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Sample records for neural constraints influencing

  1. Medical image segmentation by means of constraint satisfaction neural network

    International Nuclear Information System (INIS)

    Chen, C.T.; Tsao, C.K.; Lin, W.C.

    1990-01-01

    This paper applies the concept of constraint satisfaction neural network (CSNN) to the problem of medical image segmentation. Constraint satisfaction (or constraint propagation), the procedure to achieve global consistency through local computation, is an important paradigm in artificial intelligence. CSNN can be viewed as a three-dimensional neural network, with the two-dimensional image matrix as its base, augmented by various constraint labels for each pixel. These constraint labels can be interpreted as the connections and the topology of the neural network. Through parallel and iterative processes, the CSNN will approach a solution that satisfies the given constraints thus providing segmented regions with global consistency

  2. Medical image segmentation by a constraint satisfaction neural network

    International Nuclear Information System (INIS)

    Chen, C.T.; Tsao, E.C.K.; Lin, W.C.

    1991-01-01

    This paper proposes a class of Constraint Satisfaction Neural Networks (CSNNs) for solving the problem of medical image segmentation which can be formulated as a Constraint Satisfaction Problem (CSP). A CSNN consists of a set of objects, a set of labels for each object, a collection of constraint relations linking the labels of neighboring objects, and a topological constraint describing the neighborhood relationship among various objects. Each label for a particular object indicates one possible interpretation for that object. The CSNN can be viewed as a collection of neurons that interconnect with each other. The connections and the topology of a CSNN are used to represent the constraints in a CSP. The mechanism of the neural network is to find a solution that satisfies all the constraints in order to achieve a global consistency. The final solution outlines segmented areas and simultaneously satisfies all the constraints. This technique has been applied to medical images and the results show that this CSNN method is a very promising approach for image segmentation

  3. Training feed-forward neural networks with gain constraints

    Science.gov (United States)

    Hartman

    2000-04-01

    Inaccurate input-output gains (partial derivatives of outputs with respect to inputs) are common in neural network models when input variables are correlated or when data are incomplete or inaccurate. Accurate gains are essential for optimization, control, and other purposes. We develop and explore a method for training feedforward neural networks subject to inequality or equality-bound constraints on the gains of the learned mapping. Gain constraints are implemented as penalty terms added to the objective function, and training is done using gradient descent. Adaptive and robust procedures are devised for balancing the relative strengths of the various terms in the objective function, which is essential when the constraints are inconsistent with the data. The approach has the virtue that the model domain of validity can be extended via extrapolation training, which can dramatically improve generalization. The algorithm is demonstrated here on artificial and real-world problems with very good results and has been advantageously applied to dozens of models currently in commercial use.

  4. Neural network for nonsmooth pseudoconvex optimization with general convex constraints.

    Science.gov (United States)

    Bian, Wei; Ma, Litao; Qin, Sitian; Xue, Xiaoping

    2018-05-01

    In this paper, a one-layer recurrent neural network is proposed for solving a class of nonsmooth, pseudoconvex optimization problems with general convex constraints. Based on the smoothing method, we construct a new regularization function, which does not depend on any information of the feasible region. Thanks to the special structure of the regularization function, we prove the global existence, uniqueness and "slow solution" character of the state of the proposed neural network. Moreover, the state solution of the proposed network is proved to be convergent to the feasible region in finite time and to the optimal solution set of the related optimization problem subsequently. In particular, the convergence of the state to an exact optimal solution is also considered in this paper. Numerical examples with simulation results are given to show the efficiency and good characteristics of the proposed network. In addition, some preliminary theoretical analysis and application of the proposed network for a wider class of dynamic portfolio optimization are included. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling.

    Science.gov (United States)

    Yang, S; Wang, D

    2000-01-01

    This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.

  6. Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

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    Kmet', Tibor; Kmet'ová, Mária

    2009-09-01

    A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

  7. Lagrange constraint neural networks for massive pixel parallel image demixing

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    Szu, Harold H.; Hsu, Charles C.

    2002-03-01

    We have shown that the remote sensing optical imaging to achieve detailed sub-pixel decomposition is a unique application of blind source separation (BSS) that is truly linear of far away weak signal, instantaneous speed of light without delay, and along the line of sight without multiple paths. In early papers, we have presented a direct application of statistical mechanical de-mixing method called Lagrange Constraint Neural Network (LCNN). While the BSAO algorithm (using a posteriori MaxEnt ANN and neighborhood pixel average) is not acceptable for remote sensing, a mirror symmetric LCNN approach is all right assuming a priori MaxEnt for unknown sources to be averaged over the source statistics (not neighborhood pixel data) in a pixel-by-pixel independent fashion. LCNN reduces the computation complexity, save a great number of memory devices, and cut the cost of implementation. The Landsat system is designed to measure the radiation to deduce surface conditions and materials. For any given material, the amount of emitted and reflected radiation varies by the wavelength. In practice, a single pixel of a Landsat image has seven channels receiving 0.1 to 12 microns of radiation from the ground within a 20x20 meter footprint containing a variety of radiation materials. A-priori LCNN algorithm provides the spatial-temporal variation of mixture that is hardly de-mixable by other a-posteriori BSS or ICA methods. We have already compared the Landsat remote sensing using both methods in WCCI 2002 Hawaii. Unfortunately the absolute benchmark is not possible because of lacking of the ground truth. We will arbitrarily mix two incoherent sampled images as the ground truth. However, the constant total probability of co-located sources within the pixel footprint is necessary for the remote sensing constraint (since on a clear day the total reflecting energy is constant in neighborhood receiving pixel sensors), we have to normalized two image pixel-by-pixel as well. Then, the

  8. Commentary. Integrative Modeling and the Role of Neural Constraints

    Czech Academy of Sciences Publication Activity Database

    Bantegnie, Brice

    2017-01-01

    Roč. 8, SEP 5 (2017), s. 1-2, č. článku 1531. ISSN 1664-1078 Institutional support: RVO:67985955 Keywords : mechanistic explanation * functional analysis * mechanistic integration * reverse inference * neural plasticity * neural networks Subject RIV: AA - Philosophy ; Religion Impact factor: 2.323, year: 2016

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

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    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

  10. A one-layer recurrent neural network for non-smooth convex optimization subject to linear inequality constraints

    International Nuclear Information System (INIS)

    Liu, Xiaolan; Zhou, Mi

    2016-01-01

    In this paper, a one-layer recurrent network is proposed for solving a non-smooth convex optimization subject to linear inequality constraints. Compared with the existing neural networks for optimization, the proposed neural network is capable of solving more general convex optimization with linear inequality constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds.

  11. Neural mechanisms of social influence in adolescence.

    Science.gov (United States)

    Welborn, B Locke; Lieberman, Matthew D; Goldenberg, Diane; Fuligni, Andrew J; Galván, Adriana; Telzer, Eva H

    2016-01-01

    During the transformative period of adolescence, social influence plays a prominent role in shaping young people's emerging social identities, and can impact their propensity to engage in prosocial or risky behaviors. In this study, we examine the neural correlates of social influence from both parents and peers, two important sources of influence. Nineteen adolescents (age 16-18 years) completed a social influence task during a functional magnetic resonance imaging (fMRI) scan. Social influence from both sources evoked activity in brain regions implicated in mentalizing (medial prefrontal cortex, left temporoparietal junction, right temporoparietal junction), reward (ventromedial prefrontal cortex), and self-control (right ventrolateral prefrontal cortex). These results suggest that mental state reasoning, social reward and self-control processes may help adolescents to evaluate others' perspectives and overcome the prepotent force of their own antecedent attitudes to shift their attitudes toward those of others. Findings suggest common neural networks involved in social influence from both parents and peers. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  12. Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

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    Shu-Min Lu

    2017-01-01

    Full Text Available An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.

  13. A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Problems With Equality and Inequality Constraints.

    Science.gov (United States)

    Qin, Sitian; Yang, Xiudong; Xue, Xiaoping; Song, Jiahui

    2017-10-01

    Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches the feasible region in finite time and stays there thereafter. It is also proved that the state of the proposed neural network is convergent to an optimal solution of the related problem. Compared with the related existing recurrent neural networks for the pseudoconvex optimization problems, the proposed neural network in this paper does not need the penalty parameters and has a better convergence. Meanwhile, the proposed neural network is used to solve three nonsmooth optimization problems, and we make some detailed comparisons with the known related conclusions. In the end, some numerical examples are provided to illustrate the effectiveness of the performance of the proposed neural network.

  14. Search strategies in practice: Influence of information and task constraints.

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    Pacheco, Matheus M; Newell, Karl M

    2018-01-01

    The practice of a motor task has been conceptualized as a process of search through a perceptual-motor workspace. The present study investigated the influence of information and task constraints on the search strategy as reflected in the sequential relations of the outcome in a discrete movement virtual projectile task. The results showed that the relation between the changes of trial-to-trial movement outcome to performance level was dependent on the landscape of the task dynamics and the influence of inherent variability. Furthermore, the search was in a constrained parameter region of the perceptual-motor workspace that depended on the task constraints. These findings show that there is not a single function of trial-to-trial change over practice but rather that local search strategies (proportional, discontinuous, constant) adapt to the level of performance and the confluence of constraints to action. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Neural correlates of affective influence on choice.

    Science.gov (United States)

    Piech, Richard M; Lewis, Jade; Parkinson, Caroline H; Owen, Adrian M; Roberts, Angela C; Downing, Paul E; Parkinson, John A

    2010-03-01

    Making the right choice depends crucially on the accurate valuation of the available options in the light of current needs and goals of an individual. Thus, the valuation of identical options can vary considerably with motivational context. The present study investigated the neural structures underlying context dependent evaluation. We instructed participants to choose from food menu items based on different criteria: on their anticipated taste or on ease of preparation. The aim of the manipulation was to assess which neural sites were activated during choice guided by incentive value, and which during choice based on a value-irrelevant criterion. To assess the impact of increased motivation, affect-guided choice and cognition-guided choice was compared during the sated and hungry states. During affective choice, we identified increased activity in structures representing primarily valuation and taste (medial prefrontal cortex, insula). During cognitive choice, structures showing increased activity included those implicated in suppression and conflict monitoring (lateral orbitofrontal cortex, anterior cingulate). Hunger influenced choice-related activity in the ventrolateral prefrontal cortex. Our results show that choice is associated with the use of distinct neural structures for the pursuit of different goals. Published by Elsevier Inc.

  16. Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

    Science.gov (United States)

    Takiyama, Ken

    2017-12-01

    How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

  17. The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

    Science.gov (United States)

    Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

    2017-01-01

    The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

  18. A neural network approach to breast cancer diagnosis as a constraint satisfaction problem

    International Nuclear Information System (INIS)

    Tourassi, Georgia D.; Markey, Mia K.; Lo, Joseph Y.; Floyd, Carey E. Jr.

    2001-01-01

    A constraint satisfaction neural network (CSNN) approach is proposed for breast cancer diagnosis using mammographic and patient history findings. Initially, the diagnostic decision to biopsy was formulated as a constraint satisfaction problem. Then, an associative memory type neural network was applied to solve the problem. The proposed network has a flexible, nonhierarchical architecture that allows it to operate not only as a predictive tool but also as an analysis tool for knowledge discovery of association rules. The CSNN was developed and evaluated using a database of 500 nonpalpable breast lesions with definitive histopathological diagnosis. The CSNN diagnostic performance was evaluated using receiver operating characteristic analysis (ROC). The results of the study showed that the CSNN ROC area index was 0.84±0.02. The CSNN predictive performance is competitive with that achieved by experienced radiologists and backpropagation artificial neural networks (BP-ANNs) presented before. Furthermore, the study illustrates how CSNN can be used as a knowledge discovery tool overcoming some of the well-known limitations of BP-ANNs

  19. Neural Based Tabu Search method for solving unit commitment problem with cooling-banking constraints

    Directory of Open Access Journals (Sweden)

    Rajan Asir Christober Gnanakkan Charles

    2009-01-01

    Full Text Available This paper presents a new approach to solve short-term unit commitment problem (UCP using Neural Based Tabu Search (NBTS with cooling and banking constraints. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for next H hours. A 7-unit utility power system in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consist of 10, 26 and 34 units. Numerical results are shown to compare the superiority of the cost solutions obtained using the Tabu Search (TS method, Dynamic Programming (DP and Lagrangian Relaxation (LR methods in reaching proper unit commitment.

  20. Asymmetric continuous-time neural networks without local traps for solving constraint satisfaction problems.

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    Botond Molnár

    Full Text Available There has been a long history of using neural networks for combinatorial optimization and constraint satisfaction problems. Symmetric Hopfield networks and similar approaches use steepest descent dynamics, and they always converge to the closest local minimum of the energy landscape. For finding global minima additional parameter-sensitive techniques are used, such as classical simulated annealing or the so-called chaotic simulated annealing, which induces chaotic dynamics by addition of extra terms to the energy landscape. Here we show that asymmetric continuous-time neural networks can solve constraint satisfaction problems without getting trapped in non-solution attractors. We concentrate on a model solving Boolean satisfiability (k-SAT, which is a quintessential NP-complete problem. There is a one-to-one correspondence between the stable fixed points of the neural network and the k-SAT solutions and we present numerical evidence that limit cycles may also be avoided by appropriately choosing the parameters of the model. This optimal parameter region is fairly independent of the size and hardness of instances, this way parameters can be chosen independently of the properties of problems and no tuning is required during the dynamical process. The model is similar to cellular neural networks already used in CNN computers. On an analog device solving a SAT problem would take a single operation: the connection weights are determined by the k-SAT instance and starting from any initial condition the system searches until finding a solution. In this new approach transient chaotic behavior appears as a natural consequence of optimization hardness and not as an externally induced effect.

  1. Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

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    Gabriel A. Fonseca Guerra

    2017-12-01

    Full Text Available Constraint satisfaction problems (CSP are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems. The human brain efficiently handles CSPs both in perception and behavior using spiking neural networks (SNNs, and recent studies have demonstrated that the noise embedded within an SNN can be used as a computational resource to solve CSPs. Here, we provide a software framework for the implementation of such noisy neural solvers on the SpiNNaker massively parallel neuromorphic hardware, further demonstrating their potential to implement a stochastic search that solves instances of P and NP problems expressed as CSPs. This facilitates the exploration of new optimization strategies and the understanding of the computational abilities of SNNs. We demonstrate the basic principles of the framework by solving difficult instances of the Sudoku puzzle and of the map color problem, and explore its application to spin glasses. The solver works as a stochastic dynamical system, which is attracted by the configuration that solves the CSP. The noise allows an optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart.

  2. Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems.

    Science.gov (United States)

    Fonseca Guerra, Gabriel A; Furber, Steve B

    2017-01-01

    Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not) of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems). The human brain efficiently handles CSPs both in perception and behavior using spiking neural networks (SNNs), and recent studies have demonstrated that the noise embedded within an SNN can be used as a computational resource to solve CSPs. Here, we provide a software framework for the implementation of such noisy neural solvers on the SpiNNaker massively parallel neuromorphic hardware, further demonstrating their potential to implement a stochastic search that solves instances of P and NP problems expressed as CSPs. This facilitates the exploration of new optimization strategies and the understanding of the computational abilities of SNNs. We demonstrate the basic principles of the framework by solving difficult instances of the Sudoku puzzle and of the map color problem, and explore its application to spin glasses. The solver works as a stochastic dynamical system, which is attracted by the configuration that solves the CSP. The noise allows an optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart.

  3. The influence of end constraints on smooth pipe bends

    International Nuclear Information System (INIS)

    Thomson, G.; Spence, J.

    1981-01-01

    With present trends in the power industries towards higher operating temperatures and pressures, problems associated with the design and safety assessment of pipework systems have become increasingly complex. Within such systems, the importance of smooth pipe bends is well established. The work which will be presented will attempt to clarify the situation and unify the results. An analytical solution of the problem of a linear elastic smooth pipe bend with end constraints under in-plane bending will be presented. The analysis will deal with constraints in the form of flanged tangents of any length. The analysis employs the theorem of minimum total potential energy with suitable kinematically admissible displacements in the form of Fourier series. The integrations and minimisation were performed numerically, thereby permitting the removal of several of the assumptions made by previous authors. Typical results for flexibilities will be given along with comparisons with other works. The differences in some earlier theory are clarified and other more recent work using different solution techniques is substantiated. The bend behaviour is shown to be strongly influenced by the pipe bend parameter, the bend angle, the tangent pipe length and the bend/cross-sectional radius ratio. (orig./GL)

  4. Neural responses to exclusion predict susceptibility to social influence.

    Science.gov (United States)

    Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G

    2014-05-01

    Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.

  5. Neural Correlates of Affective Influence on Choice

    Science.gov (United States)

    Piech, Richard M.; Lewis, Jade; Parkinson, Caroline H.; Owen, Adrian M.; Roberts, Angela C.; Downing, Paul E.; Parkinson, John A.

    2010-01-01

    Making the right choice depends crucially on the accurate valuation of the available options in the light of current needs and goals of an individual. Thus, the valuation of identical options can vary considerably with motivational context. The present study investigated the neural structures underlying context dependent evaluation. We instructed…

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

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    Zhenyu Wei

    2017-12-01

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

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

    Science.gov (United States)

    Wei, Zhenyu; Zhao, Zhiying; Zheng, Yong

    2017-01-01

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

  8. The neural substrates of social influence on decision making.

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    Tomlin, Damon; Nedic, Andrea; Prentice, Deborah A; Holmes, Philip; Cohen, Jonathan D

    2013-01-01

    The mechanisms that govern human learning and decision making under uncertainty have been the focus of intense behavioral and, more recently, neuroscientific investigation. Substantial progress has been made in building models of the processes involved, and identifying underlying neural mechanisms using simple, two-alternative forced choice decision tasks. However, less attention has been given to how social information influences these processes, and the neural systems that mediate this influence. Here we sought to address these questions by using tasks similar to ones that have been used to study individual decision making behavior, and adding conditions in which participants were given trial-by-trial information about the performance of other individuals (their choices and/or their rewards) simultaneously playing the same tasks. We asked two questions: How does such information about the behavior of others influence performance in otherwise simple decision tasks, and what neural systems mediate this influence? We found that bilateral insula exhibited a parametric relationship to the degree of misalignment of the individual's performance with those of others in the group. Furthermore, activity in the bilateral insula significantly predicted participants' subsequent choices to align their behavior with others in the group when they were misaligned either in their choices (independent of success) or their degree of success (independent of specific choices). These findings add to the growing body of empirical data suggesting that the insula participates in an important way in social information processing and decision making.

  9. Complexities and constraints influencing learner performance in physical science

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    Mavhungu Abel Mafukata

    2016-01-01

    Full Text Available This paper explores complexities and constraints affecting performance and output of physical science learners in Vhembe District, Limpopo Province, South Africa. The study was motivated by the desire of the researcher to establish, profile and characterise the complexities and constraints reminiscence of poor performance of learners in physical science as measured through end-of-year Grade 12 (final year of high school education examination results. Twenty six schools (n=26 were purposively selected from three circuits of education (n=3. From these schools, two learners were randomly selected (n=52 for interviews. In addition, two circuit managers (n=2 were conveniently selected as part of Key Informant Interviews (KII. For the Focus Group Discussions (FGDs, twelve (n=12 parents were randomly selected to form two groups of six members each. Multi-factor complexities and constraints impeding performance of learners were discovered. Intensive teacher in-service programme is recommended. Community engagement should be encouraged to educate parents on the value of involvement in the education of their children. Free access learner support structures such as Homework and Extra-lessons Assistance Centre (H&EACs should be established.

  10. Neural Correlates of Social Influence Among Cannabis Users.

    Science.gov (United States)

    Gilman, Jodi M

    2017-06-01

    Although peer influence is an important factor in the initiation and maintenance of cannabis use, few studies have investigated the neural correlates of peer influence among cannabis users. The current review summarizes research on the neuroscience of social influence in cannabis users, with the goal of highlighting gaps in the literature and the need for future research. Brain regions underlying peer influence may function differently in cannabis users. Compared to non-using controls, regions of the brain underlying reward, such as the striatum, show greater connectivity with frontal regions, and also show hyperactivity when participants are presented with peer information. Other subcortical regions, such as the insula, show hypoactivation during social exclusion in cannabis users, indicating that neural responses to peer interactions may be altered in cannabis users. Although neuroscience is increasingly being used to study social behavior, few studies have specifically focused on cannabis use, and therefore it is difficult to draw conclusions about social mechanisms that may differentiate cannabis users and controls. This area of research may be a promising avenue in which to explore a critical factor underlying cannabis use and addiction.

  11. Acute Stress Influences Neural Circuits of Reward Processing

    Directory of Open Access Journals (Sweden)

    Anthony John Porcelli

    2012-11-01

    Full Text Available People often make decisions under aversive conditions such as acute stress. Yet, less is known about the process in which acute stress can influence decision-making. A growing body of research has established that reward-related information associated with the outcomes of decisions exerts a powerful influence over the choices people make and that an extensive network of brain regions, prominently featuring the striatum, is involved in the processing of this reward-related information. Thus, an important step in research on the nature of acute stress’ influence over decision-making is to examine how it may modulate responses to rewards and punishments within reward-processing neural circuitry. In the current experiment, we employed a simple reward processing paradigm – where participants received monetary rewards and punishments – known to evoke robust striatal responses. Immediately prior to performing each of two task runs, participants were exposed to acute stress (i.e., cold pressor or a no stress control procedure in a between-subjects fashion. No stress group participants exhibited a pattern of activity within the dorsal striatum and orbitofrontal cortex consistent with past research on outcome processing – specifically, differential responses for monetary rewards over punishments. In contrast, acute stress group participants’ dorsal striatum and orbitofrontal cortex demonstrated decreased sensitivity to monetary outcomes and a lack of differential activity. These findings provide insight into how neural circuits may process rewards and punishments associated with simple decisions under acutely stressful conditions.

  12. Influence of extracellular oscillations on neural communication: a computational perspective

    Directory of Open Access Journals (Sweden)

    Zoran eTiganj

    2014-02-01

    Full Text Available Neural communication generates oscillations of electric potential in the extracellular medium. In feedback, these oscillations affect the electrochemical processes within the neurons, influencing the timing and the number of action potentials. It is unclear whether this influence should be considered only as noise or it has some functional role in neural communication. Through computer simulations we investigated the effect of various sinusoidal extracellular oscillations on the timing and number of action potentials. Each simulation is based on a multicompartment model of a single neuron, which is stimulated through spatially distributed synaptic activations. A thorough analysis is conducted on a large number of simulations with different models of CA3 and CA1 pyramidal neurons which are modeled using realistic morphologies and active ion conductances. We demonstrated that the influence of the weak extracellular oscillations, which are commonly present in the brain, is rather stochastic and modest. We found that the stronger fields, which are spontaneously present in the brain only in some particular cases (e.g. during seizures or that can be induced externally, could significantly modulate spike timings.

  13. A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system

    International Nuclear Information System (INIS)

    Attaran, Seyed Mohammad; Yusof, Rubiyah; Selamat, Hazlina

    2016-01-01

    Highlights: • Decoupling of a heating, ventilation, and air conditioning system is presented. • RBF models were identified by Epsilon constraint method for temperature and humidity. • Control settings derived from optimization of the decoupled model. • Epsilon constraint-RBF based on PID controller was implemented to keep thermal comfort and minimize energy. • Enhancements of controller parameters of the HVAC system are desired. - Abstract: The energy efficiency of a heating, ventilating and air conditioning (HVAC) system optimized using a radial basis function neural network (RBFNN) combined with the epsilon constraint (EC) method is reported. The new method adopts the advanced algorithm of RBFNN for the HVAC system to estimate the residual errors, increase the control signal and reduce the error results. The objective of this study is to develop and simulate the EC-RBFNN for a self tuning PID controller for a decoupled bilinear HVAC system to control the temperature and relative humidity (RH) produced by the system. A case study indicates that the EC-RBFNN algorithm has a much better accuracy than optimization PID itself and PID-RBFNN, respectively.

  14. Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Khalid Haddouch

    2016-09-01

    Full Text Available A wide variety of real world optimization problems can be modelled as Weighted Constraint Satisfaction Problems (WCSPs. In this paper, we model this problem in terms of in original 0-1 quadratic programming subject to leaner constraints. View it performance, we use the continuous Hopfield network to solve the obtained model basing on original energy function. To validate our model, we solve several instance of benchmarking WCSP. In this regard, our approach recognizes the optimal solution of the said instances.

  15. Texture synthesis using convolutional neural networks with long-range consistency and spectral constraints

    NARCIS (Netherlands)

    Schreiber, Shaun; Geldenhuys, Jaco; Villiers, De Hendrik

    2017-01-01

    Procedural texture generation enables the creation of more rich and detailed virtual environments without the help of an artist. However, finding a flexible generative model of real world textures remains an open problem. We present a novel Convolutional Neural Network based texture model

  16. Non-neural Muscle Weakness Has Limited Influence on Complexity of Motor Control during Gait

    Directory of Open Access Journals (Sweden)

    Marije Goudriaan

    2018-01-01

    Full Text Available Cerebral palsy (CP and Duchenne muscular dystrophy (DMD are neuromuscular disorders characterized by muscle weakness. Weakness in CP has neural and non-neural components, whereas in DMD, weakness can be considered as a predominantly non-neural problem. Despite the different underlying causes, weakness is a constraint for the central nervous system when controlling gait. CP demonstrates decreased complexity of motor control during gait from muscle synergy analysis, which is reflected by a higher total variance accounted for by one synergy (tVAF1. However, it remains unclear if weakness directly contributes to higher tVAF1 in CP, or whether altered tVAF1 reflects mainly neural impairments. If muscle weakness directly contributes to higher tVAF1, then tVAF1 should also be increased in DMD. To examine the etiology of increased tVAF1, muscle activity data of gluteus medius, rectus femoris, medial hamstrings, medial gastrocnemius, and tibialis anterior were measured at self-selected walking speed, and strength data from knee extensors, knee flexors, dorsiflexors and plantar flexors, were analyzed in 15 children with CP [median (IQR age: 8.9 (2.2], 15 boys with DMD [8.7 (3.1], and 15 typical developing (TD children [8.6 (2.7]. We computed tVAF1 from 10 concatenated steps with non-negative matrix factorization, and compared tVAF1 between the three groups with a Mann-Whiney U-test. Spearman's rank correlation coefficients were used to determine if weakness in specific muscle groups contributed to altered tVAF1. No significant differences in tVAF1 were found between DMD [tVAF1: 0.60 (0.07] and TD children [0.65 (0.07], while tVAF1 was significantly higher in CP [(0.74 (0.09] than in the other groups (both p < 0.005. In CP, weakness in the plantar flexors was related to higher tVAF1 (r = −0.72. In DMD, knee extensor weakness related to increased tVAF1 (r = −0.50. These results suggest that the non-neural weakness in DMD had limited influence on

  17. 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/...

  18. An electronic system for simulation of neural networks with a micro-second real time constraint

    International Nuclear Information System (INIS)

    Chorti, Arsenia; Granado, Bertrand; Denby, Bruce; Garda, Patrick

    2001-01-01

    Neural networks implemented in hardware can perform pattern recognition very quickly, and as such have been used to advantage in the triggering systems of certain high energy physics experiments. Typically, time constants of the order of a few microseconds are required. In this paper, we present a new system. MAHARADJA, for evaluating MLP and RBF neural network paradigms in real time. The system is tested on a possible ATLAS muon triggering application suggested by the Tel Aviv ATLAS group, consisting of a 4-8-8-4 MLP which must be evaluated in 10 microseconds. The inputs to the net are dx/dz, x(z=0), dy/dz, and y(z=0), whereas the outputs give pt, tan(phi), sin(theta), and q, the charge. With a 10 MHz clock, MAHARADJA calculates the result in 6.8 microseconds; at 20 MHz, which is readily attainable, this would be reduced to only 3.4 microseconds. The system can also handle RBF networks with 3 different distance metrics (Euclidean, Manhattan and Mahalanobis), and can simulate any MLP of 10 hidden layers or less. The electronic implementation is with FPGA's, which can be optimized for a specific neural network because the number of processing elements can be modified

  19. Dynamic cultural influences on neural representations of the self.

    Science.gov (United States)

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

    2010-01-01

    People living in multicultural environments often encounter situations which require them to acquire different cultural schemas and to switch between these cultural schemas depending on their immediate sociocultural context. Prior behavioral studies show that priming cultural schemas reliably impacts mental processes and behavior underlying self-concept. However, less well understood is whether or not cultural priming affects neurobiological mechanisms underlying the self. Here we examined whether priming cultural values of individualism and collectivism in bicultural individuals affects neural activity in cortical midline structures underlying self-relevant processes using functional magnetic resonance imaging. Biculturals primed with individualistic values showed increased activation within medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) during general relative to contextual self-judgments, whereas biculturals primed with collectivistic values showed increased response within MPFC and PCC during contextual relative to general self-judgments. Moreover, degree of cultural priming was positively correlated with degree of MPFC and PCC activity during culturally congruent self-judgments. These findings illustrate the dynamic influence of culture on neural representations underlying the self and, more broadly, suggest a neurobiological basis by which people acculturate to novel environments.

  20. Sliding Mode Control for NSVs with Input Constraint Using Neural Network and Disturbance Observer

    Directory of Open Access Journals (Sweden)

    Yan-long Zhou

    2013-01-01

    Full Text Available The sliding mode control (SMC scheme is proposed for near space vehicles (NSVs with strong nonlinearity, high coupling, parameter uncertainty, and unknown time-varying disturbance based on radial basis function neural networks (RBFNNs and the nonlinear disturbance observer (NDO. Considering saturation characteristic of rudders, RBFNNs are constructed as a compensator to overcome the saturation nonlinearity. The stability of the closed-loop system is proved, and the tracking error as well as the disturbance observer error can converge to the origin through the Lyapunov analysis. Simulation results are presented to demonstrate the effectiveness of the proposed flight control scheme.

  1. Learning in fully recurrent neural networks by approaching tangent planes to constraint surfaces.

    Science.gov (United States)

    May, P; Zhou, E; Lee, C W

    2012-10-01

    In this paper we present a new variant of the online real time recurrent learning algorithm proposed by Williams and Zipser (1989). Whilst the original algorithm utilises gradient information to guide the search towards the minimum training error, it is very slow in most applications and often gets stuck in local minima of the search space. It is also sensitive to the choice of learning rate and requires careful tuning. The new variant adjusts weights by moving to the tangent planes to constraint surfaces. It is simple to implement and requires no parameters to be set manually. Experimental results show that this new algorithm gives significantly faster convergence whilst avoiding problems like local minima. Copyright © 2012 Elsevier Ltd. All rights reserved.

  2. High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

    Science.gov (United States)

    Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

    2015-09-01

    In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Hortensius, Ruud; de Gelder, Beatrice

    2014-06-01

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

  4. Zhang neural network for online solution of time-varying convex quadratic program subject to time-varying linear-equality constraints

    International Nuclear Information System (INIS)

    Zhang Yunong; Li Zhan

    2009-01-01

    In this Letter, by following Zhang et al.'s method, a recurrent neural network (termed as Zhang neural network, ZNN) is developed and analyzed for solving online the time-varying convex quadratic-programming problem subject to time-varying linear-equality constraints. Different from conventional gradient-based neural networks (GNN), such a ZNN model makes full use of the time-derivative information of time-varying coefficient. The resultant ZNN model is theoretically proved to have global exponential convergence to the time-varying theoretical optimal solution of the investigated time-varying convex quadratic program. Computer-simulation results further substantiate the effectiveness, efficiency and novelty of such ZNN model and method.

  5. Topology influences performance in the associative memory neural networks

    International Nuclear Information System (INIS)

    Lu Jianquan; He Juan; Cao Jinde; Gao Zhiqiang

    2006-01-01

    To explore how topology affects performance within Hopfield-type associative memory neural networks (AMNNs), we studied the computational performance of the neural networks with regular lattice, random, small-world, and scale-free structures. In this Letter, we found that the memory performance of neural networks obtained through asynchronous updating from 'larger' nodes to 'smaller' nodes are better than asynchronous updating in random order, especially for the scale-free topology. The computational performance of associative memory neural networks linked by the above-mentioned network topologies with the same amounts of nodes (neurons) and edges (synapses) were studied respectively. Along with topologies becoming more random and less locally disordered, we will see that the performance of associative memory neural network is quite improved. By comparing, we show that the regular lattice and random network form two extremes in terms of patterns stability and retrievability. For a network, its patterns stability and retrievability can be largely enhanced by adding a random component or some shortcuts to its structured component. According to the conclusions of this Letter, we can design the associative memory neural networks with high performance and minimal interconnect requirements

  6. Integrated systems optimization model for biofuel development: The influence of environmental constraints

    Science.gov (United States)

    Housh, M.; Ng, T.; Cai, X.

    2012-12-01

    The environmental impact is one of the major concerns of biofuel development. While many other studies have examined the impact of biofuel expansion on stream flow and water quality, this study examines the problem from the other side - will and how a biofuel production target be affected by given environmental constraints. For this purpose, an integrated model comprises of different sub-systems of biofuel refineries, transportation, agriculture, water resources and crops/ethanol market has been developed. The sub-systems are integrated into one large-scale model to guide the optimal development plan considering the interdependency between the subsystems. The optimal development plan includes biofuel refineries location and capacity, refinery operation, land allocation between biofuel and food crops, and the corresponding stream flow and nitrate load in the watershed. The watershed is modeled as a network flow, in which the nodes represent sub-watersheds and the arcs are defined as the linkage between the sub-watersheds. The runoff contribution of each sub-watershed is determined based on the land cover and the water uses in that sub-watershed. Thus, decisions of other sub-systems such as the land allocation in the land use sub-system and the water use in the refinery sub-system define the sources and the sinks of the network. Environmental policies will be addressed in the integrated model by imposing stream flow and nitrate load constraints. These constraints can be specified by location and time in the watershed to reflect the spatial and temporal variation of the regulations. Preliminary results show that imposing monthly water flow constraints and yearly nitrate load constraints will change the biofuel development plan dramatically. Sensitivity analysis is performed to examine how the environmental constraints and their spatial and the temporal distribution influence the overall biofuel development plan and the performance of each of the sub

  7. Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

    Science.gov (United States)

    Kuznetsov, A. V.; Makaryants, G. M.

    2018-01-01

    There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

  8. Numerical exploration of the influence of neural noise on the ...

    Indian Academy of Sciences (India)

    PRAKASH KUMAR

    from the lack of detailed neural biophysical elements in the present simulation. ... of perception, the lack of knowledge of all these details is equally impressive. .... I would like to thank Carmen Gómez Sos for her English language editorial ...

  9. The roles of constraint-based and dedication-based influences on user's continued online shopping behavior.

    Science.gov (United States)

    Chang, Su-Chao; Chou, Chi-Min

    2012-11-01

    The objective of this study was to determine empirically the role of constraint-based and dedication-based influences as drivers of the intention to continue using online shopping websites. Constraint-based influences consist of two variables: trust and perceived switching costs. Dedication-based influences consist of three variables: satisfaction, perceived usefulness, and trust. The current results indicate that both constraint-based and dedication-based influences are important drivers of the intention to continue using online shopping websites. The data also shows that trust has the strongest total effect on online shoppers' intention to continue using online shopping websites. In addition, the results indicate that the antecedents of constraint-based influences, technical bonds (e.g., perceived operational competence and perceived website interactivity) and social bonds (e.g., perceived relationship investment, community building, and intimacy) have indirect positive effects on the intention to continue using online shopping websites. Based on these findings, this research suggests that online shopping websites should build constraint-based and dedication-based influences to enhance user's continued online shopping behaviors simultaneously.

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Science.gov (United States)

    2017-01-01

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

  12. Cultural influences on neural basis of intergroup empathy.

    Science.gov (United States)

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

    2011-07-15

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

  13. The influence of toxicity constraints in models of chemotherapeutic protocol escalation

    KAUST Repository

    Boston, E. A. J.

    2011-04-06

    The prospect of exploiting mathematical and computational models to gain insight into the influence of scheduling on cancer chemotherapeutic effectiveness is increasingly being considered. However, the question of whether such models are robust to the inclusion of additional tumour biology is relatively unexplored. In this paper, we consider a common strategy for improving protocol scheduling that has foundations in mathematical modelling, namely the concept of dose densification, whereby rest phases between drug administrations are reduced. To maintain a manageable scope in our studies, we focus on a single cell cycle phase-specific agent with uncomplicated pharmacokinetics, as motivated by 5-Fluorouracil-based adjuvant treatments of liver micrometastases. In particular, we explore predictions of the effectiveness of dose densification and other escalations of the protocol scheduling when the influence of toxicity constraints, cell cycle phase specificity and the evolution of drug resistance are all represented within the modelling. For our specific focus, we observe that the cell cycle and toxicity should not simply be neglected in modelling studies. Our explorations also reveal the prediction that dose densification is often, but not universally, effective. Furthermore, adjustments in the duration of drug administrations are predicted to be important, especially when dose densification in isolation does not yield improvements in protocol outcomes. © The author 2011. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

  14. Stakeholders' influence on the importance of users' and clients' information and constraints during website design.

    Science.gov (United States)

    Chevalier, Aline

    2007-12-01

    The present study aims at determining the role of the stakeholder (via a user vs a client spokesperson) on the importance allocated to information and constraints considered by novice and professional web designers. Analysis showed all designers focused mainly on clients' constraints and information even when they dealt with a user spokesperson: they considered clients' constraints as more important than users' constraints. These results are new with regard to those previously obtained in web design, which showed designers considered prescribed constraints (regardless of the stakeholder to which they are related) as unavaoidable, and the vast majority of others as avoidable if required. Research is required to help web designers to ponder users' and clients' constraints and to assess whether the same patterns of results occur in other design domains.

  15. Constraints to Strategy Implementation and their Influence on Business Performance: the Case of a Waste Management Logistics Company

    Directory of Open Access Journals (Sweden)

    Chengedzai Mafini

    2016-08-01

    Full Text Available Waste management companies in developing countries often have to contend with a plethora of factors that inhibit their business performance. The primary objective of this study was to investigate the influence of constraints to strategy implementation on the business performance of a waste management logistics company in South Africa. The study was triggered by the lack of previous research focusing on constraints to strategy implementation in the waste management sector. The study employed a quantitative approach using the cross sectional survey design in which data were collected from 309 employees of a waste management logistics company based in Gauteng Province. Seven constraints to strategy implementation; namely, management practices, human resource capabilities, customer service, external orientation, internal communication, innovation and employee motivation were identified through Exploratory Factor Analysis. Pearson correlations showed that business performance is negatively affected as and when each constraint becomes more prevalent. Regression analysis showed that all constraints were statistically significant. To academics, the study provides current insights on factors impacting on business performance in waste management organisations. Management practitioners may improve the levels of business performance through structural adjustments of the seven constraints identified in this study. The study may be used as a reference point in the diagnosis of business performance related challenges in companies operating within the waste management sector.

  16. Influence of the neural microenvironment on prostate cancer.

    Science.gov (United States)

    Coarfa, Christian; Florentin, Diego; Putluri, NagiReddy; Ding, Yi; Au, Jason; He, Dandan; Ragheb, Ahmed; Frolov, Anna; Michailidis, George; Lee, MinJae; Kadmon, Dov; Miles, Brian; Smith, Christopher; Ittmann, Michael; Rowley, David; Sreekumar, Arun; Creighton, Chad J; Ayala, Gustavo

    2018-02-01

    Nerves are key factors in prostate cancer (PCa), but the functional role of innervation in prostate cancer is poorly understood. PCa induced neurogenesis and perineural invasion (PNI), are associated with aggressive disease. We denervated rodent prostates chemically and physically, before orthotopically implanting cancer cells. We also performed a human neoadjuvant clinical trial using botulinum toxin type A (Botox) and saline in the same patient, before prostatectomy. Bilateral denervation resulted in reduced tumor incidence and size in mice. Botox treatment in humans resulted in increased apoptosis of cancer cells in the Botox treated side. A similar denervation gene array profile was identified in tumors arising in denervated rodent prostates, in spinal cord injury patients and in the Botox treated side of patients. Denervation induced exhibited a signature gene profile, indicating translation and bioenergetic shutdown. Nerves also regulate basic cellular functions of non-neoplastic epithelial cells. Nerves play a role in the homeostasis of normal epithelial tissues and are involved in prostate cancer tumor survival. This study confirms that interactions between human cancer and nerves are essential to disease progression. This work may make a major impact in general cancer treatment strategies, as nerve/cancer interactions are likely important in other cancers as well. Targeting the neural microenvironment may represent a therapeutic approach for the treatment of human prostate cancer. © 2017 The Authors. The Prostate Published by Wiley Periodicals, Inc.

  17. Influence and timing of arrival of murine neural crest on pancreatic beta cell development and maturation.

    Science.gov (United States)

    Plank, Jennifer L; Mundell, Nathan A; Frist, Audrey Y; LeGrone, Alison W; Kim, Thomas; Musser, Melissa A; Walter, Teagan J; Labosky, Patricia A

    2011-01-15

    Interactions between cells from the ectoderm and mesoderm influence development of the endodermally-derived pancreas. While much is known about how mesoderm regulates pancreatic development, relatively little is understood about how and when the ectodermally-derived neural crest regulates pancreatic development and specifically, beta cell maturation. A previous study demonstrated that signals from the neural crest regulate beta cell proliferation and ultimately, beta cell mass. Here, we expand on that work to describe timing of neural crest arrival at the developing pancreatic bud and extend our knowledge of the non-cell autonomous role for neural crest derivatives in the process of beta cell maturation. We demonstrated that murine neural crest entered the pancreatic mesenchyme between the 26 and 27 somite stages (approximately 10.0 dpc) and became intermingled with pancreatic progenitors as the epithelium branched into the surrounding mesenchyme. Using a neural crest-specific deletion of the Forkhead transcription factor Foxd3, we ablated neural crest cells that migrate to the pancreatic primordium. Consistent with previous data, in the absence of Foxd3, and therefore the absence of neural crest cells, proliferation of insulin-expressing cells and insulin-positive area are increased. Analysis of endocrine cell gene expression in the absence of neural crest demonstrated that, although the number of insulin-expressing cells was increased, beta cell maturation was significantly impaired. Decreased MafA and Pdx1 expression illustrated the defect in beta cell maturation; we discovered that without neural crest, there was a reduction in the percentage of insulin-positive cells that co-expressed Glut2 and Pdx1 compared to controls. In addition, transmission electron microscopy analyses revealed decreased numbers of characteristic insulin granules and the presence of abnormal granules in insulin-expressing cells from mutant embryos. Together, these data demonstrate that

  18. Ambient temperature influences the neural benefits of exercise.

    Science.gov (United States)

    Maynard, Mark E; Chung, Chasity; Comer, Ashley; Nelson, Katharine; Tran, Jamie; Werries, Nadja; Barton, Emily A; Spinetta, Michael; Leasure, J Leigh

    2016-02-15

    Many of the neural benefits of exercise require weeks to manifest. It would be useful to accelerate onset of exercise-driven plastic changes, such as increased hippocampal neurogenesis. Exercise represents a significant challenge to the brain because it produces heat, but brain temperature does not rise during exercise in the cold. This study tested the hypothesis that exercise in cold ambient temperature would stimulate hippocampal neurogenesis more than exercise in room or hot conditions. Adult female rats had exercise access 2h per day for 5 days at either room (20 °C), cold (4.5 °C) or hot (37.5 °C) temperature. To label dividing hippocampal precursor cells, animals received daily injections of BrdU. Brains were immunohistochemically processed for dividing cells (Ki67+), surviving cells (BrdU+) and new neurons (doublecortin, DCX) in the hippocampal dentate gyrus. Animals exercising at room temperature ran significantly farther than animals exercising in cold or hot conditions (room 1490 ± 400 m; cold 440 ± 102 m; hot 291 ± 56 m). We therefore analyzed the number of Ki67+, BrdU+ and DCX+ cells normalized for shortest distance run. Contrary to our hypothesis, exercise in either cold or hot conditions generated significantly more Ki67+, BrdU+ and DCX+ cells compared to exercise at room temperature. Thus, a limited amount of running in either cold or hot ambient conditions generates more new cells than a much greater distance run at room temperature. Taken together, our results suggest a simple means by which to augment exercise effects, yet minimize exercise time. Copyright © 2015 Elsevier B.V. All rights reserved.

  19. The influence of electric fields on hippocampal neural progenitor cells.

    Science.gov (United States)

    Ariza, Carlos Atico; Fleury, Asha T; Tormos, Christian J; Petruk, Vadim; Chawla, Sagar; Oh, Jisun; Sakaguchi, Donald S; Mallapragada, Surya K

    2010-12-01

    The differentiation and proliferation of neural stem/progenitor cells (NPCs) depend on various in vivo environmental factors or cues, which may include an endogenous electrical field (EF), as observed during nervous system development and repair. In this study, we investigate the morphologic, phenotypic, and mitotic alterations of adult hippocampal NPCs that occur when exposed to two EFs of estimated endogenous strengths. NPCs treated with a 437 mV/mm direct current (DC) EF aligned perpendicularly to the EF vector and had a greater tendency to differentiate into neurons, but not into oligodendrocytes or astrocytes, compared to controls. Furthermore, NPC process growth was promoted perpendicularly and inhibited anodally in the 437 mV/mm DC EF. Yet fewer cells were observed in the DC EF, which in part was due to a decrease in cell viability. The other EF applied was a 46 mV/mm alternating current (AC) EF. However, the 46 mV/mm AC EF showed no major differences in alignment or differentiation, compared to control conditions. For both EF treatments, the percent of mitotic cells during the last 14 h of the experiment were statistically similar to controls. Reported here, to our knowledge, is the first evidence of adult NPC differentiation affected in an EF in vitro. Further investigation and application of EFs on stem cells is warranted to elucidate the utility of EFs to control phenotypic behavior. With progress, the use of EFs may be engineered to control differentiation and target the growth of transplanted cells in a stem cell-based therapy to treat nervous system disorders.

  20. Environmental influences on neural systems of relational complexity

    Directory of Open Access Journals (Sweden)

    Layne eKalbfleisch

    2013-09-01

    Full Text Available Constructivist learning theory contends that we construct knowledge by experience and that environmental context influences learning. To explore this principle, we examined the cognitive process relational complexity (RC, defined as the number of visual dimensions considered during problem solving on a matrix reasoning task and a well-documented measure of mature reasoning capacity. We sought to determine how the visual environment influences RC by examining the influence of color and visual contrast on RC in a neuroimaging task. To specify the contributions of sensory demand and relational integration to reasoning, our participants performed a non-verbal matrix task comprised of color, no-color line, or black-white visual contrast conditions parametrically varied by complexity (relations 0, 1, 2. The use of matrix reasoning is ecologically valid for its psychometric relevance and for its potential to link the processing of psychophysically specific visual properties with various levels of relational complexity during reasoning. The role of these elements is important because matrix tests assess intellectual aptitude based on these seemingly context-less exercises. This experiment is a first step toward examining the psychophysical underpinnings of performance on these types of problems. The importance of this is increased in light of recent evidence that intelligence can be linked to visual discrimination. We submit three main findings. First, color and black-white visual contrast add demand at a basic sensory level, but contributions from color and from black-white visual contrast are dissociable in cortex such that color engages a reasoning heuristic and black-white visual contrast engages a sensory heuristic. Second, color supports contextual sense-making by boosting salience resulting in faster problem solving. Lastly, when visual complexity reaches 2-relations, color and visual contrast relinquish salience to other dimensions of problem

  1. The influence of operational constraints in the production strategy definition; Influencia de restricoes operacionais na definicao da estrategia de producao

    Energy Technology Data Exchange (ETDEWEB)

    Magalhaes, Tasso C.B. de; Schiozer, Denis J. [Universidade Estadual de Campinas, SP (Brazil)

    2004-07-01

    Production strategies definition, applied to petroleum fields, must consider physical, operational and economic constraints. It is common to consider only the reservoir conditions on the optimization processes, simplifying, many times, the process by not taking into account the operational constraints due to production facilities. There are two main reasons: considering the operational constraints makes the process much complex and it is assumed that this simplification can affect the economic indicators but dos not affect significantly the optimization process (number e location of wells, for example). The capacity of a production unit can be limited by many constrains such as: maximum liquid rate, capacity of water and gas treatment, gas compression, water or gas injection, number of wells, etc. In this work, we show that these limitations have a direct influence in the oil production and consequently in the economic indicators and they can cause significant impact at production strategy definition, influencing the number of production and injection wells, their locations and their operational conditions. We presented an example of an offshore field with a limitation on the liquid rate. Production strategies were selected with and without constraints in order to observe the differences in the technical and economic indicators, such as NPV (Net Present Value), production and injection of fluids and the number and location of the production and injection wells. It was possible to observe yet that the amount and location of the wells were significantly affected by the restriction. (author)

  2. Conditions that influence the elimination of postural constraints after office employees working with VDU have received ergonomics training.

