effects of coupling strength and noise on its outcomes were studied by computer simulations of the Kuramoto model. The simulations demonstrated correct directionality for a large range of coupling strength and low sensitivity to Gaussian noise compared with pairwise coherences. The analysis was applied to resting-state fMRI data of 40 healthy young subjects to characterize the ventral visual system, motor system and default mode network (DMN). It was shown that the ventral visual system was predominantly composed of linear networks while the motor system and the DMN were composed of combined (nonlinear) networks. The ventral visual system exhibits its known temporal hierarchy, the motor system exhibits center ↔ out hierarchy and the DMN has dorsal ↔ ventral and anterior ↔ posterior organizations. The analysis can be applied in different disciplines such as seismology, or economy and in a variety of brain data including stimulus-driven fMRI, electrophysiology, EEG, and MEG, thus open new horizons in brain research. Hum Brain Mapp 38:1374-1386, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  2. 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.

  3. 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)

  4. A Switching Anti-windup Design Using Multiple Lyapunov Functions

    NARCIS (Netherlands)

    Lu, L.; Lin, Z.; Chen, J.

    2009-01-01

    This paper proposes a switching anti-windup design, which aims to enlarge the domain of attraction of the closed-loop system. Multiple anti-windup gains along with an index function that orchestrates the switching among these anti-windup gains are designed based on the min function of multiple

  5. The multiplicity function of galaxy systems

    International Nuclear Information System (INIS)

    Lucchin, F.

    1988-01-01

    The Press--Schechter approach to the mass function of cosmic structures is discussed by taking into account the effects of non-Guassian primordial perturbations and of the peak constraint, in the framework of the biased galaxy origin

  6. 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

  7. 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.

  8. 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

  9. 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

  10. Elastic tracking versus neural network tracking for very high multiplicity problems

    International Nuclear Information System (INIS)

    Harlander, M.; Gyulassy, M.

    1991-04-01

    A new Elastic Tracking (ET) algorithm is proposed for finding tracks in very high multiplicity and noisy environments. It is based on a dynamical reinterpretation and generalization of the Radon transform and is related to elastic net algorithms for geometrical optimization. ET performs an adaptive nonlinear fit to noisy data with a variable number of tracks. Its numerics is more efficient than that of the traditional Radon or Hough transform method because it avoids binning of phase space and the costly search for valid minima. Spurious local minima are avoided in ET by introducing a time-dependent effective potential. The method is shown to be very robust to noise and measurement error and extends tracking capabilities to much higher track densities than possible via local road finding or even the novel Denby-Peterson neural network tracking algorithms. 12 refs., 2 figs

  11. Stability and bifurcation in a simplified four-neuron BAM neural network with multiple delays

    Directory of Open Access Journals (Sweden)

    2006-01-01

    Full Text Available We first study the distribution of the zeros of a fourth-degree exponential polynomial. Then we apply the obtained results to a simplified bidirectional associated memory (BAM neural network with four neurons and multiple time delays. By taking the sum of the delays as the bifurcation parameter, it is shown that under certain assumptions the steady state is absolutely stable. Under another set of conditions, there are some critical values of the delay, when the delay crosses these critical values, the Hopf bifurcation occurs. Furthermore, some explicit formulae determining the stability and the direction of periodic solutions bifurcating from Hopf bifurcations are obtained by applying the normal form theory and center manifold reduction. Numerical simulations supporting the theoretical analysis are also included.

  12. Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.

    Science.gov (United States)

    Groen, Iris Ia; Greene, Michelle R; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M; Baker, Chris I

    2018-03-07

    Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

  13. Multiple soft limits of cosmological correlation functions

    International Nuclear Information System (INIS)

    Joyce, Austin; Khoury, Justin; Simonović, Marko

    2015-01-01

    We derive novel identities satisfied by inflationary correlation functions in the limit where two external momenta are taken to be small. We derive these statements in two ways: using background-wave arguments and as Ward identities following from the fixed-time path integral. Interestingly, these identities allow us to constrain some of the O(q 2 ) components of the soft limit, in contrast to their single-soft analogues. We provide several nontrivial checks of our identities both in the context of resonant non-Gaussianities and in small sound speed models. Additionally, we extend the relation at lowest order in external momenta to arbitrarily many soft legs, and comment on the many-soft extension at higher orders in the soft momentum. Finally, we consider how higher soft limits lead to identities satisfied by correlation functions in large-scale structure

  14. Neural Networks for Segregation of Multiple Objects: Visual Figure-Ground Separation and Auditory Pitch Perception.

    Science.gov (United States)

    Wyse, Lonce

    An important component of perceptual object recognition is the segmentation into coherent perceptual units of the "blooming buzzing confusion" that bombards the senses. The work presented herein develops neural network models of some key processes of pre-attentive vision and audition that serve this goal. A neural network model, called an FBF (Feature -Boundary-Feature) network, is proposed for automatic parallel separation of multiple figures from each other and their backgrounds in noisy images. Figure-ground separation is accomplished by iterating operations of a Boundary Contour System (BCS) that generates a boundary segmentation of a scene, and a Feature Contour System (FCS) that compensates for variable illumination and fills-in surface properties using boundary signals. A key new feature is the use of the FBF filling-in process for the figure-ground separation of connected regions, which are subsequently more easily recognized. The new CORT-X 2 model is a feed-forward version of the BCS that is designed to detect, regularize, and complete boundaries in up to 50 percent noise. It also exploits the complementary properties of on-cells and off -cells to generate boundary segmentations and to compensate for boundary gaps during filling-in. In the realm of audition, many sounds are dominated by energy at integer multiples, or "harmonics", of a fundamental frequency. For such sounds (e.g., vowels in speech), the individual frequency components fuse, so that they are perceived as one sound source with a pitch at the fundamental frequency. Pitch is integral to separating auditory sources, as well as to speaker identification and speech understanding. A neural network model of pitch perception called SPINET (SPatial PItch NETwork) is developed and used to simulate a broader range of perceptual data than previous spectral models. The model employs a bank of narrowband filters as a simple model of basilar membrane mechanics, spectral on-center off-surround competitive

  15. New generalized functions and multiplication of distributions

    International Nuclear Information System (INIS)

    Colombeau, J.F.

    1984-01-01

    Since its conception, Quantum Field Theory is based on 'heuristic' computations (in particular products of distributions) that, despite lots of effort, remained meaningless from a mathematical viewpoint. In this book the author presents a new mathematical theory giving a rigorous mathematical sense to these heuristic computations and, from a mathematical viewpoint, to all products of distributions. This new mathematical theory is a new theory of Generalized Functions defined on any open subset Ω of Rsup(n), which are much more general than the distributions on Ω. (Auth.)

  16. Computerized detection of multiple sclerosis candidate regions based on a level set method using an artificial neural network

    International Nuclear Information System (INIS)

    Kuwazuru, Junpei; Magome, Taiki; Arimura, Hidetaka; Yamashita, Yasuo; Oki, Masafumi; Toyofuku, Fukai; Kakeda, Shingo; Yamamoto, Daisuke

    2010-01-01

    Yamamoto et al. developed the system for computer-aided detection of multiple sclerosis (MS) candidate regions. In a level set method in their proposed method, they employed the constant threshold value for the edge indicator function related to a speed function of the level set method. However, it would be appropriate to adjust the threshold value to each MS candidate region, because the edge magnitudes in MS candidates differ from each other. Our purpose of this study was to develop a computerized detection of MS candidate regions in MR images based on a level set method using an artificial neural network (ANN). To adjust the threshold value for the edge indicator function in the level set method to each true positive (TP) and false positive (FP) region, we constructed the ANN. The ANN could provide the suitable threshold value for each candidate region in the proposed level set method so that TP regions can be segmented and FP regions can be removed. Our proposed method detected MS regions at a sensitivity of 82.1% with 0.204 FPs per slice and similarity index of MS candidate regions was 0.717 on average. (author)

  17. 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

  18. 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

  19. 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.

  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. Acute stress evokes sexually dimorphic, stressor-specific patterns of neural activation across multiple limbic brain regions in adult rats.

    Science.gov (United States)

    Sood, Ankit; Chaudhari, Karina; Vaidya, Vidita A

    2018-03-01

    Stress enhances the risk for psychiatric disorders such as anxiety and depression. Stress responses vary across sex and may underlie the heightened vulnerability to psychopathology in females. Here, we examined the influence of acute immobilization stress (AIS) and a two-day short-term forced swim stress (FS) on neural activation in multiple cortical and subcortical brain regions, implicated as targets of stress and in the regulation of neuroendocrine stress responses, in male and female rats using Fos as a neural activity marker. AIS evoked a sex-dependent pattern of neural activation within the cingulate and infralimbic subdivisions of the medial prefrontal cortex (mPFC), lateral septum (LS), habenula, and hippocampal subfields. The degree of neural activation in the mPFC, LS, and habenula was higher in males. Female rats exhibited reduced Fos positive cell numbers in the dentate gyrus hippocampal subfield, an effect not observed in males. We addressed whether the sexually dimorphic neural activation pattern noted following AIS was also observed with the short-term stress of FS. In the paraventricular nucleus of the hypothalamus and the amygdala, FS similar to AIS resulted in robust increases in neural activation in both sexes. The pattern of neural activation evoked by FS was distinct across sexes, with a heightened neural activation noted in the prelimbic mPFC subdivision and hippocampal subfields in females and differed from the pattern noted with AIS. This indicates that the sex differences in neural activation patterns observed within stress-responsive brain regions are dependent on the nature of stressor experience.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. Testing for Nonuniform Differential Item Functioning with Multiple Indicator Multiple Cause Models

    Science.gov (United States)

    Woods, Carol M.; Grimm, Kevin J.

    2011-01-01

    In extant literature, multiple indicator multiple cause (MIMIC) models have been presented for identifying items that display uniform differential item functioning (DIF) only, not nonuniform DIF. This article addresses, for apparently the first time, the use of MIMIC models for testing both uniform and nonuniform DIF with categorical indicators. A…

  8. Effects of Fluoxetine on Neural Functional Prognosis after Ischemic Stroke: A Randomized Controlled Study in China.

    Science.gov (United States)

    He, Yi-Tao; Tang, Bing-Shan; Cai, Zhi-Li; Zeng, Si-Ling; Jiang, Xin; Guo, Yi

    2016-04-01

    We investigated the effects of fluoxetine on the short-term and long-term neural functional prognoses after ischemic stroke. In this prospective randomized controlled single-blind clinical study in China, eligible patients afflicted with ischemic stroke were randomized into control and treatment groups. Patients in the treatment group received fluoxetine in addition to the basic therapies in the control group over a period of 90 days. The follow-up period was 180 days. We evaluated the effects of fluoxetine on the National Institutes of Health Stroke Scale (NIHSS) score and Barthel Index (BI) score after ischemic stroke through single- and multiple-factor analysis. The mean NIHSS score on day 180 after treatment was significantly lower in the treatment group than in the control group (P = .009). The mean BI scores on days 90 and 180 were significantly higher in the treatment group (P = .026) than in the control group (P = .011). The improvements in the NIHSS and BI scores on days 90 and 180 compared with baseline in the treatment group were all significantly greater than that in the control group (P = .033, P = .013, P = .013, P = .019, respectively). Treatment with fluoxetine was an independent factor affecting the NIHSS and BI scores on day 180 after treatment. Treatment with fluoxetine for 90 days after ischemic stroke can improve the long-term neural functional outcomes. Copyright © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.

  9. Functional Connectivity with Distinct Neural Networks Tracks Fluctuations in Gain/Loss Framing Susceptibility

    Science.gov (United States)

    Smith, David V.; Sip, Kamila E.; Delgado, Mauricio R.

    2016-01-01

    Multiple large-scale neural networks orchestrate a wide range of cognitive processes. For example, interoceptive processes related to self-referential thinking have been linked to the default-mode network (DMN); whereas exteroceptive processes related to cognitive control have been linked to the executive-control network (ECN). Although the DMN and ECN have been postulated to exert opposing effects on cognition, it remains unclear how connectivity with these spatially overlapping networks contribute to fluctuations in behavior. While previous work has suggested the medial prefrontal cortex (MPFC) is involved in behavioral change following feedback, these observations could be linked to interoceptive processes tied to DMN or exteroceptive processes tied to ECN because MPFC is positioned in both networks. To address this problem, we employed independent component analysis combined with dual-regression functional connectivity analysis. Participants made a series of financial decisions framed as monetary gains or losses. In some sessions, participants received feedback from a peer observing their choices; in other sessions, feedback was not provided. Following feedback, framing susceptibility—indexed as the increase in gambling behavior in loss frames compared to gain frames—was heightened in some participants and diminished in others. We examined whether these individual differences were linked to differences in connectivity by contrasting sessions containing feedback against those that did not contain feedback. We found two key results. As framing susceptibility increased, the MPFC increased connectivity with DMN; in contrast, temporal-parietal junction decreased connectivity with the ECN. Our results highlight how functional connectivity patterns with distinct neural networks contribute to idiosyncratic behavioral changes. PMID:25858445

  10. Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

    International Nuclear Information System (INIS)

    Eslamloueyan, R.; Shahrokhi, M.; Bozorgmehri, R.

    2003-01-01

    Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

  11. 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.

  12. 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.

  13. Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

    Science.gov (United States)

    Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

    2009-04-01

    θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

  14. 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.

  15. Evaluating Functional Decline in Patients with Multiple Sclerosis

    Science.gov (United States)

    Rosenblum, Sara; Weiss, Patrice L.

    2010-01-01

    Multiple Sclerosis (MS) is a disease with a wide-ranging impact on functional status. The aim of the study was to examine the added value of simultaneously evaluating fatigue, personal ADL and handwriting performance as indicators for functional decline among patients with MS. Participants were 50 outpatients with MS and 26 matched healthy…

  16. 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...

  17. QSAR Study of Insecticides of Phthalamide Derivatives Using Multiple Linear Regression and Artificial Neural Network Methods

    Directory of Open Access Journals (Sweden)

    Adi Syahputra

    2014-03-01

    Full Text Available Quantitative structure activity relationship (QSAR for 21 insecticides of phthalamides containing hydrazone (PCH was studied using multiple linear regression (MLR, principle component regression (PCR and artificial neural network (ANN. Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84. Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl-5-chloro-N’-((5-methylthiophen-2-ylmethylene benzohydrazide, 2-(decalinecarbamoyl-5-chloro-N’-((thiophen-2-yl-methylene benzohydrazide and 2-(decaline carbamoyl-N’-(4-fluorobenzylidene-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.

  18. A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

    Directory of Open Access Journals (Sweden)

    Yan Liu

    Full Text Available Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS treatment planning. In this work, we developed a deep learning convolutional neural network (CNN algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

  19. Human Neural Precursor Cells Promote Neurologic Recovery in a Viral Model of Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Lu Chen

    2014-06-01

    Full Text Available Using a viral model of the demyelinating disease multiple sclerosis (MS, we show that intraspinal transplantation of human embryonic stem cell-derived neural precursor cells (hNPCs results in sustained clinical recovery, although hNPCs were not detectable beyond day 8 posttransplantation. Improved motor skills were associated with a reduction in neuroinflammation, decreased demyelination, and enhanced remyelination. Evidence indicates that the reduced neuroinflammation is correlated with an increased number of CD4+CD25+FOXP3+ regulatory T cells (Tregs within the spinal cords. Coculture of hNPCs with activated T cells resulted in reduced T cell proliferation and increased Treg numbers. The hNPCs acted, in part, through secretion of TGF-β1 and TGF-β2. These findings indicate that the transient presence of hNPCs transplanted in an animal model of MS has powerful immunomodulatory effects and mediates recovery. Further investigation of the restorative effects of hNPC transplantation may aid in the development of clinically relevant MS treatments.

  20. Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2017-04-01

    Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

  1. Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

    Science.gov (United States)

    Yousefi, Mina; Krzyżak, Adam; Suen, Ching Y

    2018-05-01

    Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

    Science.gov (United States)

    Li, Zhijun; Su, Chun-Yi

    2013-09-01

    In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

  3. A simple structure wavelet transform circuit employing function link neural networks and SI filters

    Science.gov (United States)

    Mu, Li; Yigang, He

    2016-12-01

    Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.

  4. 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

  5. On Multiple Interpolation Functions of the -Genocchi Polynomials

    Directory of Open Access Journals (Sweden)

    Jin Jeong-Hee

    2010-01-01

    Full Text Available Abstract Recently, many mathematicians have studied various kinds of the -analogue of Genocchi numbers and polynomials. In the work (New approach to q-Euler, Genocchi numbers and their interpolation functions, "Advanced Studies in Contemporary Mathematics, vol. 18, no. 2, pp. 105–112, 2009.", Kim defined new generating functions of -Genocchi, -Euler polynomials, and their interpolation functions. In this paper, we give another definition of the multiple Hurwitz type -zeta function. This function interpolates -Genocchi polynomials at negative integers. Finally, we also give some identities related to these polynomials.

  6. Multiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis.

    Science.gov (United States)

    Ennouri, Karim; Ben Ayed, Rayda; Triki, Mohamed Ali; Ottaviani, Ennio; Mazzarello, Maura; Hertelli, Fathi; Zouari, Nabil

    2017-07-01

    The aim of the present work was to develop a model that supplies accurate predictions of the yields of delta-endotoxins and proteases produced by B. thuringiensis var. kurstaki HD-1. Using available medium ingredients as variables, a mathematical method, based on Plackett-Burman design (PB), was employed to analyze and compare data generated by the Bootstrap method and processed by multiple linear regressions (MLR) and artificial neural networks (ANN) including multilayer perceptron (MLP) and radial basis function (RBF) models. The predictive ability of these models was evaluated by comparison of output data through the determination of coefficient (R 2 ) and mean square error (MSE) values. The results demonstrate that the prediction of the yields of delta-endotoxin and protease was more accurate by ANN technique (87 and 89% for delta-endotoxin and protease determination coefficients, respectively) when compared with MLR method (73.1 and 77.2% for delta-endotoxin and protease determination coefficients, respectively), suggesting that the proposed ANNs, especially MLP, is a suitable new approach for determining yields of bacterial products that allow us to make more appropriate predictions in a shorter time and with less engineering effort.

  7. Neuroprotective effect of transplanted human embryonic stem cell-derived neural precursors in an animal model of multiple sclerosis.

    Directory of Open Access Journals (Sweden)

    Michal Aharonowiz

    Full Text Available BACKGROUND: Multiple sclerosis (MS is an immune mediated demyelinating disease of the central nervous system (CNS. A potential new therapeutic approach for MS is cell transplantation which may promote remyelination and suppress the inflammatory process. METHODS: We transplanted human embryonic stem cells (hESC-derived early multipotent neural precursors (NPs into the brain ventricles of mice induced with experimental autoimmune encephalomyelitis (EAE, the animal model of MS. We studied the effect of the transplanted NPs on the functional and pathological manifestations of the disease. RESULTS: Transplanted hESC-derived NPs significantly reduced the clinical signs of EAE. Histological examination showed migration of the transplanted NPs to the host white matter, however, differentiation to mature oligodendrocytes and remyelination were negligible. Time course analysis of the evolution and progression of CNS inflammation and tissue injury showed an attenuation of the inflammatory process in transplanted animals, which was correlated with the reduction of both axonal damage and demyelination. Co-culture experiments showed that hESC-derived NPs inhibited the activation and proliferation of lymph node-derived T cells in response to nonspecific polyclonal stimuli. CONCLUSIONS: The therapeutic effect of transplantation was not related to graft or host remyelination but was mediated by an immunosuppressive neuroprotective mechanism. The attenuation of EAE by hESC-derived NPs, demonstrated here, may serve as the first step towards further developments of hESC for cell therapy in MS.

  8. 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.

  9. 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

  10. Experimental design and multiple response optimization. Using the desirability function in analytical methods development.

    Science.gov (United States)

    Candioti, Luciana Vera; De Zan, María M; Cámara, María S; Goicoechea, Héctor C

    2014-06-01

    A review about the application of response surface methodology (RSM) when several responses have to be simultaneously optimized in the field of analytical methods development is presented. Several critical issues like response transformation, multiple response optimization and modeling with least squares and artificial neural networks are discussed. Most recent analytical applications are presented in the context of analytLaboratorio de Control de Calidad de Medicamentos (LCCM), Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, C.C. 242, S3000ZAA Santa Fe, ArgentinaLaboratorio de Control de Calidad de Medicamentos (LCCM), Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, C.C. 242, S3000ZAA Santa Fe, Argentinaical methods development, especially in multiple response optimization procedures using the desirability function. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

    Directory of Open Access Journals (Sweden)

    Runchun Mark Wang

    2015-05-01

    Full Text Available We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP and Spike Timing Dependent Delay Plasticity (STDDP. We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 2^26 (64M synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted and/or delayed pre-synaptic spike to the target synapse in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 2^36 (64G synaptic adaptors on a current high-end FPGA platform.

  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. 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.

  14. A Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamidreza Mousavi

    2017-01-01

    Full Text Available Recently different approaches have been developed in the field of sensor fault diagnostics based on Auto-Associative Neural Network (AANN. In this paper we present a novel algorithm called Self reconstructing Auto-Associative Neural Network (S-AANN which is able to detect and isolate single faulty sensor via reconstruction. We have also extended the algorithm to be applicable in multiple fault conditions. The algorithm uses a calibration model based on AANN. AANN can reconstruct the faulty sensor using non-faulty sensors due to correlation between the process variables, and mean of the difference between reconstructed and original data determines which sensors are faulty. The algorithms are tested on a Dimerization process. The simulation results show that the S-AANN can isolate multiple faulty sensors with low computational time that make the algorithm appropriate candidate for online applications.

  15. 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.

  16. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

    OpenAIRE

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational app...

  17. Building a functional neurocognitive theory of the multiple intelligences anatomical framework

    Directory of Open Access Journals (Sweden)

    Carlo eCerruti

    2013-12-01

    Full Text Available A key goal of educational neuroscience is to conduct constrained experimental research that is theory-driven and yet also clearly related to educators’ complex set of questions and concerns. However, the fields of education, cognitive psychology and neuroscience use different levels of description to characterize human ability. An important advance in research in educational neuroscience would be the identification of a cognitive and neurocognitive framework at a level of description relatively intuitive to educators. I argue that the theory of multiple intelligences (Gardner, 1983, a conception of the mind that motivated a past generation of teachers, may provide such an opportunity. I criticize MI for doing little to clarify for teachers a core misunderstanding, specifically that MI was only an anatomical map of the mind but not a functional theory that detailed how the mind actually processes information. In an attempt to build a functional MI theory, I integrate into MI basic principles of cognitive and neural functioning, namely interregional neural facilitation and inhibition. In so doing I hope to forge a path towards constrained experimental research that bears upon teachers’ concerns about teaching and learning.

  18. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.

    Science.gov (United States)

    Valverde, Sergi; Cabezas, Mariano; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Oliver, Arnau; Lladó, Xavier

    2017-07-15

    In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small (n≤35) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r≥0.97) also with the expected lesion volume. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Multiple neural states of representation in short-term memory? It's a matter of attention.

    Science.gov (United States)

    Larocque, Joshua J; Lewis-Peacock, Jarrod A; Postle, Bradley R

    2014-01-01

    Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time, and working memory (WM) refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing) and long-term memory (LTM-the encoding and retention of an effectively unlimited amount of information for a much longer period of time). Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson and Shiffrin, 1968; Baddeley and Hitch, 1974); and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002). In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and electroencephalography (EEG)) over conventional activation-based analysis approaches (Postle, in press). We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays.

  20. Multiple neural states of representation in short-term memory? It's a matter of attention

    Directory of Open Access Journals (Sweden)

    Joshua J Larocque

    2014-01-01

    Full Text Available Short-term memory (STM refers to the capacity-limited retention of information over a brief period of time, and working memory (WM refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing and long-term memory (LTM – the encoding and retention of an effectively unlimited amount of information for a much longer period of time. Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson & Shiffrin, 1968; Baddeley & Hitch, 1974; and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002. In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and EEG over conventional activation-based analysis approaches (Postle, in press. We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays.

  1. Functional analysis screening for multiple topographies of problem behavior.

    Science.gov (United States)

    Bell, Marlesha C; Fahmie, Tara A

    2018-04-23

    The current study evaluated a screening procedure for multiple topographies of problem behavior in the context of an ongoing functional analysis. Experimenters analyzed the function of a topography of primary concern while collecting data on topographies of secondary concern. We used visual analysis to predict the function of secondary topographies and a subsequent functional analysis to test those predictions. Results showed that a general function was accurately predicted for five of six (83%) secondary topographies. A specific function was predicted and supported for a subset of these topographies. The experimenters discuss the implication of these results for clinicians who have limited time for functional assessment. © 2018 Society for the Experimental Analysis of Behavior.

  2. 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)

  3. 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.

  4. Multiple function benefit - cost comparison of conservation buffer placement strategies

    Science.gov (United States)

    Z. Qiu; M.G. Dosskey

    2012-01-01

    Conservation buffers are considered to be effective practices for repairing impaired streams and restoring multiple ecosystem functions in degraded agricultural watersheds. Six different planning strategies for targeting their placement within watersheds were compared in terms of cost-effectiveness for environmental improvement in the 144 km² Neshanic River...

  5. Overlap in the functional neural systems involved in semantic and episodic memory retrieval.

    Science.gov (United States)

    Rajah, M N; McIntosh, A R

    2005-03-01

    Neuroimaging and neuropsychological data suggest that episodic and semantic memory may be mediated by distinct neural systems. However, an alternative perspective is that episodic and semantic memory represent different modes of processing within a single declarative memory system. To examine whether the multiple or the unitary system view better represents the data we conducted a network analysis using multivariate partial least squares (PLS ) activation analysis followed by covariance structural equation modeling (SEM) of positron emission tomography data obtained while healthy adults performed episodic and semantic verbal retrieval tasks. It is argued that if performance of episodic and semantic retrieval tasks are mediated by different memory systems, then there should differences in both regional activations and interregional correlations related to each type of retrieval task, respectively. The PLS results identified brain regions that were differentially active during episodic retrieval versus semantic retrieval. Regions that showed maximal differences in regional activity between episodic retrieval tasks were used to construct separate functional models for episodic and semantic retrieval. Omnibus tests of these functional models failed to find a significant difference across tasks for both functional models. The pattern of path coefficients for the episodic retrieval model were not different across tasks, nor were the path coefficients for the semantic retrieval model. The SEM results suggest that the same memory network/system was engaged across tasks, given the similarities in path coefficients. Therefore, activation differences between episodic and semantic retrieval may ref lect variation along a continuum of processing during task performance within the context of a single memory system.

  6. 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.

  7. Predicting Protein Function via Semantic Integration of Multiple Networks.

    Science.gov (United States)

    Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhu, Hailong

    2016-01-01

    Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet.

  8. 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.

  9. 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.

  10. Central-peripheral neural network interactions evoked by vagus nerve stimulation: functional consequences on control of cardiac function.

    Science.gov (United States)

    Ardell, Jeffrey L; Rajendran, Pradeep S; Nier, Heath A; KenKnight, Bruce H; Armour, J Andrew

    2015-11-15

    Using vagus nerve stimulation (VNS), we sought to determine the contribution of vagal afferents to efferent control of cardiac function. In anesthetized dogs, the right and left cervical vagosympathetic trunks were stimulated in the intact state, following ipsilateral or contralateral vagus nerve transection (VNTx), and then following bilateral VNTx. Stimulations were performed at currents from 0.25 to 4.0 mA, frequencies from 2 to 30 Hz, and a 500-μs pulse width. Right or left VNS evoked significantly greater current- and frequency-dependent suppression of chronotropic, inotropic, and lusitropic function subsequent to sequential VNTx. Bradycardia threshold was defined as the current first required for a 5% decrease in heart rate. The threshold for the right vs. left vagus-induced bradycardia in the intact state (2.91 ± 0.18 and 3.47 ± 0.20 mA, respectively) decreased significantly with right VNTx (1.69 ± 0.17 mA for right and 3.04 ± 0.27 mA for left) and decreased further following bilateral VNTx (1.29 ± 0.16 mA for right and 1.74 ± 0.19 mA for left). Similar effects were observed following left VNTx. The thresholds for afferent-mediated effects on cardiac parameters were 0.62 ± 0.04 and 0.65 ± 0.06 mA with right and left VNS, respectively, and were reflected primarily as augmentation. Afferent-mediated tachycardias were maintained following β-blockade but were eliminated by VNTx. The increased effectiveness and decrease in bradycardia threshold with sequential VNTx suggest that 1) vagal afferents inhibit centrally mediated parasympathetic efferent outflow and 2) the ipsilateral and contralateral vagi exert a substantial buffering capacity. The intact threshold reflects the interaction between multiple levels of the cardiac neural hierarchy. Copyright © 2015 the American Physiological Society.

  11. A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

    Directory of Open Access Journals (Sweden)

    Ozge Cagcag Yolcu

    2013-01-01

    Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

  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. 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.

  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. 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.

  17. 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

  18. Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks

    International Nuclear Information System (INIS)

    Amanifard, N.; Nariman-Zadeh, N.; Farahani, M.H.; Khalkhali, A.

    2008-01-01

    Over the past 15 years there have been several research efforts to capture the stall inception nature in axial flow compressors. However previous analytical models could not explain the formation of short-length-scale stall cells. This paper provides a new model based on evolved GMDH neural network for transient evolution of multiple short-length-scale stall cells in an axial compressor. Genetic Algorithms (GAs) are also employed for optimal design of connectivity configuration of such GMDH-type neural networks. In this way, low-pass filter (LPF) pressure trace near the rotor leading edge is modelled with respect to the variation of pressure coefficient, flow rate coefficient, and number of rotor rotations which are defined as inputs

  19. 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.

  20. A comparison of multiple regression and neural network techniques for mapping in situ pCO2 data

    International Nuclear Information System (INIS)

    Lefevre, Nathalie; Watson, Andrew J.; Watson, Adam R.

    2005-01-01

    Using about 138,000 measurements of surface pCO 2 in the Atlantic subpolar gyre (50-70 deg N, 60-10 deg W) during 1995-1997, we compare two methods of interpolation in space and time: a monthly distribution of surface pCO 2 constructed using multiple linear regressions on position and temperature, and a self-organizing neural network approach. Both methods confirm characteristics of the region found in previous work, i.e. the subpolar gyre is a sink for atmospheric CO 2 throughout the year, and exhibits a strong seasonal variability with the highest undersaturations occurring in spring and summer due to biological activity. As an annual average the surface pCO 2 is higher than estimates based on available syntheses of surface pCO 2 . This supports earlier suggestions that the sink of CO 2 in the Atlantic subpolar gyre has decreased over the last decade instead of increasing as previously assumed. The neural network is able to capture a more complex distribution than can be well represented by linear regressions, but both techniques agree relatively well on the average values of pCO 2 and derived fluxes. However, when both techniques are used with a subset of the data, the neural network predicts the remaining data to a much better accuracy than the regressions, with a residual standard deviation ranging from 3 to 11 μatm. The subpolar gyre is a net sink of CO 2 of 0.13 Gt-C/yr using the multiple linear regressions and 0.15 Gt-C/yr using the neural network, on average between 1995 and 1997. Both calculations were made with the NCEP monthly wind speeds converted to 10 m height and averaged between 1995 and 1997, and using the gas exchange coefficient of Wanninkhof

  1. Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

    Science.gov (United States)

    Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

    2013-01-01

    The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

  2. 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.

  3. Multiple functions of the S-phase checkpoint mediator.

    Science.gov (United States)

    Tanaka, Katsunori

    2010-01-01

    There is mounting evidence that replication defects are the major source of spontaneous genomic instability in cells, and that S-phase checkpoints are the principal defense against such instability. The S-phase checkpoint mediator protein Mrc1/Claspin mediates the checkpoint response to replication stress by facilitating phosphorylation of effector kinase by a sensor kinase. In this review, the multiple functions and the regulation of the S-phase checkpoint mediator are discussed.

  4. 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.

  5. 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).

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    Science.gov (United States)

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  15. 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

  16. Towards Integration of Biological and Physiological Functions at Multiple Levels

    Directory of Open Access Journals (Sweden)

    Taishin eNomura

    2010-12-01

    Full Text Available An aim of systems physiology today can be stated as to establish logical and quantitative bridges between phenomenological attributes of physiological entities such as cells and organs and physical attributes of biological entities, i.e., biological molecules, allowing us to describe and better understand physiological functions in terms of underlying biological functions. This article illustrates possible schema that can be used for promoting systems physiology by integrating quantitative knowledge of biological and physiological functions at multiple levels of time and space with the use of information technology infrastructure. Emphasis will be made for systematic, modular, hierarchical, and standardized descriptions of mathematical models of the functions and advantages for the use of them.

  17. The ALMT Gene Family Performs Multiple Functions in Plants

    Directory of Open Access Journals (Sweden)

    Jie Liu

    2018-02-01

    Full Text Available The aluminium activated malate transporter (ALMT gene family is named after the first member of the family identified in wheat (Triticum aestivum L.. The product of this gene controls resistance to aluminium (Al toxicity. ALMT genes encode transmembrane proteins that function as anion channels and perform multiple functions involving the transport of organic anions (e.g., carboxylates and inorganic anions in cells. They share a PF11744 domain and are classified in the Fusaric acid resistance protein-like superfamily, CL0307. The proteins typically have five to seven transmembrane regions in the N-terminal half and a long hydrophillic C-terminal tail but predictions of secondary structure vary. Although widely spread in plants, relatively little information is available on the roles performed by other members of this family. In this review, we summarized functions of ALMT gene families, including Al resistance, stomatal function, mineral nutrition, microbe interactions, fruit acidity, light response and seed development.

  18. 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...

  19. Multistability of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing; Cao, Jinde

    2015-11-01

    The problem of coexistence and dynamical behaviors of multiple equilibrium points is addressed for a class of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays. By virtue of the fixed point theorem, nonsmooth analysis theory and other analytical tools, some sufficient conditions are established to guarantee that such n-dimensional memristive Cohen-Grossberg neural networks can have 5(n) equilibrium points, among which 3(n) equilibrium points are locally exponentially stable. It is shown that greater storage capacity can be achieved by neural networks with the non-monotonic activation functions introduced herein than the ones with Mexican-hat-type activation function. In addition, unlike most existing multistability results of neural networks with monotonic activation functions, those obtained 3(n) locally stable equilibrium points are located both in saturated regions and unsaturated regions. The theoretical findings are verified by an illustrative example with computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. 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

  1. Speaking in Multiple Languages: Neural Correlates of Language Proficiency in Multilingual Word Production

    Science.gov (United States)

    Videsott, Gerda; Herrnberger, Barbel; Hoenig, Klaus; Schilly, Edgar; Grothe, Jo; Wiater, Werner; Spitzer, Manfred; Kiefer, Markus

    2010-01-01

    The human brain has the fascinating ability to represent and to process several languages. Although the first and further languages activate partially different brain networks, the linguistic factors underlying these differences in language processing have to be further specified. We investigated the neural correlates of language proficiency in a…

  2. Neural Dynamics Associated with Semantic and Episodic Memory for Faces: Evidence from Multiple Frequency Bands

    Science.gov (United States)

    Zion-Golumbic, Elana; Kutas, Marta; Bentin, Shlomo

    2010-01-01

    Prior semantic knowledge facilitates episodic recognition memory for faces. To examine the neural manifestation of the interplay between semantic and episodic memory, we investigated neuroelectric dynamics during the creation (study) and the retrieval (test) of episodic memories for famous and nonfamous faces. Episodic memory effects were evident…

  3. Multitasking in multiple sclerosis: can it inform vocational functioning?

    Science.gov (United States)

    Morse, Chelsea L; Schultheis, Maria T; McKeever, Joshua D; Leist, Thomas

    2013-12-01

    To examine associations between multitasking ability defined by performance on a complex task integrating multiple cognitive domains and vocational functioning in multiple sclerosis (MS). Survey data collection. Laboratory with referrals from an outpatient clinic. Community-dwelling individuals with MS (N=30) referred between October 2011 and June 2012. Not applicable. The modified Six Elements Test (SET) to measure multitasking ability, Fatigue Severity Scale to measure fatigue, several neuropsychological measures of executive functioning, and vocational status. Among the sample, 60% of individuals have reduced their work hours because of MS symptoms (cutback employment group) and 40% had maintained their work hours. Among both groups, SET performance was significantly associated with performance on several measures of neuropsychological functioning. Individuals in the cutback employment group demonstrated significantly worse overall performance on the SET (P=.041). Logistic regression was used to evaluate associations between SET performance and vocational status, while accounting for neuropsychological performance and fatigue. The overall model was significant (χ(2)3=8.65, P=.032), with fatigue [Exp(B)=.83, P=.01] and multitasking ability [Exp(B)=.60, P=.043] retained as significant predictors. Multitasking ability may play an important role in performance at work for individuals with MS. Given that multitasking was associated with vocational functioning, future efforts should assess the usefulness of incorporating multitasking ability into rehabilitation planning. Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  4. 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.

