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Sample records for neural coupling strengths

  1. Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

    International Nuclear Information System (INIS)

    Wei Duqu; Luo Xiaoshu; Zou Yanli

    2008-01-01

    We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network

  2. Cooperative effect of random and time-periodic coupling strength on synchronization transitions in one-way coupled neural system: mean field approach.

    Science.gov (United States)

    Jiancheng, Shi; Min, Luo; Chusheng, Huang

    2017-08-01

    The cooperative effect of random coupling strength and time-periodic coupling strengh on synchronization transitions in one-way coupled neural system has been investigated by mean field approach. Results show that cooperative coupling strength (CCS) plays an active role for the enhancement of synchronization transitions. There exist an optimal frequency of CCS which makes the system display the best CCS-induced synchronization transitions, a critical frequency of CCS which can not further affect the CCS-induced synchronization transitions, and a critical amplitude of CCS which can not occur the CCS-induced synchronization transitions. Meanwhile, noise intensity plays a negative role for the CCS-induced synchronization transitions. Furthermore, it is found that the novel CCS amplitude-induced synchronization transitions and CCS frequency-induced synchronization transitions are found.

  3. Long range supergravity coupling strengths

    International Nuclear Information System (INIS)

    Kenyon, I.R.

    1991-01-01

    A limit of 2x10 -13 has recently been deduced for the fractional difference between the gravitational masses of the K 0 and anti K 0 mesons. This limit is applied here to put stringent limits on the strengths of the long range vector-scalar gravitational couplings envisaged in supergravity theories. A weaker limit is inferred from the general relativistic fit to the precession of the orbit of the pulsar PSR1913+16. (orig.)

  4. Neural adaptations to electrical stimulation strength training

    NARCIS (Netherlands)

    Hortobagyi, Tibor; Maffiuletti, Nicola A.

    2011-01-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there

  5. Measuring Relative Coupling Strength in Circadian Systems.

    Science.gov (United States)

    Schmal, Christoph; Herzog, Erik D; Herzel, Hanspeter

    2018-02-01

    Modern imaging techniques allow the monitoring of circadian rhythms of single cells. Coupling between these single cellular circadian oscillators can generate coherent periodic signals on the tissue level that subsequently orchestrate physiological outputs. The strength of coupling in such systems of oscillators is often unclear. In particular, effects on coupling strength by varying cell densities, by knockouts, and by inhibitor applications are debated. In this study, we suggest to quantify the relative coupling strength via analyzing period, phase, and amplitude distributions in ensembles of individual circadian oscillators. Simulations of different oscillator networks show that period and phase distributions become narrower with increasing coupling strength. Moreover, amplitudes can increase due to resonance effects. Variances of periods and phases decay monotonically with coupling strength, and can serve therefore as measures of relative coupling strength. Our theoretical predictions are confirmed by studying recently published experimental data from PERIOD2 expression in slices of the suprachiasmatic nucleus during and after the application of tetrodotoxin (TTX). On analyzing the corresponding period, phase, and amplitude distributions, we can show that treatment with TTX can be associated with a reduced coupling strength in the system of coupled oscillators. Analysis of an oscillator network derived directly from the data confirms our conclusions. We suggest that our approach is also applicable to quantify coupling in fibroblast cultures and hepatocyte networks, and for social synchronization of circadian rhythmicity in rodents, flies, and bees.

  6. Optimal estimation of the optomechanical coupling strength

    Science.gov (United States)

    Bernád, József Zsolt; Sanavio, Claudio; Xuereb, André

    2018-06-01

    We apply the formalism of quantum estimation theory to obtain information about the value of the nonlinear optomechanical coupling strength. In particular, we discuss the minimum mean-square error estimator and a quantum Cramér-Rao-type inequality for the estimation of the coupling strength. Our estimation strategy reveals some cases where quantum statistical inference is inconclusive and merely results in the reinforcement of prior expectations. We show that these situations also involve the highest expected information losses. We demonstrate that interaction times on the order of one time period of mechanical oscillations are the most suitable for our estimation scenario, and compare situations involving different photon and phonon excitations.

  7. Perturbation theory for arbitrary coupling strength?

    Science.gov (United States)

    Mahapatra, Bimal P.; Pradhan, Noubihary

    2018-03-01

    We present a new formulation of perturbation theory for quantum systems, designated here as: “mean field perturbation theory” (MFPT), which is free from power-series-expansion in any physical parameter, including the coupling strength. Its application is thereby extended to deal with interactions of arbitrary strength and to compute system-properties having non-analytic dependence on the coupling, thus overcoming the primary limitations of the “standard formulation of perturbation theory” (SFPT). MFPT is defined by developing perturbation about a chosen input Hamiltonian, which is exactly solvable but which acquires the nonlinearity and the analytic structure (in the coupling strength) of the original interaction through a self-consistent, feedback mechanism. We demonstrate Borel-summability of MFPT for the case of the quartic- and sextic-anharmonic oscillators and the quartic double-well oscillator (QDWO) by obtaining uniformly accurate results for the ground state of the above systems for arbitrary physical values of the coupling strength. The results obtained for the QDWO may be of particular significance since “renormalon”-free, unambiguous results are achieved for its spectrum in contrast to the well-known failure of SFPT in this case.

  8. Strength of Coriolis Coupling in actinide nuclei

    International Nuclear Information System (INIS)

    Peker, L.K.; Rasmussen, J.O.; Hamilton, J.H.

    1982-01-01

    Coriolis Coupling V/sub cor/ plays an important role in deformed nuclei. V/sub cor/ is proportional to h 2 /J[j (j + 1) -Ω (Ω + 1)]/sup 1/2/ and therefore is particularly significant in the nuclei with large j and low Ω Nilsson levels close to Fermi surface: n(i/sub 13/2/) in A = 150 to 170 rare-earth nuclei and p(i/sub 13/2/) and n(j/sub 15/2/) in A greater than or equal to 224 actinide nuclei. Because of larger j (n(j/sub 15/2/) versus n(i/sub 13/2/)) and smaller deformations (β approx. = 0.22 versus β 0.28) it was reasonable to expect that in actinide nuclei Coriolis effects are stronger than in the rare earth nuclei. Recently it was realized that the strength of observed Coriolis effects depends not only on the genuine Coriolis Coupling but also on the interplay between Coriolis ad pairing forces which leads to an interference between the wave functions of two mixing rotational bands. As a consequence the effective interaction V/sub eff/ of both bands is an oscillating function of the degree of shell filling (or chemical potential lambda F). It was shown that in the rare earth nuclei this interference strongly influenced conclusions about the trends in the Coriolis coupling strength and explained many of the observed band-mixing features (the sharpness of back banding curves, details of the blocking effect etc.). From theoretical analysis it was concluded that in the majority of actinide nuclei the effective interaction V/sub eff/ is strong, and therefore the Coriolis band-mixing have to be very strong. In this paper we would like to demonstrate that contrary to these predictions experimental data suggest that Coriolis band mixing in studied actinide nuclei is relatively weak and possibly significantly weaker than in rare earth nuclei

  9. Chaotic synchronization of nearest-neighbor diffusive coupling Hindmarsh-Rose neural networks in noisy environments

    International Nuclear Information System (INIS)

    Fang Xiaoling; Yu Hongjie; Jiang Zonglai

    2009-01-01

    The chaotic synchronization of Hindmarsh-Rose neural networks linked by a nonlinear coupling function is discussed. The HR neural networks with nearest-neighbor diffusive coupling form are treated as numerical examples. By the construction of a special nonlinear-coupled term, the chaotic system is coupled symmetrically. For three and four neurons network, a certain region of coupling strength corresponding to full synchronization is given, and the effect of network structure and noise position are analyzed. For five and more neurons network, the full synchronization is very difficult to realize. All the results have been proved by the calculation of the maximum conditional Lyapunov exponent.