    Science.gov (United States)

    Montreuil, Sylvie; Laflamme, Lucie; Brisson, Chantal; Teiger, Catherine

    2006-01-01

    The goal of this article is to better understand how preventive measures are undertaken after training. It examines how certain variables, such as musculoskeletal pain, participant age and workstation and work content characteristics influence the reduction of postural constraints after office employees working with a computer have received ergonomics training. A pre-test/post-test design was used. The 207 female office workers were given 6 hours of ergonomics training. The variables were determined using a self-administered questionnaire and an observation grid filled out 2 weeks before and 6 months after the training session. The FAC and HAC were used in the data processing. The presence or absence of musculoskeletal pain had no statistically significant influence on whether or not postural constraints were eliminated. The age of the participants and the possibility of adjusting the workstation characteristics and work content produced differentiated results with regard to postural constraint reduction. We concluded that trained people succeed in taking relevant and effective measures to reduce the postural constraints found in VDUs. However other measures than work station adjustments lead to this prevention and such training must be strongly supported by the various hierarchical levels of an enterprise or an institution.

  3. Muscle Synergies Heavily Influence the Neural Control of Arm Endpoint Stiffness and Energy Consumption.

    Science.gov (United States)

    Inouye, Joshua M; Valero-Cuevas, Francisco J

    2016-02-01

    Much debate has arisen from research on muscle synergies with respect to both limb impedance control and energy consumption. Studies of limb impedance control in the context of reaching movements and postural tasks have produced divergent findings, and this study explores whether the use of synergies by the central nervous system (CNS) can resolve these findings and also provide insights on mechanisms of energy consumption. In this study, we phrase these debates at the conceptual level of interactions between neural degrees of freedom and tasks constraints. This allows us to examine the ability of experimentally-observed synergies--correlated muscle activations--to control both energy consumption and the stiffness component of limb endpoint impedance. In our nominal 6-muscle planar arm model, muscle synergies and the desired size, shape, and orientation of endpoint stiffness ellipses, are expressed as linear constraints that define the set of feasible muscle activation patterns. Quadratic programming allows us to predict whether and how energy consumption can be minimized throughout the workspace of the limb given those linear constraints. We show that the presence of synergies drastically decreases the ability of the CNS to vary the properties of the endpoint stiffness and can even preclude the ability to minimize energy. Furthermore, the capacity to minimize energy consumption--when available--can be greatly affected by arm posture. Our computational approach helps reconcile divergent findings and conclusions about task-specific regulation of endpoint stiffness and energy consumption in the context of synergies. But more generally, these results provide further evidence that the benefits and disadvantages of muscle synergies go hand-in-hand with the structure of feasible muscle activation patterns afforded by the mechanics of the limb and task constraints. These insights will help design experiments to elucidate the interplay between synergies and the mechanisms

  4. Temperament and Parenting Styles in Early Childhood Differentially Influence Neural Response to Peer Evaluation in Adolescence

    OpenAIRE

    Guyer, Amanda E.; Jarcho, Johanna M.; Pérez-Edgar, Koraly; Degnan, Kathryn A.; Pine, Daniel S.; Fox, Nathan A.; Nelson, Eric E.

    2015-01-01

    Behavioral inhibition (BI) is a temperament characterized by social reticence and withdrawal from unfamiliar or novel contexts and conveys risk for social anxiety disorder. Developmental outcomes associated with this temperament can be influenced by children’s caregiving context. The convergence of a child’s temperamental disposition and rearing environment is ultimately expressed at both the behavioral and neural levels in emotional and cognitive response patterns to social challenges. The p...

  5. Who can you trust? Behavioral and neural differences between perceptual and memory-based influences

    Directory of Open Access Journals (Sweden)

    John D Rudoy

    2009-08-01

    Full Text Available Decisions about whether to trust someone can be influenced by competing sources of information, such as analysis of facial features versus remembering specific information about the person. We hypothesized that such sources can differentially influence trustworthiness judgments depending on the circumstances in which judgments are made. In our experiments, subjects first learned face-word associations. Stimuli were trustworthy and untrustworthy faces selected on the basis of consensus judgments and personality attributes that carried either the same valence (consistent with face or the opposite valence (inconsistent with face. Subsequently, subjects rated the trustworthiness of each face. Both learned and perceptual information influenced ratings, but learned information was less influential under speeded than under non-speeded conditions. EEG data further revealed neural evidence of the processing of these two competing sources. Perceptual influences were apparent earlier than memory influences, substantiating the conclusion that time pressure can selectively disrupt memory retrieval relevant to trustworthiness attributions.

  6. Neural influences on human intestinal epithelium in vitro.

    Science.gov (United States)

    Krueger, Dagmar; Michel, Klaus; Zeller, Florian; Demir, Ihsan E; Ceyhan, Güralp O; Slotta-Huspenina, Julia; Schemann, Michael

    2016-01-15

    We present the first systematic and, up to now, most comprehensive evaluation of the basic features of epithelial functions, such as basal and nerve-evoked secretion, as well as tissue resistance, in over 2200 surgical specimens of human small and large intestine. We found no evidence for impaired nerve-evoked epithelial secretion or tissue resistance with age or disease pathologies (stomach, pancreas or colon cancer, polyps, diverticulitis, stoma reversal). This indicates the validity of future studies on epithelial secretion or resistance that are based on data from a variety of surgical specimens. ACh mainly mediated nerve-evoked and basal secretion in the small intestine, whereas vasoactive intestinal peptide and nitric oxide were the primary pro-secretory transmitters in the large intestine. The results of the present study revealed novel insights into regional differences in nerve-mediated secretion in the human intestine and comprise the basis by which to more specifically target impaired epithelial functions in the diseased gut. Knowledge on basic features of epithelial functions in the human intestine is scarce. We used Ussing chamber techniques to record basal tissue resistance (R-basal) and short circuit currents (ISC; secretion) under basal conditions (ISC-basal) and after electrical field stimulation (ISC-EFS) of nerves in 2221 resectates from 435 patients. ISC-EFS was TTX-sensitive and of comparable magnitude in the small and large intestine. ISC-EFS or R-basal were not influenced by the patients' age, sex or disease pathologies (cancer, polyps, diverticulitis). Ion substitution, bumetanide or adenylate cyclase inhibition studies suggested that ISC-EFS depended on epithelial cAMP-driven chloride and bicarbonate secretion but not on amiloride-sensitive sodium absorption. Although atropine-sensitive cholinergic components prevailed for ISC-EFS of the duodenum, jejunum and ileum, PG97-269-sensitive [vasoactive intestinal peptide (VIP) receptor 1

  7. Influence of auditory attention on sentence recognition captured by the neural phase.

    Science.gov (United States)

    Müller, Jana Annina; Kollmeier, Birger; Debener, Stefan; Brand, Thomas

    2018-03-07

    The aim of this study was to investigate whether attentional influences on speech recognition are reflected in the neural phase entrained by an external modulator. Sentences were presented in 7 Hz sinusoidally modulated noise while the neural response to that modulation frequency was monitored by electroencephalogram (EEG) recordings in 21 participants. We implemented a selective attention paradigm including three different attention conditions while keeping physical stimulus parameters constant. The participants' task was either to repeat the sentence as accurately as possible (speech recognition task), to count the number of decrements implemented in modulated noise (decrement detection task), or to do both (dual task), while the EEG was recorded. Behavioural analysis revealed reduced performance in the dual task condition for decrement detection, possibly reflecting limited cognitive resources. EEG analysis revealed no significant differences in power for the 7 Hz modulation frequency, but an attention-dependent phase difference between tasks. Further phase analysis revealed a significant difference 500 ms after sentence onset between trials with correct and incorrect responses for speech recognition, indicating that speech recognition performance and the neural phase are linked via selective attention mechanisms, at least shortly after sentence onset. However, the neural phase effects identified were small and await further investigation. © 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  8. Research ethics and Institutional Review Boards. The influence of moral constraints on emotion research.

    Science.gov (United States)

    Sontag, Michael

    2012-01-01

    Researchers in the twenty-first century face a set of challenges unknown to researchers a half century ago--the need to justify the moral acceptability of their research methods through formal review processes. However, the role that moral constraints play in the development and demise of scientific theories has largely gone unappreciated. The rise of Institutional Review Boards (IRB) in the 1960s compounded the impact of moral constraints on scientific research and on the theories that develop out of such highly monitored research. To demonstrate the effects of moral constraints on scientific theory and research, this paper offers a history and analysis of the interaction between evolving moral standards and twentieth century emotion theory. Recommendations regarding IRB reform are also reviewed. The paper concludes by arguing that, while appropriate IRB reform is important, it cannot eliminate the need for careful reflection on the broader forces that shape scientific practice and understanding.

  9. Neural Correlates of Social Influence on Risk Taking and Substance Use in Adolescents.

    Science.gov (United States)

    Telzer, Eva H; Rogers, Christina R; Van Hoorn, Jorien

    2017-09-01

    Adolescents often engage in elevated levels of risk taking that gives rise to substance use. Family and peers constitute the primary contextual risk factors for adolescent substance use. This report reviews how families and peers influence adolescent neurocognitive development to inform their risk taking and subsequent substance use. Developmental neuroscience using functional magnetic resonance imaging (fMRI) has identified regions of the brain involved in social cognition, cognitive control, and reward processing that are integrally linked to social influence on adolescent risk taking. These neural mechanisms play a role in how peer and family influence (e.g., physical presence, relationship quality, rejection) translates into adolescent substance use. Peers and families can independently, and in tandem, contribute to adolescent substance use, for better or for worse. We propose that future work utilize fMRI to investigate the neural mechanisms involved in different aspects of peer and family influence, and how these contexts uniquely and interactively influence adolescent substance use initiation and escalation across development.

  10. The Influence of Semantic Constraints on Bilingual Word Recognition during Sentence Reading

    Science.gov (United States)

    Van Assche, Eva; Drieghe, Denis; Duyck, Wouter; Welvaert, Marijke; Hartsuiker, Robert J.

    2011-01-01

    The present study investigates how semantic constraint of a sentence context modulates language-non-selective activation in bilingual visual word recognition. We recorded Dutch-English bilinguals' eye movements while they read cognates and controls in low and high semantically constraining sentences in their second language. Early and late…

  11. Noise influence on spike activation in a Hindmarsh–Rose small-world neural network

    International Nuclear Information System (INIS)

    Zhe, Sun; Micheletto, Ruggero

    2016-01-01

    We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh−Rose (H−R) neural networks. (paper)

  12. Noise influence on spike activation in a Hindmarsh-Rose small-world neural network

    Science.gov (United States)

    Zhe, Sun; Micheletto, Ruggero

    2016-07-01

    We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh-Rose (H-R) neural networks.

  13. Influence of flow constraints on the properties of the critical endpoint of symmetric nuclear matter

    Science.gov (United States)

    Ivanytskyi, A. I.; Bugaev, K. A.; Sagun, V. V.; Bravina, L. V.; Zabrodin, E. E.

    2018-06-01

    We propose a novel family of equations of state for symmetric nuclear matter based on the induced surface tension concept for the hard-core repulsion. It is shown that having only four adjustable parameters the suggested equations of state can, simultaneously, reproduce not only the main properties of the nuclear matter ground state, but the proton flow constraint up its maximal particle number densities. Varying the model parameters we carefully examine the range of values of incompressibility constant of normal nuclear matter and its critical temperature, which are consistent with the proton flow constraint. This analysis allows us to show that the physically most justified value of nuclear matter critical temperature is 15.5-18 MeV, the incompressibility constant is 270-315 MeV and the hard-core radius of nucleons is less than 0.4 fm.

  14. An assessment of energy efficiency based on environmental constraints and its influencing factors in China.

    Science.gov (United States)

    Chen, Yao; Xu, Jing-Ting

    2018-05-03

    The super-efficiency directional distance function (DDF) with data envelopment analysis (DEA) model (SEDDF-DEA) is more facilitative than to increase traditional method as a rise of energy efficiency in China, which is currently important energy development from Asia-pacific region countries. SEDDF-DEA is promoted as sustained total-factor energy efficiency (TFEE), value added outputs, and Malmquist-Luenberger productivity index (MLPI) to otherwise thorny environmental energy productivity problems with environmental constraint to concrete the means of regression model. This paper assesses the energy efficiency under environmental constraints using panel data covering the years of 2000-2015 in China. Considering the environmental constraints, the results showed that the average TFEE of the whole country followed an upward trend after 2006. The average MLPI score for the whole country increased by 10.57% during 2005-2010, which was mainly due to the progress made in developing and applying environmental technologies. The TFEE of the whole nation was promoted by the accumulation of capital stock, while it was suppressed by excessive production in secondary industries and foreign investment. The primary challenge for the northeast of China is to strengthen industrial transformation and upgrade traditional industries, as well as adjusting the economy and energy structure. The eastern and central regions of the country need to exploit clean- or low-energy industry to improve inefficiencies due to excessive consumption. The western region of China needs to implement renewable energy strategies to promote regional development.

  15. Analysis of the long-term availability of uranium: The influence of dynamic constraints and market competition

    International Nuclear Information System (INIS)

    Monnet, Antoine; Gabriel, Sophie; Percebois, Jacques

    2017-01-01

    Abstract: The availability of natural uranium has a direct impact on the global capability to sustain the demand from nuclear power plants in the coming decades. Therefore, the expansion scenarios of nuclear power should be analysed in conjunction with long-term dynamics of the uranium market. This paper presents three forms of a partial-equilibrium model of the uranium market. All forms consider global demand as exogenous (input scenarios from the literature) and regional estimates of the quantities and the costs of ultimate resources (results obtained from previous work). The three forms differ by the market constraints and the market structure considered. Comparing them highlights the role of the market structure and the impact of some key parameters of the market dynamics on the long-term availability of uranium. An important finding is the influence of two constraints: the anticipation of demand and the significant role played by the correlation between price and exploration expenses in shaping the price trends. In addition, results from simulations highlight different long-term dynamics when the producers are allocated into a limited number of regions (to simulate an oligopoly) compared to a single region (undefined number of players to simulate perfect competition). - Highlights: • The growth rate of demand during the 21st century is a key driver of price trends. • Uncertainties on ultimate resources have a limited impact in expansion scenarios. • The price-exploration correlation is a first-order dynamic constraint. • The anticipation of demand is a strong dynamic constraint related to scarcity rent. • The uranium market is better represented by a constrained oligopoly.

  16. Performance environment and nested task constraints influence long jump approach run: a preliminary study.

    Science.gov (United States)

    Panteli, Flora; Smirniotou, Athanasia; Theodorou, Apostolos

    2016-01-01

    The purpose of the study was to investigate possible changes at step pattern and technical performance of the long jump approach run in seven young long jumpers by modifying the performance environment (long jump runway versus track lane) and the nested actions (run-through with take-off versus complete long jump). Our findings suggest that the step pattern and technical aspects of the approach run are affected by environmental context and nested task constraints. In terms of environmental context, it appears that practising the training routine of run-through followed by take-off on the long jump runway allows athletes to simulate competition conditions in terms of step regulation and technical efficacy. The task of run-through followed by take-off on the track lane failed to initiate visual perception, step regulation and technical efficiency at the steps preceding the instant of take-off. In terms of nested task constraints, when run-ups were followed by jump for distance instead of only a take-off, a higher level of consistency was achieved and step regulation was based on perception-action coupling. Practising long jump run-up accuracy at a setting not containing the informational elements of the performance environment fails to develop the key elements of the skill.

  17. Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network

    International Nuclear Information System (INIS)

    Zeng, Yu-Rong; Zeng, Yi; Choi, Beomjin; Wang, Lin

    2017-01-01

    Reliable energy consumption forecasting can provide effective decision-making support for planning development strategies to energy enterprises and for establishing national energy policies. Accordingly, the present study aims to apply a hybrid intelligent approach named ADE–BPNN, the back-propagation neural network (BPNN) model supported by an adaptive differential evolution algorithm, to estimate energy consumption. Most often, energy consumption is influenced by socioeconomic factors. The proposed hybrid model incorporates gross domestic product, population, import, and export data as inputs. An improved differential evolution with adaptive mutation and crossover is utilized to find appropriate global initial connection weights and thresholds to enhance the forecasting performance of the BPNN. A comparative example and two extended examples are utilized to validate the applicability and accuracy of the proposed ADE–BPNN model. Errors of the test data sets indicate that the ADE–BPNN model can effectively predict energy consumption compared with the traditional back-propagation neural network model and other popular existing models. Moreover, mean impact value based analysis is conducted for electrical energy consumption in U.S. and total energy consumption forecasting in China to quantitatively explore the relative importance of each input variable for the improvement of effective energy consumption prediction. - Highlights: • Enhanced back-propagation neural network (ADE-BPNN) for energy consumption forecasting. • ADE-BPNN outperforms the current best models for two comparative cases. • Mean impact value approach explores socio-economic factors' relative importance. • ADE-BPNN's adjusted goodness-of-fit is 99.2% for China's energy consumption forecasting.

  18. Influence of temporal pressure constraint on the biomechanical organization of gait initiation made with or without an obstacle to clear.

    Science.gov (United States)

    Yiou, Eric; Fourcade, Paul; Artico, Romain; Caderby, Teddy

    2016-06-01

    Many daily motor tasks have to be performed under a temporal pressure constraint. This study aimed to explore the influence of such constraint on motor performance and postural stability during gait initiation. Young healthy participants initiated gait at maximal velocity under two conditions of temporal pressure: in the low-pressure condition, gait was self-initiated (self-initiated condition, SI); in the high-pressure condition, it was initiated as soon as possible after an acoustic signal (reaction-time condition, RT). Gait was initiated with and without an environmental constraint in the form of an obstacle to be cleared placed in front of participants. Results showed that the duration of postural adjustments preceding swing heel-off ("anticipatory postural adjustments", APAs) was shorter, while their amplitude was larger in RT compared to SI. These larger APAs allowed the participants to reach equivalent postural stability and motor performance in both RT and SI. In addition, the duration of the execution phase of gait initiation increased greatly in the condition with an obstacle to be cleared (OBST) compared to the condition without an obstacle (NO OBST), thereby increasing lateral instability and thus involving larger mediolateral APA. Similar effects of temporal pressure were obtained in NO OBST and OBST. This study shows the adaptability of the postural system to temporal pressure in healthy young adults initiating gait. The outcome of this study may provide a basis for better understanding the aetiology of balance impairments with the risk of falling in frail populations while performing daily complex tasks involving a whole-body progression.

  19. Temperament and Parenting Styles in Early Childhood Differentially Influence Neural Response to Peer Evaluation in Adolescence

    Science.gov (United States)

    Guyer, Amanda E.; Jarcho, Johanna M.; Pérez-Edgar, Koraly; Degnan, Kathryn A.; Pine, Daniel S.; Fox, Nathan A.; Nelson, Eric E.

    2015-01-01

    Behavioral inhibition (BI) is a temperament characterized by social reticence and withdrawal from unfamiliar or novel contexts and conveys risk for social anxiety disorder. Developmental outcomes associated with this temperament can be influenced by children’s caregiving context. The convergence of a child’s temperamental disposition and rearing environment is ultimately expressed at both the behavioral and neural levels in emotional and cognitive response patterns to social challenges. The present study used functional neuroimaging to assess the moderating effects of different parenting styles on neural response to peer rejection in two groups of adolescents characterized by their early childhood temperament (Mage = 17.89 years, N= 39, 17 males, 22 females; 18 with BI; 21 without BI). The moderating effects of authoritarian and authoritative parenting styles were examined in three brain regions linked with social anxiety: ventrolateral prefrontal cortex (vlPFC), striatum, and amygdala. In youth characterized with BI in childhood, but not in those without BI, diminished responses to peer rejection in vlPFC were associated with higher levels of authoritarian parenting. In contrast, all youth showed decreased caudate response to peer rejection at higher levels of authoritative parenting. These findings indicate that BI in early life relates to greater neurobiological sensitivity to variance in parenting styles, particularly harsh parenting, in late adolescence. These results are discussed in relation to biopsychosocial models of development. PMID:25588884

  20. Temperament and Parenting Styles in Early Childhood Differentially Influence Neural Response to Peer Evaluation in Adolescence.

    Science.gov (United States)

    Guyer, Amanda E; Jarcho, Johanna M; Pérez-Edgar, Koraly; Degnan, Kathryn A; Pine, Daniel S; Fox, Nathan A; Nelson, Eric E

    2015-07-01

    Behavioral inhibition (BI) is a temperament characterized by social reticence and withdrawal from unfamiliar or novel contexts and conveys risk for social anxiety disorder. Developmental outcomes associated with this temperament can be influenced by children's caregiving context. The convergence of a child's temperamental disposition and rearing environment is ultimately expressed at both the behavioral and neural levels in emotional and cognitive response patterns to social challenges. The present study used functional neuroimaging to assess the moderating effects of different parenting styles on neural response to peer rejection in two groups of adolescents characterized by their early childhood temperament (M(age) = 17.89 years, N = 39, 17 males, 22 females; 18 with BI; 21 without BI). The moderating effects of authoritarian and authoritative parenting styles were examined in three brain regions linked with social anxiety: ventrolateral prefrontal cortex (vlPFC), striatum, and amygdala. In youth characterized with BI in childhood, but not in those without BI, diminished responses to peer rejection in vlPFC were associated with higher levels of authoritarian parenting. In contrast, all youth showed decreased caudate response to peer rejection at higher levels of authoritative parenting. These findings indicate that BI in early life relates to greater neurobiological sensitivity to variance in parenting styles, particularly harsh parenting, in late adolescence. These results are discussed in relation to biopsychosocial models of development.

  1. Co-speech gestures influence neural activity in brain regions associated with processing semantic information.

    Science.gov (United States)

    Dick, Anthony Steven; Goldin-Meadow, Susan; Hasson, Uri; Skipper, Jeremy I; Small, Steven L

    2009-11-01

    Everyday communication is accompanied by visual information from several sources, including co-speech gestures, which provide semantic information listeners use to help disambiguate the speaker's message. Using fMRI, we examined how gestures influence neural activity in brain regions associated with processing semantic information. The BOLD response was recorded while participants listened to stories under three audiovisual conditions and one auditory-only (speech alone) condition. In the first audiovisual condition, the storyteller produced gestures that naturally accompany speech. In the second, the storyteller made semantically unrelated hand movements. In the third, the storyteller kept her hands still. In addition to inferior parietal and posterior superior and middle temporal regions, bilateral posterior superior temporal sulcus and left anterior inferior frontal gyrus responded more strongly to speech when it was further accompanied by gesture, regardless of the semantic relation to speech. However, the right inferior frontal gyrus was sensitive to the semantic import of the hand movements, demonstrating more activity when hand movements were semantically unrelated to the accompanying speech. These findings show that perceiving hand movements during speech modulates the distributed pattern of neural activation involved in both biological motion perception and discourse comprehension, suggesting listeners attempt to find meaning, not only in the words speakers produce, but also in the hand movements that accompany speech.

  2. Organizational Influences and Constraints on Community College Web-Based Media Relations

    Science.gov (United States)

    McAllister, Sheila M.; Taylor, Maureen

    2012-01-01

    Various organizational, departmental, and interdepartmental factors influence how an educational institution practices public relations. These factors may enable or hinder the ways in which communication practitioners build and maintain relationships with the media. Higher education institutions are especially in need of public relations efforts.…

  3. Trait Rumination Influences Neural Correlates of the Anticipation but Not the Consumption Phase of Reward Processing

    Directory of Open Access Journals (Sweden)

    Natália Kocsel

    2017-05-01

    Full Text Available Cumulative evidence suggests that trait rumination can be defined as an abstract information processing mode, which leads people to constantly anticipate the likely impact of present events on future events and experiences. A previous study with remitted depressed patients suggested that enhanced rumination tendencies distort brain mechanisms of anticipatory processes associated with reward and loss cues. In the present study, we explored the impact of trait rumination on neural activity during reward and loss anticipation among never-depressed people. We analyzed the data of 37 healthy controls, who performed the monetary incentive delay (MID task which was designed for the simultaneous measurement of the anticipation (motivational and consumption (hedonic phase of reward processing, during functional magnetic resonance imaging (fMRI. Our results show that rumination—after controlling for age, gender, and current mood—significantly influenced neural responses to reward (win cues compared to loss cues. Blood-oxygenation-level-dependent (BOLD activity in the left inferior frontal gyrus (IFG triangularis, left anterior insula, and left rolandic operculum was positively related to Ruminative Response Scale (RRS scores. We did not detect any significant rumination-related activations associated with win-neutral or loss-neutral cues and with reward or loss consumption. Our results highlight the influence of trait rumination on reward anticipation in a non-depressed sample. They also suggest that for never-depressed ruminators rewarding cues are more salient than loss cues. BOLD response during reward consumption did not relate to rumination, suggesting that rumination mainly relates to processing of the motivational (wanting aspect of reward rather than the hedonic (liking aspect, at least in the absence of pathological mood.

  4. Neural correlates of prosocial peer influence on public goods game donations during adolescence.

    Science.gov (United States)

    Van Hoorn, Jorien; Van Dijk, Eric; Güroğlu, Berna; Crone, Eveline A

    2016-06-01

    A unique feature of adolescent social re-orientation is heightened sensitivity to peer influence when taking risks. However, positive peer influence effects are not yet well understood. The present fMRI study tested a novel hypothesis, by examining neural correlates of prosocial peer influence on donation decisions in adolescence. Participants (age 12-16 years; N = 61) made decisions in anonymous groups about the allocation of tokens between themselves and the group in a public goods game. Two spectator groups of same-age peers-in fact youth actors-were allegedly online during some of the decisions. The task had a within-subjects design with three conditions: (1) EVALUATION: spectators evaluated decisions with likes for large donations to the group, (2) Spectator: spectators were present but no evaluative feedback was displayed and (3) Alone: no spectators nor feedback. Results showed that prosocial behavior increased in the presence of peers, and even more when participants received evaluative feedback from peers. Peer presence resulted in enhanced activity in several social brain regions including medial prefrontal cortex, temporal parietal junction (TPJ), precuneus and superior temporal sulcus. TPJ activity correlated with donations, which suggests similar networks for prosocial behavior and sensitivity to peers. These findings highlight the importance of peers in fostering prosocial development throughout adolescence. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  5. Classifying Sources Influencing Indoor Air Quality (IAQ Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Shaharil Mad Saad

    2015-05-01

    Full Text Available Monitoring indoor air quality (IAQ is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC, base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

  6. Influence of attention focus on neural activity in the human spinal cord during thermal sensory stimulation.

    Science.gov (United States)

    Stroman, Patrick W; Coe, Brian C; Munoz, Doug P

    2011-01-01

    Perceptions of sensation and pain in healthy people are believed to be the net result of sensory input and descending modulation from brainstem and cortical regions depending on emotional and cognitive factors. Here, the influence of attention on neural activity in the spinal cord during thermal sensory stimulation of the hand was investigated with functional magnetic resonance imaging by systematically varying the participants' attention focus across and within repeated studies. Attention states included (1) attention to the stimulus by rating the sensation and (2) attention away from the stimulus by performing various mental tasks of watching a movie and identifying characters, detecting the direction of coherently moving dots within a randomly moving visual field and answering mentally-challenging questions. Functional MRI results spanning the cervical spinal cord and brainstem consistently demonstrated that the attention state had a significant influence on the activity detected in the cervical spinal cord, as well as in brainstem regions involved with the descending analgesia system. These findings have important implications for the detection and study of pain, and improved characterization of the effects of injury or disease. Copyright © 2011 Elsevier Inc. All rights reserved.

  7. The influences and neural correlates of past and present during gambling in humans.

    Science.gov (United States)

    Sacré, Pierre; Subramanian, Sandya; Kerr, Matthew S D; Kahn, Kevin; Johnson, Matthew A; Bulacio, Juan; González-Martínez, Jorge A; Sarma, Sridevi V; Gale, John T

    2017-12-07

    During financial decision-making tasks, humans often make "rational" decisions, where they maximize expected reward. However, this rationality may compete with a bias that reflects past outcomes. That is, if one just lost money or won money, this may impact future decisions. It is unclear how past outcomes influence future decisions in humans, and how neural circuits encode present and past information. In this study, six human subjects performed a financial decision-making task while we recorded local field potentials from multiple brain structures. We constructed a model for each subject characterizing bets on each trial as a function of present and past information. The models suggest that some patients are more influenced by previous trial outcomes (i.e., previous return and risk) than others who stick to more fixed decision strategies. In addition, past return and present risk modulated with the activity in the cuneus; while present return and past risk modulated with the activity in the superior temporal gyrus and the angular gyrus, respectively. Our findings suggest that these structures play a role in decision-making beyond their classical functions by incorporating predictions and risks in humans' decision strategy, and provide new insight into how humans link their internal biases to decisions.

  8. Neural responses to smoking stimuli are influenced by smokers' attitudes towards their own smoking behaviour.

    Directory of Open Access Journals (Sweden)

    Bastian Stippekohl

    Full Text Available An important feature of addiction is the high drug craving that may promote the continuation of consumption. Environmental stimuli classically conditioned to drug-intake have a strong motivational power for addicts and can elicit craving. However, addicts differ in the attitudes towards their own consumption behavior: some are content with drug taking (consonant users whereas others are discontent (dissonant users. Such differences may be important for clinical practice because the experience of dissonance might enhance the likelihood to consider treatment. This fMRI study investigated in smokers whether these different attitudes influence subjective and neural responses to smoking stimuli. Based on self-characterization, smokers were divided into consonant and dissonant smokers. These two groups were presented smoking stimuli and neutral stimuli. Former studies have suggested differences in the impact of smoking stimuli depending on the temporal stage of the smoking ritual they are associated with. Therefore, we used stimuli associated with the beginning (BEGIN-smoking-stimuli and stimuli associated with the terminal stage (END-smoking-stimuli of the smoking ritual as distinct stimulus categories. Stimulus ratings did not differ between both groups. Brain data showed that BEGIN-smoking-stimuli led to enhanced mesolimbic responses (amygdala, hippocampus, insula in dissonant compared to consonant smokers. In response to END-smoking-stimuli, dissonant smokers showed reduced mesocortical responses (orbitofrontal cortex, subcallosal cortex compared to consonant smokers. These results suggest that smoking stimuli with a high incentive value (BEGIN-smoking-stimuli are more appetitive for dissonant than consonant smokers at least on the neural level. To the contrary, smoking stimuli with low incentive value (END-smoking-stimuli seem to be less appetitive for dissonant smokers than consonant smokers. These differences might be one reason why dissonant

  9. Neural responses to smoking stimuli are influenced by smokers' attitudes towards their own smoking behaviour.

    Science.gov (United States)

    Stippekohl, Bastian; Winkler, Markus H; Walter, Bertram; Kagerer, Sabine; Mucha, Ronald F; Pauli, Paul; Vaitl, Dieter; Stark, Rudolf

    2012-01-01

    An important feature of addiction is the high drug craving that may promote the continuation of consumption. Environmental stimuli classically conditioned to drug-intake have a strong motivational power for addicts and can elicit craving. However, addicts differ in the attitudes towards their own consumption behavior: some are content with drug taking (consonant users) whereas others are discontent (dissonant users). Such differences may be important for clinical practice because the experience of dissonance might enhance the likelihood to consider treatment. This fMRI study investigated in smokers whether these different attitudes influence subjective and neural responses to smoking stimuli. Based on self-characterization, smokers were divided into consonant and dissonant smokers. These two groups were presented smoking stimuli and neutral stimuli. Former studies have suggested differences in the impact of smoking stimuli depending on the temporal stage of the smoking ritual they are associated with. Therefore, we used stimuli associated with the beginning (BEGIN-smoking-stimuli) and stimuli associated with the terminal stage (END-smoking-stimuli) of the smoking ritual as distinct stimulus categories. Stimulus ratings did not differ between both groups. Brain data showed that BEGIN-smoking-stimuli led to enhanced mesolimbic responses (amygdala, hippocampus, insula) in dissonant compared to consonant smokers. In response to END-smoking-stimuli, dissonant smokers showed reduced mesocortical responses (orbitofrontal cortex, subcallosal cortex) compared to consonant smokers. These results suggest that smoking stimuli with a high incentive value (BEGIN-smoking-stimuli) are more appetitive for dissonant than consonant smokers at least on the neural level. To the contrary, smoking stimuli with low incentive value (END-smoking-stimuli) seem to be less appetitive for dissonant smokers than consonant smokers. These differences might be one reason why dissonant smokers

  10. Shades of grey; Assessing the contribution of the magno- and parvocellular systems to neural processing of the retinal input in the human visual system from the influence of neural population size and its discharge activity on the VEP.

    Science.gov (United States)

    Marcar, Valentine L; Baselgia, Silvana; Lüthi-Eisenegger, Barbara; Jäncke, Lutz

    2018-03-01

    Retinal input processing in the human visual system involves a phasic and tonic neural response. We investigated the role of the magno- and parvocellular systems by comparing the influence of the active neural population size and its discharge activity on the amplitude and latency of four VEP components. We recorded the scalp electric potential of 20 human volunteers viewing a series of dartboard images presented as a pattern reversing and pattern on-/offset stimulus. These patterns were designed to vary both neural population size coding the temporal- and spatial luminance contrast property and the discharge activity of the population involved in a systematic manner. When the VEP amplitude reflected the size of the neural population coding the temporal luminance contrast property of the image, the influence of luminance contrast followed the contrast response function of the parvocellular system. When the VEP amplitude reflected the size of the neural population responding to the spatial luminance contrast property the image, the influence of luminance contrast followed the contrast response function of the magnocellular system. The latencies of the VEP components examined exhibited the same behavior across our stimulus series. This investigation demonstrates the complex interplay of the magno- and parvocellular systems on the neural response as captured by the VEP. It also demonstrates a linear relationship between stimulus property, neural response, and the VEP and reveals the importance of feedback projections in modulating the ongoing neural response. In doing so, it corroborates the conclusions of our previous study.

  11. Neural mechanisms of the influence of popularity on adolescent ratings of music.

    Science.gov (United States)

    Berns, Gregory S; Capra, C Monica; Moore, Sara; Noussair, Charles

    2010-02-01

    It is well-known that social influences affect consumption decisions. We used functional magnetic resonance imaging (fMRI) to elucidate the neural mechanisms associated with social influence with regard to a common consumer good: music. Our study population was adolescents, age 12-17. Music is a common purchase in this age group, and it is widely believed that adolescent behavior is influenced by perceptions of popularity in their reference group. Using 15-s clips of songs from MySpace.com, we obtained behavioral measures of preferences and neurobiological responses to the songs. The data were gathered with, and without, the overall popularity of the song revealed. Song popularity had a significant effect on the participants' likability ratings of the songs. fMRI results showed a strong correlation between the participants' rating and activity in the caudate nucleus, a region previously implicated in reward-driven actions. The tendency to change one's evaluation of a song was positively correlated with activation in the anterior insula and anterior cingulate, two regions that are associated with physiological arousal and negative affective states. Sensitivity to popularity was linked to lower activation levels in the middle temporal gyrus, suggesting a lower depth of musical semantic processing. Our results suggest that a principal mechanism whereby popularity ratings affect consumer choice is through the anxiety generated by the mismatch between one's own preferences and others'. This mismatch anxiety motivates people to switch their choices in the direction of the consensus. Our data suggest that this is a major force behind the conformity observed in music tastes in some teenagers. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  12. Determining the Neural Substrate for Encoding a Memory of Human Pain and the Influence of Anxiety.

    Science.gov (United States)

    Tseng, Ming-Tsung; Kong, Yazhuo; Eippert, Falk; Tracey, Irene

    2017-12-06

    To convert a painful stimulus into a briefly maintainable construct when the painful stimulus is no longer accessible is essential to guide human behavior and avoid dangerous situations. Because of the aversive nature of pain, this encoding process might be influenced by emotional aspects and could thus vary across individuals, but we have yet to understand both the basic underlying neural mechanisms as well as potential interindividual differences. Using fMRI in combination with a delayed-discrimination task in healthy volunteers of both sexes, we discovered that brain regions involved in this working memory encoding process were dissociable according to whether the to-be-remembered stimulus was painful or not, with the medial thalamus and the rostral anterior cingulate cortex encoding painful and the primary somatosensory cortex encoding nonpainful stimuli. Encoding of painful stimuli furthermore significantly enhanced functional connectivity between the thalamus and medial prefrontal cortex (mPFC). With regards to emotional aspects influencing encoding processes, we observed that more anxious participants showed significant performance advantages when encoding painful stimuli. Importantly, only during the encoding of pain, the interindividual differences in anxiety were associated with the strength of coupling between medial thalamus and mPFC, which was furthermore related to activity in the amygdala. These results indicate not only that there is a distinct signature for the encoding of a painful experience in humans, but also that this encoding process involves a strong affective component. SIGNIFICANCE STATEMENT To convert the sensation of pain into a briefly maintainable construct is essential to guide human behavior and avoid dangerous situations. Although this working memory encoding process is implicitly contained in the majority of studies, the underlying neural mechanisms remain unclear. Using fMRI in a delayed-discrimination task, we found that the

  13. A neural networks application for the study of the influence of transport conditions on the working performance

    Science.gov (United States)

    Anghel, D.-C.; Ene, A.; Ştirbu, C.; Sicoe, G.

    2017-10-01

    This paper presents a study about the factors that influence the working performances of workers in the automotive industry. These factors regard mainly the transportations conditions, taking into account the fact that a large number of workers live in places that are far away of the enterprise. The quantitative data obtained from this study will be generalized by using a neural network, software simulated. The neural network is able to estimate the performance of workers even for the combinations of input factors that had been not recorded by the study. The experimental data obtained from the study will be divided in two classes. The first class that contains approximately 80% of data will be used by the Java software for the training of the neural network. The weights resulted from the training process will be saved in a text file. The other class that contains the rest of the 20% of experimental data will be used to validate the neural network. The training and the validation of the networks are performed in a Java software (TrainAndValidate java class). We designed another java class, Test.java that will be used with new input data, for new situations. The experimental data collected from the study. The software that simulated the neural network. The software that estimates the working performance, when new situations are met. This application is useful for human resources department of an enterprise. The output results are not quantitative. They are qualitative (from low performance to high performance, divided in five classes).

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

    Directory of Open Access Journals (Sweden)

    Ying eXie

    2016-02-01

    Full Text Available Event-related potentials (ERPs were used in this study to explore the neural mechanism of obedience and conformity on the model of online book purchasing. Participants were asked to decide as quickly as possible whether to buy a book based on limited information including its title, keywords and number of positive and negative reviews. Obedience was induced by forcing participants to buy books which received mostly negative reviews. In contrast, conformity was aroused by majority influence (caused by positive and negative comments. P3 and N2, two kinds of ERP components related to social cognitive process, were measured and recorded with electroencephalogram (EEG test. The results show that compared with conformity decisions, obedience decisions induced greater cognitive conflicts. In ERP measurements, greater amplitudes of N2 component were observed in the context of obedience. However, consistency level did not make a difference on P3 peak latency for both conformity and obedience. This shows that classification process is implicit in both conformity and obedience decision-making. In addition, for both conformity and obedience decisions, augmented P3 was observed when the reviews consistency (either negative or positive was higher.

  15. Evaluating the Influence of Road Lighting on Traffic Safety at Accesses Using An Artificial Neural Network.

    Science.gov (United States)

    Xu, Yueru; Ye, Zhirui; Wang, Yuan; Wang, Chao; Sun, Cuicui

    2018-05-18

    This paper focuses on the effect of road lighting on road safety at accesses and tries to quantitatively analyze the relationship between road lighting and road safety. An Artificial Neural Network (ANN) was applied in this study. This method is one of the most popular machine-learning methods in recent years and does not require any pre-defined assumptions. This method was applied using field data collected from ten road segments in Nanjing, Jiangsu Province, China. The results show that the impact of road lighting on road safety at accesses is significant. In addition, road lighting has greater influence when vehicle speeds are higher or the number of lanes is larger. A threshold illuminance was also found in this paper, and the results show that the safety level at accesses will become stable when reaching this value. The improvement of illuminance can decrease the speed variation among vehicles and improve the safety level. In addition, high-grade roads need better illuminance at accesses. A threshold value can also be obtained based on related variables and used to develop scientific guidelines for traffic management organizations.

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

    Science.gov (United States)

    Xie, Ying; Chen, Mingliang; Lai, Hongxia; Zhang, Wuke; Zhao, Zhen; Anwar, Ch Mahmood

    2016-01-01

    Event-related potentials (ERPs) were used in this study to explore the neural mechanism of obedience and conformity on the model of online book purchasing. Participants were asked to decide as quickly as possible whether to buy a book based on limited information including its title, keywords and number of positive and negative reviews. Obedience was induced by forcing participants to buy books which received mostly negative reviews. In contrast, conformity was aroused by majority influence (caused by positive and negative comments). P3 and N2, two kinds of ERP components related to social cognitive process, were measured and recorded with electroencephalogram (EEG) test. The results show that compared with conformity decisions, obedience decisions induced greater cognitive conflicts. In ERP measurements, greater amplitudes of N2 component were observed in the context of obedience. However, consistency level did not make a difference on P3 peak latency for both conformity and obedience. This shows that classification process is implicit in both conformity and obedience decision-making. In addition, for both conformity and obedience decisions, augmented P3 was observed when the reviews consistency (either negative or positive) was higher.

  17. The neural correlates of biomechanical constraints in hand laterality judgment task performed from other person's perspective: A near-infrared spectroscopy study.

    Directory of Open Access Journals (Sweden)

    Shuang Meng

    Full Text Available Previous studies, mainly using a first-person perspective (1PP, have shown that the judgments of the hand laterality judgment (HLJ task are dependent on biomechanical constraints (BC. Specifically, differing reaction times (RT for hand pictures rotated medially or laterally around the mid sagittal plane are attributed to the BC effect on motor imagery. In contrast, we investigated whether the HLJ task is also subject to BC when performed from a third-person perspective (3PP as well as 1PP using near-infrared spectroscopy (NIRS to measure the brain activity of prefrontal cortex (PFC in right-handed participants assigned to 1PP or 3PP groups. The 1PP group judged whether a presented hand was their own left or right hand, and the 3PP group whether it was the other's left or right hand. Using their HLJ task error rates, the 1PP and 3PP groups were subdivided into an Error Group (EG and No Error Group (NEG. For the 1PP group, both EG and NEG showed a significant Hand Laterality × Orientation interaction for RT, indicating the BC effect on motor imagery. For the 3PP group, however, neither EG nor NEG showed the interaction, even though EG showed a significantly longer RT than NEG. These results suggest that the 3PP EG appropriately followed the 3PP task instruction, while the NEG might have taken 1PP. However, the 3PP EG NIRS profile of left PFC showed a significant Hand Laterality × Orientation interaction, while the 1PP EG did not. More noteworthy is that the left PFC activation of EG showed an interaction between the 1PP and 3PP groups when the left hand was presented. Furthermore, in the NEG, the PFC activation was not influenced by the BC in either the 1PP or 3PP condition. These results indicate that BC interferes with the HLJ task performed from the 1PP and 3PP.

  18. The neural correlates of biomechanical constraints in hand laterality judgment task performed from other person's perspective: A near-infrared spectroscopy study.

    Science.gov (United States)

    Meng, Shuang; Oi, Misato; Saito, Godai; Saito, Hirofumi

    2017-01-01

    Previous studies, mainly using a first-person perspective (1PP), have shown that the judgments of the hand laterality judgment (HLJ) task are dependent on biomechanical constraints (BC). Specifically, differing reaction times (RT) for hand pictures rotated medially or laterally around the mid sagittal plane are attributed to the BC effect on motor imagery. In contrast, we investigated whether the HLJ task is also subject to BC when performed from a third-person perspective (3PP) as well as 1PP using near-infrared spectroscopy (NIRS) to measure the brain activity of prefrontal cortex (PFC) in right-handed participants assigned to 1PP or 3PP groups. The 1PP group judged whether a presented hand was their own left or right hand, and the 3PP group whether it was the other's left or right hand. Using their HLJ task error rates, the 1PP and 3PP groups were subdivided into an Error Group (EG) and No Error Group (NEG). For the 1PP group, both EG and NEG showed a significant Hand Laterality × Orientation interaction for RT, indicating the BC effect on motor imagery. For the 3PP group, however, neither EG nor NEG showed the interaction, even though EG showed a significantly longer RT than NEG. These results suggest that the 3PP EG appropriately followed the 3PP task instruction, while the NEG might have taken 1PP. However, the 3PP EG NIRS profile of left PFC showed a significant Hand Laterality × Orientation interaction, while the 1PP EG did not. More noteworthy is that the left PFC activation of EG showed an interaction between the 1PP and 3PP groups when the left hand was presented. Furthermore, in the NEG, the PFC activation was not influenced by the BC in either the 1PP or 3PP condition. These results indicate that BC interferes with the HLJ task performed from the 1PP and 3PP.

  19. Constraint Differentiation

    DEFF Research Database (Denmark)

    Mödersheim, Sebastian Alexander; Basin, David; Viganò, Luca

    2010-01-01

    We introduce constraint differentiation, a powerful technique for reducing search when model-checking security protocols using constraint-based methods. Constraint differentiation works by eliminating certain kinds of redundancies that arise in the search space when using constraints to represent...... results show that constraint differentiation substantially reduces search and considerably improves the performance of OFMC, enabling its application to a wider class of problems....

  20. Religious beliefs influence neural substrates of self-reflection in Tibetans.

    Science.gov (United States)

    Wu, Yanhong; Wang, Cheng; He, Xi; Mao, Lihua; Zhang, Li

    2010-06-01

    Previous transcultural neuroimaging studies have shown that the neural substrates of self-reflection can be shaped by different cultures. There are few studies, however, on the neural activity of self-reflection where religion is viewed as a form of cultural expression. The present study examined the self-processing of two Chinese ethnic groups (Han and Tibetan) to investigate the significant role of religion on the functional anatomy of self-representation. We replicated the previous results in Han participants with the ventral medial prefrontal cortex and left anterior cingulate cortex showing stronger activation in self-processing when compared with other-processing conditions. However, no typical self-reference pattern was identified in Tibetan participants on behavioral or neural levels. This could be explained by the minimal subjective sense of 'I-ness' in Tibetan Buddhists. Our findings lend support to the presumed role of culture and religion in shaping the neural substrate of self.

  1. Beauty is in the belief of the beholder: cognitive influences on the neural response to facial attractiveness.

    Science.gov (United States)

    Thiruchselvam, Ravi; Harper, Jessica; Homer, Abigail L

    2016-12-01

    Judgments of facial attractiveness are central to decision-making in various domains, but little is known about the extent to which they are malleable. In this study, we used EEG/ERP methods to examine two novel influences on neural and subjective responses to facial attractiveness: an observer's expectation and repetition. In each trial of our task, participants viewed either an ordinary or attractive face. To alter expectations, the faces were preceded by a peer-rating that ostensibly reflected the overall attractiveness value assigned to that face by other individuals. To examine the impact of repetition, trials were presented twice throughout the experimental session. Results showed that participants' expectations about a person's attractiveness level powerfully altered both the neural response (i.e. the late positive potential; LPP) and self-reported attractiveness ratings. Intriguingly, repetition enhanced both the LPP and self-reported attractiveness as well. Exploratory analyses further suggested that both observer expectation and repetition modulated early neural responses (i.e. the early posterior negativity; EPN) elicited by facial attractiveness. Collectively, these results highlight novel influences on a core social judgment that underlies individuals' affective lives. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  2. Beauty is in the belief of the beholder: cognitive influences on the neural response to facial attractiveness

    Science.gov (United States)

    Thiruchselvam, Ravi; Harper, Jessica; Homer, Abigail L.