  5. 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.

  6. Global existence of periodic solutions in a simplified four-neuron BAM neural network model with multiple delays

    Directory of Open Access Journals (Sweden)

    2006-01-01

    Full Text Available We consider a simplified bidirectional associated memory (BAM neural network model with four neurons and multiple time delays. The global existence of periodic solutions bifurcating from Hopf bifurcations is investigated by applying the global Hopf bifurcation theorem due to Wu and Bendixson's criterion for high-dimensional ordinary differential equations due to Li and Muldowney. It is shown that the local Hopf bifurcation implies the global Hopf bifurcation after the second critical value of the sum of two delays. Numerical simulations supporting the theoretical analysis are also included.

  7. Assessment of the expected construction company’s net profit using neural network and multiple regression models

    Directory of Open Access Journals (Sweden)

    H.H. Mohamad

    2013-09-01

    This research aims to develop a mathematical model for assessing the expected net profit of any construction company. To achieve the research objective, four steps were performed. First, the main factors affecting firms’ net profit were identified. Second, pertinent data regarding the net profit factors were collected. Third, two different net profit models were developed using the Multiple Regression (MR and the Neural Network (NN techniques. The validity of the proposed models was also investigated. Finally, the results of both MR and NN models were compared to investigate the predictive capabilities of the two models.

  8. QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

    Directory of Open Access Journals (Sweden)

    Rachid Darnag

    2017-02-01

    Full Text Available Support vector machines (SVM represent one of the most promising Machine Learning (ML tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR models using molecular descriptors. Multiple linear regression (MLR and artificial neural networks (ANNs were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated.

  9. 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.

  10. Prediction of beta-turns and beta-turn types by a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN).

    Science.gov (United States)

    Kirschner, Andreas; Frishman, Dmitrij

    2008-10-01

    Prediction of beta-turns from amino acid sequences has long been recognized as an important problem in structural bioinformatics due to their frequent occurrence as well as their structural and functional significance. Because various structural features of proteins are intercorrelated, secondary structure information has been often employed as an additional input for machine learning algorithms while predicting beta-turns. Here we present a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN) capable of predicting multiple mutually dependent structural motifs and demonstrate its efficiency in recognizing three aspects of protein structure: beta-turns, beta-turn types, and secondary structure. The advantage of our method compared to other predictors is that it does not require any external input except for sequence profiles because interdependencies between different structural features are taken into account implicitly during the learning process. In a sevenfold cross-validation experiment on a standard test dataset our method exhibits the total prediction accuracy of 77.9% and the Mathew's Correlation Coefficient of 0.45, the highest performance reported so far. It also outperforms other known methods in delineating individual turn types. We demonstrate how simultaneous prediction of multiple targets influences prediction performance on single targets. The MOLEBRNN presented here is a generic method applicable in a variety of research fields where multiple mutually depending target classes need to be predicted. http://webclu.bio.wzw.tum.de/predator-web/.

  11. Psychological functioning in primary progressive versus secondary progressive multiple sclerosis

    DEFF Research Database (Denmark)

    Vleugels, L; Pfennings, L E; Pouwer, F

    1998-01-01

    Psychological functioning in two types of multiple sclerosis (MS) patients is assessed: primary progressive (PP) and secondary progressive (SP) patients. On the basis of differences in clinical course and underlying pathology we hypothesized that primary progressive patients and secondary...... progressive patients might have different psychological functioning. Seventy patients treated in an MS centre were examined cross-sectionally. Forty had an SP course of MS and 30 a PP course. The 33 male and 37 female patients had a mean age of 48.4 years (SD 11.2) and mean age of onset of MS of 30.7 years...... (SD 11.1). Patients completed questionnaires measuring among others the following aspects of psychological functioning: depression (BDI, SCL-90), anxiety (STAI, SCL-90), agoraphobia (SCL-90), somatic complaints (SCL-90), hostility (SCL-90) and attitude towards handicap (GHAS). Patients with a PP...

  12. 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

  13. Neural Dynamics of Multiple Object Processing in Mild Cognitive Impairment and Alzheimer's Disease: Future Early Diagnostic Biomarkers?

    Science.gov (United States)

    Bagattini, Chiara; Mazza, Veronica; Panizza, Laura; Ferrari, Clarissa; Bonomini, Cristina; Brignani, Debora

    2017-01-01

    The aim of this study was to investigate the behavioral and electrophysiological dynamics of multiple object processing (MOP) in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to test whether its neural signatures may represent reliable diagnostic biomarkers. Behavioral performance and event-related potentials [N2pc and contralateral delay activity (CDA)] were measured in AD, MCI, and healthy controls during a MOP task, which consisted in enumerating a variable number of targets presented among distractors. AD patients showed an overall decline in accuracy for both small and large target quantities, whereas in MCI patients, only enumeration of large quantities was impaired. N2pc, a neural marker of attentive individuation, was spared in both AD and MCI patients. In contrast, CDA, which indexes visual short term memory abilities, was altered in both groups of patients, with a non-linear pattern of amplitude modulation along the continuum of the disease: a reduction in AD and an increase in MCI. These results indicate that AD pathology shows a progressive decline in MOP, which is associated to the decay of visual short-term memory mechanisms. Crucially, CDA may be considered as a useful neural signature both to distinguish between healthy and pathological aging and to characterize the different stages along the AD continuum, possibly becoming a reliable candidate for an early diagnostic biomarker of AD pathology.

  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. Mode Shape Analysis of Multiple Cracked Functionally Graded Timoshenko Beams

    Directory of Open Access Journals (Sweden)

    Tran Van Lien

    Full Text Available Abstract The present paper addresses free vibration of multiple cracked Timoshenko beams made of Functionally Graded Material (FGM. Cracks are modeled by rotational spring of stiffness calculated from the crack depth and material properties vary according to the power law throughout the beam thickness. Governing equations for free vibration of the beam are formulated with taking into account actual position of the neutral plane. The obtained frequency equation and mode shapes are used for analysis of the beam mode shapes in dependence on the material and crack parameters. Numerical results validate usefulness of the proposed herein theory and show that mode shapes are good indication for detecting multiple cracks in Timoshenko FGM beams.

  16. PERSPECTIVE: Translational neural engineering: multiple perspectives on bringing benchtop research into the clinical domain

    Science.gov (United States)

    Rousche, Patrick; Schneeweis, David M.; Perreault, Eric J.; Jensen, Winnie

    2008-03-01

    A half-day forum to address a wide range of issues related to translational neural engineering was conducted at the annual meeting of the Biomedical Engineering Society. Successful practitioners of translational neural engineering from academics, clinical medicine and industry were invited to share a diversity of perspectives and experiences on the translational process. The forum was targeted towards traditional academic researchers who may be interested in the expanded funding opportunities available for translational research that emphasizes product commercialization and clinical implementation. The seminar was funded by the NIH with support from the Rehabilitation Institute of Chicago. We report here a summary of the speaker viewpoints with particular focus on extracting successful strategies for engaging in or conducting translational neural engineering research. Daryl Kipke, PhD, (Department of Biomedical Engineering at the University of Michigan) and Molly Shoichet, PhD, (Department of Chemical Engineering at the University of Toronto) gave details of their extensive experience with product commercialization while holding primary appointments in academic departments. They both encouraged strong clinical input at very early stages of research. Neurosurgeon Fady Charbel, MD, (Department of Neurosurgery at the University of Illinois at Chicago) discussed his role in product commercialization as a clinician. Todd Kuiken, MD, PhD, (Director of the Neural Engineering for Artificial Limbs at the Rehabilitation Institute of Chicago, affiliated with Northwestern University) also a clinician, described a model of translational engineering that emphasized the development of clinically relevant technology, without a strong commercialization imperative. The clinicians emphasized the importance of communicating effectively with engineers. Representing commercial neural engineering was Doug Sheffield, PhD, (Director of New Technology at Vertis Neuroscience, Inc.) who

  17. Translational neural engineering: multiple perspectives on bringing benchtop research into the clinical domain.

    Science.gov (United States)

    Rousche, Patrick; Schneeweis, David M; Perreault, Eric J; Jensen, Winnie

    2008-03-01

    A half-day forum to address a wide range of issues related to translational neural engineering was conducted at the annual meeting of the Biomedical Engineering Society. Successful practitioners of translational neural engineering from academics, clinical medicine and industry were invited to share a diversity of perspectives and experiences on the translational process. The forum was targeted towards traditional academic researchers who may be interested in the expanded funding opportunities available for translational research that emphasizes product commercialization and clinical implementation. The seminar was funded by the NIH with support from the Rehabilitation Institute of Chicago. We report here a summary of the speaker viewpoints with particular focus on extracting successful strategies for engaging in or conducting translational neural engineering research. Daryl Kipke, PhD, (Department of Biomedical Engineering at the University of Michigan) and Molly Shoichet, PhD, (Department of Chemical Engineering at the University of Toronto) gave details of their extensive experience with product commercialization while holding primary appointments in academic departments. They both encouraged strong clinical input at very early stages of research. Neurosurgeon Fady Charbel, MD, (Department of Neurosurgery at the University of Illinois at Chicago) discussed his role in product commercialization as a clinician. Todd Kuiken, MD, PhD, (Director of the Neural Engineering for Artificial Limbs at the Rehabilitation Institute of Chicago, affiliated with Northwestern University) also a clinician, described a model of translational engineering that emphasized the development of clinically relevant technology, without a strong commercialization imperative. The clinicians emphasized the importance of communicating effectively with engineers. Representing commercial neural engineering was Doug Sheffield, PhD, (Director of New Technology at Vertis Neuroscience, Inc.) who

  18. Transcriptional and Genomic Targets of Neural Stem Cells for Functional Recovery after Hemorrhagic Stroke

    Directory of Open Access Journals (Sweden)

    Le Zhang

    2017-01-01

    Full Text Available Hemorrhagic stroke is a life-threatening disease characterized by a sudden rupture of cerebral blood vessels, and it is widely believed that neural cell death occurs after exposure to blood metabolites or subsequently damaged cells. Neural stem cells (NSCs, which maintain neurogenesis and are found in subgranular zone and subventricular zone, are thought to be an endogenous neuroprotective mechanism for these brain injuries. However, due to the complexity of NSCs and their microenvironment, current strategies cannot satisfactorily enhance functional recovery after hemorrhagic stroke. It is well known that transcriptional and genomic pathways play important roles in ensuring the normal functions of NSCs, including proliferation, migration, differentiation, and neural reconnection. Recently, emerging evidence from the use of new technologies such as next-generation sequencing and transcriptome profiling has provided insight into our understanding of genomic function and regulation of NSCs. In the present article, we summarize and present the current data on the control of NSCs at both the transcriptional and genomic levels. Using bioinformatics methods, we sought to predict novel therapeutic targets of endogenous neurogenesis and exogenous NSC transplantation for functional recovery after hemorrhagic stroke, which could also advance our understanding of its pathophysiology.

  19. Alteration in neonatal nutrition causes perturbations in hypothalamic neural circuits controlling reproductive function.

    Science.gov (United States)

    Caron, Emilie; Ciofi, Philippe; Prevot, Vincent; Bouret, Sebastien G

    2012-08-15

    It is increasingly accepted that alterations of the early life environment may have lasting impacts on physiological functions. In particular, epidemiological and animal studies have indicated that changes in growth and nutrition during childhood and adolescence can impair reproductive function. However, the precise biological mechanisms that underlie these programming effects of neonatal nutrition on reproduction are still poorly understood. Here, we used a mouse model of divergent litter size to investigate the effects of early postnatal overnutrition and undernutrition on the maturation of hypothalamic circuits involved in reproductive function. Neonatally undernourished females display attenuated postnatal growth associated with delayed puberty and defective development of axonal projections from the arcuate nucleus to the preoptic region. These alterations persist into adulthood and specifically affect the organization of neural projections containing kisspeptin, a key neuropeptide involved in pubertal activation and fertility. Neonatal overfeeding also perturbs the development of neural projections from the arcuate nucleus to the preoptic region, but it does not result in alterations in kisspeptin projections. These studies indicate that alterations in the early nutritional environment cause lasting and deleterious effects on the organization of neural circuits involved in the control of reproduction, and that these changes are associated with lifelong functional perturbations.

  20. Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation

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    Chunqing Li

    2012-01-01

    Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.

  1. The neural network involved in a bimanual tactile-tactile matching discrimination task: a functional imaging study at 3 T

    Energy Technology Data Exchange (ETDEWEB)

    Habas, Christophe; Cabanis, Emmanuel A. [UPMC Paris 6, Service de NeuroImagerie, Hopital des Quinze-Vingts, Paris (France)

    2007-08-15

    The cerebral and cerebellar network involved in a bimanual object recognition was studied in blood oxygenation dependent level functional magnetic resonance imaging (fMRI). Nine healthy right-handed volunteers were scanned (1) while performing bilateral finger movements (nondiscrimination motor task), and (2) while performing a bimanual tactile-tactile matching discrimination task using small chess pieces (tactile discrimination task). Extensive activations were specifically observed in the parietal (SII, superior lateral lobule), insular, prefrontal, cingulate and neocerebellar cortices (HVIII), with a left predominance in motor areas, during the tactile discrimination task in contrast to the findings during the nondiscrimination motor task. Bimanual tactile-tactile matching discrimination recruits multiple sensorimotor and associative cerebral and neocerebellar networks (including the cerebellar second homunculus, HVIII), comparable to the neural circuits involved in unimanual tactile object recognition. (orig.)

  2. Anger under control: neural correlates of frustration as a function of trait aggression.

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    Christina M Pawliczek

    Full Text Available Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed two extreme groups, one including individuals reporting high (n=21 and one reporting low (n=18 trait aggression. Using functional magnetic resonance imaging (fMRI at 3T, all participants were put through a frustration task comprising unsolvable anagrams of German nouns. Despite similar behavioral performance, males with high trait aggression reported higher ratings of negative affect and anger after the frustration task. Moreover, they showed relatively decreased activation in the frontal brain regions and the dorsal anterior cingulate cortex (dACC as well as relatively less amygdala activation in response to frustration. Our findings indicate distinct frontal and limbic processing mechanisms following frustration modulated by trait aggression. In response to a frustrating event, HA individuals show some of the personality characteristics and neural processing patterns observed in abnormally aggressive populations. Highlighting the impact of aggressive traits on the behavioral and neural responses to frustration in non-psychiatric extreme groups can facilitate further characterization of neural dysfunctions underlying psychiatric disorders that involve abnormal frustration processing and aggression.

  3. Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro.

    Science.gov (United States)

    Kirwan, Peter; Turner-Bridger, Benita; Peter, Manuel; Momoh, Ayiba; Arambepola, Devika; Robinson, Hugh P C; Livesey, Frederick J

    2015-09-15

    A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology. © 2015. Published by The Company of Biologists Ltd.

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

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    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.

  5. Could LC-NE-Dependent Adjustment of Neural Gain Drive Functional Brain Network Reorganization?

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    Carole Guedj

    2017-01-01

    Full Text Available The locus coeruleus-norepinephrine (LC-NE system is thought to act at synaptic, cellular, microcircuit, and network levels to facilitate cognitive functions through at least two different processes, not mutually exclusive. Accordingly, as a reset signal, the LC-NE system could trigger brain network reorganizations in response to salient information in the environment and/or adjust the neural gain within its target regions to optimize behavioral responses. Here, we provide evidence of the co-occurrence of these two mechanisms at the whole-brain level, in resting-state conditions following a pharmacological stimulation of the LC-NE system. We propose that these two mechanisms are interdependent such that the LC-NE-dependent adjustment of the neural gain inferred from the clustering coefficient could drive functional brain network reorganizations through coherence in the gamma rhythm. Via the temporal dynamic of gamma-range band-limited power, the release of NE could adjust the neural gain, promoting interactions only within the neuronal populations whose amplitude envelopes are correlated, thus making it possible to reorganize neuronal ensembles, functional networks, and ultimately, behavioral responses. Thus, our proposal offers a unified framework integrating the putative influence of the LC-NE system on both local- and long-range adjustments of brain dynamics underlying behavioral flexibility.

  6. Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

    Science.gov (United States)

    Golmohammadi, Hassan

    2009-11-30

    A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

  7. Synergic Functions of miRNAs Determine Neuronal Fate of Adult Neural Stem Cells

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    Meritxell Pons-Espinal

    2017-04-01

    Full Text Available Summary: Adult neurogenesis requires the precise control of neuronal versus astrocyte lineage determination in neural stem cells. While microRNAs (miRNAs are critically involved in this step during development, their actions in adult hippocampal neural stem cells (aNSCs has been unclear. As entry point to address that question we chose DICER, an endoribonuclease essential for miRNA biogenesis and other RNAi-related processes. By specific ablation of Dicer in aNSCs in vivo and in vitro, we demonstrate that miRNAs are required for the generation of new neurons, but not astrocytes, in the adult murine hippocampus. Moreover, we identify 11 miRNAs, of which 9 have not been previously characterized in neurogenesis, that determine neurogenic lineage fate choice of aNSCs at the expense of astrogliogenesis. Finally, we propose that the 11 miRNAs sustain adult hippocampal neurogenesis through synergistic modulation of 26 putative targets from different pathways. : In this article, the authors demonstrate that Dicer-dependent miRNAs are required for the generation of new neurons, but not astrocytes, in the adult hippocampus in vivo and in vitro. The authors identify a new set of 11 miRNAs that synergistically converge on multiple targets in different pathways to sustain neurogenic lineage fate commitment in aNSCs. Keywords: mouse, hippocampus, neural stem cells, fate choice, adult neurogenesis, astrogliogenesis, DICER, microRNAs, synergy

  8. Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

    Science.gov (United States)

    Liu, Qingshan; Wang, Jun

    2011-04-01

    This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

  9. A model for integrating elementary neural functions into delayed-response behavior.

    Directory of Open Access Journals (Sweden)

    Thomas Gisiger

    2006-04-01

    Full Text Available It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning, and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task, or recalling from this image another one that has been associated with it during training (delayed-pair association task. The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

  10. A model for integrating elementary neural functions into delayed-response behavior.

    Science.gov (United States)

    Gisiger, Thomas; Kerszberg, Michel

    2006-04-01

    It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

  11. Neural Correlates of Symptom Dimensions in Pediatric Obsessive-Compulsive Disorder: A Functional Magnetic Resonance Imaging Study

    Science.gov (United States)

    Gilbert, Andrew R.; Akkal, Dalila; Almeida, Jorge R. C.; Mataix-Cols, David; Kalas, Catherine; Devlin, Bernie; Birmaher, Boris; Phillips, Mary L.

    2009-01-01

    The use of functional magnetic resonance imaging on a group of pediatric subjects with obsessive compulsive disorder reveals that this group has reduced activity in neural regions underlying emotional processing, cognitive processing, and motor performance as compared to control subjects.

  12. Novel stability criteria for uncertain delayed Cohen-Grossberg neural networks using discretized Lyapunov functional

    International Nuclear Information System (INIS)

    Souza, Fernando O.; Palhares, Reinaldo M.; Ekel, Petr Ya.

    2009-01-01

    This paper deals with the stability analysis of delayed uncertain Cohen-Grossberg neural networks (CGNN). The proposed methodology consists in obtaining new robust stability criteria formulated as linear matrix inequalities (LMIs) via the Lyapunov-Krasovskii theory. Particularly one stability criterion is derived from the selection of a parameter-dependent Lyapunov-Krasovskii functional, which allied with the Gu's discretization technique and a simple strategy that decouples the system matrices from the functional matrices, assures a less conservative stability condition. Two computer simulations are presented to support the improved theoretical results.

  13. Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation.

    Science.gov (United States)

    Mall, Susmita; Chakraverty, S

    2016-08-01

    Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced by expansion block of input pattern using Hermite orthogonal polynomials. A feedforward neural network model with the unsupervised error backpropagation principle is used for modifying the network parameters and minimizing the computed error function. The Van der Pol-Duffing and Duffing oscillator equations may not be solved exactly. Here, approximate solutions of these types of equations have been obtained by applying the HeNN model for the first time. Three mathematical example problems and two real-life application problems of Van der Pol-Duffing oscillator equation, extracting the features of early mechanical failure signal and weak signal detection problems, are solved using the proposed HeNN method. HeNN approximate solutions have been compared with results obtained by the well known Runge-Kutta method. Computed results are depicted in term of graphs. After training the HeNN model, we may use it as a black box to get numerical results at any arbitrary point in the domain. Thus, the proposed HeNN method is efficient. The results reveal that this method is reliable and can be applied to other nonlinear problems too.

  14. Neural mechanisms of subclinical depressive symptoms in women: a pilot functional brain imaging study

    Directory of Open Access Journals (Sweden)

    Felder Jennifer N

    2012-09-01

    Full Text Available Abstract Background Studies of individuals who do not meet criteria for major depressive disorder (MDD but with subclinical levels of depressive symptoms may aid in the identification of neurofunctional abnormalities that possibly precede and predict the development of MDD. The purpose of this study was to evaluate relations between subclinical levels of depressive symptoms and neural activation patterns during tasks previously shown to differentiate individuals with and without MDD. Methods Functional magnetic resonance imaging (fMRI was used to assess neural activations during active emotion regulation, a resting state scan, and reward processing. Participants were twelve females with a range of depressive symptoms who did not meet criteria for MDD. Results Increased depressive symptom severity predicted (1 decreased left midfrontal gyrus activation during reappraisal of sad stimuli; (2 increased right midfrontal gyrus activation during distraction from sad stimuli; (3 increased functional connectivity between a precuneus seed region and left orbitofrontal cortex during a resting state scan; and (4 increased paracingulate activation during non-win outcomes during a reward-processing task. Conclusions These pilot data shed light on relations between subclinical levels of depressive symptoms in the absence of a formal MDD diagnosis and neural activation patterns. Future studies will be needed to test the utility of these activation patterns for predicting MDD onset in at-risk samples.

  15. Assessment of patients with multiple sclerosis according to tests of the Multiple Sclerosis Functional Composite

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    Anelise Ineu Figueiredo

    Full Text Available Abstract Introduction: The Multiple Sclerosis Functional Composite (MSFC is a scale that evaluates the functional and cognitive aspects of patients with multiple sclerosis (MS. Objective: To compare the performance of individuals with the relapsing-remitting form of MS (RRMS with a group of healthy subjects using the MSFC. Methods: Twenty subjects were investigated in this study, consisting of 10 patients with clinical diagnosis of RRMS and 10 controls with similar gender and age to the group with the disease. The three tests that comprise the MSFC were used for the evaluation of gait, upper limb motor function and cognition (memory and processing speed. Student's t-test was used to assess data with normal distribution and data with skewed distribution were evaluated using the Mann-Whitney test. Results: The results showed that the patients with RRMS took longer to perform the locomotion test (6.91 ± 2.35 compared to the control group (5.16 ± 1.28. The MS group (22.06 ± 5.44 also showed greater difficulty in performing a task with the dominant upper limb compared to the healthy subjects (17.79 ± 2.96. No statistically significant difference was found between the groups in the performance of cognitive tasks (p = .65. Conclusion: The use of the MSFC tests proved valuable for measuring possible motor and cognitive impairments in patients with RRMS. Thus, it is suggested that this scale is adopted in clinical practice, improving therapies for the treatment of MS patients and thereby providing them a better quality of life.

  16. Altered Immune Function Associated with Disordered Neural Connectivity and Executive Dysfunctions: A Neurophysiological Study on Children with Autism Spectrum Disorders

    Science.gov (United States)

    Han, Yvonne M. Y.; Chan, Agnes S.; Sze, Sophia L.; Cheung, Mei-Chun; Wong, Chun-kwok; Lam, Joseph M. K.; Poon, Priscilla M. K.

    2013-01-01

    Previous studies have shown that children with autism spectrum disorders (ASDs) have impaired executive function, disordered neural connectivity, and abnormal immunologic function. The present study examined whether these abnormalities were associated. Seventeen high-functioning (HFA) and 17 low-functioning (LFA) children with ASD, aged 8-17…

  17. Progressive multiple sclerosis, cognitive function, and quality of life.

    Science.gov (United States)

    Højsgaard Chow, Helene; Schreiber, Karen; Magyari, Melinda; Ammitzbøll, Cecilie; Börnsen, Lars; Romme Christensen, Jeppe; Ratzer, Rikke; Soelberg Sørensen, Per; Sellebjerg, Finn

    2018-02-01

    Patients with progressive multiple sclerosis (MS) often have cognitive impairment in addition to physical impairment. The burden of cognitive and physical impairment progresses over time, and may be major determinants of quality of life. The aim of this study was to assess to which degree quality of life correlates with physical and cognitive function in progressive MS. This is a retrospective study of 52 patients with primary progressive ( N  = 18) and secondary progressive MS ( N  = 34). Physical disability was assessed using the Expanded Disability Status Scale, Timed 25 Foot Walk (T25FW) test and 9-Hole Peg Test (9HPT). Cognitive function was assessed using Symbol Digit Modalities Test (SDMT), Paced Auditory Serial Addition Test, and Trail Making Test B (TRAIL-B). In addition, quality of life was assessed by the Short Form 36 (SF-36) questionnaire. Only measures of cognitive function correlated with the overall SF-36 quality of life score and the Mental Component Summary score from the SF-36. The only physical measure that correlated with a measure of quality of life was T25FW test, which correlated with the Physical Component Summary from the SF-36. We found no other significant correlations between the measures of cognitive function and the overall physical measures but interestingly, we found a possible relationship between the 9HPT score for the nondominant hand and the SDMT and TRAIL-B. Our findings support inclusion of measures of cognitive function in the assessment of patients with progressive MS as these correlated closer with quality of life than measures of physical impairment.

  18. 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.

  19. Identifying thematic roles from neural representations measured by functional magnetic resonance imaging.

    Science.gov (United States)

    Wang, Jing; Cherkassky, Vladimir L; Yang, Ying; Chang, Kai-Min Kevin; Vargas, Robert; Diana, Nicholas; Just, Marcel Adam

    2016-01-01

    The generativity and complexity of human thought stem in large part from the ability to represent relations among concepts and form propositions. The current study reveals how a given object such as rabbit is neurally encoded differently and identifiably depending on whether it is an agent ("the rabbit punches the monkey") or a patient ("the monkey punches the rabbit"). Machine-learning classifiers were trained on functional magnetic resonance imaging (fMRI) data evoked by a set of short videos that conveyed agent-verb-patient propositions. When tested on a held-out video, the classifiers were able to reliably identify the thematic role of an object from its associated fMRI activation pattern. Moreover, when trained on one subset of the study participants, classifiers reliably identified the thematic roles in the data of a left-out participant (mean accuracy = .66), indicating that the neural representations of thematic roles were common across individuals.

  20. Probability of detection as a function of multiple influencing parameters

    Energy Technology Data Exchange (ETDEWEB)

    Pavlovic, Mato

    2014-10-15

    Non-destructive testing is subject to measurement uncertainties. In safety critical applications the reliability assessment of its capability to detect flaws is therefore necessary. In most applications, the flaw size is the single most important parameter that influences the probability of detection (POD) of the flaw. That is why the POD is typically calculated and expressed as a function of the flaw size. The capability of the inspection system to detect flaws is established by comparing the size of reliably detected flaw with the size of the flaw that is critical for the structural integrity. Applications where several factors have an important influence on the POD are investigated in this dissertation. To devise a reliable estimation of the NDT system capability it is necessary to express the POD as a function of all these factors. A multi-parameter POD model is developed. It enables POD to be calculated and expressed as a function of several influencing parameters. The model was tested on the data from the ultrasonic inspection of copper and cast iron components with artificial flaws. Also, a technique to spatially present POD data called the volume POD is developed. The fusion of the POD data coming from multiple inspections of the same component with different sensors is performed to reach the overall POD of the inspection system.

  1. Fast, multiple optimizations of quadratic dose objective functions in IMRT

    International Nuclear Information System (INIS)

    Breedveld, Sebastiaan; Storchi, Pascal R M; Keijzer, Marleen; Heijmen, Ben J M

    2006-01-01

    Inverse treatment planning for intensity-modulated radiotherapy may include time consuming, multiple minimizations of an objective function. In this paper, methods are presented to speed up the process of (repeated) minimization of the well-known quadratic dose objective function, extended with a smoothing term that ensures generation of clinically acceptable beam profiles. In between two subsequent optimizations, the voxel-dependent importance factors of the quadratic terms will generally be adjusted, based on an intermediate plan evaluation. The objective function has been written in matrix-vector format, facilitating the use of a recently published, fast quadratic minimization algorithm, instead of commonly applied gradient-based methods. This format also reduces the calculation time in between subsequent minimizations, related to adjustment of the voxel-dependent importance factors. Sparse matrices are used to limit the required amount of computer memory. For three patients, comparisons have been made with a gradient method. Mean speed improvements of up to a factor of 37 have been achieved

  2. Wavefield reconstruction inversion with a multiplicative cost function

    Science.gov (United States)

    da Silva, Nuno V.; Yao, Gang

    2018-01-01

    We present a method for the automatic estimation of the trade-off parameter in the context of wavefield reconstruction inversion (WRI). WRI formulates the inverse problem as an optimisation problem, minimising the data misfit while penalising with a wave equation constraining term. The trade-off between the two terms is balanced by a scaling factor that balances the contributions of the data-misfit term and the constraining term to the value of the objective function. If this parameter is too large then it implies penalizing for the wave equation imposing a hard constraint in the inversion. If it is too small, then this leads to a poorly constrained solution as it is essentially penalizing for the data misfit and not taking into account the physics that explains the data. This paper introduces a new approach for the formulation of WRI recasting its formulation into a multiplicative cost function. We demonstrate that the proposed method outperforms the additive cost function when the trade-off parameter is appropriately scaled in the latter, when adapting it throughout the iterations, and when the data is contaminated with Gaussian random noise. Thus this work contributes with a framework for a more automated application of WRI.

  3. Probability of detection as a function of multiple influencing parameters

    International Nuclear Information System (INIS)

    Pavlovic, Mato

    2014-01-01

    Non-destructive testing is subject to measurement uncertainties. In safety critical applications the reliability assessment of its capability to detect flaws is therefore necessary. In most applications, the flaw size is the single most important parameter that influences the probability of detection (POD) of the flaw. That is why the POD is typically calculated and expressed as a function of the flaw size. The capability of the inspection system to detect flaws is established by comparing the size of reliably detected flaw with the size of the flaw that is critical for the structural integrity. Applications where several factors have an important influence on the POD are investigated in this dissertation. To devise a reliable estimation of the NDT system capability it is necessary to express the POD as a function of all these factors. A multi-parameter POD model is developed. It enables POD to be calculated and expressed as a function of several influencing parameters. The model was tested on the data from the ultrasonic inspection of copper and cast iron components with artificial flaws. Also, a technique to spatially present POD data called the volume POD is developed. The fusion of the POD data coming from multiple inspections of the same component with different sensors is performed to reach the overall POD of the inspection system.

  4. Functional Magnetic Resonance Imaging Evaluation of Auricular Percutaneous Electrical Neural Field Stimulation for Fibromyalgia: Protocol for a Feasibility Study.

    Science.gov (United States)

    Gebre, Melat; Woodbury, Anna; Napadow, Vitaly; Krishnamurthy, Venkatagiri; Krishnamurthy, Lisa C; Sniecinski, Roman; Crosson, Bruce

    2018-02-06

    Fibromyalgia is a chronic pain state that includes widespread musculoskeletal pain, fatigue, psychiatric symptoms, cognitive and sleep disturbances, and multiple somatic symptoms. Current therapies are often insufficient or come with significant risks, and while there is an increasing demand for non-pharmacologic and especially non-opioid pain management such as that offered through complementary and alternative medicine therapies, there is currently insufficient evidence to recommend these therapies. Percutaneous electrical neural stimulation (PENS) is an evidence-based treatment option for pain conditions that involves electrical current stimulation through needles inserted into the skin. Percutaneous electrical neural field stimulation (PENFS) of the auricle is similar to PENS, but instead of targeting a single neurovascular bundle, PENFS stimulates the entire ear, covering all auricular branches of the cranial nerves, including the vagus nerve. The neural mechanisms of PENFS for fibromyalgia symptom relief are unknown. We hypothesize that PENFS treatment will decrease functional brain connectivity between the default mode network (DMN) and right posterior insula in fibromyalgia patients. We expect that the decrease in functional connectivity between the DMN and insula will correlate with patient-reported analgesic improvements as indicated by the Defense and Veterans Pain Rating Scale (DVPRS) and will be anti-correlated with patient-reported analgesic medication consumption. Exploratory analyses will be performed for further hypothesis generation. A total of 20 adults from the Atlanta Veterans Affairs Medical Center diagnosed with fibromyalgia will be randomized into 2 groups: 10 subjects to a control (standard therapy) group and 10 subjects to a PENFS treatment group. The pragmatic, standard therapy group will include pharmacologic treatments such as anticonvulsants, non-steroidal anti-inflammatory drugs, topical agents and physical therapy individualized to

  5. Adaptive Neural Control of Nonaffine Nonlinear Systems without Differential Condition for Nonaffine Function

    Directory of Open Access Journals (Sweden)

    Chaojiao Sun

    2016-01-01

    Full Text Available An adaptive neural control scheme is proposed for nonaffine nonlinear system without using the implicit function theorem or mean value theorem. The differential conditions on nonaffine nonlinear functions are removed. The control-gain function is modeled with the nonaffine function probably being indifferentiable. Furthermore, only a semibounded condition for nonaffine nonlinear function is required in the proposed method, and the basic idea of invariant set theory is then constructively introduced to cope with the difficulty in the control design for nonaffine nonlinear systems. It is rigorously proved that all the closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Finally, simulation examples are provided to demonstrate the effectiveness of the designed method.