  10. Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.

    Science.gov (United States)

    Ly, Cheng; Marsat, Gary

    2018-02-01

    Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling. Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous neural attributes alter firing rate heterogeneity, accounting for the effect with various sensory stimuli. The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks: the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) when firing rate heterogeneity is low and is negatively correlated with excitability with high firing rate heterogeneity. We also show how our theory can be used to predict effective neural architecture. We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and discuss how it can ultimately shape population codes.

  11. Neural network error correction for solving coupled ordinary differential equations

    Science.gov (United States)

    Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

    1992-01-01

    A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

  12. Intermediate coupling collision strengths from LS coupled R-matrix elements

    International Nuclear Information System (INIS)

    Clark, R.E.H.

    1978-01-01

    Fine structure collision strength for transitions between two groups of states in intermediate coupling and with inclusion of configuration mixing are obtained from LS coupled reactance matrix elements (R-matrix elements) and a set of mixing coefficients. The LS coupled R-matrix elements are transformed to pair coupling using Wigner 6-j coefficients. From these pair coupled R-matrix elements together with a set of mixing coefficients, R-matrix elements are obtained which include the intermediate coupling and configuration mixing effects. Finally, from the latter R-matrix elements, collision strengths for fine structure transitions are computed (with inclusion of both intermediate coupling and configuration mixing). (Auth.)

  13. Strength at Home Couples Program to Prevent Military Partner Violence

    Science.gov (United States)

    2017-10-01

    AWARD NUMBER: W81XWH-15-1-0374 TITLE: Strength at Home Couples Program to Prevent Military Partner Violence PRINCIPAL INVESTIGATOR: Casey T...SUBTITLE 5a. CONTRACT NUMBER Strength at Home Couples Program to Prevent Military Partner Violence 5b. GRANT NUMBER W81XWH-15-1-0374 5c. PROGRAM...7 9. Appendices…………………………………………………………………………………..7 1 Annual Report for Period: Sep 30, 2016 to Sept 29, 2017 Strength at Home

  14. Stochastic synchronization of coupled neural networks with intermittent control

    International Nuclear Information System (INIS)

    Yang Xinsong; Cao Jinde

    2009-01-01

    In this Letter, we study the exponential stochastic synchronization problem for coupled neural networks with stochastic noise perturbations. Based on Lyapunov stability theory, inequality techniques, the properties of Weiner process, and adding different intermittent controllers, several sufficient conditions are obtained to ensure exponential stochastic synchronization of coupled neural networks with or without coupling delays under stochastic perturbations. These stochastic synchronization criteria are expressed in terms of several lower-dimensional linear matrix inequalities (LMIs) and can be easily verified. Moreover, the results of this Letter are applicable to both directed and undirected weighted networks. A numerical example and its simulations are offered to show the effectiveness of our new results.

  15. Synchronization of chaotic neural networks via output or state coupling

    International Nuclear Information System (INIS)

    Lu Hongtao; Leeuwen, C. van

    2006-01-01

    We consider the problem of global exponential synchronization between two identical chaotic neural networks that are linearly and unidirectionally coupled. We formulate a general framework for the synchronization problem in which one chaotic neural network, working as the driving system (or master), sends its output or state values to the other, which serves as the response system (or slave). We use Lyapunov functions to establish general theoretical conditions for designing the coupling matrix. Neither symmetry nor negative (positive) definiteness of the coupling matrix are required; under less restrictive conditions, the two coupled chaotic neural networks can achieve global exponential synchronization regardless of their initial states. Detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws

  16. A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment.

    Science.gov (United States)

    Wen, Dong; Xue, Qing; Lu, Chengbiao; Guan, Xinyong; Wang, Yuping; Li, Xiaoli

    2014-08-01

    Recently, the synchronization between neural signals has been widely used as a key indicator of brain function. To understand comprehensively the effect of synchronization on the brain function, accurate computation of the synchronization strength among multivariate neural series from the whole brain is necessary. In this study, we proposed a method named global coupling index (GCI) to estimate the synchronization strength of multiple neural signals. First of all, performance of the GCI method was evaluated by analyzing simulated EEG signals from a multi-channel neural mass model, including the effects of the frequency band, the coupling coefficient, and the signal noise ratio. Then, the GCI method was applied to analyze the EEG signals from 12 mild cognitive impairment (MCI) subjects and 12 normal controls (NC). The results showed that GCI method had two major advantages over the global synchronization index (GSI) or S-estimator. Firstly, simulation data showed that the GCI method provided both a more robust result on the frequency band and a better performance on the coupling coefficients. Secondly, the actual EEG data demonstrated that GCI method was more sensitive in differentiating the MCI from control subjects, in terms of the global synchronization strength of neural series of specific alpha, beta1 and beta2 frequency bands. Hence, it is suggested that GCI is a better method over GSI and S-estimator to estimate the synchronization strength of multivariate neural series for predicting the MCI from the whole brain EEG recordings. Copyright © 2014. Published by Elsevier Ltd.

  17. Automatic gain control of neural coupling during cooperative hand movements.

    Science.gov (United States)

    Thomas, F A; Dietz, V; Schrafl-Altermatt, M

    2018-04-13

    Cooperative hand movements (e.g. opening a bottle) are controlled by a task-specific neural coupling, reflected in EMG reflex responses contralateral to the stimulation site. In this study the contralateral reflex responses in forearm extensor muscles to ipsilateral ulnar nerve stimulation was analyzed at various resistance and velocities of cooperative hand movements. The size of contralateral reflex responses was closely related to the level of forearm muscle activation required to accomplish the various cooperative hand movement tasks. This indicates an automatic gain control of neural coupling that allows a rapid matching of corrective forces exerted at both sides of an object with the goal 'two hands one action'.

  18. How single node dynamics enhances synchronization in neural networks with electrical coupling

    International Nuclear Information System (INIS)

    Bonacini, E.; Burioni, R.; Di Volo, M.; Groppi, M.; Soresina, C.; Vezzani, A.

    2016-01-01

    The stability of the completely synchronous state in neural networks with electrical coupling is analytically investigated applying both the Master Stability Function approach (MSF), developed by Pecora and Carroll (1998), and the Connection Graph Stability method (CGS) proposed by Belykh et al. (2004). The local dynamics is described by Morris–Lecar model for spiking neurons and by Hindmarsh–Rose model in spike, burst, irregular spike and irregular burst regimes. The combined application of both CGS and MSF methods provides an efficient estimate of the synchronization thresholds, namely bounds for the coupling strength ranges in which the synchronous state is stable. In all the considered cases, we observe that high values of coupling strength tend to synchronize the system. Furthermore, we observe a correlation between the single node attractor and the local stability properties given by MSF. The analytical results are compared with numerical simulations on a sample network, with excellent agreement.