    2016-01-01

    Judgments of facial attractiveness are central to decision-making in various domains, but little is known about the extent to which they are malleable. In this study, we used EEG/ERP methods to examine two novel influences on neural and subjective responses to facial attractiveness: an observer’s expectation and repetition. In each trial of our task, participants viewed either an ordinary or attractive face. To alter expectations, the faces were preceded by a peer-rating that ostensibly reflected the overall attractiveness value assigned to that face by other individuals. To examine the impact of repetition, trials were presented twice throughout the experimental session. Results showed that participants’ expectations about a person’s attractiveness level powerfully altered both the neural response (i.e. the late positive potential; LPP) and self-reported attractiveness ratings. Intriguingly, repetition enhanced both the LPP and self-reported attractiveness as well. Exploratory analyses further suggested that both observer expectation and repetition modulated early neural responses (i.e. the early posterior negativity; EPN) elicited by facial attractiveness. Collectively, these results highlight novel influences on a core social judgment that underlies individuals’ affective lives. PMID:27522090

  3. The Power of the Like in Adolescence: Effects of Peer Influence on Neural and Behavioral Responses to Social Media.

    Science.gov (United States)

    Sherman, Lauren E; Payton, Ashley A; Hernandez, Leanna M; Greenfield, Patricia M; Dapretto, Mirella

    2016-07-01

    We investigated a unique way in which adolescent peer influence occurs on social media. We developed a novel functional MRI (fMRI) paradigm to simulate Instagram, a popular social photo-sharing tool, and measured adolescents' behavioral and neural responses to likes, a quantifiable form of social endorsement and potential source of peer influence. Adolescents underwent fMRI while viewing photos ostensibly submitted to Instagram. They were more likely to like photos depicted with many likes than photos with few likes; this finding showed the influence of virtual peer endorsement and held for both neutral photos and photos of risky behaviors (e.g., drinking, smoking). Viewing photos with many (compared with few) likes was associated with greater activity in neural regions implicated in reward processing, social cognition, imitation, and attention. Furthermore, when adolescents viewed risky photos (as opposed to neutral photos), activation in the cognitive-control network decreased. These findings highlight possible mechanisms underlying peer influence during adolescence. © The Author(s) 2016.

  4. Strategies influence neural activity for feedback learning across child and adolescent development.

    Science.gov (United States)

    Peters, Sabine; Koolschijn, P Cédric M P; Crone, Eveline A; Van Duijvenvoorde, Anna C K; Raijmakers, Maartje E J

    2014-09-01

    Learning from feedback is an important aspect of executive functioning that shows profound improvements during childhood and adolescence. This is accompanied by neural changes in the feedback-learning network, which includes pre-supplementary motor area (pre- SMA)/anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), superior parietal cortex (SPC), and the basal ganglia. However, there can be considerable differences within age ranges in performance that are ascribed to differences in strategy use. This is problematic for traditional approaches of analyzing developmental data, in which age groups are assumed to be homogenous in strategy use. In this study, we used latent variable models to investigate if underlying strategy groups could be detected for a feedback-learning task and whether there were differences in neural activation patterns between strategies. In a sample of 268 participants between ages 8 to 25 years, we observed four underlying strategy groups, which were cut across age groups and varied in the optimality of executive functioning. These strategy groups also differed in neural activity during learning; especially the most optimal performing group showed more activity in DLPFC, SPC and pre-SMA/ACC compared to the other groups. However, age differences remained an important contributor to neural activation, even when correcting for strategy. These findings contribute to the debate of age versus performance predictors of neural development, and highlight the importance of studying individual differences in strategy use when studying development. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Neural bases of motivated reasoning: an FMRI study of emotional constraints on partisan political judgment in the 2004 U.S. Presidential election.

    Science.gov (United States)

    Westen, Drew; Blagov, Pavel S; Harenski, Keith; Kilts, Clint; Hamann, Stephan

    2006-11-01

    Research on political judgment and decision-making has converged with decades of research in clinical and social psychology suggesting the ubiquity of emotion-biased motivated reasoning. Motivated reasoning is a form of implicit emotion regulation in which the brain converges on judgments that minimize negative and maximize positive affect states associated with threat to or attainment of motives. To what extent motivated reasoning engages neural circuits involved in "cold" reasoning and conscious emotion regulation (e.g., suppression) is, however, unknown. We used functional neuroimaging to study the neural responses of 30 committed partisans during the U.S. Presidential election of 2004. We presented subjects with reasoning tasks involving judgments about information threatening to their own candidate, the opposing candidate, or neutral control targets. Motivated reasoning was associated with activations of the ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, insular cortex, and lateral orbital cortex. As predicted, motivated reasoning was not associated with neural activity in regions previously linked to cold reasoning tasks and conscious (explicit) emotion regulation. The findings provide the first neuroimaging evidence for phenomena variously described as motivated reasoning, implicit emotion regulation, and psychological defense. They suggest that motivated reasoning is qualitatively distinct from reasoning when people do not have a strong emotional stake in the conclusions reached.

  6. Investigating the Influence of Box-Constraints on the Solution of a Total Variation Model via an Efficient Primal-Dual Method

    Directory of Open Access Journals (Sweden)

    Andreas Langer

    2018-01-01

    Full Text Available In this paper, we investigate the usefulness of adding a box-constraint to the minimization of functionals consisting of a data-fidelity term and a total variation regularization term. In particular, we show that in certain applications an additional box-constraint does not effect the solution at all, i.e., the solution is the same whether a box-constraint is used or not. On the contrary, i.e., for applications where a box-constraint may have influence on the solution, we investigate how much it effects the quality of the restoration, especially when the regularization parameter, which weights the importance of the data term and the regularizer, is chosen suitable. In particular, for such applications, we consider the case of a squared L 2 data-fidelity term. For computing a minimizer of the respective box-constrained optimization problems a primal-dual semi-smooth Newton method is presented, which guarantees superlinear convergence.

  7. Emotional expectations influence neural sensitivity to fearful faces in humans:An event-related potential study

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The present study tested whether neural sensitivity to salient emotional facial expressions was influenced by emotional expectations induced by a cue that validly predicted the expression of a subsequently presented target face. Event-related potentials (ERPs) elicited by fearful and neutral faces were recorded while participants performed a gender discrimination task under cued (‘expected’) and uncued (‘unexpected’) conditions. The behavioral results revealed that accuracy was lower for fearful compared with neutral faces in the unexpected condition, while accuracy was similar for fearful and neutral faces in the expected condition. ERP data revealed increased amplitudes in the P2 component and 200–250 ms interval for unexpected fearful versus neutral faces. By contrast, ERP responses were similar for fearful and neutral faces in the expected condition. These findings indicate that human neural sensitivity to fearful faces is modulated by emotional expectations. Although the neural system is sensitive to unpredictable emotionally salient stimuli, sensitivity to salient stimuli is reduced when these stimuli are predictable.

  8. Influence of the neural tube/notochord complex on MyoD expression and cellular proliferation in chicken embryos

    Directory of Open Access Journals (Sweden)

    H.J. Alves

    2003-02-01

    Full Text Available Important advances have been made in understanding the genetic processes that control skeletal muscle formation. Studies conducted on quails detected a delay in the myogenic program of animals selected for high growth rates. These studies have led to the hypothesis that a delay in myogenesis would allow somitic cells to proliferate longer and consequently increase the number of embryonic myoblasts. To test this hypothesis, recently segmented somites and part of the unsegmented paraxial mesoderm were separated from the neural tube/notochord complex in HH12 chicken embryos. In situ hybridization and competitive RT-PCR revealed that MyoD transcripts, which are responsible for myoblast determination, were absent in somites separated from neural tube/notochord (1.06 and 0.06 10-3 attomol MyoD/1 attomol ß-actin for control and separated somites, respectively; P<0.01. However, reapproximation of these structures allowed MyoD to be expressed in somites. Cellular proliferation was analyzed by immunohistochemical detection of incorporated BrdU, a thymidine analogue. A smaller but not significant (P = 0.27 number of proliferating cells was observed in somites that had been separated from neural tube/notochord (27 and 18 for control and separated somites, respectively. These results confirm the influence of the axial structures on MyoD activation but do not support the hypothesis that in the absence of MyoD transcripts the cellular proliferation would be maintained for a longer period of time.

  9. Neural processing of speech in children is influenced by bilingual experience

    Science.gov (United States)

    Krizman, Jennifer; Slater, Jessica; Skoe, Erika; Marian, Viorica; Kraus, Nina

    2014-01-01

    Language experience fine-tunes how the auditory system processes sound. For example, bilinguals, relative to monolinguals, have more robust evoked responses to speech that manifest as stronger neural encoding of the fundamental frequency (F0) and greater across-trial consistency. However, it is unknown whether such enhancements increase with increasing second language experience. We predict that F0 amplitude and neural consistency scale with dual-language experience during childhood, such that more years of bilingual experience leads to more robust F0 encoding and greater neural consistency. To test this hypothesis, we recorded auditory brainstem responses to the synthesized syllables ‘ba’ and ‘ga’ in two groups of bilingual children who were matched for age at test (8.4+/−0.67 years) but differed in their age of second language acquisition. One group learned English and Spanish simultaneously from birth (n=13), while the second group learned the two languages sequentially (n=15), spending on average their first four years as monolingual Spanish speakers. We find that simultaneous bilinguals have a larger F0 response to ‘ba’ and ‘ga’ and a more consistent response to ‘ba’ compared to sequential bilinguals. We also demonstrate that these neural enhancements positively relate with years of bilingual experience. These findings support the notion that bilingualism enhances subcortical auditory processing. PMID:25445377

  10. Folic acid supplementation influences the distribution of neural tube defect subtypes : A registry-based study

    NARCIS (Netherlands)

    Bergman, J. E. H.; Otten, E.; Verheij, J. B. G. M.; de Walle, H. E. K.

    Periconceptional folic acid (FA) reduces neural tube defect (NTD) risk, but seems to have a varying effect per NTD subtype. We aimed to study the effect of FA supplementation on NTD subtype distribution using data from EUROCAT Northern Netherlands. We included all birth types with non-syndromal NTDs

  11. The influence of mental fatigue and motivation on neural network dynamics; an EEG coherence study

    NARCIS (Netherlands)

    Lorist, Monicque M.; Bezdan, Eniko; Caat, Michael ten; Span, Mark M.; Roerdink, Jos B.T.M.; Maurits, Natasha M.

    2009-01-01

    The purpose of the present study is to examine the effects of mental fatigue and motivation on neural network dynamics activated during task switching. Mental fatigue was induced by 2 h of continuous performance; after which subjects were motivated by using social comparison and monetary reward as

  12. Are lexical tones musical? Native language's influence on neural response to pitch in different domains.

    Science.gov (United States)

    Chen, Ao; Peter, Varghese; Wijnen, Frank; Schnack, Hugo; Burnham, Denis

    2018-04-21

    Language experience shapes musical and speech pitch processing. We investigated whether speaking a lexical tone language natively modulates neural processing of pitch in language and music as well as their correlation. We tested tone language (Mandarin Chinese), and non-tone language (Dutch) listeners in a passive oddball paradigm measuring mismatch negativity (MMN) for (i) Chinese lexical tones and (ii) three-note musical melodies with similar pitch contours. For lexical tones, Chinese listeners showed a later MMN peak than the non-tone language listeners, whereas for MMN amplitude there were no significant differences between groups. Dutch participants also showed a late discriminative negativity (LDN). In the music condition two MMNs, corresponding to the two notes that differed between the standard and the deviant were found for both groups, and an LDN were found for both the Dutch and the Chinese listeners. The music MMNs were significantly right lateralized. Importantly, significant correlations were found between the lexical tone and the music MMNs for the Dutch but not the Chinese participants. The results suggest that speaking a tone language natively does not necessarily enhance neural responses to pitch either in language or in music, but that it does change the nature of neural pitch processing: non-tone language speakers appear to perceive lexical tones as musical, whereas for tone language speakers, lexical tones and music may activate different neural networks. Neural resources seem to be assigned differently for the lexical tones and for musical melodies, presumably depending on the presence or absence of long-term phonological memory traces. Copyright © 2018 Elsevier Inc. All rights reserved.

  13. The influence of lifestyle on cardiovascular risk factors. Analysis using a neural network.

    Science.gov (United States)

    Gueli, Nicoló; Piccirillo, Gianfanco; Troisi, Giovanni; Cicconetti, Paolo; Meloni, Fortunato; Ettorre, Evaristo; Verico, Paola; D'Arcangelo, Enzo; Cacciafesta, Mauro

    2005-01-01

    The cardiovascular pathologies are the most common causes of death in the elderly patient. To single out the main risk factors in order to effectively prevent the onset of the disease, the authors experimented a special computerized tool, the neural network, that works out a mathematical relation that can obtain certain data (defined as output) as a function of other data (defined as input). Data were processed from a sample of 276 subjects of both sexes aged 26-69 years old. The output data were: high/low cholesterolemia, HDL cholesterol, triglyceridemia with respect to an established cut-off; the input data were: sex, age, build, weight, married/single, number of children, number of cigarettes smoked/day, amount of wine and number of cups of coffee. We conclude that: (i) a relationship exists, deduced from a neural network, between a set of input variables and a dichotomous output variable; (ii) this relationship can be expressed as a mathematical function; (iii) a neural network, having learned the data on a sufficiently large population, can provide valid predictive data for a single individual with a high probability (up to 93.33%) that the response it gives is correct. In this study, such a result is found for two of the three cardiovascular risk indicators considered (cholesterol and triglycerides); (iv) the repetition of the neural network analysis of the cases in question after a "pruning" operation provided a somewhat less good performance; (v) a statistical analysis conducted on those same cases has confirmed the existence of a strong relationship between the input and the output variables. Therefore the neural network is a valid instrument for providing predictive in a single subject on cardiovascular pathology risks.

  14. 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.

  15. Polycose Taste Pre-Exposure Fails to Influence Behavioral and Neural Indices of Taste Novelty

    OpenAIRE

    Barot, Sabiha K.; Bernstein, Ilene L.

    2005-01-01

    Taste novelty can strongly modulate the speed and efficacy of taste aversion learning. Novel sweet tastes enhance c-Fos-like immunoreactivity (FLI) in the central amygdala and insular cortex. The present studies examined whether this neural correlate of novelty extends to different taste types by measuring FLI signals after exposure to novel and familiar polysaccharide (Polycose®) and salt (NaCl) tastes. Novel Polycose not only failed to elevate FLI expression in central amygdala and insular ...

  16. Latent iron deficiency at birth influences auditory neural maturation in late preterm and term infants.

    Science.gov (United States)

    Choudhury, Vivek; Amin, Sanjiv B; Agarwal, Asha; Srivastava, L M; Soni, Arun; Saluja, Satish

    2015-11-01

    In utero latent iron deficiency has been associated with abnormal neurodevelopmental outcomes during childhood. Its concomitant effect on auditory neural maturation has not been well studied in late preterm and term infants. The objective was to determine whether in utero iron status is associated with auditory neural maturation in late preterm and term infants. This prospective cohort study was performed at Sir Ganga Ram Hospital, New Delhi, India. Infants with a gestational age ≥34 wk were eligible unless they met the exclusion criteria: craniofacial anomalies, chromosomal disorders, hemolytic disease, multiple gestation, third-trimester maternal infection, chorioamnionitis, toxoplasmosis, other infections, rubella, cytomegalovirus infection, and herpes simplex virus infections (TORCH), Apgar score 75 ng/mL) at birth. Twenty-three infants had latent iron deficiency. Infants with latent iron deficiency had significantly prolonged wave V latencies (7.10 ± 0.68 compared with 6.60 ± 0.66), III-V interpeak latencies (2.37 ± 0.64 compared with 2.07 ± 0.33), and I-V interpeak latencies (5.10 ± 0.57 compared with 4.72 ± 0.56) compared with infants with normal iron status (P neural maturation in infants at ≥34 wk gestational age. This trial was registered at clinicaltrials.gov as NCT02503397. © 2015 American Society for Nutrition.

  17. The influence of immunosuppressive drugs on neural stem/progenitor cell fate in vitro

    Energy Technology Data Exchange (ETDEWEB)

    Skardelly, Marco, E-mail: Marco.Skardelly@med.uni-tuebingen.de [Department of Neurosurgery, University Hospital, Leipzig (Germany); Translational Centre for Regenerative Medicine, University of Leipzig, Leipzig (Germany); Glien, Anja; Groba, Claudia; Schlichting, Nadine [Department of Neurosurgery, University Hospital, Leipzig (Germany); Kamprad, Manja [Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Leipzig (Germany); Meixensberger, Juergen [Department of Neurosurgery, University Hospital, Leipzig (Germany); Milosevic, Javorina [Translational Centre for Regenerative Medicine, University of Leipzig, Leipzig (Germany)

    2013-12-10

    In allogenic and xenogenic transplantation, adequate immunosuppression plays a major role in graft survival, especially over the long term. The effect of immunosuppressive drugs on neural stem/progenitor cell fate has not been sufficiently explored. The focus of this study is to systematically investigate the effects of the following four different immunotherapeutic strategies on human neural progenitor cell survival/death, proliferation, metabolic activity, differentiation and migration in vitro: (1) cyclosporine A (CsA), a calcineurin inhibitor; (2) everolimus (RAD001), an mTOR-inhibitor; (3) mycophenolic acid (MPA, mycophenolate), an inhibitor of inosine monophosphate dehydrogenase and (4) prednisolone, a steroid. At the minimum effective concentration (MEC), we found a prominent decrease in hNPCs' proliferative capacity (BrdU incorporation), especially for CsA and MPA, and an alteration of the NAD(P)H-dependent metabolic activity. Cell death rate, neurogenesis, gliogenesis and cell migration remained mostly unaffected under these conditions for all four immunosuppressants, except for apoptotic cell death, which was significantly increased by MPA treatment. - Highlights: • Four immunosuppresants (ISs) were tested in human neural progenitor cells in vitro. • Cyclosporine A and mycophenolic acid showed a prominent anti-proliferative activity • Mycophenolic acid exhibited a significant pro-apoptotic effect. • NAD(P)H-dependent metabolic activity was occasionally induced by ISs. • Neuronal differentiation and migration potential remained unaffected by ISs treatment.

  18. The influence of motherhood on neural systems for reward processing in low income, minority, young women.

    Science.gov (United States)

    Moses-Kolko, Eydie L; Forbes, Erika E; Stepp, Stephanie; Fraser, David; Keenan, Kate E; Guyer, Amanda E; Chase, Henry W; Phillips, Mary L; Zevallos, Carlos R; Guo, Chaohui; Hipwell, Alison E

    2016-04-01

    Given the association between maternal caregiving behavior and heightened neural reward activity in experimental animal studies, the present study examined whether motherhood in humans positively modulates reward-processing neural circuits, even among mothers exposed to various life stressors and depression. Subjects were 77 first-time mothers and 126 nulliparous young women from the Pittsburgh Girls Study, a longitudinal study beginning in childhood. Subjects underwent a monetary reward task during functional magnetic resonance imaging in addition to assessment of current depressive symptoms. Life stress was measured by averaging data collected between ages 8-15 years. Using a region-of-interest approach, we conducted hierarchical regression to examine the relationship of psychosocial factors (life stress and current depression) and motherhood with extracted ventral striatal (VST) response to reward anticipation. Whole-brain regression analyses were performed post-hoc to explore non-striatal regions associated with reward anticipation in mothers vs nulliparous women. Anticipation of monetary reward was associated with increased neural activity in expected regions including caudate, orbitofrontal, occipital, superior and middle frontal cortices. There was no main effect of motherhood nor motherhood-by-psychosocial factor interaction effect on VST response during reward anticipation. Depressive symptoms were associated with increased VST activity across the entire sample. In exploratory whole brain analysis, motherhood was associated with increased somatosensory cortex activity to reward (FWE cluster forming threshold preward anticipation-related VST activity nor does motherhood modulate the impact of depression or life stress on VST activity. Future studies are needed to evaluate whether earlier postpartum assessment of reward function, inclusion of mothers with more severe depressive symptoms, and use of reward tasks specific for social reward might reveal an

  19. The BDNF Val66Met Polymorphism Influences Reading Ability and Patterns of Neural Activation in Children.

    Directory of Open Access Journals (Sweden)

    Kaja K Jasińska

    Full Text Available Understanding how genes impact the brain's functional activation for learning and cognition during development remains limited. We asked whether a common genetic variant in the BDNF gene (the Val66Met polymorphism modulates neural activation in the young brain during a critical period for the emergence and maturation of the neural circuitry for reading. In animal models, the bdnf variation has been shown to be associated with the structure and function of the developing brain and in humans it has been associated with multiple aspects of cognition, particularly memory, which are relevant for the development of skilled reading. Yet, little is known about the impact of the Val66Met polymorphism on functional brain activation in development, either in animal models or in humans. Here, we examined whether the BDNF Val66Met polymorphism (dbSNP rs6265 is associated with children's (age 6-10 neural activation patterns during a reading task (n = 81 using functional magnetic resonance imaging (fMRI, genotyping, and standardized behavioral assessments of cognitive and reading development. Children homozygous for the Val allele at the SNP rs6265 of the BDNF gene outperformed Met allele carriers on reading comprehension and phonological memory, tasks that have a strong memory component. Consistent with these behavioral findings, Met allele carriers showed greater activation in reading-related brain regions including the fusiform gyrus, the left inferior frontal gyrus and left superior temporal gyrus as well as greater activation in the hippocampus during a word and pseudoword reading task. Increased engagement of memory and spoken language regions for Met allele carriers relative to Val/Val homozygotes during reading suggests that Met carriers have to exert greater effort required to retrieve phonological codes.

  20. The BDNF Val66Met Polymorphism Influences Reading Ability and Patterns of Neural Activation in Children.

    Science.gov (United States)

    Jasińska, Kaja K; Molfese, Peter J; Kornilov, Sergey A; Mencl, W Einar; Frost, Stephen J; Lee, Maria; Pugh, Kenneth R; Grigorenko, Elena L; Landi, Nicole

    2016-01-01

    Understanding how genes impact the brain's functional activation for learning and cognition during development remains limited. We asked whether a common genetic variant in the BDNF gene (the Val66Met polymorphism) modulates neural activation in the young brain during a critical period for the emergence and maturation of the neural circuitry for reading. In animal models, the bdnf variation has been shown to be associated with the structure and function of the developing brain and in humans it has been associated with multiple aspects of cognition, particularly memory, which are relevant for the development of skilled reading. Yet, little is known about the impact of the Val66Met polymorphism on functional brain activation in development, either in animal models or in humans. Here, we examined whether the BDNF Val66Met polymorphism (dbSNP rs6265) is associated with children's (age 6-10) neural activation patterns during a reading task (n = 81) using functional magnetic resonance imaging (fMRI), genotyping, and standardized behavioral assessments of cognitive and reading development. Children homozygous for the Val allele at the SNP rs6265 of the BDNF gene outperformed Met allele carriers on reading comprehension and phonological memory, tasks that have a strong memory component. Consistent with these behavioral findings, Met allele carriers showed greater activation in reading-related brain regions including the fusiform gyrus, the left inferior frontal gyrus and left superior temporal gyrus as well as greater activation in the hippocampus during a word and pseudoword reading task. Increased engagement of memory and spoken language regions for Met allele carriers relative to Val/Val homozygotes during reading suggests that Met carriers have to exert greater effort required to retrieve phonological codes.

  1. Influence of aging on the neural correlates of autobiographical, episodic, and semantic memory retrieval.

    Science.gov (United States)

    St-Laurent, Marie; Abdi, Hervé; Burianová, Hana; Grady, Cheryl L

    2011-12-01

    We used fMRI to assess the neural correlates of autobiographical, semantic, and episodic memory retrieval in healthy young and older adults. Participants were tested with an event-related paradigm in which retrieval demand was the only factor varying between trials. A spatio-temporal partial least square analysis was conducted to identify the main patterns of activity characterizing the groups across conditions. We identified brain regions activated by all three memory conditions relative to a control condition. This pattern was expressed equally in both age groups and replicated previous findings obtained in a separate group of younger adults. We also identified regions whose activity differentiated among the different memory conditions. These patterns of differentiation were expressed less strongly in the older adults than in the young adults, a finding that was further confirmed by a barycentric discriminant analysis. This analysis showed an age-related dedifferentiation in autobiographical and episodic memory tasks but not in the semantic memory task or the control condition. These findings suggest that the activation of a common memory retrieval network is maintained with age, whereas the specific aspects of brain activity that differ with memory content are more vulnerable and less selectively engaged in older adults. Our results provide a potential neural mechanism for the well-known age differences in episodic/autobiographical memory, and preserved semantic memory, observed when older adults are compared with younger adults.

  2. Neural correlates of informational cascades: brain mechanisms of social influence on belief updating.

    Science.gov (United States)

    Huber, Rafael E; Klucharev, Vasily; Rieskamp, Jörg

    2015-04-01

    Informational cascades can occur when rationally acting individuals decide independently of their private information and follow the decisions of preceding decision-makers. In the process of updating beliefs, differences in the weighting of private and publicly available social information may modulate the probability that a cascade starts in a decisive way. By using functional magnetic resonance imaging, we examined neural activity while participants updated their beliefs based on the decisions of two fictitious stock market traders and their own private information, which led to a final decision of buying one of two stocks. Computational modeling of the behavioral data showed that a majority of participants overweighted private information. Overweighting was negatively correlated with the probability of starting an informational cascade in trials especially prone to conformity. Belief updating by private information was related to activity in the inferior frontal gyrus/anterior insula, the dorsolateral prefrontal cortex and the parietal cortex; the more a participant overweighted private information, the higher the activity in the inferior frontal gyrus/anterior insula and the lower in the parietal-temporal cortex. This study explores the neural correlates of overweighting of private information, which underlies the tendency to start an informational cascade. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  3. A Concept for Extending the Applicability of Constraint-Induced Movement Therapy through Motor Cortex Activity Feedback Using a Neural Prosthesis

    Directory of Open Access Journals (Sweden)

    Tomas E. Ward

    2007-01-01

    Full Text Available This paper describes a concept for the extension of constraint-induced movement therapy (CIMT through the use of feedback of primary motor cortex activity. CIMT requires residual movement to act as a source of feedback to the patient, thus preventing its application to those with no perceptible movement. It is proposed in this paper that it is possible to provide feedback of the motor cortex effort to the patient by measurement with near infrared spectroscopy (NIRS. Significant changes in such effort may be used to drive rehabilitative robotic actuators, for example. This may provide a possible avenue for extending CIMT to patients hitherto excluded as a result of severity of condition. In support of such a paradigm, this paper details the current status of CIMT and related attempts to extend rehabilitation therapy through the application of technology. An introduction to the relevant haemodynamics is given including a description of the basic technology behind a suitable NIRS system. An illustration of the proposed therapy is described using a simple NIRS system driving a robotic arm during simple upper-limb unilateral isometric contraction exercises with healthy subjects.

  4. Consuming Almonds vs. Isoenergetic Baked Food Does Not Differentially Influence Postprandial Appetite or Neural Reward Responses to Visual Food Stimuli.

    Science.gov (United States)

    Sayer, R Drew; Dhillon, Jaapna; Tamer, Gregory G; Cornier, Marc-Andre; Chen, Ningning; Wright, Amy J; Campbell, Wayne W; Mattes, Richard D

    2017-07-27

    Nuts have high energy and fat contents, but nut intake does not promote weight gain or obesity, which may be partially explained by their proposed high satiety value. The primary aim of this study was to assess the effects of consuming almonds versus a baked food on postprandial appetite and neural responses to visual food stimuli. Twenty-two adults (19 women and 3 men) with a BMI between 25 and 40 kg/m² completed the current study during a 12-week behavioral weight loss intervention. Participants consumed either 28 g of whole, lightly salted roasted almonds or a serving of a baked food with equivalent energy and macronutrient contents in random order on two testing days prior to and at the end of the intervention. Pre- and postprandial appetite ratings and functional magnetic resonance imaging scans were completed on all four testing days. Postprandial hunger, desire to eat, fullness, and neural responses to visual food stimuli were not different following consumption of almonds and the baked food, nor were they influenced by weight loss. These results support energy and macronutrient contents as principal determinants of postprandial appetite and do not support a unique satiety effect of almonds independent of these variables.

  5. Polycose taste pre-exposure fails to influence behavioral and neural indices of taste novelty.

    Science.gov (United States)

    Barot, Sabiha K; Bernstein, Ilene L

    2005-12-01

    Taste novelty can strongly modulate the speed and efficacy of taste aversion learning. Novel sweet tastes enhance c-Fos-like immunoreactivity (FLI) in the central amygdala and insular cortex. The present studies examined whether this neural correlate of novelty extends to different taste types by measuring FLI signals after exposure to novel and familiar polysaccharide (Polycose) and salt (NaCl) tastes. Novel Polycose not only failed to elevate FLI expression in central amygdala and insular cortex, but also failed to induce stronger taste aversion learning than familiar Polycose. Novel NaCl, on the other hand, showed patterns of FLI activation and aversion learning similar to that of novel sweet tastes. Possible reasons for the resistance of Polycose to typical pre-exposure effects are discussed. Copyright (c) 2006 APA, all rights reserved.

  6. The influence of cochlear traveling wave and neural adaptation on auditory brainstem responses

    DEFF Research Database (Denmark)

    Junius, D.; Dau, Torsten

    2005-01-01

    of the responses to the single components, as a function of stimulus level. In the first experiment, a single rising chirp was temporally and spectrally embedded in two steady-state tones. In the second experiment, the stimulus consisted of a continuous alternating train of chirps: each rising chirp was followed...... by the temporally reversed (falling) chirp. In both experiments, the transitions between stimulus components were continuous. For stimulation levels up to approximately 70 dB SPL, the responses to the embedded chirp corresponded to the responses to the single chirp. At high stimulus levels (80-100 dB SPL......), disparities occurred between the responses, reflecting a nonlinearity in the processing when neural activity is integrated across frequency. In the third experiment, the effect of within-train rate on wave-V response was investigated. The response to the chirp presented at a within-train rate of 95 Hz...

  7. Neural computations underpinning the strategic management of influence in advice giving.

    Science.gov (United States)

    Hertz, Uri; Palminteri, Stefano; Brunetti, Silvia; Olesen, Cecilie; Frith, Chris D; Bahrami, Bahador

    2017-12-19

    Research on social influence has focused mainly on the target of influence (e.g., consumer and voter); thus, the cognitive and neurobiological underpinnings of the source of the influence (e.g., politicians and salesmen) remain unknown. Here, in a three-sided advice-giving game, two advisers competed to influence a client by modulating their own confidence in their advice about which lottery the client should choose. We report that advisers' strategy depends on their level of influence on the client and their merit relative to one another. Moreover, blood-oxygenation-level-dependent (BOLD) signal in the temporo-parietal junction is modulated by adviser's current level of influence on the client, and relative merit prediction error affects activity in medial-prefrontal cortex. Both types of social information modulate ventral striatum response. By demonstrating what happens in our mind and brain when we try to influence others, these results begin to explain the biological mechanisms that shape inter-individual differences in social conduct.

  8. Who Can You Trust? Behavioral and Neural Differences Between Perceptual and Memory-Based Influences

    OpenAIRE

    Rudoy, John D.; Paller, Ken A.

    2009-01-01

    Decisions about whether to trust someone can be influenced by competing sources of information, such as analysis of facial features versus remembering specific information about the person. We hypothesized that such sources can differentially influence trustworthiness judgments depending on the circumstances in which judgments are made. In our experiments, subjects first learned face-word associations. Stimuli were trustworthy and untrustworthy faces, selected on the basis of consensus judgme...

  9. Direct and inverse neural networks modelling applied to study the influence of the gas diffusion layer properties on PBI-based PEM fuel cells

    Energy Technology Data Exchange (ETDEWEB)

    Lobato, Justo; Canizares, Pablo; Rodrigo, Manuel A.; Linares, Jose J. [Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004 Ciudad Real (Spain); Piuleac, Ciprian-George; Curteanu, Silvia [Faculty of Chemical Engineering and Environmental Protection, Department of Chemical Engineering, ' ' Gh. Asachi' ' Technical University Iasi Bd. D. Mangeron, No. 71A, 700050 IASI (Romania)

    2010-08-15

    This article shows the application of a very useful mathematical tool, artificial neural networks, to predict the fuel cells results (the value of the tortuosity and the cell voltage, at a given current density, and therefore, the power) on the basis of several properties that define a Gas Diffusion Layer: Teflon content, air permeability, porosity, mean pore size, hydrophobia level. Four neural networks types (multilayer perceptron, generalized feedforward network, modular neural network, and Jordan-Elman neural network) have been applied, with a good fitting between the predicted and the experimental values in the polarization curves. A simple feedforward neural network with one hidden layer proved to be an accurate model with good generalization capability (error about 1% in the validation phase). A procedure based on inverse neural network modelling was able to determine, with small errors, the initial conditions leading to imposed values for characteristics of the fuel cell. In addition, the use of this tool has been proved to be very attractive in order to predict the cell performance, and more interestingly, the influence of the properties of the gas diffusion layer on the cell performance, allowing possible enhancements of this material by changing some of its properties. (author)

  10. Threatened species richness along a Himalayan elevational gradient: quantifying the influences of human population density, range size, and geometric constraints.

    Science.gov (United States)

    Paudel, Prakash Kumar; Sipos, Jan; Brodie, Jedediah F

    2018-02-07

    A crucial step in conserving biodiversity is to identify the distributions of threatened species and the factors associated with species threat status. In the biodiversity hotspot of the Himalaya, very little is known about which locations harbour the highest diversity of threatened species and whether diversity of such species is related to area, mid-domain effects (MDE), range size, or human density. In this study, we assessed the drivers of variation in richness of threatened birds, mammals, reptiles, actinopterygii, and amphibians along an elevational gradient in Nepal Himalaya. Although geometric constraints (MDE), species range size, and human population density were significantly related to threatened species richness, the interaction between range size and human population density was of greater importance. Threatened species richness was positively associated with human population density and negatively associated with range size. In areas with high richness of threatened species, species ranges tend to be small. The preponderance of species at risk of extinction at low elevations in the subtropical biodiversity hotspot could be due to the double impact of smaller range sizes and higher human density.

  11. Solar constraints

    International Nuclear Information System (INIS)

    Provost, J.

    1984-01-01

    Accurate tests of the theory of stellar structure and evolution are available from the Sun's observations. The solar constraints are reviewed, with a special attention to the recent progress in observing global solar oscillations. Each constraint is sensitive to a given region of the Sun. The present solar models (standard, low Z, mixed) are discussed with respect to neutrino flux, low and high degree five-minute oscillations and low degree internal gravity modes. It appears that actually there do not exist solar models able to fully account for all the observed quantities. (Auth.)

  12. Cultural influences on the neural correlate of moral decision making processes.

    Science.gov (United States)

    Han, Hyemin; Glover, Gary H; Jeong, Changwoo

    2014-02-01

    This study compares the neural substrate of moral decision making processes between Korean and American participants. By comparison with Americans, Korean participants showed increased activity in the right putamen associated with socio-intuitive processes and right superior frontal gyrus associated with cognitive control processes under a moral-personal condition, and in the right postcentral sulcus associated with mental calculation in familiar contexts under a moral-impersonal condition. On the other hand, American participants showed a significantly higher degree of activity in the bilateral anterior cingulate cortex (ACC) associated with conflict resolution under the moral-personal condition, and in the right medial frontal gyrus (MFG) associated with simple cognitive branching in non-familiar contexts under the moral-impersonal condition when a more lenient threshold was applied, than Korean participants. These findings support the ideas of the interactions between the cultural background, education, and brain development, proposed in the field of cultural psychology and educational psychology. The study introduces educational implications relevant to moral psychologists and educators. Copyright © 2013 Elsevier B.V. All rights reserved.

  13. Attribution of intentional causation influences the perception of observed movements: Behavioural evidence and neural correlates

    Directory of Open Access Journals (Sweden)

    James W Moore

    2013-01-01

    Full Text Available Recent research on human agency suggests that intentional causation is associated with a subjective compression in the temporal interval between actions and their effects. That is, intentional movements and their causal effects are perceived as closer together in time than equivalent unintentional movements and their causal effects. This so-called intentional binding effect is consistently found for one’s own self-generated actions. It has also been suggested that intentional binding occurs when observing intentional movements of others. However, this evidence is undermined by limitations of the paradigm used. In the current study we aimed to overcome these limitations using a more rigorous design in combination with functional Magnetic Resonance Imaging (fMRI to explore the neural underpinnings of intentional binding of observed movements. In particular, we aimed to identify brain areas sensitive to the interaction between intentionality and causality attributed to the observed action. Our behavioural results confirmed the occurrence of intentional binding for observed movements using this more rigorous paradigm. Our fMRI results highlighted a collection of brain regions whose activity was sensitive to the interaction between intentionality and causation. Intriguingly, these brain regions have previously been implicated in the sense of agency over one’s own movements. We discuss the implications of these results for intentional binding specifically, and the sense of agency more generally.

  14. The influence of chromatic context on binocular color rivalry: Perception and neural representation

    Science.gov (United States)

    Hong, Sang Wook; Shevell, Steven K.

    2008-01-01

    The predominance of rivalrous targets is affected by surrounding context when stimuli rival in orientation, motion or color. This study investigated the influence of chromatic context on binocular color rivalry. The predominance of rivalrous chromatic targets was measured in various surrounding contexts. The first experiment showed that a chromatic surround's influence was stronger when the surround was uniform or a grating with luminance contrast (chromatic/black grating) compared to an equiluminant grating (chromatic/white). The second experiment revealed virtually no effect of the orientation of the surrounding chromatic context, using chromatically rivalrous vertical gratings. These results are consistent with a chromatic representation of the context by a non-oriented, chromatically selective and spatially antagonistic receptive field. Neither a double-opponent receptive field nor a receptive field without spatial antagonism accounts for the influence of context on binocular color rivalry. PMID:18331750

  15. The influence of the dwell time deviation constraint (DTDC) parameter on dosimetry with IPSA optimisation for HDR prostate brachytherapy

    International Nuclear Information System (INIS)

    Smith, Ryan L.; Millar, Jeremy L.; Panettieri, Vanessa; Mason, Natasha; Lancaster, Craig; Francih, Rick D.

    2015-01-01

    To investigate how the dwell time deviation constraint (DTDC) parameter, applied to inverse planning by simulated annealing (IPSA) optimisation limits large dwell times from occurring in each catheter and to characterise the effect on the resulting dosimetry for prostate high dose rate (HDR) brachytherapy treatment plans. An unconstrained IPSA optimised treatment plan, using the Oncentra Brachytherapy treatment planning system (version 4.3, Nucletron an Elekta company, Elekta AB, Stockholm, Sweden), was generated for 20 consecutive HDR prostate brachytherapy patients, with the DTDC set to zero. Successive constrained optimisation plans were also created for each patient by increasing the DTDC parameter by 0.2, up to a maximum value of 1.0. We defined a “plan modulation index”, to characterise the change of dwell time modulation as the DTDC parameter was increased. We calculated the dose volume histogram indices for the PTV (D90, V100, V150, V200%) and urethra (D10%) to characterise the effect on the resulting dosimetry. The average PTV D90% decreases as the DTDC is applied, on average by only 1.5 %, for a DTDC = 0.4. The measures of high dose regions in the PTV, V150 and V200%, increase on average by less than 5 and 2 % respectively. The net effect of DTDC on the modulation of dwell times has been characterised by the introduction of the plan modulation index. DTDC applied during IPSA optimisation of HDR prostate brachytherapy plans reduce the occurrence of large isolated dwell times within individual catheters. The mechanism by which DTDC works has been described and its effect on the modulation of dwell times has been characterised. The authors recommend using a DTDC parameter no greater than 0.4 to obtain a plan with dwell time modulation comparable to a geometric optimised plan. This yielded on average a 1.5 % decrease in PTV coverage and an acceptable increase in V150%, without compromising the urethral dose.

  16. Neuromodulation and developmental contextual influences on neural and cognitive plasticity across the lifespan.

    Science.gov (United States)

    Li, Shu-Chen

    2013-11-01

    Behavioral, cognitive, and motivational development entails co-constructive interactions between the environmental and social influences from the developmental context, on the one hand, and the individual's neurobiological inheritance, on the other hand. Key brain networks underlying cognition, emotion, and motivation are innervated by major transmitter systems (e.g., the catecholamines and acetylcholine). Thus, the maturation and senescence of neurotransmitter systems have direct implications for lifespan development. In addition to reviewing evidence on life age differences in dopaminergic modulation and cognitive development, this brief review selectively highlights recent findings on how important influences from the developmental context, such as reward-mediated motivational processes, transgenerational stress transmission, psychosocial stress, and cognitive interventions, may, in part, exert their effects on brain and behavioral development through their effects on neuromodulatory mechanisms. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. The constraints

    International Nuclear Information System (INIS)

    Jones, P.M.S.

    1987-01-01

    There are considerable incentives for the use of nuclear in preference to other sources for base load electricity generation in most of the developed world. These are economic, strategic, environmental and climatic. However, there are two potential constraints which could hinder the development of nuclear power to its full economic potential. These are public opinion and financial regulations which distort the nuclear economic advantage. The concerns of the anti-nuclear lobby are over safety, (especially following the Chernobyl accident), the management of radioactive waste, the potential effects of large scale exposure of the population to radiation and weapons proliferation. These are discussed. The financial constraint is over two factors, the availability of funds and the perception of cost, both of which are discussed. (U.K.)

  18. Imaging Posture Veils Neural Signals

    Directory of Open Access Journals (Sweden)

    Robert T Thibault

    2016-10-01

    Full Text Available Whereas modern brain imaging often demands holding body positions incongruent with everyday life, posture governs both neural activity and cognitive performance. Humans commonly perform while upright; yet, many neuroimaging methodologies require participants to remain motionless and adhere to non-ecological comportments within a confined space. This inconsistency between ecological postures and imaging constraints undermines the transferability and generalizability of many a neuroimaging assay.Here we highlight the influence of posture on brain function and behavior. Specifically, we challenge the tacit assumption that brain processes and cognitive performance are comparable across a spectrum of positions. We provide an integrative synthesis regarding the increasingly prominent influence of imaging postures on autonomic function, mental capacity, sensory thresholds, and neural activity. Arguing that neuroimagers and cognitive scientists could benefit from considering the influence posture wields on both general functioning and brain activity, we examine existing imaging technologies and the potential of portable and versatile imaging devices (e.g., functional near infrared spectroscopy. Finally, we discuss ways that accounting for posture may help unveil the complex brain processes of everyday cognition.

  19. Variety identification of wheat using mass spectrometry with neural networks and the influence of mass spectra processing prior to neural network analysis

    DEFF Research Database (Denmark)

    Sørensen, Helle Aagaard; Sperotto, Maria Maddalena; Petersen, M.

    2002-01-01

    The performance of matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks in wheat variety classification is further evaluated.(1) Two principal issues were studied: (a) the number of varieties that could be classified correctly; and (b) various means of....... With the final method, it was possible to classify 30 wheat varieties with 87% correctly classified mass spectra and a correlation coefficient of 0.90....

  20. Influences of social reward experience on behavioral responses to drugs of abuse: Review of shared and divergent neural plasticity mechanisms for sexual reward and drugs of abuse.

    Science.gov (United States)

    Beloate, Lauren N; Coolen, Lique M

    2017-12-01

    Different factors influence the development of drug addiction in humans, including social reward experiences. In animals, experience with social rewards, such as sexual behavior, pair bonding, social and environmental enrichment, can be protective. However, loss or lack of social rewards can lead to a vulnerability to drug-seeking behavior. The effects of social reward experience on drug-seeking behavior are associated with changes in the neural pathways that control drug-related behavior. This review will provide an introduction and overview of the mesolimbic pathway and the influence of social reward experience on drug-seeking behavior in rodents. Moreover, the research from our laboratory on effects of sexual experience and loss of sex reward on psychostimulant and opiate reward will be reviewed. Finally, we will review current knowledge of the neural mechanisms that underlie these interactions. Investigations of the neural underpinnings by which social and drug rewards interact contribute to improved understanding of the neural basis of vulnerability for drug addiction and reward-related behaviors in general. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Body Position Influences Which Neural Structures Are Recruited by Lumbar Transcutaneous Spinal Cord Stimulation.

    Directory of Open Access Journals (Sweden)

    Simon M Danner

    Full Text Available Transcutaneous stimulation of the human lumbosacral spinal cord is used to evoke spinal reflexes and to neuromodulate altered sensorimotor function following spinal cord injury. Both applications require the reliable stimulation of afferent posterior root fibers. Yet under certain circumstances, efferent anterior root fibers can be co-activated. We hypothesized that body position influences the preferential stimulation of sensory or motor fibers. Stimulus-triggered responses to transcutaneous spinal cord stimulation were recorded using surface-electromyography from quadriceps, hamstrings, tibialis anterior, and triceps surae muscles in 10 individuals with intact nervous systems in the supine, standing and prone positions. Single and paired (30-ms inter-stimulus intervals biphasic stimulation pulses were applied through surface electrodes placed on the skin between the T11 and T12 inter-spinous processes referenced to electrodes on the abdomen. The paired stimulation was applied to evaluate the origin of the evoked electromyographic response; trans-synaptic responses would be suppressed whereas direct efferent responses would almost retain their amplitude. We found that responses to the second stimulus were decreased to 14%±5% of the amplitude of the response to the initial pulse in the supine position across muscles, to 30%±5% in the standing, and to only 80%±5% in the prone position. Response thresholds were lowest during standing and highest in the prone position and response amplitudes were largest in the supine and smallest in the prone position. The responses obtained in the supine and standing positions likely resulted from selective stimulation of sensory fibers while concomitant motor-fiber stimulation occurred in the prone position. We assume that changes of root-fiber paths within the generated electric field when in the prone position increase the stimulation thresholds of posterior above those of anterior root fibers. Thus, we

  2. The Ambiguous Role of Constraints in Creativity

    DEFF Research Database (Denmark)

    Biskjær, Michael Mose; Onarheim, Balder; Wiltschnig, Stefan

    2011-01-01

    The relationship between creativity and constraints is often described in the literature either in rather imprecise, general concepts or in relation to very specific domains. Cross-domain and cross-disciplinary takes on how the handling of constraints influences creative activities are rare. In t......-disciplinary research into the ambiguous role of constraints in creativity....

  3. Creativity from Constraints in Engineering Design

    DEFF Research Database (Denmark)

    Onarheim, Balder

    2012-01-01

    This paper investigates the role of constraints in limiting and enhancing creativity in engineering design. Based on a review of literature relating constraints to creativity, the paper presents a longitudinal participatory study from Coloplast A/S, a major international producer of disposable...... and ownership of formal constraints played a crucial role in defining their influence on creativity – along with the tacit constraints held by the designers. The designers were found to be highly constraint focused, and four main creative strategies for constraint manipulation were observed: blackboxing...