  6. Protection of visual functions by human neural progenitors in a rat model of retinal disease.

    Directory of Open Access Journals (Sweden)

    David M Gamm

    2007-03-01

    Full Text Available A promising clinical application for stem and progenitor cell transplantation is in rescue therapy for degenerative diseases. This strategy seeks to preserve rather than restore host tissue function by taking advantage of unique properties often displayed by these versatile cells. In studies using different neurodegenerative disease models, transplanted human neural progenitor cells (hNPC protected dying host neurons within both the brain and spinal cord. Based on these reports, we explored the potential of hNPC transplantation to rescue visual function in an animal model of retinal degeneration, the Royal College of Surgeons rat.Animals received unilateral subretinal injections of hNPC or medium alone at an age preceding major photoreceptor loss. Principal outcomes were quantified using electroretinography, visual acuity measurements and luminance threshold recordings from the superior colliculus. At 90-100 days postnatal, a time point when untreated rats exhibit little or no retinal or visual function, hNPC-treated eyes retained substantial retinal electrical activity and visual field with near-normal visual acuity. Functional efficacy was further enhanced when hNPC were genetically engineered to secrete glial cell line-derived neurotrophic factor. Histological examination at 150 days postnatal showed hNPC had formed a nearly continuous pigmented layer between the neural retina and retinal pigment epithelium, as well as distributed within the inner retina. A concomitant preservation of host cone photoreceptors was also observed.Wild type and genetically modified human neural progenitor cells survive for prolonged periods, migrate extensively, secrete growth factors and rescue visual functions following subretinal transplantation in the Royal College of Surgeons rat. These results underscore the potential therapeutic utility of hNPC in the treatment of retinal degenerative diseases and suggest potential mechanisms underlying their effect in

  7. Multiple image x-radiography for functional lung imaging

    Science.gov (United States)

    Aulakh, G. K.; Mann, A.; Belev, G.; Wiebe, S.; Kuebler, W. M.; Singh, B.; Chapman, D.

    2018-01-01

    Detection and visualization of lung tissue structures is impaired by predominance of air. However, by using synchrotron x-rays, refraction of x-rays at the interface of tissue and air can be utilized to generate contrast which may in turn enable quantification of lung optical properties. We utilized multiple image radiography, a variant of diffraction enhanced imaging, at the Canadian light source to quantify changes in unique x-ray optical properties of lungs, namely attenuation, refraction and ultra small-angle scatter (USAXS or width) contrast ratios as a function of lung orientation in free-breathing or respiratory-gated mice before and after intra-nasal bacterial endotoxin (lipopolysaccharide) instillation. The lung ultra small-angle scatter and attenuation contrast ratios were significantly higher 9 h post lipopolysaccharide instillation compared to saline treatment whereas the refraction contrast decreased in magnitude. In ventilated mice, end-expiratory pressures result in an increase in ultra small-angle scatter contrast ratio when compared to end-inspiratory pressures. There were no detectable changes in lung attenuation or refraction contrast ratio with change in lung pressure alone. In effect, multiple image radiography can be applied towards following optical properties of lung air-tissue barrier over time during pathologies such as acute lung injury.

  8. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

    Science.gov (United States)

    Subramaniam, Karuna; Hooker, Christine I.; Biagianti, Bruno; Fisher, Melissa; Nagarajan, Srikantan; Vinogradov, Sophia

    2015-01-01

    Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC) participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ) participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life. PMID:26413478

  9. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

    Directory of Open Access Journals (Sweden)

    Karuna Subramaniam

    2015-01-01

    Full Text Available Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life.

  10. Time Series Analysis of Soil Radon Data Using Multiple Linear Regression and Artificial Neural Network in Seismic Precursory Studies

    Science.gov (United States)

    Singh, S.; Jaishi, H. P.; Tiwari, R. P.; Tiwari, R. C.

    2017-07-01

    This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake.

  11. Neural correlates of alerting and orienting impairment in multiple sclerosis patients.

    Science.gov (United States)

    Vázquez-Marrufo, Manuel; Galvao-Carmona, Alejandro; González-Rosa, Javier J; Hidalgo-Muñoz, Antonio R; Borges, Mónica; Ruiz-Peña, Juan Luis; Izquierdo, Guillermo

    2014-01-01

    A considerable percentage of multiple sclerosis patients have attentional impairment, but understanding its neurophysiological basis remains a challenge. The Attention Network Test allows 3 attentional networks to be studied. Previous behavioural studies using this test have shown that the alerting network is impaired in multiple sclerosis. The aim of this study was to identify neurophysiological indexes of the attention impairment in relapsing-remitting multiple sclerosis patients using this test. After general slowing had been removed in patients group to isolate the effects of each condition, some behavioral differences between them were obtained. About Contingent Negative Variation, a statistically significant decrement were found in the amplitude for Central and Spatial Cue Conditions for patient group (pmultiple sclerosis. P1 and N1 delayed latencies are evidence of the demyelination process that causes impairment in the first steps of the visual sensory processing. Lastly, P3 amplitude shows a general decrease for the pathological group probably indexing a more central impairment. These results suggest that the Attention Network Test give evidence of multiple levels of attention impairment, which could help in the assessment and treatment of relapsing-remitting multiple sclerosis patients.

  12. Trait approach and avoidance motivation: lateralized neural activity associated with executive function.

    Science.gov (United States)

    Spielberg, Jeffrey M; Miller, Gregory A; Engels, Anna S; Herrington, John D; Sutton, Bradley P; Banich, Marie T; Heller, Wendy

    2011-01-01

    Motivation and executive function are both necessary for the completion of goal-directed behavior. Research investigating the manner in which these processes interact is beginning to emerge and has implicated middle frontal gyrus (MFG) as a site of interaction for relevant neural mechanisms. However, this research has focused on state motivation, and it has not examined functional lateralization. The present study examined the impact of trait levels of approach and avoidance motivation on neural processes associated with executive function. Functional magnetic resonance imaging was conducted while participants performed a color-word Stroop task. Analyses identified brain regions in which trait approach and avoidance motivation (measured by questionnaires) moderated activation associated with executive control. Approach was hypothesized to be associated with left-lateralized MFG activation, whereas avoidance was hypothesized to be associated with right-lateralized MFG activation. Results supported both hypotheses. Present findings implicate areas of middle frontal gyrus in top-down control to guide behavior in accordance with motivational goals. Copyright © 2010 Elsevier Inc. All rights reserved.

  13. 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.

  14. Dynamic neural network of insight: a functional magnetic resonance imaging study on solving Chinese 'chengyu' riddles.

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    Qingbai Zhao

    Full Text Available The key components of insight include breaking mental sets and forming the novel, task-related associations. The majority of researchers have agreed that the anterior cingulate cortex may mediate processes of breaking one's mental set, while the exact neural correlates of forming novel associations are still debatable. In the present study, we used a paradigm of answer selection to explore brain activations of insight by using event-related functional magnetic resonance imaging during solving Chinese 'chengyu' (in Chinese pinyin riddles. Based on the participant's choice, the trials were classified into the insight and non-insight conditions. Both stimulus-locked and response-locked analyses are conducted to detect the neural activity corresponding to the early and late periods of insight solution, respectively. Our data indicate that the early period of insight solution shows more activation in the middle temporal gyrus, the middle frontal gyrus and the anterior cingulate cortex. These activities might be associated to the extensive semantic processing, as well as detecting and resolving cognitive conflicts. In contrast, the late period of insight solution produced increased activities in the hippocampus and the amygdala, possibly reflecting the forming of novel association and the concomitant "Aha" feeling. Our study supports the key role of hippocampus in forming novel associations, and indicates a dynamic neural network during insight solution.

  15. Neural correlates of alerting and orienting impairment in multiple sclerosis patients.

    Directory of Open Access Journals (Sweden)

    Manuel Vázquez-Marrufo

    Full Text Available BACKGROUND: A considerable percentage of multiple sclerosis patients have attentional impairment, but understanding its neurophysiological basis remains a challenge. The Attention Network Test allows 3 attentional networks to be studied. Previous behavioural studies using this test have shown that the alerting network is impaired in multiple sclerosis. The aim of this study was to identify neurophysiological indexes of the attention impairment in relapsing-remitting multiple sclerosis patients using this test. RESULTS: After general slowing had been removed in patients group to isolate the effects of each condition, some behavioral differences between them were obtained. About Contingent Negative Variation, a statistically significant decrement were found in the amplitude for Central and Spatial Cue Conditions for patient group (p<0.05. ANOVAs showed for the patient group a significant latency delay for P1 and N1 components (p<0.05 and a decrease of P3 amplitude for congruent and incongruent stimuli (p<0.01. With regard to correlation analysis, PASAT-3s and SDMT showed significant correlations with behavioral measures of the Attention Network Test (p<0.01 and an ERP parameter (CNV amplitude. CONCLUSIONS: Behavioral data are highly correlated with the neuropsychological scores and show that the alerting and orienting mechanisms in the patient group were impaired. Reduced amplitude for the Contingent Negative Variation in the patient group suggests that this component could be a physiological marker related to the alerting and orienting impairment in relapsing-remitting multiple sclerosis. P1 and N1 delayed latencies are evidence of the demyelination process that causes impairment in the first steps of the visual sensory processing. Lastly, P3 amplitude shows a general decrease for the pathological group probably indexing a more central impairment. These results suggest that the Attention Network Test give evidence of multiple levels of attention

  16. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.

    Science.gov (United States)

    Hoellinger, Thomas; Petieau, Mathieu; Duvinage, Matthieu; Castermans, Thierry; Seetharaman, Karthik; Cebolla, Ana-Maria; Bengoetxea, Ana; Ivanenko, Yuri; Dan, Bernard; Cheron, Guy

    2013-01-01

    The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  17. Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

    Science.gov (United States)

    Smith, Kent W.; Sasaki, M. S.

    1979-01-01

    A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

  18. A potential neural substrate for processing functional classes of complex acoustic signals.

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    Isabelle George

    Full Text Available Categorization is essential to all cognitive processes, but identifying the neural substrates underlying categorization processes is a real challenge. Among animals that have been shown to be able of categorization, songbirds are particularly interesting because they provide researchers with clear examples of categories of acoustic signals allowing different levels of recognition, and they possess a system of specialized brain structures found only in birds that learn to sing: the song system. Moreover, an avian brain nucleus that is analogous to the mammalian secondary auditory cortex (the caudo-medial nidopallium, or NCM has recently emerged as a plausible site for sensory representation of birdsong, and appears as a well positioned brain region for categorization of songs. Hence, we tested responses in this non-primary, associative area to clear and distinct classes of songs with different functions and social values, and for a possible correspondence between these responses and the functional aspects of songs, in a highly social songbird species: the European starling. Our results clearly show differential neuronal responses to the ethologically defined classes of songs, both in the number of neurons responding, and in the response magnitude of these neurons. Most importantly, these differential responses corresponded to the functional classes of songs, with increasing activation from non-specific to species-specific and from species-specific to individual-specific sounds. These data therefore suggest a potential neural substrate for sorting natural communication signals into categories, and for individual vocal recognition of same-species members. Given the many parallels that exist between birdsong and speech, these results may contribute to a better understanding of the neural bases of speech.

  19. 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

  20. Two-Photon Functional Imaging of the Auditory Cortex in Behaving Mice: From Neural Networks to Single Spines

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    Ruijie Li

    2018-04-01

    Full Text Available In vivo two-photon Ca2+ imaging is a powerful tool for recording neuronal activities during perceptual tasks and has been increasingly applied to behaving animals for acute or chronic experiments. However, the auditory cortex is not easily accessible to imaging because of the abundant temporal muscles, arteries around the ears and their lateral locations. Here, we report a protocol for two-photon Ca2+ imaging in the auditory cortex of head-fixed behaving mice. By using a custom-made head fixation apparatus and a head-rotated fixation procedure, we achieved two-photon imaging and in combination with targeted cell-attached recordings of auditory cortical neurons in behaving mice. Using synthetic Ca2+ indicators, we recorded the Ca2+ transients at multiple scales, including neuronal populations, single neurons, dendrites and single spines, in auditory cortex during behavior. Furthermore, using genetically encoded Ca2+ indicators (GECIs, we monitored the neuronal dynamics over days throughout the process of associative learning. Therefore, we achieved two-photon functional imaging at multiple scales in auditory cortex of behaving mice, which extends the tool box for investigating the neural basis of audition-related behaviors.

  1. 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

  2. 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...

  3. Expandable and Rapidly Differentiating Human Induced Neural Stem Cell Lines for Multiple Tissue Engineering Applications

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    Dana M. Cairns

    2016-09-01

    Full Text Available Limited availability of human neurons poses a significant barrier to progress in biological and preclinical studies of the human nervous system. Current stem cell-based approaches of neuron generation are still hindered by prolonged culture requirements, protocol complexity, and variability in neuronal differentiation. Here we establish stable human induced neural stem cell (hiNSC lines through the direct reprogramming of neonatal fibroblasts and adult adipose-derived stem cells. These hiNSCs can be passaged indefinitely and cryopreserved as colonies. Independently of media composition, hiNSCs robustly differentiate into TUJ1-positive neurons within 4 days, making them ideal for innervated co-cultures. In vivo, hiNSCs migrate, engraft, and contribute to both central and peripheral nervous systems. Lastly, we demonstrate utility of hiNSCs in a 3D human brain model. This method provides a valuable interdisciplinary tool that could be used to develop drug screening applications as well as patient-specific disease models related to disorders of innervation and the brain.

  4. Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

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    Muammar Sadrawi

    2015-01-01

    Full Text Available This study evaluated the depth of anesthesia (DoA index using artificial neural networks (ANN which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD is utilized to purify between the electroencephalography (EEG signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG, heart rate (HR, pulse, systolic blood pressure (SBP, diastolic blood pressure (DBP, and signal quality index (SQI to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS. The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.

  5. 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.

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

    Science.gov (United States)

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

    2016-01-01

    Neural plasticity is a major factor driving cortical reorganization after stroke. We here tested whether repetitively enhancing motor cortex plasticity by means of intermittent theta-burst stimulation (iTBS) prior to physiotherapy might promote recovery of function early after stroke. Functional magnetic resonance imaging (fMRI) was used to elucidate underlying neural mechanisms. Twenty-six hospitalized, first-ever stroke patients (time since stroke: 1–16 days) with hand motor deficits were enrolled in a sham-controlled design and pseudo-randomized into 2 groups. iTBS was administered prior to physiotherapy on 5 consecutive days either over ipsilesional primary motor cortex (M1-stimulation group) or parieto-occipital vertex (control-stimulation group). Hand motor function, cortical excitability, and resting-state fMRI were assessed 1 day prior to the first stimulation and 1 day after the last stimulation. Recovery of grip strength was significantly stronger in the M1-stimulation compared to the control-stimulation group. Higher levels of motor network connectivity were associated with better motor outcome. Consistently, control-stimulated patients featured a decrease in intra- and interhemispheric connectivity of the motor network, which was absent in the M1-stimulation group. Hence, adding iTBS to prime physiotherapy in recovering stroke patients seems to interfere with motor network degradation, possibly reflecting alleviation of post-stroke diaschisis. PMID:26980614

  7. 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.

  8. Neural activation and functional connectivity during motor imagery of bimanual everyday actions.

    Directory of Open Access Journals (Sweden)

    André J Szameitat

    Full Text Available Bimanual actions impose intermanual coordination demands not present during unimanual actions. We investigated the functional neuroanatomical correlates of these coordination demands in motor imagery (MI of everyday actions using functional magnetic resonance imaging (fMRI. For this, 17 participants imagined unimanual actions with the left and right hand as well as bimanual actions while undergoing fMRI. A univariate fMRI analysis showed no reliable cortical activations specific to bimanual MI, indicating that intermanual coordination demands in MI are not associated with increased neural processing. A functional connectivity analysis based on psychophysiological interactions (PPI, however, revealed marked increases in connectivity between parietal and premotor areas within and between hemispheres. We conclude that in MI of everyday actions intermanual coordination demands are primarily met by changes in connectivity between areas and only moderately, if at all, by changes in the amount of neural activity. These results are the first characterization of the neuroanatomical correlates of bimanual coordination demands in MI. Our findings support the assumed equivalence of overt and imagined actions and highlight the differences between uni- and bimanual actions. The findings extent our understanding of the motor system and may aid the development of clinical neurorehabilitation approaches based on mental practice.

  9. Contact psychophysiological and neural functions with technical and tactical readiness volleyball

    Directory of Open Access Journals (Sweden)

    I.D. Glazyrin

    2013-06-01

    Full Text Available Set the level of neural development, psycho-physiological functions in highly skilled volleyball players. Defined technical and tactical preparedness highly skilled volleyball players in the competitive period of the annual cycle of training. The study involved six masters of sport and 8 candidates for the master of sports. That the quality of play activities and the successful execution of technical elements depend on functional mobility, strength and reactivity of nerve processes, associative thinking, memory and attention. The results, which may have a prognostic value. It is shown that the neurodynamic functions are genetically determined. It is recommended to use them for the initial recruitment and selection stages for sports improvement. The necessity influence the types of thinking, memory and attention in the training process of volleyball players.

  10. Associations between proprioceptive neural pathway structural connectivity and balance in people with multiple sclerosis

    OpenAIRE

    Brett W Fling; Brett W Fling; Geetanjali eGera-Dutta; Heather eSchlueter; Michelle H Cameron; Michelle H Cameron; Fay B Horak; Fay B Horak

    2014-01-01

    Mobility and balance impairments are a hallmark of multiple sclerosis (MS), affecting nearly half of patients at presentation and resulting in decreased activity and participation, falls, injuries, and reduced quality of life. A growing body of work suggests that balance impairments in people with mild MS are primarily the result of deficits in proprioception, the ability to determine body position in space in the absence of vision. A better understanding of the pathophysiology of balance dis...

  11. Associations between Proprioceptive Neural Pathway Structural Connectivity and Balance in People with Multiple Sclerosis

    OpenAIRE

    Fling, Brett W.; Dutta, Geetanjali Gera; Schlueter, Heather; Cameron, Michelle H.; Horak, Fay B.

    2014-01-01

    Mobility and balance impairments are a hallmark of multiple sclerosis (MS), affecting nearly half of patients at presentation and resulting in decreased activity and participation, falls, injuries, and reduced quality of life. A growing body of work suggests that balance impairments in people with mild MS are primarily the result of deficits in proprioception, the ability to determine body position in space in the absence of vision. A better understanding of the pathophysiology of balance dis...

  12. Cell surface glycan engineering of neural stem cells augments neurotropism and improves recovery in a murine model of multiple sclerosis

    KAUST Repository

    Merzaban, Jasmeen; Imitola, Jaime; Starossom, Sarah C.; Zhu, Bing; Wang, Yue; Lee, Jack; Ali, Amal J.; Olah, Marta; AbuElela, Ayman; Khoury, Samia J.; Sackstein, Robert

    2015-01-01

    Neural stem cell (NSC)-based therapies offer potential for neural repair in central nervous system (CNS) inflammatory and degenerative disorders. Typically, these conditions present with multifocal CNS lesions making it impractical to inject NSCs

  13. Medicare capitation model, functional status, and multiple comorbidities: model accuracy

    Science.gov (United States)

    Noyes, Katia; Liu, Hangsheng; Temkin-Greener, Helena

    2012-01-01

    Objective This study examined financial implications of CMS-Hierarchical Condition Categories (HCC) risk-adjustment model on Medicare payments for individuals with comorbid chronic conditions. Study Design The study used 1992-2000 data from the Medicare Current Beneficiary Survey and corresponding Medicare claims. The pairs of comorbidities were formed based on the prior evidence about possible synergy between these conditions and activities of daily living (ADL) deficiencies and included heart disease and cancer, lung disease and cancer, stroke and hypertension, stroke and arthritis, congestive heart failure (CHF) and osteoporosis, diabetes and coronary artery disease, CHF and dementia. Methods For each beneficiary, we calculated the actual Medicare cost ratio as the ratio of the individual’s annualized costs to the mean annual Medicare cost of all people in the study. The actual Medicare cost ratios, by ADLs, were compared to the HCC ratios under the CMS-HCC payment model. Using multivariate regression models, we tested whether having the identified pairs of comorbidities affects the accuracy of CMS-HCC model predictions. Results The CMS-HCC model underpredicted Medicare capitation payments for patients with hypertension, lung disease, congestive heart failure and dementia. The difference between the actual costs and predicted payments was partially explained by beneficiary functional status and less than optimal adjustment for these chronic conditions. Conclusions Information about beneficiary functional status should be incorporated in reimbursement models since underpaying providers for caring for population with multiple comorbidities may provide severe disincentives for managed care plans to enroll such individuals and to appropriately manage their complex and costly conditions. PMID:18837646

  14. Functional Magnetic Resonance Imaging with Concurrent Urodynamic Testing Identifies Brain Structures Involved in Micturition Cycle in Patients with Multiple Sclerosis.

    Science.gov (United States)

    Khavari, Rose; Karmonik, Christof; Shy, Michael; Fletcher, Sophie; Boone, Timothy

    2017-02-01

    Neurogenic lower urinary tract dysfunction, which is common in patients with multiple sclerosis, has a significant impact on quality of life. In this study we sought to determine brain activity processes during the micturition cycle in female patients with multiple sclerosis and neurogenic lower urinary tract dysfunction. We report brain activity on functional magnetic resonance imaging and simultaneous urodynamic testing in 23 ambulatory female patients with multiple sclerosis. Individual functional magnetic resonance imaging activation maps at strong desire to void and at initiation of voiding were calculated and averaged at Montreal Neuroimaging Institute. Areas of significant activation were identified in these average maps. Subgroup analysis was performed in patients with elicitable neurogenic detrusor overactivity or detrusor-sphincter dyssynergia. Group analysis of all patients at strong desire to void yielded areas of activation in regions associated with executive function (frontal gyrus), emotional regulation (cingulate gyrus) and motor control (putamen, cerebellum and precuneus). Comparison of the average change in activation between previously reported healthy controls and patients with multiple sclerosis showed predominantly stronger, more focal activation in the former and lower, more diffused activation in the latter. Patients with multiple sclerosis who had demonstrable neurogenic detrusor overactivity and detrusor-sphincter dyssynergia showed a trend toward distinct brain activation at full urge and at initiation of voiding respectively. We successfully studied brain activation during the entire micturition cycle in female patients with neurogenic lower urinary tract dysfunction and multiple sclerosis using a concurrent functional magnetic resonance imaging/urodynamic testing platform. Understanding the central neural processes involved in specific parts of micturition in patients with neurogenic lower urinary tract dysfunction may identify areas

  15. Motor imagery learning modulates functional connectivity of multiple brain systems in resting state.

    Science.gov (United States)

    Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

    2014-01-01

    Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.

  16. Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

    Science.gov (United States)

    Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

    2014-01-01

    Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning. PMID:24465577

  17. Remodeling Functional Connectivity in Multiple Sclerosis: A Challenging Therapeutic Approach.

    Science.gov (United States)

    Stampanoni Bassi, Mario; Gilio, Luana; Buttari, Fabio; Maffei, Pierpaolo; Marfia, Girolama A; Restivo, Domenico A; Centonze, Diego; Iezzi, Ennio

    2017-01-01

    Neurons in the central nervous system are organized in functional units interconnected to form complex networks. Acute and chronic brain damage disrupts brain connectivity producing neurological signs and/or symptoms. In several neurological diseases, particularly in Multiple Sclerosis (MS), structural imaging studies cannot always demonstrate a clear association between lesion site and clinical disability, originating the "clinico-radiological paradox." The discrepancy between structural damage and disability can be explained by a complex network perspective. Both brain networks architecture and synaptic plasticity may play important roles in modulating brain networks efficiency after brain damage. In particular, long-term potentiation (LTP) may occur in surviving neurons to compensate network disconnection. In MS, inflammatory cytokines dramatically interfere with synaptic transmission and plasticity. Importantly, in addition to acute and chronic structural damage, inflammation could contribute to reduce brain networks efficiency in MS leading to worse clinical recovery after a relapse and worse disease progression. These evidence suggest that removing inflammation should represent the main therapeutic target in MS; moreover, as synaptic plasticity is particularly altered by inflammation, specific strategies aimed at promoting LTP mechanisms could be effective for enhancing clinical recovery. Modulation of plasticity with different non-invasive brain stimulation (NIBS) techniques has been used to promote recovery of MS symptoms. Better knowledge of features inducing brain disconnection in MS is crucial to design specific strategies to promote recovery and use NIBS with an increasingly tailored approach.

  18. Remodeling Functional Connectivity in Multiple Sclerosis: A Challenging Therapeutic Approach

    Directory of Open Access Journals (Sweden)

    Mario Stampanoni Bassi

    2017-12-01

    Full Text Available Neurons in the central nervous system are organized in functional units interconnected to form complex networks. Acute and chronic brain damage disrupts brain connectivity producing neurological signs and/or symptoms. In several neurological diseases, particularly in Multiple Sclerosis (MS, structural imaging studies cannot always demonstrate a clear association between lesion site and clinical disability, originating the “clinico-radiological paradox.” The discrepancy between structural damage and disability can be explained by a complex network perspective. Both brain networks architecture and synaptic plasticity may play important roles in modulating brain networks efficiency after brain damage. In particular, long-term potentiation (LTP may occur in surviving neurons to compensate network disconnection. In MS, inflammatory cytokines dramatically interfere with synaptic transmission and plasticity. Importantly, in addition to acute and chronic structural damage, inflammation could contribute to reduce brain networks efficiency in MS leading to worse clinical recovery after a relapse and worse disease progression. These evidence suggest that removing inflammation should represent the main therapeutic target in MS; moreover, as synaptic plasticity is particularly altered by inflammation, specific strategies aimed at promoting LTP mechanisms could be effective for enhancing clinical recovery. Modulation of plasticity with different non-invasive brain stimulation (NIBS techniques has been used to promote recovery of MS symptoms. Better knowledge of features inducing brain disconnection in MS is crucial to design specific strategies to promote recovery and use NIBS with an increasingly tailored approach.

  19. Multiple spectral channels in branchiopods. I. Vision in dim light and neural correlates.

    Science.gov (United States)

    Lessios, Nicolas; Rutowski, Ronald L; Cohen, Jonathan H; Sayre, Marcel E; Strausfeld, Nicholas J

    2018-05-22

    Animals that have true color vision possess several spectral classes of photoreceptors. Pancrustaceans (Hexapoda+Crustacea) that integrate spectral information about their reconstructed visual world do so from photoreceptor terminals supplying their second optic neuropils, with subsequent participation of the third (lobula) and deeper centers (optic foci). Here, we describe experiments and correlative neural arrangements underlying convergent visual pathways in two species of branchiopod crustaceans that have to cope with a broad range of spectral ambience and illuminance in ephemeral pools, yet possess just two optic neuropils, the lamina and the optic tectum. Electroretinographic recordings and multimodel inference based on modeled spectral absorptance were used to identify the most likely number of spectral photoreceptor classes in their compound eyes. Recordings from the retina provide support for four color channels. Neuroanatomical observations resolve arrangements in their laminas that suggest signal summation at low light intensities, incorporating chromatic channels. Neuroanatomical observations demonstrate that spatial summation in the lamina of the two species are mediated by quite different mechanisms, both of which allow signals from several ommatidia to be pooled at single lamina monopolar cells. We propose that such summation provides sufficient signal for vision at intensities equivalent to those experienced by insects in terrestrial habitats under dim starlight. Our findings suggest that despite the absence of optic lobe neuropils necessary for spectral discrimination utilized by true color vision, four spectral photoreceptor classes have been maintained in Branchiopoda for vision at very low light intensities at variable ambient wavelengths that typify conditions in ephemeral freshwater habitats. © 2018. Published by The Company of Biologists Ltd.

  20. Neural stem cells encapsulated in a functionalized self-assembling peptide hydrogel for brain tissue engineering.

    Science.gov (United States)

    Cheng, Tzu-Yun; Chen, Ming-Hong; Chang, Wen-Han; Huang, Ming-Yuan; Wang, Tzu-Wei

    2013-03-01

    Brain injury is almost irreparable due to the poor regenerative capability of neural tissue. Nowadays, new therapeutic strategies have been focused on stem cell therapy and supplying an appropriate three dimensional (3D) matrix for the repair of injured brain tissue. In this study, we specifically linked laminin-derived IKVAV motif on the C-terminal to enrich self-assembling peptide RADA(16) as a functional peptide-based scaffold. Our purpose is providing a functional self-assembling peptide 3D hydrogel with encapsulated neural stem cells to enhance the reconstruction of the injured brain. The physiochemical properties reported that RADA(16)-IKVAV can self-assemble into nanofibrous morphology with bilayer β-sheet structure and become gelationed hydrogel with mechanical stiffness similar to brain tissue. The in vitro results showed that the extended IKVAV sequence can serve as a signal or guiding cue to direct the encapsulated neural stem cells (NSCs) adhesion and then towards neuronal differentiation. Animal study was conducted in a rat brain surgery model to demonstrate the damage in cerebral neocortex/neopallium loss. The results showed that the injected peptide solution immediately in situ formed the 3D hydrogel filling up the cavity and bridging the gaps. The histological analyses revealed the RADA(16)-IKVAV self-assembling peptide hydrogel not only enhanced survival of encapsulated NSCs but also reduced the formation of glial astrocytes. The peptide hydrogel with IKVAV extended motifs also showed the support of encapsulated NSCs in neuronal differentiation and the improvement in brain tissue regeneration after 6 weeks post-transplantation. Copyright © 2012 Elsevier Ltd. All rights reserved.

  1. Rapid and Objective Assessment of Neural Function in Autism Spectrum Disorder Using Transient Visual Evoked Potentials.

    Directory of Open Access Journals (Sweden)

    Paige M Siper

    Full Text Available There is a critical need to identify biomarkers and objective outcome measures that can be used to understand underlying neural mechanisms in autism spectrum disorder (ASD. Visual evoked potentials (VEPs offer a noninvasive technique to evaluate the functional integrity of neural mechanisms, specifically visual pathways, while probing for disease pathophysiology.Transient VEPs (tVEPs were obtained from 96 unmedicated children, including 37 children with ASD, 36 typically developing (TD children, and 23 unaffected siblings (SIBS. A conventional contrast-reversing checkerboard condition was compared to a novel short-duration condition, which was developed to enable objective data collection from severely affected populations who are often excluded from electroencephalographic (EEG studies.Children with ASD showed significantly smaller amplitudes compared to TD children at two of the earliest critical VEP components, P60-N75 and N75-P100. SIBS showed intermediate responses relative to ASD and TD groups. There were no group differences in response latency. Frequency band analyses indicated significantly weaker responses for the ASD group in bands encompassing gamma-wave activity. Ninety-two percent of children with ASD were able to complete the short-duration condition compared to 68% for the standard condition.The current study establishes the utility of a short-duration tVEP test for use in children at varying levels of functioning and describes neural abnormalities in children with idiopathic ASD. Implications for excitatory/inhibitory balance as well as the potential application of VEP for use in clinical trials are discussed.

  2. Mobilization of Neural Precursors in the Circulating Blood of Patients with Multiple Sclerosis

    Science.gov (United States)

    2013-09-01

    Bongarzone ER. Expression of sonic hedgehog targeted genes in peripheral blood mononuclear cells of patients with multiple sclerosis. Society for...recovery in a rat spinal cord injury model. Spine (Phila Pa 1976) 34:249-254. Schwartz PH, Bryant PJ, Fuja TJ, Su H, O’Dowd DK, Klassen H (2003...Print Program#/Poster#: 322.13 Presentation Title: Expression of sonic  hedgehog  targeted genes in peripheral blood mononuclear cells of patients with

  3. Neural signatures of economic parameters during decision-making: a functional MRI (FMRI), electroencephalography (EEG) and autonomic monitoring study.

    Science.gov (United States)

    Minati, Ludovico; Grisoli, Marina; Franceschetti, Silvana; Epifani, Francesca; Granvillano, Alice; Medford, Nick; Harrison, Neil A; Piacentini, Sylvie; Critchley, Hugo D

    2012-01-01

    Adaptive behaviour requires an ability to obtain rewards by choosing between different risky options. Financial gambles can be used to study effective decision-making experimentally, and to distinguish processes involved in choice option evaluation from outcome feedback and other contextual factors. Here, we used a paradigm where participants evaluated 'mixed' gambles, each presenting a potential gain and a potential loss and an associated variable outcome probability. We recorded neural responses using autonomic monitoring, electroencephalography (EEG) and functional neuroimaging (fMRI), and used a univariate, parametric design to test for correlations with the eleven economic parameters that varied across gambles, including expected value (EV) and amount magnitude. Consistent with behavioural economic theory, participants were risk-averse. Gamble evaluation generated detectable autonomic responses, but only weak correlations with outcome uncertainty were found, suggesting that peripheral autonomic feedback does not play a major role in this task. Long-latency stimulus-evoked EEG potentials were sensitive to expected gain and expected value, while alpha-band power reflected expected loss and amount magnitude, suggesting parallel representations of distinct economic qualities in cortical activation and central arousal. Neural correlates of expected value representation were localized using fMRI to ventromedial prefrontal cortex, while the processing of other economic parameters was associated with distinct patterns across lateral prefrontal, cingulate, insula and occipital cortices including default-mode network and early visual areas. These multimodal data provide complementary evidence for distributed substrates of choice evaluation across multiple, predominantly cortical, brain systems wherein distinct regions are preferentially attuned to specific economic features. Our findings extend biologically-plausible models of risky decision-making while providing

  4. Examination of cognitive fatigue in multiple sclerosis using functional magnetic resonance imaging and diffusion tensor imaging.

    Science.gov (United States)

    Genova, Helen M; Rajagopalan, Venkateswaran; Deluca, John; Das, Abhijit; Binder, Allison; Arjunan, Aparna; Chiaravalloti, Nancy; Wylie, Glenn

    2013-01-01

    The present study investigated the neural correlates of cognitive fatigue in Multiple Sclerosis (MS), looking specifically at the relationship between self-reported fatigue and objective measures of cognitive fatigue. In Experiment 1, functional magnetic resonance imaging (fMRI) was used to examine where in the brain BOLD activity covaried with "state" fatigue, assessed during performance of a task designed to induce cognitive fatigue while in the scanner. In Experiment 2, diffusion tensor imaging (DTI) was used to examine where in the brain white matter damage correlated with increased "trait" fatigue in individuals with MS, assessed by the Fatigue Severity Scale (FSS) completed outside the scanning session. During the cognitively fatiguing task, the MS group had increased brain activity associated with fatigue in the caudate as compared with HCs. DTI findings revealed that reduced fractional anisotropy in the anterior internal capsule was associated with increased self-reported fatigue on the FSS. Results are discussed in terms of identifying a "fatigue-network" in MS.

  5. Examination of cognitive fatigue in multiple sclerosis using functional magnetic resonance imaging and diffusion tensor imaging.

    Directory of Open Access Journals (Sweden)

    Helen M Genova

    Full Text Available The present study investigated the neural correlates of cognitive fatigue in Multiple Sclerosis (MS, looking specifically at the relationship between self-reported fatigue and objective measures of cognitive fatigue. In Experiment 1, functional magnetic resonance imaging (fMRI was used to examine where in the brain BOLD activity covaried with "state" fatigue, assessed during performance of a task designed to induce cognitive fatigue while in the scanner. In Experiment 2, diffusion tensor imaging (DTI was used to examine where in the brain white matter damage correlated with increased "trait" fatigue in individuals with MS, assessed by the Fatigue Severity Scale (FSS completed outside the scanning session. During the cognitively fatiguing task, the MS group had increased brain activity associated with fatigue in the caudate as compared with HCs. DTI findings revealed that reduced fractional anisotropy in the anterior internal capsule was associated with increased self-reported fatigue on the FSS. Results are discussed in terms of identifying a "fatigue-network" in MS.