  19. Dynamics of coupled mode solitons in bursting neural networks

    Science.gov (United States)

    Nfor, N. Oma; Ghomsi, P. Guemkam; Moukam Kakmeni, F. M.

    2018-02-01

    Using an electrically coupled chain of Hindmarsh-Rose neural models, we analytically derived the nonlinearly coupled complex Ginzburg-Landau equations. This is realized by superimposing the lower and upper cutoff modes of wave propagation and by employing the multiple scale expansions in the semidiscrete approximation. We explore the modified Hirota method to analytically obtain the bright-bright pulse soliton solutions of our nonlinearly coupled equations. With these bright solitons as initial conditions of our numerical scheme, and knowing that electrical signals are the basis of information transfer in the nervous system, it is found that prior to collisions at the boundaries of the network, neural information is purely conveyed by bisolitons at lower cutoff mode. After collision, the bisolitons are completely annihilated and neural information is now relayed by the upper cutoff mode via the propagation of plane waves. It is also shown that the linear gain of the system is inextricably linked to the complex physiological mechanisms of ion mobility, since the speeds and spatial profiles of the coupled nerve impulses vary with the gain. A linear stability analysis performed on the coupled system mainly confirms the instability of plane waves in the neural network, with a glaring example of the transition of weak plane waves into a dark soliton and then static kinks. Numerical simulations have confirmed the annihilation phenomenon subsequent to collision in neural systems. They equally showed that the symmetry breaking of the pulse solution of the system leaves in the network static internal modes, sometime referred to as Goldstone modes.

  20. Analysis of Neural-BOLD Coupling through Four Models of the Neural Metabolic Demand

    Directory of Open Access Journals (Sweden)

    Christopher W Tyler

    2015-12-01

    Full Text Available The coupling of the neuronal energetics to the blood-oxygen-level-dependent (BOLD response is still incompletely understood. To address this issue, we compared the fits of four plausible models of neurometabolic coupling dynamics to available data for simultaneous recordings of the local field potential (LFP and the local BOLD response recorded from monkey primary visual cortex over a wide range of stimulus durations. The four models of the metabolic demand driving the BOLD response were: direct coupling with the overall LFP; rectified coupling to the LFP; coupling with a slow adaptive component of the implied neural population response; and coupling with the non-adaptive intracellular input signal defined by the stimulus time course. Taking all stimulus durations into account, the results imply that the BOLD response is most closely coupled with metabolic demand derived from the intracellular input waveform, without significant influence from the adaptive transients and nonlinearities exhibited by the LFP waveform.

  1. MULTISPECTRAL PANSHARPENING APPROACH USING PULSE-COUPLED NEURAL NETWORK SEGMENTATION

    Directory of Open Access Journals (Sweden)

    X. J. Li

    2018-04-01

    Full Text Available The paper proposes a novel pansharpening method based on the pulse-coupled neural network segmentation. In the new method, uniform injection gains of each region are estimated through PCNN segmentation rather than through a simple square window. Since PCNN segmentation agrees with the human visual system, the proposed method shows better spectral consistency. Our experiments, which have been carried out for both suburban and urban datasets, demonstrate that the proposed method outperforms other methods in multispectral pansharpening.

  2. artificial neural network model for low strength rc beam shear capacity

    African Journals Online (AJOL)

    User

    RESEARCH PAPER. Keywords: Shear strength, reinforced concrete, Artificial Neural Network, design equations ... searchers using artificial intelligence to im- prove on theoretical ...... benefit to humanity or a waste of time?” The. Structural ...

  3. Computer simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling

    Directory of Open Access Journals (Sweden)

    Ferris Daniel P

    2010-12-01

    Full Text Available Abstract Background When humans perform rhythmic upper and lower limb locomotor-like movements, there is an excitatory effect of upper limb exertion on lower limb muscle recruitment. To investigate potential neural mechanisms for this behavioral observation, we developed computer simulations modeling interlimb neural pathways among central pattern generators. We hypothesized that enhancement of muscle recruitment from interlimb spinal mechanisms was not sufficient to explain muscle enhancement levels observed in experimental data. Methods We used Matsuoka oscillators for the central pattern generators (CPG and determined parameters that enhanced amplitudes of rhythmic steady state bursts. Potential mechanisms for output enhancement were excitatory and inhibitory sensory feedback gains, excitatory and inhibitory interlimb coupling gains, and coupling geometry. We first simulated the simplest case, a single CPG, and then expanded the model to have two CPGs and lastly four CPGs. In the two and four CPG models, the lower limb CPGs did not receive supraspinal input such that the only mechanisms available for enhancing output were interlimb coupling gains and sensory feedback gains. Results In a two-CPG model with inhibitory sensory feedback gains, only excitatory gains of ipsilateral flexor-extensor/extensor-flexor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 26%. In a two-CPG model with excitatory sensory feedback gains, excitatory gains of contralateral flexor-flexor/extensor-extensor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 100%. However, within a given excitatory sensory feedback gain, enhancement due to excitatory interlimb gains could only reach levels up to 20%. Interconnecting four CPGs to have ipsilateral flexor-extensor/extensor-flexor coupling, contralateral flexor-flexor/extensor-extensor coupling, and bilateral flexor-extensor/extensor-flexor coupling could enhance

  4. Extended Neural Metastability in an Embodied Model of Sensorimotor Coupling

    Directory of Open Access Journals (Sweden)

    Miguel Aguilera

    2016-09-01

    Full Text Available The hypothesis that brain organization is based on mechanisms of metastable synchronization in neural assemblies has been popularized during the last decades of neuroscientific research. Nevertheless, the role of body and environment for understanding the functioning of metastable assemblies is frequently dismissed. The main goal of this paper is to investigate the contribution of sensorimotor coupling to neural and behavioural metastability using a minimal computational model of plastic neural ensembles embedded in a robotic agent in a behavioural preference task. Our hypothesis is that, under some conditions, the metastability of the system is not restricted to the brain but extends to the system composed by the interaction of brain, body and environment. We test this idea, comparing an agent in continuous interaction with its environment in a task demanding behavioural flexibility with an equivalent model from the point of view of 'internalist neuroscience'. A statistical characterization of our model and tools from information theory allows us to show how (1 the bidirectional coupling between agent and environment brings the system closer to a regime of criticality and triggers the emergence of additional metastable states which are not found in the brain in isolation but extended to the whole system of sensorimotor interaction, (2 the synaptic plasticity of the agent is fundamental to sustain open structures in the neural controller of the agent flexibly engaging and disengaging different behavioural patterns that sustain sensorimotor metastable states, and (3 these extended metastable states emerge when the agent generates an asymmetrical circular loop of causal interaction with its environment, in which the agent responds to variability of the environment at fast timescales while acting over the environment at slow timescales, suggesting the constitution of the agent as an autonomous entity actively modulating its sensorimotor coupling

  5. Extended Neural Metastability in an Embodied Model of Sensorimotor Coupling.

    Science.gov (United States)