  4. The Influence of Genre Constraints on Author Representation in Medical Research Articles. The French Indefinite Pronoun On in IMRAD Research Articles

    Directory of Open Access Journals (Sweden)

    Anje Müller Gjesdal

    2013-07-01

    Full Text Available Scientific discourse is characterized by highly normative and strict genre constraints on language use, both on the macro (text and micro (word level. The purpose of this article is to study the distribution of the French indefinite pronoun on and its interpretative values across the sections of French-language medical articles (KIAP corpus in the IMRAD format (Introduction, Methods, Results and Discussion. The main hypothesis is that the IMRAD structure entails a specific distribution of macro level textual structures (author roles, argumentation, rhetorical functions, and that this is reflected in the distribution of micro level linguistic markers, such as the pronoun on. Previous studies based on a more limited material (Gjesdal, 2008 indicate that the variation in the interpretative values of on seems to be influenced by the IMRAD format, and, furthermore, that the different values seem to correspond to different author roles. Particular emphasis will be put on the influence of the linear sequencing of text imposed by the IMRAD format and the distribution of author roles and speech acts across the text.Le discours scientifique se caractérise par des contraintes rigoureuses sur la production linguistique au niveau micro-linguistique (mot aussi bien qu’au niveau macro-linguistique (texte. Cet article a pour objectif d’étudier la distribution du pronom on et ses valeurs interprétatives à travers les sections d’articles médicaux sous le format IMRAD (Introduction, Methods, Results and Discussion dans un corpus d’articles médicaux (le corpus KIAP. L’hypothèse principale est que la structure IMRAD impose une distribution spécifique de structures textuelles (rôles d’auteur, argumentation, fonctions rhétoriques qui sera à son tour reflétée dans la distribution des marqueurs micro-linguistiques comme le pronom on.

  5. The power of the “like” in adolescence: Effects of peer influence on neural and behavioral responses to social media

    Science.gov (United States)

    Sherman, Lauren E.; Payton, Ashley A.; Hernandez, Leanna M.; Greenfield, Patricia M.; Dapretto, Mirella

    2016-01-01

    We investigated a unique way in which adolescent peer influence occurs on social media. We developed a novel fMRI paradigm to simulate the popular social photo-sharing tool Instagram, and measured adolescents’ behavioral and neural responses to “Likes,” a quantifiable form of social endorsement and potential source of peer influence. Adolescents underwent fMRI while viewing photographs ostensibly submitted to Instagram. Adolescents were more likely to Like photos depicted with many Likes and refrain from Liking photos with few Likes – indicating the influence of virtual peer endorsement, a finding that held for both neutral photos and photos of risky behaviors (e.g., drinking, smoking). Viewing photographs with many (vs. few) Likes was associated with greater activity in neural regions implicated in reward processing, social cognition, imitation, and attention. Furthermore, when adolescents viewed risky (vs. non-risky) photographs, activation in the cognitive control network decreased. These findings highlight possible mechanisms underlying peer influence during adolescence. PMID:27247125

  6. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

    The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

  7. Influences of hand dominance on the maintenance of benefits after home-based modified constraint-induced movement therapy in individuals with stroke

    Directory of Open Access Journals (Sweden)

    Renata C. M. Lima

    2014-10-01

    Full Text Available Objective: To investigate the influence of hand dominance on the maintenance of gains after home-based modified constraint-induced movement therapy (mCIMT. Method: Aprevious randomized controlled trial was conducted to examine the addition of trunk restraint to the mCIMT. Twenty-two chronic stroke survivors with mild to moderate motor impairments received individual home-based mCIMT with or without trunk restraints, five times per week, three hours daily over two weeks. In this study, the participants were separated into dominant group, which had their paretic upper limb as dominant before the stroke (n=8, and non-dominant group (n=14 for analyses. The ability to perform unimanual tasks was measured by the Wolf Motor Function Test (WMFT and the Motor Activity Log (MAL, whereas the capacity to perform bimanual tasks was measured using the Bilateral Activity Assessment Scale (BAAS. Results: Analysis revealed significant positive effects on the MAL amount of use and quality of the movement scales, as well as on the BAAS scores after intervention, with no differences between groups. Both groups maintained the bimanual improvements during follow-ups (BAAS-seconds 0.1, 95% CI -10.0 to 10.0, however only the dominant group maintained the unilateral improvements (MAL-amount of use: 1.5, 95% CI 0.7 to 2.3; MAL-quality: 1.3, 95% CI 0.5 to 2.1. Conclusions: Upper limb dominance did not interfere with the acquisition of upper limb skills after mCIMT. However, the participants whose paretic upper limb was dominant demonstrated better abilities to maintain the unilateral gains. The bilateral improvements were maintained, regardless of upper limb dominance.

  8. What’s the Gist? The influence of schemas on the neural correlates underlying true and false memories

    Science.gov (United States)

    Webb, Christina E.; Turney, Indira C.; Dennis, Nancy A.

    2017-01-01

    The current study used a novel scene paradigm to investigate the role of encoding schemas on memory. Specifically, the study examined the influence of a strong encoding schema on retrieval of both schematic and non-schematic information, as well as false memories for information associated with the schema. Additionally, the separate roles of recollection and familiarity in both veridical and false memory retrieval were examined. The study identified several novel results. First, while many common neural regions mediated both schematic and non-schematic retrieval success, schematic recollection exhibited greater activation in visual cortex and hippocampus, regions commonly shown to mediate detailed retrieval. More effortful cognitive control regions in the prefrontal and parietal cortices, on the other hand, supported non-schematic recollection, while lateral temporal cortices supported familiarity-based retrieval of non-schematic items. Second, both true and false recollection, as well as familiarity, were mediated by activity in left middle temporal gyrus, a region associated with semantic processing and retrieval of schematic gist. Moreover, activity in this region was greater for both false recollection and false familiarity, suggesting a greater reliance on lateral temporal cortices for retrieval of illusory memories, irrespective of memory strength. Consistent with previous false memory studies, visual cortex showed increased activity for true compared to false recollection, suggesting that visual cortices are critical for distinguishing between previously viewed targets and related lures at retrieval. Additionally, the absence of common visual activity between true and false retrieval suggests that, unlike previous studies utilizing visual stimuli, when false memories are predicated on schematic gist and not perceptual overlap, there is little reliance on visual processes during false memory retrieval. Finally, the medial temporal lobe exhibited an

  9. Peak torque and rate of torque development influence on repeated maximal exercise performance: contractile and neural contributions.

    Science.gov (United States)

    Morel, Baptiste; Rouffet, David M; Saboul, Damien; Rota, Samuel; Clémençon, Michel; Hautier, Christophe A

    2015-01-01

    Rapid force production is critical to improve performance and prevent injuries. However, changes in rate of force/torque development caused by the repetition of maximal contractions have received little attention. The aim of this study was to determine the relative influence of rate of torque development (RTD) and peak torque (T(peak)) on the overall performance (i.e. mean torque, T(mean)) decrease during repeated maximal contractions and to investigate the contribution of contractile and neural mechanisms to the alteration of the various mechanical variables. Eleven well-trained men performed 20 sets of 6-s isokinetic maximal knee extensions at 240° · s(-1), beginning every 30 seconds. RTD, T(peak) and T(mean) as well as the Rate of EMG Rise (RER), peak EMG (EMG(peak)) and mean EMG (EMG(mean)) of the vastus lateralis were monitored for each contraction. A wavelet transform was also performed on raw EMG signal for instant mean frequency (if(mean)) calculation. A neuromuscular testing procedure was carried out before and immediately after the fatiguing protocol including evoked RTD (eRTD) and maximal evoked torque (eT(peak)) induced by high frequency doublet (100 Hz). T(mean) decrease was correlated to RTD and T(peak) decrease (R(²) = 0.62; p<0.001; respectively β=0.62 and β=0.19). RER, eRTD and initial if(mean) (0-225 ms) decreased after 20 sets (respectively -21.1 ± 14.1, -25 ± 13%, and ~20%). RTD decrease was correlated to RER decrease (R(²) = 0.36; p<0.05). The eT(peak) decreased significantly after 20 sets (24 ± 5%; p<0.05) contrary to EMG(peak) (-3.2 ± 19.5 %; p=0.71). Our results show that reductions of RTD explained part of the alterations of the overall performance during repeated moderate velocity maximal exercise. The reductions of RTD were associated to an impairment of the ability of the central nervous system to maximally activate the muscle in the first milliseconds of the contraction.

  10. Influence of the Training Set Value on the Quality of the Neural Network to Identify Selected Moulding Sand Properties

    Directory of Open Access Journals (Sweden)

    Jakubski J.

    2013-06-01

    Full Text Available Artificial neural networks are one of the modern methods of the production optimisation. An attempt to apply neural networks for controlling the quality of bentonite moulding sands is presented in this paper. This is the assessment method of sands suitability by means of detecting correlations between their individual parameters. This paper presents the next part of the study on usefulness of artificial neural networks to support rebonding of green moulding sand, using chosen properties of moulding sands, which can be determined fast. The effect of changes in the training set quantity on the quality of the network is presented in this article. It has been shown that a small change in the data set would change the quality of the network, and may also make it necessary to change the type of network in order to obtain good results.

  11. Buffering social influence: neural correlates of response inhibition predict driving safety in the presence of a peer.

    Science.gov (United States)

    Cascio, Christopher N; Carp, Joshua; O'Donnell, Matthew Brook; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G; Falk, Emily B

    2015-01-01

    Adolescence is a period characterized by increased sensitivity to social cues, as well as increased risk-taking in the presence of peers. For example, automobile crashes are the leading cause of death for adolescents, and driving with peers increases the risk of a fatal crash. Growing evidence points to an interaction between neural systems implicated in cognitive control and social and emotional context in predicting adolescent risk. We tested such a relationship in recently licensed teen drivers. Participants completed an fMRI session in which neural activity was measured during a response inhibition task, followed by a separate driving simulator session 1 week later. Participants drove alone and with a peer who was randomly assigned to express risk-promoting or risk-averse social norms. The experimentally manipulated social context during the simulated drive moderated the relationship between individual differences in neural activity in the hypothesized cognitive control network (right inferior frontal gyrus, BG) and risk-taking in the driving context a week later. Increased activity in the response inhibition network was not associated with risk-taking in the presence of a risky peer but was significantly predictive of safer driving in the presence of a cautious peer, above and beyond self-reported susceptibility to peer pressure. Individual differences in recruitment of the response inhibition network may allow those with stronger inhibitory control to override risky tendencies when in the presence of cautious peers. This relationship between social context and individual differences in brain function expands our understanding of neural systems involved in top-down cognitive control during adolescent development.

  12. [Analysis of the Characteristics of Infantile Small World Neural Network Node Properties Correlated with the Influencing Factors].

    Science.gov (United States)

    Qu, Haibo; Lu, Su; Zhang, Wenjing; Xiao, Yuan; Ning, Gang; Sun, Huaiqiang

    2016-10-01

    We applied resting-state functional magnetic resonance imaging(rfMRI)combined with graph theory to analyze 90 regions of the infantile small world neural network of the whole brain.We tried to get the following two points clear:1 whether the parameters of the node property of the infantile small world neural network are correlated with the level of infantile intelligence development;2 whether the parameters of the infantile small world neural network are correlated with the children’s baseline parameters,i.e.,the demographic parameters such as gender,age,parents’ education level,etc.Twelve cases of healthy infants were included in the investigation(9males and 3females with the average age of 33.42±8.42 months.)We then evaluated the level of infantile intelligence of all the cases and graded by Gesell Development Scale Test.We used a Siemens 3.0T Trio imaging system to perform resting-state(rs)EPI scans,and collected the BOLD functional Magnetic Resonance Imaging(fMRI)data.We performed the data processing with Statistical Parametric Mapping 5(SPM5)based on Matlab environment.Furthermore,we got the attributes of the whole brain small world and node attributes of 90 encephalic regions of templates of Anatomatic Automatic Labeling(ALL).At last,we carried out correlation study between the above-mentioned attitudes,intelligence scale parameters and demographic data.The results showed that many node attributes of small world neural network were closely correlated with intelligence scale parameters.Betweeness was mainly centered in thalamus,superior frontal gyrus,and occipital lobe(negative correlation).The r value of superior occipital gyrus associated with the individual and social intelligent scale was-0.729(P=0.007);degree was mainly centered in amygdaloid nucleus,superior frontal gyrus,and inferior parietal gyrus(positive correlation).The r value of inferior parietal gyrus associated with the gross motor intelligent scale was 0.725(P=0.008);efficiency was mainly

  13. Simultaneous influence of gas mixture composition and process temperature on Fe2O3->FeO reduction kinetics: neural network modeling

    Directory of Open Access Journals (Sweden)

    K. Piotrowski

    2005-09-01

    Full Text Available The kinetics of Fe2O3->FeO reaction was investigated. The thermogravimetric (TGA data covered the reduction of hematite both by pure species (nitrogen diluted CO or H2 and by their mixture. The conventional analysis has indicated that initially the reduction of hematite is a complex, surface controlled process, however once a thin layer of lower oxidation state iron oxides (magnetite, wüstite is formed on the surface, it changes to diffusion control. Artificial Neural Network (ANN has proved to be a convenient tool for modeling of this complex, heterogeneous reaction runs within the both (kinetic and diffusion regions, correctly considering influence of temperature and gas composition effects and their complex interactions. ANN's model shows the capability to mimic some extreme (minimum of the reaction rate within the determined temperature window, while the Arrhenius dependency is of limited use.

  14. Influence of neural mobilization of lower limbs on the functional performance and dynamic balance in asymptomatic individuals: a cross-over randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Nunes Guilherme S.

    2017-12-01

    Full Text Available Purpose. To verify the influence of neural mobilization (NM applied to the lower limbs on functional performance and dynamic balance in asymptomatic individuals. Methods. The total of 30 asymptomatic participants (15 women and 15 men; age, 30.1 ± 6.7 years; height, 1.70 ± 0.1 m; body mass, 73.1 ± 13.4 kg were enrolled in this cross-over randomized controlled trial. The participants received NM of the femoral, sciatic, and tibial nerves, as well as static stretching (SS of the following muscles: hamstring, lumbar, piriformis, hip adductors, hip flexors, quadriceps, and triceps surae. The order of applying NM and SS was randomly decided and the interventions were performed at least 48 hours apart. Functional performance was measured by performance in vertical jump (VJ and dynamic balance was measured with the Star Excursion Balance Test (SEBT. Results. There were no differences between NM and SS for height (cm in VJ (p = 0.16 or in the distance reached (% in the SEBT, normalized by lower limb length (dominant limb: anterior, p = 0.35; posterolateral, p = 0.69; posteromedial, p = 0.50 / non-dominant limb: anterior, p = 0.68; posterolateral, p = 1.00; posteromedial, p = 0.77. Conclusions. NM did not exert any influence on functional performance or dynamic balance. Thereby, having no positive or negative impact on performance, NM can be used at any time of treatment.

  15. Causal influence in neural systems: Reconciling mechanistic-reductionist and statistical perspectives. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino & S.L. Bressler

    Science.gov (United States)

    Griffiths, John D.

    2015-12-01

    The modern understanding of the brain as a large, complex network of interacting elements is a natural consequence of the Neuron Doctrine [1,2] that has been bolstered in recent years by the tools and concepts of connectomics. In this abstracted, network-centric view, the essence of neural and cognitive function derives from the flows between network elements of activity and information - or, more generally, causal influence. The appropriate characterization of causality in neural systems, therefore, is a question at the very heart of systems neuroscience.

  16. Application of artificial neural network model for groundwater level forecasting in a river island with artificial influencing factors

    Science.gov (United States)

    Lee, Sanghoon; Yoon, Heesung; Park, Byeong-Hak; Lee, Kang-Kun

    2017-04-01

    Groundwater use has been increased for various purposes like agriculture, industry or drinking water in recent years, the issue related to sustainability on the groundwater use also has been raised. Accordingly, forecasting the groundwater level is of great importance for planning sustainable use of groundwater. In a small island surrounded by the Han River, South Korea, seasonal fluctuation of the groundwater level is characterized by multiple factors such as recharge/discharge event of the Paldang dam, Water Curtain Cultivation (WCC) during the winter season, operation of Groundwater Heat Pump System (GWHP). For a period when the dam operation is only occurred in the study area, a prediction of the groundwater level can be easily achieved by a simple cross-correlation model. However, for a period when the WCC and the GWHP systems are working together, the groundwater level prediction is challenging due to its unpredictable operation of the two systems. This study performed Artificial Neural Network (ANN) model to forecast the groundwater level in the river area reflecting the various predictable/unpredictable factors. For constructing the ANN models, two monitoring wells, YSN1 and YSO8, which are located near the injection and abstraction wells for the GWHP system were selected, respectively. By training with the groundwater level data measured in January 2015 to August 2015, response of groundwater level by each of the surface water level, the WCC and the GWHP system were evaluated. Consequentially, groundwater levels in December 2015 to March 2016 were predicted by ANN models, providing optimal fits in comparison to the observed water levels. This study suggests that the ANN model is a useful tool to forecast the groundwater level in terms of the management of groundwater. Acknowledgement : Financial support was provided by the "R&D Project on Environmental Management of Geologic CO2 Storage" from the KEITI (Project Number: 2014001810003) This research was

  17. Neural correlates of stress-induced and cue-induced drug craving: influences of sex and cocaine dependence.

    Science.gov (United States)

    Potenza, Marc N; Hong, Kwang-ik Adam; Lacadie, Cheryl M; Fulbright, Robert K; Tuit, Keri L; Sinha, Rajita

    2012-04-01

    Although stress and drug cue exposure each increase drug craving and contribute to relapse in cocaine dependence, no previous research has directly examined the neural correlates of stress-induced and drug cue-induced craving in cocaine-dependent women and men relative to comparison subjects. Functional MRI was used to assess responses to individualized scripts for stress, drug/alcohol cue and neutral-relaxing-imagery conditions in 30 abstinent cocaine-dependent individuals (16 women, 14 men) and 36 healthy recreational-drinking comparison subjects (18 women, 18 men). Significant three-way interactions between diagnostic group, sex, and script condition were observed in multiple brain regions including the striatum, insula, and anterior and posterior cingulate. Within women, group-by-condition interactions were observed involving these regions and were attributable to relatively increased regional activations in cocaine-dependent women during the stress and, to a lesser extent, neutral-relaxing conditions. Within men, group main effects were observed involving these same regions, with cocaine-dependent men demonstrating relatively increased activation across conditions, with the main contributions from the drug and neutral-relaxing conditions. In men and women, subjective drug-induced craving measures correlated positively with corticostriatal-limbic activations. In cocaine dependence, corticostriatal-limbic hyperactivity appears to be linked to stress cues in women, drug cues in men, and neutral-relaxing conditions in both. These findings suggest that sex should be taken into account in the selection of therapies in the treatment of addiction, particularly those targeting stress reduction.

  18. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  19. Financing Constraints and Entrepreneurship

    OpenAIRE

    William R. Kerr; Ramana Nanda

    2009-01-01

    Financing constraints are one of the biggest concerns impacting potential entrepreneurs around the world. Given the important role that entrepreneurship is believed to play in the process of economic growth, alleviating financing constraints for would-be entrepreneurs is also an important goal for policymakers worldwide. We review two major streams of research examining the relevance of financing constraints for entrepreneurship. We then introduce a framework that provides a unified perspecti...

  20. Temporal Concurrent Constraint Programming

    DEFF Research Database (Denmark)

    Nielsen, Mogens; Valencia Posso, Frank Dan

    2002-01-01

    The ntcc calculus is a model of non-deterministic temporal concurrent constraint programming. In this paper we study behavioral notions for this calculus. In the underlying computational model, concurrent constraint processes are executed in discrete time intervals. The behavioral notions studied...... reflect the reactive interactions between concurrent constraint processes and their environment, as well as internal interactions between individual processes. Relationships between the suggested notions are studied, and they are all proved to be decidable for a substantial fragment of the calculus...

  1. A new perspective on behavioral inconsistency and neural noise in aging: Compensatory speeding of neural communication

    Directory of Open Access Journals (Sweden)

    S. Lee Hong

    2012-09-01

    Full Text Available This paper seeks to present a new perspective on the aging brain. Here, we make connections between two key phenomena of brain aging: 1 increased neural noise or random background activity; and 2 slowing of brain activity. Our perspective proposes the possibility that the slowing of neural processing due to decreasing nerve conduction velocities leads to a compensatory speeding of neuron firing rates. These increased firing rates lead to a broader distribution of power in the frequency spectrum of neural oscillations, which we propose, can just as easily be interpreted as neural noise. Compensatory speeding of neural activity, as we present, is constrained by the: A availability of metabolic energy sources; and B competition for frequency bandwidth needed for neural communication. We propose that these constraints lead to the eventual inability to compensate for age-related declines in neural function that are manifested clinically as deficits in cognition, affect, and motor behavior.

  2. Mobility and Position Error Analysis of a Complex Planar Mechanism with Redundant Constraints

    Science.gov (United States)

    Sun, Qipeng; Li, Gangyan

    2018-03-01

    Nowadays mechanisms with redundant constraints have been created and attracted much attention for their merits. The mechanism of the redundant constraints in a mechanical system is analyzed in this paper. A analysis method of Planar Linkage with a repetitive structure is proposed to get the number and type of constraints. According to the difference of applications and constraint characteristics, the redundant constraints are divided into the theoretical planar redundant constraints and the space-planar redundant constraints. And the calculation formula for the number of redundant constraints and type of judging method are carried out. And a complex mechanism with redundant constraints is analyzed of the influence about redundant constraints on mechanical performance. With the combination of theoretical derivation and simulation research, a mechanism analysis method is put forward about the position error of complex mechanism with redundant constraints. It points out the direction on how to eliminate or reduce the influence of redundant constraints.

  3. The influence of L-opsin gene polymorphisms and neural ageing on spatio-chromatic contrast sensitivity in 20-71 year olds.

    Science.gov (United States)

    Dees, Elise W; Gilson, Stuart J; Neitz, Maureen; Baraas, Rigmor C

    2015-11-01

    Chromatic contrast sensitivity may be a more sensitive measure of an individual's visual function than achromatic contrast sensitivity. Here, the first aim was to quantify individual- and age-related variations in chromatic contrast sensitivity to a range of spatial frequencies for stimuli along two complementary directions in color space. The second aim was to examine whether polymorphisms at specific amino acid residues of the L- and M-opsin genes (OPN1LW and OPN1MW) known to affect spectral tuning of the photoreceptors could influence spatio-chromatic contrast sensitivity. Chromatic contrast sensitivity functions were measured in 50 healthy individuals (20-71 years) employing a novel pseudo-isochromatic grating stimulus. The spatio-chromatic contrast sensitivity functions were found to be low pass for all subjects, independent of age and color vision. The results revealed a senescent decline in spatio-chromatic contrast sensitivity. There were considerable between-individual differences in sensitivity within each age decade for individuals 49 years old or younger, and age did not predict sensitivity for these age decades alone. Forty-six subjects (including a color deficient male and eight female carriers) were genotyped for L- and M-opsin genes. The Ser180Ala polymorphisms on the L-opsin gene were found to influence the subject's color discrimination and their sensitivity to spatio-chromatic patterns. The results expose the significant role of neural and genetic factors in the deterioration of visual function with increasing age. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Temporal Concurrent Constraint Programming

    DEFF Research Database (Denmark)

    Nielsen, Mogens; Palamidessi, Catuscia; Valencia, Frank Dan

    2002-01-01

    The ntcc calculus is a model of non-deterministic temporal concurrent constraint programming. In this paper we study behavioral notions for this calculus. In the underlying computational model, concurrent constraint processes are executed in discrete time intervals. The behavioral notions studied...

  5. Evaluating Distributed Timing Constraints

    DEFF Research Database (Denmark)

    Kristensen, C.H.; Drejer, N.

    1994-01-01

    In this paper we describe a solution to the problem of implementing time-optimal evaluation of timing constraints in distributed real-time systems.......In this paper we describe a solution to the problem of implementing time-optimal evaluation of timing constraints in distributed real-time systems....

  6. Theory of Constraints (TOC)

    DEFF Research Database (Denmark)

    Michelsen, Aage U.

    2004-01-01

    Tankegangen bag Theory of Constraints samt planlægningsprincippet Drum-Buffer-Rope. Endvidere skitse af The Thinking Process.......Tankegangen bag Theory of Constraints samt planlægningsprincippet Drum-Buffer-Rope. Endvidere skitse af The Thinking Process....

  7. Neural overlap in processing music and speech.

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L

    2015-03-19

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  8. Neural overlap in processing music and speech

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L.

    2015-01-01

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. PMID:25646513

  9. Constraint-based reachability

    Directory of Open Access Journals (Sweden)

    Arnaud Gotlieb

    2013-02-01

    Full Text Available Iterative imperative programs can be considered as infinite-state systems computing over possibly unbounded domains. Studying reachability in these systems is challenging as it requires to deal with an infinite number of states with standard backward or forward exploration strategies. An approach that we call Constraint-based reachability, is proposed to address reachability problems by exploring program states using a constraint model of the whole program. The keypoint of the approach is to interpret imperative constructions such as conditionals, loops, array and memory manipulations with the fundamental notion of constraint over a computational domain. By combining constraint filtering and abstraction techniques, Constraint-based reachability is able to solve reachability problems which are usually outside the scope of backward or forward exploration strategies. This paper proposes an interpretation of classical filtering consistencies used in Constraint Programming as abstract domain computations, and shows how this approach can be used to produce a constraint solver that efficiently generates solutions for reachability problems that are unsolvable by other approaches.

  10. Neurometaplasticity: Glucoallostasis control of plasticity of the neural networks of error commission, detection, and correction modulates neuroplasticity to influence task precision

    Science.gov (United States)

    Welcome, Menizibeya O.; Dane, Şenol; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

    2017-12-01

    The term "metaplasticity" is a recent one, which means plasticity of synaptic plasticity. Correspondingly, neurometaplasticity simply means plasticity of neuroplasticity, indicating that a previous plastic event determines the current plasticity of neurons. Emerging studies suggest that neurometaplasticity underlie many neural activities and neurobehavioral disorders. In our previous work, we indicated that glucoallostasis is essential for the control of plasticity of the neural network that control error commission, detection and correction. Here we review recent works, which suggest that task precision depends on the modulatory effects of neuroplasticity on the neural networks of error commission, detection, and correction. Furthermore, we discuss neurometaplasticity and its role in error commission, detection, and correction.

  11. 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

  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. Resources, constraints and capabilities

    NARCIS (Netherlands)

    Dhondt, S.; Oeij, P.R.A.; Schröder, A.

    2018-01-01

    Human and financial resources as well as organisational capabilities are needed to overcome the manifold constraints social innovators are facing. To unlock the potential of social innovation for the whole society new (social) innovation friendly environments and new governance structures

  14. Design with Nonlinear Constraints

    KAUST Repository

    Tang, Chengcheng

    2015-01-01

    . The first application is the design of meshes under both geometric and static constraints, including self-supporting polyhedral meshes that are not height fields. Then, with a formulation bridging mesh based and spline based representations, the application

  15. Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

    Directory of Open Access Journals (Sweden)

    Alexander eHuk

    2012-10-01

    Full Text Available A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP. In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1 empirical questions not yet answered by existing data; (2 implementation issues related to how neural circuits could actually implement the mechanisms suggested by both physiology and psychology; and (3 ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general encoding-decoding framework that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

  16. Influence of DAT1 and COMT variants on neural activation during response inhibition in adolescents with attention-deficit/hyperactivity disorder and healthy controls

    NARCIS (Netherlands)

    van Rooij, D.; Hoekstra, P. J.; Bralten, J.; Hakobjan, M.; Oosterlaan, J.; Franke, B.; Rommelse, N.; Buitelaar, J. K.; Hartman, C. A.

    2015-01-01

    Background. Impairment of response inhibition has been implicated in attention-deficit/hyperactivity disorder (ADHD). Dopamine neurotransmission has been linked to the behavioural and neural correlates of response inhibition. The current study aimed to investigate the relationship of polymorphisms

  17. Dynamics and causality constraints

    International Nuclear Information System (INIS)

    Sousa, Manoelito M. de

    2001-04-01

    The physical meaning and the geometrical interpretation of causality implementation in classical field theories are discussed. Causality in field theory are kinematical constraints dynamically implemented via solutions of the field equation, but in a limit of zero-distance from the field sources part of these constraints carries a dynamical content that explains old problems of classical electrodynamics away with deep implications to the nature of physicals interactions. (author)

  18. Route constraints model based on polychromatic sets

    Science.gov (United States)

    Yin, Xianjun; Cai, Chao; Wang, Houjun; Li, Dongwu

    2018-03-01

    With the development of unmanned aerial vehicle (UAV) technology, the fields of its application are constantly expanding. The mission planning of UAV is especially important, and the planning result directly influences whether the UAV can accomplish the task. In order to make the results of mission planning for unmanned aerial vehicle more realistic, it is necessary to consider not only the physical properties of the aircraft, but also the constraints among the various equipment on the UAV. However, constraints among the equipment of UAV are complex, and the equipment has strong diversity and variability, which makes these constraints difficult to be described. In order to solve the above problem, this paper, referring to the polychromatic sets theory used in the advanced manufacturing field to describe complex systems, presents a mission constraint model of UAV based on polychromatic sets.

  19. Momentum constraint relaxation

    International Nuclear Information System (INIS)

    Marronetti, Pedro

    2006-01-01

    Full relativistic simulations in three dimensions invariably develop runaway modes that grow exponentially and are accompanied by violations of the Hamiltonian and momentum constraints. Recently, we introduced a numerical method (Hamiltonian relaxation) that greatly reduces the Hamiltonian constraint violation and helps improve the quality of the numerical model. We present here a method that controls the violation of the momentum constraint. The method is based on the addition of a longitudinal component to the traceless extrinsic curvature A ij -tilde, generated by a vector potential w i , as outlined by York. The components of w i are relaxed to solve approximately the momentum constraint equations, slowly pushing the evolution towards the space of solutions of the constraint equations. We test this method with simulations of binary neutron stars in circular orbits and show that it effectively controls the growth of the aforementioned violations. We also show that a full numerical enforcement of the constraints, as opposed to the gentle correction of the momentum relaxation scheme, results in the development of instabilities that stop the runs shortly

  20. Selection of new constraints

    International Nuclear Information System (INIS)

    Sugier, A.

    2003-01-01

    The selected new constraints should be consistent with the scale of concern i.e. be expressed roughly as fractions or multiples of the average annual background. They should take into account risk considerations and include the values of the currents limits, constraints and other action levels. The recommendation is to select four leading values for the new constraints: 500 mSv ( single event or in a decade) as a maximum value, 0.01 mSv/year as a minimum value; and two intermediate values: 20 mSv/year and 0.3 mSv/year. This new set of dose constraints, representing basic minimum standards of protection for the individuals taking into account the specificity of the exposure situations are thus coherent with the current values which can be found in ICRP Publications. A few warning need however to be noticed: There is no more multi sources limit set by ICRP. The coherence between the proposed value of dose constraint (20 mSv/year) and the current occupational dose limit of 20 mSv/year is valid only if the workers are exposed to one single source. When there is more than one source, it will be necessary to apportion. The value of 1000 mSv lifetimes used for relocation can be expressed into annual dose, which gives approximately 10 mSv/year and is coherent with the proposed dose constraint. (N.C.)

  1. Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

    OpenAIRE

    Wu, Zhonghua; Lu, Jingchao; Shi, Jingping; Liu, Yang; Zhou, Qing

    2017-01-01

    This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) tech...

  2. Neurally and mathematically motivated architecture for language and thought.

    Science.gov (United States)

    Perlovsky, L I; Ilin, R

    2010-01-01

    Neural structures of interaction between thinking and language are unknown. This paper suggests a possible architecture motivated by neural and mathematical considerations. A mathematical requirement of computability imposes significant constraints on possible architectures consistent with brain neural structure and with a wealth of psychological knowledge. How language interacts with cognition. Do we think with words, or is thinking independent from language with words being just labels for decisions? Why is language learned by the age of 5 or 7, but acquisition of knowledge represented by learning to use this language knowledge takes a lifetime? This paper discusses hierarchical aspects of language and thought and argues that high level abstract thinking is impossible without language. We discuss a mathematical technique that can model the joint language-thought architecture, while overcoming previously encountered difficulties of computability. This architecture explains a contradiction between human ability for rational thoughtful decisions and irrationality of human thinking revealed by Tversky and Kahneman; a crucial role in this contradiction might be played by language. The proposed model resolves long-standing issues: how the brain learns correct words-object associations; why animals do not talk and think like people. We propose the role played by language emotionality in its interaction with thought. We relate the mathematical model to Humboldt's "firmness" of languages; and discuss possible influence of language grammar on its emotionality. Psychological and brain imaging experiments related to the proposed model are discussed. Future theoretical and experimental research is outlined.

  3. Wave transmission prediction of multilayer floating breakwater using neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Patil, S.G.; Hegde, A.V.

    In the present study, an artificial neural network method has been applied for wave transmission prediction of multilayer floating breakwater. Two neural network models are constructed based on the parameters which influence the wave transmission...

  4. Stability prediction of berm breakwater using neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Rao, S.; Manjunath, Y.R.

    In the present study, an artificial neural network method has been applied to predict the stability of berm breakwaters. Four neural network models are constructed based on the parameters which influence the stability of breakwater. Training...

  5. Misconceptions and constraints

    International Nuclear Information System (INIS)

    Whitten, M.; Mahon, R.

    2005-01-01

    In theory, the sterile insect technique (SIT) is applicable to a wide variety of invertebrate pests. However, in practice, the approach has been successfully applied to only a few major pests. Chapters in this volume address possible reasons for this discrepancy, e.g. Klassen, Lance and McInnis, and Robinson and Hendrichs. The shortfall between theory and practice is partly due to the persistence of some common misconceptions, but it is mainly due to one constraint, or a combination of constraints, that are biological, financial, social or political in nature. This chapter's goal is to dispel some major misconceptions, and view the constraints as challenges to overcome, seeing them as opportunities to exploit. Some of the common misconceptions include: (1) released insects retain residual radiation, (2) females must be monogamous, (3) released males must be fully sterile, (4) eradication is the only goal, (5) the SIT is too sophisticated for developing countries, and (6) the SIT is not a component of an area-wide integrated pest management (AW-IPM) strategy. The more obvious constraints are the perceived high costs of the SIT, and the low competitiveness of released sterile males. The perceived high up-front costs of the SIT, their visibility, and the lack of private investment (compared with alternative suppression measures) emerge as serious constraints. Failure to appreciate the true nature of genetic approaches, such as the SIT, may pose a significant constraint to the wider adoption of the SIT and other genetically-based tactics, e.g. transgenic genetically modified organisms (GMOs). Lack of support for the necessary underpinning strategic research also appears to be an important constraint. Hence the case for extensive strategic research in ecology, population dynamics, genetics, and insect behaviour and nutrition is a compelling one. Raising the competitiveness of released sterile males remains the major research objective of the SIT. (author)

  6. Neural crest contributions to the lamprey head

    Science.gov (United States)

    McCauley, David W.; Bronner-Fraser, Marianne

    2003-01-01

    The neural crest is a vertebrate-specific cell population that contributes to the facial skeleton and other derivatives. We have performed focal DiI injection into the cranial neural tube of the developing lamprey in order to follow the migratory pathways of discrete groups of cells from origin to destination and to compare neural crest migratory pathways in a basal vertebrate to those of gnathostomes. The results show that the general pathways of cranial neural crest migration are conserved throughout the vertebrates, with cells migrating in streams analogous to the mandibular and hyoid streams. Caudal branchial neural crest cells migrate ventrally as a sheet of cells from the hindbrain and super-pharyngeal region of the neural tube and form a cylinder surrounding a core of mesoderm in each pharyngeal arch, similar to that seen in zebrafish and axolotl. In addition to these similarities, we also uncovered important differences. Migration into the presumptive caudal branchial arches of the lamprey involves both rostral and caudal movements of neural crest cells that have not been described in gnathostomes, suggesting that barriers that constrain rostrocaudal movement of cranial neural crest cells may have arisen after the agnathan/gnathostome split. Accordingly, neural crest cells from a single axial level contributed to multiple arches and there was extensive mixing between populations. There was no apparent filling of neural crest derivatives in a ventral-to-dorsal order, as has been observed in higher vertebrates, nor did we find evidence of a neural crest contribution to cranial sensory ganglia. These results suggest that migratory constraints and additional neural crest derivatives arose later in gnathostome evolution.

  7. Occupational dose constraint

    International Nuclear Information System (INIS)

    Heilbron Filho, Paulo Fernando Lavalle; Xavier, Ana Maria

    2005-01-01

    The revision process of the international radiological protection regulations has resulted in the adoption of new concepts, such as practice, intervention, avoidable and restriction of dose (dose constraint). The latter deserving of special mention since it may involve reducing a priori of the dose limits established both for the public and to individuals occupationally exposed, values that can be further reduced, depending on the application of the principle of optimization. This article aims to present, with clarity, from the criteria adopted to define dose constraint values to the public, a methodology to establish the dose constraint values for occupationally exposed individuals, as well as an example of the application of this methodology to the practice of industrial radiography

  8. Psychological constraints on egalitarianism

    DEFF Research Database (Denmark)

    Kasperbauer, Tyler Joshua

    2015-01-01

    processes motivating people to resist various aspects of egalitarianism. I argue for two theses, one normative and one descriptive. The normative thesis holds that egalitarians must take psychological constraints into account when constructing egalitarian ideals. I draw from non-ideal theories in political...... philosophy, which aim to construct moral goals with current social and political constraints in mind, to argue that human psychology must be part of a non-ideal theory of egalitarianism. The descriptive thesis holds that the most fundamental psychological challenge to egalitarian ideals comes from what......Debates over egalitarianism for the most part are not concerned with constraints on achieving an egalitarian society, beyond discussions of the deficiencies of egalitarian theory itself. This paper looks beyond objections to egalitarianism as such and investigates the relevant psychological...

  9. Exploring the Spatial-Temporal Disparities of Urban Land Use Economic Efficiency in China and Its Influencing Factors under Environmental Constraints Based on a Sequential Slacks-Based Model

    Directory of Open Access Journals (Sweden)

    Hualin Xie

    2015-07-01

    Full Text Available Using a sequential slack-based measure (SSBM model, this paper analyzes the spatiotemporal disparities of urban land use economic efficiency (ULUEE under environmental constraints, and its influencing factors in 270 cities across China from 2003–2012. The main results are as follows: (1 The average ULUEE for Chinese cities is only 0.411, and out of the 270 cities, only six cities are always efficient in urban land use in the study period. Most cities have a lot of room to improve the economic output of secondary and tertiary industries, as well as environmental protection work; (2 The eastern region of China enjoys the highest ULUEE, followed by the western and central regions. Super-scale cities show the best performance of all four city scales, followed by large-scale, small-scale and medium-scale cities. Cities with relatively developed economies and less pollutant discharge always have better ULUEE; (3 The results of slack variables analysis show that most cities have problems such as the labor surplus, over-development, excessive pollutant discharge, economic output shortage, and unreasonable use of funds is the most serious one; (4 The regression results of the influencing factors show that improvements of the per capita GDP and land use intensity are helpful to raise ULUEE. The urbanization rate and the proportion of foreign enterprises’ output account for the total output in the secondary and tertiary industries only have the same effect in some regions and city scales. The land management policy and land leasing policy have negative impact on the ULUEE in all the three regions and four city scales; (5 Some targeted policy goals are proposed, including the reduction of surplus labor, and pay more attention to environmental protection. Most importantly, effective implementation of land management policies from the central government, and stopping blind leasing of land to make up the local government’s financial deficit would be very

  10. University Course Timetabling using Constraint Programming

    Directory of Open Access Journals (Sweden)

    Hadi Shahmoradi

    2017-03-01

    Full Text Available University course timetabling problem is a challenging and time-consuming task on the overall structure of timetable in every academic environment. The problem deals with many factors such as the number of lessons, classes, teachers, students and working time, and these are influenced by some hard and soft constraints. The aim of solving this problem is to assign courses and classes to teachers and students, so that the restrictions are held. In this paper, a constraint programming method is proposed to satisfy maximum constraints and expectation, in order to address university timetabling problem. For minimizing the penalty of soft constraints, a cost function is introduced and AHP method is used for calculating its coefficients. The proposed model is tested on department of management, University of Isfahan dataset using OPL on the IBM ILOG CPLEX Optimization Studio platform. A statistical analysis has been conducted and shows the performance of the proposed approach in satisfying all hard constraints and also the satisfying degree of the soft constraints is on maximum desirable level. The running time of the model is less than 20 minutes that is significantly better than the non-automated ones.

  11. Constraint-based scheduling applying constraint programming to scheduling problems

    CERN Document Server

    Baptiste, Philippe; Nuijten, Wim

    2001-01-01

    Constraint Programming is a problem-solving paradigm that establishes a clear distinction between two pivotal aspects of a problem: (1) a precise definition of the constraints that define the problem to be solved and (2) the algorithms and heuristics enabling the selection of decisions to solve the problem. It is because of these capabilities that Constraint Programming is increasingly being employed as a problem-solving tool to solve scheduling problems. Hence the development of Constraint-Based Scheduling as a field of study. The aim of this book is to provide an overview of the most widely used Constraint-Based Scheduling techniques. Following the principles of Constraint Programming, the book consists of three distinct parts: The first chapter introduces the basic principles of Constraint Programming and provides a model of the constraints that are the most often encountered in scheduling problems. Chapters 2, 3, 4, and 5 are focused on the propagation of resource constraints, which usually are responsibl...

  12. Constraints on Dbar uplifts

    International Nuclear Information System (INIS)

    Alwis, S.P. de

    2016-01-01

    We discuss constraints on KKLT/KKLMMT and LVS scenarios that use anti-branes to get an uplift to a deSitter vacuum, coming from requiring the validity of an effective field theory description of the physics. We find these are not always satisfied or are hard to satisfy.

  13. Ecosystems emerging. 5: Constraints

    Czech Academy of Sciences Publication Activity Database

    Patten, B. C.; Straškraba, Milan; Jorgensen, S. E.

    2011-01-01

    Roč. 222, č. 16 (2011), s. 2945-2972 ISSN 0304-3800 Institutional research plan: CEZ:AV0Z50070508 Keywords : constraint * epistemic * ontic Subject RIV: EH - Ecology, Behaviour Impact factor: 2.326, year: 2011 http://www.sciencedirect.com/science/article/pii/S0304380011002274

  14. Constraints and Ambiguity

    DEFF Research Database (Denmark)

    Dove, Graham; Biskjær, Michael Mose; Lundqvist, Caroline Emilie

    2017-01-01

    groups of students building three models each. We studied groups building with traditional plastic bricks and also using a digital environment. The building tasks students undertake, and our subsequent analysis, are informed by the role constraints and ambiguity play in creative processes. Based...

  15. The influence of emotional priming on the neural substrates of memory: a prospective fMRI study using portrait art stimuli.

    Science.gov (United States)

    Baeken, Chris; De Raedt, Rudi; Van Schuerbeek, Peter; De Mey, Johan; Bossuyt, Axel; Luypaert, Robert

    2012-07-16

    Events coupled with an emotional context seem to be better retained than non-emotional events. The aim of our study was to investigate whether an emotional context could influence the neural substrates of memory associations with novel portrait art stimuli. In the current prospective fMRI study, we have investigated for one specific visual art form (modern artistic portraits with a high degree of abstraction) whether memory is influenced by priming with emotional facial pictures. In total forty healthy female volunteers in the same age range were recruited for the study. Twenty of these women participated in a prospective brain imaging memory paradigm and were asked to memorize a series of similar looking, but different portraits. After randomization, for twelve participants (Group 1), a third of the portraits was emotionally primed with approach-related pictures (smiling baby faces), a third with withdrawal-related pictures (baby faces with severe dermatological conditions), and another third with neutral images. Group 2 consisted of eight participants and they were not primed. Then, during an fMRI session 2h later, these portraits were viewed in random order intermixed with a set of new (previously unseen) ones, and the participants had to decide for each portrait whether or not they had already been seen. In a separate experiment, a different sample of twenty healthy females (Group 3) rated their mood after being exposed to the same art stimuli, without priming. The portraits did not evoke significant mood changes by themselves, supporting their initial neutral emotional character (Group 3). The correct decision on whether the portraits were Familiar of Unfamiliar led to similar neuronal activations in brain areas implicated in visual and attention processing for both groups (Groups 1 and 2). In contrast, whereas primed participants showed significant higher neuronal activities in the left midline superior frontal cortex (Brodmann area (BA) 6), unprimed

  16. Do You Believe It? Verbal Suggestions Influence the Clinical and Neural Effects of Escitalopram in Social Anxiety Disorder: A Randomized Trial.

    Science.gov (United States)

    Faria, Vanda; Gingnell, Malin; Hoppe, Johanna M; Hjorth, Olof; Alaie, Iman; Frick, Andreas; Hultberg, Sara; Wahlstedt, Kurt; Engman, Jonas; Månsson, Kristoffer N T; Carlbring, Per; Andersson, Gerhard; Reis, Margareta; Larsson, Elna-Marie; Fredrikson, Mats; Furmark, Tomas

    2017-10-01

    administration yielded significantly better outcome on the LSAS-SR (adjusted difference 21.17, 95% CI 10.69-31.65, psocial anxiety with treatment correlated significantly with enhanced posterior cingulate (z threshold 3.24, p=0.0006) and attenuated amygdala (z threshold 2.70, p=0.003) activity. The clinical and neural effects of escitalopram were markedly influenced by verbal suggestions. This points to a pronounced placebo component in SSRI-treatment of SAD and favors a biopsychosocial over a biomedical explanatory model for SSRI efficacy. The Swedish Research Council for Working Life and Social Research (grant 2011-1368), the Swedish Research Council (grant 421-2013-1366), Riksbankens Jubileumsfond - the Swedish Foundation for Humanities and Social Sciences (grant P13-1270:1). Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  17. The effects of perceived leisure constraints among Korean university students

    Science.gov (United States)

    Sae-Sook Oh; Sei-Yi Oh; Linda L. Caldwell

    2002-01-01

    This study is based on Crawford, Jackson, and Godbey's model of leisure constraints (1991), and examines the relationships between the influences of perceived constraints, frequency of participation, and health status in the context of leisure-time outdoor activities. The study was based on a sample of 234 Korean university students. This study provides further...

  18. Leukemia inhibitory factor (LIF) enhances MAP2 + and HUC/D + neurons and influences neurite extension during differentiation of neural progenitors derived from human embryonic stem cells.

    Science.gov (United States)

    Leukemia Inhibitory Factor (L1F), a member of the Interleukin 6 cytokine family, has a role in differentiation of Human Neural Progenitor (hNP) cells in vitro. hNP cells, derived from Human Embryonic Stem (hES) cells, have an unlimited capacity for self-renewal in monolayer cultu...

  19. The DNA glycosylases OGG1 and NEIL3 influence differentiation potential, proliferation, and senescence-associated signs in neural stem cells

    International Nuclear Information System (INIS)

    Reis, Amilcar; Hermanson, Ola

    2012-01-01

    Highlights: ► DNA glycosylases OGG1 and NEIL3 are required for neural stem cell state. ► No effect on cell viability by OGG1 or NEIL3 knockdown in neural stem cells. ► OGG1 or NEIL3 RNA knockdown result in decreased proliferation and differentiation. ► Increased HP1γ immunoreactivity after NEIL3 knockdown suggests premature senescence. -- Abstract: Embryonic neural stem cells (NSCs) exhibit self-renewal and multipotency as intrinsic characteristics that are key parameters for proper brain development. When cells are challenged by oxidative stress agents the resulting DNA lesions are repaired by DNA glycosylases through the base excision repair (BER) pathway as a means to maintain the fidelity of the genome, and thus, proper cellular characteristics. The functional roles for DNA glycosylases in NSCs have however remained largely unexplored. Here we demonstrate that RNA knockdown of the DNA glycosylases OGG1 and NEIL3 decreased NSC differentiation ability and resulted in decreased expression of both neuronal and astrocytic genes after mitogen withdrawal, as well as the stem cell marker Musashi-1. Furthermore, while cell survival remained unaffected, NEIL3 deficient cells displayed decreased cell proliferation rates along with an increase in HP1γ immunoreactivity, a sign of premature senescence. Our results suggest that DNA glycosylases play multiple roles in governing essential neural stem cell characteristics.