  6. Neural correlates of own- and other-race face recognition in children: a functional near-infrared spectroscopy study.

    Science.gov (United States)

    Ding, Xiao Pan; Fu, Genyue; Lee, Kang

    2014-01-15

    The present study used the functional Near-infrared Spectroscopy (fNIRS) methodology to investigate the neural correlates of elementary school children's own- and other-race face processing. An old-new paradigm was used to assess children's recognition ability of own- and other-race faces. FNIRS data revealed that other-race faces elicited significantly greater [oxy-Hb] changes than own-race faces in the right middle frontal gyrus and inferior frontal gyrus regions (BA9) and the left cuneus (BA18). With increased age, the [oxy-Hb] activity differences between own- and other-race faces, or the neural other-race effect (NORE), underwent significant changes in these two cortical areas: at younger ages, the neural response to the other-race faces was modestly greater than that to the own-race faces, but with increased age, the neural response to the own-race faces became increasingly greater than that to the other-race faces. Moreover, these areas had strong regional functional connectivity with a swath of the cortical regions in terms of the neural other-race effect that also changed with increased age. We also found significant and positive correlations between the behavioral other-race effect (reaction time) and the neural other-race effect in the right middle frontal gyrus and inferior frontal gyrus regions (BA9). These results taken together suggest that children, like adults, devote different amounts of neural resources to processing own- and other-race faces, but the size and direction of the neural other-race effect and associated functional regional connectivity change with increased age. © 2013.

  7. Functional Roles of Neural Preparatory Processes in a Cued Stroop Task Revealed by Linking Electrophysiology with Behavioral Performance.

    Science.gov (United States)

    Wang, Chao; Ding, Mingzhou; Kluger, Benzi M

    2015-01-01

    It is well established that cuing facilitates behavioral performance and that different aspects of instructional cues evoke specific neural preparatory processes in cued task-switching paradigms. To deduce the functional role of these neural preparatory processes the majority of studies vary aspects of the experimental paradigm and describe how these variations alter markers of neural preparatory processes. Although these studies provide important insights, they also have notable limitations, particularly in terms of understanding the causal or functional relationship of neural markers to cognitive and behavioral processes. In this study, we sought to address these limitations and uncover the functional roles of neural processes by examining how variability in the amplitude of neural preparatory processes predicts behavioral performance to subsequent stimuli. To achieve this objective 16 young adults were recruited to perform a cued Stroop task while their brain activity was measured using high-density electroencephalography. Four temporally overlapping but functionally and topographically distinct cue-triggered event related potentials (ERPs) were identified: 1) A left-frontotemporal negativity (250-700 ms) that was positively associated with word-reading performance; 2) a midline-frontal negativity (450-800 ms) that was positively associated with color-naming and incongruent performance; 3) a left-frontal negativity (450-800 ms) that was positively associated with switch trial performance; and 4) a centroparietal positivity (450-800 ms) that was positively associated with performance for almost all trial types. These results suggest that at least four dissociable cognitive processes are evoked by instructional cues in the present task, including: 1) domain-specific task facilitation; 2) switch-specific task-set reconfiguration; 3) preparation for response conflict; and 4) proactive attentional control. Examining the relationship between ERPs and behavioral

  8. Functional Roles of Neural Preparatory Processes in a Cued Stroop Task Revealed by Linking Electrophysiology with Behavioral Performance.

    Directory of Open Access Journals (Sweden)

    Chao Wang

    Full Text Available It is well established that cuing facilitates behavioral performance and that different aspects of instructional cues evoke specific neural preparatory processes in cued task-switching paradigms. To deduce the functional role of these neural preparatory processes the majority of studies vary aspects of the experimental paradigm and describe how these variations alter markers of neural preparatory processes. Although these studies provide important insights, they also have notable limitations, particularly in terms of understanding the causal or functional relationship of neural markers to cognitive and behavioral processes. In this study, we sought to address these limitations and uncover the functional roles of neural processes by examining how variability in the amplitude of neural preparatory processes predicts behavioral performance to subsequent stimuli. To achieve this objective 16 young adults were recruited to perform a cued Stroop task while their brain activity was measured using high-density electroencephalography. Four temporally overlapping but functionally and topographically distinct cue-triggered event related potentials (ERPs were identified: 1 A left-frontotemporal negativity (250-700 ms that was positively associated with word-reading performance; 2 a midline-frontal negativity (450-800 ms that was positively associated with color-naming and incongruent performance; 3 a left-frontal negativity (450-800 ms that was positively associated with switch trial performance; and 4 a centroparietal positivity (450-800 ms that was positively associated with performance for almost all trial types. These results suggest that at least four dissociable cognitive processes are evoked by instructional cues in the present task, including: 1 domain-specific task facilitation; 2 switch-specific task-set reconfiguration; 3 preparation for response conflict; and 4 proactive attentional control. Examining the relationship between ERPs and behavioral

  9. Motor sequence learning-induced neural efficiency in functional brain connectivity.

    Science.gov (United States)

    Karim, Helmet T; Huppert, Theodore J; Erickson, Kirk I; Wollam, Mariegold E; Sparto, Patrick J; Sejdić, Ervin; VanSwearingen, Jessie M

    2017-02-15

    Previous studies have shown the functional neural circuitry differences before and after an explicitly learned motor sequence task, but have not assessed these changes during the process of motor skill learning. Functional magnetic resonance imaging activity was measured while participants (n=13) were asked to tap their fingers to visually presented sequences in blocks that were either the same sequence repeated (learning block) or random sequences (control block). Motor learning was associated with a decrease in brain activity during learning compared to control. Lower brain activation was noted in the posterior parietal association area and bilateral thalamus during the later periods of learning (not during the control). Compared to the control condition, we found the task-related motor learning was associated with decreased connectivity between the putamen and left inferior frontal gyrus and left middle cingulate brain regions. Motor learning was associated with changes in network activity, spatial extent, and connectivity. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Global Approximations to Cost and Production Functions using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Efthymios G. Tsionas

    2009-06-01

    Full Text Available The estimation of cost and production functions in economics relies on standard specifications which are less than satisfactory in numerous situations. However, instead of fitting the data with a pre-specified model, Artificial Neural Networks (ANNs let the data itself serve as evidence to support the modelrs estimation of the underlying process. In this context, the proposed approach combines the strengths of economics, statistics and machine learning research and the paper proposes a global approximation to arbitrary cost and production functions, respectively, given by ANNs. Suggestions on implementation are proposed and empirical application relies on standard techniques. All relevant measures such as Returns to Scale (RTS and Total Factor Productivity (TFP may be computed routinely.

  11. Functional studies of microRNAs in neural stem cells: problems and perspectives.

    Directory of Open Access Journals (Sweden)

    Malin eÅkerblom

    2012-02-01

    Full Text Available In adult mammals, neural stem cells (NSCs are found in two niches of the brain; the subventricular zone at the lateral ventricle and the subgranular zone of the dentate gyrus in the hippocampus. Neurogenesis is a complex process that is tightly controlled on a molecular level. Recently, microRNAs (miRNAs have been implicated to play a central role in the regulation of NCSs. miRNAs are small, endogenously expressed RNAs that regulate gene expression at the post-transcriptional level. However, functional studies of miRNAs are complicated due to current technical limitations. In this review we describe recent findings about miRNAs in NSCs looking closely at miR-124, miR-9 and let-7. We also highlight technical strategies used to investigate miRNA function, accentuating limitations and potentials.

  12. Microfluidic production of multiple emulsions and functional microcapsules

    NARCIS (Netherlands)

    Lee, Tae Yong; Choi, Tae Min; Shim, Tae Soup; Frijns, Raoul A.M.; Kim, Shin Hyun

    2016-01-01

    Recent advances in microfluidics have enabled the controlled production of multiple-emulsion drops with onion-like topology. The multiple-emulsion drops possess an intrinsic core-shell geometry, which makes them useful as templates to create microcapsules with a solid membrane. High flexibility

  13. A common functional neural network for overt production of speech and gesture.

    Science.gov (United States)

    Marstaller, L; Burianová, H

    2015-01-22

    The perception of co-speech gestures, i.e., hand movements that co-occur with speech, has been investigated by several studies. The results show that the perception of co-speech gestures engages a core set of frontal, temporal, and parietal areas. However, no study has yet investigated the neural processes underlying the production of co-speech gestures. Specifically, it remains an open question whether Broca's area is central to the coordination of speech and gestures as has been suggested previously. The objective of this study was to use functional magnetic resonance imaging to (i) investigate the regional activations underlying overt production of speech, gestures, and co-speech gestures, and (ii) examine functional connectivity with Broca's area. We hypothesized that co-speech gesture production would activate frontal, temporal, and parietal regions that are similar to areas previously found during co-speech gesture perception and that both speech and gesture as well as co-speech gesture production would engage a neural network connected to Broca's area. Whole-brain analysis confirmed our hypothesis and showed that co-speech gesturing did engage brain areas that form part of networks known to subserve language and gesture. Functional connectivity analysis further revealed a functional network connected to Broca's area that is common to speech, gesture, and co-speech gesture production. This network consists of brain areas that play essential roles in motor control, suggesting that the coordination of speech and gesture is mediated by a shared motor control network. Our findings thus lend support to the idea that speech can influence co-speech gesture production on a motoric level. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

  14. A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

    Science.gov (United States)

    Yeh, Wei-Chang

    Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

  15. Localized states in an unbounded neural field equation with smooth firing rate function: a multi-parameter analysis.

    Science.gov (United States)

    Faye, Grégory; Rankin, James; Chossat, Pascal

    2013-05-01

    The existence of spatially localized solutions in neural networks is an important topic in neuroscience as these solutions are considered to characterize working (short-term) memory. We work with an unbounded neural network represented by the neural field equation with smooth firing rate function and a wizard hat spatial connectivity. Noting that stationary solutions of our neural field equation are equivalent to homoclinic orbits in a related fourth order ordinary differential equation, we apply normal form theory for a reversible Hopf bifurcation to prove the existence of localized solutions; further, we present results concerning their stability. Numerical continuation is used to compute branches of localized solution that exhibit snaking-type behaviour. We describe in terms of three parameters the exact regions for which localized solutions persist.

  16. SYNTHESIS AND REDUCED LOGIC GATE REALIZATION OF MULTI-VALUED LOGIC FUNCTIONS USING NEURAL NETWORK DEPLOYMENT ALGORITHM

    Directory of Open Access Journals (Sweden)

    A. K. CHOWDHURY

    2016-02-01

    Full Text Available In this paper an evolutionary technique for synthesizing Multi-Valued Logic (MVL functions using Neural Network Deployment Algorithm (NNDA is presented. The algorithm is combined with back-propagation learning capability and neural MVL operators. This research article is done to observe the anomalistic characteristics of MVL neural operators and their role in synthesis. The advantages of NNDA-MVL algorithm is demonstrated with realization of synthesized many valued functions with lesser MVL operators. The characteristic feature set consists of MVL gate count, network link count, network propagation delay and accuracy achieved in training. In brief, this paper depicts an effort of reduced network size for synthesized MVL functions. Trained MVL operators improve the basic architecture by reducing MIN gate and interlink connection by 52.94% and 23.38% respectively.

  17. 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.

  18. Classification of ion mobility spectra by functional groups using neural networks

    Science.gov (United States)

    Bell, S.; Nazarov, E.; Wang, Y. F.; Eiceman, G. A.

    1999-01-01

    Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

  19. 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.

  20. Robust fixed-time synchronization for uncertain complex-valued neural networks with discontinuous activation functions.

    Science.gov (United States)

    Ding, Xiaoshuai; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E; Hayat, Tasawar

    2017-06-01

    This paper is concerned with the fixed-time synchronization for a class of complex-valued neural networks in the presence of discontinuous activation functions and parameter uncertainties. Fixed-time synchronization not only claims that the considered master-slave system realizes synchronization within a finite time segment, but also requires a uniform upper bound for such time intervals for all initial synchronization errors. To accomplish the target of fixed-time synchronization, a novel feedback control procedure is designed for the slave neural networks. By means of the Filippov discontinuity theories and Lyapunov stability theories, some sufficient conditions are established for the selection of control parameters to guarantee synchronization within a fixed time, while an upper bound of the settling time is acquired as well, which allows to be modulated to predefined values independently on initial conditions. Additionally, criteria of modified controller for assurance of fixed-time anti-synchronization are also derived for the same system. An example is included to illustrate the proposed methodologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Neural loss aversion differences between depression patients and healthy individuals: A functional MRI investigation.

    Science.gov (United States)

    Chandrasekhar Pammi, V S; Pillai Geethabhavan Rajesh, Purushothaman; Kesavadas, Chandrasekharan; Rappai Mary, Paramban; Seema, Satish; Radhakrishnan, Ashalatha; Sitaram, Ranganatha

    2015-04-01

    Neuroeconomics employs neuroscience techniques to explain decision-making behaviours. Prospect theory, a prominent model of decision-making, features a value function with parameters for risk and loss aversion. Recent work with normal participants identified activation related to loss aversion in brain regions including the amygdala, ventral striatum, and ventromedial prefrontal cortex. However, the brain network for loss aversion in pathologies such as depression has yet to be identified. The aim of the current study is to employ the value function from prospect theory to examine behavioural and neural manifestations of loss aversion in depressed and healthy individuals to identify the neurobiological markers of loss aversion in economic behaviour. We acquired behavioural data and fMRI scans while healthy controls and patients with depression performed an economic decision-making task. Behavioural loss aversion was higher in patients with depression than in healthy controls. fMRI results revealed that the two groups shared a brain network for value function including right ventral striatum, ventromedial prefrontal cortex, and right amygdala. However, the neural loss aversion results revealed greater activations in the right dorsal striatum and the right anterior insula for controls compared with patients with depression, and higher activations in the midbrain region ventral tegmental area for patients with depression compared with controls. These results suggest that while the brain network for loss aversion is shared between depressed and healthy individuals, some differences exist with respect to differential activation of additional areas. Our findings are relevant to identifying neurobiological markers for altered decision-making in the depressed. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  2. 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

  3. Effects of topography on the functional development of human neural progenitor cells.

    Science.gov (United States)

    Wu, Ze-Zhi; Kisaalita, William S; Wang, Lina; Zachman, Angela L; Zhao, Yiping; Hasneen, Kowser; Machacek, Dave; Stice, Steven L

    2010-07-01

    We have fabricated a topographical substrate with a packed polystyrene bead array for the development of cell-based assay systems targeting voltage-gated calcium channels (VGCCs). Human neural progenitor cells (H945RB.3) cultured on both flat and topographical substrates were analyzed in terms of morphological spreading, neuronal commitment, resting membrane potential (V(m)) establishment and VGCC function development. We found, by SEM imaging, that arrayed substrates, formed with both sub-micrometer (of 0.51 microm in mean diameter) and micrometer (of 1.98 microm in mean diameter) beads, were capable of promoting the spreading of the progenitor cells as compared with the flat polystyrene surfaces. With the micrometer beads, it was found that arrayed substrates facilitated the neural progenitor cells' maintenance of less negative V(m) values upon differentiation with bFGF starvation, which favored predominant neuronal commitment. Almost all the progenitor cells were responsive to 50 mM K(+) depolarization with an increase in [Ca(2+)](i) either before or upon differentiation, suggesting the expression of functional VGCCs. Compared to the flat polystyrene surfaces, microbead arrayed substrates facilitated the development of higher VGCC responsiveness by the progenitor cells upon differentiation. The enhancement of both VGCC responsiveness and cell spreading by arrays of micrometer beads was most significant on day 14 into differentiation, which was the latest time point of measurement in this study. This study thus rationalized the possibility for future substrate topography engineering to manipulate ion channel function and to meet the challenge of low VGCC responsiveness found in early drug discovery.

  4. Behavioral and Neural Correlates of Executive Function: Interplay between Inhibition and Updating Processes.

    Science.gov (United States)

    Kim, Na Young; Wittenberg, Ellen; Nam, Chang S

    2017-01-01

    This study investigated the interaction between two executive function processes, inhibition and updating, through analyses of behavioral, neurophysiological, and effective connectivity metrics. Although, many studies have focused on behavioral effects of executive function processes individually, few studies have examined the dynamic causal interactions between these two functions. A total of twenty participants from a local university performed a dual task combing flanker and n-back experimental paradigms, and completed the Operation Span Task designed to measure working memory capacity. We found that both behavioral (accuracy and reaction time) and neurophysiological (P300 amplitude and alpha band power) metrics on the inhibition task (i.e., flanker task) were influenced by the updating load (n-back level) and modulated by working memory capacity. Using independent component analysis, source localization (DIPFIT), and Granger Causality analysis of the EEG time-series data, the present study demonstrated that manipulation of cognitive demand in a dual executive function task influenced the causal neural network. We compared connectivity across three updating loads (n-back levels) and found that experimental manipulation of working memory load enhanced causal connectivity of a large-scale neurocognitive network. This network contains the prefrontal and parietal cortices, which are associated with inhibition and updating executive function processes. This study has potential applications in human performance modeling and assessment of mental workload, such as the design of training materials and interfaces for those performing complex multitasking under stress.

  5. Behavioral and Neural Correlates of Executive Function: Interplay between Inhibition and Updating Processes

    Directory of Open Access Journals (Sweden)

    Na Young Kim

    2017-06-01

    Full Text Available This study investigated the interaction between two executive function processes, inhibition and updating, through analyses of behavioral, neurophysiological, and effective connectivity metrics. Although, many studies have focused on behavioral effects of executive function processes individually, few studies have examined the dynamic causal interactions between these two functions. A total of twenty participants from a local university performed a dual task combing flanker and n-back experimental paradigms, and completed the Operation Span Task designed to measure working memory capacity. We found that both behavioral (accuracy and reaction time and neurophysiological (P300 amplitude and alpha band power metrics on the inhibition task (i.e., flanker task were influenced by the updating load (n-back level and modulated by working memory capacity. Using independent component analysis, source localization (DIPFIT, and Granger Causality analysis of the EEG time-series data, the present study demonstrated that manipulation of cognitive demand in a dual executive function task influenced the causal neural network. We compared connectivity across three updating loads (n-back levels and found that experimental manipulation of working memory load enhanced causal connectivity of a large-scale neurocognitive network. This network contains the prefrontal and parietal cortices, which are associated with inhibition and updating executive function processes. This study has potential applications in human performance modeling and assessment of mental workload, such as the design of training materials and interfaces for those performing complex multitasking under stress.

  6. Function of FEZF1 during early neural differentiation of human embryonic stem cells.

    Science.gov (United States)

    Liu, Xin; Su, Pei; Lu, Lisha; Feng, Zicen; Wang, Hongtao; Zhou, Jiaxi

    2018-01-01

    The understanding of the mechanism underlying human neural development has been hampered due to lack of a cellular system and complicated ethical issues. Human embryonic stem cells (hESCs) provide an invaluable model for dissecting human development because of unlimited self-renewal and the capacity to differentiate into nearly all cell types in the human body. In this study, using a chemical defined neural induction protocol and molecular profiling, we identified Fez family zinc finger 1 (FEZF1) as a potential regulator of early human neural development. FEZF1 is rapidly up-regulated during neural differentiation in hESCs and expressed before PAX6, a well-established marker of early human neural induction. We generated FEZF1-knockout H1 hESC lines using CRISPR-CAS9 technology and found that depletion of FEZF1 abrogates neural differentiation of hESCs. Moreover, loss of FEZF1 impairs the pluripotency exit of hESCs during neural specification, which partially explains the neural induction defect caused by FEZF1 deletion. However, enforced expression of FEZF1 itself fails to drive neural differentiation in hESCs, suggesting that FEZF1 is necessary but not sufficient for neural differentiation from hESCs. Taken together, our findings identify one of the earliest regulators expressed upon neural induction and provide insight into early neural development in human.

  7. Zebrafish msxB, msxC and msxE function together to refine the neural-nonneural border and regulate cranial placodes and neural crest development.

    Science.gov (United States)

    Phillips, Bryan T; Kwon, Hye-Joo; Melton, Colt; Houghtaling, Paul; Fritz, Andreas; Riley, Bruce B

    2006-06-15

    The zebrafish muscle segment homeobox genes msxB, msxC and msxE are expressed in partially overlapping domains in the neural crest and preplacodal ectoderm. We examined the roles of these msx genes in early development. Disrupting individual msx genes causes modest variable defects, whereas disrupting all three produces a reproducible severe phenotype, suggesting functional redundancy. Neural crest differentiation is blocked at an early stage. Preplacodal development begins normally, but placodes arising from the msx expression domain later show elevated apoptosis and are reduced in size. Cell proliferation is normal in these tissues. Unexpectedly, Msx-deficient embryos become ventralized by late gastrulation whereas misexpression of msxB dorsalizes the embryo. These effects appear to involve Distal-less (Dlx) protein activity, as loss of dlx3b and dlx4b suppresses ventralization in Msx-depleted embryos. At the same time, Msx-depletion restores normal preplacodal gene expression to dlx3b-dlx4b mutants. These data suggest that mutual antagonism between Msx and Dlx proteins achieves a balance of function required for normal preplacodal differentiation and placement of the neural-nonneural border.

  8. Neural correlates of gait variability in people with multiple sclerosis with fall history.

    Science.gov (United States)

    Kalron, Alon; Allali, Gilles; Achiron, Anat

    2018-05-28

    Investigate the association between step time variability and related brain structures in accordance with fall status in people with multiple sclerosis (PwMS). The study included 225 PwMS. A whole-brain MRI was performed by a high-resolution 3.0-Telsa MR scanner in addition to volumetric analysis based on 3D T1-weighted images using the FreeSurfer image analysis suite. Step time variability was measured by an electronic walkway. Participants were defined as "fallers" (at least two falls during the previous year) and "non-fallers". One hundred and five PwMS were defined as fallers and had a greater step time variability compared to non-fallers (5.6% (S.D.=3.4) vs. 3.4% (S.D.=1.5); p=0.001). MS fallers exhibited a reduced volume in the left caudate and both cerebellum hemispheres compared to non-fallers. By using a linear regression analysis no association was found between gait variability and related brain structures in the total cohort and non-fallers group. However, the analysis found an association between the left hippocampus and left putamen volumes with step time variability in the faller group; p=0.031, 0.048, respectively, controlling for total cranial volume, walking speed, disability, age and gender. Nevertheless, according to the hierarchical regression model, the contribution of these brain measures to predict gait variability was relatively small compared to walking speed. An association between low left hippocampal, putamen volumes and step time variability was found in PwMS with a history of falls, suggesting brain structural characteristics may be related to falls and increased gait variability in PwMS. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  9. Associations between proprioceptive neural pathway structural connectivity and balance in people with multiple sclerosis

    Directory of Open Access Journals (Sweden)

    Brett W Fling

    2014-10-01

    Full Text Available Mobility and balance impairments are a hallmark of multiple sclerosis (MS, affecting nearly half of patients at presentation and resulting in decreased activity and participation, falls, injuries, and reduced quality of life. A growing body of work suggests that balance impairments in people with mild MS are primarily the result of deficits in proprioception, the ability to determine body position in space in the absence of vision. A better understanding of the pathophysiology of balance disturbances in MS is needed to develop evidence-based rehabilitation approaches. The purpose of the current study was to 1 map the cortical proprioceptive pathway in-vivo using diffusion weighted imaging and 2 assess associations between proprioceptive pathway white matter microstructural integrity and performance on clinical and behavioral balance tasks. We hypothesized that people with MS (PwMS would have reduced integrity of cerebral proprioceptive pathways, and that reduced white matter microstructure within these tracts would be strongly related to proprioceptive-based balance deficits. We found poorer balance control on proprioceptive-based tasks and reduced white matter microstructural integrity of the cortical proprioceptive tracts in PwMS compared with age-matched healthy controls. Microstructural integrity of this pathway in the right hemisphere was also strongly associated with proprioceptive-based balance control in PwMS and controls. Conversely, while white matter integrity of the right hemisphere’s proprioceptive pathway was significantly correlated with overall balance performance in healthy controls, there was no such relationship in PwMS. These results augment existing literature suggesting that balance control in PwMS may become more dependent upon 1 cerebellar-regulated proprioceptive control, 2 the vestibular system, and/or 3 the visual system.

  10. Efficacy of peripheral lidocaine application (neural therapy) in the treatment of neurogenic detrusor overactivity in multiple sclerosis patients.

    Science.gov (United States)

    Tamam, Yusuf; Özdemir, Hasan Hüseyin; Gedik, Abdullah; Tamam, Cüneyt; Nazlıkul, Hüseyin

    2017-09-01

    Many agents and treatments are used in the treatment of neurogenic detrusor overactivity (NDO) in MS patients, but no study has been conducted on the use of peripheric lidocaine (neural therapy-NT) on MS patients. We evaluated the effects of local administration of lidocaine on NDO in Multiple Sclerosis (MS) patients. For each patient local anesthetic lidocaine was injected at each session. Sessions were held once a week for 5 weeks. At each session, Th 10-L1, urogenital segment intradermal injections, Frankenhauser, and sacral epidural injections were given. The patients had clinical and urodynamic assessment 1 month before and 3, 9, and 12 months after NT. In addition, multiple sclerosis quality of life inventory (MSQL-54) and bladder control scale (BLCS) was performed for patients. Twenty-eight patients were included in the study (8 males, 20 females). The patients' average age was 31.7 ± 8.1 years. The injection therapy significantly improved volume at first involuntary bladder contraction (FCV), maximal detrusor pression during filling (P det. max.), maximal cystometric bladder capacity (MCC) after 3 months. Also, the MSQL-54 and BLCS scores were improved with treatment. However, these improvements reached a maximum 3 months after treatment, but from the 9 month a regression was seen in the parameters, and after 12 months the findings were seen to be slightly above their basal levels. These results suggest that NDO treatment in MS patients could be an effective treatment which is easy and has very few side effects, and is cost effective. © 2017 Wiley Periodicals, Inc.

  11. Signaling through three chemokine receptors triggers the migration of transplanted neural precursor cells in a model of multiple sclerosis.

    Science.gov (United States)

    Cohen, Mikhal E; Fainstein, Nina; Lavon, Iris; Ben-Hur, Tamir

    2014-09-01

    Multiple sclerosis (MS) is a multifocal disease, and precursor cells need to migrate into the multiple lesions in order to exert their therapeutic effects. Therefore, cell migration is a crucial element in regenerative processes in MS, dictating the route of delivery, when cell transplantation is considered. We have previously shown that inflammation triggers migration of multi-potential neural precursor cells (NPCs) into the white matter of experimental autoimmune encephalomyelitis (EAE) rodents, a widely used model of MS. Here we investigated the molecular basis of this attraction. NPCs were grown from E13 embryonic mouse brains and transplanted into the lateral cerebral ventricles of EAE mice. Transplanted NPC migration was directed by three tissue-derived chemokines. Stromal cell-derived factor-1α, monocyte chemo-attractant protein-1 and hepatocyte growth factor were expressed in the EAE brain and specifically in microglia and astrocytes. Their cognate receptors, CXCR4, CCR2 or c-Met were constitutively expressed on NPCs. Selective blockage of CXCR4, CCR2 or c-Met partially inhibited NPC migration in EAE brains. Blocking all three receptors had an additive effect and resulted in profound inhibition of NPC migration, as compared to extensive migration of control NPCs. The inflammation-triggered NPC migration into white matter tracts was dependent on a motile NPC phenotype. Specifically, depriving NPCs from epidermal growth factor (EGF) prevented the induction of glial commitment and a motile phenotype (as indicated by an in vitro motility assay), hampering their response to neuroinflammation. In conclusion, signaling via three chemokine systems accounts for most of the inflammation-induced, tissue-derived attraction of transplanted NPCs into white matter tracts during EAE. Copyright © 2014. Published by Elsevier B.V.

  12. Fluvoxamine stimulates oligodendrogenesis of cultured neural stem cells and attenuates inflammation and demyelination in an animal model of multiple sclerosis.

    Science.gov (United States)

    Ghareghani, Majid; Zibara, Kazem; Sadeghi, Heibatollah; Dokoohaki, Shima; Sadeghi, Hossein; Aryanpour, Roya; Ghanbari, Amir

    2017-07-07

    Multiple Sclerosis (MS) require medications controlling severity of the pathology and depression, affecting more than half of the patients. In this study, the effect of antidepressant drug fluvoxamine, a selective serotonin reuptake inhibitor, was investigated in vitro and in vivo. Nanomolar concentrations of fluvoxamine significantly increased cell viability and proliferation of neural stem cells (NSCs) through increasing mRNA expression of Notch1, Hes1 and Ki-67, and protein levels of NICD. Also, physiological concentrations of fluvoxamine were optimal for NSC differentiation toward oligodendrocytes, astrocytes and neurons. In addition, fluvoxamine attenuated experimental autoimmune encephalomyelitis (EAE) severity, a rat MS model, by significantly decreasing its clinical scores. Moreover, fluvoxamine treated EAE rats showed a decrease in IFN-γ serum levels and an increase in IL-4, pro- and anti-inflammatory cytokines respectively, compared to untreated EAE rats. Furthermore, immune cell infiltration and demyelination plaque significantly decreased in spinal cords of fluvoxamine-treated rats, which was accompanied by an increase in protein expression of MBP and GFAP positive cells and a decrease in lactate serum levels, a new biomarker of MS progression. In summary, besides its antidepressant activity, fluvoxamine stimulates proliferation and differentiation of NSCs particularly toward oligodendrocytes, a producer of CNS myelin.

  13. The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

    International Nuclear Information System (INIS)

    Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari; Askarian, Mehrdad; Movahedi, Mohammad Mehdi; Hosseini, Somayyeh; Jahandideh, Mina

    2009-01-01

    Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R 2 were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R 2 confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.

  14. Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2013-01-01

    Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

  15. 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

  16. Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data.

    Science.gov (United States)

    Deshpande, Gopikrishna; Wang, Peng; Rangaprakash, D; Wilamowski, Bogdan

    2015-12-01

    Automated recognition and classification of brain diseases are of tremendous value to society. Attention deficit hyperactivity disorder (ADHD) is a diverse spectrum disorder whose diagnosis is based on behavior and hence will benefit from classification utilizing objective neuroimaging measures. Toward this end, an international competition was conducted for classifying ADHD using functional magnetic resonance imaging data acquired from multiple sites worldwide. Here, we consider the data from this competition as an example to illustrate the utility of fully connected cascade (FCC) artificial neural network (ANN) architecture for performing classification. We employed various directional and nondirectional brain connectivity-based methods to extract discriminative features which gave better classification accuracy compared to raw data. Our accuracy for distinguishing ADHD from healthy subjects was close to 90% and between the ADHD subtypes was close to 95%. Further, we show that, if properly used, FCC ANN performs very well compared to other classifiers such as support vector machines in terms of accuracy, irrespective of the feature used. Finally, the most discriminative connectivity features provided insights about the pathophysiology of ADHD and showed reduced and altered connectivity involving the left orbitofrontal cortex and various cerebellar regions in ADHD.

  17. Functional neuroimaging studies in addiction: multisensory drug stimuli and neural cue reactivity.

    Science.gov (United States)

    Yalachkov, Yavor; Kaiser, Jochen; Naumer, Marcus J

    2012-02-01

    Neuroimaging studies on cue reactivity have substantially contributed to the understanding of addiction. In the majority of studies drug cues were presented in the visual modality. However, exposure to conditioned cues in real life occurs often simultaneously in more than one sensory modality. Therefore, multisensory cues should elicit cue reactivity more consistently than unisensory stimuli and increase the ecological validity and the reliability of brain activation measurements. This review includes the data from 44 whole-brain functional neuroimaging studies with a total of 1168 subjects (812 patients and 356 controls). Correlations between neural cue reactivity and clinical covariates such as craving have been reported significantly more often for multisensory than unisensory cues in the motor cortex, insula and posterior cingulate cortex. Thus, multisensory drug cues are particularly effective in revealing brain-behavior relationships in neurocircuits of addiction responsible for motivation, craving awareness and self-related processing. Copyright © 2011 Elsevier Ltd. All rights reserved.

  18. Neural plasticity in functional and anatomical MRI studies of children with Tourette syndrome

    DEFF Research Database (Denmark)

    Eichele, Heike; Plessen, Kerstin J

    2012-01-01

    Background: Tourette syndrome (TS) is a neuropsychiatric disorder with childhood onset characterized by chronic motor and vocal tics. The typical clinical course of an attenuation of symptoms during adolescence in parallel with the emerging self-regulatory control during development suggests...... that plastic processes may play an important role in the development of tic symptoms. Methods: We conducted a systematic search to identify existing imaging studies (both anatomical and functional magnetic resonance imaging [fMRI]) in young persons under the age of 19 years with TS. Results: The final search...... compensatory pathways in children with TS. Along with alterations in regions putatively representing the origin of tics, deviations in several other regions most likely represent an activity-dependent neural plasticity that help to modulate tic severity, such as the prefrontal cortex, but also in the corpus...

  19. Forecasting business cycle with chaotic time series based on neural network with weighted fuzzy membership functions

    International Nuclear Information System (INIS)

    Chai, Soo H.; Lim, Joon S.

    2016-01-01

    This study presents a forecasting model of cyclical fluctuations of the economy based on the time delay coordinate embedding method. The model uses a neuro-fuzzy network called neural network with weighted fuzzy membership functions (NEWFM). The preprocessed time series of the leading composite index using the time delay coordinate embedding method are used as input data to the NEWFM to forecast the business cycle. A comparative study is conducted using other methods based on wavelet transform and Principal Component Analysis for the performance comparison. The forecasting results are tested using a linear regression analysis to compare the approximation of the input data against the target class, gross domestic product (GDP). The chaos based model captures nonlinear dynamics and interactions within the system, which other two models ignore. The test results demonstrated that chaos based method significantly improved the prediction capability, thereby demonstrating superior performance to the other methods.

  20. The neural correlates of coloured music: a functional MRI investigation of auditory-visual synaesthesia.

    Science.gov (United States)

    Neufeld, J; Sinke, C; Dillo, W; Emrich, H M; Szycik, G R; Dima, D; Bleich, S; Zedler, M

    2012-01-01

    In auditory-visual synaesthesia, all kinds of sound can induce additional visual experiences. To identify the brain regions mainly involved in this form of synaesthesia, functional magnetic resonance imaging (fMRI) has been used during non-linguistic sound perception (chords and pure tones) in synaesthetes and non-synaesthetes. Synaesthetes showed increased activation in the left inferior parietal cortex (IPC), an area involved in multimodal integration, feature binding and attention guidance. No significant group-differences could be detected in area V4, which is known to be related to colour vision and form processing. The results support the idea of the parietal cortex acting as sensory nexus area in auditory-visual synaesthesia, and as a common neural correlate for different types of synaesthesia. Copyright © 2011 Elsevier Ltd. All rights reserved.

  1. 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)

  2. The microbiota-gut-brain axis as a key regulator of neural function and the stress response: Implications for human and animal health.