    Aguilera, Miguel; Bedia, Manuel G; Barandiaran, Xabier E

    2016-01-01

    The hypothesis that brain organization is based on mechanisms of metastable synchronization in neural assemblies has been popularized during the last decades of neuroscientific research. Nevertheless, the role of body and environment for understanding the functioning of metastable assemblies is frequently dismissed. The main goal of this paper is to investigate the contribution of sensorimotor coupling to neural and behavioral metastability using a minimal computational model of plastic neural ensembles embedded in a robotic agent in a behavioral preference task. Our hypothesis is that, under some conditions, the metastability of the system is not restricted to the brain but extends to the system composed by the interaction of brain, body and environment. We test this idea, comparing an agent in continuous interaction with its environment in a task demanding behavioral flexibility with an equivalent model from the point of view of "internalist neuroscience." A statistical characterization of our model and tools from information theory allow us to show how (1) the bidirectional coupling between agent and environment brings the system closer to a regime of criticality and triggers the emergence of additional metastable states which are not found in the brain in isolation but extended to the whole system of sensorimotor interaction, (2) the synaptic plasticity of the agent is fundamental to sustain open structures in the neural controller of the agent flexibly engaging and disengaging different behavioral patterns that sustain sensorimotor metastable states, and (3) these extended metastable states emerge when the agent generates an asymmetrical circular loop of causal interaction with its environment, in which the agent responds to variability of the environment at fast timescales while acting over the environment at slow timescales, suggesting the constitution of the agent as an autonomous entity actively modulating its sensorimotor coupling with the world. We

  6. Dependence of synchronization transitions on mean field approach in two-way coupled neural system

    Science.gov (United States)

    Shi, J. C.; Luo, M.; Huang, C. S.

    2018-03-01

    This work investigates the synchronization transitions in two-way coupled neural system by mean field approach. Results show that, there exists a critical noise intensity for the synchronization transitions, i.e., above (or below) the critical noise intensity, the synchronization transitions are decreased (or hardly change) with increasing the noise intensity. Meanwhile, the heterogeneity effect plays a negative role for the synchronization transitions, and above critical coupling strength, the heterogeneity effect on synchronization transitions can be negligible. Furthermore, when an external signal is introduced into the coupled system, the novel frequency-induced and amplitude-induced synchronization transitions are found, and there exist an optimal frequency and an optimal amplitude of external signal which makes the system to display the best synchronization transitions. In particular, it is observed that the synchronization transitions can not be further affected above critical frequency of external signal.

  7. Implementation of a pulse coupled neural network in FPGA.

    Science.gov (United States)

    Waldemark, J; Millberg, M; Lindblad, T; Waldemark, K; Becanovic, V

    2000-06-01

    The Pulse Coupled neural network, PCNN, is a biologically inspired neural net and it can be used in various image analysis applications, e.g. time-critical applications in the field of image pre-processing like segmentation, filtering, etc. a VHDL implementation of the PCNN targeting FPGA was undertaken and the results presented here. The implementation contains many interesting features. By pipelining the PCNN structure a very high throughput of 55 million neuron iterations per second could be achieved. By making the coefficients re-configurable during operation, a complete recognition system could be implemented on one, or maybe two, chip(s). Reconsidering the ranges and resolutions of the constants may save a lot of hardware, since the higher resolution requires larger multipliers, adders, memories etc.

  8. Quantum correlations dynamics of three-qubit states coupled to an XY spin chain: Role of coupling strengths

    International Nuclear Information System (INIS)

    Yin Shao-Ying; Song Jie; Xu Xue-Xin; Zhou Ke-Ya; Liu Shu-Tian; Liu Qing-Xin

    2017-01-01

    We investigate the prominent impacts of coupling strengths on the evolution of entanglement and quantum discord for a three-qubit system coupled to an XY spin-chain environment. In the case of a pure W state, more robust, even larger nonzero quantum correlations can be obtained by tailoring the coupling strengths between the qubits and the environment. For a mixed state consisting of the GHZ and W states, the dynamics of entanglement and quantum discord can characterize the critical point of quantum phase transition. Remarkably, a large nonzero quantum discord is generally retained, while the nonzero entanglement can only be obtained as the system-environment coupling satisfies certain conditions. We also find that the impact of each qubit’s coupling strength on the quantum correlation dynamics strongly depends on the variation schemes of the system-environment couplings. (paper)

  9. Electrolarynx Voice Recognition Utilizing Pulse Coupled Neural Network

    Directory of Open Access Journals (Sweden)

    Fatchul Arifin

    2010-08-01

    Full Text Available The laryngectomies patient has no ability to speak normally because their vocal chords have been removed. The easiest option for the patient to speak again is by using electrolarynx speech. This tool is placed on the lower chin. Vibration of the neck while speaking is used to produce sound. Meanwhile, the technology of "voice recognition" has been growing very rapidly. It is expected that the technology of "voice recognition" can also be used by laryngectomies patients who use electrolarynx.This paper describes a system for electrolarynx speech recognition. Two main parts of the system are feature extraction and pattern recognition. The Pulse Coupled Neural Network – PCNN is used to extract the feature and characteristic of electrolarynx speech. Varying of β (one of PCNN parameter also was conducted. Multi layer perceptron is used to recognize the sound patterns. There are two kinds of recognition conducted in this paper: speech recognition and speaker recognition. The speech recognition recognizes specific speech from every people. Meanwhile, speaker recognition recognizes specific speech from specific person. The system ran well. The "electrolarynx speech recognition" has been tested by recognizing of “A” and "not A" voice. The results showed that the system had 94.4% validation. Meanwhile, the electrolarynx speaker recognition has been tested by recognizing of “saya” voice from some different speakers. The results showed that the system had 92.2% validation. Meanwhile, the best β parameter of PCNN for electrolarynx recognition is 3.

  10. Neural dissociations in attitude strength: Distinct regions of cingulate cortex track ambivalence and certainty.

    Science.gov (United States)

    Luttrell, Andrew; Stillman, Paul E; Hasinski, Adam E; Cunningham, William A

    2016-04-01

    People's behaviors are often guided by valenced responses to objects in the environment. Beyond positive and negative evaluations, attitudes research has documented the importance of attitude strength--qualities of an attitude that enhance or attenuate its impact and durability. Although neuroscience research has extensively investigated valence, little work exists on other related variables like metacognitive judgments about one's attitudes. It remains unclear, then, whether the various indicators of attitude strength represent a single underlying neural process or whether they reflect independent processes. To examine this, we used functional MRI (fMRI) to identify the neural correlates of attitude strength. Specifically, we focus on ambivalence and certainty, which represent metacognitive judgments that people can make about their evaluations. Although often correlated, prior neuroscience research suggests that these 2 attributes may have distinct neural underpinnings. We investigate this by having participants make evaluative judgments of visually presented words while undergoing fMRI. After scanning, participants rated the degree of ambivalence and certainty they felt regarding their attitudes toward each word. We found that these 2 judgments corresponded to distinct brain regions' activity during the process of evaluation. Ambivalence corresponded to activation in anterior cingulate cortex, dorsomedial prefrontal cortex, and posterior cingulate cortex. Certainty, however, corresponded to activation in unique areas of the precuneus/posterior cingulate cortex. These results support a model treating ambivalence and certainty as distinct, though related, attitude strength variables, and we discuss implications for both attitudes and neuroscience research. (c) 2016 APA, all rights reserved).