  20. The DNA glycosylases OGG1 and NEIL3 influence differentiation potential, proliferation, and senescence-associated signs in neural stem cells

    Energy Technology Data Exchange (ETDEWEB)

    Reis, Amilcar [Linnaeus Center in Developmental Biology for Regenerative Medicine (DBRM), Department of Neuroscience, Karolinska Institutet, SE 17177 Stockholm (Sweden); Hermanson, Ola, E-mail: ola.hermanson@ki.se [Linnaeus Center in Developmental Biology for Regenerative Medicine (DBRM), Department of Neuroscience, Karolinska Institutet, SE 17177 Stockholm (Sweden)

    2012-07-13

    Highlights: Black-Right-Pointing-Pointer DNA glycosylases OGG1 and NEIL3 are required for neural stem cell state. Black-Right-Pointing-Pointer No effect on cell viability by OGG1 or NEIL3 knockdown in neural stem cells. Black-Right-Pointing-Pointer OGG1 or NEIL3 RNA knockdown result in decreased proliferation and differentiation. Black-Right-Pointing-Pointer Increased HP1{gamma} immunoreactivity after NEIL3 knockdown suggests premature senescence. -- Abstract: Embryonic neural stem cells (NSCs) exhibit self-renewal and multipotency as intrinsic characteristics that are key parameters for proper brain development. When cells are challenged by oxidative stress agents the resulting DNA lesions are repaired by DNA glycosylases through the base excision repair (BER) pathway as a means to maintain the fidelity of the genome, and thus, proper cellular characteristics. The functional roles for DNA glycosylases in NSCs have however remained largely unexplored. Here we demonstrate that RNA knockdown of the DNA glycosylases OGG1 and NEIL3 decreased NSC differentiation ability and resulted in decreased expression of both neuronal and astrocytic genes after mitogen withdrawal, as well as the stem cell marker Musashi-1. Furthermore, while cell survival remained unaffected, NEIL3 deficient cells displayed decreased cell proliferation rates along with an increase in HP1{gamma} immunoreactivity, a sign of premature senescence. Our results suggest that DNA glycosylases play multiple roles in governing essential neural stem cell characteristics.

  1. Developmental constraint of insect audition

    Directory of Open Access Journals (Sweden)

    Strauß Johannes

    2006-12-01

    Full Text Available Abstract Background Insect ears contain very different numbers of sensory cells, from only one sensory cell in some moths to thousands of sensory cells, e.g. in cicadas. These differences still await functional explanation and especially the large numbers in cicadas remain puzzling. Insects of the different orders have distinct developmental sequences for the generation of auditory organs. These sensory cells might have different functions depending on the developmental stages. Here we propose that constraints arising during development are also important for the design of insect ears and might influence cell numbers of the adults. Presentation of the hypothesis We propose that the functional requirements of the subadult stages determine the adult complement of sensory units in the auditory system of cicadas. The hypothetical larval sensory organ should function as a vibration receiver, representing a functional caenogenesis. Testing the hypothesis Experiments at different levels have to be designed to test the hypothesis. Firstly, the neuroanatomy of the larval sense organ should be analyzed to detail. Secondly, the function should be unraveled neurophysiologically and behaviorally. Thirdly, the persistence of the sensory cells and the rebuilding of the sensory organ to the adult should be investigated. Implications of the hypothesis Usually, the evolution of insect ears is viewed with respect to physiological and neuronal mechanisms of sound perception. This view should be extended to the development of sense organs. Functional requirements during postembryonic development may act as constraints for the evolution of adult organs, as exemplified with the auditory system of cicadas.

  2. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  3. Robustness of the ATLAS pixel clustering neural network algorithm

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00407780; The ATLAS collaboration

    2016-01-01

    Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. In the ATLAS track reconstruction algorithm, an artificial neural network is utilised to identify and split clusters of neighbouring read-out elements in the ATLAS pixel detector created by multiple charged particles. The robustness of the neural network algorithm is presented, probing its sensitivity to uncertainties in the detector conditions. The robustness is studied by evaluating the stability of the algorithm's performance under a range of variations in the inputs to the neural networks. Within reasonable variation magnitudes, the neural networks prove to be robust to most variation types.

  4. Stereo, Shading, and Surfaces: Curvature Constraints Couple Neural Computations

    Science.gov (United States)

    2014-04-23

    century of gestalt psychology in visual perception : I. perceptual grouping and figure-ground organization,’’ Psychol. Bull., vol. 138, no. 6, pp. 1172–1217...questions they are attempting to answer. Knowing the answers could suggest insights from neuro- science to guide engineering theories and applications; at...The intrinsic images model has its roots in Land and McCann’s retinex theory , which was developed to explain color constancy. Retinex is based on the

  5. Constraints and Creativity in NPD - Testing the Impact of 'Late Constraints'

    DEFF Research Database (Denmark)

    Onarheim, Balder; Valgeirsdóttir, Dagný

    experiment was conducted, involving 12 teams of industrial designers from three different countries, each team working on two 30 minutes design tasks. In one condition all constraints were given at the start, and in the other one new radical constraint was added after 12 minutes. The output from all 24 tasks......The aim of the presented work is to investigate how the timing of project constraints can influence the creativity of the output in New Product Development (NPD) projects. When seeking to produce a creative output, is it beneficial to know all constraints when initiating a project...... was assessed for creativity using the Consensual Assessment Technique (CAT), and a comparative within-subjects analysis found no significant different between the two conditions. Controlling for task and assessor a small but non-significant effect was found, in favor of the ‘late constraint’ condition. Thus...

  6. Hopfield neural network in HEP track reconstruction

    International Nuclear Information System (INIS)

    Muresan, R.; Pentia, M.

    1997-01-01

    In experimental particle physics, pattern recognition problems, specifically for neural network methods, occur frequently in track finding or feature extraction. Track finding is a combinatorial optimization problem. Given a set of points in Euclidean space, one tries the reconstruction of particle trajectories, subject to smoothness constraints.The basic ingredients in a neural network are the N binary neurons and the synaptic strengths connecting them. In our case the neurons are the segments connecting all possible point pairs.The dynamics of the neural network is given by a local updating rule wich evaluates for each neuron the sign of the 'upstream activity'. An updating rule in the form of sigmoid function is given. The synaptic strengths are defined in terms of angle between the segments and the lengths of the segments implied in the track reconstruction. An algorithm based on Hopfield neural network has been developed and tested on the track coordinates measured by silicon microstrip tracking system

  7. Optimal neural computations require analog processors

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    This paper discusses some of the limitations of hardware implementations of neural networks. The authors start by presenting neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural networks. Further, the focus will be on hardware imposed constraints. They will present recent results for three different alternatives of parallel implementations of neural networks: digital circuits, threshold gate circuits, and analog circuits. The area and the delay will be related to the neurons` fan-in and to the precision of their synaptic weights. The main conclusion is that hardware-efficient solutions require analog computations, and suggests the following two alternatives: (i) cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow the use of the third dimension (e.g. using optical interconnections).

  8. Managing Constraint Generators in Retail Design Processes

    DEFF Research Database (Denmark)

    Münster, Mia Borch; Haug, Anders

    case studies of fashion store design projects, the present paper addresses this gap. The and six case studies of fashion store design projects, the present paper sheds light on the types of constraints generated by the relevant constraint generators. The paper shows that in the cases studied......Retail design concepts are complex designs meeting functional and aesthetic demands. During a design process a retail designer has to consider various constraint generators such as stakeholder interests, physical limitations and restrictions. Obviously the architectural site, legislators...... and landlords need to be considered as well as the interest of the client and brand owner. Furthermore the users need to be taken into account in order to develop an interesting and functional shopping and working environments. Finally, suppliers and competitors may influence the design with regard...

  9. Graphical constraints: a graphical user interface for constraint problems

    OpenAIRE

    Vieira, Nelson Manuel Marques

    2015-01-01

    A constraint satisfaction problem is a classical artificial intelligence paradigm characterized by a set of variables (each variable with an associated domain of possible values), and a set of constraints that specify relations among subsets of these variables. Solutions are assignments of values to all variables that satisfy all the constraints. Many real world problems may be modelled by means of constraints. The range of problems that can use this representation is very diverse and embrace...

  10. Distance Constraint Satisfaction Problems

    Science.gov (United States)

    Bodirsky, Manuel; Dalmau, Victor; Martin, Barnaby; Pinsker, Michael

    We study the complexity of constraint satisfaction problems for templates Γ that are first-order definable in ({ Z}; {suc}), the integers with the successor relation. Assuming a widely believed conjecture from finite domain constraint satisfaction (we require the tractability conjecture by Bulatov, Jeavons and Krokhin in the special case of transitive finite templates), we provide a full classification for the case that Γ is locally finite (i.e., the Gaifman graph of Γ has finite degree). We show that one of the following is true: The structure Γ is homomorphically equivalent to a structure with a certain majority polymorphism (which we call modular median) and CSP(Γ) can be solved in polynomial time, or Γ is homomorphically equivalent to a finite transitive structure, or CSP(Γ) is NP-complete.

  11. Constraint-based scheduling

    Science.gov (United States)

    Zweben, Monte

    1993-01-01

    The GERRY scheduling system developed by NASA Ames with assistance from the Lockheed Space Operations Company, and the Lockheed Artificial Intelligence Center, uses a method called constraint-based iterative repair. Using this technique, one encodes both hard rules and preference criteria into data structures called constraints. GERRY repeatedly attempts to improve schedules by seeking repairs for violated constraints. The system provides a general scheduling framework which is being tested on two NASA applications. The larger of the two is the Space Shuttle Ground Processing problem which entails the scheduling of all the inspection, repair, and maintenance tasks required to prepare the orbiter for flight. The other application involves power allocation for the NASA Ames wind tunnels. Here the system will be used to schedule wind tunnel tests with the goal of minimizing power costs. In this paper, we describe the GERRY system and its application to the Space Shuttle problem. We also speculate as to how the system would be used for manufacturing, transportation, and military problems.

  12. Activation of Group II Metabotropic Glutamate Receptors Increases Proliferation but does not Influence Neuronal Differentiation of a Human Neural Stem Cell Line

    DEFF Research Database (Denmark)

    Dindler, Anne; Blaabjerg, Morten; Kamand, Morad

    2018-01-01

    of pharmacological activation and inhibition of mGluR2/3 on proliferation, differentiation and viability of a human neural stem cell line. Immunofluorescence staining revealed the presence of mGluR2/3 receptors on both proliferating and differentiating stem cells, including cells differentiated into β-tubulin III....... Western blot analysis revealed that the active, dimeric form of mGluR2/3 was mainly present on the proliferating cells, which may explain our findings. The present study emphasises the importance of glutamate and mGluRs on regulation of human neural stem cells and suggests a significant role of mGluR2....../3 during cell proliferation. This article is protected by copyright. All rights reserved....

  13. Attentional states influence early neural responses associated with motivational processes: local vs. global attentional scope and N1 amplitude to appetitive stimuli.

    Science.gov (United States)

    Gable, Philip A; Harmon-Jones, Eddie

    2011-05-01

    Positive affects vary in the degree with which they are associated with approach motivation, the drive to approach an object or a goal. High approach-motivated positive affects cause a narrowing of attention, whereas low approach-motivated positive affects causes a broadening of attention. The current study was designed to extend this work by examining whether the relationship between motivation and attentional bias was bi-directional. Specifically, the experiment investigated whether a manipulated local attentional scope would cause greater approach motivational processing than a global attentional scope as measured by neural processes as early as 100 ms. As compared to a global attentional scope, a local attentional scope caused greater neural processing associated with approach motivation as measured by the N1 to appetitive pictures. Copyright © 2011 Elsevier B.V. All rights reserved.

  14. Long-chain polyunsaturated fatty acids (LCPUFA) from genesis to senescence: the influence of LCPUFA on neural development, aging, and neurodegeneration.

    Science.gov (United States)

    Janssen, Carola I F; Kiliaan, Amanda J

    2014-01-01

    Many clinical and animal studies demonstrate the importance of long-chain polyunsaturated fatty acids (LCPUFA) in neural development and neurodegeneration. This review will focus on involvement of LCPUFA from genesis to senescence. The LCPUFA docosahexaenoic acid and arachidonic acid are important components of neuronal membranes, while eicosapentaenoic acid, docosahexaenoic acid, and arachidonic acid also affect cardiovascular health and inflammation. In neural development, LCPUFA deficiency can lead to severe disorders like schizophrenia and attention deficit hyperactivity disorder. Perinatal LCPUFA supplementation demonstrated beneficial effects in neural development in humans and rodents resulting in improved cognition and sensorimotor integration. In normal aging, the effect of LCPUFA on prevention of cognitive impairment will be discussed. LCPUFA are important for neuronal membrane integrity and function, and also contribute in prevention of brain hypoperfusion. Cerebral perfusion can be compromised as result of obesity, cerebrovascular disease, hypertension, or diabetes mellitus type 2. Last, we will focus on the role of LCPUFA in most common neurodegenerative diseases like Alzheimer's disease and Parkinson's disease. These disorders are characterized by impaired cognition and connectivity and both clinical and animal supplementation studies have shown the potential of LCPUFA to decrease neurodegeneration and inflammation. This review shows that LCPUFA are essential throughout life. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. Efficient Searching with Linear Constraints

    DEFF Research Database (Denmark)

    Agarwal, Pankaj K.; Arge, Lars Allan; Erickson, Jeff

    2000-01-01

    We show how to preprocess a set S of points in d into an external memory data structure that efficiently supports linear-constraint queries. Each query is in the form of a linear constraint xd a0+∑d−1i=1 aixi; the data structure must report all the points of S that satisfy the constraint. This pr...

  16. Deepening Contractions and Collateral Constraints

    DEFF Research Database (Denmark)

    Jensen, Henrik; Ravn, Søren Hove; Santoro, Emiliano

    and occasionally non-binding credit constraints. Easier credit access increases the likelihood that constraints become slack in the face of expansionary shocks, while contractionary shocks are further amplified due to tighter constraints. As a result, busts gradually become deeper than booms. Based...

  17. Reduction Of Constraints For Coupled Operations

    International Nuclear Information System (INIS)

    Raszewski, F.; Edwards, T.

    2009-01-01

    the High Level Waste (HLW) System Plan. As with the first phase of testing for sludge-only operations, replacement of the homogeneity constraint with the alumina and sum of alkali constraints will ensure acceptable product durability over the compositional region evaluated. Although these study glasses only provide limited data in a large compositional region, the approach and results are consistent with previous studies that challenged the homogeneity constraint for sludge-only operations. That is, minimal benefit is gained by imposing the homogeneity constraint if the other PCCS constraints are satisfied. The normalized boron releases of all of the glasses are well below the Environmental Assessment (EA) glass results, regardless of thermal history. Although one of the glasses had a normalized boron release of approximately 10 g/L and was not predictable, the glass is still considered acceptable. This particular glass has a low Al 2 O 3 concentration, which may have attributed to the anomalous behavior. Given that poor durability has been previously observed in other glasses with low Al 2 O 3 and Fe 2 O 3 concentrations, including the sludge-only reduction of constraints study, further investigations appear to be warranted. Based on the results of this study, it is recommended that the homogeneity constraint (in its entirety with the associated low frit/high frit constraints) be eliminated for coupled operations as defined by Revision 14 of the HLW System Plan with up to 2 wt% TiO 2 . The use of the alumina and sum of alkali constraints should be continued along with the variability study to determine the predictability of the current durability models and/or that the glasses are acceptable with respect to durability. The use of a variability study for each batch is consistent with the glass product control program and it will help to assess new streams or compositional changes. It is also recommended that the influence of alumina and alkali on durability be studied

  18. The Effects of Two Levels of Linguistic Constraint on Echolalia and Generative Language Production in Children with Autism.

    Science.gov (United States)

    Rydell, Patrick J.; Mirenda, Pat

    1991-01-01

    This study of 3 boys (ages 5-6) with autism found that adult high-constraint antecedent utterances elicited more verbal utterances in general, including subjects' echolalia; adult low-constraint utterances elicited more subject high-constraint utterances; and the degree of adult-utterance constraint did not influence the mean lengths of subjects'…

  19. A Recurrent Neural Network for Nonlinear Fractional Programming

    Directory of Open Access Journals (Sweden)

    Quan-Ju Zhang

    2012-01-01

    Full Text Available This paper presents a novel recurrent time continuous neural network model which performs nonlinear fractional optimization subject to interval constraints on each of the optimization variables. The network is proved to be complete in the sense that the set of optima of the objective function to be minimized with interval constraints coincides with the set of equilibria of the neural network. It is also shown that the network is primal and globally convergent in the sense that its trajectory cannot escape from the feasible region and will converge to an exact optimal solution for any initial point being chosen in the feasible interval region. Simulation results are given to demonstrate further the global convergence and good performance of the proposing neural network for nonlinear fractional programming problems with interval constraints.

  20. Design with Nonlinear Constraints

    KAUST Repository

    Tang, Chengcheng

    2015-12-10

    Most modern industrial and architectural designs need to satisfy the requirements of their targeted performance and respect the limitations of available fabrication technologies. At the same time, they should reflect the artistic considerations and personal taste of the designers, which cannot be simply formulated as optimization goals with single best solutions. This thesis aims at a general, flexible yet e cient computational framework for interactive creation, exploration and discovery of serviceable, constructible, and stylish designs. By formulating nonlinear engineering considerations as linear or quadratic expressions by introducing auxiliary variables, the constrained space could be e ciently accessed by the proposed algorithm Guided Projection, with the guidance of aesthetic formulations. The approach is introduced through applications in different scenarios, its effectiveness is demonstrated by examples that were difficult or even impossible to be computationally designed before. The first application is the design of meshes under both geometric and static constraints, including self-supporting polyhedral meshes that are not height fields. Then, with a formulation bridging mesh based and spline based representations, the application is extended to developable surfaces including origami with curved creases. Finally, general approaches to extend hard constraints and soft energies are discussed, followed by a concluding remark outlooking possible future studies.

  1. Searching for genomic constraints

    Energy Technology Data Exchange (ETDEWEB)

    Lio` , P [Cambridge, Univ. (United Kingdom). Genetics Dept.; Ruffo, S [Florence, Univ. (Italy). Fac. di Ingegneria. Dipt. di Energetica ` S. Stecco`

    1998-01-01

    The authors have analyzed general properties of very long DNA sequences belonging to simple and complex organisms, by using different correlation methods. They have distinguished those base compositional rules that concern the entire genome which they call `genomic constraints` from the rules that depend on the `external natural selection` acting on single genes, i. e. protein-centered constraints. They show that G + C content, purine / pyrimidine distributions and biological complexity of the organism are the most important factors which determine base compositional rules and genome complexity. Three main facts are here reported: bacteria with high G + C content have more restrictions on base composition than those with low G + C content; at constant G + C content more complex organisms, ranging from prokaryotes to higher eukaryotes (e.g. human) display an increase of repeats 10-20 nucleotides long, which are also partly responsible for long-range correlations; work selection of length 3 to 10 is stronger in human and in bacteria for two distinct reasons. With respect to previous studies, they have also compared the genomic sequence of the archeon Methanococcus jannaschii with those of bacteria and eukaryotes: it shows sometimes an intermediate statistical behaviour.

  2. Searching for genomic constraints

    International Nuclear Information System (INIS)

    Lio', P.; Ruffo, S.

    1998-01-01

    The authors have analyzed general properties of very long DNA sequences belonging to simple and complex organisms, by using different correlation methods. They have distinguished those base compositional rules that concern the entire genome which they call 'genomic constraints' from the rules that depend on the 'external natural selection' acting on single genes, i. e. protein-centered constraints. They show that G + C content, purine / pyrimidine distributions and biological complexity of the organism are the most important factors which determine base compositional rules and genome complexity. Three main facts are here reported: bacteria with high G + C content have more restrictions on base composition than those with low G + C content; at constant G + C content more complex organisms, ranging from prokaryotes to higher eukaryotes (e.g. human) display an increase of repeats 10-20 nucleotides long, which are also partly responsible for long-range correlations; work selection of length 3 to 10 is stronger in human and in bacteria for two distinct reasons. With respect to previous studies, they have also compared the genomic sequence of the archeon Methanococcus jannaschii with those of bacteria and eukaryotes: it shows sometimes an intermediate statistical behaviour

  3. Minimal investment risk of a portfolio optimization problem with budget and investment concentration constraints

    Science.gov (United States)

    Shinzato, Takashi

    2017-02-01

    In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investment concentration constraint (as well as the budget constraint), it is intuitive that the minimal investment risk for the problem with an investment concentration constraint can be larger than that without the constraint (that is, with only the budget constraint). Moreover, a numerical experiment shows the effectiveness of our proposed analysis. In contrast, the standard operations research approach failed to identify accurately the minimal investment risk of the portfolio optimization problem.

  4. Controlled neural network application in track-match problem

    International Nuclear Information System (INIS)

    Baginyan, S.A.; Ososkov, G.A.

    1993-01-01

    Track-match problem of high energy physics (HEP) data handling is formulated in terms of incidence matrices. The corresponding Hopfield neural network is developed to solve this type of constraint satisfaction problems (CSP). A special concept of the controlled neural network is proposed as a basis of an algorithm for the effective CSP solution. Results of comparable calculations show the very high performance of this algorithm against conventional search procedures. 8 refs.; 1 fig.; 1 tab

  5. Overlapping constraint for variational surface reconstruction

    DEFF Research Database (Denmark)

    Aanæs, Henrik; Solem, J.E.

    2005-01-01

    In this paper a counter example, illustrating a shortcoming in most variational formulations for 3D surface estimation, is presented. The nature of this shortcoming is a lack of an overlapping constraint. A remedy for this shortcoming is presented in the form of a penalty function with an analysi...... of the effects of this function on surface motion. For practical purposes, this will only have minor influence on current methods. However, the insight provided in the analysis is likely to influence future developments in the field of variational surface reconstruction....

  6. Short-Lived Human Umbilical Cord-Blood-Derived Neural Stem Cells Influence the Endogenous Secretome and Increase the Number of Endogenous Neural Progenitors in a Rat Model of Lacunar Stroke.

    Science.gov (United States)

    Jablonska, Anna; Drela, Katarzyna; Wojcik-Stanaszek, Luiza; Janowski, Miroslaw; Zalewska, Teresa; Lukomska, Barbara

    2016-11-01

    Stroke is the leading cause of severe disability, and lacunar stroke is related to cognitive decline and hemiparesis. There is no effective treatment for the majority of patients with stroke. Thus, stem cell-based regenerative medicine has drawn a growing body of attention due to the capabilities for trophic factor expression and neurogenesis enhancement. Moreover, it was shown in an experimental autoimmune encephalomyelitis (EAE) model that even short-lived stem cells can be therapeutic, and we have previously observed that phenomenon indirectly. Here, in a rat model of lacunar stroke, we investigated the molecular mechanisms underlying the positive therapeutic effects of short-lived human umbilical cord-blood-derived neural stem cells (HUCB-NSCs) through the distinct measurement of exogenous human and endogenous rat trophic factors. We have also evaluated neurogenesis and metalloproteinase activity as cellular components of therapeutic activity. As expected, we observed an increased proliferation and migration of progenitors, as well as metalloproteinase activity up to 14 days post transplantation. These changes were most prominent at the 7-day time point when we observed 30 % increases in the number of bromodeoxyuridine (BrdU)-positive cells in HUCB-NSC transplanted animals. The expression of human trophic factors was present until 7 days post transplantation, which correlated well with the survival of the human graft. For these 7 days, the level of messenger RNA (mRNA) in the analyzed trophic factors was from 300-fold for CNTF to 10,000-fold for IGF, much higher compared to constitutive expression in HUCB-NSCs in vitro. What is interesting is that there was no increase in the expression of rat trophic factors during the human graft survival, compared to that in non-transplanted animals. However, there was a prolongation of a period of increased trophic expression until 14 days post transplantation, while, in non-transplanted animals, there was a

  7. Information Extraction with Character-level Neural Networks and Free Noisy Supervision

    OpenAIRE

    Meerkamp, Philipp; Zhou, Zhengyi

    2016-01-01

    We present an architecture for information extraction from text that augments an existing parser with a character-level neural network. The network is trained using a measure of consistency of extracted data with existing databases as a form of noisy supervision. Our architecture combines the ability of constraint-based information extraction systems to easily incorporate domain knowledge and constraints with the ability of deep neural networks to leverage large amounts of data to learn compl...

  8. Supergravity constraints on monojets

    International Nuclear Information System (INIS)

    Nandi, S.

    1986-01-01

    In the standard model, supplemented by N = 1 minimal supergravity, all the supersymmetric particle masses can be expressed in terms of a few unknown parameters. The resulting mass relations, and the laboratory and the cosmological bounds on these superpartner masses are used to put constraints on the supersymmetric origin of the CERN monojets. The latest MAC data at PEP excludes the scalar quarks, of masses up to 45 GeV, as the origin of these monojets. The cosmological bounds, for a stable photino, excludes the mass range necessary for the light gluino-heavy squark production interpretation. These difficulties can be avoided by going beyond the minimal supergravity theory. Irrespective of the monojets, the importance of the stable γ as the source of the cosmological dark matter is emphasized

  9. Temporal Concurrent Constraint Programming

    DEFF Research Database (Denmark)

    Valencia, Frank Dan

    Concurrent constraint programming (ccp) is a formalism for concurrency in which agents interact with one another by telling (adding) and asking (reading) information in a shared medium. Temporal ccp extends ccp by allowing agents to be constrained by time conditions. This dissertation studies...... temporal ccp by developing a process calculus called ntcc. The ntcc calculus generalizes the tcc model, the latter being a temporal ccp model for deterministic and synchronouss timed reactive systems. The calculus is built upon few basic ideas but it captures several aspects of timed systems. As tcc, ntcc...... structures, robotic devises, multi-agent systems and music applications. The calculus is provided with a denotational semantics that captures the reactive computations of processes in the presence of arbitrary environments. The denotation is proven to be fully-abstract for a substantial fragment...

  10. Recent Advances in Neural Recording Microsystems

    Directory of Open Access Journals (Sweden)

    Benoit Gosselin

    2011-04-01

    Full Text Available The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

  11. Minimal Flavor Constraints for Technicolor

    DEFF Research Database (Denmark)

    Sakuma, Hidenori; Sannino, Francesco

    2010-01-01

    We analyze the constraints on the the vacuum polarization of the standard model gauge bosons from a minimal set of flavor observables valid for a general class of models of dynamical electroweak symmetry breaking. We will show that the constraints have a strong impact on the self-coupling and mas......We analyze the constraints on the the vacuum polarization of the standard model gauge bosons from a minimal set of flavor observables valid for a general class of models of dynamical electroweak symmetry breaking. We will show that the constraints have a strong impact on the self...

  12. Social Constraints on Animate Vision

    National Research Council Canada - National Science Library

    Breazeal, Cynthia; Edsinger, Aaron; Fitzpatrick, Paul; Scassellati, Brian

    2000-01-01

    .... In humanoid robotic systems, or in any animate vision system that interacts with people, social dynamics provide additional levels of constraint and provide additional opportunities for processing economy...

  13. Modifier constraint in alkali borophosphate glasses using topological constraint theory

    Energy Technology Data Exchange (ETDEWEB)

    Li, Xiang [Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237 (China); Zeng, Huidan, E-mail: hdzeng@ecust.edu.cn [Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237 (China); Jiang, Qi [Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237 (China); Zhao, Donghui [Unifrax Corporation, Niagara Falls, NY 14305 (United States); Chen, Guorong [Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237 (China); Wang, Zhaofeng; Sun, Luyi [Department of Chemical & Biomolecular Engineering and Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT 06269 (United States); Chen, Jianding [Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237 (China)

    2016-12-01

    In recent years, composition-dependent properties of glasses have been successfully predicted using the topological constraint theory. The constraints of the glass network are derived from two main parts: network formers and network modifiers. The constraints of the network formers can be calculated on the basis of the topological structure of the glass. However, the latter cannot be accurately calculated in this way, because of the existing of ionic bonds. In this paper, the constraints of the modifier ions in phosphate glasses were thoroughly investigated using the topological constraint theory. The results show that the constraints of the modifier ions are gradually increased with the addition of alkali oxides. Furthermore, an improved topological constraint theory for borophosphate glasses is proposed by taking the composition-dependent constraints of the network modifiers into consideration. The proposed theory is subsequently evaluated by analyzing the composition dependence of the glass transition temperature in alkali borophosphate glasses. This method is supposed to be extended to other similar glass systems containing alkali ions.

  14. Seismological Constraints on Geodynamics

    Science.gov (United States)

    Lomnitz, C.

    2004-12-01

    Earth is an open thermodynamic system radiating heat energy into space. A transition from geostatic earth models such as PREM to geodynamical models is needed. We discuss possible thermodynamic constraints on the variables that govern the distribution of forces and flows in the deep Earth. In this paper we assume that the temperature distribution is time-invariant, so that all flows vanish at steady state except for the heat flow Jq per unit area (Kuiken, 1994). Superscript 0 will refer to the steady state while x denotes the excited state of the system. We may write σ 0=(J{q}0ṡX{q}0)/T where Xq is the conjugate force corresponding to Jq, and σ is the rate of entropy production per unit volume. Consider now what happens after the occurrence of an earthquake at time t=0 and location (0,0,0). The earthquake introduces a stress drop Δ P(x,y,z) at all points of the system. Response flows are directed along the gradients toward the epicentral area, and the entropy production will increase with time as (Prigogine, 1947) σ x(t)=σ 0+α {1}/(t+β )+α {2}/(t+β )2+etc A seismological constraint on the parameters may be obtained from Omori's empirical relation N(t)=p/(t+q) where N(t) is the number of aftershocks at time t following the main shock. It may be assumed that p/q\\sim\\alpha_{1}/\\beta times a constant. Another useful constraint is the Mexican-hat geometry of the seismic transient as obtained e.g. from InSAR radar interferometry. For strike-slip events such as Landers the distribution of \\DeltaP is quadrantal, and an oval-shaped seismicity gap develops about the epicenter. A weak outer triggering maxiμm is found at a distance of about 17 fault lengths. Such patterns may be extracted from earthquake catalogs by statistical analysis (Lomnitz, 1996). Finally, the energy of the perturbation must be at least equal to the recovery energy. The total energy expended in an aftershock sequence can be found approximately by integrating the local contribution over

  15. Implicit Motives and Men's Perceived Constraint in Fatherhood.

    Science.gov (United States)

    Ruppen, Jessica; Waldvogel, Patricia; Ehlert, Ulrike

    2016-01-01

    Research shows that implicit motives influence social relationships. However, little is known about their role in fatherhood and, particularly, how men experience their paternal role. Therefore, this study examined the association of implicit motives and fathers' perceived constraint due to fatherhood. Furthermore, we explored their relation to fathers' life satisfaction. Participants were fathers with biological children ( N = 276). They were asked to write picture stories, which were then coded for implicit affiliation and power motives. Perceived constraint and life satisfaction were assessed on a visual analog scale. A higher implicit need for affiliation was significantly associated with lower perceived constraint, whereas the implicit need for power had the opposite effect. Perceived constraint had a negative influence on life satisfaction. Structural equation modeling revealed significant indirect effects of implicit affiliation and power motives on life satisfaction mediated by perceived constraint. Our findings indicate that men with a higher implicit need for affiliation experience less constraint due to fatherhood, resulting in higher life satisfaction. The implicit need for power, however, results in more perceived constraint and is related to decreased life satisfaction.

  16. Implicit Motives and Men’s Perceived Constraint in Fatherhood

    Science.gov (United States)

    Ruppen, Jessica; Waldvogel, Patricia; Ehlert, Ulrike

    2016-01-01

    Research shows that implicit motives influence social relationships. However, little is known about their role in fatherhood and, particularly, how men experience their paternal role. Therefore, this study examined the association of implicit motives and fathers’ perceived constraint due to fatherhood. Furthermore, we explored their relation to fathers’ life satisfaction. Participants were fathers with biological children (N = 276). They were asked to write picture stories, which were then coded for implicit affiliation and power motives. Perceived constraint and life satisfaction were assessed on a visual analog scale. A higher implicit need for affiliation was significantly associated with lower perceived constraint, whereas the implicit need for power had the opposite effect. Perceived constraint had a negative influence on life satisfaction. Structural equation modeling revealed significant indirect effects of implicit affiliation and power motives on life satisfaction mediated by perceived constraint. Our findings indicate that men with a higher implicit need for affiliation experience less constraint due to fatherhood, resulting in higher life satisfaction. The implicit need for power, however, results in more perceived constraint and is related to decreased life satisfaction. PMID:27933023

  17. Implicit Motives and Men’s Perceived Constraint in Fatherhood

    Directory of Open Access Journals (Sweden)

    Jessica Ruppen

    2016-11-01

    Full Text Available Research shows that implicit motives influence social relationships. However, little is known about their role in fatherhood and, particularly, how men experience their paternal role. Therefore, this study examined the association of implicit motives and fathers’ perceived constraint due to fatherhood. Furthermore, we explored their relation to fathers’ life satisfaction. Participants were fathers with biological children (N = 276. They were asked to write picture stories, which were then coded for implicit affiliation and power motives. Perceived constraint and life satisfaction were assessed on a visual analog scale. A higher implicit need for affiliation was significantly associated with lower perceived constraint, whereas the implicit need for power had the opposite effect. Perceived constraint had a negative influence on life satisfaction. Structural equation modeling revealed significant indirect effects of implicit affiliation and power motives on life satisfaction mediated by perceived constraint. Our findings indicate that men with a higher implicit need for affiliation experience less constraint due to fatherhood, resulting in higher life satisfaction. The implicit need for power, however, results in more perceived constraint and is related to decreased life satisfaction.

  18. An Evolutionary Optimization Framework for Neural Networks and Neuromorphic Architectures

    Energy Technology Data Exchange (ETDEWEB)

    Schuman, Catherine D [ORNL; Plank, James [University of Tennessee (UT); Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)

    2016-01-01

    As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.

  19. Discrete-time BAM neural networks with variable delays

    Science.gov (United States)

    Liu, Xin-Ge; Tang, Mei-Lan; Martin, Ralph; Liu, Xin-Bi

    2007-07-01

    This Letter deals with the global exponential stability of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Using a Lyapunov functional, and linear matrix inequality techniques (LMI), we derive a new delay-dependent exponential stability criterion for BAM neural networks with variable delays. As this criterion has no extra constraints on the variable delay functions, it can be applied to quite general BAM neural networks with a broad range of time delay functions. It is also easy to use in practice. An example is provided to illustrate the theoretical development.

  20. Discrete-time BAM neural networks with variable delays

    International Nuclear Information System (INIS)

    Liu Xinge; Tang Meilan; Martin, Ralph; Liu Xinbi

    2007-01-01

    This Letter deals with the global exponential stability of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Using a Lyapunov functional, and linear matrix inequality techniques (LMI), we derive a new delay-dependent exponential stability criterion for BAM neural networks with variable delays. As this criterion has no extra constraints on the variable delay functions, it can be applied to quite general BAM neural networks with a broad range of time delay functions. It is also easy to use in practice. An example is provided to illustrate the theoretical development

  1. Balancing Structural and Temporal Constraints in Multitasking Contexts

    OpenAIRE

    Salvucci, Dario D.; Kujala, Tuomo

    2016-01-01

    Recent research has shown that when people multitask, both the subtask structure and the temporal constraints of the component tasks strongly influence people’s task-switching behavior. In this paper, we propose an integrated theoretical account and associated computational model that aims to quantify how people balance structural and temporal constraints in everyday multitasking. We validate the theory using data from an empirical study in which drivers performed a vi...

  2. Bureaucratic Dilemmas: Civil servants between political responsiveness and normative constraints

    DEFF Research Database (Denmark)

    Christensen, Jørgen Grønnegård; Opstrup, Niels

    2017-01-01

    The interaction between political executives and civil servants rests on a delicate balance between political responsiveness and the duty of civil servants and ministers to respect legal and other normative constraints on executive authority. In Danish central government, this balance is stressed...... by norms that define the correct behavior when the civil service provides ministers with political advice and assistance. Organizational factors strongly influence civil servants’ behavior when they have to balance responsiveness against constraints on their role as political advisers. Moreover, civil...

  3. Constraints in distortion-invariant target recognition system simulation

    Science.gov (United States)

    Iftekharuddin, Khan M.; Razzaque, Md A.

    2000-11-01

    Automatic target recognition (ATR) is a mature but active research area. In an earlier paper, we proposed a novel ATR approach for recognition of targets varying in fine details, rotation, and translation using a Learning Vector Quantization (LVQ) Neural Network (NN). The proposed approach performed segmentation of multiple objects and the identification of the objects using LVQNN. In this current paper, we extend the previous approach for recognition of targets varying in rotation, translation, scale, and combination of all three distortions. We obtain the analytical results of the system level design to show that the approach performs well with some constraints. The first constraint determines the size of the input images and input filters. The second constraint shows the limits on amount of rotation, translation, and scale of input objects. We present the simulation verification of the constraints using DARPA's Moving and Stationary Target Recognition (MSTAR) images with different depression and pose angles. The simulation results using MSTAR images verify the analytical constraints of the system level design.

  4. Observational constraints on interstellar chemistry

    International Nuclear Information System (INIS)

    Winnewisser, G.

    1984-01-01

    The author points out presently existing observational constraints in the detection of interstellar molecular species and the limits they may cast on our knowledge of interstellar chemistry. The constraints which arise from the molecular side are summarised and some technical difficulties encountered in detecting new species are discussed. Some implications for our understanding of molecular formation processes are considered. (Auth.)

  5. Market segmentation using perceived constraints

    Science.gov (United States)

    Jinhee Jun; Gerard Kyle; Andrew Mowen

    2008-01-01

    We examined the practical utility of segmenting potential visitors to Cleveland Metroparks using their constraint profiles. Our analysis identified three segments based on their scores on the dimensions of constraints: Other priorities--visitors who scored the highest on 'other priorities' dimension; Highly Constrained--visitors who scored relatively high on...

  6. Fixed Costs and Hours Constraints

    Science.gov (United States)

    Johnson, William R.

    2011-01-01

    Hours constraints are typically identified by worker responses to questions asking whether they would prefer a job with more hours and more pay or fewer hours and less pay. Because jobs with different hours but the same rate of pay may be infeasible when there are fixed costs of employment or mandatory overtime premia, the constraint in those…

  7. An Introduction to 'Creativity Constraints'

    DEFF Research Database (Denmark)

    Onarheim, Balder; Biskjær, Michael Mose

    2013-01-01

    Constraints play a vital role as both restrainers and enablers in innovation processes by governing what the creative agent/s can and cannot do, and what the output can and cannot be. Notions of constraints are common in creativity research, but current contributions are highly dispersed due to n...

  8. Constraint Programming for Context Comprehension

    DEFF Research Database (Denmark)

    Christiansen, Henning

    2014-01-01

    A close similarity is demonstrated between context comprehension, such as discourse analysis, and constraint programming. The constraint store takes the role of a growing knowledge base learned throughout the discourse, and a suitable con- straint solver does the job of incorporating new pieces...

  9. The influence of information status on pronoun resolution in Mandarin Chinese: Evidence from ERPs

    Directory of Open Access Journals (Sweden)

    Xiaodong eXu

    2015-07-01

    Full Text Available The purpose of this study is to shed light on the neural mechanisms underlying the modulation of pronoun resolution processes by the information status of the antecedent. Information status was manipulated by using a structurally-based constraint (e.g., order of mention as well as a pragmatically-based constraint (i.e., topichood. We found that the pronouns referring to topic entities (the initial NP in SOV structure in Experiment 1 and OSV structure in Experiment 2 elicited attenuated P600 responses compared to the pronouns referring to non-topic entities (the initial NP in SVO structure or the second NP in OSV structure in both experiments when potential interference from structural constraints was controlled. The linear structural constraint, namely the order of mention, had no clear influence on the P600 effect when the syntactic structural constraint was held constant (i.e., when both entities were syntactic subjects, regardless of whether one (Experiment 1 or two (Experiment 2 animate antecedents were present. These findings suggest that pragmatically encoded features such as topichood and givenness can be processed separately from structural constraints such as order of mention to promote the salient status of a referent and thereby facilitate pronoun interpretation.

  10. Testing for a cultural influence on reading for meaning in the developing brain: the neural basis of semantic processing in Chinese children

    Directory of Open Access Journals (Sweden)

    Tai-Li Chou

    2009-11-01

    Full Text Available Functional magnetic resonance imaging (fMRI was used to explore the neural correlates of semantic judgments in a group of 8- to 15-year-old Chinese children. Participants were asked to indicate if pairs of Chinese characters presented visually were related in meaning. The related pairs were arranged in a continuous variable according to association strength. Pairs of characters with weaker semantic association elicited greater activation in the mid ventral region (BA 45 of left inferior frontal gyrus, suggesting increased demands on the process of selecting appropriate semantic features. By contrast, characters with stronger semantic association elicited greater activation in left inferior parietal lobule (BA 39, suggesting stronger integration of highly related features. In addition, there was a developmental increase, similar to previously reported findings in English, in left posterior middle temporal gyrus (BA 21, suggesting that older children have more elaborated semantic representations. There were additional age-related increases in the posterior region of left inferior parietal lobule and in the ventral regions of left inferior frontal gyrus, suggesting that reading acquisition relies more on the mapping from orthography to semantics in Chinese children as compared to previously reported findings in English.

  11. Vocabulary Constraint on Texts

    Directory of Open Access Journals (Sweden)

    C. Sutarsyah

    2008-01-01

    Full Text Available This case study was carried out in the English Education Department of State University of Malang. The aim of the study was to identify and describe the vocabulary in the reading text and to seek if the text is useful for reading skill development. A descriptive qualitative design was applied to obtain the data. For this purpose, some available computer programs were used to find the description of vocabulary in the texts. It was found that the 20 texts containing 7,945 words are dominated by low frequency words which account for 16.97% of the words in the texts. The high frequency words occurring in the texts were dominated by function words. In the case of word levels, it was found that the texts have very limited number of words from GSL (General Service List of English Words (West, 1953. The proportion of the first 1,000 words of GSL only accounts for 44.6%. The data also show that the texts contain too large proportion of words which are not in the three levels (the first 2,000 and UWL. These words account for 26.44% of the running words in the texts.  It is believed that the constraints are due to the selection of the texts which are made of a series of short-unrelated texts. This kind of text is subject to the accumulation of low frequency words especially those of content words and limited of words from GSL. It could also defeat the development of students' reading skills and vocabulary enrichment.

  12. RBF neural network based H∞ H∞ H∞ synchronization for ...

    Indian Academy of Sciences (India)

    Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of disturbance to an H ∞ norm constraint. It is shown that finding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved ...

  13. A General Connectionist Model of Attitude Structure and Change: The ACS (Attitudes as Constraint Satisfaction) Model

    Science.gov (United States)

    Monroe, Brian M.; Read, Stephen J.

    2008-01-01

    A localist, parallel constraint satisfaction, artificial neural network model is presented that accounts for a broad collection of attitude and attitude-change phenomena. The network represents the attitude object and cognitions and beliefs related to the attitude, as well as how to integrate a persuasive message into this network. Short-term…

  14. 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.

  15. Neural Tube Defects

    Science.gov (United States)

    Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the ... that she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In ...

  16. An optimal power-dispatching system using neural networks for the electrochemical process of zinc depending on varying prices of electricity.

    Science.gov (United States)

    Yang, Chunhua; Deconinck, G; Gui, Weihua; Li, Yonggang

    2002-01-01

    Depending on varying prices of electricity, an optimal power-dispatching system (OPDS) is developed to minimize the cost of power consumption in the electrochemical process of zinc (EPZ). Due to the complexity of the EPZ, the main factors influencing the power consumption are determined by qualitative analysis, and a series of conditional experiments is conducted to acquire sufficient data, then two backpropagation neural networks are used to describe these relationships quantitatively. An equivalent Hopfield neural network is constructed to solve the optimization problem where a penalty function is introduced into the network energy function so as to meet the equality constraints, and inequality constraints are removed by alteration of the Sigmoid function. This OPDS was put into service in a smeltery in 1998. The cost of power consumption has decreased significantly, the total electrical energy consumption is reduced, and it is also beneficial to balancing the load of the power grid. The actual results show the effectiveness of the OPDS. This paper introduces a successful industrial application and mainly presents how to utilize neural networks to solve particular problems for the real world.

  17. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  18. Prediction of soil urea conversion and quantification of the importance degrees of influencing factors through a new combinatorial model based on cluster method and artificial neural network.

    Science.gov (United States)

    Lei, Tao; Guo, Xianghong; Sun, Xihuan; Ma, Juanjuan; Zhang, Shaowen; Zhang, Yong

    2018-05-01

    Quantitative prediction of soil urea conversion is crucial in determining the mechanism of nitrogen transformation and understanding the dynamics of soil nutrients. This study aimed to establish a combinatorial prediction model (MCA-F-ANN) for soil urea conversion and quantify the relative importance degrees (RIDs) of influencing factors with the MCA-F-ANN method. Data samples were obtained from laboratory culture experiments, and soil nitrogen content and physicochemical properties were measured every other day. Results showed that when MCA-F-ANN was used, the mean-absolute-percent error values of NH 4 + -N, NO 3 - -N, and NH 3 contents were 3.180%, 2.756%, and 3.656%, respectively. MCA-F-ANN predicted urea transformation under multi-factor coupling conditions more accurately than traditional models did. The RIDs of reaction time (RT), electrical conductivity (EC), temperature (T), pH, nitrogen application rate (F), and moisture content (W) were 32.2%-36.5%, 24.0%-28.9%, 12.8%-15.2%, 9.8%-12.5%, 7.8%-11.0%, and 3.5%-6.0%, respectively. The RIDs of the influencing factors in a descending order showed the pattern RT > EC > T > pH > F > W. RT and EC were the key factors in the urea conversion process. The prediction accuracy of urea transformation process was improved, and the RIDs of the influencing factors were quantified. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. Concurrent tACS-fMRI Reveals Causal Influence of Power Synchronized Neural Activity on Resting State fMRI Connectivity.

    Science.gov (United States)

    Bächinger, Marc; Zerbi, Valerio; Moisa, Marius; Polania, Rafael; Liu, Quanying; Mantini, Dante; Ruff, Christian; Wenderoth, Nicole

    2017-05-03

    Resting state fMRI (rs-fMRI) is commonly used to study the brain's intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSNs). It has been hypothesized that slow rs-fMRI oscillations (5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices. The two driving tACS signals were tailored to the individual's α rhythm (8-12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either α oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS). Power-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared with phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brain's intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity. SIGNIFICANCE STATEMENT Resting state fMRI (rs-fMRI) has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes. It was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to

  20. Machine tongues. X. Constraint languages

    Energy Technology Data Exchange (ETDEWEB)

    Levitt, D.

    Constraint languages and programming environments will help the designer produce a lucid description of a problem domain, and then of particular situations and problems in it. Early versions of these languages were given descriptions of real world domain constraints, like the operation of electrical and mechanical parts. More recently, the author has automated a vocabulary for describing musical jazz phrases, using constraint language as a jazz improviser. General constraint languages will handle all of these domains. Once the model is in place, the system will connect built-in code fragments and algorithms to answer questions about situations; that is, to help solve problems. Bugs will surface not in code, but in designs themselves. 15 references.

  1. Fluid convection, constraint and causation

    Science.gov (United States)

    Bishop, Robert C.

    2012-01-01

    Complexity—nonlinear dynamics for my purposes in this essay—is rich with metaphysical and epistemological implications but is receiving sustained philosophical analysis only recently. I will explore some of the subtleties of causation and constraint in Rayleigh–Bénard convection as an example of a complex phenomenon, and extract some lessons for further philosophical reflection on top-down constraint and causation particularly with respect to causal foundationalism. PMID:23386955

  2. Management of Constraint Generators in Fashion Store Design Processes

    DEFF Research Database (Denmark)

    Borch Münster, Mia; Haug, Anders

    2017-01-01

    of the literature and eight case studies of fashion store design projects. Findings: The paper shows that the influence of the constraint generators decreases during the design process except for supplier-generated constraints, which increase in the final stages of the design process. The paper argues...... is on fashion store design, the findings may, to some degree, be applicable to other types of store design projects. Practical implications: The understandings provided by this paper may help designers to deal proactively with constraints, reducing the use of resources to alter design proposals. Originality......Purpose: Retail design concepts are complex designs meeting functional and aesthetic demands from various constraint generators. However, the literature on this topic is sparse and offers only little support for store designers to deal with such challenges. To address this issue, the purpose...