    Science.gov (United States)

    Wiley, N C; Dinan, T G; Ross, R P; Stanton, C; Clarke, G; Cryan, J F

    2017-07-01

    The brain-gut-microbiota axis comprises an extensive communication network between the brain, the gut, and the microbiota residing there. Development of a diverse gut microbiota is vital for multiple features of behavior and physiology, as well as many fundamental aspects of brain structure and function. Appropriate early-life assembly of the gut microbiota is also believed to play a role in subsequent emotional and cognitive development. If the composition, diversity, or assembly of the gut microbiota is impaired, this impairment can have a negative impact on host health and lead to disorders such as obesity, diabetes, inflammatory diseases, and even potentially neuropsychiatric illnesses, including anxiety and depression. Therefore, much research effort in recent years has focused on understanding the potential of targeting the intestinal microbiota to prevent and treat such disorders. This review aims to explore the influence of the gut microbiota on host neural function and behavior, particularly those of relevance to stress-related disorders. The involvement of microbiota in diverse neural functions such as myelination, microglia function, neuronal morphology, and blood-brain barrier integrity across the life span, from early life to adolescence to old age, will also be discussed. Nurturing an optimal gut microbiome may also prove beneficial in animal science as a means to manage stressful situations and to increase productivity of farm animals. The implications of these observations are manifold, and researchers are hopeful that this promising body of preclinical work can be successfully translated to the clinic and beyond.

  3. Functional MRI studies of the neural mechanisms of human brain attentional networks

    International Nuclear Information System (INIS)

    Hao Jing; Li Kuncheng; Chen Qi; Wang Yan; Peng Xiaozhe; Zhou Xiaolin

    2005-01-01

    Objective: To identify the neural mechanisms of the anterior attention network (AAN) and posterior attention network (PAN) , investigate the possible interaction between them with event-related functional MRI(ER-fMRI). Methods: Eight right-handed healthy volunteers participated in the experiment designed with inhibition of return in visual orienting and Stroop color-word interference effect. The fMRI data were collected on Siemens 1.5 T Sonata MRI systems and analyzed by AFNI to generate the activation map. Results: The data sets from 6 of 8 subjects were used in the study. The functional localizations of the Stroop and IOR, which manifest the function of the AAN and PAN respectively, were consistent with previous imaging researches. On cued locations, left inferior parietal lobule (IPL), area MT/V5, right dorsolateral prefrontal cortex (DLPFC) and left anterior cingulated cortex (ACC) were significantly activated. On uncued locations, right superior parietal lobule (SPL) and bilateral area MT/V5 were significantly activated. Conclusion: The AAN exerts control over the PAN, while its function can be in turn modulated by the PAN. There are interaction between the AAN and PAN. In addition, it is also proved that ER-fMRI is a feasible method to revise preexisting cognitive model and theory. (authors)

  4. The neural substrate and functional integration of uncertainty in decision making: an information theory approach.

    Science.gov (United States)

    Goñi, Joaquín; Aznárez-Sanado, Maite; Arrondo, Gonzalo; Fernández-Seara, María; Loayza, Francis R; Heukamp, Franz H; Pastor, María A

    2011-03-09

    Decision making can be regarded as the outcome of cognitive processes leading to the selection of a course of action among several alternatives. Borrowing a central measurement from information theory, Shannon entropy, we quantified the uncertainties produced by decisions of participants within an economic decision task under different configurations of reward probability and time. These descriptors were used to obtain blood oxygen level-dependent (BOLD) signal correlates of uncertainty and two clusters codifying the Shannon entropy of task configurations were identified: a large cluster including parts of the right middle cingulate cortex (MCC) and left and right pre-supplementary motor areas (pre-SMA) and a small cluster at the left anterior thalamus. Subsequent functional connectivity analyses using the psycho-physiological interactions model identified areas involved in the functional integration of uncertainty. Results indicate that clusters mostly located at frontal and temporal cortices experienced an increased connectivity with the right MCC and left and right pre-SMA as the uncertainty was higher. Furthermore, pre-SMA was also functionally connected to a rich set of areas, most of them associative areas located at occipital and parietal lobes. This study provides a map of the human brain segregation and integration (i.e., neural substrate and functional connectivity respectively) of the uncertainty associated to an economic decision making paradigm.

  5. The neural substrate and functional integration of uncertainty in decision making: an information theory approach.

    Directory of Open Access Journals (Sweden)

    Joaquín Goñi

    Full Text Available Decision making can be regarded as the outcome of cognitive processes leading to the selection of a course of action among several alternatives. Borrowing a central measurement from information theory, Shannon entropy, we quantified the uncertainties produced by decisions of participants within an economic decision task under different configurations of reward probability and time. These descriptors were used to obtain blood oxygen level-dependent (BOLD signal correlates of uncertainty and two clusters codifying the Shannon entropy of task configurations were identified: a large cluster including parts of the right middle cingulate cortex (MCC and left and right pre-supplementary motor areas (pre-SMA and a small cluster at the left anterior thalamus. Subsequent functional connectivity analyses using the psycho-physiological interactions model identified areas involved in the functional integration of uncertainty. Results indicate that clusters mostly located at frontal and temporal cortices experienced an increased connectivity with the right MCC and left and right pre-SMA as the uncertainty was higher. Furthermore, pre-SMA was also functionally connected to a rich set of areas, most of them associative areas located at occipital and parietal lobes. This study provides a map of the human brain segregation and integration (i.e., neural substrate and functional connectivity respectively of the uncertainty associated to an economic decision making paradigm.

  6. Efficient second order Algorithms for Function Approximation with Neural Networks. Application to Sextic Potentials

    International Nuclear Information System (INIS)

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

    2009-01-01

    The problem of determining the analytical description for a set of data arises in numerous sciences and applications and can be referred to as data modeling or system identification. Neural networks are a convenient means of representation because they are known to be universal approximates that can learn data. The desired task is usually obtained by a learning procedure which consists in adjusting the s ynaptic weights . For this purpose, many learning algorithms have been proposed to update these weights. The convergence for these learning algorithms is a crucial criterion for neural networks to be useful in different applications. The aim of the present contribution is to use a training algorithm for feed forward wavelet networks used for function approximation. The training is based on the minimization of the least-square cost function. The minimization is performed by iterative second order gradient-based methods. We make use of the Levenberg-Marquardt algorithm to train the architecture of the chosen network and, then, the training procedure starts with a simple gradient method which is followed by a BFGS (Broyden, Fletcher, Glodfarb et Shanno) algorithm. The performances of the two algorithms are then compared. Our method is then applied to determine the energy of the ground state associated to a sextic potential. In fact, the Schrodinger equation does not always admit an exact solution and one has, generally, to solve it numerically. To this end, the sextic potential is, firstly, approximated with the above outlined wavelet network and, secondly, implemented into a numerical scheme. Our results are in good agreement with the ones found in the literature.

  7. Neutron generators with size scalability, ease of fabrication and multiple ion source functionalities

    Science.gov (United States)

    Elizondo-Decanini, Juan M

    2014-11-18

    A neutron generator is provided with a flat, rectilinear geometry and surface mounted metallizations. This construction provides scalability and ease of fabrication, and permits multiple ion source functionalities.

  8. Functional link between the hypocretin and serotonin systems in the neural control of breathing and central chemosensitivity.

    Science.gov (United States)

    Corcoran, Andrea E; Richerson, George B; Harris, Michael B

    2015-07-01

    Serotonin (5-HT)-synthesizing neurons of the medullary raphe are putative central chemoreceptors, proposed to be one of potentially multiple brain stem chemosensitive cell types and loci interacting to produce the respiratory chemoreflex. Hypocretin-synthesizing neurons of the lateral hypothalamus are important contributors to arousal state, thermoregulation, and feeding behavior and are also reportedly involved in the hypercapnic ventilatory response. Recently, a functional interaction was found between the hypocretin system and 5-HT neurons of the dorsal raphe. The validity and potential significance of hypocretin modulation of medullary raphe 5-HT neurons, however, is unknown. As such, the purpose of this study was to explore functional interactions between the hypocretin system and 5-HT system of the medullary raphe on baseline respiratory output and central chemosensitivity. To explore such interactions, we used the neonatal in vitro medullary slice preparation derived from wild-type (WT) mice (normal 5-HT function) and a knockout strain lacking all central 5-HT neurons (Lmx1b(f/f/p) mice). We examined effects of acidosis, hypocretin-1, a hypocretin receptor antagonist (SB-408124), and the effect of the antagonist on the response to acidosis. We confirmed the critical role of 5-HT neurons in central chemosensitivity given that the increased hypoglossal burst frequency with acidosis, characteristic of WT mice, was absent in preparations derived from Lmx1b(f/f/p) mice. We also found that hypocretin facilitated baseline neural ventilatory output in part through 5-HT neurons. Although the impact of hypocretin on 5-HT neuronal sensitivity to acidosis is still unclear, hypocretins did appear to mediate the burst duration response to acidosis via serotonergic mechanisms.

  9. Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.

    Science.gov (United States)

    Li, Man; Ling, Cheng; Xu, Qi; Gao, Jingyang

    2018-02-01

    Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

  10. Implantable liquid metal-based flexible neural microelectrode array and its application in recovering animal locomotion functions

    Science.gov (United States)

    Guo, Rui; Liu, Jing

    2017-10-01

    With significant advantages in rapidly restoring the nerve function, electrical stimulation of nervous tissue is a crucial treatment of peripheral nerve injuries leading to common movement disorder. However, the currently available stimulating electrodes generally based on rigid conductive materials would cause a potential mechanical mismatch with soft neural tissues which thus reduces long-term effects of electrical stimulation. Here, we proposed and fabricated a flexible neural microelectrode array system based on the liquid metal GaIn alloy (75.5% Ga and 24.5% In by weight) and via printing approach. Such an alloy with a unique low melting point (10.35 °C) owns excellent electrical conductivity and high compliance, which are beneficial to serve as implantable flexible neural electrodes. The flexible neural microelectrode array embeds four liquid metal electrodes and stretchable interconnects in a PDMS membrane (500 µm in thickness) that possess a lower elastic modulus (1.055 MPa), which is similar to neural tissues with elastic moduli in the 0.1-1.5 MPa range. The electrical experiments indicate that the liquid metal interconnects could sustain over 7000 mechanical stretch cycles with resistance approximately staying at 4 Ω. Over the conceptual experiments on animal sciatic nerve electrical stimulation, the dead bullfrog implanted with flexible neural microelectrode array could even rhythmically contract and move its lower limbs under the electrical stimulations from the implant. This demonstrates a highly efficient way for quickly recovering biological nerve functions. Further, the good biocompatibility of the liquid metal material was justified via a series of biological experiments. This liquid metal modality for neural stimulation is expected to play important roles as biologic electrodes to overcome the fundamental mismatch in mechanics between biological tissues and electronic devices in the coming time.

  11. Implantable liquid metal-based flexible neural microelectrode array and its application in recovering animal locomotion functions

    International Nuclear Information System (INIS)

    Guo, Rui; Liu, Jing

    2017-01-01

    With significant advantages in rapidly restoring the nerve function, electrical stimulation of nervous tissue is a crucial treatment of peripheral nerve injuries leading to common movement disorder. However, the currently available stimulating electrodes generally based on rigid conductive materials would cause a potential mechanical mismatch with soft neural tissues which thus reduces long-term effects of electrical stimulation. Here, we proposed and fabricated a flexible neural microelectrode array system based on the liquid metal GaIn alloy (75.5% Ga and 24.5% In by weight) and via printing approach. Such an alloy with a unique low melting point (10.35 °C) owns excellent electrical conductivity and high compliance, which are beneficial to serve as implantable flexible neural electrodes. The flexible neural microelectrode array embeds four liquid metal electrodes and stretchable interconnects in a PDMS membrane (500 µ m in thickness) that possess a lower elastic modulus (1.055 MPa), which is similar to neural tissues with elastic moduli in the 0.1–1.5 MPa range. The electrical experiments indicate that the liquid metal interconnects could sustain over 7000 mechanical stretch cycles with resistance approximately staying at 4 Ω. Over the conceptual experiments on animal sciatic nerve electrical stimulation, the dead bullfrog implanted with flexible neural microelectrode array could even rhythmically contract and move its lower limbs under the electrical stimulations from the implant. This demonstrates a highly efficient way for quickly recovering biological nerve functions. Further, the good biocompatibility of the liquid metal material was justified via a series of biological experiments. This liquid metal modality for neural stimulation is expected to play important roles as biologic electrodes to overcome the fundamental mismatch in mechanics between biological tissues and electronic devices in the coming time. (paper)

  12. Short-term plasticity as a neural mechanism supporting memory and attentional functions.

    Science.gov (United States)

    Jääskeläinen, Iiro P; Ahveninen, Jyrki; Andermann, Mark L; Belliveau, John W; Raij, Tommi; Sams, Mikko

    2011-11-08

    Based on behavioral studies, several relatively distinct perceptual and cognitive functions have been defined in cognitive psychology such as sensory memory, short-term memory, and selective attention. Here, we review evidence suggesting that some of these functions may be supported by shared underlying neuronal mechanisms. Specifically, we present, based on an integrative review of the literature, a hypothetical model wherein short-term plasticity, in the form of transient center-excitatory and surround-inhibitory modulations, constitutes a generic processing principle that supports sensory memory, short-term memory, involuntary attention, selective attention, and perceptual learning. In our model, the size and complexity of receptive fields/level of abstraction of neural representations, as well as the length of temporal receptive windows, increases as one steps up the cortical hierarchy. Consequently, the type of input (bottom-up vs. top down) and the level of cortical hierarchy that the inputs target, determine whether short-term plasticity supports purely sensory vs. semantic short-term memory or attentional functions. Furthermore, we suggest that rather than discrete memory systems, there are continuums of memory representations from short-lived sensory ones to more abstract longer-duration representations, such as those tapped by behavioral studies of short-term memory. Copyright © 2011 Elsevier B.V. All rights reserved.

  13. Executive functions in mild cognitive impairment: emergence and breakdown of neural plasticity.

    Science.gov (United States)

    Clément, Francis; Gauthier, Serge; Belleville, Sylvie

    2013-05-01

    Our goal was to test the effect of disease severity on the brain activation associated with two executive processes: manipulation and divided attention. This was achieved by administrating a manipulation task and a divided attention task using functional magnetic resonance imaging to 24 individuals with mild cognitive impairment (MCI) and 14 healthy controls matched for age, sex and education. The Mattis Dementia Rating Scale was used to divide persons with MCI into those with better and worse cognitive performances. Both tasks were associated with more brain activation in the MCI group with higher cognition than in healthy controls, particularly in the left frontal areas. Correlational analyses indicated that greater activation in a frontostriatal network hyperactivated by the higher-cognition group was related with better task performance, suggesting that these activations may support functional reorganization of a compensatory nature. By contrast, the lower-cognition group failed to show greater cerebral hyperactivation than controls during the divided attention task and, during the manipulation task, and showed less brain activation than controls in the left ventrolateral cortex, a region commonly hypoactivated in patients with Alzheimer's disease. These findings indicate that, during the early phase of MCI, executive functioning benefits from neural reorganization, but that a breakdown of this brain plasticity characterizes the late stages of MCI. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Different methods to define utility functions yield similar results but engage different neural processes

    Directory of Open Access Journals (Sweden)

    Marcus Heldmann

    2009-10-01

    Full Text Available Although the concept of utility is fundamental to many economic theories, up to now a generally accepted method determining a subject’s utility function is not available. We investigated two methods that are used in economic sciences for describing utility functions by using response-locked event-related potentials in order to assess their neural underpinnings. For defining the certainty equivalent (CE, we used a lottery game with probabilities to win p=0.5, for identifying the subjects’ utility functions directly a standard bisection task was applied. Although the lottery tasks’ payoffs were only hypothetical, a pronounced negativity was observed resembling the error related negativity (ERN previously described in action monitoring research, but this occurred only for choices far away from the indifference point between money and lottery. By contrast, the bisection task failed to evoke an ERN irrespective of the responses’ correctness. Based on these findings we are reasoning that only decisions made in the lottery task achieved a level of subjective relevance that activates cognitive-emotional monitoring. In terms of economic sciences, our findings support the view that the bisection method is unaffected by any kind of probability valuation or other parameters related to risk and in combination with the lottery task can, therefore, be used to differentiate between payoff and probability valuation.

  15. Functional neural changes associated with acquired amusia across different stages of recovery after stroke.

    Science.gov (United States)

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

    2017-09-12

    Brain damage causing acquired amusia disrupts the functional music processing system, creating a unique opportunity to investigate the critical neural architectures of musical processing in the brain. In this longitudinal fMRI study of stroke patients (N = 41) with a 6-month follow-up, we used natural vocal music (sung with lyrics) and instrumental music stimuli to uncover brain activation and functional network connectivity changes associated with acquired amusia and its recovery. In the acute stage, amusic patients exhibited decreased activation in right superior temporal areas compared to non-amusic patients during instrumental music listening. During the follow-up, the activation deficits expanded to comprise a wide-spread bilateral frontal, temporal, and parietal network. The amusics showed less activation deficits to vocal music, suggesting preserved processing of singing in the amusic brain. Compared to non-recovered amusics, recovered amusics showed increased activation to instrumental music in bilateral frontoparietal areas at 3 months and in right middle and inferior frontal areas at 6 months. Amusia recovery was also associated with increased functional connectivity in right and left frontoparietal attention networks to instrumental music. Overall, our findings reveal the dynamic nature of deficient activation and connectivity patterns in acquired amusia and highlight the role of dorsal networks in amusia recovery.

  16. A new paradigm of electrical stimulation to enhance sensory neural function.

    Science.gov (United States)

    Breen, Paul P; ÓLaighin, Gearóid; McIntosh, Caroline; Dinneen, Sean F; Quinlan, Leo R; Serrador, Jorge M

    2014-08-01

    The ability to improve peripheral neural transmission would have significant therapeutic potential in medicine. A technology of this kind could be used to restore and/or enhance sensory function in individuals with depressed sensory function, such as older adults or patients with peripheral neuropathies. The goal of this study was to investigate if a new paradigm of subsensory electrical noise stimulation enhances somatosensory function. Vibration (50Hz) was applied with a Neurothesiometer to the plantar aspect of the foot in the presence or absence of subsensory electrical noise (1/f type). The noise was applied at a proximal site, on a defined region of the tibial nerve path above the ankle. Vibration perception thresholds (VPT) of younger adults were measured in control and experimental conditions, in the absence or presence of noise respectively. An improvement of ∼16% in VPT was found in the presence of noise. These are the first data to demonstrate that modulation of axonal transmission with externally applied electrical noise improves perception of tactile stimuli in humans. Copyright © 2014 IPEM. All rights reserved.

  17. 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…

  18. Experiencing Past and Future Personal Events: Functional Neuroimaging Evidence on the Neural Bases of Mental Time Travel

    Science.gov (United States)

    Botzung, Anne; Denkova, Ekaterina; Manning, Lilianne

    2008-01-01

    Functional MRI was used in healthy subjects to investigate the existence of common neural structures supporting re-experiencing the past and pre-experiencing the future. Past and future events evocation appears to involve highly similar patterns of brain activation including, in particular, the medial prefrontal cortex, posterior regions and the…

  19. Targeting Lumbar Spinal Neural Circuitry by Epidural Stimulation to Restore Motor Function After Spinal Cord Injury.

    Science.gov (United States)

    Minassian, Karen; McKay, W Barry; Binder, Heinrich; Hofstoetter, Ursula S

    2016-04-01

    Epidural spinal cord stimulation has a long history of application for improving motor control in spinal cord injury. This review focuses on its resurgence following the progress made in understanding the underlying neurophysiological mechanisms and on recent reports of its augmentative effects upon otherwise subfunctional volitional motor control. Early work revealed that the spinal circuitry involved in lower-limb motor control can be accessed by stimulating through electrodes placed epidurally over the posterior aspect of the lumbar spinal cord below a paralyzing injury. Current understanding is that such stimulation activates large-to-medium-diameter sensory fibers within the posterior roots. Those fibers then trans-synaptically activate various spinal reflex circuits and plurisegmentally organized interneuronal networks that control more complex contraction and relaxation patterns involving multiple muscles. The induced change in responsiveness of this spinal motor circuitry to any residual supraspinal input via clinically silent translesional neural connections that have survived the injury may be a likely explanation for rudimentary volitional control enabled by epidural stimulation in otherwise paralyzed muscles. Technological developments that allow dynamic control of stimulation parameters and the potential for activity-dependent beneficial plasticity may further unveil the remarkable capacity of spinal motor processing that remains even after severe spinal cord injuries.

  20. The use of artificial neural network analysis and multiple regression for trap quality evaluation: a case study of the Northern Kuqa Depression of Tarim Basin in western China

    Energy Technology Data Exchange (ETDEWEB)

    Guangren Shi; Xingxi Zhou; Guangya Zhang; Xiaofeng Shi; Honghui Li [Research Institute of Petroleum Exploration and Development, Beijing (China)

    2004-03-01

    Artificial neural network analysis is found to be far superior to multiple regression when applied to the evaluation of trap quality in the Northern Kuqa Depression, a gas-rich depression of Tarim Basin in western China. This is because this technique can correlate the complex and non-linear relationship between trap quality and related geological factors, whereas multiple regression can only describe a linear relationship. However, multiple regression can work as an auxiliary tool, as it is suited to high-speed calculations and can indicate the degree of dependence between the trap quality and its related geological factors which artificial neural network analysis cannot. For illustration, we have investigated 30 traps in the Northern Kuqa Depression. For each of the traps, the values of 14 selected geological factors were all known. While geologists were also able to assign individual trap quality values to 27 traps, they were less certain about the values for the other three traps. Multiple regression and artificial neural network analysis were, therefore, respectively used to ascertain these values. Data for the 27 traps were used as known sample data, while the three traps were used as prediction candidates. Predictions from artificial neural network analysis are found to agree with exploration results: where simulation predicted high trap quality, commercial quality flows were afterwards found, and where low trap quality is indicated, no such discoveries have yet been made. On the other hand, multiple regression results indicate the order of dependence of the trap quality on geological factors, which reconciles with what geologists have commonly recognized. We can conclude, therefore, that the application of artificial neural network analysis with the aid of multiple regression to trap evaluation in the Northern Kuqa Depression has been quite successful. To ensure the precision of the above mentioned geological factors and their related parameters for each

  1. Neural correlates of the popular music phenomenon: evidence from functional MRI and PET imaging

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Qiaozhen [The Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Psychiatry, Hangzhou (China); Zhejiang University Medical PET Center, Hangzhou (China); Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou (China); Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou (China); The Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Nuclear Medicine, Hangzhou, Zhejiang (China); Zhang, Ying; Hou, Haifeng; Du, Fenglei; Wu, Shuang; Chen, Lin; Shen, Yehua; Chao, Fangfang; Zhang, Hong; Tian, Mei [Zhejiang University Medical PET Center, Hangzhou (China); Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou (China); Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou (China); The Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Nuclear Medicine, Hangzhou, Zhejiang (China); Chung, June-key [Seoul National University Hospital, Department of Nuclear Medicine, Seoul (Korea, Republic of)

    2017-06-15

    Music can induce different emotions. However, its neural mechanism remains unknown. The aim of this study was to use functional magnetic resonance imaging (fMRI) and position emission tomography (PET) imaging for mapping of neural changes under the most popular music in healthy volunteers. Blood-oxygen-level-dependent (BOLD) fMRI and monoamine receptor PET imaging with {sup 11}C-N-methylspiperone ({sup 11}C-NMSP) were conducted under the popular music Gangnam Style and light music A Comme Amour in healthy subjects. PET and fMRI images were analyzed by using the Statistical Parametric Mapping software (SPM). Significantly increased fMRI BOLD signals were found in the bilateral superior temporal cortices, left cerebellum, left putamen and right thalamus cortex. Monoamine receptor availability was increased significantly in the left superior temporal gyrus and left putamen, but decreased in the bilateral superior occipital cortices under the Gangnam Style compared with the light music condition. Significant positive correlation was found between {sup 11}C-NMSP binding and fMRI BOLD signals in the left temporal cortex. Furthermore, increased {sup 11}C-NMSP binding in the left putamen was positively correlated with the mood arousal level score under the Gangnam Style condition. Popular music Gangnam Style can arouse pleasure experience and strong emotional response. The left putamen is positively correlated with the mood arousal level score under the Gangnam Style condition. Our results revealed characteristic patterns of brain activity associated with Gangnam Style, and may also provide more general insights into the music-induced emotional processing. (orig.)

  2. Neural plasticity in functional and anatomical MRI studies of children with Tourette syndrome.

    Science.gov (United States)

    Eichele, Heike; Plessen, Kerstin J

    2013-01-01

    Tourette syndrome (TS) is a neuropsychiatric disorder with childhood onset characterized by chronic motor and vocal tics. The typical clinical course of an attenuation of symptoms during adolescence in parallel with the emerging self-regulatory control during development suggests that plastic processes may play an important role in the development of tic symptoms. We conducted a systematic search to identify existing imaging studies (both anatomical and functional magnetic resonance imaging [fMRI]) in young persons under the age of 19 years with TS. The final search resulted in 13 original studies, which were reviewed with a focus on findings suggesting adaptive processes (using fMRI) and plasticity (using anatomical MRI). Differences in brain activation compared to healthy controls during tasks that require overriding of prepotent responses help to understand compensatory pathways in children with TS. Along with alterations in regions putatively representing the origin of tics, deviations in several other regions most likely represent an activity-dependent neural plasticity that help to modulate tic severity, such as the prefrontal cortex, but also in the corpus callosum and the limbic system. Factors that potentially influence the development of adaptive changes in the brain of children with TS are age, comorbidity with other developmental disorders, medication use, IQ along with study-design or MRI techniques for acquisition, and analysis of data. The most prominent limitation of all studies is their cross-sectional design. Longitudinal studies extending to younger age groups and to children at risk for developing TS hopefully will confirm findings of neural plasticity in future investigations.

  3. Functionally segregated neural substrates for arbitrary audiovisual paired-association learning.

    Science.gov (United States)

    Tanabe, Hiroki C; Honda, Manabu; Sadato, Norihiro

    2005-07-06

    To clarify the neural substrates and their dynamics during crossmodal association learning, we conducted functional magnetic resonance imaging (MRI) during audiovisual paired-association learning of delayed matching-to-sample tasks. Thirty subjects were involved in the study; 15 performed an audiovisual paired-association learning task, and the remainder completed a control visuo-visual task. Each trial consisted of the successive presentation of a pair of stimuli. Subjects were asked to identify predefined audiovisual or visuo-visual pairs by trial and error. Feedback for each trial was given regardless of whether the response was correct or incorrect. During the delay period, several areas showed an increase in the MRI signal as learning proceeded: crossmodal activity increased in unimodal areas corresponding to visual or auditory areas, and polymodal responses increased in the occipitotemporal junction and parahippocampal gyrus. This pattern was not observed in the visuo-visual intramodal paired-association learning task, suggesting that crossmodal associations might be formed by binding unimodal sensory areas via polymodal regions. In both the audiovisual and visuo-visual tasks, the MRI signal in the superior temporal sulcus (STS) in response to the second stimulus and feedback peaked during the early phase of learning and then decreased, indicating that the STS might be key to the creation of paired associations, regardless of stimulus type. In contrast to the activity changes in the regions discussed above, there was constant activity in the frontoparietal circuit during the delay period in both tasks, implying that the neural substrates for the formation and storage of paired associates are distinct from working memory circuits.

  4. Neural correlates of the popular music phenomenon: evidence from functional MRI and PET imaging.

    Science.gov (United States)

    Chen, Qiaozhen; Zhang, Ying; Hou, Haifeng; Du, Fenglei; Wu, Shuang; Chen, Lin; Shen, Yehua; Chao, Fangfang; Chung, June-Key; Zhang, Hong; Tian, Mei

    2017-06-01

    Music can induce different emotions. However, its neural mechanism remains unknown. The aim of this study was to use functional magnetic resonance imaging (fMRI) and position emission tomography (PET) imaging for mapping of neural changes under the most popular music in healthy volunteers. Blood-oxygen-level-dependent (BOLD) fMRI and monoamine receptor PET imaging with 11 C-N-methylspiperone ( 11 C-NMSP) were conducted under the popular music Gangnam Style and light music A Comme Amour in healthy subjects. PET and fMRI images were analyzed by using the Statistical Parametric Mapping software (SPM). Significantly increased fMRI BOLD signals were found in the bilateral superior temporal cortices, left cerebellum, left putamen and right thalamus cortex. Monoamine receptor availability was increased significantly in the left superior temporal gyrus and left putamen, but decreased in the bilateral superior occipital cortices under the Gangnam Style compared with the light music condition. Significant positive correlation was found between 11 C-NMSP binding and fMRI BOLD signals in the left temporal cortex. Furthermore, increased 11 C-NMSP binding in the left putamen was positively correlated with the mood arousal level score under the Gangnam Style condition. Popular music Gangnam Style can arouse pleasure experience and strong emotional response. The left putamen is positively correlated with the mood arousal level score under the Gangnam Style condition. Our results revealed characteristic patterns of brain activity associated with Gangnam Style, and may also provide more general insights into the music-induced emotional processing.

  5. Neural correlates of the popular music phenomenon: evidence from functional MRI and PET imaging

    International Nuclear Information System (INIS)

    Chen, Qiaozhen; Zhang, Ying; Hou, Haifeng; Du, Fenglei; Wu, Shuang; Chen, Lin; Shen, Yehua; Chao, Fangfang; Zhang, Hong; Tian, Mei; Chung, June-key

    2017-01-01

    Music can induce different emotions. However, its neural mechanism remains unknown. The aim of this study was to use functional magnetic resonance imaging (fMRI) and position emission tomography (PET) imaging for mapping of neural changes under the most popular music in healthy volunteers. Blood-oxygen-level-dependent (BOLD) fMRI and monoamine receptor PET imaging with "1"1C-N-methylspiperone ("1"1C-NMSP) were conducted under the popular music Gangnam Style and light music A Comme Amour in healthy subjects. PET and fMRI images were analyzed by using the Statistical Parametric Mapping software (SPM). Significantly increased fMRI BOLD signals were found in the bilateral superior temporal cortices, left cerebellum, left putamen and right thalamus cortex. Monoamine receptor availability was increased significantly in the left superior temporal gyrus and left putamen, but decreased in the bilateral superior occipital cortices under the Gangnam Style compared with the light music condition. Significant positive correlation was found between "1"1C-NMSP binding and fMRI BOLD signals in the left temporal cortex. Furthermore, increased "1"1C-NMSP binding in the left putamen was positively correlated with the mood arousal level score under the Gangnam Style condition. Popular music Gangnam Style can arouse pleasure experience and strong emotional response. The left putamen is positively correlated with the mood arousal level score under the Gangnam Style condition. Our results revealed characteristic patterns of brain activity associated with Gangnam Style, and may also provide more general insights into the music-induced emotional processing. (orig.)

  6. Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model.

    Directory of Open Access Journals (Sweden)

    Guido Gigante

    2015-11-01

    Full Text Available Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.

  7. Multiple neural states of representation in short-term memory? It’s a matter of attention

    Science.gov (United States)

    LaRocque, Joshua J.; Lewis-Peacock, Jarrod A.; Postle, Bradley R.

    2014-01-01

    Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time, and working memory (WM) refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing) and long-term memory (LTM—the encoding and retention of an effectively unlimited amount of information for a much longer period of time). Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson and Shiffrin, 1968; Baddeley and Hitch, 1974); and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002). In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and electroencephalography (EEG)) over conventional activation-based analysis approaches (Postle, in press). We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays. PMID:24478671

  8. 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

  9. All-trans retinoic acid promotes neural lineage entry by pluripotent embryonic stem cells via multiple pathways

    Directory of Open Access Journals (Sweden)

    Fang Bo

    2009-07-01

    Full Text Available Abstract Background All-trans retinoic acid (RA is one of the most important morphogens with pleiotropic actions. Its embryonic distribution correlates with neural differentiation in the developing central nervous system. To explore the precise effects of RA on neural differentiation of mouse embryonic stem cells (ESCs, we detected expression of RA nuclear receptors and RA-metabolizing enzymes in mouse ESCs and investigated the roles of RA in adherent monolayer culture. Results Upon addition of RA, cell differentiation was directed rapidly and exclusively into the neural lineage. Conversely, pharmacological interference with RA signaling suppressed this neural differentiation. Inhibition of fibroblast growth factor (FGF signaling did not suppress significantly neural differentiation in RA-treated cultures. Pharmacological interference with extracellular signal-regulated kinase (ERK pathway or activation of Wnt pathway effectively blocked the RA-promoted neural specification. ERK phosphorylation was enhanced in RA-treated cultures at the early stage of differentiation. Conclusion RA can promote neural lineage entry by ESCs in adherent monolayer culture systems. This effect depends on RA signaling and its crosstalk with the ERK and Wnt pathways.

  10. Cooperative Control for Multiple Autonomous Vehicles Using Descriptor Functions

    Directory of Open Access Journals (Sweden)

    Marta Niccolini

    2014-01-01

    Full Text Available The paper presents a novel methodology for the control management of a swarm of autonomous vehicles. The vehicles, or agents, may have different skills, and be employed for different missions. The methodology is based on the definition of descriptor functions that model the capabilities of the single agent and each task or mission. The swarm motion is controlled by minimizing a suitable norm of the error between agents’ descriptor functions and other descriptor functions which models the entire mission. The validity of the proposed technique is tested via numerical simulation, using different task assignment scenarios.

  11. Temporal entrainment of cognitive functions: musical mnemonics induce brain plasticity and oscillatory synchrony in neural networks underlying memory.

    Science.gov (United States)

    Thaut, Michael H; Peterson, David A; McIntosh, Gerald C

    2005-12-01

    In a series of experiments, we have begun to investigate the effect of music as a mnemonic device on learning and memory and the underlying plasticity of oscillatory neural networks. We used verbal learning and memory tests (standardized word lists, AVLT) in conjunction with electroencephalographic analysis to determine differences between verbal learning in either a spoken or musical (verbal materials as song lyrics) modality. In healthy adults, learning in both the spoken and music condition was associated with significant increases in oscillatory synchrony across all frequency bands. A significant difference between the spoken and music condition emerged in the cortical topography of the learning-related synchronization. When using EEG measures as predictors during learning for subsequent successful memory recall, significantly increased coherence (phase-locked synchronization) within and between oscillatory brain networks emerged for music in alpha and gamma bands. In a similar study with multiple sclerosis patients, superior learning and memory was shown in the music condition when controlled for word order recall, and subjects were instructed to sing back the word lists. Also, the music condition was associated with a significant power increase in the low-alpha band in bilateral frontal networks, indicating increased neuronal synchronization. Musical learning may access compensatory pathways for memory functions during compromised PFC functions associated with learning and recall. Music learning may also confer a neurophysiological advantage through the stronger synchronization of the neuronal cell assemblies underlying verbal learning and memory. Collectively our data provide evidence that melodic-rhythmic templates as temporal structures in music may drive internal rhythm formation in recurrent cortical networks involved in learning and memory.

  12. Robustness of Multiple Objective Decision Analysis Preference Functions

    National Research Council Canada - National Science Library

    Klimack, William

    2002-01-01

    .... The impact of these differences was examined to improve implementation efficiency. The robustness of the decision model was examined with respect to the preference functions to reduce the time burden imposed on the decision maker...