  11. Critical features of coupling parameter in synchronization of small world neural networks

    International Nuclear Information System (INIS)

    Li Yanlong; Ma Jun; Xu Wenke; Li Hongbo; Wu Min

    2008-01-01

    The critical features of a coupling parameter in the synchronization of small world neural networks are investigated. A power law decay form is observed in this spatially extended system, the larger linked degree becomes, the larger critical coupling intensity. There exists maximal and minimal critical coupling intensity for synchronization in the extended system. An approximate synchronization diagram has been constructed. In the case of partial coupling, a primary result is presented about the critical coupling fraction for various linked degree of networks

  12. Topological probability and connection strength induced activity in complex neural networks

    International Nuclear Information System (INIS)

    Du-Qu, Wei; Bo, Zhang; Dong-Yuan, Qiu; Xiao-Shu, Luo

    2010-01-01

    Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world (SW) connections. Network elements are described by two-dimensional map neurons (2DMNs) with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied. (general)

  13. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

    Science.gov (United States)

    Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

    2017-06-07

    Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

  14. Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network

    Directory of Open Access Journals (Sweden)

    Haiyan Mo

    2013-01-01

    Full Text Available In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes. In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes. The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model.

  15. Precise measurement of coupling strength and high temperature quantum effect in a nonlinearly coupled qubit-oscillator system

    Science.gov (United States)

    Ge, Li; Zhao, Nan

    2018-04-01

    We study the coherence dynamics of a qubit coupled to a harmonic oscillator with both linear and quadratic interactions. As long as the linear coupling strength is much smaller than the oscillator frequency, the long time behavior of the coherence is dominated by the quadratic coupling strength g 2. The coherence decays and revives at a period , with the width of coherence peak decreasing as the temperature increases, hence providing a way to measure g 2 precisely without cooling. Unlike the case of linear coupling, here the coherence dynamics never reduces to the classical limit in which the oscillator is classical. Finally, the validity of linear coupling approximation is discussed and the coherence under Hahn-echo is evaluated.

  16. Dependence of some electromagnetic properties of superconductors on coupling strength

    International Nuclear Information System (INIS)

    Marsiglio, F.; Carbotte, J.P.; Blezius, J.

    1990-01-01

    We have calculated select electromagnetic properties for many real superconductors based on tunneling-derived electron-phonon spectral densities. We use this data to fit coefficients in semiphenomenological forms derived through a series of approximations to the exact microscopic expressions. It is found that the derived forms represent well the strong-coupling corrections

  17. The networks scale and coupling parameter in synchronization of neural networks with diluted synapses

    International Nuclear Information System (INIS)

    Li Yanlong; Ma Jun; Chen Yuhong; Xu Wenke; Wang Yinghai

    2008-01-01

    In this paper the influence of the networks scale on the coupling parameter in the synchronization of neural networks with diluted synapses is investigated. Using numerical simulations, an exponential decay form is observed in the extreme case of global coupling among networks and full connection in each network; the larger linked degree becomes, the larger critical coupling intensity becomes; and the oscillation phenomena in the relationship of critical coupling intensity and the number of neural networks layers in the case of small-scale networks are found

  18. Prediction of compression strength of high performance concrete using artificial neural networks

    International Nuclear Information System (INIS)

    Torre, A; Moromi, I; Garcia, F; Espinoza, P; Acuña, L

    2015-01-01

    High-strength concrete is undoubtedly one of the most innovative materials in construction. Its manufacture is simple and is carried out starting from essential components (water, cement, fine and aggregates) and a number of additives. Their proportions have a high influence on the final strength of the product. This relations do not seem to follow a mathematical formula and yet their knowledge is crucial to optimize the quantities of raw materials used in the manufacture of concrete. Of all mechanical properties, concrete compressive strength at 28 days is most often used for quality control. Therefore, it would be important to have a tool to numerically model such relationships, even before processing. In this aspect, artificial neural networks have proven to be a powerful modeling tool especially when obtaining a result with higher reliability than knowledge of the relationships between the variables involved in the process. This research has designed an artificial neural network to model the compressive strength of concrete based on their manufacturing parameters, obtaining correlations of the order of 0.94

  19. Precise strength of the πNN coupling constant

    International Nuclear Information System (INIS)

    Ericson, T.E.O.; Loiseau, B.; Rahm, J.; Blomgren, J.; Olsson, N.; Thomas, A. W.

    1999-01-01

    We report here a preliminary value for the πNN coupling constant deduced from the Goldberger-Miyazawa-Oehme sum rule for forward πN scattering. As in our previous determination from np backward differential scattering cross sections we give a critical discussion of the analysis with careful attention not only to the statistical, but also to the systematic uncertainties. Our preliminary evaluation gives g 2 c =13.99(24)

  20. Precise strength of the $\\pi$NN coupling constant

    CERN Document Server

    Ericson, Torleif Eric Oskar; Rahm, J; Blomgren, J; Olsson, N; Thomas, A W

    1998-01-01

    We report here a preliminary value for the piNN coupling constant deduced from the GMO sumrule for forward piN scattering. As in our previous determination from np backward differential scattering cross sections we give a critical discussion of the analysis with careful attention not only to the statistical, but also to the systematic uncertainties. Our preliminary evaluation gives $g^2_c$(GMO) = 13.99(24).

  1. Event-driven simulation of neural population synchronization facilitated by electrical coupling.

    Science.gov (United States)

    Carrillo, Richard R; Ros, Eduardo; Barbour, Boris; Boucheny, Christian; Coenen, Olivier

    2007-02-01

    Most neural communication and processing tasks are driven by spikes. This has enabled the application of the event-driven simulation schemes. However the simulation of spiking neural networks based on complex models that cannot be simplified to analytical expressions (requiring numerical calculation) is very time consuming. Here we describe briefly an event-driven simulation scheme that uses pre-calculated table-based neuron characterizations to avoid numerical calculations during a network simulation, allowing the simulation of large-scale neural systems. More concretely we explain how electrical coupling can be simulated efficiently within this computation scheme, reproducing synchronization processes observed in detailed simulations of neural populations.

  2. Determination of the quark coupling strength vertical bar V-ub vertical bar using baryonic decays

    NARCIS (Netherlands)

    Aaij, R.; Adeva, B.; Adinolfi, M.; Older, A. A.; Ajaltouni, Z.; Akar, S.; Albrecht, J.; Alessio, F.; Alexander, M.; Ali, S.; Alkhazov, G.; Cartelle, P. Alvarez; Alves, A. A.; Amato, S.; Amerio, S.; Amhis, Y.; An, L.; Anderlini, L.; Andreotti, M.; Andrews, J. E.; Appleby, R. B.; Gutierrez, O. Aquines; Archilli, F.; Artamonov, A.; Artuso, M.; Aslanides, E.; Auriemma, G.; Baalouch, M.; Bachmann, S.; Back, J. J.; Badalov, A.; Baesso, C.; Baldini, W.; Barlow, R. J.; Barschel, C.; Barsuk, S.; Barter, W.; Batozskaya, V.; Battista, V.; Beaucourt, L.; Beddow, J.; Bedeschi, F.; Bediaga, I.; Bel, L. J.; Belyaev, I.; Ben-Haim, E.; Bencivenni, G.; Onderwater, C. J. G.; Pellegrino, A.; Tolk, S.