  3. Grammatical constraints on phonological encoding in speech production.

    Science.gov (United States)

    Heller, Jordana R; Goldrick, Matthew

    2014-12-01

    To better understand the influence of grammatical encoding on the retrieval and encoding of phonological word-form information during speech production, we examine how grammatical class constraints influence the activation of phonological neighbors (words phonologically related to the target--e.g., MOON, TWO for target TUNE). Specifically, we compare how neighbors that share a target's grammatical category (here, nouns) influence its planning and retrieval, assessed by picture naming latencies, and phonetic encoding, assessed by word productions in picture names, when grammatical constraints are strong (in sentence contexts) versus weak (bare naming). Within-category (noun) neighbors influenced planning time and phonetic encoding more strongly in sentence contexts. This suggests that grammatical encoding constrains phonological processing; the influence of phonological neighbors is grammatically dependent. Moreover, effects on planning times could not fully account for phonetic effects, suggesting that phonological interaction affects articulation after speech onset. These results support production theories integrating grammatical, phonological, and phonetic processes.

  4. What the success of brain imaging implies about the neural code.

    Science.gov (United States)

    Guest, Olivia; Love, Bradley C

    2017-01-19

    The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI's limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution, the neural code must be smooth at the voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we determine which coding schemes are plausible given both fMRI's successes and its limitations in measuring neural activity. Deep neural network approaches, which have been forwarded as computational accounts of the ventral stream, are consistent with the success of fMRI, though functional smoothness breaks down in the later network layers. These results have implications for the nature of the neural code and ventral stream, as well as what can be successfully investigated with fMRI.

  5. A NOS1 variant implicated in cognitive performance influences evoked neural responses during a high density EEG study of early visual perception.

    Science.gov (United States)

    O'Donoghue, Therese; Morris, Derek W; Fahey, Ciara; Da Costa, Andreia; Foxe, John J; Hoerold, Doreen; Tropea, Daniela; Gill, Michael; Corvin, Aiden; Donohoe, Gary

    2012-05-01

    The nitric oxide synthasase-1 gene (NOS1) has been implicated in mental disorders including schizophrenia and variation in cognition. The NOS1 variant rs6490121 identified in a genome wide association study of schizophrenia has recently been associated with variation in general intelligence and working memory in both patients and healthy participants. Whether this variant is also associated with variation in early sensory processing remains unclear. We investigated differences in the P1 visual evoked potential in a high density EEG study of 54 healthy participants. Given both NOS1's association with cognition and recent evidence that cognitive performance and P1 response are correlated, we investigated whether NOS1's effect on P1 response was independent of its effects on cognition using CANTAB's spatial working memory (SWM) task. We found that carriers of the previously identified risk "G" allele showed significantly lower P1 responses than non-carriers. We also found that while P1 response and SWM performance were correlated, NOS1 continued to explain a significant proportion of variation in P1 response even when its effects on cognition were accounted for. The schizophrenia implicated NOS1 variants rs6490121 influences visual sensory processing as measured by the P1 response, either as part of the gene's pleiotropic effects on multiple aspects of brain function, or because of a primary influence on sensory processing that mediates the effects already seen in higher cognitive processes. Copyright © 2011 Wiley-Liss, Inc.

  6. A one-layer recurrent neural network for constrained nonsmooth optimization.

    Science.gov (United States)

    Liu, Qingshan; Wang, Jun

    2011-10-01

    This paper presents a novel one-layer recurrent neural network modeled by means of a differential inclusion for solving nonsmooth optimization problems, in which the number of neurons in the proposed neural network is the same as the number of decision variables of optimization problems. Compared with existing neural networks for nonsmooth optimization problems, the global convexity condition on the objective functions and constraints is relaxed, which allows the objective functions and constraints to be nonconvex. It is proven that the state variables of the proposed neural network are convergent to optimal solutions if a single design parameter in the model is larger than a derived lower bound. Numerical examples with simulation results substantiate the effectiveness and illustrate the characteristics of the proposed neural network.

  7. A one-layer recurrent neural network for constrained nonconvex optimization.

    Science.gov (United States)

    Li, Guocheng; Yan, Zheng; Wang, Jun

    2015-01-01

    In this paper, a one-layer recurrent neural network is proposed for solving nonconvex optimization problems subject to general inequality constraints, designed based on an exact penalty function method. It is proved herein that any neuron state of the proposed neural network is convergent to the feasible region in finite time and stays there thereafter, provided that the penalty parameter is sufficiently large. The lower bounds of the penalty parameter and convergence time are also estimated. In addition, any neural state of the proposed neural network is convergent to its equilibrium point set which satisfies the Karush-Kuhn-Tucker conditions of the optimization problem. Moreover, the equilibrium point set is equivalent to the optimal solution to the nonconvex optimization problem if the objective function and constraints satisfy given conditions. Four numerical examples are provided to illustrate the performances of the proposed neural network.

  8. Non-invasive neural stimulation

    Science.gov (United States)

    Tyler, William J.; Sanguinetti, Joseph L.; Fini, Maria; Hool, Nicholas

    2017-05-01

    Neurotechnologies for non-invasively interfacing with neural circuits have been evolving from those capable of sensing neural activity to those capable of restoring and enhancing human brain function. Generally referred to as non-invasive neural stimulation (NINS) methods, these neuromodulation approaches rely on electrical, magnetic, photonic, and acoustic or ultrasonic energy to influence nervous system activity, brain function, and behavior. Evidence that has been surmounting for decades shows that advanced neural engineering of NINS technologies will indeed transform the way humans treat diseases, interact with information, communicate, and learn. The physics underlying the ability of various NINS methods to modulate nervous system activity can be quite different from one another depending on the energy modality used as we briefly discuss. For members of commercial and defense industry sectors that have not traditionally engaged in neuroscience research and development, the science, engineering and technology required to advance NINS methods beyond the state-of-the-art presents tremendous opportunities. Within the past few years alone there have been large increases in global investments made by federal agencies, foundations, private investors and multinational corporations to develop advanced applications of NINS technologies. Driven by these efforts NINS methods and devices have recently been introduced to mass markets via the consumer electronics industry. Further, NINS continues to be explored in a growing number of defense applications focused on enhancing human dimensions. The present paper provides a brief introduction to the field of non-invasive neural stimulation by highlighting some of the more common methods in use or under current development today.

  9. The effect of the neural activity on topological properties of growing neural networks.

    Science.gov (United States)

    Gafarov, F M; Gafarova, V R

    2016-09-01

    The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

  10. The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior.

    Science.gov (United States)

    Orr, Mark G; Thrush, Roxanne; Plaut, David C

    2013-01-01

    The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual's pre-existing belief structure and the beliefs of others in the individual's social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics.

  11. The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior.

    Directory of Open Access Journals (Sweden)

    Mark G Orr

    Full Text Available The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior, does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence. To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual's pre-existing belief structure and the beliefs of others in the individual's social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics.

  12. Legal, ethical,and economic constraints

    International Nuclear Information System (INIS)

    Libassi, F.P.; Donaldson, L.F.

    1980-01-01

    This paper considers the legal, ethical, and economic constraints to developing a comprehensive knowledge of the biological effects of ionizing radiation. These constraints are not fixed and immutable; rather they are determined by the political process. Political issues cannot be evaded. The basic objective of developing a comprehensive knowledge about the biological effects of ionizing radiation exists as an objective not only because we wish to add to the store of human knowledge but also because we have important use for that knowledge. It will assist our decision-makers to make choices that affect us all. These choices require both hard factual information and application of political judgment. Research supplies some of the hard factual information and should be as free as possible from political influence in its execution. At the same time, the political choices that must be made influence the direction and nature of the research program as a whole. Similarly, the legal, ethical, and economic factors that constrain our ability to expand knowledge through research reflect a judgment by political agents that values other than expansion of knowledge should be recognized and given effect

  13. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

    We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

  14. Dlx proteins position the neural plate border and determine adjacent cell fates.

    Science.gov (United States)

    Woda, Juliana M; Pastagia, Julie; Mercola, Mark; Artinger, Kristin Bruk

    2003-01-01

    The lateral border of the neural plate is a major source of signals that induce primary neurons, neural crest cells and cranial placodes as well as provide patterning cues to mesodermal structures such as somites and heart. Whereas secreted BMP, FGF and Wnt proteins influence the differentiation of neural and non-neural ectoderm, we show here that members of the Dlx family of transcription factors position the border between neural and non-neural ectoderm and are required for the specification of adjacent cell fates. Inhibition of endogenous Dlx activity in Xenopus embryos with an EnR-Dlx homeodomain fusion protein expands the neural plate into non-neural ectoderm tissue whereas ectopic activation of Dlx target genes inhibits neural plate differentiation. Importantly, the stereotypic pattern of border cell fates in the adjacent ectoderm is re-established only under conditions where the expanded neural plate abuts Dlx-positive non-neural ectoderm. Experiments in which presumptive neural plate was grafted to ventral ectoderm reiterate induction of neural crest and placodal lineages and also demonstrate that Dlx activity is required in non-neural ectoderm for the production of signals needed for induction of these cells. We propose that Dlx proteins regulate intercellular signaling across the interface between neural and non-neural ectoderm that is critical for inducing and patterning adjacent cell fates.

  15. Connecting Neural Coding to Number Cognition: A Computational Account

    Science.gov (United States)

    Prather, Richard W.

    2012-01-01

    The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has…

  16. Developmental constraints on behavioural flexibility.

    Science.gov (United States)

    Holekamp, Kay E; Swanson, Eli M; Van Meter, Page E

    2013-05-19

    We suggest that variation in mammalian behavioural flexibility not accounted for by current socioecological models may be explained in part by developmental constraints. From our own work, we provide examples of constraints affecting variation in behavioural flexibility, not only among individuals, but also among species and higher taxonomic units. We first implicate organizational maternal effects of androgens in shaping individual differences in aggressive behaviour emitted by female spotted hyaenas throughout the lifespan. We then compare carnivores and primates with respect to their locomotor and craniofacial adaptations. We inquire whether antagonistic selection pressures on the skull might impose differential functional constraints on evolvability of skulls and brains in these two orders, thus ultimately affecting behavioural flexibility in each group. We suggest that, even when carnivores and primates would theoretically benefit from the same adaptations with respect to behavioural flexibility, carnivores may nevertheless exhibit less behavioural flexibility than primates because of constraints imposed by past adaptations in the morphology of the limbs and skull. Phylogenetic analysis consistent with this idea suggests greater evolutionary lability in relative brain size within families of primates than carnivores. Thus, consideration of developmental constraints may help elucidate variation in mammalian behavioural flexibility.

  17. Data assimilation with inequality constraints

    Science.gov (United States)

    Thacker, W. C.

    If values of variables in a numerical model are limited to specified ranges, these restrictions should be enforced when data are assimilated. The simplest option is to assimilate without regard for constraints and then to correct any violations without worrying about additional corrections implied by correlated errors. This paper addresses the incorporation of inequality constraints into the standard variational framework of optimal interpolation with emphasis on our limited knowledge of the underlying probability distributions. Simple examples involving only two or three variables are used to illustrate graphically how active constraints can be treated as error-free data when background errors obey a truncated multi-normal distribution. Using Lagrange multipliers, the formalism is expanded to encompass the active constraints. Two algorithms are presented, both relying on a solution ignoring the inequality constraints to discover violations to be enforced. While explicitly enforcing a subset can, via correlations, correct the others, pragmatism based on our poor knowledge of the underlying probability distributions suggests the expedient of enforcing them all explicitly to avoid the computationally expensive task of determining the minimum active set. If additional violations are encountered with these solutions, the process can be repeated. Simple examples are used to illustrate the algorithms and to examine the nature of the corrections implied by correlated errors.

  18. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

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

  19. Returns, productivity and constraints analyses of cassava/maize ...

    African Journals Online (AJOL)

    The study was conducted in Isi-Uzo LGA of Enugu State, and evaluated the productivity and profitability of cassava/maize/melon mixed cropping with the aim to determine the degree and direction of influence of the production factors and identification of constraints militating against the enterprise. Multistage and purposive ...

  20. Effect Of Credit Constraint On Production Efficiency Of Farm ...

    African Journals Online (AJOL)

    Credit constraint in agriculture affects not only the purchasing power of producers to procure farm inputs and to cover operating costs in the short run, but also their capacity to make farm-related investments as well as risk behavior in technology choice and adoption. These, in turn, influence technical efficiencies of the ...

  1. Prospects for nuclear terrorism: psychological motivations and constraints

    International Nuclear Information System (INIS)

    Post, J.M.

    1987-01-01

    In considering the implications of psychological understandings to the specific case of nuclear terrorism, it is emphasized that distorted decision making does not equate to totally irrational decision making. In certain circumstances, however, the distorted individual and group decision-making psychology could influence the group toward a high-risk option such as nuclear terrorism. For terrorists operating within their own national boundaries, a terrorist act producing mass casualties would generally be counterproductive. For groups acting across national boundaries, however, this constraint does not apply to nearly the same degree. Although the opprobrium of the West will be a constraint for some, it will not be equally so for all terrorist groups. The degree of disincentive will relate in particular to the major audience of influence. Also, there are the terrorist losers who are being shunted aside and losing the recognition they seek. Such a group could justify a terrorist spectacular in order to regain influence on the basis of a what have we got to lose rationale. In thinking about the possibility of nuclear terrorism, it is important to distinguish between the actual detonation of a device and the use of a device for extortion and influence. The constraints against the latter are significantly reduced in contrast to acts producing mass casualties. The constraints are even more reduced in the case of the plausible nuclear hoax, an option that can be expected to become more frequent

  2. A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

    Science.gov (United States)

    Liu, Qingshan; Guo, Zhishan; Wang, Jun

    2012-02-01

    In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

  3. Constraint programming and decision making

    CERN Document Server

    Kreinovich, Vladik

    2014-01-01

    In many application areas, it is necessary to make effective decisions under constraints. Several area-specific techniques are known for such decision problems; however, because these techniques are area-specific, it is not easy to apply each technique to other applications areas. Cross-fertilization between different application areas is one of the main objectives of the annual International Workshops on Constraint Programming and Decision Making. Those workshops, held in the US (El Paso, Texas), in Europe (Lyon, France), and in Asia (Novosibirsk, Russia), from 2008 to 2012, have attracted researchers and practitioners from all over the world. This volume presents extended versions of selected papers from those workshops. These papers deal with all stages of decision making under constraints: (1) formulating the problem of multi-criteria decision making in precise terms, (2) determining when the corresponding decision problem is algorithmically solvable; (3) finding the corresponding algorithms, and making...

  4. Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

    Science.gov (United States)

    Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

    2011-05-01

    A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

  5. Effect of Auditory Constraints on Motor Learning Depends on Stage of Recovery Post Stroke

    Directory of Open Access Journals (Sweden)

    Viswanath eAluru

    2014-06-01

    Full Text Available In order to develop evidence-based rehabilitation protocols post stroke, one must first reconcile the vast heterogeneity in the post-stroke population and develop protocols to facilitate motor learning in the various subgroups. The main purpose of this study is to show that auditory constraints interact with the stage of recovery post stroke to influence motor learning. We characterized the stages of upper limb recovery using task-based kinematic measures in twenty subjects with chronic hemiparesis, and used a bimanual wrist extension task using a custom-made wrist trainer to facilitate learning of wrist extension in the paretic hand under four auditory conditions: 1 without auditory cueing; 2 to non-musical happy sounds; 3 to self-selected music; and 4 to a metronome beat set at a comfortable tempo. Two bimanual trials (15 s each were followed by one unimanual trial with the paretic hand over six cycles under each condition. Clinical metrics, wrist and arm kinematics and electromyographic activity were recorded. Hierarchical cluster analysis with the Mahalanobis metric based on baseline speed and extent of wrist movement stratified subjects into three distinct groups which reflected their stage of recovery: spastic paresis, spastic co-contraction, and minimal paresis. In spastic paresis, the metronome beat increased wrist extension, but also increased muscle co-activation across the wrist. In contrast, in spastic co-contraction, no auditory stimulation increased wrist extension and reduced co-activation. In minimal paresis, wrist extension did not improve under any condition. The results suggest that auditory task constraints interact with stage of recovery during motor learning after stroke, perhaps due to recruitment of distinct neural substrates over the course of recovery. The findings advance our understanding of the mechanisms of progression of motor recovery and lay the foundation for personalized treatment algorithms post stroke.

  6. Epidural anaesthesia with levobupivacaine and ropivacaine : effects of age on the pharmacokinetics, neural blockade and haemodynamics

    NARCIS (Netherlands)

    Simon, Mischa J.G.

    2006-01-01

    Epidural neural blockade results from processes after the administration of a local anaesthetic in the epidural space until the uptake in neural tissue. The pharmacokinetics, neural blockade and haemodynamics after epidural anaesthesia may be influenced by several factors, with age as the most

  7. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...... is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V...

  8. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  9. Evolutionary Algorithms For Neural Networks Binary And Real Data Classification

    Directory of Open Access Journals (Sweden)

    Dr. Hanan A.R. Akkar

    2015-08-01

    Full Text Available Artificial neural networks are complex networks emulating the way human rational neurons process data. They have been widely used generally in prediction clustering classification and association. The training algorithms that used to determine the network weights are almost the most important factor that influence the neural networks performance. Recently many meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to achieve better neural performance. This paper aims to use recently proposed algorithms for optimizing neural networks weights comparing these algorithms performance with other classical meta-heuristic algorithms used for the same purpose. However to evaluate the performance of such algorithms for training neural networks we examine such algorithms to classify four opposite binary XOR clusters and classification of continuous real data sets such as Iris and Ecoli.

  10. Constraint elimination in dynamical systems

    Science.gov (United States)

    Singh, R. P.; Likins, P. W.

    1989-01-01

    Large space structures (LSSs) and other dynamical systems of current interest are often extremely complex assemblies of rigid and flexible bodies subjected to kinematical constraints. A formulation is presented for the governing equations of constrained multibody systems via the application of singular value decomposition (SVD). The resulting equations of motion are shown to be of minimum dimension.

  11. Constraint Programming versus Mathematical Programming

    DEFF Research Database (Denmark)

    Hansen, Jesper

    2003-01-01

    Constraint Logic Programming (CLP) is a relatively new technique from the 80's with origins in Computer Science and Artificial Intelligence. Lately, much research have been focused on ways of using CLP within the paradigm of Operations Research (OR) and vice versa. The purpose of this paper...

  12. Sterile neutrino constraints from cosmology

    DEFF Research Database (Denmark)

    Hamann, Jan; Hannestad, Steen; Raffelt, Georg G.

    2012-01-01

    The presence of light particles beyond the standard model's three neutrino species can profoundly impact the physics of decoupling and primordial nucleosynthesis. I review the observational signatures of extra light species, present constraints from recent data, and discuss the implications of po...... of possible sterile neutrinos with O(eV)-masses for cosmology....

  13. Intertemporal consumption and credit constraints

    DEFF Research Database (Denmark)

    Leth-Petersen, Søren

    2010-01-01

    There is continuing controversy over the importance of credit constraints. This paper investigates whether total household expenditure and debt is affected by an exogenous increase in access to credit provided by a credit market reform that enabled Danish house owners to use housing equity...

  14. Financial Constraints: Explaining Your Position.

    Science.gov (United States)

    Cargill, Jennifer

    1988-01-01

    Discusses the importance of educating library patrons about the library's finances and the impact of budget constraints and the escalating cost of serials on materials acquisition. Steps that can be taken in educating patrons by interpreting and publicizing financial information are suggested. (MES)

  15. Central neural pathways for thermoregulation

    Science.gov (United States)

    Morrison, Shaun F.; Nakamura, Kazuhiro

    2010-01-01

    Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

  16. Consumers, food and convenience: The long way from resource constraints to actual consumption patterns

    DEFF Research Database (Denmark)

    Scholderer, Joachim; Grunert, Klaus G.

    2005-01-01

    that the influence of resource constraints on actual convenience behaviours is doubly mediated, first by perceptions of resource constraints, and then by convenience orientations. In Study 1, the model is calibrated based on a sample of 1000 French respondents with main responsibility for food shopping and meal...

  17. Effects of Social Constraints on Career Maturity: The Mediating Effect of the Time Perspective

    Science.gov (United States)

    Kim, Kyung-Nyun; Oh, Se-Hee

    2013-01-01

    Previous studies have provided mixed results for the effects of social constraints on career maturity. However, there has been growing interest in these effects from the time perspective. Few studies have examined the effects of social constraints on the time perspective which in turn influences career maturity. This study examines the mediating…

  18. Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

    Directory of Open Access Journals (Sweden)

    Zhonghua Wu

    2017-01-01

    Full Text Available This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.

  19. Neural Networks: Implementations and Applications

    OpenAIRE

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  20. Race modulates neural activity during imitation

    Science.gov (United States)

    Losin, Elizabeth A. Reynolds; Iacoboni, Marco; Martin, Alia; Cross, Katy A.; Dapretto, Mirella

    2014-01-01

    Imitation plays a central role in the acquisition of culture. People preferentially imitate others who are self-similar, prestigious or successful. Because race can indicate a person's self-similarity or status, race influences whom people imitate. Prior studies of the neural underpinnings of imitation have not considered the effects of race. Here we measured neural activity with fMRI while European American participants imitated meaningless gestures performed by actors of their own race, and two racial outgroups, African American, and Chinese American. Participants also passively observed the actions of these actors and their portraits. Frontal, parietal and occipital areas were differentially activated while participants imitated actors of different races. More activity was present when imitating African Americans than the other racial groups, perhaps reflecting participants' reported lack of experience with and negative attitudes towards this group, or the group's lower perceived social status. This pattern of neural activity was not found when participants passively observed the gestures of the actors or simply looked at their faces. Instead, during face-viewing neural responses were overall greater for own-race individuals, consistent with prior race perception studies not involving imitation. Our findings represent a first step in elucidating neural mechanisms involved in cultural learning, a process that influences almost every aspect of our lives but has thus far received little neuroscientific study. PMID:22062193

  1. Computational modeling of neural plasticity for self-organization of neural networks.

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

    Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  2. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  3. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    changes or to abandon the strong identity thesis altogether. Were one to pursue a theory according to which consciousness is not an epiphenomenon to brain processes, consciousness may in fact affect its own neural basis. The neural correlate of consciousness is often seen as a stable structure, that is...

  4. Influence of the Different Primary Cancers and Different Types of Bone Metastasis on the Lesion-based Artificial Neural Network Value Calculated by a Computer-aided Diagnostic System,BONENAVI, on Bone Scintigraphy Images

    Directory of Open Access Journals (Sweden)

    TAKURO ISODA

    2017-01-01

    Full Text Available Objective(s: BONENAVI, a computer-aided diagnostic system, is used in bone scintigraphy. This system provides the artificial neural network (ANN and bone scan index (BSI values. ANN is associated with the possibility of bone metastasis, while BSI is related to the amount of bone metastasis. The degree of uptake on bone scintigraphy can be affected by the type of bone metastasis. Therefore, the ANN value provided by BONENAVI may be influenced by the characteristics of bone metastasis. In this study, we aimed to assess the relationship between ANN value and characteristics of bone metastasis. Methods: We analyzed 50 patients (36 males, 14 females; age range: 42–87 yrs, median age: 72.5 yrs with prostate, breast, or lung cancer who had undergone bone scintigraphy and were diagnosed with bone metastasis (32 cases of prostate cancer, nine cases of breast cancer, and nine cases of lung cancer. Those who had received systematic therapy over the past years were excluded. Bone metastases were diagnosed clinically, and the type of bone metastasis (osteoblastic, mildly osteoblastic,osteolytic, and mixed components was decided visually by the agreement of two radiologists. We compared the ANN values (case-based and lesion-based among the three primary cancers and four types of bone metastasis.Results: There was no significant difference in case-based ANN values among prostate, breast, and lung cancers. However, the lesion-based ANN values were the highest in cases with prostate cancer and the lowest in cases of lung cancer (median values: prostate cancer, 0.980; breast cancer, 0.909; and lung cancer, 0.864. Mildly osteoblastic lesions showed significantly lower ANN values than the other three types of bone metastasis (median values: osteoblastic, 0.939; mildly osteoblastic, 0.788; mixed type, 0.991; and osteolytic, 0.969. The possibility of a lesion-based ANN value below 0.5 was 10.9% for bone metastasis in prostate cancer, 12.9% for breast cancer, and 37

  5. Feature extraction using convolutional neural network for classifying breast density in mammographic images

    Science.gov (United States)

    Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

    2017-03-01

    Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

  6. Avoiding object by robot using neural network

    International Nuclear Information System (INIS)

    Prasetijo, D.W.

    1997-01-01

    A Self controlling robot is necessary in the robot application in which operator control is difficult. Serial method such as process on the computer of van newman is difficult to be applied for self controlling robot. In this research, Neural network system for robotic control system was developed by performance expanding at the SCARA. In this research, it was shown that SCARA with application at Neural network system can avoid blocking objects without influence by number and density of the blocking objects, also departure and destination paint. robot developed by this study also can control its moving by self

  7. Stimulation and recording electrodes for neural prostheses

    CERN Document Server

    Pour Aryan, Naser; Rothermel, Albrecht

    2015-01-01

    This book provides readers with basic principles of the electrochemistry of the electrodes used in modern, implantable neural prostheses. The authors discuss the boundaries and conditions in which the electrodes continue to function properly for long time spans, which are required when designing neural stimulator devices for long-term in vivo applications. Two kinds of electrode materials, titanium nitride and iridium are discussed extensively, both qualitatively and quantitatively. The influence of the counter electrode on the safety margins and electrode lifetime in a two electrode system is explained. Electrode modeling is handled in a final chapter.

  8. A compendium of chameleon constraints

    International Nuclear Information System (INIS)

    Burrage, Clare; Sakstein, Jeremy

    2016-01-01

    The chameleon model is a scalar field theory with a screening mechanism that explains how a cosmologically relevant light scalar can avoid the constraints of intra-solar-system searches for fifth-forces. The chameleon is a popular dark energy candidate and also arises in f ( R ) theories of gravity. Whilst the chameleon is designed to avoid historical searches for fifth-forces it is not unobservable and much effort has gone into identifying the best observables and experiments to detect it. These results are not always presented for the same models or in the same language, a particular problem when comparing astrophysical and laboratory searches making it difficult to understand what regions of parameter space remain. Here we present combined constraints on the chameleon model from astrophysical and laboratory searches for the first time and identify the remaining windows of parameter space. We discuss the implications for cosmological chameleon searches and future small-scale probes.

  9. A compendium of chameleon constraints

    Energy Technology Data Exchange (ETDEWEB)

    Burrage, Clare [School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD (United Kingdom); Sakstein, Jeremy, E-mail: clare.burrage@nottingham.ac.uk, E-mail: jeremy.sakstein@port.ac.uk [Center for Particle Cosmology, Department of Physics and Astronomy, University of Pennsylvania, 209 S. 33rd St., Philadelphia, PA 19104 (United States)

    2016-11-01

    The chameleon model is a scalar field theory with a screening mechanism that explains how a cosmologically relevant light scalar can avoid the constraints of intra-solar-system searches for fifth-forces. The chameleon is a popular dark energy candidate and also arises in f ( R ) theories of gravity. Whilst the chameleon is designed to avoid historical searches for fifth-forces it is not unobservable and much effort has gone into identifying the best observables and experiments to detect it. These results are not always presented for the same models or in the same language, a particular problem when comparing astrophysical and laboratory searches making it difficult to understand what regions of parameter space remain. Here we present combined constraints on the chameleon model from astrophysical and laboratory searches for the first time and identify the remaining windows of parameter space. We discuss the implications for cosmological chameleon searches and future small-scale probes.

  10. Self-Imposed Creativity Constraints

    DEFF Research Database (Denmark)

    Biskjaer, Michael Mose

    2013-01-01

    Abstract This dissertation epitomizes three years of research guided by the research question: how can we conceptualize creative self-binding as a resource in art and design processes? Concretely, the dissertation seeks to offer insight into the puzzling observation that highly skilled creative...... practitioners sometimes freely and intentionally impose rigid rules, peculiar principles, and other kinds of creative obstructions on themselves as a means to spur momentum in the process and reach a distinctly original outcome. To investigate this the dissertation is composed of four papers (Part II) framed...... of analysis. Informed by the insight that constraints both enable and restrain creative agency, the dissertation’s main contention is that creative self- binding may profitably be conceptualized as the exercise of self-imposed creativity constraints. Thus, the dissertation marks an analytical move from vague...

  11. Unitarity constraints on trimaximal mixing

    International Nuclear Information System (INIS)

    Kumar, Sanjeev

    2010-01-01

    When the neutrino mass eigenstate ν 2 is trimaximally mixed, the mixing matrix is called trimaximal. The middle column of the trimaximal mixing matrix is identical to tribimaximal mixing and the other two columns are subject to unitarity constraints. This corresponds to a mixing matrix with four independent parameters in the most general case. Apart from the two Majorana phases, the mixing matrix has only one free parameter in the CP conserving limit. Trimaximality results in interesting interplay between mixing angles and CP violation. A notion of maximal CP violation naturally emerges here: CP violation is maximal for maximal 2-3 mixing. Similarly, there is a natural constraint on the deviation from maximal 2-3 mixing which takes its maximal value in the CP conserving limit.

  12. Macroscopic constraints on string unification

    International Nuclear Information System (INIS)

    Taylor, T.R.

    1989-03-01

    The comparison of sting theory with experiment requires a huge extrapolation from the microscopic distances, of order of the Planck length, up to the macroscopic laboratory distances. The quantum effects give rise to large corrections to the macroscopic predictions of sting unification. I discus the model-independent constraints on the gravitational sector of string theory due to the inevitable existence of universal Fradkin-Tseytlin dilatons. 9 refs

  13. Financial Constraints and Franchising Decisions

    OpenAIRE

    Kai-Uwe Kuhn; Francine Lafontaine; Ying Fan

    2013-01-01

    We study how the financial constraints of agents affect the behavior of principals in the context of franchising. We develop an empirical model of franchising starting with a principal-agent framework that emphasizes the role of franchisees' collateral from an incentive perspective. We estimate the determinants of chains' entry (into franchising) and growth decisions using data on franchised chains and data on local macroeconomic conditions. In particular, we use collateralizable housing weal...

  14. Analysis of Space Tourism Constraints

    Science.gov (United States)

    Bonnal, Christophe

    2002-01-01

    Space tourism appears today as a new Eldorado in a relatively near future. Private operators are already proposing services for leisure trips in Low Earth Orbit, and some happy few even tested them. But are these exceptional events really marking the dawn of a new space age ? The constraints associated to the space tourism are severe : - the economical balance of space tourism is tricky; development costs of large manned - the technical definition of such large vehicles is challenging, mainly when considering - the physiological aptitude of passengers will have a major impact on the mission - the orbital environment will also lead to mission constraints on aspects such as radiation, However, these constraints never appear as show-stoppers and have to be dealt with pragmatically: - what are the recommendations one can make for future research in the field of space - which typical roadmap shall one consider to develop realistically this new market ? - what are the synergies with the conventional missions and with the existing infrastructure, - how can a phased development start soon ? The paper proposes hints aiming at improving the credibility of Space Tourism and describes the orientations to follow in order to solve the major hurdles found in such an exciting development.

  15. Infrared Constraint on Ultraviolet Theories

    Energy Technology Data Exchange (ETDEWEB)

    Tsai, Yuhsin [Cornell Univ., Ithaca, NY (United States)

    2012-08-01

    While our current paradigm of particle physics, the Standard Model (SM), has been extremely successful at explaining experiments, it is theoretically incomplete and must be embedded into a larger framework. In this thesis, we review the main motivations for theories beyond the SM (BSM) and the ways such theories can be constrained using low energy physics. The hierarchy problem, neutrino mass and the existence of dark matter (DM) are the main reasons why the SM is incomplete . Two of the most plausible theories that may solve the hierarchy problem are the Randall-Sundrum (RS) models and supersymmetry (SUSY). RS models usually suffer from strong flavor constraints, while SUSY models produce extra degrees of freedom that need to be hidden from current experiments. To show the importance of infrared (IR) physics constraints, we discuss the flavor bounds on the anarchic RS model in both the lepton and quark sectors. For SUSY models, we discuss the difficulties in obtaining a phenomenologically allowed gaugino mass, its relation to R-symmetry breaking, and how to build a model that avoids this problem. For the neutrino mass problem, we discuss the idea of generating small neutrino masses using compositeness. By requiring successful leptogenesis and the existence of warm dark matter (WDM), we can set various constraints on the hidden composite sector. Finally, to give an example of model independent bounds from collider experiments, we show how to constrain the DM–SM particle interactions using collider results with an effective coupling description.

  16. Isocurvature constraints on portal couplings

    Energy Technology Data Exchange (ETDEWEB)

    Kainulainen, Kimmo; Nurmi, Sami; Vaskonen, Ville [Department of Physics, University of Jyväskylä, P.O.Box 35 (YFL), FI-40014 University of Jyväskylä (Finland); Tenkanen, Tommi; Tuominen, Kimmo, E-mail: kimmo.kainulainen@jyu.fi, E-mail: sami.t.nurmi@jyu.fi, E-mail: tommi.tenkanen@helsinki.fi, E-mail: kimmo.i.tuominen@helsinki.fi, E-mail: ville.vaskonen@jyu.fi [Department of Physics, University of Helsinki P.O. Box 64, FI-00014, Helsinki (Finland)

    2016-06-01

    We consider portal models which are ultraweakly coupled with the Standard Model, and confront them with observational constraints on dark matter abundance and isocurvature perturbations. We assume the hidden sector to contain a real singlet scalar s and a sterile neutrino ψ coupled to s via a pseudoscalar Yukawa term. During inflation, a primordial condensate consisting of the singlet scalar s is generated, and its contribution to the isocurvature perturbations is imprinted onto the dark matter abundance. We compute the total dark matter abundance including the contributions from condensate decay and nonthermal production from the Standard Model sector. We then use the Planck limit on isocurvature perturbations to derive a novel constraint connecting dark matter mass and the singlet self coupling with the scale of inflation: m {sub DM}/GeV ∼< 0.2λ{sub s}{sup 3/8} ( H {sub *}/10{sup 11} GeV){sup −3/2}. This constraint is relevant in most portal models ultraweakly coupled with the Standard Model and containing light singlet scalar fields.

  17. A two-layer recurrent neural network for nonsmooth convex optimization problems.

    Science.gov (United States)

    Qin, Sitian; Xue, Xiaoping

    2015-06-01

    In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush-Kuhn-Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems.

  18. Relaxations of semiring constraint satisfaction problems

    CSIR Research Space (South Africa)

    Leenen, L

    2007-03-01

    Full Text Available The Semiring Constraint Satisfaction Problem (SCSP) framework is a popular approach for the representation of partial constraint satisfaction problems. In this framework preferences can be associated with tuples of values of the variable domains...

  19. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  20. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible

  1. Dynamics of neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  2. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    Rajpal, P.S.; Shishodia, K.S.; Sekhon, G.S.

    2006-01-01

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  3. Global asymptotic stability of hybrid bidirectional associative memory neural networks with time delays

    International Nuclear Information System (INIS)

    Arik, Sabri

    2006-01-01

    This Letter presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results 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. The results are also compared with the previous results derived in the literature

  4. 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.

  5. Global asymptotic stability of hybrid bidirectional associative memory neural networks with time delays

    Science.gov (United States)

    Arik, Sabri

    2006-02-01

    This Letter presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results 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. The results are also compared with the previous results derived in the literature.

  6. Transmission and capacity pricing and constraints

    International Nuclear Information System (INIS)

    Fusco, M.

    1999-01-01

    A series of overhead viewgraphs accompanied this presentation which discussed the following issues regarding the North American electric power industry: (1) capacity pricing transmission constraints, (2) nature of transmission constraints, (3) consequences of transmission constraints, and (4) prices as market evidence. Some solutions suggested for pricing constraints included the development of contingent contracts, back-up power in supply regions, and new line capacity construction. 8 tabs., 20 figs

  7. Ant colony optimization and constraint programming

    CERN Document Server

    Solnon, Christine

    2013-01-01

    Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search

  8. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-07-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

  9. ANT Advanced Neural Tool

    International Nuclear Information System (INIS)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-01-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs

  10. Streaming Weak Submodularity: Interpreting Neural Networks on the Fly

    OpenAIRE

    Elenberg, Ethan R.; Dimakis, Alexandros G.; Feldman, Moran; Karbasi, Amin

    2017-01-01

    In many machine learning applications, it is important to explain the predictions of a black-box classifier. For example, why does a deep neural network assign an image to a particular class? We cast interpretability of black-box classifiers as a combinatorial maximization problem and propose an efficient streaming algorithm to solve it subject to cardinality constraints. By extending ideas from Badanidiyuru et al. [2014], we provide a constant factor approximation guarantee for our algorithm...

  11. Optimal deep neural networks for sparse recovery via Laplace techniques

    OpenAIRE

    Limmer, Steffen; Stanczak, Slawomir

    2017-01-01

    This paper introduces Laplace techniques for designing a neural network, with the goal of estimating simplex-constraint sparse vectors from compressed measurements. To this end, we recast the problem of MMSE estimation (w.r.t. a pre-defined uniform input distribution) as the problem of computing the centroid of some polytope that results from the intersection of the simplex and an affine subspace determined by the measurements. Owing to the specific structure, it is shown that the centroid ca...

  12. Learning and Parallelization Boost Constraint Search

    Science.gov (United States)

    Yun, Xi

    2013-01-01

    Constraint satisfaction problems are a powerful way to abstract and represent academic and real-world problems from both artificial intelligence and operations research. A constraint satisfaction problem is typically addressed by a sequential constraint solver running on a single processor. Rather than construct a new, parallel solver, this work…

  13. A general treatment of dynamic integrity constraints

    NARCIS (Netherlands)

    de Brock, EO

    This paper introduces a general, set-theoretic model for expressing dynamic integrity constraints, i.e., integrity constraints on the state changes that are allowed in a given state space. In a managerial context, such dynamic integrity constraints can be seen as representations of "real world"

  14. Linear programming based on neural networks for radiotherapy treatment planning

    International Nuclear Information System (INIS)

    Xingen Wu; Limin Luo

    2000-01-01

    In this paper, we propose a neural network model for linear programming that is designed to optimize radiotherapy treatment planning (RTP). This kind of neural network can be easily implemented by using a kind of 'neural' electronic system in order to obtain an optimization solution in real time. We first give an introduction to the RTP problem and construct a non-constraint objective function for the neural network model. We adopt a gradient algorithm to minimize the objective function and design the structure of the neural network for RTP. Compared to traditional linear programming methods, this neural network model can reduce the time needed for convergence, the size of problems (i.e., the number of variables to be searched) and the number of extra slack and surplus variables needed. We obtained a set of optimized beam weights that result in a better dose distribution as compared to that obtained using the simplex algorithm under the same initial condition. The example presented in this paper shows that this model is feasible in three-dimensional RTP. (author)

  15. Neck muscle biomechanics and neural control.

    Science.gov (United States)

    Fice, Jason Bradley; Siegmund, Gunter P; Blouin, Jean-Sebastien

    2018-04-18

    The mechanics, morphometry, and geometry of our joints, segments and muscles are fundamental biomechanical properties intrinsic to human neural control. The goal of our study was to investigate if the biomechanical actions of individual neck muscles predicts their neural control. Specifically, we compared the moment direction & variability produced by electrical stimulation of a neck muscle (biomechanics) to their preferred activation direction & variability (neural control). Subjects sat upright with their head fixed to a 6-axis load cell and their torso restrained. Indwelling wire electrodes were placed into the sternocleidomastoid (SCM), splenius capitis (SPL), and semispinalis capitis (SSC) muscles. The electrically stimulated direction was defined as the moment direction produced when a current (2-19mA) was passed through each muscle's electrodes. Preferred activation direction was defined as the vector sum of the spatial tuning curve built from RMS EMG when subjects produced isometric moments at 7.5% and 15% of their maximum voluntary contraction (MVC) in 26 3D directions. The spatial tuning curves at 15% MVC were well-defined (unimodal, pbiomechanics but, as activation increases, biomechanical constraints in part dictate the activation of synergistic neck muscles.

  16. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  17. Neural networks for aircraft control

    Science.gov (United States)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  18. Active Neural Localization

    OpenAIRE

    Chaplot, Devendra Singh; Parisotto, Emilio; Salakhutdinov, Ruslan

    2018-01-01

    Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of tradition...

  19. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  20. Constraint Specialisation in Horn Clause Verification

    DEFF Research Database (Denmark)

    Kafle, Bishoksan; Gallagher, John Patrick

    2015-01-01

    We present a method for specialising the constraints in constrained Horn clauses with respect to a goal. We use abstract interpretation to compute a model of a query-answer transformation of a given set of clauses and a goal. The effect is to propagate the constraints from the goal top......-down and propagate answer constraints bottom-up. Our approach does not unfold the clauses at all; we use the constraints from the model to compute a specialised version of each clause in the program. The approach is independent of the abstract domain and the constraints theory underlying the clauses. Experimental...

  1. Constraint specialisation in Horn clause verification

    DEFF Research Database (Denmark)

    Kafle, Bishoksan; Gallagher, John Patrick

    2017-01-01

    We present a method for specialising the constraints in constrained Horn clauses with respect to a goal. We use abstract interpretation to compute a model of a query–answer transformed version of a given set of clauses and a goal. The constraints from the model are then used to compute...... a specialised version of each clause. The effect is to propagate the constraints from the goal top-down and propagate answer constraints bottom-up. The specialisation procedure can be repeated to yield further specialisation. The approach is independent of the abstract domain and the constraint theory...

  2. Nuclear energy and external constraints

    International Nuclear Information System (INIS)

    Lattes, R.; Thiriet, L.

    1983-01-01

    The structural factors of this crisis probably predominate over factors arising out the economic situation, even if explanations vary in this respect. In this article devoted to nuclear energy, a possible means of Loosering external constraints the current international economic environment is firstly outlined; the context in which the policies of industrialized countries, and therefore that of France, must be developed. An examination of the possible role of energy policies in general and nuclear policies in particular as an instrument of economic policy in providing a partial solution to this crisis, will then enable to quantitatively evaluate the effects of such policies at a national level [fr

  3. Classification of urine sediment based on convolution neural network

    Science.gov (United States)

    Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

    2018-04-01

    By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

  4. How multiple causes combine: independence constraints on causal inference.

    Science.gov (United States)

    Liljeholm, Mimi

    2015-01-01

    According to the causal power view, two core constraints-that causes occur independently (i.e., no confounding) and influence their effects independently-serve as boundary conditions for causal induction. This study investigated how violations of these constraints modulate uncertainty about the existence and strength of a causal relationship. Participants were presented with pairs of candidate causes that were either confounded or not, and that either interacted or exerted their influences independently. Consistent with the causal power view, uncertainty about the existence and strength of causal relationships was greater when causes were confounded or interacted than when unconfounded and acting independently. An elemental Bayesian causal model captured differences in uncertainty due to confounding but not those due to an interaction. Implications of distinct sources of uncertainty for the selection of contingency information and causal generalization are discussed.

  5. A neural network approach to job-shop scheduling.

    Science.gov (United States)

    Zhou, D N; Cherkassky, V; Baldwin, T R; Olson, D E

    1991-01-01

    A novel analog computational network is presented for solving NP-complete constraint satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linear programming network described by D.W. Tank and J.J. Hopfield (1985), which also uses a linear cost function for a simple optimization problem. It is shown how to map a difficult constraint-satisfaction problem onto a simple neural net in which the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the authors' approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the traveling salesman problem-type Hopfield approach and integer linear programming approach of J.P.S. Foo and Y. Takefuji (1988), in terms of the quality of the solution and the network complexity.

  6. Global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms

    International Nuclear Information System (INIS)

    Wang Jian; Lu Junguo

    2008-01-01

    In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits

  7. Impact parameter determination for 40Ca + 40Ca reactions using a neural network

    International Nuclear Information System (INIS)

    Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.

    1995-01-01

    A neural network is used for the impact parameter determination in 40 Ca + 40 Ca reactions at energies between 35 and 70 AMeV. A special attention is devoted to the effect of experimental constraints such as the detection efficiency. An overall improvement of the impact parameter determination of 25% is obtained with the neural network. The neural network technique is then used in the analysis of the Ca+Ca data at 35 AMeV and allows separation of three different class of events among the selected 'complete' events. (authors). 8 refs., 5 figs

  8. Micro-compression testing: A critical discussion of experimental constraints

    International Nuclear Information System (INIS)

    Kiener, D.; Motz, C.; Dehm, G.

    2009-01-01

    Micro-compression testing is a promising technique for determining mechanical properties at small length scales since it has several benefits over nanoindentation. However, as for all new techniques, experimental constraints influencing the results of such a micro-mechanical test must be considered. Here we investigate constraints imposed by the sample geometry, the pile-up of dislocations at the sample top and base, and the lateral stiffness of the testing setup. Using a focused ion beam milling setup, single crystal Cu specimens with different geometries and crystal orientations were fabricated. Tapered samples served to investigate the influence of strain gradients, while stiff sample top coatings and undeformable substrates depict the influence of dislocation pile-ups at these interfaces. The lateral system stiffness was reduced by placing specimens on top of needles. Samples were loaded using an in situ indenter in a scanning electron microscope in load controlled or displacement controlled mode. The observed differences in the mechanical response with respect to the experimental imposed constraints are discussed and lead to the conclusion that controlling the lateral system stiffness is the most important point

  9. Research on Fault Diagnosis Method Based on Rule Base Neural Network

    Directory of Open Access Journals (Sweden)

    Zheng Ni

    2017-01-01

    Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

  10. Neural network modeling of emotion

    Science.gov (United States)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  11. A one-layer recurrent neural network for constrained nonsmooth invex optimization.

    Science.gov (United States)

    Li, Guocheng; Yan, Zheng; Wang, Jun

    2014-02-01

    Invexity is an important notion in nonconvex optimization. In this paper, a one-layer recurrent neural network is proposed for solving constrained nonsmooth invex optimization problems, designed based on an exact penalty function method. It is proved herein that any state of the proposed neural network is globally convergent to the optimal solution set of constrained invex optimization problems, with a sufficiently large penalty parameter. In addition, any neural state is globally convergent to the unique optimal solution, provided that the objective function and constraint functions are pseudoconvex. Moreover, any neural state is globally convergent to the feasible region in finite time and stays there thereafter. The lower bounds of the penalty parameter and convergence time are also estimated. Two numerical examples are provided to illustrate the performances of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

  12. Thermomechanical constraints and constitutive formulations in thermoelasticity

    Directory of Open Access Journals (Sweden)

    Baek S.

    2003-01-01

    Full Text Available We investigate three classes of constraints in a thermoelastic body: (i a deformation-temperature constraint, (ii a deformation-entropy constraint, and (iii a deformation-energy constraint. These constraints are obtained as limits of unconstrained thermoelastic materials and we show that constraints (ii and (iii are equivalent. By using a limiting procedure, we show that for the constraint (i, the entropy plays the role of a Lagrange multiplier while for (ii and (iii, the absolute temperature plays the role of Lagrange multiplier. We further demonstrate that the governing equations for materials subject to constraint (i are identical to those of an unconstrained material whose internal energy is an affine function of the entropy, while those for materials subject to constraints (ii and (iii are identical to those of an unstrained material whose Helmholtz potential is affine in the absolute temperature. Finally, we model the thermoelastic response of a peroxide-cured vulcanizate of natural rubber and show that imposing the constraint in which the volume change depends only on the internal energy leads to very good predictions (compared to experimental results of the stress and temperature response under isothermal and isentropic conditions.

  13. Statistical mechanics of attractor neural network models with synaptic depression

    International Nuclear Information System (INIS)

    Igarashi, Yasuhiko; Oizumi, Masafumi; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato

    2009-01-01

    Synaptic depression is known to control gain for presynaptic inputs. Since cortical neurons receive thousands of presynaptic inputs, and their outputs are fed into thousands of other neurons, the synaptic depression should influence macroscopic properties of neural networks. We employ simple neural network models to explore the macroscopic effects of synaptic depression. Systems with the synaptic depression cannot be analyzed due to asymmetry of connections with the conventional equilibrium statistical-mechanical approach. Thus, we first propose a microscopic dynamical mean field theory. Next, we derive macroscopic steady state equations and discuss the stabilities of steady states for various types of neural network models.