  13. Neural Temporal Dynamics of Facial Emotion Processing: Age Effects and Relationship to Cognitive Function

    Directory of Open Access Journals (Sweden)

    Xiaoyan Liao

    2017-06-01

    Full Text Available This study used event-related potentials (ERPs to investigate the effects of age on neural temporal dynamics of processing task-relevant facial expressions and their relationship to cognitive functions. Negative (sad, afraid, angry, and disgusted, positive (happy, and neutral faces were presented to 30 older and 31 young participants who performed a facial emotion categorization task. Behavioral and ERP indices of facial emotion processing were analyzed. An enhanced N170 for negative faces, in addition to intact right-hemispheric N170 for positive faces, was observed in older adults relative to their younger counterparts. Moreover, older adults demonstrated an attenuated within-group N170 laterality effect for neutral faces, while younger adults showed the opposite pattern. Furthermore, older adults exhibited sustained temporo-occipital negativity deflection over the time range of 200–500 ms post-stimulus, while young adults showed posterior positivity and subsequent emotion-specific frontal negativity deflections. In older adults, decreased accuracy for labeling negative faces was positively correlated with Montreal Cognitive Assessment Scores, and accuracy for labeling neutral faces was negatively correlated with age. These findings suggest that older people may exert more effort in structural encoding for negative faces and there are different response patterns for the categorization of different facial emotions. Cognitive functioning may be related to facial emotion categorization deficits observed in older adults. This may not be attributable to positivity effects: it may represent a selective deficit for the processing of negative facial expressions in older adults.

  14. Encoding neural and synaptic functionalities in electron spin: A pathway to efficient neuromorphic computing

    Science.gov (United States)

    Sengupta, Abhronil; Roy, Kaushik

    2017-12-01

    Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform every day. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this paper attempts to provide a review of the recent developments in the field of spintronic device based neuromorphic computing. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing device structures mimicking neural and synaptic functionalities is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations.

  15. Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density.

    Science.gov (United States)

    Lęski, Szymon; Kublik, Ewa; Swiejkowski, Daniel A; Wróbel, Andrzej; Wójcik, Daniel K

    2010-12-01

    Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4 × 5 × 7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.

  16. Visual working memory load-related changes in neural activity and functional connectivity.

    Directory of Open Access Journals (Sweden)

    Ling Li

    Full Text Available BACKGROUND: Visual working memory (VWM helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we recorded electroencephalography (EEG from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4-8 Hz, alpha- (8-12 Hz, beta- (12-32 Hz, and gamma- (32-40 Hz frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. CONCLUSIONS/SIGNIFICANCE: We suggest that the differences in theta- and alpha- bands between LVF and RVF

  17. Visual Working Memory Load-Related Changes in Neural Activity and Functional Connectivity

    Science.gov (United States)

    Li, Ling; Zhang, Jin-Xiang; Jiang, Tao

    2011-01-01

    Background Visual working memory (VWM) helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. Methodology/Principal Findings In this study, we recorded electroencephalography (EEG) from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF) memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP) at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4–8 Hz), alpha- (8–12 Hz), beta- (12–32 Hz), and gamma- (32–40 Hz) frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF) WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. Conclusions/Significance We suggest that the differences in theta- and alpha- bands between LVF and RVF conditions in

  18. Single-Cell Transcriptomics and Fate Mapping of Ependymal Cells Reveals an Absence of Neural Stem Cell Function.

    Science.gov (United States)

    Shah, Prajay T; Stratton, Jo A; Stykel, Morgan Gail; Abbasi, Sepideh; Sharma, Sandeep; Mayr, Kyle A; Koblinger, Kathrin; Whelan, Patrick J; Biernaskie, Jeff

    2018-05-03

    Ependymal cells are multi-ciliated cells that form the brain's ventricular epithelium and a niche for neural stem cells (NSCs) in the ventricular-subventricular zone (V-SVZ). In addition, ependymal cells are suggested to be latent NSCs with a capacity to acquire neurogenic function. This remains highly controversial due to a lack of prospective in vivo labeling techniques that can effectively distinguish ependymal cells from neighboring V-SVZ NSCs. We describe a transgenic system that allows for targeted labeling of ependymal cells within the V-SVZ. Single-cell RNA-seq revealed that ependymal cells are enriched for cilia-related genes and share several stem-cell-associated genes with neural stem or progenitors. Under in vivo and in vitro neural-stem- or progenitor-stimulating environments, ependymal cells failed to demonstrate any suggestion of latent neural-stem-cell function. These findings suggest remarkable stability of ependymal cell function and provide fundamental insights into the molecular signature of the V-SVZ niche. Copyright © 2018 Elsevier Inc. All rights reserved.

  19. Multiple Functional Variants in cis Modulate PDYN Expression.

    Science.gov (United States)

    Babbitt, Courtney C; Silverman, Jesse S; Haygood, Ralph; Reininga, Jennifer M; Rockman, Matthew V; Wray, Gregory A

    2010-02-01

    Understanding genetic variation and its functional consequences within cis-regulatory regions remains an important challenge in human genetics and evolution. Here, we present a fine-scale functional analysis of segregating variation within the cis-regulatory region of prodynorphin, a gene that encodes an endogenous opioid precursor with roles in cognition and disease. In order to characterize the functional consequences of segregating variation in cis in a region under balancing selection in different human populations, we examined associations between specific polymorphisms and gene expression in vivo and in vitro. We identified five polymorphisms within the 5' flanking region that affect transcript abundance: a 68-bp repeat recognized in prior studies, as well as two microsatellites and two single nucleotide polymorphisms not previously implicated as functional variants. The impact of these variants on transcription differs by brain region, sex, and cell type, implying interactions between cis genotype and the differentiated state of cells. The effects of individual variants on expression level are not additive in some combinations, implying epistatic interactions between nearby variants. These data reveal an unexpectedly complex relationship between segregating genetic variation and its expression-trait consequences and highlights the importance of close functional scrutiny of natural genetic variation within even relatively well-studied cis-regulatory regions.

  20. Mechanisms underlying metabolic and neural defects in zebrafish and human multiple acyl-CoA dehydrogenase deficiency (MADD.

    Directory of Open Access Journals (Sweden)

    Yuanquan Song

    2009-12-01

    Full Text Available In humans, mutations in electron transfer flavoprotein (ETF or electron transfer flavoprotein dehydrogenase (ETFDH lead to MADD/glutaric aciduria type II, an autosomal recessively inherited disorder characterized by a broad spectrum of devastating neurological, systemic and metabolic symptoms. We show that a zebrafish mutant in ETFDH, xavier, and fibroblast cells from MADD patients demonstrate similar mitochondrial and metabolic abnormalities, including reduced oxidative phosphorylation, increased aerobic glycolysis, and upregulation of the PPARG-ERK pathway. This metabolic dysfunction is associated with aberrant neural proliferation in xav, in addition to other neural phenotypes and paralysis. Strikingly, a PPARG antagonist attenuates aberrant neural proliferation and alleviates paralysis in xav, while PPARG agonists increase neural proliferation in wild type embryos. These results show that mitochondrial dysfunction, leading to an increase in aerobic glycolysis, affects neurogenesis through the PPARG-ERK pathway, a potential target for therapeutic intervention.

  1. The hypocretins/orexins: integrators of multiple physiological functions

    Science.gov (United States)

    Li, Jingcheng; Hu, Zhian; Lecea, Luis

    2014-01-01

    The hypocretins (Hcrts), also known as orexins, are two peptides derived from a single precursor produced in the posterior lateral hypothalamus. Over the past decade, the orexin system has been associated with numerous physiological functions, including sleep/arousal, energy homeostasis, endocrine, visceral functions and pathological states, such as narcolepsy and drug abuse. Here, we review the discovery of Hcrt/orexins and their receptors and propose a hypothesis as to how the orexin system orchestrates these multifaceted physiological functions. Linked ArticlesThis article is part of a themed section on Orexin Receptors. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2014.171.issue-2 PMID:24102345

  2. Using domain-specific basic functions for the analysis of supervised artificial neural networks

    NARCIS (Netherlands)

    van der Zwaag, B.J.

    2003-01-01

    Since the early development of artificial neural networks, researchers have tried to analyze trained neural networks in order to gain insight into their behavior. For certain applications and in certain problem domains this has been successful, for example by the development of so-called rule

  3. Multiple sclerosis impairs regional functional connectivity in the cerebellum

    DEFF Research Database (Denmark)

    Dogonowski, Anne-Marie; Andersen, Kasper Winther; Madsen, Kristoffer Hougaard

    2013-01-01

    in the cerebellum in MS. This might be caused by a functional disruption of cortico-ponto-cerebellar and spino-cerebellar inputs, since patients with higher lesion load in the left cerebellar peduncles showed a stronger reduction in cerebellar homogeneity. In patients, two clusters in the left posterior cerebellum...

  4. Tyrosine Residues Regulate Multiple Nuclear Functions of P54nrb.

    Science.gov (United States)

    Lee, Ahn R; Hung, Wayne; Xie, Ning; Liu, Liangliang; He, Leye; Dong, Xuesen

    2017-04-01

    The non-POU-domain-containing octamer binding protein (NONO; also known as p54nrb) has various nuclear functions ranging from transcription, RNA splicing, DNA synthesis and repair. Although tyrosine phosphorylation has been proposed to account for the multi-functional properties of p54nrb, direct evidence on p54nrb as a phosphotyrosine protein remains unclear. To investigate the tyrosine phosphorylation status of p54nrb, we performed site-directed mutagenesis on the five tyrosine residues of p54nrb, replacing the tyrosine residues with phenylalanine or alanine, and immunoblotted for tyrosine phosphorylation. We then preceded with luciferase reporter assays, RNA splicing minigene assays, co-immunoprecipitation, and confocal microscopy to study the function of p54nrb tyrosine residues on transcription, RNA splicing, protein-protein interaction, and cellular localization. We found that p54nrb was not phosphorylated at tyrosine residues. Rather, it has non-specific binding affinity to anti-phosphotyrosine antibodies. However, replacement of tyrosine with phenylalanine altered p54nrb activities in transcription co-repression and RNA splicing in gene context-dependent fashions by means of differential regulation of p54nrb protein association with its interacting partners and co-regulators of transcription and splicing. These results demonstrate that tyrosine residues, regardless of phosphorylation status, are important for p54nrb function. J. Cell. Physiol. 232: 852-861, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  5. Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

    Directory of Open Access Journals (Sweden)

    Satoru Hiwa

    2016-01-01

    Full Text Available Functional near-infrared spectroscopy (fNIRS is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups.

  6. Neural regulation of the kidney function in rats with cisplatin induced renal failure

    Science.gov (United States)

    Goulding, Niamh E.; Johns, Edward J.

    2015-01-01

    Aim: Chronic kidney disease (CKD) is often associated with a disturbed cardiovascular homeostasis. This investigation explored the role of the renal innervation in mediating deranged baroreflex control of renal sympathetic nerve activity (RSNA) and renal excretory function in cisplatin-induced renal failure. Methods: Rats were either intact or bilaterally renally denervated 4 days prior to receiving cisplatin (5 mg/kg i.p.) and entered a chronic metabolic study for 8 days. At day 8, other groups of rats were prepared for acute measurement of RSNA or renal function with either intact or denervated kidneys. Results: Following the cisplatin challenge, creatinine clearance was 50% lower while fractional sodium excretion and renal cortical and medullary TGF-β1 concentrations were 3–4 fold higher in both intact and renally denervated rats compared to control rats. In cisplatin-treated rats, the maximal gain of the high-pressure baroreflex curve was only 20% that of control rats, but following renal denervation not different from that of renally denervated control rats. Volume expansion reduced RSNA by 50% in control and in cisplatin-treated rats but only following bilateral renal denervation. The volume expansion mediated natriuresis/diuresis was absent in the cisplatin-treated rats but was normalized following renal denervation. Conclusions: Cisplatin-induced renal injury impaired renal function and caused a sympatho-excitation with blunting of high and low pressure baroreflex regulation of RSNA, which was dependent on the renal innervation. It is suggested that in man with CKD there is a dysregulation of the neural control of the kidney mediated by its sensory innervation. PMID:26175693

  7. Functional connectivity and information flow of the respiratory neural network in chronic obstructive pulmonary disease.

    Science.gov (United States)

    Yu, Lianchun; De Mazancourt, Marine; Hess, Agathe; Ashadi, Fakhrul R; Klein, Isabelle; Mal, Hervé; Courbage, Maurice; Mangin, Laurence

    2016-08-01

    Breathing involves a complex interplay between the brainstem automatic network and cortical voluntary command. How these brain regions communicate at rest or during inspiratory loading is unknown. This issue is crucial for several reasons: (i) increased respiratory loading is a major feature of several respiratory diseases, (ii) failure of the voluntary motor and cortical sensory processing drives is among the mechanisms that precede acute respiratory failure, (iii) several cerebral structures involved in responding to inspiratory loading participate in the perception of dyspnea, a distressing symptom in many disease. We studied functional connectivity and Granger causality of the respiratory network in controls and patients with chronic obstructive pulmonary disease (COPD), at rest and during inspiratory loading. Compared with those of controls, the motor cortex area of patients exhibited decreased connectivity with their contralateral counterparts and no connectivity with the brainstem. In the patients, the information flow was reversed at rest with the source of the network shifted from the medulla towards the motor cortex. During inspiratory loading, the system was overwhelmed and the motor cortex became the sink of the network. This major finding may help to understand why some patients with COPD are prone to acute respiratory failure. Network connectivity and causality were related to lung function and illness severity. We validated our connectivity and causality results with a mathematical model of neural network. Our findings suggest a new therapeutic strategy involving the modulation of brain activity to increase motor cortex functional connectivity and improve respiratory muscles performance in patients. Hum Brain Mapp 37:2736-2754, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  8. 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.

  9. 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...

  10. 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.

  11. Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model

    DEFF Research Database (Denmark)

    Tønnesen, Jan; Parish, Clare L; Sørensen, Andreas T

    2011-01-01

    Intrastriatal grafts of stem cell-derived dopamine (DA) neurons induce behavioral recovery in animal models of Parkinson's disease (PD), but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral...... of post-synaptic currents, and functional expression of DA D₂ autoreceptors. These properties resembled those recorded from identical cells in acute slices of intrastriatal grafts in the 6-hydroxy-DA-induced mouse PD model and from DA neurons in intact substantia nigra. Optogenetic activation...... using optogenetics that ectopically grafted stem cell-derived DA neurons become functionally integrated in the DA-denervated striatum. Further optogenetic dissection of the synaptic wiring between grafted and host neurons will be crucial to clarify the cellular and synaptic mechanisms underlying...

  12. The multiple roles and functions of English in South Korea

    Directory of Open Access Journals (Sweden)

    Gene Vasilopoulos

    2013-10-01

    Full Text Available In the field of language and identity, the subcategory of gender has been an area of growing interest (Pavlenko, 2001; Norton & Pavlenko, 2004; Menard-Warwick, 2008; and Higgins, 2010. Adopting the view of gender as “a system of social relationships and discursive practices” (Norton & Pavlenko, 2004, p. 504, social context is fundamental in understanding how gender relates to foreign language learning. This qualitative study focused on the extent to which gender impacts English language learning and English language use in the context of teaching English as a foreign language in South Korea. More specifically, it investigates how gender shapes self and social identity, and how these identities relate to English language learning and English language use, at present and/or in the future, in both real and/or imagined communities. Four male and four female participants were selected using purposive homogenous sampling techniques based on the criteria of having lived abroad in an English speaking community for over 5 years—a criterion which assumes the formation of self and social identity in addition to their native Korean L1. Data was collected through multiple methods including open-ended questionnaires, in-depth interviews, and focus group discussions. Interview and questionnaire data reveals gender differences in the symbolic meaning of English language, the relevance of English in self and social positioning, and the role of English in shaping future professional trajectories with males situating themselves in international contexts and females in the local.

  13. Functional Disturbances Within Frontostriatal Circuits Across Multiple Childhood Psychopathologies

    Science.gov (United States)

    Marsh, Rachel; Maia, Tiago V.; Peterson, Bradley S.

    2009-01-01

    Objective Neuroimaging studies of healthy individuals inform us about the normative maturation of the frontostriatal circuits that subserve self-regulatory control processes. Findings from these studies can be used as a reference frame against which to compare the aberrant development of these processes in individuals across a wide range of childhood psychopathologies. Method The authors reviewed extensive neuroimaging evidence for the presence of abnormalities in frontostriatal circuits in children and adults with Tourette’s syndrome and obsessive-compulsive disorder (OCD) as well as a more limited number of imaging studies of adolescents and adults with anorexia nervosa or bulimia nervosa that, together, implicate dysregulation of frontostriatal control systems in the pathogenesis of these eating disorders. Results The presence of an impaired capacity for self-regulatory control that derives from abnormal development of frontostriatal circuits likely interacts in similar ways with normally occurring somatic sensations and motor urges, intrusive thoughts, sensations of hunger, and preoccupation with body shape and weight to contribute, respectively, to the development of the tics of Tourette’s syndrome, the obsessions of OCD, the binge eating behaviors of bulimia, and the self-starvation of anorexia. Conclusions Analogous brain mechanisms in parallel frontostriatal circuits, or even in differing portions of the same frontostriatal circuit, may underlie the differing behavioral disturbances in these multiple disorders, although further research is needed to confirm this hypothesis. PMID:19448188

  14. Infants’ neural responses to facial emotion in the prefrontal cortex are correlated with temperament: A functional near-infrared spectroscopy study

    Directory of Open Access Journals (Sweden)

    Miranda M Ravicz

    2015-07-01

    Full Text Available Accurate decoding of facial expressions is critical for human communication, particularly during infancy, before formal language has developed. Different facial emotions elicit distinct neural responses within the first months of life. However, there are broad individual differences in such responses, such that the same emotion can elicit different brain responses in different infants. In this study we sought to investigate such differences in the processing of emotional faces by analyzing infants’ cortical metabolic responses to face stimuli and examining whether individual differences in these responses might vary as a function of infant temperament.Seven-month-old infants (N = 24 were shown photographs of women portraying happy expressions, and neural activity was recorded using functional near-infrared spectroscopy (fNIRS. Temperament data were collected using the Revised Infant Behavior Questionnaire Short Form, which assesses the broad temperament factors of Surgency/Extraversion (S/E, Negative Emotionality (NE, and Orienting/Regulation (O/R. We observed that oxyhemoglobin (oxyHb responses to happy face stimuli were negatively correlated with infant temperament factors in channels over the left prefrontal cortex (uncorrected for multiple comparisons. To investigate the brain activity underlying this association, and to explore the use of fNIRS in measuring cortical asymmetry, we analyzed hemispheric asymmetry with respect to temperament groups. Results showed preferential activation of the left hemisphere in low-NE infants in response to smiling faces.These results suggest that individual differences in temperament are associated with differential prefrontal oxyHb responses to faces. Overall, these analyses contribute to our current understanding of face processing during infancy, demonstrate the use of fNIRS in measuring prefrontal asymmetry, and illuminate the neural correlates of face processing as modulated by temperament.

  15. Neural correlates of social cooperation and non-cooperation as a function of psychopathy.

    Science.gov (United States)

    Rilling, James K; Glenn, Andrea L; Jairam, Meeta R; Pagnoni, Giuseppe; Goldsmith, David R; Elfenbein, Hanie A; Lilienfeld, Scott O

    2007-06-01

    Psychopathy is a disorder involving a failure to experience many emotions that are necessary for appropriate social behavior. In this study, we probed the behavioral, emotional, and neural correlates of psychopathic traits within the context of a dyadic social interaction. Thirty subjects were imaged with functional magnetic resonance imaging while playing an iterated Prisoner's Dilemma game with human confederates who were outside the scanner. Subjects also completed two self-report psychopathy questionnaires. Subjects scoring higher on psychopathy, particularly males, defected more often and were less likely to continue cooperating after establishing mutual cooperation with a partner. Further, they experienced more outcomes in which their cooperation was not reciprocated (cooperate-defect outcome). After such outcomes, subjects scoring high in psychopathy showed less amygdala activation, suggesting weaker aversive conditioning to those outcomes. Compared with low-psychopathy subjects, subjects higher in psychopathy also showed weaker activation within orbitofrontal cortex when choosing to cooperate and showed weaker activation within dorsolateral prefrontal and rostral anterior cingulate cortex when choosing to defect. These findings suggest that whereas subjects scoring low on psychopathy have emotional biases toward cooperation that can only be overcome with effortful cognitive control, subjects scoring high on psychopathy have an opposing bias toward defection that likewise can only be overcome with cognitive effort.

  16. 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.

  17. 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.

  18. Melatonin enhances neural stem cell differentiation and engraftment by increasing mitochondrial function.

    Science.gov (United States)

    Mendivil-Perez, Miguel; Soto-Mercado, Viviana; Guerra-Librero, Ana; Fernandez-Gil, Beatriz I; Florido, Javier; Shen, Ying-Qiang; Tejada, Miguel A; Capilla-Gonzalez, Vivian; Rusanova, Iryna; Garcia-Verdugo, José M; Acuña-Castroviejo, Darío; López, Luis Carlos; Velez-Pardo, Carlos; Jimenez-Del-Rio, Marlene; Ferrer, José M; Escames, Germaine

    2017-09-01

    Neural stem cells (NSCs) are regarded as a promising therapeutic approach to protecting and restoring damaged neurons in neurodegenerative diseases (NDs) such as Parkinson's disease and Alzheimer's disease (PD and AD, respectively). However, new research suggests that NSC differentiation is required to make this strategy effective. Several studies have demonstrated that melatonin increases mature neuronal markers, which reflects NSC differentiation into neurons. Nevertheless, the possible involvement of mitochondria in the effects of melatonin during NSC differentiation has not yet been fully established. We therefore tested the impact of melatonin on NSC proliferation and differentiation in an attempt to determine whether these actions depend on modulating mitochondrial activity. We measured proliferation and differentiation markers, mitochondrial structural and functional parameters as well as oxidative stress indicators and also evaluated cell transplant engraftment. This enabled us to show that melatonin (25 μM) induces NSC differentiation into oligodendrocytes and neurons. These effects depend on increased mitochondrial mass/DNA/complexes, mitochondrial respiration, and membrane potential as well as ATP synthesis in NSCs. It is also interesting to note that melatonin prevented oxidative stress caused by high levels of mitochondrial activity. Finally, we found that melatonin enriches NSC engraftment in the ND mouse model following transplantation. We concluded that a combined therapy involving transplantation of NSCs pretreated with pharmacological doses of melatonin could efficiently restore neuronal cell populations in PD and AD mouse models depending on mitochondrial activity promotion. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  19. Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters

    Directory of Open Access Journals (Sweden)

    Amir LAKZIAN

    2010-09-01

    Full Text Available This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC, -33 kPa, and permanent wilting point (PWP, -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN, Artificial Neural Network (ANN and pedotransfer functions (PTF were used to predict the soil water at each sampling location. Mean square deviation (MSD and its components, index of agreement (d, root mean square difference (RMSD and normalized RMSD (RMSDr were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.

  20. 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.

  1. Behavioral and neural correlates of executive functioning in musicians and non-musicians.

    Directory of Open Access Journals (Sweden)

    Jennifer Zuk

    Full Text Available Executive functions (EF are cognitive capacities that allow for planned, controlled behavior and strongly correlate with academic abilities. Several extracurricular activities have been shown to improve EF, however, the relationship between musical training and EF remains unclear due to methodological limitations in previous studies. To explore this further, two experiments were performed; one with 30 adults with and without musical training and one with 27 musically trained and untrained children (matched for general cognitive abilities and socioeconomic variables with a standardized EF battery. Furthermore, the neural correlates of EF skills in musically trained and untrained children were investigated using fMRI. Adult musicians compared to non-musicians showed enhanced performance on measures of cognitive flexibility, working memory, and verbal fluency. Musically trained children showed enhanced performance on measures of verbal fluency and processing speed, and significantly greater activation in pre-SMA/SMA and right VLPFC during rule representation and task-switching compared to musically untrained children. Overall, musicians show enhanced performance on several constructs of EF, and musically trained children further show heightened brain activation in traditional EF regions during task-switching. These results support the working hypothesis that musical training may promote the development and maintenance of certain EF skills, which could mediate the previously reported links between musical training and enhanced cognitive skills and academic achievement.

  2. Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function

    Directory of Open Access Journals (Sweden)

    Faa-Jeng Lin

    2017-01-01

    Full Text Available An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF is proposed in this study. First, a photovoltaic (PV power plant with a battery energy storage system (BESS is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plant are converted to the dq-axis current components. The resulted q-axis current is the input of the PFNN-AMF power smoothing control, and the output is a smoothing PV power curve to achieve the effect of PV power smoothing. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. In addition, a personal computer- (PC- based PV power plant emulator and BESS are built for the experimentation. From the experimental results of various irradiance variation conditions, the effectiveness of the proposed intelligent PV power smoothing control can be verified.

  3. 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.

  4. Impaired neural networks for approximate calculation in dyscalculic children: a functional MRI study

    Directory of Open Access Journals (Sweden)

    Dosch Mengia

    2006-09-01

    Full Text Available Abstract Background Developmental dyscalculia (DD is a specific learning disability affecting the acquisition of mathematical skills in children with otherwise normal general intelligence. The goal of the present study was to examine cerebral mechanisms underlying DD. Methods Eighteen children with DD aged 11.2 ± 1.3 years and twenty age-matched typically achieving schoolchildren were investigated using functional magnetic resonance imaging (fMRI during trials testing approximate and exact mathematical calculation, as well as magnitude comparison. Results Children with DD showed greater inter-individual variability and had weaker activation in almost the entire neuronal network for approximate calculation including the intraparietal sulcus, and the middle and inferior frontal gyrus of both hemispheres. In particular, the left intraparietal sulcus, the left inferior frontal gyrus and the right middle frontal gyrus seem to play crucial roles in correct approximate calculation, since brain activation correlated with accuracy rate in these regions. In contrast, no differences between groups could be found for exact calculation and magnitude comparison. In general, fMRI revealed similar parietal and prefrontal activation patterns in DD children compared to controls for all conditions. Conclusion In conclusion, there is evidence for a deficient recruitment of neural resources in children with DD when processing analog magnitudes of numbers.

  5. 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.

  6. Ras proteins have multiple functions in vegetative cells of Dictyostelium.

    Science.gov (United States)

    Bolourani, Parvin; Spiegelman, George; Weeks, Gerald

    2010-11-01

    During the aggregation of Dictyostelium cells, signaling through RasG is more important in regulating cyclic AMP (cAMP) chemotaxis, whereas signaling through RasC is more important in regulating the cAMP relay. However, RasC is capable of substituting for RasG for chemotaxis, since rasG⁻ cells are only partially deficient in chemotaxis, whereas rasC⁻/rasG⁻ cells are totally incapable of chemotaxis. In this study we have examined the possible functional overlap between RasG and RasC in vegetative cells by comparing the vegetative cell properties of rasG⁻, rasC⁻, and rasC⁻/rasG⁻ cells. In addition, since RasD, a protein not normally found in vegetative cells, is expressed in vegetative rasG⁻ and rasC⁻/rasG⁻ cells and appears to partially compensate for the absence of RasG, we have also examined the possible functional overlap between RasG and RasD by comparing the properties of rasG⁻ and rasC⁻/rasG⁻ cells with those of the mutant cells expressing higher levels of RasD. The results of these two lines of investigation show that RasD is capable of totally substituting for RasG for cytokinesis and growth in suspension, whereas RasC is without effect. In contrast, for chemotaxis to folate, RasC is capable of partially substituting for RasG, but RasD is totally without effect. Finally, neither RasC nor RasD is able to substitute for the role that RasG plays in regulating actin distribution and random motility. These specificity studies therefore delineate three distinct and none-overlapping functions for RasG in vegetative cells.

  7. A Powerful Test for Comparing Multiple Regression Functions.

    Science.gov (United States)

    Maity, Arnab

    2012-09-01

    In this article, we address the important problem of comparison of two or more population regression functions. Recently, Pardo-Fernández, Van Keilegom and González-Manteiga (2007) developed test statistics for simple nonparametric regression models: Y(ij) = θ(j)(Z(ij)) + σ(j)(Z(ij))∊(ij), based on empirical distributions of the errors in each population j = 1, … , J. In this paper, we propose a test for equality of the θ(j)(·) based on the concept of generalized likelihood ratio type statistics. We also generalize our test for other nonparametric regression setups, e.g, nonparametric logistic regression, where the loglikelihood for population j is any general smooth function [Formula: see text]. We describe a resampling procedure to obtain the critical values of the test. In addition, we present a simulation study to evaluate the performance of the proposed test and compare our results to those in Pardo-Fernández et al. (2007).

  8. Acquisition and processing of multiple functional investigations in cardiology

    International Nuclear Information System (INIS)

    Baruthio, J.; Constantinesco, A.; Chavas, M.; Meyer, P.; Dumitresco, B.; Chambron, J.; Nikitine, S.; Voegtlin, R.

    1978-01-01

    This work is based on a acquisition and pretreating Service of data and signals resulting from several types of functional explorations in cardiology. This Service, through a specialised phone-line, is connected to the Medical Centre of Informatic where the different treatments and on occasion the recording of data are carried out. This functions according to the Real Time Executive System and the clinical results are send back to the investigation unit. The main types of cardiologic analysis as studied by the system are envisaged: measure in real time of the cardiac output and analysis of the radiocardiogram, measure and display of the thoracic potentials, spectral analysis of the vectorcardiogram, quantitative cineangiocardiography and analysis of intracardiac pressures. This system is particularly interesting in that it may treat several processes which could ask for a great capacity of storing and an important calculus power, see for instance the study of thoracic potentials. This type of analysis could not be envisaged financially with isolated calculators. The set of results obtained enables us to investigate thoroughly and precisely the cardiac performance [fr

  9. Validating archetypes for the Multiple Sclerosis Functional Composite.

    Science.gov (United States)

    Braun, Michael; Brandt, Alexander Ulrich; Schulz, Stefan; Boeker, Martin

    2014-08-03

    Numerous information models for electronic health records, such as openEHR archetypes are available. The quality of such clinical models is important to guarantee standardised semantics and to facilitate their interoperability. However, validation aspects are not regarded sufficiently yet. The objective of this report is to investigate the feasibility of archetype development and its community-based validation process, presuming that this review process is a practical way to ensure high-quality information models amending the formal reference model definitions. A standard archetype development approach was applied on a case set of three clinical tests for multiple sclerosis assessment: After an analysis of the tests, the obtained data elements were organised and structured. The appropriate archetype class was selected and the data elements were implemented in an iterative refinement process. Clinical and information modelling experts validated the models in a structured review process. Four new archetypes were developed and publicly deployed in the openEHR Clinical Knowledge Manager, an online platform provided by the openEHR Foundation. Afterwards, these four archetypes were validated by domain experts in a team review. The review was a formalised process, organised in the Clinical Knowledge Manager. Both, development and review process turned out to be time-consuming tasks, mostly due to difficult selection processes between alternative modelling approaches. The archetype review was a straightforward team process with the goal to validate archetypes pragmatically. The quality of medical information models is crucial to guarantee standardised semantic representation in order to improve interoperability. The validation process is a practical way to better harmonise models that diverge due to necessary flexibility left open by the underlying formal reference model definitions.This case study provides evidence that both community- and tool-enabled review processes

  10. Epidemiologic study of neural tube defects in Los Angeles County. I. Prevalence at birth based on multiple sources of case ascertainment

    Energy Technology Data Exchange (ETDEWEB)

    Sever, L.E. (Pacific Northwest Lab., Richland, WA); Sanders, M.; Monsen, R.

    1982-01-01

    Epidemiologic studies of the neural tube defects (NTDs), anencephalus and spina bifida, have for the most part been based on single sources of case ascertainment in past studies. The present investigation attempts total ascertainment of NTD cases in the newborn population of Los Angeles County residents for the period 1966 to 1972. Design of the study, sources of data, and estimates of prevalence rates based on single and multiple sources of case ascertainment are here discussed. Anencephalus cases totaled 448, spina bifida 442, and encephalocele 72, giving prevalence rates of 0.52, 0.51, and 0.08 per 1000 total births, respectively, for these neural tube defects - rates considered to be low. The Los Angeles County prevalence rates are compared with those of other recent North American studies and support is provided for earlier suggestions of low rates on the West Coast.

  11. Neuromuscular adaptations to long-term progressive resistance training translates to improved functional capacity for people with multiple sclerosis and is maintained at follow-up

    DEFF Research Database (Denmark)

    Kjolhede, T.; Vissing, K.; de Place, L.

    2015-01-01

    BACKGROUND: Progressive resistance training (PRT) is acknowledged to effectively improve muscle strength for people with multiple sclerosis (PwMS), but diverging results exist regarding whether such improvements translates to improved functional capacity, possibly relating to insufficient duration......: This study was a randomised controlled trial, with a training group and a waitlist group undergoing supervised PRT for 24 weeks initially or after 24 weeks of habitual lifestyle, respectively. Functional capacity, isometric muscle strength of knee extensors and flexors, neural drive and thigh muscle cross......-sectional area was measured at baseline, after 24 and 48 weeks. RESULTS: The training group significantly improved neuromuscular function of the knee extensors and flexors, which translated to improvements in functional capacity. Furthermore, the improved functional capacity was maintained after 24 weeks of self...

  12. Bisphenol A affects androgen receptor function via multiple mechanisms.

    Science.gov (United States)

    Teng, Christina; Goodwin, Bonnie; Shockley, Keith; Xia, Menghang; Huang, Ruili; Norris, John; Merrick, B Alex; Jetten, Anton M; Austin, Christopher P; Tice, Raymond R

    2013-05-25

    Bisphenol A (BPA), is a well-known endocrine disruptor compound (EDC) that affects the normal development and function of the female and male reproductive system, however the mechanisms of action remain unclear. To investigate the molecular mechanisms of how BPA may affect ten different nuclear receptors, stable cell lines containing individual nuclear receptor ligand binding domain (LBD)-linked to the β-Gal reporter were examined by a quantitative high throughput screening (qHTS) format in the Tox21 Screening Program of the NIH. The results showed that two receptors, estrogen receptor alpha (ERα) and androgen receptor (AR), are affected by BPA in opposite direction. To confirm the observed effects of BPA on ERα and AR, we performed transient transfection experiments with full-length receptors and their corresponding response elements linked to luciferase reporters. We also included in this study two BPA analogs, bisphenol AF (BPAF) and bisphenol S (BPS). As seen in African green monkey kidney CV1 cells, the present study confirmed that BPA and BPAF act as ERα agonists (half maximal effective concentration EC50 of 10-100 nM) and as AR antagonists (half maximal inhibitory concentration IC50 of 1-2 μM). Both BPA and BPAF antagonized AR function via competitive inhibition of the action of synthetic androgen R1881. BPS with lower estrogenic activity (EC50 of 2.2 μM), did not compete with R1881 for AR binding, when tested at 30 μM. Finally, the effects of BPA were also evaluated in a nuclear translocation assays using EGPF-tagged receptors. Similar to 17β-estradiol (E2) which was used as control, BPA was able to enhance ERα nuclear foci formation but at a 100-fold higher concentration. Although BPA was able to bind AR, the nuclear translocation was reduced. Furthermore, BPA was unable to induce functional foci in the nuclei and is consistent with the transient transfection study that BPA is unable to activate AR. Published by Elsevier Ireland Ltd.

  13. Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks

    International Nuclear Information System (INIS)

    Nishitani, Y.; Kaneko, Y.; Ueda, M.; Fujii, E.; Morie, T.