    In the Standard Model of particle physics, the strength of the couplings of the b quark to the u and c quarks, vertical bar V-ub vertical bar and vertical bar V-ub vertical bar, are governed by the coupling of the quarks to the Higgs boson. Using data from the LHCb experiment at the Large Hadron

  3. Impact of dispersed coupling strength on the free running periods of circadian rhythms

    Science.gov (United States)

    Gu, Changgui; Rohling, Jos H. T.; Liang, Xiaoming; Yang, Huijie

    2016-03-01

    The dominant endogenous clock, named the suprachiasmatic nucleus (SCN), regulates circadian rhythms of behavioral and physiological activity in mammals. One of the main characteristics of the SCN is that the animal maintains a circadian rhythm with a period close to 24 h in the absence of a daily light-dark cycle (called the free running period). The free running period varies among species due to heterogeneity of the SCN network. Previous studies have shown that the heterogeneity in cellular coupling as well as in intrinsic neuronal periods shortens the free running period. Furthermore, as derived from experiments, one neuron's coupling strength is negatively associated with its period. It is unknown what the effects of this association between coupling strength and period are on the free running period and how the heterogeneity in coupling strength influences this free running period. In the present study we found that in the presence of a negative relationship between one neuron's coupling strength and its period, surprisingly, the dispersion of coupling strengths increases the free running period. Our present finding may shed new light on the understanding of the heterogeneous SCN network and provides an alternative explanation for the diversity of free running periods between species.

  4. Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons

    Science.gov (United States)

    Ma, Ying; Shaik, Mohammed A.; Kozberg, Mariel G.; Portes, Jacob P.; Timerman, Dmitriy

    2016-01-01

    Brain hemodynamics serve as a proxy for neural activity in a range of noninvasive neuroimaging techniques including functional magnetic resonance imaging (fMRI). In resting-state fMRI, hemodynamic fluctuations have been found to exhibit patterns of bilateral synchrony, with correlated regions inferred to have functional connectivity. However, the relationship between resting-state hemodynamics and underlying neural activity has not been well established, making the neural underpinnings of functional connectivity networks unclear. In this study, neural activity and hemodynamics were recorded simultaneously over the bilateral cortex of awake and anesthetized Thy1-GCaMP mice using wide-field optical mapping. Neural activity was visualized via selective expression of the calcium-sensitive fluorophore GCaMP in layer 2/3 and 5 excitatory neurons. Characteristic patterns of resting-state hemodynamics were accompanied by more rapidly changing bilateral patterns of resting-state neural activity. Spatiotemporal hemodynamics could be modeled by convolving this neural activity with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Simultaneous imaging and electrophysiology confirmed that Thy1-GCaMP signals are well-predicted by multiunit activity. Neurovascular coupling between resting-state neural activity and hemodynamics was robust and fast in awake animals, whereas coupling in urethane-anesthetized animals was slower, and in some cases included lower-frequency (resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying patterns of excitatory neural activity. The patterns of bilaterally-symmetric spontaneous neural activity revealed by wide-field Thy1-GCaMP imaging may depict the neural foundation of functional connectivity networks detected in resting-state fMRI. PMID:27974609

  5. Strength of the trilinear Higgs boson coupling in technicolor models

    International Nuclear Information System (INIS)

    Doff, A.; Natale, A.A.

    2006-01-01

    In the standard model of elementary particles the fermion and gauge boson masses are generated due to the interaction of these particles with elementary Higgs scalar bosons. Despite its success there are some points in the model as, for instance, the enormous range of masses between the lightest and heaviest fermions and other peculiarities that could be better explained at a deeper level. The nature of the Higgs boson is one of the most important problems in particle physics, and there are many questions that may be answered in the near future by LHC experiments, such as: Is the Higgs boson, if it exists at all, elementary or composite? What are the symmetries behind the Higgs mechanism? There are many variants for the Higgs mechanism. Our interest in this work will be focused in the models of electroweak symmetry breaking via strongly interacting theories of technicolor (TC) type. In these theories the Higgs boson is a composite of the so called technifermions, and at some extent any model where the Higgs boson is not an elementary field follows more or less the same ideas of the technicolor models. In extensions of the standard model the scalar self-couplings can be enhanced, like in the supersymmetric version. If the same happens in models of dynamical symmetry breaking, as far as we know, has not been investigated up to now, and this study is the motivation of our work. Although technicolor is a non-Abelian gauge theory it is not necessarily similar to QCD, and most of the work in this area try to find the TC dynamics dealing with the particle content of the theory in order to obtain a technifermion self-energy that does not lead to phenomenological problems as in the scheme known as walking technicolor. In this work we will consider a very general Ansatz for the technifermion self-energy, which is an essential ingredient to compute the scalar self-couplings. This Ansatz interpolates between all known forms of technifermionic self-energy. As we vary some

  6. Pinning Synchronization of Delayed Neural Networks with Nonlinear Inner-Coupling

    Directory of Open Access Journals (Sweden)

    Yangling Wang

    2011-01-01

    Full Text Available Without assuming the symmetry and irreducibility of the outer-coupling weight configuration matrices, we investigate the pinning synchronization of delayed neural networks with nonlinear inner-coupling. Some delay-dependent controlled stability criteria in terms of linear matrix inequality (LMI are obtained. An example is presented to show the application of the criteria obtained in this paper.

  7. Estimation of RC slab-column joints effective strength using neural networks

    Directory of Open Access Journals (Sweden)

    A. A. Shah

    Full Text Available The nominal strength of slab-column joints made of highstrength concrete (HSC columns and normal strength concrete (NSC slabs is of great importance in structural design and construction of concrete buildings. This topic has been intensively studied during the last decades. Different types of column-slab joints have been investigated experimentally providing a basis for developing design provisions. However, available data does not cover all classes of concretes, reinforcements, and possible loading cases for the proper calculation of joint stresses necessary for design purposes. New numerical methods based on modern software seem to be effective and may allow reliable prediction of column-slab joint strength. The current research is focused on analysis of available experimental data on different slab-to-column joints with the aim of predicting the nominal strength of slabcolumn joint. Neural networks technique is proposed herein using MATLAB routines developed to analyze available experimental data. The obtained results allow prediction of the effective strength of column-slab joints with accuracy and good correlation coefficients when compared to regression based models. The proposed method enables the user to predict the effective design of column-slab joints without the need for conservative safety coefficients generally promoted and used by most construction codes.

  8. Reconstruction of coupling architecture of neural field networks from vector time series

    Science.gov (United States)

    Sysoev, Ilya V.; Ponomarenko, Vladimir I.; Pikovsky, Arkady

    2018-04-01

    We propose a method of reconstruction of the network coupling matrix for a basic voltage-model of the neural field dynamics. Assuming that the multivariate time series of observations from all nodes are available, we describe a technique to find coupling constants which is unbiased in the limit of long observations. Furthermore, the method is generalized for reconstruction of networks with time-delayed coupling, including the reconstruction of unknown time delays. The approach is compared with other recently proposed techniques.

  9. Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

    Directory of Open Access Journals (Sweden)

    David Breuer

    2014-03-01

    Full Text Available How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.

  10. Complex Dynamics of Delay-Coupled Neural Networks

    Science.gov (United States)

    Mao, Xiaochen

    2016-09-01

    This paper reveals the complicated dynamics of a delay-coupled system that consists of a pair of sub-networks and multiple bidirectional couplings. Time delays are introduced into the internal connections and network-couplings, respectively. The stability and instability of the coupled network are discussed. The sufficient conditions for the existence of oscillations are given. Case studies of numerical simulations are given to validate the analytical results. Interesting and complicated neuronal activities are observed numerically, such as rest states, periodic oscillations, multiple switches of rest states and oscillations, and the coexistence of different types of oscillations.