  14. Adult Mammalian Neural Stem Cells and Neurogenesis: Five Decades Later

    Science.gov (United States)

    Bond, Allison M.; Ming, Guo-li; Song, Hongjun

    2015-01-01

    Summary Adult somatic stem cells in various organs maintain homeostatic tissue regeneration and enhance plasticity. Since its initial discovery five decades ago, investigations of adult neurogenesis and neural stem cells have led to an established and expanding field that has significantly influenced many facets of neuroscience, developmental biology and regenerative medicine. Here we review recent progress and focus on questions related to adult mammalian neural stem cells that also apply to other somatic stem cells. We further discuss emerging topics that are guiding the field toward better understanding adult neural stem cells and ultimately applying these principles to improve human health. PMID:26431181

  15. Neural responses to macronutrients: hedonic and homeostatic mechanisms.

    Science.gov (United States)

    Tulloch, Alastair J; Murray, Susan; Vaicekonyte, Regina; Avena, Nicole M

    2015-05-01

    The brain responds to macronutrients via intricate mechanisms. We review how the brain's neural systems implicated in homeostatic control of feeding and hedonic responses are influenced by the ingestion of specific types of food. We discuss how these neural systems are dysregulated in preclinical models of obesity. Findings from these studies can increase our understanding of overeating and, perhaps in some cases, the development of obesity. In addition, a greater understanding of the neural circuits affected by the consumption of specific macronutrients, and by obesity, might lead to new treatments and strategies for preventing unhealthy weight gain. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

  16. From physical dose constraints to equivalent uniform dose constraints in inverse radiotherapy planning

    International Nuclear Information System (INIS)

    Thieke, Christian; Bortfeld, Thomas; Niemierko, Andrzej; Nill, Simeon

    2003-01-01

    Optimization algorithms in inverse radiotherapy planning need information about the desired dose distribution. Usually the planner defines physical dose constraints for each structure of the treatment plan, either in form of minimum and maximum doses or as dose-volume constraints. The concept of equivalent uniform dose (EUD) was designed to describe dose distributions with a higher clinical relevance. In this paper, we present a method to consider the EUD as an optimization constraint by using the method of projections onto convex sets (POCS). In each iteration of the optimization loop, for the actual dose distribution of an organ that violates an EUD constraint a new dose distribution is calculated that satisfies the EUD constraint, leading to voxel-based physical dose constraints. The new dose distribution is found by projecting the current one onto the convex set of all dose distributions fulfilling the EUD constraint. The algorithm is easy to integrate into existing inverse planning systems, and it allows the planner to choose between physical and EUD constraints separately for each structure. A clinical case of a head and neck tumor is optimized using three different sets of constraints: physical constraints for all structures, physical constraints for the target and EUD constraints for the organs at risk, and EUD constraints for all structures. The results show that the POCS method converges stable and given EUD constraints are reached closely

  17. Parallel consensual neural networks.

    Science.gov (United States)

    Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

    1997-01-01

    A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

  18. Metric approach to quantum constraints

    International Nuclear Information System (INIS)

    Brody, Dorje C; Hughston, Lane P; Gustavsson, Anna C T

    2009-01-01

    A framework for deriving equations of motion for constrained quantum systems is introduced and a procedure for its implementation is outlined. In special cases, the proposed new method, which takes advantage of the fact that the space of pure states in quantum mechanics has both a symplectic structure and a metric structure, reduces to a quantum analogue of the Dirac theory of constraints in classical mechanics. Explicit examples involving spin-1/2 particles are worked out in detail: in the first example, our approach coincides with a quantum version of the Dirac formalism, while the second example illustrates how a situation that cannot be treated by Dirac's approach can nevertheless be dealt with in the present scheme.

  19. Neural complexity: A graph theoretic interpretation

    Science.gov (United States)

    Barnett, L.; Buckley, C. L.; Bullock, S.

    2011-04-01

    One of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end, Tononi [Proc. Natl. Acad. Sci. USA.PNASA60027-842410.1073/pnas.91.11.5033 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system’s dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns [Cereb. Cortex53OPAV1047-321110.1093/cercor/10.2.127 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. While De Lucia [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.71.016114 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work. In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular, we explicitly establish a dependency of neural complexity on cyclic graph motifs.

  20. Cosmographic Constraints and Cosmic Fluids

    Directory of Open Access Journals (Sweden)

    Salvatore Capozziello

    2013-12-01

    Full Text Available The problem of reproducing dark energy effects is reviewed here with particular interest devoted to cosmography. We summarize some of the most relevant cosmological models, based on the assumption that the corresponding barotropic equations of state evolve as the universe expands, giving rise to the accelerated expansion. We describe in detail the ΛCDM (Λ-Cold Dark Matter and ωCDM models, considering also some specific examples, e.g., Chevallier–Polarsky–Linder, the Chaplygin gas and the Dvali–Gabadadze–Porrati cosmological model. Finally, we consider the cosmological consequences of f(R and f(T gravities and their impact on the framework of cosmography. Keeping these considerations in mind, we point out the model-independent procedure related to cosmography, showing how to match the series of cosmological observables to the free parameters of each model. We critically discuss the role played by cosmography, as a selection criterion to check whether a particular model passes or does not present cosmological constraints. In so doing, we find out cosmological bounds by fitting the luminosity distance expansion of the redshift, z, adopting the recent Union 2.1 dataset of supernovae, combined with the baryonic acoustic oscillation and the cosmic microwave background measurements. We perform cosmographic analyses, imposing different priors on the Hubble rate present value. In addition, we compare our results with recent PLANCK limits, showing that the ΛCDM and ωCDM models seem to be the favorite with respect to other dark energy models. However, we show that cosmographic constraints on f(R and f(T cannot discriminate between extensions of General Relativity and dark energy models, leading to a disadvantageous degeneracy problem.

  1. Causality Constraints in Conformal Field Theory

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Causality places nontrivial constraints on QFT in Lorentzian signature, for example fixing the signs of certain terms in the low energy Lagrangian. In d-dimensional conformal field theory, we show how such constraints are encoded in crossing symmetry of Euclidean correlators, and derive analogous constraints directly from the conformal bootstrap (analytically). The bootstrap setup is a Lorentzian four-point function corresponding to propagation through a shockwave. Crossing symmetry fixes the signs of certain log terms that appear in the conformal block expansion, which constrains the interactions of low-lying operators. As an application, we use the bootstrap to rederive the well known sign constraint on the (∂φ)4 coupling in effective field theory, from a dual CFT. We also find constraints on theories with higher spin conserved currents. Our analysis is restricted to scalar correlators, but we argue that similar methods should also impose nontrivial constraints on the interactions of spinni...

  2. Constraint Embedding for Multibody System Dynamics

    Science.gov (United States)

    Jain, Abhinandan

    2009-01-01

    This paper describes a constraint embedding approach for the handling of local closure constraints in multibody system dynamics. The approach uses spatial operator techniques to eliminate local-loop constraints from the system and effectively convert the system into tree-topology systems. This approach allows the direct derivation of recursive O(N) techniques for solving the system dynamics and avoiding the expensive steps that would otherwise be required for handling the closedchain dynamics. The approach is very effective for systems where the constraints are confined to small-subgraphs within the system topology. The paper provides background on the spatial operator O(N) algorithms, the extensions for handling embedded constraints, and concludes with some examples of such constraints.

  3. Use of dose constraints in public exposure

    International Nuclear Information System (INIS)

    Tageldein, Amged

    2015-02-01

    An overview of the dose constraints in public exposures has been carried out in this project. The establishment, development and the application of the concept of dose constraints are reviewed with regards to public exposure. The role of dose constraints in the process of optimization of radiation protection was described and has been showed that the concept of the dose constraints along with many other concept of radiation protection is widely applied in the optimization of exposure to radiation. From the beginning of the establishment of dose constraints as a concept in radiation protection, the International Commission of Radiological Protection (ICRP) has published a number of documents that provides detailed application related to radiation protection and safety of public exposure from ionizing radiation. This work provides an overview of such publications and related documents with special emphasis on optimization of public exposure using dose constraints. (au)

  4. Causality constraints in conformal field theory

    Energy Technology Data Exchange (ETDEWEB)

    Hartman, Thomas; Jain, Sachin; Kundu, Sandipan [Department of Physics, Cornell University,Ithaca, New York (United States)

    2016-05-17

    Causality places nontrivial constraints on QFT in Lorentzian signature, for example fixing the signs of certain terms in the low energy Lagrangian. In d dimensional conformal field theory, we show how such constraints are encoded in crossing symmetry of Euclidean correlators, and derive analogous constraints directly from the conformal bootstrap (analytically). The bootstrap setup is a Lorentzian four-point function corresponding to propagation through a shockwave. Crossing symmetry fixes the signs of certain log terms that appear in the conformal block expansion, which constrains the interactions of low-lying operators. As an application, we use the bootstrap to rederive the well known sign constraint on the (∂ϕ){sup 4} coupling in effective field theory, from a dual CFT. We also find constraints on theories with higher spin conserved currents. Our analysis is restricted to scalar correlators, but we argue that similar methods should also impose nontrivial constraints on the interactions of spinning operators.

  5. Constraint-based Word Segmentation for Chinese

    DEFF Research Database (Denmark)

    Christiansen, Henning; Bo, Li

    2014-01-01

    -hoc and statistically based methods. In this paper, we show experiments of implementing different approaches to CWSP in the framework of CHR Grammars [Christiansen, 2005] that provides a constraint solving approach to language analysis. CHR Grammars are based upon Constraint Handling Rules, CHR [Frühwirth, 1998, 2009......], which is a declarative, high-level programming language for specification and implementation of constraint solvers....

  6. Stability Constraints for Robust Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Amanda G. S. Ottoni

    2015-01-01

    Full Text Available This paper proposes an approach for the robust stabilization of systems controlled by MPC strategies. Uncertain SISO linear systems with box-bounded parametric uncertainties are considered. The proposed approach delivers some constraints on the control inputs which impose sufficient conditions for the convergence of the system output. These stability constraints can be included in the set of constraints dealt with by existing MPC design strategies, in this way leading to the “robustification” of the MPC.

  7. Some cosmological constraints on gauge theories

    International Nuclear Information System (INIS)

    Schramm, D.N.

    1983-01-01

    In these lectures, a review is made of various constraints cosmology may place on gauge theories. Particular emphasis is placed on those constraints obtainable from Big Bang Nucleosynthesis, with only brief mention made of Big Bang Baryosynthesis. There is also a considerable discussion of astrophysical constraints on masses and lifetimes of neutrinos with specific mention of the 'missing mass (light)' problem of galactic dynamics. (orig./HSI)

  8. Neural Architectures for Control

    Science.gov (United States)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  9. Generalized Pauli constraints in small atoms

    DEFF Research Database (Denmark)

    Schilling, Christian; Altunbulak, Murat; Knecht, Stefan

    2018-01-01

    investigations have found evidence that these constraints are exactly saturated in several physically relevant systems, e.g., in a certain electronic state of the beryllium atom. It has been suggested that, in such cases, the constraints, rather than the details of the Hamiltonian, dictate the system......'s qualitative behavior. Here, we revisit this question with state-of-the-art numerical methods for small atoms. We find that the constraints are, in fact, not exactly saturated, but that they lie much closer to the surface defined by the constraints than the geometry of the problem would suggest. While...

  10. Production Team Maintenance: Systemic Constraints Impacting Implementation

    National Research Council Canada - National Science Library

    Moore, Terry

    1997-01-01

    .... Identified constraints included: integrating the PTM positioning strategy into the AMC corporate strategic planning process, manpower modeling simulator limitations, labor force authorizations and decentralization...

  11. Review of Minimal Flavor Constraints for Technicolor

    DEFF Research Database (Denmark)

    S. Fukano, Hidenori; Sannino, Francesco

    2010-01-01

    We analyze the constraints on the the vacuum polarization of the standard model gauge bosons from a minimal set of flavor observables valid for a general class of models of dynamical electroweak symmetry breaking. We will show that the constraints have a strong impact on the self-coupling and mas......We analyze the constraints on the the vacuum polarization of the standard model gauge bosons from a minimal set of flavor observables valid for a general class of models of dynamical electroweak symmetry breaking. We will show that the constraints have a strong impact on the self...

  12. Toward an automaton Constraint for Local Search

    Directory of Open Access Journals (Sweden)

    Jun He

    2009-10-01

    Full Text Available We explore the idea of using finite automata to implement new constraints for local search (this is already a successful technique in constraint-based global search. We show how it is possible to maintain incrementally the violations of a constraint and its decision variables from an automaton that describes a ground checker for that constraint. We establish the practicality of our approach idea on real-life personnel rostering problems, and show that it is competitive with the approach of [Pralong, 2007].

  13. Notes on Timed Concurrent Constraint Programming

    DEFF Research Database (Denmark)

    Nielsen, Mogens; Valencia, Frank D.

    2004-01-01

    and program reactive systems. This note provides a comprehensive introduction to the background for and central notions from the theory of tccp. Furthermore, it surveys recent results on a particular tccp calculus, ntcc, and it provides a classification of the expressive power of various tccp languages.......A constraint is a piece of (partial) information on the values of the variables of a system. Concurrent constraint programming (ccp) is a model of concurrency in which agents (also called processes) interact by telling and asking information (constraints) to and from a shared store (a constraint...

  14. Identification of generalized state transfer matrix using neural networks

    International Nuclear Information System (INIS)

    Zhu Changchun

    2001-01-01

    The research is introduced on identification of generalized state transfer matrix of linear time-invariant (LTI) system by use of neural networks based on LM (Levenberg-Marquart) algorithm. Firstly, the generalized state transfer matrix is defined. The relationship between the identification of state transfer matrix of structural dynamics and the identification of the weight matrix of neural networks has been established in theory. A singular layer neural network is adopted to obtain the structural parameters as a powerful tool that has parallel distributed processing ability and the property of adaptation or learning. The constraint condition of weight matrix of the neural network is deduced so that the learning and training of the designed network can be more effective. The identified neural network can be used to simulate the structural response excited by any other signals. In order to cope with its further application in practical problems, some noise (5% and 10%) is expected to be present in the response measurements. Results from computer simulation studies show that this method is valid and feasible

  15. 2D neural hardware versus 3D biological ones

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological inspiration with respect to their power of computation: the mapping onto silicon lacking the third dimension of biological nets. This translates into reduced fan-in, and leads to reduced precision. The main conclusion is that one is faced with the following alternatives: (1) try to cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow one to use the third dimension, e.g. using optical interconnections.

  16. Sacred or Neural?

    DEFF Research Database (Denmark)

    Runehov, Anne Leona Cesarine

    Are religious spiritual experiences merely the product of the human nervous system? Anne L.C. Runehov investigates the potential of contemporary neuroscience to explain religious experiences. Following the footsteps of Michael Persinger, Andrew Newberg and Eugene d'Aquili she defines...... the terminological bounderies of "religious experiences" and explores the relevant criteria for the proper evaluation of scientific research, with a particular focus on the validity of reductionist models. Runehov's theis is that the perspectives looked at do not necessarily exclude each other but can be merged....... The question "sacred or neural?" becomes a statement "sacred and neural". The synergies thus produced provide manifold opportunities for interdisciplinary dialogue and research....

  17. Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

    Science.gov (United States)

    Xu, Bin; Yang, Chenguang; Pan, Yongping

    2015-10-01

    This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

  18. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.

    1990-11-15

    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  19. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  20. Development and application of a unified balancing approach with multiple constraints

    Science.gov (United States)

    Zorzi, E. S.; Lee, C. C.; Giordano, J. C.

    1985-01-01

    The development of a general analytic approach to constrained balancing that is consistent with past influence coefficient methods is described. The approach uses Lagrange multipliers to impose orbit and/or weight constraints; these constraints are combined with the least squares minimization process to provide a set of coupled equations that result in a single solution form for determining correction weights. Proper selection of constraints results in the capability to: (1) balance higher speeds without disturbing previously balanced modes, thru the use of modal trial weight sets; (2) balance off-critical speeds; and (3) balance decoupled modes by use of a single balance plane. If no constraints are imposed, this solution form reduces to the general weighted least squares influence coefficient method. A test facility used to examine the use of the general constrained balancing procedure and application of modal trial weight ratios is also described.

  1. Determining discourses: Constraints and resources influencing early career science teachers

    Science.gov (United States)

    Grindstaff, Kelly E.

    This study explores the thinking and practices of five early-career teachers of grades eight to ten science, in relation to their histories, schools, students, and larger cultural and political forces. All the teachers are young women, two in their fourth year of teaching, who teach together in an affluent suburb, along with one first-year teacher. The other two are first-year teachers who teach in an urban setting. All of these teachers most closely associated good science teaching with forming relationships with students. They filtered science content through a lens of relevance (mostly to everyday life) and interest for students. Thus they filtered science content through a commitment to serving students, which makes sense since I argue that the primary motivations for teaching had more to do with working with students and helping people than the disciplines of science. Thus, within the discourse of the supremacy of curriculum and the prevalence of testing, these teachers enact hybrid practices which focus on covering content -- to help ensure the success of students -- and on relevance and interest, which has more to do with teaching styles and personality than disciplines of science. Ideas of good teaching are not very focused on science, which contradicts the type of support they seek and utilize around science content. This presents a challenge to pre- and in-service education and support to question what student success means, what concern for students entails and how to connect caring and concern for students with science.

  2. Discretionary time of Chinese college students: Activities and impact of SARS-induced constraints on choices

    Science.gov (United States)

    He Yang; Susan Hutchinson; Harry Zinn; Alan Watson

    2011-01-01

    How people make choices about activity engagement during discretionary time is a topic of increasing interest to those studying quality of life issues. Assuming choices are made to maximize individual welfare, several factors are believed to influence these choices. Constraints theory from the leisure research literature suggests these choices are heavily influenced by...

  3. Hopfield neural network in HEP track reconstruction

    International Nuclear Information System (INIS)

    Muresan, Raluca; Pentia, Mircea

    1996-01-01

    This work uses neural network technique (Hopfield method) to reconstruct particle tracks starting from a data set obtained with a coordinate detector system placed around a high energy accelerated particle interaction region. A learning algorithm for finding the optimal connection of the signal points have been elaborated and tested. We used a single layer neutral network with constraints in order to obtain the particle tracks drawn through the detected signal points. The dynamics of the systems is given by the MFT equations which determine the system evolution to a minimum energy function. We carried out a computing program that has been tested on a lot of Monte Carlo simulated data. With this program we obtained good results even for noise/signal ratio 200. (authors)

  4. Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems

    OpenAIRE

    Ortiz-Bayliss, José Carlos; Amaya, Ivan; Conant-Pablos, Santiago Enrique; Terashima-Marín, Hugo

    2018-01-01

    When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differen...

  5. The watercolor effect: spacing constraints.

    Science.gov (United States)

    Devinck, Frédéric; Spillmann, Lothar

    2009-12-01

    The watercolor effect (WCE) is a long-range color assimilation effect occurring within an area enclosed by a light chromatic contour, which in turn is surrounded by a dark chromatic contour. Here, we studied the effects of chromatic modulation of the WCE for different kinds of spacing between and within the inducing contours, using a hue-cancellation method. When an empty zone or interspace was inserted between the inducing contours (radial spacing), the hue shift required to null the induced coloration rapidly decreased with increasing spacing between the two contours. Similarly, when the continuous contours were replaced by dotted contours (lateral spacing), the shift in chromaticity quickly decreased with increasing distance between the dots. In this case, the decrease was similar for chains of paired dots ("in-phase") and chains of unpaired dots ("out-of-phase"). Results demonstrate that the WCE is strongest when the two inducing contours are spatially contiguous and continuous. The neural implications of these findings are discussed.

  6. Physical activity participation and constraints among athletic training students.

    Science.gov (United States)

    Stanek, Justin; Rogers, Katherine; Anderson, Jordan

    2015-02-01

    Researchers have examined the physical activity (PA) habits of certified athletic trainers; however, none have looked specifically at athletic training students. To assess PA participation and constraints to participation among athletic training students. Cross-sectional study. Entry-level athletic training education programs (undergraduate and graduate) across the United States. Participants were 1125 entry-level athletic training students. Self-reported PA participation, including a calculated PA index based on a typical week. Leisure constraints and demographic data were also collected. Only 22.8% (252/1105) of athletic training students were meeting the American College of Sports Medicine recommendations for PA through moderate-intensity cardiorespiratory exercise. Although 52.3% (580/1105) were meeting the recommendations through vigorous-intensity cardiorespiratory exercise, 60.5% (681/1125) were meeting the recommendations based on the combined total of moderate or vigorous cardiorespiratory exercise. In addition, 57.2% (643/1125) of respondents met the recommendations for resistance exercise. Exercise habits of athletic training students appear to be better than the national average and similar to those of practicing athletic trainers. Students reported structural constraints such as lack of time due to work or studies as the most significant barrier to exercise participation. Athletic training students experienced similar constraints to PA participation as practicing athletic trainers, and these constraints appeared to influence their exercise participation during their entry-level education. Athletic training students may benefit from a greater emphasis on work-life balance during their entry-level education to promote better health and fitness habits.

  7. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

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

  8. Neural correlates of consciousness

    African Journals Online (AJOL)

    neural cells.1 Under this approach, consciousness is believed to be a product of the ... possible only when the 40 Hz electrical hum is sustained among the brain circuits, ... expect the brain stem ascending reticular activating system. (ARAS) and the ... related synchrony of cortical neurons.11 Indeed, stimulation of brainstem ...

  9. Neural Networks and Micromechanics

    Science.gov (United States)

    Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

    The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.

  10. 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

  11. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  12. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  13. Neural underpinnings of music

    DEFF Research Database (Denmark)

    Vuust, Peter; Gebauer, Line K; Witek, Maria A G

    2014-01-01

    . According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Fourth, empirical studies of neural and behavioral effects of syncopation, polyrhythm and groove will be reported, and we...

  14. Monocular Visual Odometry Based on Trifocal Tensor Constraint

    Science.gov (United States)

    Chen, Y. J.; Yang, G. L.; Jiang, Y. X.; Liu, X. Y.

    2018-02-01

    For the problem of real-time precise localization in the urban street, a monocular visual odometry based on Extend Kalman fusion of optical-flow tracking and trifocal tensor constraint is proposed. To diminish the influence of moving object, such as pedestrian, we estimate the motion of the camera by extracting the features on the ground, which improves the robustness of the system. The observation equation based on trifocal tensor constraint is derived, which can form the Kalman filter alone with the state transition equation. An Extend Kalman filter is employed to cope with the nonlinear system. Experimental results demonstrate that, compares with Yu’s 2-step EKF method, the algorithm is more accurate which meets the needs of real-time accurate localization in cities.

  15. Optimal Stopping with Information Constraint

    International Nuclear Information System (INIS)

    Lempa, Jukka

    2012-01-01

    We study the optimal stopping problem proposed by Dupuis and Wang (Adv. Appl. Probab. 34:141–157, 2002). In this maximization problem of the expected present value of the exercise payoff, the underlying dynamics follow a linear diffusion. The decision maker is not allowed to stop at any time she chooses but rather on the jump times of an independent Poisson process. Dupuis and Wang (Adv. Appl. Probab. 34:141–157, 2002), solve this problem in the case where the underlying is a geometric Brownian motion and the payoff function is of American call option type. In the current study, we propose a mild set of conditions (covering the setup of Dupuis and Wang in Adv. Appl. Probab. 34:141–157, 2002) on both the underlying and the payoff and build and use a Markovian apparatus based on the Bellman principle of optimality to solve the problem under these conditions. We also discuss the interpretation of this model as optimal timing of an irreversible investment decision under an exogenous information constraint.

  16. Linear determining equations for differential constraints

    International Nuclear Information System (INIS)

    Kaptsov, O V

    1998-01-01

    A construction of differential constraints compatible with partial differential equations is considered. Certain linear determining equations with parameters are used to find such differential constraints. They generalize the classical determining equations used in the search for admissible Lie operators. As applications of this approach equations of an ideal incompressible fluid and non-linear heat equations are discussed

  17. Optimal Portfolio Choice with Wash Sale Constraints

    DEFF Research Database (Denmark)

    Astrup Jensen, Bjarne; Marekwica, Marcel

    2011-01-01

    We analytically solve the portfolio choice problem in the presence of wash sale constraints in a two-period model with one risky asset. Our results show that wash sale constraints can heavily affect portfolio choice of investors with unrealized losses. The trading behavior of such investors...

  18. Freedom and constraint analysis and optimization

    NARCIS (Netherlands)

    Brouwer, Dannis Michel; Boer, Steven; Aarts, Ronald G.K.M.; Meijaard, Jacob Philippus; Jonker, Jan B.

    2011-01-01

    Many mathematical and intuitive methods for constraint analysis of mechanisms have been proposed. In this article we compare three methods. Method one is based on Grüblers equation. Method two uses an intuitive analysis method based on opening kinematic loops and evaluating the constraints at the

  19. Network Design with Node Degree Balance Constraints

    DEFF Research Database (Denmark)

    Pedersen, Michael Berliner; Crainic, Teodor Gabriel

    This presentation discusses an extension to the network design model where there in addition to the flow conservation constraints also are constraints that require design conservation. This means that the number of arcs entering and leaving a node must be the same. As will be shown the model has ...

  20. Constraint solving for direct manipulation of features

    NARCIS (Netherlands)

    Lourenco, D.; Oliveira, P.; Noort, A.; Bidarra, R.

    2006-01-01

    In current commercial feature modeling systems, support for direct manipulation of features is not commonly available. This is partly due to the strong reliance of such systems on constraints, but also to the lack of speed of current constraint solvers. In this paper, an approach to the optimization

  1. A Temporal Concurrent Constraint Programming Calculus

    DEFF Research Database (Denmark)

    Palamidessi, Catuscia; Valencia Posso, Frank Darwin

    2001-01-01

    The tcc model is a formalism for reactive concurrent constraint programming. In this paper we propose a model of temporal concurrent constraint programming which adds to tcc the capability of modeling asynchronous and non-deterministic timed behavior. We call this tcc extension the ntcc calculus...

  2. Modifier constraints in alkali ultraphosphate glasses

    DEFF Research Database (Denmark)

    Rodrigues, B.P.; Mauro, J.C.; Yue, Yuanzheng

    2014-01-01

    In applying the recently introduced concept of cationic constraint strength [J. Chem. Phys. 140, 214501 (2014)] to bond constraint theory (BCT) of binary phosphate glasses in the ultraphosphate region of xR2O-(1-x)P2O5 (with x ≤ 0.5 and R = {Li, Na, Cs}), we demonstrate that a fundamental limitat...

  3. Specifying Dynamic and Deontic Integrity Constraints

    NARCIS (Netherlands)

    Wieringa, Roelf J.; Meyer, John-Jules; Weigand, Hans

    In the dominant view of knowledge bases (KB's), a KB is a set of facts (atomic sentences) and integrity constraints (IC's). An IC is then a sentence which must at least be consistent with the other sentences in the KB, This view obliterates the distinction between, for example, the constraint that

  4. UAV Trajectory Modeling Using Neural Networks

    Science.gov (United States)

    Xue, Min

    2017-01-01

    Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural

  5. Solar system constraints on disformal gravity theories

    International Nuclear Information System (INIS)

    Ip, Hiu Yan; Schmidt, Fabian; Sakstein, Jeremy

    2015-01-01

    Disformal theories of gravity are scalar-tensor theories where the scalar couples derivatively to matter via the Jordan frame metric. These models have recently attracted interest in the cosmological context since they admit accelerating solutions. We derive the solution for a static isolated mass in generic disformal gravity theories and transform it into the parameterised post-Newtonian form. This allows us to investigate constraints placed on such theories by local tests of gravity. The tightest constraints come from preferred-frame effects due to the motion of the Solar System with respect to the evolving cosmological background field. The constraints we obtain improve upon the previous solar system constraints by two orders of magnitude, and constrain the scale of the disformal coupling for generic models to ℳ ∼> 100 eV. These constraints render all disformal effects irrelevant for cosmology

  6. Short-sale Constraints and Credit Runs

    DEFF Research Database (Denmark)

    Venter, Gyuri

    ), creditors with high private signals are more lenient to roll over debt, and a bank with lower asset quality remains solvent. This leads to higher allocative efficiency in the real economy. My result thus implies that the decrease in average informativeness due to short-sale constraints can be more than......This paper studies how short-sale constraints affect the informational efficiency of market prices and the link between prices and economic activity. I show that under short-sale constraints security prices contain less information. However, short-sale constraints increase the informativeness...... the price of an asset the bank holds. I show that short-selling constraints in the financial market lead to the revival of self-fulfilling beliefs about the beliefs and actions of others, and create multiple equilibria. In the equilibrium where agents rely more on public information (i.e., the price...

  7. Revisiting the simplicity constraints and coherent intertwiners

    International Nuclear Information System (INIS)

    Dupuis, Maite; Livine, Etera R

    2011-01-01

    In the context of loop quantum gravity and spinfoam models, the simplicity constraints are essential in that they allow one to write general relativity as a constrained topological BF theory. In this work, we apply the recently developed U(N) framework for SU(2) intertwiners to the issue of imposing the simplicity constraints to spin network states. More particularly, we focus on solving on individual intertwiners in the 4D Euclidean theory. We review the standard way of solving the simplicity constraints using coherent intertwiners and we explain how these fit within the U(N) framework. Then we show how these constraints can be written as a closed u(N) algebra and we propose a set of U(N) coherent states that solves all the simplicity constraints weakly for an arbitrary Immirzi parameter.

  8. Rhythmic constraints in durational control

    NARCIS (Netherlands)

    Grover, C.N.; Terken, J.M.B.

    1994-01-01

    Two potential factors in durational control are addressed. First, we investigate whether lengthening a syllable implies lengthening all of its constituent phonemes in a regular way. Analysis of a small corpus of syllables shows that this is not the case. Second, we investigate the influence of

  9. Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

    Directory of Open Access Journals (Sweden)

    Zhonghua Wu

    2017-02-01

    Full Text Available A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.

  10. Robo signaling regulates the production of cranial neural crest cells.

    Science.gov (United States)

    Li, Yan; Zhang, Xiao-Tan; Wang, Xiao-Yu; Wang, Guang; Chuai, Manli; Münsterberg, Andrea; Yang, Xuesong

    2017-12-01

    Slit/Robo signaling plays an important role in the guidance of developing neurons in developing embryos. However, it remains obscure whether and how Slit/Robo signaling is involved in the production of cranial neural crest cells. In this study, we examined Robo1 deficient mice to reveal developmental defects of mouse cranial frontal and parietal bones, which are derivatives of cranial neural crest cells. Therefore, we determined the production of HNK1 + cranial neural crest cells in early chick embryo development after knock-down (KD) of Robo1 expression. Detection of markers for pre-migratory and migratory neural crest cells, PAX7 and AP-2α, showed that production of both was affected by Robo1 KD. In addition, we found that the transcription factor slug is responsible for the aberrant delamination/EMT of cranial neural crest cells induced by Robo1 KD, which also led to elevated expression of E- and N-Cadherin. N-Cadherin expression was enhanced when blocking FGF signaling with dominant-negative FGFR1 in half of the neural tube. Taken together, we show that Slit/Robo signaling influences the delamination/EMT of cranial neural crest cells, which is required for cranial bone development. Copyright © 2017. Published by Elsevier Inc.

  11. Understanding the Implications of Neural Population Activity on Behavior

    Science.gov (United States)

    Briguglio, John

    Learning how neural activity in the brain leads to the behavior we exhibit is one of the fundamental questions in Neuroscience. In this dissertation, several lines of work are presented to that use principles of neural coding to understand behavior. In one line of work, we formulate the efficient coding hypothesis in a non-traditional manner in order to test human perceptual sensitivity to complex visual textures. We find a striking agreement between how variable a particular texture signal is and how sensitive humans are to its presence. This reveals that the efficient coding hypothesis is still a guiding principle for neural organization beyond the sensory periphery, and that the nature of cortical constraints differs from the peripheral counterpart. In another line of work, we relate frequency discrimination acuity to neural responses from auditory cortex in mice. It has been previously observed that optogenetic manipulation of auditory cortex, in addition to changing neural responses, evokes changes in behavioral frequency discrimination. We are able to account for changes in frequency discrimination acuity on an individual basis by examining the Fisher information from the neural population with and without optogenetic manipulation. In the third line of work, we address the question of what a neural population should encode given that its inputs are responses from another group of neurons. Drawing inspiration from techniques in machine learning, we train Deep Belief Networks on fake retinal data and show the emergence of Garbor-like filters, reminiscent of responses in primary visual cortex. In the last line of work, we model the state of a cortical excitatory-inhibitory network during complex adaptive stimuli. Using a rate model with Wilson-Cowan dynamics, we demonstrate that simple non-linearities in the signal transferred from inhibitory to excitatory neurons can account for real neural recordings taken from auditory cortex. This work establishes and tests

  12. Different-Level Simultaneous Minimization Scheme for Fault Tolerance of Redundant Manipulator Aided with Discrete-Time Recurrent Neural Network.

    Science.gov (United States)

    Jin, Long; Liao, Bolin; Liu, Mei; Xiao, Lin; Guo, Dongsheng; Yan, Xiaogang

    2017-01-01

    By incorporating the physical constraints in joint space, a different-level simultaneous minimization scheme, which takes both the robot kinematics and robot dynamics into account, is presented and investigated for fault-tolerant motion planning of redundant manipulator in this paper. The scheme is reformulated as a quadratic program (QP) with equality and bound constraints, which is then solved by a discrete-time recurrent neural network. Simulative verifications based on a six-link planar redundant robot manipulator substantiate the efficacy and accuracy of the presented acceleration fault-tolerant scheme, the resultant QP and the corresponding discrete-time recurrent neural network.

  13. Hamiltonian constraint in polymer parametrized field theory

    International Nuclear Information System (INIS)

    Laddha, Alok; Varadarajan, Madhavan

    2011-01-01

    Recently, a generally covariant reformulation of two-dimensional flat spacetime free scalar field theory known as parametrized field theory was quantized using loop quantum gravity (LQG) type ''polymer'' representations. Physical states were constructed, without intermediate regularization structures, by averaging over the group of gauge transformations generated by the constraints, the constraint algebra being a Lie algebra. We consider classically equivalent combinations of these constraints corresponding to a diffeomorphism and a Hamiltonian constraint, which, as in gravity, define a Dirac algebra. Our treatment of the quantum constraints parallels that of LQG and obtains the following results, expected to be of use in the construction of the quantum dynamics of LQG: (i) the (triangulated) Hamiltonian constraint acts only on vertices, its construction involves some of the same ambiguities as in LQG and its action on diffeomorphism invariant states admits a continuum limit, (ii) if the regulating holonomies are in representations tailored to the edge labels of the state, all previously obtained physical states lie in the kernel of the Hamiltonian constraint, (iii) the commutator of two (density weight 1) Hamiltonian constraints as well as the operator correspondent of their classical Poisson bracket converge to zero in the continuum limit defined by diffeomorphism invariant states, and vanish on the Lewandowski-Marolf habitat, (iv) the rescaled density 2 Hamiltonian constraints and their commutator are ill-defined on the Lewandowski-Marolf habitat despite the well-definedness of the operator correspondent of their classical Poisson bracket there, (v) there is a new habitat which supports a nontrivial representation of the Poisson-Lie algebra of density 2 constraints.

  14. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Natural Constraints to Species Diversification.

    Directory of Open Access Journals (Sweden)

    Eric Lewitus

    2016-08-01

    Full Text Available Identifying modes of species diversification is fundamental to our understanding of how biodiversity changes over evolutionary time. Diversification modes are captured in species phylogenies, but characterizing the landscape of diversification has been limited by the analytical tools available for directly comparing phylogenetic trees of groups of organisms. Here, we use a novel, non-parametric approach and 214 family-level phylogenies of vertebrates representing over 500 million years of evolution to identify major diversification modes, to characterize phylogenetic space, and to evaluate the bounds and central tendencies of species diversification. We identify five principal patterns of diversification to which all vertebrate families hold. These patterns, mapped onto multidimensional space, constitute a phylogenetic space with distinct properties. Firstly, phylogenetic space occupies only a portion of all possible tree space, showing family-level phylogenies to be constrained to a limited range of diversification patterns. Secondly, the geometry of phylogenetic space is delimited by quantifiable trade-offs in tree size and the heterogeneity and stem-to-tip distribution of branching events. These trade-offs are indicative of the instability of certain diversification patterns and effectively bound speciation rates (for successful clades within upper and lower limits. Finally, both the constrained range and geometry of phylogenetic space are established by the differential effects of macroevolutionary processes on patterns of diversification. Given these properties, we show that the average path through phylogenetic space over evolutionary time traverses several diversification stages, each of which is defined by a different principal pattern of diversification and directed by a different macroevolutionary process. The identification of universal patterns and natural constraints to diversification provides a foundation for understanding the

  16. Natural Constraints to Species Diversification.

    Science.gov (United States)

    Lewitus, Eric; Morlon, Hélène

    2016-08-01

    Identifying modes of species diversification is fundamental to our understanding of how biodiversity changes over evolutionary time. Diversification modes are captured in species phylogenies, but characterizing the landscape of diversification has been limited by the analytical tools available for directly comparing phylogenetic trees of groups of organisms. Here, we use a novel, non-parametric approach and 214 family-level phylogenies of vertebrates representing over 500 million years of evolution to identify major diversification modes, to characterize phylogenetic space, and to evaluate the bounds and central tendencies of species diversification. We identify five principal patterns of diversification to which all vertebrate families hold. These patterns, mapped onto multidimensional space, constitute a phylogenetic space with distinct properties. Firstly, phylogenetic space occupies only a portion of all possible tree space, showing family-level phylogenies to be constrained to a limited range of diversification patterns. Secondly, the geometry of phylogenetic space is delimited by quantifiable trade-offs in tree size and the heterogeneity and stem-to-tip distribution of branching events. These trade-offs are indicative of the instability of certain diversification patterns and effectively bound speciation rates (for successful clades) within upper and lower limits. Finally, both the constrained range and geometry of phylogenetic space are established by the differential effects of macroevolutionary processes on patterns of diversification. Given these properties, we show that the average path through phylogenetic space over evolutionary time traverses several diversification stages, each of which is defined by a different principal pattern of diversification and directed by a different macroevolutionary process. The identification of universal patterns and natural constraints to diversification provides a foundation for understanding the deep-time evolution of

  17. Natural Constraints to Species Diversification

    Science.gov (United States)

    Lewitus, Eric; Morlon, Hélène

    2016-01-01

    Identifying modes of species diversification is fundamental to our understanding of how biodiversity changes over evolutionary time. Diversification modes are captured in species phylogenies, but characterizing the landscape of diversification has been limited by the analytical tools available for directly comparing phylogenetic trees of groups of organisms. Here, we use a novel, non-parametric approach and 214 family-level phylogenies of vertebrates representing over 500 million years of evolution to identify major diversification modes, to characterize phylogenetic space, and to evaluate the bounds and central tendencies of species diversification. We identify five principal patterns of diversification to which all vertebrate families hold. These patterns, mapped onto multidimensional space, constitute a phylogenetic space with distinct properties. Firstly, phylogenetic space occupies only a portion of all possible tree space, showing family-level phylogenies to be constrained to a limited range of diversification patterns. Secondly, the geometry of phylogenetic space is delimited by quantifiable trade-offs in tree size and the heterogeneity and stem-to-tip distribution of branching events. These trade-offs are indicative of the instability of certain diversification patterns and effectively bound speciation rates (for successful clades) within upper and lower limits. Finally, both the constrained range and geometry of phylogenetic space are established by the differential effects of macroevolutionary processes on patterns of diversification. Given these properties, we show that the average path through phylogenetic space over evolutionary time traverses several diversification stages, each of which is defined by a different principal pattern of diversification and directed by a different macroevolutionary process. The identification of universal patterns and natural constraints to diversification provides a foundation for understanding the deep-time evolution of

  18. Few-body hypernuclear constraints

    International Nuclear Information System (INIS)

    Gibson, B.F.

    1993-01-01

    Since the discovery of the first hyperfragment in a balloon flown emulsion stack some two score years ago, physicists have worked to understand how the addition of the strangeness degree of freedom alters the picture of nuclei and the baryon-baryon force. Because the Λ and Σ masses differ markedly from that of the proton and neutron, SU (3) symmetry is broken. How it is broken is a question of importance to the fundamental understanding of the baryon-baryon interaction. New dynamical symmetries, forbidden by the Pauli principle in conventional nuclei, appear. Three-body forces play a more significant role. A binding anomaly in A = 5 as well as a possible spin inversion between ground and excited states in A = 4 appear. Surprisingly narrow structure near the threshold for Σ production has been reported in the 4 He (K - , π - ) spectrum while no corresponding structure is observed in the companion 4 He(K - , π + ) spectrum; this has been interpreted as evidence for a Σ 4 He bound state. Finally, the reported observation of ΛΛ-hypernuclei, in particular ΛΛ 6 He, bears directly upon the possibilities for the prediction of a bound H particle--the S = -2 dibaryon. Although it is not feasible to invert the analysis and determine the interaction from the data on few-body systems, it is possible to utilize these data to constrain the models, provided one is careful. The author will explore briefly the constraints which the few-body data impose and the level of understanding that has been achieved

  19. Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: A systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database

    International Nuclear Information System (INIS)

    Dietzel, Matthias; Baltzer, Pascal A.T.; Dietzel, Andreas; Zoubi, Ramy; Gröschel, Tobias; Burmeister, Hartmut P.; Bogdan, Martin; Kaiser, Werner A.

    2012-01-01

    Rationale and objectives: Differential diagnosis of lesions in MR-Mammography (MRM) remains a complex task. The aim of this MRM study was to design and to test robustness of Artificial Neural Network architectures to predict malignancy using a large clinical database. Materials and methods: For this IRB-approved investigation standardized protocols and study design were applied (T1w-FLASH; 0.1 mmol/kgBW Gd-DTPA; T2w-TSE; histological verification after MRM). All lesions were evaluated by two experienced (>500 MRM) radiologists in consensus. In every lesion, 18 previously published descriptors were assessed and documented in the database. An Artificial Neural Network (ANN) was developed to process this database (The-MathWorks/Inc., feed-forward-architecture/resilient back-propagation-algorithm). All 18 descriptors were set as input variables, whereas histological results (malignant vs. benign) was defined as classification variable. Initially, the ANN was optimized in terms of “Training Epochs” (TE), “Hidden Layers” (HL), “Learning Rate” (LR) and “Neurons” (N). Robustness of the ANN was addressed by repeated evaluation cycles (n: 9) with receiver operating characteristics (ROC) analysis of the results applying 4-fold Cross Validation. The best network architecture was identified comparing the corresponding Area under the ROC curve (AUC). Results: Histopathology revealed 436 benign and 648 malignant lesions. Enhancing the level of complexity could not increase diagnostic accuracy of the network (P: n.s.). The optimized ANN architecture (TE: 20, HL: 1, N: 5, LR: 1.2) was accurate (mean-AUC 0.888; P: <0.001) and robust (CI: 0.885–0.892; range: 0.880–0.898). Conclusion: The optimized neural network showed robust performance and high diagnostic accuracy for prediction of malignancy on unknown data.

  20. Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: a systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database.

    Science.gov (United States)

    Dietzel, Matthias; Baltzer, Pascal A T; Dietzel, Andreas; Zoubi, Ramy; Gröschel, Tobias; Burmeister, Hartmut P; Bogdan, Martin; Kaiser, Werner A

    2012-07-01

    Differential diagnosis of lesions in MR-Mammography (MRM) remains a complex task. The aim of this MRM study was to design and to test robustness of Artificial Neural Network architectures to predict malignancy using a large clinical database. For this IRB-approved investigation standardized protocols and study design were applied (T1w-FLASH; 0.1 mmol/kgBW Gd-DTPA; T2w-TSE; histological verification after MRM). All lesions were evaluated by two experienced (>500 MRM) radiologists in consensus. In every lesion, 18 previously published descriptors were assessed and documented in the database. An Artificial Neural Network (ANN) was developed to process this database (The-MathWorks/Inc., feed-forward-architecture/resilient back-propagation-algorithm). All 18 descriptors were set as input variables, whereas histological results (malignant vs. benign) was defined as classification variable. Initially, the ANN was optimized in terms of "Training Epochs" (TE), "Hidden Layers" (HL), "Learning Rate" (LR) and "Neurons" (N). Robustness of the ANN was addressed by repeated evaluation cycles (n: 9) with receiver operating characteristics (ROC) analysis of the results applying 4-fold Cross Validation. The best network architecture was identified comparing the corresponding Area under the ROC curve (AUC). Histopathology revealed 436 benign and 648 malignant lesions. Enhancing the level of complexity could not increase diagnostic accuracy of the network (P: n.s.). The optimized ANN architecture (TE: 20, HL: 1, N: 5, LR: 1.2) was accurate (mean-AUC 0.888; P: <0.001) and robust (CI: 0.885-0.892; range: 0.880-0.898). The optimized neural network showed robust performance and high diagnostic accuracy for prediction of malignancy on unknown data. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  1. Neural Crossroads in the Hematopoietic Stem Cell Niche.

    Science.gov (United States)

    Agarwala, Sobhika; Tamplin, Owen J

    2018-05-29

    The hematopoietic stem cell (HSC) niche supports steady-state hematopoiesis and responds to changing needs during stress and disease. The nervous system is an important regulator of the niche, and its influence is established early in development when stem cells are specified. Most research has focused on direct innervation of the niche, however recent findings show there are different modes of neural control, including globally by the central nervous system (CNS) and hormone release, locally by neural crest-derived mesenchymal stem cells, and intrinsically by hematopoietic cells that express neural receptors and neurotransmitters. Dysregulation between neural and hematopoietic systems can contribute to disease, however new therapeutic opportunities may be found among neuroregulator drugs repurposed to support hematopoiesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. Wind power prediction based on genetic neural network

    Science.gov (United States)

    Zhang, Suhan

    2017-04-01

    The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

  3. Shaping the learning curve: epigenetic dynamics in neural plasticity

    Directory of Open Access Journals (Sweden)

    Zohar Ziv Bronfman

    2014-07-01

    Full Text Available A key characteristic of learning and neural plasticity is state-dependent acquisition dynamics reflected by the non-linear learning curve that links increase in learning with practice. Here we propose that the manner by which epigenetic states of individual cells change during learning contributes to the shape of the neural and behavioral learning curve. We base our suggestion on recent studies showing that epigenetic mechanisms such as DNA methylation, histone acetylation and RNA-mediated gene regulation are intimately involved in the establishment and maintenance of long-term neural plasticity, reflecting specific learning-histories and influencing future learning. Our model, which is the first to suggest a dynamic molecular account of the shape of the learning curve, leads to several testable predictions regarding the link between epigenetic dynamics at the promoter, gene-network and neural-network levels. This perspective opens up new avenues for therapeutic interventions in neurological pathologies.

  4. Constraint Handling Rules with Binders, Patterns and Generic Quantification

    NARCIS (Netherlands)

    Serrano, Alejandro; Hage, J.

    2017-01-01

    Constraint Handling Rules provide descriptions for constraint solvers. However, they fall short when those constraints specify some binding structure, like higher-rank types in a constraint-based type inference algorithm. In this paper, the term syntax of constraints is replaced by λ-tree syntax, in

  5. Creating buzz: the neural correlates of effective message propagation.

    Science.gov (United States)

    Falk, Emily B; Morelli, Sylvia A; Welborn, B Locke; Dambacher, Karl; Lieberman, Matthew D

    2013-07-01

    Social interaction promotes the spread of values, attitudes, and behaviors. Here, we report on neural responses to ideas that are destined to spread. We scanned message communicators using functional MRI during their initial exposure to the to-be-communicated ideas. These message communicators then had the opportunity to spread the messages and their corresponding subjective evaluations to message recipients outside the scanner. Successful ideas were associated with neural responses in the communicators' mentalizing systems and reward systems when they first heard the messages, prior to spreading them. Similarly, individuals more able to spread their own views to others produced greater mentalizing-system activity during initial encoding. Unlike prior social-influence studies that focused on the individuals being influenced, this investigation focused on the brains of influencers. Successful social influence is reliably associated with an influencer-to-be's state of mind when first encoding ideas.