    2012-01-01

    Spike-timing-dependent synaptic plasticity (STDP) is demonstrated in a synapse device based on a ferroelectric-gate field-effect transistor (FeFET). STDP is a key of the learning functions observed in human brains, where the synaptic weight changes only depending on the spike timing of the pre- and post-neurons. The FeFET is composed of the stacked oxide materials with ZnO/Pr(Zr,Ti)O 3 (PZT)/SrRuO 3 . In the FeFET, the channel conductance can be altered depending on the density of electrons induced by the polarization of PZT film, which can be controlled by applying the gate voltage in a non-volatile manner. Applying a pulse gate voltage enables the multi-valued modulation of the conductance, which is expected to be caused by a change in PZT polarization. This variation depends on the height and the duration of the pulse gate voltage. Utilizing these characteristics, symmetric and asymmetric STDP learning functions are successfully implemented in the FeFET-based synapse device by applying the non-linear pulse gate voltage generated from a set of two pulses in a sampling circuit, in which the two pulses correspond to the spikes from the pre- and post-neurons. The three-terminal structure of the synapse device enables the concurrent learning, in which the weight update can be performed without canceling signal transmission among neurons, while the neural networks using the previously reported two-terminal synapse devices need to stop signal transmission for learning.

  14. New bioactive motifs and their use in functionalized self-assembling peptides for NSC differentiation and neural tissue engineering

    Science.gov (United States)

    Gelain, F.; Cigognini, D.; Caprini, A.; Silva, D.; Colleoni, B.; Donegá, M.; Antonini, S.; Cohen, B. E.; Vescovi, A.

    2012-04-01

    Developing functionalized biomaterials for enhancing transplanted cell engraftment in vivo and stimulating the regeneration of injured tissues requires a multi-disciplinary approach customized for the tissue to be regenerated. In particular, nervous tissue engineering may take a great advantage from the discovery of novel functional motifs fostering transplanted stem cell engraftment and nervous fiber regeneration. Using phage display technology we have discovered new peptide sequences that bind to murine neural stem cell (NSC)-derived neural precursor cells (NPCs), and promote their viability and differentiation in vitro when linked to LDLK12 self-assembling peptide (SAPeptide). We characterized the newly functionalized LDLK12 SAPeptides via atomic force microscopy, circular dichroism and rheology, obtaining nanostructured hydrogels that support human and murine NSC proliferation and differentiation in vitro. One functionalized SAPeptide (Ac-FAQ), showing the highest stem cell viability and neural differentiation in vitro, was finally tested in acute contusive spinal cord injury in rats, where it fostered nervous tissue regrowth and improved locomotor recovery. Interestingly, animals treated with the non-functionalized LDLK12 had an axon sprouting/regeneration intermediate between Ac-FAQ-treated animals and controls. These results suggest that hydrogels functionalized with phage-derived peptides may constitute promising biomimetic scaffolds for in vitro NSC differentiation, as well as regenerative therapy of the injured nervous system. Moreover, this multi-disciplinary approach can be used to customize SAPeptides for other specific tissue engineering applications.Developing functionalized biomaterials for enhancing transplanted cell engraftment in vivo and stimulating the regeneration of injured tissues requires a multi-disciplinary approach customized for the tissue to be regenerated. In particular, nervous tissue engineering may take a great advantage from the

  15. Melatonin in Plants - Diversity of Levels and Multiplicity of Functions.

    Science.gov (United States)

    Hardeland, Rüdiger

    2016-01-01

    Melatonin has been detected in numerous plant species. A particularly surprising finding concerns the highly divergent levels of melatonin that vary between species, organs and environmental conditions, from a few pg/g to over 20 μg/g, reportedly up to 200 μg/g. Highest values have been determined in oily seeds and in plant organs exposed to high UV radiation. The divergency of melatonin concentrations is discussed under various functional aspects and focused on several open questions. This comprises differences in precursor availability, catabolism, the relative contribution of isoenzymes of the melatonin biosynthetic pathway, and differences in rate limitation by either serotonin N-acetyltransferase or N-acetylserotonin O-methyltransferase. Other differences are related to the remarkable pleiotropy of melatonin, which exhibits properties as a growth regulator and morphogenetic factor, actually debated in terms of auxin-like effects, and as a signaling molecule that modulates pathways of ethylene, abscisic, jasmonic and salicylic acids and is involved in stress tolerance, pathogen defense and delay of senescence. In the context of high light/UV intensities, elevated melatonin levels exceed those required for signaling via stress-related phytohormones and may comprise direct antioxidant and photoprotectant properties, perhaps with a contribution of its oxidatively formed metabolites, such as N (1)-acetyl-N (2)-formyl-5-methoxykynuramine and its secondary products. High melatonin levels in seeds may also serve antioxidative protection and have been shown to promote seed viability and germination capacity.

  16. Vestigial preference functions in neural networks and túngara frogs.

    OpenAIRE

    Phelps, S. M.; Ryan, M. J.; Rand, A. S.

    2001-01-01

    Although there is a growing interest in understanding how perceptual mechanisms influence behavioral evolution, few studies have addressed how perception itself is shaped by evolutionary forces. We used a combination of artificial neural network models and behavioral experiments to investigate how evolutionary history influenced the perceptual processes used in mate choice by female túngara frogs. We manipulated the evolutionary history of artificial neural network models and observed an emer...

  17. Gold nanoparticles functionalized with a fragment of the neural cell adhesion molecule L1 stimulate L1-mediated functions

    Science.gov (United States)

    Schulz, Florian; Lutz, David; Rusche, Norman; Bastús, Neus G.; Stieben, Martin; Höltig, Michael; Grüner, Florian; Weller, Horst; Schachner, Melitta; Vossmeyer, Tobias; Loers, Gabriele

    2013-10-01

    The neural cell adhesion molecule L1 is involved in nervous system development and promotes regeneration in animal models of acute and chronic injury of the adult nervous system. To translate these conducive functions into therapeutic approaches, a 22-mer peptide that encompasses a minimal and functional L1 sequence of the third fibronectin type III domain of murine L1 was identified and conjugated to gold nanoparticles (AuNPs) to obtain constructs that interact homophilically with the extracellular domain of L1 and trigger the cognate beneficial L1-mediated functions. Covalent conjugation was achieved by reacting mixtures of two cysteine-terminated forms of this L1 peptide and thiolated poly(ethylene) glycol (PEG) ligands (~2.1 kDa) with citrate stabilized AuNPs of two different sizes (~14 and 40 nm in diameter). By varying the ratio of the L1 peptide-PEG mixtures, an optimized layer composition was achieved that resulted in the expected homophilic interaction of the AuNPs. These AuNPs were stable as tested over a time period of 30 days in artificial cerebrospinal fluid and interacted with the extracellular domain of L1 on neurons and Schwann cells, as could be shown by using cells from wild-type and L1-deficient mice. In vitro, the L1-derivatized particles promoted neurite outgrowth and survival of neurons from the central and peripheral nervous system and stimulated Schwann cell process formation and proliferation. These observations raise the hope that, in combination with other therapeutic approaches, L1 peptide-functionalized AuNPs may become a useful tool to ameliorate the deficits resulting from acute and chronic injuries of the mammalian nervous system.The neural cell adhesion molecule L1 is involved in nervous system development and promotes regeneration in animal models of acute and chronic injury of the adult nervous system. To translate these conducive functions into therapeutic approaches, a 22-mer peptide that encompasses a minimal and functional L1

  18. Function of One Regular Separable Relation Set Decided for the Minimal Covering in Multiple Valued Logic

    Directory of Open Access Journals (Sweden)

    Liu Yu Zhen

    2016-01-01

    Full Text Available Multiple-valued logic is an important branch of the computer science and technology. Multiple-valued logic studies the theory, multiple-valued circuit & multiple-valued system, and the applications of multiple-valued logic included.In the theory of multiple-valued logic, one primary and important problem is the completeness of function sets, which can be solved depending on the decision for all the precomplete sets(also called maximal closed sets of K-valued function sets noted by PK*, and another is the decision for Sheffer function, which can be totally solved by picking out all of the minimal covering of the precomplete sets. In the function structure theory of multi-logic, decision on Sheffer function is an important role. It contains structure and decision of full multi-logic and partial multi-logic. Its decision is closely related to decision of completeness of function which can be done by deciding the minimal covering of full multi-logic and partial-logic. By theory of completeness of partial multi-logic, we prove that function of one regular separable relation is not minimal covering of PK* under the condition of m = 2, σ = e.

  19. Design Of the Approximation Function of a Pedometer based on Artificial Neural Network for the Healthy Life Style Promotion in Diabetic Patients

    OpenAIRE

    Vega Corona, Antonio; Zárate Banda, Magdalena; Barron Adame, Jose Miguel; Martínez Celorio, René Alfredo; Andina de la Fuente, Diego

    2008-01-01

    The present study describes the design of an Artificial Neural Network to synthesize the Approximation Function of a Pedometer for the Healthy Life Style Promotion. Experimentally, the approximation function is synthesized using three basic digital pedometers of low cost, these pedometers were calibrated with an advanced pedometer that calculates calories consumed and computes distance travelled with personal stride input. The synthesized approximation function by means of the designed neural...

  20. Multiple loci associated with renal function in African Americans.

    Directory of Open Access Journals (Sweden)

    Daniel Shriner

    Full Text Available The incidence of chronic kidney disease varies by ethnic group in the USA, with African Americans displaying a two-fold higher rate than European Americans. One of the two defining variables underlying staging of chronic kidney disease is the glomerular filtration rate. Meta-analysis in individuals of European ancestry has identified 23 genetic loci associated with the estimated glomerular filtration rate (eGFR. We conducted a follow-up study of these 23 genetic loci using a population-based sample of 1,018 unrelated admixed African Americans. We included in our follow-up study two variants in APOL1 associated with end-stage kidney disease discovered by admixture mapping in admixed African Americans. To address confounding due to admixture, we estimated local ancestry at each marker and global ancestry. We performed regression analysis stratified by local ancestry and combined the resulting regression estimates across ancestry strata using an inverse variance-weighted fixed effects model. We found that 11 of the 24 loci were significantly associated with eGFR in our sample. The effect size estimates were not significantly different between the subgroups of individuals with two copies of African ancestry vs. two copies of European ancestry for any of the 11 loci. In contrast, allele frequencies were significantly different at 10 of the 11 loci. Collectively, the 11 loci, including four secondary signals revealed by conditional analyses, explained 14.2% of the phenotypic variance in eGFR, in contrast to the 1.4% explained by the 24 loci in individuals of European ancestry. Our findings provide insight into the genetic basis of variation in renal function among admixed African Americans.

  1. Estimation of Multiple Point Sources for Linear Fractional Order Systems Using Modulating Functions

    KAUST Repository

    Belkhatir, Zehor; Laleg-Kirati, Taous-Meriem

    2017-01-01

    This paper proposes an estimation algorithm for the characterization of multiple point inputs for linear fractional order systems. First, using polynomial modulating functions method and a suitable change of variables the problem of estimating

  2. Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids.

    Science.gov (United States)

    Fragkaki, A G; Farmaki, E; Thomaidis, N; Tsantili-Kakoulidou, A; Angelis, Y S; Koupparis, M; Georgakopoulos, C

    2012-09-21

    The comparison among different modelling techniques, such as multiple linear regression, partial least squares and artificial neural networks, has been performed in order to construct and evaluate models for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. The performance of the quantitative structure-retention relationship study, using the multiple linear regression and partial least squares techniques, has been previously conducted. In the present study, artificial neural networks models were constructed and used for the prediction of relative retention times of anabolic androgenic steroids, while their efficiency is compared with that of the models derived from the multiple linear regression and partial least squares techniques. For overall ranking of the models, a novel procedure [Trends Anal. Chem. 29 (2010) 101-109] based on sum of ranking differences was applied, which permits the best model to be selected. The suggested models are considered useful for the estimation of relative retention times of designer steroids for which no analytical data are available. Copyright © 2012 Elsevier B.V. All rights reserved.

  3. Functional electrical stimulation controlled by artificial neural networks: pilot experiments with simple movements are promising for rehabilitation applications.

    Science.gov (United States)

    Ferrante, Simona; Pedrocchi, Alessandra; Iannò, Marco; De Momi, Elena; Ferrarin, Maurizio; Ferrigno, Giancarlo

    2004-01-01

    This study falls within the ambit of research on functional electrical stimulation for the design of rehabilitation training for spinal cord injured patients. In this context, a crucial issue is the control of the stimulation parameters in order to optimize the patterns of muscle activation and to increase the duration of the exercises. An adaptive control system (NEURADAPT) based on artificial neural networks (ANNs) was developed to control the knee joint in accordance with desired trajectories by stimulating quadriceps muscles. This strategy includes an inverse neural model of the stimulated limb in the feedforward line and a neural network trained on-line in the feedback loop. NEURADAPT was compared with a linear closed-loop proportional integrative derivative (PID) controller and with a model-based neural controller (NEUROPID). Experiments on two subjects (one healthy and one paraplegic) show the good performance of NEURADAPT, which is able to reduce the time lag introduced by the PID controller. In addition, control systems based on ANN techniques do not require complicated calibration procedures at the beginning of each experimental session. After the initial learning phase, the ANN, thanks to its generalization capacity, is able to cope with a certain range of variability of skeletal muscle properties.

  4. 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

  5. Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.

    Science.gov (United States)

    Testolin, Alberto; Zorzi, Marco

    2016-01-01

    Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.

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

    Science.gov (United States)

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

    2010-02-01

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

  7. 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...

  8. Dissecting CNBP, a zinc-finger protein required for neural crest development, in its structural and functional domains.

    Science.gov (United States)

    Armas, Pablo; Agüero, Tristán H; Borgognone, Mariana; Aybar, Manuel J; Calcaterra, Nora B

    2008-10-17

    Cellular nucleic-acid-binding protein (CNBP) plays an essential role in forebrain and craniofacial development by controlling cell proliferation and survival to mediate neural crest expansion. CNBP binds to single-stranded nucleic acids and displays nucleic acid chaperone activity in vitro. The CNBP family shows a conserved modular organization of seven Zn knuckles and an arginine-glycine-glycine (RGG) box between the first and second Zn knuckles. The participation of these structural motifs in CNBP biochemical activities has still not been addressed. Here, we describe the generation of CNBP mutants that dissect the protein into regions with structurally and functionally distinct properties. Mutagenesis approaches were followed to generate: (i) an amino acid replacement that disrupted the fifth Zn knuckle; (ii) N-terminal deletions that removed the first Zn knuckle and the RGG box, or the RGG box alone; and (iii) a C-terminal deletion that eliminated the three last Zn knuckles. Mutant proteins were overexpressed in Escherichia coli, purified, and used to analyze their biochemical features in vitro, or overexpressed in Xenopus laevis embryos to study their function in vivo during neural crest cell development. We found that the Zn knuckles are required, but not individually essential, for CNBP biochemical activities, whereas the RGG box is essential for RNA-protein binding and nucleic acid chaperone activity. Removal of the RGG box allowed CNBP to preserve a weak single-stranded-DNA-binding capability. A mutant mimicking the natural N-terminal proteolytic CNBP form behaved as the RGG-deleted mutant. By gain-of-function and loss-of-function experiments in Xenopus embryos, we confirmed the participation of CNBP in neural crest development, and we demonstrated that the CNBP mutants lacking the N-terminal region or the RGG box alone may act as dominant negatives in vivo. Based on these data, we speculate about the existence of a specific proteolytic mechanism for the

  9. In Vivo Transplantation of Enteric Neural Crest Cells into Mouse Gut; Engraftment, Functional Integration and Long-Term Safety.

    Directory of Open Access Journals (Sweden)

    Julie E Cooper

    Full Text Available Enteric neuropathies are severe gastrointestinal disorders with unsatisfactory outcomes. We aimed to investigate the potential of enteric neural stem cell therapy approaches for such disorders by transplanting mouse enteric neural crest cells (ENCCs into ganglionic and aganglionic mouse gut in vivo and analysing functional integration and long-term safety.Neurospheres generated from yellow fluorescent protein (YFP expressing ENCCs selected from postnatal Wnt1-cre;R26R-YFP/YFP murine gut were transplanted into ganglionic hindgut of wild-type littermates or aganglionic hindgut of Ednrbtm1Ywa mice (lacking functional endothelin receptor type-B. Intestines were then assessed for ENCC integration and differentiation using immunohistochemistry, cell function using calcium imaging, and long-term safety using PCR to detect off-target YFP expression.YFP+ ENCCs engrafted, proliferated and differentiated into enteric neurons and glia within recipient ganglionic gut. Transplanted cells and their projections spread along the endogenous myenteric plexus to form branching networks. Electrical point stimulation of endogenous nerve fibres resulted in calcium transients (F/F0 = 1.16 ± 0.01;43 cells, n = 6 in YFP+ transplanted ENCCs (abolished with TTX. Long-term follow-up (24 months showed transplanted ENCCs did not give rise to tumours or spread to other organs (PCR negative in extraintestinal sites. In aganglionic gut ENCCs similarly spread and differentiated to form neuronal and glial networks with projections closely associated with endogenous neural networks of the transition zone.Transplanted ENCCs successfully engrafted into recipient ganglionic and aganglionic gut showing appropriate spread, localisation and, importantly, functional integration without any long-term safety issues. This study provides key support for the development and use of enteric neural stem cell therapies.

  10. Sequential optimization of matrix chain multiplication relative to different cost functions

    KAUST Repository

    Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2011-01-01

    In this paper, we present a methodology to optimize matrix chain multiplication sequentially relative to different cost functions such as total number of scalar multiplications, communication overhead in a multiprocessor environment, etc. For n matrices our optimization procedure requires O(n 3) arithmetic operations per one cost function. This work is done in the framework of a dynamic programming extension that allows sequential optimization relative to different criteria. © 2011 Springer-Verlag Berlin Heidelberg.

  11. Neural Priming in Human Frontal Cortex: Multiple Forms of Learning Reduce Demands on the Prefrontal Executive System

    Science.gov (United States)

    Race, Elizabeth A.; Shanker, Shanti; Wagner, Anthony D.

    2009-01-01

    Past experience is hypothesized to reduce computational demands in PFC by providing bottom-up predictive information that informs subsequent stimulus-action mapping. The present fMRI study measured cortical activity reductions ("neural priming"/"repetition suppression") during repeated stimulus classification to investigate the mechanisms through…

  12. Functional cross-talk between the cellular prion protein and the neural cell adhesion molecule is critical for neuronal differentiation of neural stem/precursor cells.

    Science.gov (United States)

    Prodromidou, Kanella; Papastefanaki, Florentia; Sklaviadis, Theodoros; Matsas, Rebecca

    2014-06-01

    Cellular prion protein (PrP) is prominently expressed in brain, in differentiated neurons but also in neural stem/precursor cells (NPCs). The misfolding of PrP is a central event in prion diseases, yet the physiological function of PrP is insufficiently understood. Although PrP has been reported to associate with the neural cell adhesion molecule (NCAM), the consequences of concerted PrP-NCAM action in NPC physiology are unknown. Here, we generated NPCs from the subventricular zone (SVZ) of postnatal day 5 wild-type and PrP null (-/-) mice and observed that PrP is essential for proper NPC proliferation and neuronal differentiation. Moreover, we found that PrP is required for the NPC response to NCAM-induced neuronal differentiation. In the absence of PrP, NCAM not only fails to promote neuronal differentiation but also induces an accumulation of doublecortin-positive neuronal progenitors at the proliferation stage. In agreement, we noted an increase in cycling neuronal progenitors in the SVZ of PrP-/- mice compared with PrP+/+ mice, as evidenced by double labeling for the proliferation marker Ki67 and doublecortin as well as by 5-bromo-2'-deoxyuridine incorporation experiments. Additionally, fewer newly born neurons were detected in the rostral migratory stream of PrP-/- mice. Analysis of the migration of SVZ cells in microexplant cultures from wild-type and PrP-/- mice revealed no differences between genotypes or a role for NCAM in this process. Our data demonstrate that PrP plays a critical role in neuronal differentiation of NPCs and suggest that this function is, at least in part, NCAM-dependent. © 2014 AlphaMed Press.

  13. Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

    Science.gov (United States)

    Shastri, Niket; Pathak, Kamlesh

    2018-05-01

    The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

  14. Dissociation between neural and vascular responses to sympathetic stimulation : contribution of local adrenergic receptor function

    Science.gov (United States)

    Jacob, G.; Costa, F.; Shannon, J.; Robertson, D.; Biaggioni, I.

    2000-01-01

    Sympathetic activation produced by various stimuli, eg, mental stress or handgrip, evokes regional vascular responses that are often nonhomogeneous. This phenomenon is believed to be the consequence of the recruitment of differential central neural pathways or of a sympathetically mediated vasodilation. The purpose of this study was to determine whether a similar heterogeneous response occurs with cold pressor stimulation and to test the hypothesis that local differences in adrenergic receptor function could be in part responsible for this diversity. In 8 healthy subjects, local norepinephrine spillover and blood flow were measured in arms and legs at baseline and during sympathetic stimulation induced by baroreflex mechanisms (nitroprusside infusion) or cold pressor stimulation. At baseline, legs had higher vascular resistance (27+/-5 versus 17+/-2 U, P=0.05) despite lower norepinephrine spillover (0.28+/-0.04 versus 0.4+/-0.05 mg. min(-1). dL(-1), P=0.03). Norepinephrine spillover increased similarly in both arms and legs during nitroprusside infusion and cold pressor stimulation. On the other hand, during cold stimulation, vascular resistance increased in arms but not in legs (20+/-9% versus -7+/-4%, P=0.03). Increasing doses of isoproterenol and phenylephrine were infused intra-arterially in arms and legs to estimate beta-mediated vasodilation and alpha-induced vasoconstriction, respectively. beta-Mediated vasodilation was significantly lower in legs compared with arms. Thus, we report a dissociation between norepinephrine spillover and vascular responses to cold stress in lower limbs characterized by a paradoxical decrease in local resistance despite increases in sympathetic activity. The differences observed in adrenergic receptor responses cannot explain this phenomenon.

  15. Cell surface glycan engineering of neural stem cells augments neurotropism and improves recovery in a murine model of multiple sclerosis

    KAUST Repository

    Merzaban, Jasmeen S.

    2015-09-13

    Neural stem cell (NSC)-based therapies offer potential for neural repair in central nervous system (CNS) inflammatory and degenerative disorders. Typically, these conditions present with multifocal CNS lesions making it impractical to inject NSCs locally, thus mandating optimization of vascular delivery of the cells to involved sites. Here, we analyzed NSCs for expression of molecular effectors of cell migration and found that these cells are natively devoid of E-selectin ligands. Using glycosyltransferase-programmed stereosubstitution (GPS), we glycan engineered the cell surface of NSCs ("GPS-NSCs") with resultant enforced expression of the potent E-selectin ligand HCELL (hematopoietic cell E-/L-selectin ligand) and of an E-selectin-binding glycoform of neural cell adhesion molecule ("NCAM-E"). Following intravenous (i.v.) injection, short-term homing studies demonstrated that, compared with buffer-treated (control) NSCs, GPS-NSCs showed greater neurotropism. Administration of GPS-NSC significantly attenuated the clinical course of experimental autoimmune encephalomyelitis (EAE), with markedly decreased inflammation and improved oligodendroglial and axonal integrity, but without evidence of long-term stem cell engraftment. Notably, this effect of NSC is not a universal property of adult stem cells, as administration of GPS-engineered mouse hematopoietic stem/progenitor cells did not improve EAE clinical course. These findings highlight the utility of cell surface glycan engineering to boost stem cell delivery in neuroinflammatory conditions and indicate that, despite the use of a neural tissue-specific progenitor cell population, neural repair in EAE results from endogenous repair and not from direct, NSC-derived cell replacement.

  16. Programmable release of multiple protein drugs from aptamer-functionalized hydrogels via nucleic acid hybridization.

    Science.gov (United States)

    Battig, Mark R; Soontornworajit, Boonchoy; Wang, Yong

    2012-08-01

    Polymeric delivery systems have been extensively studied to achieve localized and controlled release of protein drugs. However, it is still challenging to control the release of multiple protein drugs in distinct stages according to the progress of disease or treatment. This study successfully demonstrates that multiple protein drugs can be released from aptamer-functionalized hydrogels with adjustable release rates at predetermined time points using complementary sequences (CSs) as biomolecular triggers. Because both aptamer-protein interactions and aptamer-CS hybridization are sequence-specific, aptamer-functionalized hydrogels constitute a promising polymeric delivery system for the programmable release of multiple protein drugs to treat complex human diseases.

  17. Using multiple schedules during functional communication training to promote rapid transfer of treatment effects.

    Science.gov (United States)

    Fisher, Wayne W; Greer, Brian D; Fuhrman, Ashley M; Querim, Angie C

    2015-12-01

    Multiple schedules with signaled periods of reinforcement and extinction have been used to thin reinforcement schedules during functional communication training (FCT) to make the intervention more practical for parents and teachers. We evaluated whether these signals would also facilitate rapid transfer of treatment effects across settings and therapists. With 2 children, we conducted FCT in the context of mixed (baseline) and multiple (treatment) schedules introduced across settings or therapists using a multiple baseline design. Results indicated that when the multiple schedules were introduced, the functional communication response came under rapid discriminative control, and problem behavior remained at near-zero rates. We extended these findings with another individual by using a more traditional baseline in which problem behavior produced reinforcement. Results replicated those of the previous participants and showed rapid reductions in problem behavior when multiple schedules were implemented across settings. © Society for the Experimental Analysis of Behavior.

  18. A functional neuroimaging study assessing gender differences in the neural mechanisms underlying the ability to resist impulsive desires.

    Science.gov (United States)

    Diekhof, Esther K; Keil, Maria; Obst, Katrin U; Henseler, Ilona; Dechent, Peter; Falkai, Peter; Gruber, Oliver

    2012-09-14

    There is ample evidence of gender differences in neural processes and behavior. Differences in reward-related behaviors have been linked to either temporary or permanent organizational influences of gonadal hormones on the mesolimbic dopamine system and reward-related activation. Still, little is known about the association between biological gender and the neural underpinnings of the ability to resist reward-related impulses. Here we assessed with functional magnetic resonance imaging which neural processes enable men and women to successfully control their desire for immediate reward when this is required by a higher-order goal (i.e., during a 'desire-reason dilemma'; Diekhof and Gruber, 2010). Thirty-two participants (16 females) were closely matched for age, personality characteristics (e.g., novelty seeking) and behavioral performance in the 'desire-reason task'. On the neural level, men and women showed similarities in the general response of the nucleus accumbens and of the ventral tegmental area to predictors of immediate reward, but they differed in additional brain mechanisms that enabled self-controlled decisions against the preference for immediate reward. Firstly, men exhibited a stronger reduction of activation in the ventral pallidum, putamen, temporal pole and pregenual anterior cingulate cortex during the 'desire-reason dilemma'. Secondly, connectivity analyses revealed a significant change in the direction of the connectivity between anteroventral prefrontal cortex and nucleus accumbens during decisions counteracting the reward-related impulse when comparing men and women. Together, these findings support the view of a sexual dimorphism that manifested in the recruitment of gender-specific neural resources during the successful deployment of self-control. Copyright © 2012 Elsevier B.V. All rights reserved.

  19. Neural mechanism of lmplicit and explicit memory retrieval: functional MR imaging

    International Nuclear Information System (INIS)

    Kang, Heoung Keun; Jeong, Gwang Woo; Park, Tae Jin; Seo, Jeong Jin; Kim, Hyung Joong; Eun, Sung Jong; Chung, Tae Woong

    2003-01-01

    To identify, using functional MR imaging, distinct cerebral centers and to evaluate the neural mechanism associated with implicit and explicit retrieval of words during conceptual processing. Seven healthy volunteers aged 21-25 (mean, 22) years underwent BOLD-based fMR imaging using a 1.5T signa horizon echospeed MR system. To activate the cerebral cortices, a series of tasks was performed as follows: the encoding of two-syllable words, and implicit and explicit retrieval of previously learned words during conceptual processing. The activation paradigm consisted of a cycle of alternating periods of 30 seconds of stimulation and 30 seconds of rest. Stimulation was accomplished by encoding eight two-syllable words and the retrieval of previously presented words, while the control condition was a white screen with a small fixed cross. During the tasks we acquired ten slices (6 mm slice thickness, 1 mm gap) parallel to the AC-PC line, and the resulting functional activation maps were reconstructed using a statistical parametric mapping program (SPM99). A comparison of activation ratios (percentages), based on the number of volunteers, showed that activation of Rhs-35, PoCiG-23 and ICiG-26·30 was associated with explicit retrieval only; other brain areas were activated during the performance of both implicit and explicit retrieval tasks. Activation ratios were higher for explicit tasks than for implicit; in the cingulate gyrus and temporal lobe they were 30% and 10% greater, respectively. During explicit retrieval, a distinct brain activation index (percentage) was seen in the temporal, parietal, and occipital lobe and cingulate gyrus, and PrCeG-4, Pr/ PoCeG-43 in the frontal lobe. During implicit retrieval, on the other hand, activity was greater in the frontal lobe, including the areas of SCA-25, SFG/MFG-10, IFG-44·45, OrbG-11·47, SFG-6·8 and MFG-9·46. Overall, activation was lateralized mainly in the left hemisphere during both implicit and explicit retrieval

  20. Neural mechanism of lmplicit and explicit memory retrieval: functional MR imaging

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Heoung Keun; Jeong, Gwang Woo; Park, Tae Jin; Seo, Jeong Jin; Kim, Hyung Joong; Eun, Sung Jong; Chung, Tae Woong [Chonnam National University Medical School, Gwangju (Korea, Republic of)

    2003-03-01

    To identify, using functional MR imaging, distinct cerebral centers and to evaluate the neural mechanism associated with implicit and explicit retrieval of words during conceptual processing. Seven healthy volunteers aged 21-25 (mean, 22) years underwent BOLD-based fMR imaging using a 1.5T signa horizon echospeed MR system. To activate the cerebral cortices, a series of tasks was performed as follows: the encoding of two-syllable words, and implicit and explicit retrieval of previously learned words during conceptual processing. The activation paradigm consisted of a cycle of alternating periods of 30 seconds of stimulation and 30 seconds of rest. Stimulation was accomplished by encoding eight two-syllable words and the retrieval of previously presented words, while the control condition was a white screen with a small fixed cross. During the tasks we acquired ten slices (6 mm slice thickness, 1 mm gap) parallel to the AC-PC line, and the resulting functional activation maps were reconstructed using a statistical parametric mapping program (SPM99). A comparison of activation ratios (percentages), based on the number of volunteers, showed that activation of Rhs-35, PoCiG-23 and ICiG-26{center_dot}30 was associated with explicit retrieval only; other brain areas were activated during the performance of both implicit and explicit retrieval tasks. Activation ratios were higher for explicit tasks than for implicit; in the cingulate gyrus and temporal lobe they were 30% and 10% greater, respectively. During explicit retrieval, a distinct brain activation index (percentage) was seen in the temporal, parietal, and occipital lobe and cingulate gyrus, and PrCeG-4, Pr/ PoCeG-43 in the frontal lobe. During implicit retrieval, on the other hand, activity was greater in the frontal lobe, including the areas of SCA-25, SFG/MFG-10, IFG-44{center_dot}45, OrbG-11{center_dot}47, SFG-6{center_dot}8 and MFG-9{center_dot}46. Overall, activation was lateralized mainly in the left

  1. The artist emerges: visual art learning alters neural structure and function.

    Science.gov (United States)

    Schlegel, Alexander; Alexander, Prescott; Fogelson, Sergey V; Li, Xueting; Lu, Zhengang; Kohler, Peter J; Riley, Enrico; Tse, Peter U; Meng, Ming

    2015-01-15

    How does the brain mediate visual artistic creativity? Here we studied behavioral and neural changes in drawing and painting students compared to students who did not study art. We investigated three aspects of cognition vital to many visual artists: creative cognition, perception, and perception-to-action. We found that the art students became more creative via the reorganization of prefrontal white matter but did not find any significant changes in perceptual ability or related neural activity in the art students relative to the control group. Moreover, the art students improved in their ability to sketch human figures from observation, and multivariate patterns of cortical and cerebellar activity evoked by this drawing task became increasingly separable between art and non-art students. Our findings suggest that the emergence of visual artistic skills is supported by plasticity in neural pathways that enable creative cognition and mediate perceptuomotor integration. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. Apoptosis in neural crest cells by functional loss of APC tumor suppressor gene

    Science.gov (United States)

    Hasegawa, Sumitaka; Sato, Tomoyuki; Akazawa, Hiroshi; Okada, Hitoshi; Maeno, Akiteru; Ito, Masaki; Sugitani, Yoshinobu; Shibata, Hiroyuki; Miyazaki, Jun-ichi; Katsuki, Motoya; Yamauchi, Yasutaka; Yamamura, Ken-ichi; Katamine, Shigeru; Noda, Tetsuo

    2002-01-01

    Apc is a gene associated with familial adenomatous polyposis coli (FAP) and its inactivation is a critical step in colorectal tumor formation. The protein product, adenomatous polyposis coli (APC), acts to down-regulate intracellular levels of β-catenin, a key signal transducer in the Wnt signaling. Conditional targeting of Apc in the neural crest of mice caused massive apoptosis of cephalic and cardiac neural crest cells at about 11.5 days post coitum, resulting in craniofacial and cardiac anomalies at birth. Notably, the apoptotic cells localized in the regions where β-catenin had accumulated. In contrast to its role in colorectal epithelial cells, inactivation of APC leads to dysregulation of β-catenin/Wnt signaling with resultant apoptosis in certain tissues including neural crest cells. PMID:11756652

  3. Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

    Directory of Open Access Journals (Sweden)

    M. Kumar

    2016-01-01

    Full Text Available Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN and the Cat Swarm Optimization (CSO is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR, have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.

  4. Functional neural correlates of mindfulness meditations in comparison with psychotherapy, pharmacotherapy and placebo effect. Is there a link?

    Science.gov (United States)

    Chiesa, Alberto; Brambilla, Paolo; Serretti, Alessandro

    2010-06-01

    Chiesa A, Brambilla P, Serretti A. Functional neural correlates of mindfulness meditations in comparison with psychotherapy, pharmacotherapy and placebo effect. Is there a link? Mindfulness meditations (MM) are a group of meditation practices which are increasingly receiving attention. The aim of the present work is to review current findings about the neural correlates of MM and compare such findings with other specific and non-specific treatments. A literature search was undertaken using MEDLINE, ISI web of knowledge, the Cochrane database and references of retrieved articles. Studies which focused on the functional neural correlates of MM, psychotherapy, pharmacotherapy and placebo published up to August 2009 were screened in order to be considered for the inclusion. Main findings suggest that long-term MM practice allows a more flexible emotional regulation by engaging frontal cortical structures to dampen automatic amygdala activation. A large overlap exists between cerebral areas activated during MM, psychotherapy, pharmacotherapy and those activated by placebo. However, while MM, psychotherapy and placebo seem to act through a top-down regulation, antidepressants seem to act through a bottom-up process. MM seem to target specific brain areas related to emotions and emotional regulation. Similar mechanisms have been observed also in other interventions, particularly psychotherapy.

  5. A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

    Science.gov (United States)

    Kappel, David; Legenstein, Robert; Habenschuss, Stefan; Hsieh, Michael; Maass, Wolfgang

    2018-01-01

    Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.

  6. Knowledge-based approach for functional MRI analysis by SOM neural network using prior labels from Talairach stereotaxic space

    Science.gov (United States)

    Erberich, Stephan G.; Willmes, Klaus; Thron, Armin; Oberschelp, Walter; Huang, H. K.