  11. Energy efficient neural stimulation: coupling circuit design and membrane biophysics.

    Science.gov (United States)

    Foutz, Thomas J; Ackermann, D Michael; Kilgore, Kevin L; McIntyre, Cameron C

    2012-01-01

    The delivery of therapeutic levels of electrical current to neural tissue is a well-established treatment for numerous indications such as Parkinson's disease and chronic pain. While the neuromodulation medical device industry has experienced steady clinical growth over the last two decades, much of the core technology underlying implanted pulse generators remain unchanged. In this study we propose some new methods for achieving increased energy-efficiency during neural stimulation. The first method exploits the biophysical features of excitable tissue through the use of a centered-triangular stimulation waveform. Neural activation with this waveform is achieved with a statistically significant reduction in energy compared to traditional rectangular waveforms. The second method demonstrates energy savings that could be achieved by advanced circuitry design. We show that the traditional practice of using a fixed compliance voltage for constant-current stimulation results in substantial energy loss. A portion of this energy can be recuperated by adjusting the compliance voltage to real-time requirements. Lastly, we demonstrate the potential impact of axon fiber diameter on defining the energy-optimal pulse-width for stimulation. When designing implantable pulse generators for energy efficiency, we propose that the future combination of a variable compliance system, a centered-triangular stimulus waveform, and an axon diameter specific stimulation pulse-width has great potential to reduce energy consumption and prolong battery life in neuromodulation devices.

  12. Energy efficient neural stimulation: coupling circuit design and membrane biophysics.

    Directory of Open Access Journals (Sweden)

    Thomas J Foutz

    Full Text Available The delivery of therapeutic levels of electrical current to neural tissue is a well-established treatment for numerous indications such as Parkinson's disease and chronic pain. While the neuromodulation medical device industry has experienced steady clinical growth over the last two decades, much of the core technology underlying implanted pulse generators remain unchanged. In this study we propose some new methods for achieving increased energy-efficiency during neural stimulation. The first method exploits the biophysical features of excitable tissue through the use of a centered-triangular stimulation waveform. Neural activation with this waveform is achieved with a statistically significant reduction in energy compared to traditional rectangular waveforms. The second method demonstrates energy savings that could be achieved by advanced circuitry design. We show that the traditional practice of using a fixed compliance voltage for constant-current stimulation results in substantial energy loss. A portion of this energy can be recuperated by adjusting the compliance voltage to real-time requirements. Lastly, we demonstrate the potential impact of axon fiber diameter on defining the energy-optimal pulse-width for stimulation. When designing implantable pulse generators for energy efficiency, we propose that the future combination of a variable compliance system, a centered-triangular stimulus waveform, and an axon diameter specific stimulation pulse-width has great potential to reduce energy consumption and prolong battery life in neuromodulation devices.