  6. Analysis of neural data

    CERN Document Server

    Kass, Robert E; Brown, Emery N

    2014-01-01

    Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

  7. On the neural mechanisms subserving consciousness and attention

    Directory of Open Access Journals (Sweden)

    Catherine eTallon-Baudry

    2012-01-01

    Full Text Available Consciousness, as described in the experimental literature, is a multi-faceted phenomenon, that impinges on other well-studied concepts such as attention and control. Do consciousness and attention refer to different aspects of the same core phenomenon, or do they correspond to distinct functions? One possibility to address this question is to examine the neural mechanisms underlying consciousness and attention. If consciousness and attention pertain to the same concept, they should rely on shared neural mechanisms. Conversely, if their underlying mechanisms are distinct, then consciousness and attention should be considered as distinct entities. This paper therefore reviews neurophysiological facts arguing in favor or against a tight relationship between consciousness and attention. Three neural mechanisms that have been associated with both attention and consciousness are examined (neural amplification, involvement of the fronto-parietal network, and oscillatory synchrony, to conclude that the commonalities between attention and consciousness at the neural level may have been overestimated. Last but not least, experiments in which both attention and consciousness were probed at the neural level point toward a dissociation between the two concepts. It therefore appears from this review that consciousness and attention rely on distinct neural properties, although they can interact at the behavioral level. It is proposed that a "cumulative influence model", in which attention and consciousness correspond to distinct neural mechanisms feeding a single decisional process leading to behavior, fits best with available neural and behavioral data. In this view, consciousness should not be considered as a top-level executive function but should rather be defined by its experiential properties.

  8. Forecasting macroeconomic variables using neural network models and three automated model selection techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    2016-01-01

    When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. To alleviate the problem, White (2006) presented a solution (QuickNet) that conv...

  9. Deep Neural Yodelling

    OpenAIRE

    Pfäffli, Daniel (Autor/in)

    2018-01-01

    Yodel music differs from most other genres by exercising the transition from chest voice to falsetto with an audible glottal stop which is recognised even by laymen. Yodel often consists of a yodeller with a choir accompaniment. In Switzerland, it is differentiated between the natural yodel and yodel songs. Today's approaches to music generation with machine learning algorithms are based on neural networks, which are best described by stacked layers of neurons which are connected with neurons...

  10. Neural networks for triggering

    International Nuclear Information System (INIS)

    Denby, B.; Campbell, M.; Bedeschi, F.; Chriss, N.; Bowers, C.; Nesti, F.

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab

  11. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  12. Rotation Invariance Neural Network

    OpenAIRE

    Li, Shiyuan

    2017-01-01

    Rotation invariance and translation invariance have great values in image recognition tasks. In this paper, we bring a new architecture in convolutional neural network (CNN) named cyclic convolutional layer to achieve rotation invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap target. Last but not least, this architecture can achieve one-shot learning in some cases using thos...

  13. Neural Mechanisms of Foraging

    OpenAIRE

    Kolling, Nils; Behrens, Timothy EJ; Mars, Rogier B; Rushworth, Matthew FS

    2012-01-01

    Behavioural economic studies, involving limited numbers of choices, have provided key insights into neural decision-making mechanisms. By contrast, animals’ foraging choices arise in the context of sequences of encounters with prey/food. On each encounter the animal chooses to engage or whether the environment is sufficiently rich that searching elsewhere is merited. The cost of foraging is also critical. We demonstrate humans can alternate between two modes of choice, comparative decision-ma...

  14. QCD unitarity constraints on Reggeon Field Theory

    Energy Technology Data Exchange (ETDEWEB)

    Kovner, Alex [Physics Department, University of Connecticut,2152 Hillside Road, Storrs, CT 06269 (United States); Levin, Eugene [Departemento de Física, Universidad Técnica Federico Santa María,and Centro Científico-Tecnológico de Valparaíso,Avda. Espana 1680, Casilla 110-V, Valparaíso (Chile); Department of Particle Physics, Tel Aviv University,Tel Aviv 69978 (Israel); Lublinsky, Michael [Physics Department, Ben-Gurion University of the Negev,Beer Sheva 84105 (Israel); Physics Department, University of Connecticut,2152 Hillside Road, Storrs, CT 06269 (United States)

    2016-08-04

    We point out that the s-channel unitarity of QCD imposes meaningful constraints on a possible form of the QCD Reggeon Field Theory. We show that neither the BFKL nor JIMWLK nor Braun’s Hamiltonian satisfy the said constraints. In a toy, zero transverse dimensional case we construct a model that satisfies the analogous constraint and show that at infinite energy it indeed tends to a “black disk limit' as opposed to the model with triple Pomeron vertex only, routinely used as a toy model in the literature.

  15. QCD unitarity constraints on Reggeon Field Theory

    International Nuclear Information System (INIS)

    Kovner, Alex; Levin, Eugene; Lublinsky, Michael

    2016-01-01

    We point out that the s-channel unitarity of QCD imposes meaningful constraints on a possible form of the QCD Reggeon Field Theory. We show that neither the BFKL nor JIMWLK nor Braun’s Hamiltonian satisfy the said constraints. In a toy, zero transverse dimensional case we construct a model that satisfies the analogous constraint and show that at infinite energy it indeed tends to a “black disk limit' as opposed to the model with triple Pomeron vertex only, routinely used as a toy model in the literature.

  16. Liquidity Constraints and Fiscal Stabilization Policy

    DEFF Research Database (Denmark)

    Kristoffersen, Mark Strøm

    It is often claimed that the presence of liquidity constrained households enhances the need for and the effects of fi…scal stabilization policies. This paper studies this in a model of a small open economy with liquidity constrained households. The results show that the consequences of liquidity...... constraints are more complex than previously thought: The optimal stabilization policy in case of productivity shocks is independent of the liquidity constraints, and the presence of liquidity constraints tends to reduce the need for an active policy stabilizing productivity shocks....

  17. Use of dose constraints for occupational exposure

    International Nuclear Information System (INIS)

    Kaijage, Tunu

    2015-02-01

    The use of dose constraints for occupational exposure was reviewed in this project. The role of dose constraints as used in optimization of protection of workers was described. Different issues to be considered in application of the concept and challenges associated with their implementation were also discussed. The situation where dose constraints could be misinterpreted to dose limits is also explained as the two are clearly differentiated by the International Commission of Radiological Protection (ICRP) Publication 103. Moreover, recommendations to all parties responsible for protection and safety of workers were discussed. (au)

  18. Constraint satisfaction problems CSP formalisms and techniques

    CERN Document Server

    Ghedira, Khaled

    2013-01-01

    A Constraint Satisfaction Problem (CSP) consists of a set of variables, a domain of values for each variable and a set of constraints. The objective is to assign a value for each variable such that all constraints are satisfied. CSPs continue to receive increased attention because of both their high complexity and their omnipresence in academic, industrial and even real-life problems. This is why they are the subject of intense research in both artificial intelligence and operations research. This book introduces the classic CSP and details several extensions/improvements of both formalisms a

  19. Expressing Model Constraints Visually with VMQL

    DEFF Research Database (Denmark)

    Störrle, Harald

    2011-01-01

    ) for specifying constraints on UML models. We examine VMQL's usability by controlled experiments and its expressiveness by a representative sample. We conclude that VMQL is less expressive than OCL, although expressive enough for most of the constraints in the sample. In terms of usability, however, VMQL......OCL is the de facto standard language for expressing constraints and queries on UML models. However, OCL expressions are very difficult to create, understand, and maintain, even with the sophisticated tool support now available. In this paper, we propose to use the Visual Model Query Language (VMQL...

  20. Efeitos do potencial de ação neural sobre a percepção de fala em usuários de implante coclear Influence of evoked compound action potential on speech perception in cochlear implant users

    Directory of Open Access Journals (Sweden)

    Mariana Cardoso Guedes

    2007-08-01

    Full Text Available O Potencial de Ação Composto Evocado Eletricamente reflete a atividade do nervo auditivo, podendo ser registrado através dos eletrodos do implante coclear. A determinação dos elementos neurais estimuláveis pode contribuir para explicar a variabilidade de desempenho entre indivíduos implantados. OBJETIVO: Comparar o desempenho nos testes de percepção da fala entre pacientes que apresentaram e que não apresentaram potencial de ação composto evocado eletricamente no momento intra-operatório. MATERIAL E MÉTODO: Estudo prospectivo no qual 100 indivíduos usuários do implante coclear Nucleus 24 foram divididos em dois grupos de acordo com a presença ou ausência do potencial de ação intra-operatório. Após 6 meses de uso do dispositivo, os resultados dos testes de percepção de fala foram comparados entre os grupos. RESULTADOS: O potencial foi observado em 72% dos pacientes. A percepção no teste de frases em formato aberto foi melhor nos indivíduos com presença de potencial (média 82,8% contra 41,0%, p = 0,005. Houve associação entre ausência do potencial e etiologia da surdez por meningite. CONCLUSÃO: Ausência de potencial neural intraoperatório esteve associada ao pior desempenho na percepção da fala e à etiologia da surdez por meningite. Por outro lado, a presença do potencial de ação intraoperatório sugere ótimo prognóstico.Electrically Evoked Compound Action Potential is a measure of synchronous cochlear nerve fibers activity elicited by electrical stimulation of the cochlear implant. The electrophysiological nerve responses may contribute to explain the variability in individual performance of cochlear implant recipients. AIM: To compare speech perception tests’ performances of cochlear implant users according to the presence or absence of intraoperative neural telemetry responses. MATERIAL AND METHOD: Prospective study design with 100 "Nucleus 24" cochlear implant users divided in two groups according

  1. Reconstruction of sparse connectivity in neural networks from spike train covariances

    International Nuclear Information System (INIS)

    Pernice, Volker; Rotter, Stefan

    2013-01-01

    The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of recurrent connections on covariances can be characterized directly in linear models, where connectivity in the network is described by a matrix of linear coupling kernels. However, as indirect connections also give rise to covariances, the inverse problem of inferring network structure from covariances can generally not be solved unambiguously. Here we study to what degree this ambiguity can be resolved if the sparseness of neural networks is taken into account. To reconstruct a sparse network, we determine the minimal set of linear couplings consistent with the measured covariances by minimizing the L 1 norm of the coupling matrix under appropriate constraints. Contrary to intuition, after stochastic optimization of the coupling matrix, the resulting estimate of the underlying network is directed, despite the fact that a symmetric matrix of count covariances is used for inference. The performance of the new method is best if connections are neither exceedingly sparse, nor too dense, and it is easily applicable for networks of a few hundred nodes. Full coupling kernels can be obtained from the matrix of full covariance functions. We apply our method to networks of leaky integrate-and-fire neurons in an asynchronous–irregular state, where spike train covariances are well described by a linear model. (paper)

  2. FPGA Dynamic Power Minimization through Placement and Routing Constraints

    Directory of Open Access Journals (Sweden)

    Deepak Agarwal

    2006-08-01

    Full Text Available Field-programmable gate arrays (FPGAs are pervasive in embedded systems requiring low-power utilization. A novel power optimization methodology for reducing the dynamic power consumed by the routing of FPGA circuits by modifying the constraints applied to existing commercial tool sets is presented. The power optimization techniques influence commercial FPGA Place and Route (PAR tools by translating power goals into standard throughput and placement-based constraints. The Low-Power Intelligent Tool Environment (LITE is presented, which was developed to support the experimentation of power models and power optimization algorithms. The generated constraints seek to implement one of four power optimization approaches: slack minimization, clock tree paring, N-terminal net colocation, and area minimization. In an experimental study, we optimize dynamic power of circuits mapped into 0.12 μm Xilinx Virtex-II FPGAs. Results show that several optimization algorithms can be combined on a single design, and power is reduced by up to 19.4%, with an average power savings of 10.2%.

  3. Natural language acquisition in large scale neural semantic networks

    Science.gov (United States)

    Ealey, Douglas

    This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

  4. Proposal of Constraints Analysis Method Based on Network Model for Task Planning

    Science.gov (United States)

    Tomiyama, Tomoe; Sato, Tatsuhiro; Morita, Toyohisa; Sasaki, Toshiro

    Deregulation has been accelerating several activities toward reengineering business processes, such as railway through service and modal shift in logistics. Making those activities successful, business entities have to regulate new business rules or know-how (we call them ‘constraints’). According to the new constraints, they need to manage business resources such as instruments, materials, workers and so on. In this paper, we propose a constraint analysis method to define constraints for task planning of the new business processes. To visualize each constraint's influence on planning, we propose a network model which represents allocation relations between tasks and resources. The network can also represent task ordering relations and resource grouping relations. The proposed method formalizes the way of defining constraints manually as repeatedly checking the network structure and finding conflicts between constraints. Being applied to crew scheduling problems shows that the method can adequately represent and define constraints of some task planning problems with the following fundamental features, (1) specifying work pattern to some resources, (2) restricting the number of resources for some works, (3) requiring multiple resources for some works, (4) prior allocation of some resources to some works and (5) considering the workload balance between resources.

  5. Neural Based Orthogonal Data Fitting The EXIN Neural Networks

    CERN Document Server

    Cirrincione, Giansalvo

    2008-01-01

    Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

  6. Robustness of the ATLAS pixel clustering neural network algorithm

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00407780; The ATLAS collaboration

    2016-01-01

    Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. The algorithms depend heavily on accurate estimation of the position of particles as they traverse the inner detector elements. An artificial neural network algorithm is utilised to identify and split clusters of neighbouring read-out elements in the ATLAS pixel detector created by multiple charged particles. The method recovers otherwise lost tracks in dense environments where particles are separated by distances comparable to the size of the detector read-out elements. Such environments are highly relevant for LHC run 2, e.g. in searches for heavy resonances. Within the scope of run 2 track reconstruction performance and upgrades, the robustness of the neural network algorithm will be presented. The robustness has been studied by evaluating the stability of the algorithm’s performance under a range of variations in the pixel detector conditions.

  7. Attractor neural networks with resource-efficient synaptic connectivity

    Science.gov (United States)

    Pehlevan, Cengiz; Sengupta, Anirvan

    Memories are thought to be stored in the attractor states of recurrent neural networks. Here we explore how resource constraints interplay with memory storage function to shape synaptic connectivity of attractor networks. We propose that given a set of memories, in the form of population activity patterns, the neural circuit choses a synaptic connectivity configuration that minimizes a resource usage cost. We argue that the total synaptic weight (l1-norm) in the network measures the resource cost because synaptic weight is correlated with synaptic volume, which is a limited resource, and is proportional to neurotransmitter release and post-synaptic current, both of which cost energy. Using numerical simulations and replica theory, we characterize optimal connectivity profiles in resource-efficient attractor networks. Our theory explains several experimental observations on cortical connectivity profiles, 1) connectivity is sparse, because synapses are costly, 2) bidirectional connections are overrepresented and 3) are stronger, because attractor states need strong recurrence.

  8. Emulation of Neural Networks on a Nanoscale Architecture

    International Nuclear Information System (INIS)

    Eshaghian-Wilner, Mary M; Friesz, Aaron; Khitun, Alex; Navab, Shiva; Parker, Alice C; Wang, Kang L; Zhou, Chongwu

    2007-01-01

    In this paper, we propose using a nanoscale spin-wave-based architecture for implementing neural networks. We show that this architecture can efficiently realize highly interconnected neural network models such as the Hopfield model. In our proposed architecture, no point-to-point interconnection is required, so unlike standard VLSI design, no fan-in/fan-out constraint limits the interconnectivity. Using spin-waves, each neuron could broadcast to all other neurons simultaneously and similarly a neuron could concurrently receive and process multiple data. Therefore in this architecture, the total weighted sum to each neuron can be computed by the sum of the values from all the incoming waves to that neuron. In addition, using the superposition property of waves, this computation can be done in O(1) time, and neurons can update their states quite rapidly

  9. Dose constraints, what are they now?

    International Nuclear Information System (INIS)

    Lazo, T.

    2005-01-01

    The concept of a source-related dose constraint was first introduced in ICPR publication 60. The idea was to provide a number that individual exposures from a single, specific source should not exceed, and below which optimisation of protection should take place. Dose constraints were applied to occupational and public exposures from practices. In order to simplify and clarify the ICRP's recommendations, the latest draft, RPO5, presents dose constraints again, and with the same meaning as in publication 60. However, the dose constraints are now applied in all situations, not just practices. This new approach does provide simplification, in that a single concept is applied to all types of exposures (normal situations, accident situations, and existing situations). However, the approach and numerical values that are selected by regulatory authorities for the application of the concept, particularly in normal situations which are also subject to dose limits, will be crucial to the implementation of the system of radiological protection. (author)

  10. Biological constraints do not entail cognitive closure.

    Science.gov (United States)

    Vlerick, Michael

    2014-12-01

    From the premise that our biology imposes cognitive constraints on our epistemic activities, a series of prominent authors--most notably Fodor, Chomsky and McGinn--have argued that we are cognitively closed to certain aspects and properties of the world. Cognitive constraints, they argue, entail cognitive closure. I argue that this is not the case. More precisely, I detect two unwarranted conflations at the core of arguments deriving closure from constraints. The first is a conflation of what I will refer to as 'representation' and 'object of representation'. The second confuses the cognitive scope of the assisted mind for that of the unassisted mind. Cognitive closure, I conclude, cannot be established from pointing out the (uncontroversial) existence of cognitive constraints. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. institutional and resource constraints that inhibit contractor ...

    African Journals Online (AJOL)

    p2333147

    Keywords: Institutions; small-scale contractor performance; sugar industry. ABSTRACT ..... diverse cultural settings, women, specifically widowed or single women, have a .... constraints on business growth, such as the work limitations placed.

  12. Constraint theory multidimensional mathematical model management

    CERN Document Server

    Friedman, George J

    2017-01-01

    Packed with new material and research, this second edition of George Friedman’s bestselling Constraint Theory remains an invaluable reference for all engineers, mathematicians, and managers concerned with modeling. As in the first edition, this text analyzes the way Constraint Theory employs bipartite graphs and presents the process of locating the “kernel of constraint” trillions of times faster than brute-force approaches, determining model consistency and computational allowability. Unique in its abundance of topological pictures of the material, this book balances left- and right-brain perceptions to provide a thorough explanation of multidimensional mathematical models. Much of the extended material in this new edition also comes from Phan Phan’s PhD dissertation in 2011, titled “Expanding Constraint Theory to Determine Well-Posedness of Large Mathematical Models.” Praise for the first edition: "Dr. George Friedman is indisputably the father of the very powerful methods of constraint theory...

  13. Constraint-based Attribute and Interval Planning

    Science.gov (United States)

    Jonsson, Ari; Frank, Jeremy

    2013-01-01

    In this paper we describe Constraint-based Attribute and Interval Planning (CAIP), a paradigm for representing and reasoning about plans. The paradigm enables the description of planning domains with time, resources, concurrent activities, mutual exclusions among sets of activities, disjunctive preconditions and conditional effects. We provide a theoretical foundation for the paradigm, based on temporal intervals and attributes. We then show how the plans are naturally expressed by networks of constraints, and show that the process of planning maps directly to dynamic constraint reasoning. In addition, we de ne compatibilities, a compact mechanism for describing planning domains. We describe how this framework can incorporate the use of constraint reasoning technology to improve planning. Finally, we describe EUROPA, an implementation of the CAIP framework.

  14. Automated constraint placement to maintain pile shape

    KAUST Repository

    Hsu, Shu-Wei; Keyser, John

    2012-01-01

    structure. Next, for stabilizing the structure, we pick suitable objects from those passing the equilibrium analysis and then restrict their DOFs by managing the insertion of constraints on them. The method is suitable for controlling stacking behavior

  15. Cosmological constraints on Brans-Dicke theory.

    Science.gov (United States)

    Avilez, A; Skordis, C

    2014-07-04

    We report strong cosmological constraints on the Brans-Dicke (BD) theory of gravity using cosmic microwave background data from Planck. We consider two types of models. First, the initial condition of the scalar field is fixed to give the same effective gravitational strength Geff today as the one measured on Earth, GN. In this case, the BD parameter ω is constrained to ω>692 at the 99% confidence level, an order of magnitude improvement over previous constraints. In the second type, the initial condition for the scalar is a free parameter leading to a somewhat stronger constraint of ω>890, while Geff is constrained to 0.981theory and are valid for any Horndeski theory, the most general second-order scalar-tensor theory, which approximates the BD theory on cosmological scales. In this sense, our constraints place strong limits on possible modifications of gravity that might explain cosmic acceleration.

  16. CONSTRAINTS TO USE OF MOBILE TELEPHONY FOR ...

    African Journals Online (AJOL)

    Key words: Constraints, mobile telephony, frequency, farmers and telecommunications service ... efficient sharing of agricultural information ... calls on the mobile phone without the need .... adequate training on the use of mobile .... Job Market.

  17. Modernizing China's Military: Opportunities and Constraints

    National Research Council Canada - National Science Library

    Crane, Keith; Cliff, Roger; Medeiros, Evan; Mulvenon, James; Overholt, William

    2005-01-01

    The purpose of this study is to assess future resource constraints on, and potential domestic economic and industrial contributions to, the ability of the Chinese military to become a significant threat to U.S. forces by 2025...

  18. Neural Elements for Predictive Coding

    Directory of Open Access Journals (Sweden)

    Stewart SHIPP

    2016-11-01

    Full Text Available Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backwards in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forwards and backwards pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a updates in the microcircuitry of primate visual cortex, and (b rapid technical advances made

  19. Neural Elements for Predictive Coding.

    Science.gov (United States)

    Shipp, Stewart

    2016-01-01

    Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural

  20. Optimal capital stock and financing constraints

    OpenAIRE

    Saltari, Enrico; Giuseppe, Travaglini

    2011-01-01

    In this paper we show that financing constraints affect the optimal level of capital stock even when the financing constraint is ineffective. This happens when the firm rationally anticipates that access to external financing resources may be rationed in the future. We will show that with these expectations, the optimal investment policy is to invest less in any given period, thereby lowering the desired optimal capital stock in the long run.

  1. Credit Constraints, Political Instability, and Capital Accumulation

    OpenAIRE

    Risto Herrala; Rima Turk-Ariss

    2013-01-01

    We investigate the complex interactions between credit constraints, political instability, and capital accumulation using a novel approach based on Kiyotaki and Moore’s (1997) theoretical framework. Drawing on a unique firm-level data set from Middle-East and North Africa (MENA), empirical findings point to a large and significant effect of credit conditions on capital accumulation and suggest that continued political unrest worsens credit constraints. The results support the view that financ...

  2. Cyclic labellings with constraints at two distances

    OpenAIRE

    Leese, R; Noble, S D

    2004-01-01

    Motivated by problems in radio channel assignment, we consider the vertex-labelling of graphs with non-negative integers. The objective is to minimise the span of the labelling, subject to constraints imposed at graph distances one and two. We show that the minimum span is (up to rounding) a piecewise linear function of the constraints, and give a complete specification, together with associated optimal assignments, for trees and cycles.

  3. Portfolios with nonlinear constraints and spin glasses

    Science.gov (United States)

    Gábor, Adrienn; Kondor, I.

    1999-12-01

    In a recent paper Galluccio, Bouchaud and Potters demonstrated that a certain portfolio problem with a nonlinear constraint maps exactly onto finding the ground states of a long-range spin glass, with the concomitant nonuniqueness and instability of the optimal portfolios. Here we put forward geometric arguments that lead to qualitatively similar conclusions, without recourse to the methods of spin glass theory, and give two more examples of portfolio problems with convex nonlinear constraints.

  4. Future Cosmological Constraints From Fast Radio Bursts

    Science.gov (United States)

    Walters, Anthony; Weltman, Amanda; Gaensler, B. M.; Ma, Yin-Zhe; Witzemann, Amadeus

    2018-03-01

    We consider the possible observation of fast radio bursts (FRBs) with planned future radio telescopes, and investigate how well the dispersions and redshifts of these signals might constrain cosmological parameters. We construct mock catalogs of FRB dispersion measure (DM) data and employ Markov Chain Monte Carlo analysis, with which we forecast and compare with existing constraints in the flat ΛCDM model, as well as some popular extensions that include dark energy equation of state and curvature parameters. We find that the scatter in DM observations caused by inhomogeneities in the intergalactic medium (IGM) poses a big challenge to the utility of FRBs as a cosmic probe. Only in the most optimistic case, with a high number of events and low IGM variance, do FRBs aid in improving current constraints. In particular, when FRBs are combined with CMB+BAO+SNe+H 0 data, we find the biggest improvement comes in the {{{Ω }}}{{b}}{h}2 constraint. Also, we find that the dark energy equation of state is poorly constrained, while the constraint on the curvature parameter, Ω k , shows some improvement when combined with current constraints. When FRBs are combined with future baryon acoustic oscillation (BAO) data from 21 cm Intensity Mapping, we find little improvement over the constraints from BAOs alone. However, the inclusion of FRBs introduces an additional parameter constraint, {{{Ω }}}{{b}}{h}2, which turns out to be comparable to existing constraints. This suggests that FRBs provide valuable information about the cosmological baryon density in the intermediate redshift universe, independent of high-redshift CMB data.

  5. Classifying medical relations in clinical text via convolutional neural networks.

    Science.gov (United States)

    He, Bin; Guan, Yi; Dai, Rui

    2018-05-16

    Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. Copyright © 2018. Published by Elsevier B.V.

  6. Trimaran Resistance Artificial Neural Network

    Science.gov (United States)

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  7. Generalized Pauli constraints in small atoms

    Science.gov (United States)

    Schilling, Christian; Altunbulak, Murat; Knecht, Stefan; Lopes, Alexandre; Whitfield, James D.; Christandl, Matthias; Gross, David; Reiher, Markus

    2018-05-01

    The natural occupation numbers of fermionic systems are subject to nontrivial constraints, which include and extend the original Pauli principle. A recent mathematical breakthrough has clarified their mathematical structure and has opened up the possibility of a systematic analysis. Early investigations have found evidence that these constraints are exactly saturated in several physically relevant systems, e.g., in a certain electronic state of the beryllium atom. It has been suggested that, in such cases, the constraints, rather than the details of the Hamiltonian, dictate the system's qualitative behavior. Here, we revisit this question with state-of-the-art numerical methods for small atoms. We find that the constraints are, in fact, not exactly saturated, but that they lie much closer to the surface defined by the constraints than the geometry of the problem would suggest. While the results seem incompatible with the statement that the generalized Pauli constraints drive the behavior of these systems, they suggest that the qualitatively correct wave-function expansions can in some systems already be obtained on the basis of a limited number of Slater determinants, which is in line with numerical evidence from quantum chemistry.

  8. Neuroadaptive Fault-Tolerant Control of Nonlinear Systems Under Output Constraints and Actuation Faults.

    Science.gov (United States)

    Zhao, Kai; Song, Yongduan; Shen, Zhixi

    2018-02-01

    In this paper, a neuroadaptive fault-tolerant tracking control method is proposed for a class of time-delay pure-feedback systems in the presence of external disturbances and actuation faults. The proposed controller can achieve prescribed transient and steady-state performance, despite uncertain time delays and output constraints as well as actuation faults. By combining a tangent barrier Lyapunov-Krasovskii function with the dynamic surface control technique, the neural network unit in the developed control scheme is able to take its action from the very beginning and play its learning/approximating role safely during the entire system operational envelope, leading to enhanced control performance without the danger of violating compact set precondition. Furthermore, prescribed transient performance and output constraints are strictly ensured in the presence of nonaffine uncertainties, external disturbances, and undetectable actuation faults. The control strategy is also validated by numerical simulation.

  9. EDITORIAL: Special issue on applied neurodynamics: from neural dynamics to neural engineering Special issue on applied neurodynamics: from neural dynamics to neural engineering

    Science.gov (United States)

    Chiel, Hillel J.; Thomas, Peter J.

    2011-12-01

    , the sun, earth and moon) proved to be far more difficult. In the late nineteenth century, Poincaré made significant progress on this problem, introducing a geometric method of reasoning about solutions to differential equations (Diacu and Holmes 1996). This work had a powerful impact on mathematicians and physicists, and also began to influence biology. In his 1925 book, based on his work starting in 1907, and that of others, Lotka used nonlinear differential equations and concepts from dynamical systems theory to analyze a wide variety of biological problems, including oscillations in the numbers of predators and prey (Lotka 1925). Although little was known in detail about the function of the nervous system, Lotka concluded his book with speculations about consciousness and the implications this might have for creating a mathematical formulation of biological systems. Much experimental work in the 1930s and 1940s focused on the biophysical mechanisms of excitability in neural tissue, and Rashevsky and others continued to apply tools and concepts from nonlinear dynamical systems theory as a means of providing a more general framework for understanding these results (Rashevsky 1960, Landahl and Podolsky 1949). The publication of Hodgkin and Huxley's classic quantitative model of the action potential in 1952 created a new impetus for these studies (Hodgkin and Huxley 1952). In 1955, FitzHugh published an important paper that summarized much of the earlier literature, and used concepts from phase plane analysis such as asymptotic stability, saddle points, separatrices and the role of noise to provide a deeper theoretical and conceptual understanding of threshold phenomena (Fitzhugh 1955, Izhikevich and FitzHugh 2006). The Fitzhugh-Nagumo equations constituted an important two-dimensional simplification of the four-dimensional Hodgkin and Huxley equations, and gave rise to an extensive literature of analysis. Many of the papers in this special issue build on tools

  10. Representational constraints on children's suggestibility.

    Science.gov (United States)

    Ceci, Stephen J; Papierno, Paul B; Kulkofsky, Sarah

    2007-06-01

    In a multistage experiment, twelve 4- and 9-year-old children participated in a triad rating task. Their ratings were mapped with multidimensional scaling, from which euclidean distances were computed to operationalize semantic distance between items in target pairs. These children and age-mates then participated in an experiment that employed these target pairs in a story, which was followed by a misinformation manipulation. Analyses linked individual and developmental differences in suggestibility to children's representations of the target items. Semantic proximity was a strong predictor of differences in suggestibility: The closer a suggested distractor was to the original item's representation, the greater was the distractor's suggestive influence. The triad participants' semantic proximity subsequently served as the basis for correctly predicting memory performance in the larger group. Semantic proximity enabled a priori counterintuitive predictions of reverse age-related trends to be confirmed whenever the distance between representations of items in a target pair was greater for younger than for older children.

  11. Neural mechanisms of human perceptual choice under focused and divided attention.

    Science.gov (United States)

    Wyart, Valentin; Myers, Nicholas E; Summerfield, Christopher

    2015-02-25

    Perceptual decisions occur after the evaluation and integration of momentary sensory inputs, and dividing attention between spatially disparate sources of information impairs decision performance. However, it remains unknown whether dividing attention degrades the precision of sensory signals, precludes their conversion into decision signals, or dampens the integration of decision information toward an appropriate response. Here we recorded human electroencephalographic (EEG) activity while participants categorized one of two simultaneous and independent streams of visual gratings according to their average tilt. By analyzing trial-by-trial correlations between EEG activity and the information offered by each sample, we obtained converging behavioral and neural evidence that dividing attention between left and right visual fields does not dampen the encoding of sensory or decision information. Under divided attention, momentary decision information from both visual streams was encoded in slow parietal signals without interference but was lost downstream during their integration as reflected in motor mu- and beta-band (10-30 Hz) signals, resulting in a "leaky" accumulation process that conferred greater behavioral influence to more recent samples. By contrast, sensory inputs that were explicitly cued as irrelevant were not converted into decision signals. These findings reveal that a late cognitive bottleneck on information integration limits decision performance under divided attention, and places new capacity constraints on decision-theoretic models of information integration under cognitive load. Copyright © 2015 the authors 0270-6474/15/353485-14$15.00/0.

  12. The known unknowns: neural representation of second-order uncertainty, and ambiguity

    Science.gov (United States)

    Bach, Dominik R.; Hulme, Oliver; Penny, William D.; Dolan, Raymond J.

    2011-01-01

    Predictions provided by action-outcome probabilities entail a degree of (first-order) uncertainty. However, these probabilities themselves can be imprecise and embody second-order uncertainty. Tracking second-order uncertainty is important for optimal decision making and reinforcement learning. Previous functional magnetic resonance imaging investigations of second-order uncertainty in humans have drawn on an economic concept of ambiguity, where action-outcome associations in a gamble are either known (unambiguous) or completely unknown (ambiguous). Here, we relaxed the constraints associated with a purely categorical concept of ambiguity and varied the second-order uncertainty of gambles continuously, quantified as entropy over second-order probabilities. We show that second-order uncertainty influences decisions in a pessimistic way by biasing second-order probabilities, and that second-order uncertainty is negatively correlated with posterior cingulate cortex activity. The category of ambiguous (compared to non-ambiguous) gambles also biased choice in a similar direction, but was associated with distinct activation of a posterior parietal cortical area; an activation that we show reflects a different computational mechanism. Our findings indicate that behavioural and neural responses to second-order uncertainty are distinct from those associated with ambiguity and may call for a reappraisal of previous data. PMID:21451019

  13. Neural mechanisms of human perceptual choice under focused and divided attention

    Science.gov (United States)

    Wyart, Valentin; Myers, Nicholas E.; Summerfield, Christopher

    2015-01-01

    Perceptual decisions occur after evaluation and integration of momentary sensory inputs, and dividing attention between spatially disparate sources of information impairs decision performance. However, it remains unknown whether dividing attention degrades the precision of sensory signals, precludes their conversion into decision signals, or dampens the integration of decision information towards an appropriate response. Here we recorded human electroencephalographic (EEG) activity whilst participants categorised one of two simultaneous and independent streams of visual gratings according to their average tilt. By analyzing trial-by-trial correlations between EEG activity and the information offered by each sample, we obtained converging behavioural and neural evidence that dividing attention between left and right visual fields does not dampen the encoding of sensory or decision information. Under divided attention, momentary decision information from both visual streams was encoded in slow parietal signals without interference but was lost downstream during their integration as reflected in motor mu- and beta-band (10–30 Hz) signals, resulting in a ‘leaky’ accumulation process which conferred greater behavioural influence to more recent samples. By contrast, sensory inputs that were explicitly cued as irrelevant were not converted into decision signals. These findings reveal that a late cognitive bottleneck on information integration limits decision performance under divided attention, and place new capacity constraints on decision-theoretic models of information integration under cognitive load. PMID:25716848

  14. Constraint-Muse: A Soft-Constraint Based System for Music Therapy

    Science.gov (United States)

    Hölzl, Matthias; Denker, Grit; Meier, Max; Wirsing, Martin

    Monoidal soft constraints are a versatile formalism for specifying and solving multi-criteria optimization problems with dynamically changing user preferences. We have developed a prototype tool for interactive music creation, called Constraint Muse, that uses monoidal soft constraints to ensure that a dynamically generated melody harmonizes with input from other sources. Constraint Muse provides an easy to use interface based on Nintendo Wii controllers and is intended to be used in music therapy for people with Parkinson’s disease and for children with high-functioning autism or Asperger’s syndrome.

  15. Optimization of multilayer neural network parameters for speaker recognition

    Science.gov (United States)

    Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka

    2016-05-01

    This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.

  16. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  17. Neural Mechanisms Underlying Risk and Ambiguity Attitudes.

    Science.gov (United States)

    Blankenstein, Neeltje E; Peper, Jiska S; Crone, Eveline A; van Duijvenvoorde, Anna C K

    2017-11-01

    Individual differences in attitudes to risk (a taste for risk, known probabilities) and ambiguity (a tolerance for uncertainty, unknown probabilities) differentially influence risky decision-making. However, it is not well understood whether risk and ambiguity are coded differently within individuals. Here, we tested whether individual differences in risk and ambiguity attitudes were reflected in distinct neural correlates during choice and outcome processing of risky and ambiguous gambles. To these ends, we developed a neuroimaging task in which participants ( n = 50) chose between a sure gain and a gamble, which was either risky or ambiguous, and presented decision outcomes (gains, no gains). From a separate task in which the amount, probability, and ambiguity level were varied, we estimated individuals' risk and ambiguity attitudes. Although there was pronounced neural overlap between risky and ambiguous gambling in a network typically related to decision-making under uncertainty, relatively more risk-seeking attitudes were associated with increased activation in valuation regions of the brain (medial and lateral OFC), whereas relatively more ambiguity-seeking attitudes were related to temporal cortex activation. In addition, although striatum activation was observed during reward processing irrespective of a prior risky or ambiguous gamble, reward processing after an ambiguous gamble resulted in enhanced dorsomedial PFC activation, possibly functioning as a general signal of uncertainty coding. These findings suggest that different neural mechanisms reflect individual differences in risk and ambiguity attitudes and that risk and ambiguity may impact overt risk-taking behavior in different ways.

  18. Memory Consolidation and Neural Substrate of Reward

    Directory of Open Access Journals (Sweden)

    Redolar-Ripoll, Diego

    2012-08-01

    Full Text Available The aim of this report is to analyze the relationships between reward and learning and memory processes. Different studies have described how information about rewards influences behavior and how the brain uses this reward information to control learning and memory processes. Reward nature seems to be processed in different ways by neurons in different brain structures, ranging from the detection and perception of rewards to the use of information about predicted rewards for the control of goal-directed behavior. The neural substrate underling this processing of reward information is a reliable way of improving learning and memory processes. Evidence from several studies indicates that this neural system can facilitate memory consolidation in a wide variety of learning tasks. From a molecular perspective, certain cardinal features of reward have been described as forms of memory. Studies of human addicts and studies in animal models of addiction show that chronic drug exposure produces stable changes in the brain at the cellular and molecular levels that underlie the long-lasting behavioral plasticity associated with addiction. These molecular and cellular adaptations involved in addiction are also implicated in learning and memory processes. Dopamine seems to be a critical common signal to activate different genetic mechanisms that ultimately remodel synapses and circuits. Despite memory is an active and complex process mediated by different brain areas, the neural substrate of reward is able to improve memory consolidation in a several paradigms. We believe that there are many equivalent traits between reward and learning and memory processes.

  19. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

    In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

  20. Analysis of Recurrent Analog Neural Networks

    Directory of Open Access Journals (Sweden)

    Z. Raida

    1998-06-01

    Full Text Available In this paper, an original rigorous analysis of recurrent analog neural networks, which are built from opamp neurons, is presented. The analysis, which comes from the approximate model of the operational amplifier, reveals causes of possible non-stable states and enables to determine convergence properties of the network. Results of the analysis are discussed in order to enable development of original robust and fast analog networks. In the analysis, the special attention is turned to the examination of the influence of real circuit elements and of the statistical parameters of processed signals to the parameters of the network.

  1. Echoes in correlated neural systems

    International Nuclear Information System (INIS)

    Helias, M; Tetzlaff, T; Diesmann, M

    2013-01-01

    Correlations are employed in modern physics to explain microscopic and macroscopic phenomena, like the fractional quantum Hall effect and the Mott insulator state in high temperature superconductors and ultracold atoms. Simultaneously probed neurons in the intact brain reveal correlations between their activity, an important measure to study information processing in the brain that also influences the macroscopic signals of neural activity, like the electroencephalogram (EEG). Networks of spiking neurons differ from most physical systems: the interaction between elements is directed, time delayed, mediated by short pulses and each neuron receives events from thousands of neurons. Even the stationary state of the network cannot be described by equilibrium statistical mechanics. Here we develop a quantitative theory of pairwise correlations in finite-sized random networks of spiking neurons. We derive explicit analytic expressions for the population-averaged cross correlation functions. Our theory explains why the intuitive mean field description fails, how the echo of single action potentials causes an apparent lag of inhibition with respect to excitation and how the size of the network can be scaled while maintaining its dynamical state. Finally, we derive a new criterion for the emergence of collective oscillations from the spectrum of the time-evolution propagator. (paper)

  2. Parasympathetic neural activity accounts for the lowering of exercise heart rate at high altitude

    DEFF Research Database (Denmark)

    Boushel, Robert Christopher; Calbet, J A; Rådegran, G

    2001-01-01

    In chronic hypoxia, both heart rate (HR) and cardiac output (Q) are reduced during exercise. The role of parasympathetic neural activity in lowering HR is unresolved, and its influence on Q and oxygen transport at high altitude has never been studied.......In chronic hypoxia, both heart rate (HR) and cardiac output (Q) are reduced during exercise. The role of parasympathetic neural activity in lowering HR is unresolved, and its influence on Q and oxygen transport at high altitude has never been studied....

  3. 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...

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

    Science.gov (United States)

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

    2009-09-01

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

  5. Determining the confidence levels of sensor outputs using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Broten, G S; Wood, H C [Saskatchewan Univ., Saskatoon, SK (Canada). Dept. of Electrical Engineering

    1996-12-31

    This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network`s ability to determine the confidence level is influenced by the complexity of the sensor`s response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

  6. Neural correlates of math anxiety – an overview and implications

    OpenAIRE

    Artemenko, Christina; Daroczy, Gabriella; Nuerk, Hans-Christoph

    2015-01-01

    Math anxiety is a common phenomenon which can have a negative impact on numerical and arithmetic performance. However, so far little is known about the underlying neurocognitive mechanisms. This mini review provides an overview of studies investigating the neural correlates of math anxiety which provide several hints regarding its influence on math performance: while behavioral studies mostly observe an influence of math anxiety on difficult math tasks, neurophysiological studies show that pr...

  7. Neural mechanisms of emotional regulation and decision making

    OpenAIRE

    Gospic, Katarina

    2011-01-01

    Emotions influence our perception and decision making. It is of great importance to understand the neurophysiology behind these processes as they influence human core functions. Moreover, knowledge within this field is required in order to develop new medical therapies for pathological conditions that involve dysregulation of emotions. In this thesis the neural mechanisms of emotional regulation and decision making were investigated using different pharmacological manipul...

  8. Neural information processing in cognition: we start to understand the orchestra, but where is the conductor?

    Directory of Open Access Journals (Sweden)

    Guenther ePalm

    2016-01-01

    Full Text Available Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article.

  9. Neural Network Control for the Probe Landing Based on Proportional Integral Observer

    Directory of Open Access Journals (Sweden)

    Yuanchun Li

    2015-01-01

    Full Text Available For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.

  10. Neural Information Processing in Cognition: We Start to Understand the Orchestra, but Where is the Conductor?

    Science.gov (United States)

    Palm, Günther

    2016-01-01

    Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article. PMID:26858632

  11. Global asymptotic stability analysis of bidirectional associative memory neural networks with time delays.

    Science.gov (United States)

    Arik, Sabri

    2005-05-01

    This paper presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all continuous nonmonotonic neuron activation functions. It is shown that in some special cases of the results, the stability criteria can be easily checked. Some examples are also given to compare the results with the previous results derived in the literature.

  12. 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.

  13. Analysis of neural networks

    CERN Document Server

    Heiden, Uwe

    1980-01-01

    The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica­ ted throughout the text. However, they are not explored in de­ tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev­ els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be­ havior of neurons or neuron pools. In this respect the essay is writt...

  14. Neural Synchronization and Cryptography

    Science.gov (United States)

    Ruttor, Andreas

    2007-11-01

    Neural networks can synchronize by learning from each other. In the case of discrete weights full synchronization is achieved in a finite number of steps. Additional networks can be trained by using the inputs and outputs generated during this process as examples. Several learning rules for both tasks are presented and analyzed. In the case of Tree Parity Machines synchronization is much faster than learning. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. They show that the partners can reach any desired level of security by just increasing the synaptic depth. Then the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Further improvements of security are possible by replacing the random inputs with queries generated by the partners.

  15. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  16. Neural networks at the Tevatron

    International Nuclear Information System (INIS)

    Badgett, W.; Burkett, K.; Campbell, M.K.; Wu, D.Y.; Bianchin, S.; DeNardi, M.; Pauletta, G.; Santi, L.; Caner, A.; Denby, B.; Haggerty, H.; Lindsey, C.S.; Wainer, N.; Dall'Agata, M.; Johns, K.; Dickson, M.; Stanco, L.; Wyss, J.L.

    1992-10-01

    This paper summarizes neural network applications at the Fermilab Tevatron, including the first online hardware application in high energy physics (muon tracking): the CDF and DO neural network triggers; offline quark/gluon discrimination at CDF; ND a new tool for top to multijets recognition at CDF

  17. Neural Networks for the Beginner.

    Science.gov (United States)

    Snyder, Robin M.

    Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…

  18. Fuzzy Constraint-Based Agent Negotiation

    Institute of Scientific and Technical Information of China (English)

    Menq-Wen Lin; K. Robert Lai; Ting-Jung Yu

    2005-01-01

    Conflicts between two or more parties arise for various reasons and perspectives. Thus, resolution of conflicts frequently relies on some form of negotiation. This paper presents a general problem-solving framework for modeling multi-issue multilateral negotiation using fuzzy constraints. Agent negotiation is formulated as a distributed fuzzy constraint satisfaction problem (DFCSP). Fuzzy constrains are thus used to naturally represent each agent's desires involving imprecision and human conceptualization, particularly when lexical imprecision and subjective matters are concerned. On the other hand, based on fuzzy constraint-based problem-solving, our approach enables an agent not only to systematically relax fuzzy constraints to generate a proposal, but also to employ fuzzy similarity to select the alternative that is subject to its acceptability by the opponents. This task of problem-solving is to reach an agreement that benefits all agents with a high satisfaction degree of fuzzy constraints, and move towards the deal more quickly since their search focuses only on the feasible solution space. An application to multilateral negotiation of a travel planning is provided to demonstrate the usefulness and effectiveness of our framework.

  19. Pair Production Constraints on Superluminal Neutrinos Revisited

    International Nuclear Information System (INIS)

    Brodsky, Stanley

    2012-01-01

    We revisit the pair creation constraint on superluminal neutrinos considered by Cohen and Glashow in order to clarify which types of superluminal models are constrained. We show that a model in which the superluminal neutrino is effectively light-like can evade the Cohen-Glashow constraint. In summary, any model for which the CG pair production process operates is excluded because such timelike neutrinos would not be detected by OPERA or other experiments. However, a superluminal neutrino which is effectively lightlike with fixed p 2 can evade the Cohen-Glashow constraint because of energy-momentum conservation. The coincidence involved in explaining the SN1987A constraint certainly makes such a picture improbable - but it is still intrinsically possible. The lightlike model is appealing in that it does not violate Lorentz symmetry in particle interactions, although one would expect Hughes-Drever tests to turn up a violation eventually. Other evasions of the CG constraints are also possible; perhaps, e.g., the neutrino takes a 'short cut' through extra dimensions or suffers anomalous acceleration in matter. Irrespective of the OPERA result, Lorentz-violating interactions remain possible, and ongoing experimental investigation of such possibilities should continue.

  20. Diffusion Processes Satisfying a Conservation Law Constraint

    Directory of Open Access Journals (Sweden)

    J. Bakosi

    2014-01-01

    Full Text Available We investigate coupled stochastic differential equations governing N nonnegative continuous random variables that satisfy a conservation principle. In various fields a conservation law requires a set of fluctuating variables to be nonnegative and (if appropriately normalized sum to one. As a result, any stochastic differential equation model to be realizable must not produce events outside of the allowed sample space. We develop a set of constraints on the drift and diffusion terms of such stochastic models to ensure that both the nonnegativity and the unit-sum conservation law constraints are satisfied as the variables evolve in time. We investigate the consequences of the developed constraints on the Fokker-Planck equation, the associated system of stochastic differential equations, and the evolution equations of the first four moments of the probability density function. We show that random variables, satisfying a conservation law constraint, represented by stochastic diffusion processes, must have diffusion terms that are coupled and nonlinear. The set of constraints developed enables the development of statistical representations of fluctuating variables satisfying a conservation law. We exemplify the results with the bivariate beta process and the multivariate Wright-Fisher, Dirichlet, and Lochner’s generalized Dirichlet processes.