    2002-04-01

    Among the methods proposed for the analysis of functional MR we have previously introduced a model-independent analysis based on the self-organizing map (SOM) neural network technique. The SOM neural network can be trained to identify the temporal patterns in voxel time-series of individual functional MRI (fMRI) experiments. The separated classes consist of activation, deactivation and baseline patterns corresponding to the task-paradigm. While the classification capability of the SOM is not only based on the distinctness of the patterns themselves but also on their frequency of occurrence in the training set, a weighting or selection of voxels of interest should be considered prior to the training of the neural network to improve pattern learning. Weighting of interesting voxels by means of autocorrelation or F-test significance levels has been used successfully, but still a large number of baseline voxels is included in the training. The purpose of this approach is to avoid the inclusion of these voxels by using three different levels of segmentation and mapping from Talairach space: (1) voxel partitions at the lobe level, (2) voxel partitions at the gyrus level and (3) voxel partitions at the cell level (Brodmann areas). The results of the SOM classification based on these mapping levels in comparison to training with all brain voxels are presented in this paper.

  7. Psychometrics of Multiple Choice Questions with Non-Functioning Distracters: Implications to Medical Education.

    Science.gov (United States)

    Deepak, Kishore K; Al-Umran, Khalid Umran; AI-Sheikh, Mona H; Dkoli, B V; Al-Rubaish, Abdullah

    2015-01-01

    The functionality of distracters in a multiple choice question plays a very important role. We examined the frequency and impact of functioning and non-functioning distracters on psychometric properties of 5-option items in clinical disciplines. We analyzed item statistics of 1115 multiple choice questions from 15 summative assessments of undergraduate medical students and classified the items into five groups by their number of non-functioning distracters. We analyzed the effect of varying degree of non-functionality ranging from 0 to 4, on test reliability, difficulty index, discrimination index and point biserial correlation. The non-functionality of distracters inversely affected the test reliability and quality of items in a predictable manner. The non-functioning distracters made the items easier and lowered the discrimination index significantly. Three non-functional distracters in a 5-option MCQ significantly affected all psychometric properties (p psychometrically as effective as 5-option items. Our study reveals that a multiple choice question with 3 functional options provides lower most limit of item format that has adequate psychometric property. The test containing items with less number of functioning options have significantly lower reliability. The distracter function analysis and revision of nonfunctioning distracters can serve as important methods to improve the psychometrics and reliability of assessment.

  8. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  9. Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2014-01-01

    Full Text Available The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.

  10. Brief Report: Anomalous Neural Deactivations and Functional Connectivity during Receptive Language in Autism Spectrum Disorder--A Functional MRI Study

    Science.gov (United States)

    Karten, Ariel; Hirsch, Joy

    2015-01-01

    Neural mechanisms that underlie language disability in autism spectrum disorder (ASD) have been associated with reduced excitatory processes observed as positive blood oxygen level dependent (BOLD) responses. However, negative BOLD responses (NBR) associated with language and inhibitory processes have been less studied in ASD. In this study,…

  11. Multiple POU-binding motifs, recognized by tissue-specific nuclear factors, are important for Dll1 gene expression in neural stem cells

    International Nuclear Information System (INIS)

    Nakayama, Kohzo; Nagase, Kazuko; Tokutake, Yuriko; Koh, Chang-Sung; Hiratochi, Masahiro; Ohkawara, Takeshi; Nakayama, Noriko

    2004-01-01

    We cloned the 5'-flanking region of the mouse homolog of the Delta gene (Dll1) and demonstrated that the sequence between nucleotide position -514 and -484 in the 5'-flanking region of Dll1 played a critical role in the regulation of its tissue-specific expression in neural stem cells (NSCs). Further, we showed that multiple POU-binding motifs, located within this short sequence of 30 bp, were essential for transcriptional activation of Dll1 and also that multiple tissue-specific nuclear factors recognized these POU-binding motifs in various combinations through differentiation of NSCs. Thus, POU-binding factors may play an important role in Dll1 expression in developing NSCs

  12. On functional determinants of matrix differential operators with multiple zero modes

    NARCIS (Netherlands)

    Falco, G.M.; Fedorenko, Andrey A; Gruzberg, Ilya A

    2017-01-01

    We generalize the method of computing functional determinants with a single excluded zero eigenvalue developed by McKane and Tarlie to differential operators with multiple zero eigenvalues. We derive general formulas for such functional determinants of $r\\times r$ matrix second order differential

  13. Linking and Psychological Functioning in a Chinese Sample: The Multiple Mediation of Response to Positive Affect

    Science.gov (United States)

    Yang, Hongfei; Li, Juan

    2016-01-01

    The present study examined the associations between linking, response to positive affect, and psychological functioning in Chinese college students. The results of conducting multiple mediation analyses indicated that emotion- and self-focused positive rumination mediated the relationship between linking and psychological functioning, whereas…

  14. Effects of Functional Mobility Skills Training for Adults with Severe Multiple Disabilities

    Science.gov (United States)

    Whinnery, Stacie B.; Whinnery, Keith W.

    2011-01-01

    This study investigated the effects of a functional mobility program on the functional standing and walking skills of five adults with developmental disabilities. The Mobility Opportunities Via Education (MOVE) Curriculum was implemented using a multiple-baseline across subjects design. Repeated measures were taken during baseline, intervention…

  15. Micropatterning of Functional Conductive Polymers with Multiple Surface Chemistries in Register

    DEFF Research Database (Denmark)

    Lind, Johan Ulrik; Acikgöz, Canet; Daugaard, Anders Egede

    2012-01-01

    A versatile procedure is presented for fast and efficient micropatterning of multiple types of covalently bound surface chemistry in perfect register on and between conductive polymer microcircuits. The micropatterning principle is applied to several types of native and functionalized PEDOT (poly(3...... functionalized conjugated polymer systems....

  16. A comparative study between the use of artificial neural networks and multiple linear regression for caustic concentration prediction in a stage of alumina production

    Directory of Open Access Journals (Sweden)

    Giovanni Leopoldo Rozza

    2015-09-01

    Full Text Available With world becoming each day a global village, enterprises continuously seek to optimize their internal processes to hold or improve their competitiveness and make better use of natural resources. In this context, decision support tools are an underlying requirement. Such tools are helpful on predicting operational issues, avoiding cost risings, loss of productivity, work-related accident leaves or environmental disasters. This paper has its focus on the prediction of spent liquor caustic concentration of Bayer process for alumina production. Caustic concentration measuring is essential to keep it at expected levels, otherwise quality issues might arise. The organization requests caustic concentration by chemical analysis laboratory once a day, such information is not enough to issue preventive actions to handle process inefficiencies that will be known only after new measurement on the next day. Thereby, this paper proposes using Multiple Linear Regression and Artificial Neural Networks techniques a mathematical model to predict the spent liquor´s caustic concentration. Hence preventive actions will occur in real time. Such models were built using software tool for numerical computation (MATLAB and a statistical analysis software package (SPSS. The models output (predicted caustic concentration were compared with the real lab data. We found evidence suggesting superior results with use of Artificial Neural Networks over Multiple Linear Regression model. The results demonstrate that replacing laboratorial analysis by the forecasting model to support technical staff on decision making could be feasible.

  17. Curvelet-domain multiple matching method combined with cubic B-spline function

    Science.gov (United States)

    Wang, Tong; Wang, Deli; Tian, Mi; Hu, Bin; Liu, Chengming

    2018-05-01

    Since the large amount of surface-related multiple existed in the marine data would influence the results of data processing and interpretation seriously, many researchers had attempted to develop effective methods to remove them. The most successful surface-related multiple elimination method was proposed based on data-driven theory. However, the elimination effect was unsatisfactory due to the existence of amplitude and phase errors. Although the subsequent curvelet-domain multiple-primary separation method achieved better results, poor computational efficiency prevented its application. In this paper, we adopt the cubic B-spline function to improve the traditional curvelet multiple matching method. First, select a little number of unknowns as the basis points of the matching coefficient; second, apply the cubic B-spline function on these basis points to reconstruct the matching array; third, build constraint solving equation based on the relationships of predicted multiple, matching coefficients, and actual data; finally, use the BFGS algorithm to iterate and realize the fast-solving sparse constraint of multiple matching algorithm. Moreover, the soft-threshold method is used to make the method perform better. With the cubic B-spline function, the differences between predicted multiple and original data diminish, which results in less processing time to obtain optimal solutions and fewer iterative loops in the solving procedure based on the L1 norm constraint. The applications to synthetic and field-derived data both validate the practicability and validity of the method.

  18. DMPD: Suppressor of cytokine signaling (SOCS) 2, a protein with multiple functions. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 17070092 Suppressor of cytokine signaling (SOCS) 2, a protein with multiple function...Epub 2006 Oct 27. (.png) (.svg) (.html) (.csml) Show Suppressor of cytokine signaling (SOCS) 2, a protein with multiple function...SOCS) 2, a protein with multiple functions. Authors Rico-Bautista E, Flores-Morales A, Fernandez-Perez L. Pu

  19. Availability of thermodynamic system with multiple performance parameters based on vector-universal generating function

    International Nuclear Information System (INIS)

    Cai Qi; Shang Yanlong; Chen Lisheng; Zhao Yuguang

    2013-01-01

    Vector-universal generating function was presented to analyze the availability of thermodynamic system with multiple performance parameters. Vector-universal generating function of component's performance was defined, the arithmetic model based on vector-universal generating function was derived for the thermodynamic system, and the calculation method was given for state probability of multi-state component. With the stochastic simulation of the degeneration trend of the multiple factors, the system availability with multiple performance parameters was obtained under composite factors. It is shown by an example that the results of the availability obtained by the binary availability analysis method are somewhat conservative, and the results considering parameter failure based on vector-universal generating function reflect the operation characteristics of the thermodynamic system better. (authors)

  20. The Burden of Binge and Heavy Drinking on the Brain: Effects on Adolescent and Young Adult Neural Structure and Function

    Directory of Open Access Journals (Sweden)

    Anita Cservenka

    2017-06-01

    Full Text Available Introduction: Adolescence and young adulthood are periods of continued biological and psychosocial maturation. Thus, there may be deleterious effects of consuming large quantities of alcohol on neural development and associated cognition during this time. The purpose of this mini review is to highlight neuroimaging research that has specifically examined the effects of binge and heavy drinking on adolescent and young adult brain structure and function.Methods: We review cross-sectional and longitudinal studies of young binge and heavy drinkers that have examined brain structure (e.g., gray and white matter volume, cortical thickness, white matter microstructure and investigated brain response using functional magnetic resonance imaging (fMRI.Results: Binge and heavy-drinking adolescents and young adults have systematically thinner and lower volume in prefrontal cortex and cerebellar regions, and attenuated white matter development. They also show elevated brain activity in fronto-parietal regions during working memory, verbal learning, and inhibitory control tasks. In response to alcohol cues, relative to controls or light-drinking individuals, binge and heavy drinkers show increased neural response mainly in mesocorticolimbic regions, including the striatum, anterior cingulate cortex (ACC, hippocampus, and amygdala. Mixed findings are present in risky decision-making tasks, which could be due to large variation in task design and analysis.Conclusions: These findings suggest altered neural structure and activity in binge and heavy-drinking youth may be related to the neurotoxic effects of consuming alcohol in large quantities during a highly plastic neurodevelopmental period, which could result in neural reorganization, and increased risk for developing an alcohol use disorder (AUD.

  1. FGL-functionalized self-assembling nanofiber hydrogel as a scaffold for spinal cord-derived neural stem cells

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jian [Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 (China); Zheng, Jin [Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 (China); Zheng, Qixin, E-mail: zheng-qx@163.com [Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 (China); Wu, Yongchao; Wu, Bin; Huang, Shuai; Fang, Weizhi; Guo, Xiaodong [Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 (China)

    2015-01-01

    A class of designed self-assembling peptide nanofiber scaffolds has been shown to be a good biomimetic material in tissue engineering. Here, we specifically made a new peptide hydrogel scaffold FGLmx by mixing the pure RADA{sub 16} and designer functional peptide RADA{sub 16}-FGL solution, and we analyzed the physiochemical properties of each peptide with atomic force microscopy (AFM) and circular dichroism (CD). In addition, we examined the biocompatibility and bioactivity of FGLmx as well as RADA{sub 16} scaffold on spinal cord-derived neural stem cells (SC-NSCs) isolated from neonatal rats. Our results showed that RADA{sub 16}-FGL displayed a weaker β-sheet structure and FGLmx could self-assemble into nanofibrous morphology. Moreover, we found that FGLmx was not only noncytotoxic to SC-NSCs but also promoted SC-NSC proliferation and migration into the three-dimensional (3-D) scaffold, meanwhile, the adhesion and lineage differentiation of SC-NSCs on FGLmx were similar to that on RADA{sub 16}. Our results indicated that the FGL-functionalized peptide scaffold might be very beneficial for tissue engineering and suggested its further application for spinal cord injury (SCI) repair. - Highlights: • RADA{sub 16} and RADA{sub 16}-FGL peptides were synthesized and characterized. • Rat spinal cord neural stem cells were successfully isolated and characterized. • We provided an induction method for mixed differentiation of neural stem cells. • FGL scaffold had good biocompatibility and bioactivity with neural stem cells.

  2. Evaluation of autonomic functions of patients with multiple system atrophy and Parkinson's disease by head-up tilt test.

    Science.gov (United States)

    Watano, Chikako; Shiota, Yuri; Onoda, Keiichi; Sheikh, Abdullah Md; Mishima, Seiji; Nitta, Eri; Yano, Shozo; Yamaguchi, Shuhei; Nagai, Atsushi

    2018-02-01

    The aim of this study was to evaluate the autonomic neural function in Parkinson's disease (PD) and multiple system atrophy (MSA) with head-up tilt test and spectral analysis of cardiovascular parameters. This study included 15 patients with MSA, 15 patients with PD, and 29 healthy control (HC) subjects. High frequency power of the RR interval (RR-HF), the ratio of low frequency power of RR interval to RR-HF (RR-LF/HF) and LF power of systolic BP were used to evaluate parasympathetic, cardiac sympathetic and vasomotor sympathetic functions, respectively. Both patients with PD and MSA showed orthostatic hypotension and lower parasympathetic function (RR-HF) at tilt position as compared to HC subjects. Cardiac sympathetic function (RR-LF/HF) was significantly high in patients with PD than MSA at supine position. RR-LF/HF tended to increase in MSA and HC, but decreased in PD by tilting. Consequently, the change of the ratio due to tilting (ΔRR-LF/HF) was significantly lower in patients with PD than in HC subjects. Further analysis showed that compared to mild stage of PD, RR-LF/HF at the supine position was significantly higher in advanced stage. By tilting, it was increased in mild stage and decreased in the advanced stage of PD, causing ΔRR-LF/HF to decrease significantly in the advanced stage. Thus, we demonstrated that spectral analysis of cardiovascular parameters is useful to identify sympathetic and parasympathetic disorders in MSA and PD. High cardiac sympathetic function at the supine position, and its reduction by tilting might be a characteristic feature of PD, especially in the advanced stage.

  3. Introduction to neural networks

    International Nuclear Information System (INIS)

    Pavlopoulos, P.

    1996-01-01

    This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix

  4. Expression and function of orphan nuclear receptor TLX in adult neural stem cells.

    Science.gov (United States)

    Shi, Yanhong; Chichung Lie, D; Taupin, Philippe; Nakashima, Kinichi; Ray, Jasodhara; Yu, Ruth T; Gage, Fred H; Evans, Ronald M

    2004-01-01

    The finding of neurogenesis in the adult brain led to the discovery of adult neural stem cells. TLX was initially identified as an orphan nuclear receptor expressed in vertebrate forebrains and is highly expressed in the adult brain. The brains of TLX-null mice have been reported to have no obvious defects during embryogenesis; however, mature mice suffer from retinopathies, severe limbic defects, aggressiveness, reduced copulation and progressively violent behaviour. Here we show that TLX maintains adult neural stem cells in an undifferentiated, proliferative state. We show that TLX-expressing cells isolated by fluorescence-activated cell sorting (FACS) from adult brains can proliferate, self-renew and differentiate into all neural cell types in vitro. By contrast, TLX-null cells isolated from adult mutant brains fail to proliferate. Reintroducing TLX into FACS-sorted TLX-null cells rescues their ability to proliferate and to self-renew. In vivo, TLX mutant mice show a loss of cell proliferation and reduced labelling of nestin in neurogenic areas in the adult brain. TLX can silence glia-specific expression of the astrocyte marker GFAP in neural stem cells, suggesting that transcriptional repression may be crucial in maintaining the undifferentiated state of these cells.

  5. Molecular target discovery for neural repair in the functional genomics era

    NARCIS (Netherlands)

    Verhaagen, J.; van Kesteren, R.E.; Bossers, K.A.; Mac Gillavry, H.D.; Mason, M.R.; Smit, A.B.

    2012-01-01

    A comprehensive understanding of the molecular pathways activated by traumatic neural injury is of major importance for the development of treatments for spinal cord injury (SCI). High-throughput gene expression profiling is a powerful approach to reveal genome-wide changes in gene expression during

  6. 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

  7. Differences in Neural Activation as a Function of Risk-taking Task Parameters

    Directory of Open Access Journals (Sweden)

    Eliza eCongdon

    2013-09-01

    Full Text Available Despite evidence supporting a relationship between impulsivity and naturalistic risk-taking, the relationship of impulsivity with laboratory-based measures of risky decision-making remains unclear. One factor contributing to this gap in our understanding is the degree to which different risky decision-making tasks vary in their details. We conducted an fMRI investigation of the Angling Risk Task (ART, which is an improved behavioral measure of risky decision-making. In order to examine whether the observed pattern of neural activation was specific to the ART or generalizable, we also examined correlates of the Balloon Analogue Risk Taking (BART task in the same sample of 23 healthy adults. Exploratory analyses were conducted to examine the relationship between neural activation, performance, impulsivity and self-reported risk-taking. While activation in a valuation network was associated with reward tracking during the ART but not the BART, increased fronto-cingulate activation was seen during risky choice trials in the BART as compared to the ART. Thus, neural activation during risky decision-making trials differed between the two tasks, and this observation was likely driven by differences in task parameters, namely the absence vs. presence of ambiguity and/or stationary vs. increasing probability of loss on the ART and BART, respectively. Exploratory association analyses suggest that sensitivity of neural response to the magnitude of potential reward during the ART was associated with a suboptimal performance strategy, higher scores on a scale of dysfunctional impulsivity and a greater likelihood of engaging in risky behaviors, while this pattern was not seen for the BART. Our results suggest that the ART is decomposable and associated with distinct patterns of neural activation; this represents a preliminary step towards characterizing a behavioral measure of risky decision-making that may support a better understanding of naturalistic risk-taking.

  8. Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

    Directory of Open Access Journals (Sweden)

    S. Venkatesh

    2012-01-01

    Full Text Available Partial discharge (PD is a major cause of failure of power apparatus and hence its measurement and analysis have emerged as a vital field in assessing the condition of the insulation system. Several efforts have been undertaken by researchers to classify PD pulses utilizing artificial intelligence techniques. Recently, the focus has shifted to the identification of multiple sources of PD since it is often encountered in real-time measurements. Studies have indicated that classification of multi-source PD becomes difficult with the degree of overlap and that several techniques such as mixed Weibull functions, neural networks, and wavelet transformation have been attempted with limited success. Since digital PD acquisition systems record data for a substantial period, the database becomes large, posing considerable difficulties during classification. This research work aims firstly at analyzing aspects concerning classification capability during the discrimination of multisource PD patterns. Secondly, it attempts at extending the previous work of the authors in utilizing the novel approach of probabilistic neural network versions for classifying moderate sets of PD sources to that of large sets. The third focus is on comparing the ability of partition-based algorithms, namely, the labelled (learning vector quantization and unlabelled (K-means versions, with that of a novel hypergraph-based clustering method in providing parsimonious sets of centers during classification.

  9. Multiple victimization experiences of urban elementary school students: associations with psychosocial functioning and academic performance.

    Science.gov (United States)

    Holt, Melissa K; Finkelhor, David; Kantor, Glenda Kaufman

    2007-05-01

    This study explored the victimization experiences of urban elementary school students to determine whether subsets of youth emerged with similar victimization profiles (e.g., no victimization, multiple types of victimization). It also evaluated whether multiple victimization was associated with greater psychological distress and lower academic performance. Participants were 689 fifth grade students from an urban, ethnically diverse school district in the Northeast. Youth completed self-report measures in school about bullying victimization, victimization in the home and community, and psychosocial functioning. Cluster analysis suggested the existence of three distinct youth profiles: those with minimal victimization, those victimized primarily by their peers, and those with multiple types of victimizations. As hypothesized, youth with multiple victimizations experienced more psychological distress and earned lower grades than their peers. Findings highlight the heterogeneity of youth victimization experiences and their relations to functioning, and have implications for treatment planning among practitioners working with youth.

  10. 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.

  11. Decomposing metaphor processing at the cognitive and neural level through functional magnetic resonance imaging.

    Science.gov (United States)

    Bambini, Valentina; Gentili, Claudio; Ricciardi, Emiliano; Bertinetto, Pier Marco; Pietrini, Pietro

    2011-10-10

    Prior neuroimaging studies on metaphor comprehension have tended to focus on the role of the right hemisphere, without reaching consensus and leaving aside the functional architecture of this process. The present work aimed to break down metaphor comprehension into its functional components. The study rationale is two-fold: on the one hand, the large-scale network model as emerging in cognitive neuroscience led us to a consideration of metaphor as supported by a distributed and bilateral network; on the other hand, we based on the accounts of figurative language put forward in pragmatics and cognitive science to postulate a decomposition of such a network into multiple sub-systems. During scanning, participants implicitly processed metaphorical (familiar and unfamiliar) and non-metaphorical passages, while being explicitly involved in an adjective matching task to be performed after reading the target passages. Several regions showed greater activity to metaphors as compared to non-metaphors, including left and right inferior frontal gyrus, right superior temporal gyrus, left angular gyrus, and anterior cingulate. This pattern of activations, markedly bilateral, can be decomposed into circumscribed functional sub-systems mediating different aspects of metaphor resolution, as foreseen in the pragmatic and cognitive literature: (a) the conceptual/pragmatic machinery in the bilateral inferior frontal gyrus and in the left angular gyrus, which supports the integration of linguistic material and world knowledge in context; (b) the attentional component in the anterior cingulate and prefrontal areas, which is set to monitor and filter for the relevant aspects of context and for the appropriate meanings; (c) the Theory of Mind system along the right superior temporal sulcus, which deals with the recognition of speakers' communicative intentions and is more extensively activated by unfamiliar metaphors. The results have several implications for the field of neuropragmatics

  12. Aerobic capacity explains physical functioning and participation in patients with multiple sclerosis-related fatigue

    OpenAIRE

    Emma Rosalie Driehuis; Lizanne Eva van den Akker; Vincent de Groot; Heleen Beckerman

    2018-01-01

    Objective: To investigate whether aerobic capacity explains the level of self-reported physical activity, physical functioning, and participation and autonomy in daily living in persons with multiple sclerosis-related fatigue. Design: A cross-sectional study. Patients: Sixty-two participants with multiple sclerosis-related fatigue. Methods: Aerobic capacity was measured with a leg ergometer and was expressed as maximal oxygen uptake (VO2max, in ...

  13. Robust Template Decomposition without Weight Restriction for Cellular Neural Networks Implementing Arbitrary Boolean Functions Using Support Vector Classifiers

    Directory of Open Access Journals (Sweden)

    Yih-Lon Lin

    2013-01-01

    Full Text Available If the given Boolean function is linearly separable, a robust uncoupled cellular neural network can be designed as a maximal margin classifier. On the other hand, if the given Boolean function is linearly separable but has a small geometric margin or it is not linearly separable, a popular approach is to find a sequence of robust uncoupled cellular neural networks implementing the given Boolean function. In the past research works using this approach, the control template parameters and thresholds are restricted to assume only a given finite set of integers, and this is certainly unnecessary for the template design. In this study, we try to remove this restriction. Minterm- and maxterm-based decomposition algorithms utilizing the soft margin and maximal margin support vector classifiers are proposed to design a sequence of robust templates implementing an arbitrary Boolean function. Several illustrative examples are simulated to demonstrate the efficiency of the proposed method by comparing our results with those produced by other decomposition methods with restricted weights.

  14. Towards Finding the Global Minimum of the D-Wave Objective Function for Improved Neural Network Regressions

    Science.gov (United States)

    Dorband, J. E.

    2017-12-01

    The D-Wave 2X has successfully been used for regression analysis to derive carbon flux data from OCO-2 CO2 concentration using neural networks. The samples returned from the D-Wave should represent the minimum of an objective function presented to it. An accurate as possible minimum function value is needed for this analysis. Samples from the D-Wave are near minimum, but seldom are the global minimum of the function due to quantum noise. Two methods for improving the accuracy of minimized values represented by the samples returned from the D-Wave are presented. The first method finds a new sample with a minimum value near each returned D-Wave sample. The second method uses all the returned samples to find a more global minimum sample. We present three use-cases performed using the former method. In the first use case, it is demonstrated that an objective function with random qubits and coupler coefficients had an improved minimum. In the second use case, the samples corrected by the first method can improve the training of a Boltzmann machine neural network. The third use case demonstrated that using the first method can improve virtual qubit accuracy.The later method was also performed on the first use case.

  15. 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.

  16. Induced Neural Stem Cells Achieve Long-Term Survival and Functional Integration in the Adult Mouse Brain

    Directory of Open Access Journals (Sweden)

    Kathrin Hemmer

    2014-09-01

    Full Text Available Differentiated cells can be converted directly into multipotent neural stem cells (i.e., induced neural stem cells [iNSCs]. iNSCs offer an attractive alternative to induced pluripotent stem cell (iPSC technology with regard to regenerative therapies. Here, we show an in vivo long-term analysis of transplanted iNSCs in the adult mouse brain. iNSCs showed sound in vivo long-term survival rates without graft overgrowths. The cells displayed a neural multilineage potential with a clear bias toward astrocytes and a permanent downregulation of progenitor and cell-cycle markers, indicating that iNSCs are not predisposed to tumor formation. Furthermore, the formation of synaptic connections as well as neuronal and glial electrophysiological properties demonstrated that differentiated iNSCs migrated, functionally integrated, and interacted with the existing neuronal circuitry. We conclude that iNSC long-term transplantation is a safe procedure; moreover, it might represent an interesting tool for future personalized regenerative applications.

  17. Induced neural stem cells achieve long-term survival and functional integration in the adult mouse brain.

    Science.gov (United States)

    Hemmer, Kathrin; Zhang, Mingyue; van Wüllen, Thea; Sakalem, Marna; Tapia, Natalia; Baumuratov, Aidos; Kaltschmidt, Christian; Kaltschmidt, Barbara; Schöler, Hans R; Zhang, Weiqi; Schwamborn, Jens C

    2014-09-09

    Differentiated cells can be converted directly into multipotent neural stem cells (i.e., induced neural stem cells [iNSCs]). iNSCs offer an attractive alternative to induced pluripotent stem cell (iPSC) technology with regard to regenerative therapies. Here, we show an in vivo long-term analysis of transplanted iNSCs in the adult mouse brain. iNSCs showed sound in vivo long-term survival rates without graft overgrowths. The cells displayed a neural multilineage potential with a clear bias toward astrocytes and a permanent downregulation of progenitor and cell-cycle markers, indicating that iNSCs are not predisposed to tumor formation. Furthermore, the formation of synaptic connections as well as neuronal and glial electrophysiological properties demonstrated that differentiated iNSCs migrated, functionally integrated, and interacted with the existing neuronal circuitry. We conclude that iNSC long-term transplantation is a safe procedure; moreover, it might represent an interesting tool for future personalized regenerative applications. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  18. Multiple types of synchronization analysis for discontinuous Cohen-Grossberg neural networks with time-varying delays.

    Science.gov (United States)

    Li, Jiarong; Jiang, Haijun; Hu, Cheng; Yu, Zhiyong

    2018-03-01

    This paper is devoted to the exponential synchronization, finite time synchronization, and fixed-time synchronization of Cohen-Grossberg neural networks (CGNNs) with discontinuous activations and time-varying delays. Discontinuous feedback controller and Novel adaptive feedback controller are designed to realize global exponential synchronization, finite time synchronization and fixed-time synchronization by adjusting the values of the parameters ω in the controller. Furthermore, the settling time of the fixed-time synchronization derived in this paper is less conservative and more accurate. Finally, some numerical examples are provided to show the effectiveness and flexibility of the results derived in this paper. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  20. Correlation functions with fusion-channel multiplicity in W3 Toda field theory

    International Nuclear Information System (INIS)

    Belavin, Vladimir; Estienne, Benoit; Foda, Omar; Santachiara, Raoul

    2016-01-01

    Current studies of W N Toda field theory focus on correlation functions such that the W N highest-weight representations in the fusion channels are multiplicity-free. In this work, we study W 3 Toda 4-point functions with multiplicity in the fusion channel. The conformal blocks of these 4-point functions involve matrix elements of a fully-degenerate primary field with a highest-weight in the adjoint representation of sl 3 , and a fully-degenerate primary field with a highest-weight in the fundamental representation of sl 3 . We show that, when the fusion rules do not involve multiplicities, the matrix elements of the fully-degenerate adjoint field, between two arbitrary descendant states, can be computed explicitly, on equal footing with the matrix elements of the semi-degenerate fundamental field. Using null-state conditions, we obtain a fourth-order Fuchsian differential equation for the conformal blocks. Using Okubo theory, we show that, due to the presence of multiplicities, this differential equation belongs to a class of Fuchsian equations that is different from those that have appeared so far in W N theories. We solve this equation, compute its monodromy group, and construct the monodromy-invariant correlation functions. This computation shows in detail how the ambiguities that are caused by the presence of multiplicities are fixed by requiring monodromy-invariance.

  1. Correlation functions with fusion-channel multiplicity in W{sub 3} Toda field theory

    Energy Technology Data Exchange (ETDEWEB)

    Belavin, Vladimir [I.E. Tamm Department of Theoretical Physics, P.N. Lebedev Physical Institute,Leninsky Avenue 53, 119991 Moscow (Russian Federation); Department of Quantum Physics, Institute for Information Transmission Problems,Bolshoy Karetny per. 19, 127994 Moscow (Russian Federation); Estienne, Benoit [LPTHE, CNRS and Université Pierre et Marie Curie,Sorbonne Universités, 4 Place Jussieu, 75252 Paris Cedex 05 (France); Foda, Omar [School of Mathematics and Statistics, University of Melbourne,Parkville, Victoria 3010 (Australia); Santachiara, Raoul [LPTMS, CNRS (UMR 8626), Université Paris-Saclay,15 rue Georges Clémenceau, 91405 Orsay (France)

    2016-06-22

    Current studies of W{sub N} Toda field theory focus on correlation functions such that the W{sub N} highest-weight representations in the fusion channels are multiplicity-free. In this work, we study W{sub 3} Toda 4-point functions with multiplicity in the fusion channel. The conformal blocks of these 4-point functions involve matrix elements of a fully-degenerate primary field with a highest-weight in the adjoint representation of sl{sub 3}, and a fully-degenerate primary field with a highest-weight in the fundamental representation of sl{sub 3}. We show that, when the fusion rules do not involve multiplicities, the matrix elements of the fully-degenerate adjoint field, between two arbitrary descendant states, can be computed explicitly, on equal footing with the matrix elements of the semi-degenerate fundamental field. Using null-state conditions, we obtain a fourth-order Fuchsian differential equation for the conformal blocks. Using Okubo theory, we show that, due to the presence of multiplicities, this differential equation belongs to a class of Fuchsian equations that is different from those that have appeared so far in W{sub N} theories. We solve this equation, compute its monodromy group, and construct the monodromy-invariant correlation functions. This computation shows in detail how the ambiguities that are caused by the presence of multiplicities are fixed by requiring monodromy-invariance.

  2. Integrated analysis of genetic, behavioral, and biochemical data implicates neural stem cell-induced changes in immunity, neurotransmission and mitochondrial function in Dementia with Lewy Body mice.

    Science.gov (United States)

    Lakatos, Anita; Goldberg, Natalie R S; Blurton-Jones, Mathew

    2017-03-10

    We previously demonstrated that transplantation of murine neural stem cells (NSCs) can improve motor and cognitive function in a transgenic model of Dementia with Lewy Bodies (DLB). These benefits occurred without changes in human α-synuclein pathology and were mediated in part by stem cell-induced elevation of brain-derived neurotrophic factor (BDNF). However, instrastriatal NSC transplantation likely alters the brain microenvironment via multiple mechanisms that may synergize to promote cognitive and motor recovery. The underlying neurobiology that mediates such restoration no doubt involves numerous genes acting in concert to modulate signaling within and between host brain cells and transplanted NSCs. In order to identify functionally connected gene networks and additional mechanisms that may contribute to stem cell-induced benefits, we performed weighted gene co-expression network analysis (WGCNA) on striatal tissue isolated from NSC- and vehicle-injected wild-type and DLB mice. Combining continuous behavioral and biochemical data with genome wide expression via network analysis proved to be a powerful approach; revealing significant alterations in immune response, neurotransmission, and mitochondria function. Taken together, these data shed further light on the gene network and biological processes that underlie the therapeutic effects of NSC transplantation on α-synuclein induced cognitive and motor impairments, thereby highlighting additional therapeutic targets for synucleinopathies.

  3. Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control.

    Science.gov (United States)

    Vlachos, Ioannis; Deniz, Taşkin; Aertsen, Ad; Kumar, Arvind

    2016-02-01

    There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.

  4. Protein distance constraints predicted by neural networks and probability density functions

    DEFF Research Database (Denmark)

    Lund, Ole; Frimand, Kenneth; Gorodkin, Jan

    1997-01-01

    We predict interatomic C-α distances by two independent data driven methods. The first method uses statistically derived probability distributions of the pairwise distance between two amino acids, whilst the latter method consists of a neural network prediction approach equipped with windows taki...... method based on the predicted distances is presented. A homepage with software, predictions and data related to this paper is available at http://www.cbs.dtu.dk/services/CPHmodels/...

  5. 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)

  6. Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model.

    Directory of Open Access Journals (Sweden)

    Jan Tønnesen

    Full Text Available Intrastriatal grafts of stem cell-derived dopamine (DA neurons induce behavioral recovery in animal models of Parkinson's disease (PD, but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral mesencephalon of tyrosine hydroxylase-GFP transgenic mice were expanded as neurospheres and transplanted into organotypic cultures of wild type mouse striatum. Differentiated GFP-labeled DA neurons in the grafts exhibited mature neuronal properties, including spontaneous firing of action potentials, presence of post-synaptic currents, and functional expression of DA D₂ autoreceptors. These properties resembled those recorded from identical cells in acute slices of intrastriatal grafts in the 6-hydroxy-DA-induced mouse PD model and from DA neurons in intact substantia nigra. Optogenetic activation or inhibition of grafted cells and host neurons using channelrhodopsin-2 (ChR2 and halorhodopsin (NpHR, respectively, revealed complex, bi-directional synaptic interactions between grafted cells and host neurons and extensive synaptic connectivity within the graft. Our data demonstrate for the first time using optogenetics that ectopically grafted stem cell-derived DA neurons become functionally integrated in the DA-denervated striatum. Further optogenetic dissection of the synaptic wiring between grafted and host neurons will be crucial to clarify the cellular and synaptic mechanisms underlying behavioral recovery as well as adverse effects following stem cell-based DA cell replacement strategies in PD.