  13. Determination of the quark coupling strength $|V_{ub}|$ using baryonic decays

    CERN Document Server

    Aaij, Roel; Adinolfi, Marco; Affolder, Anthony; Ajaltouni, Ziad; Akar, Simon; Albrecht, Johannes; Alessio, Federico; Alexander, Michael; Ali, Suvayu; Alkhazov, Georgy; Alvarez Cartelle, Paula; Alves Jr, Antonio Augusto; Amato, Sandra; Amerio, Silvia; Amhis, Yasmine; An, Liupan; Anderlini, Lucio; Anderson, Jonathan; Andreotti, Mirco; Andrews, Jason; Appleby, Robert; Aquines Gutierrez, Osvaldo; Archilli, Flavio; Artamonov, Alexander; Artuso, Marina; Aslanides, Elie; Auriemma, Giulio; Baalouch, Marouen; Bachmann, Sebastian; Back, John; Badalov, Alexey; Baesso, Clarissa; Baldini, Wander; Barlow, Roger; Barschel, Colin; Barsuk, Sergey; Barter, William; Batozskaya, Varvara; Battista, Vincenzo; Bay, Aurelio; Beaucourt, Leo; Beddow, John; Bedeschi, Franco; Bediaga, Ignacio; Belyaev, Ivan; Ben-Haim, Eli; Bencivenni, Giovanni; Benson, Sean; Benton, Jack; Berezhnoy, Alexander; Bernet, Roland; Bertolin, Alessandro; Bettler, Marc-Olivier; van Beuzekom, Martinus; Bien, Alexander; Bifani, Simone; Bird, Thomas; Bizzeti, Andrea; Blake, Thomas; Blanc, Frédéric; Blouw, Johan; Blusk, Steven; Bocci, Valerio; Bondar, Alexander; Bondar, Nikolay; Bonivento, Walter; Borghi, Silvia; Borsato, Martino; Bowcock, Themistocles; Bowen, Espen Eie; Bozzi, Concezio; Braun, Svende; Brett, David; Britsch, Markward; Britton, Thomas; Brodzicka, Jolanta; Brook, Nicholas; Bursche, Albert; Buytaert, Jan; Cadeddu, Sandro; Calabrese, Roberto; Calvi, Marta; Calvo Gomez, Miriam; Campana, Pierluigi; Campora Perez, Daniel; Capriotti, Lorenzo; Carbone, Angelo; Carboni, Giovanni; Cardinale, Roberta; Cardini, Alessandro; Carniti, Paolo; Carson, Laurence; Carvalho Akiba, Kazuyoshi; Casanova Mohr, Raimon; Casse, Gianluigi; Cassina, Lorenzo; Castillo Garcia, Lucia; Cattaneo, Marco; Cauet, Christophe; Cavallero, Giovanni; Cenci, Riccardo; Charles, Matthew; Charpentier, Philippe; Chefdeville, Maximilien; Chen, Shanzhen; Cheung, Shu-Faye; Chiapolini, Nicola; Chrzaszcz, Marcin; Cid Vidal, Xabier; Ciezarek, Gregory; Clarke, Peter; Clemencic, Marco; Cliff, Harry; Closier, Joel; Coco, Victor; Cogan, Julien; Cogneras, Eric; Cogoni, Violetta; Cojocariu, Lucian; Collazuol, Gianmaria; Collins, Paula; Comerma-Montells, Albert; Contu, Andrea; Cook, Andrew; Coombes, Matthew; Coquereau, Samuel; Corti, Gloria; Corvo, Marco; Counts, Ian; Couturier, Benjamin; Cowan, Greig; Craik, Daniel Charles; Crocombe, Andrew; Cruz Torres, Melissa Maria; Cunliffe, Samuel; Currie, Robert; D'Ambrosio, Carmelo; Dalseno, Jeremy; David, Pieter; Davis, Adam; De Bruyn, Kristof; De Capua, Stefano; De Cian, Michel; De Miranda, Jussara; De Paula, Leandro; De Silva, Weeraddana; De Simone, Patrizia; Dean, Cameron Thomas; Decamp, Daniel; Deckenhoff, Mirko; Del Buono, Luigi; Déléage, Nicolas; Derkach, Denis; Deschamps, Olivier; Dettori, Francesco; Dey, Biplab; Di Canto, Angelo; Di Ruscio, Francesco; Dijkstra, Hans; Donleavy, Stephanie; Dordei, Francesca; Dorigo, Mirco; Dosil Suárez, Alvaro; Dossett, David; Dovbnya, Anatoliy; Dreimanis, Karlis; Dufour, Laurent; Dujany, Giulio; Dupertuis, Frederic; Durante, Paolo; Dzhelyadin, Rustem; Dziurda, Agnieszka; Dzyuba, Alexey; Easo, Sajan; Egede, Ulrik; Egorychev, Victor; Eidelman, Semen; Eisenhardt, Stephan; Eitschberger, Ulrich; Ekelhof, Robert; Eklund, Lars; El Rifai, Ibrahim; Elsasser, Christian; Ely, Scott; Esen, Sevda; Evans, Hannah Mary; Evans, Timothy; Falabella, Antonio; Färber, Christian; Farinelli, Chiara; Farley, Nathanael; Farry, Stephen; Fay, Robert; Ferguson, Dianne; Fernandez Albor, Victor; Ferreira Rodrigues, Fernando; Ferro-Luzzi, Massimiliano; Filippov, Sergey; Fiore, Marco; Fiorini, Massimiliano; Firlej, Miroslaw; Fitzpatrick, Conor; Fiutowski, Tomasz; Fol, Philip; Fontana, Marianna; Fontanelli, Flavio; Forty, Roger; Francisco, Oscar; Frank, Markus; Frei, Christoph; Frosini, Maddalena; Fu, Jinlin; Furfaro, Emiliano; Gallas Torreira, Abraham; Galli, Domenico; Gallorini, Stefano; Gambetta, Silvia; Gandelman, Miriam; Gandini, Paolo; Gao, Yuanning; García Pardiñas, Julián; Garofoli, Justin; Garra Tico, Jordi; Garrido, Lluis; Gascon, David; Gaspar, Clara; Gastaldi, Ugo; Gauld, Rhorry; Gavardi, Laura; Gazzoni, Giulio; Geraci, Angelo; Gersabeck, Evelina; Gersabeck, Marco; Gershon, Timothy; Ghez, Philippe; Gianelle, Alessio; Gianì, Sebastiana; Gibson, Valerie; Giubega, Lavinia-Helena; Gligorov, Vladimir; Göbel, Carla; Golubkov, Dmitry; Golutvin, Andrey; Gomes, Alvaro; Gotti, Claudio; Grabalosa Gándara, Marc; Graciani Diaz, Ricardo; Granado Cardoso, Luis Alberto; Graugés, Eugeni; Graverini, Elena; Graziani, Giacomo; Grecu, Alexandru; Greening, Edward; Gregson, Sam; Griffith, Peter; Grillo, Lucia; Grünberg, Oliver; Gui, Bin; Gushchin, Evgeny; Guz, Yury; Gys, Thierry; Hadjivasiliou, Christos; Haefeli, Guido; Haen, Christophe; Haines, Susan; Hall, Samuel; Hamilton, Brian; Hampson, Thomas; Han, Xiaoxue; Hansmann-Menzemer, Stephanie; Harnew, Neville; Harnew, Samuel; Harrison, Jonathan; He, Jibo; Head, Timothy; Heijne, Veerle; Hennessy, Karol; Henrard, Pierre; Henry, Louis; Hernando Morata, Jose Angel; van Herwijnen, Eric; Heß, Miriam; Hicheur, Adlène; Hill, Donal; Hoballah, Mostafa; Hombach, Christoph; Hulsbergen, Wouter; Humair, Thibaud; Hussain, Nazim; Hutchcroft, David; Hynds, Daniel; Idzik, Marek; Ilten, Philip; Jacobsson, Richard; Jaeger, Andreas; Jalocha, Pawel; Jans, Eddy; Jawahery, Abolhassan; Jing, Fanfan; John, Malcolm; Johnson, Daniel; Jones, Christopher; Joram, Christian; Jost, Beat; Jurik, Nathan; Kandybei, Sergii; Kanso, Walaa; Karacson, Matthias; Karbach, Moritz; Karodia, Sarah; Kelsey, Matthew; Kenyon, Ian; Kenzie, Matthew; Ketel, Tjeerd; Khanji, Basem; Khurewathanakul, Chitsanu; Klaver, Suzanne; Klimaszewski, Konrad; Kochebina, Olga; Kolpin, Michael; Komarov, Ilya; Koopman, Rose; Koppenburg, Patrick; Korolev, Mikhail; Kravchuk, Leonid; Kreplin, Katharina; Kreps, Michal; Krocker, Georg; Krokovny, Pavel; Kruse, Florian; Kucewicz, Wojciech; Kucharczyk, Marcin; Kudryavtsev, Vasily; Kurek, Krzysztof; Kvaratskheliya, Tengiz; La Thi, Viet Nga; Lacarrere, Daniel; Lafferty, George; Lai, Adriano; Lambert, Dean; Lambert, Robert W; Lanfranchi, Gaia; Langenbruch, Christoph; Langhans, Benedikt; Latham, Thomas; Lazzeroni, Cristina; Le Gac, Renaud; van Leerdam, Jeroen; Lees, Jean-Pierre; Lefèvre, Regis; Leflat, Alexander; Lefrançois, Jacques; Leroy, Olivier; Lesiak, Tadeusz; Leverington, Blake; Li, Yiming; Likhomanenko, Tatiana; Liles, Myfanwy; Lindner, Rolf; Linn, Christian; Lionetto, Federica; Liu, Bo; Lohn, Stefan; Longstaff, Iain; Lopes, Jose; Lowdon, Peter; Lucchesi, Donatella; Luo, Haofei; Lupato, Anna; Luppi, Eleonora; Lupton, Oliver; Machefert, Frederic; Maciuc, Florin; Maev, Oleg; Maguire, Kevin; Malde, Sneha; Malinin, Alexander; Manca, Giulia; Mancinelli, Giampiero; Manning, Peter Michael; Mapelli, Alessandro; Maratas, Jan; Marchand, Jean François; Marconi, Umberto; Marin Benito, Carla; Marino, Pietro; Märki, Raphael; Marks, Jörg; Martellotti, Giuseppe; Martinelli, Maurizio; Martinez Santos, Diego; Martinez Vidal, Fernando; Martins Tostes, Danielle; Massafferri, André; Matev, Rosen; Mathad, Abhijit; Mathe, Zoltan; Matteuzzi, Clara; Mauri, Andrea; Maurin, Brice; Mazurov, Alexander; McCann, Michael; McCarthy, James; McNab, Andrew; McNulty, Ronan; Meadows, Brian; Meier, Frank; Meissner, Marco; Merk, Marcel; Milanes, Diego Alejandro; Minard, Marie-Noelle; Molina Rodriguez, Josue; Monteil, Stephane; Morandin, Mauro; Morawski, Piotr; Mordà, Alessandro; Morello, Michael Joseph; Moron, Jakub; Morris, Adam Benjamin; Mountain, Raymond; Muheim, Franz; Müller, Katharina; Mussini, Manuel; Muster, Bastien; Naik, Paras; Nakada, Tatsuya; Nandakumar, Raja; Nasteva, Irina; Needham, Matthew; Neri, Nicola; Neubert, Sebastian; Neufeld, Niko; Neuner, Max; Nguyen, Anh Duc; Nguyen, Thi-Dung; Nguyen-Mau, Chung; Niess, Valentin; Niet, Ramon; Nikitin, Nikolay; Nikodem, Thomas; Novoselov, Alexey; O'Hanlon, Daniel Patrick; Oblakowska-Mucha, Agnieszka; Obraztsov, Vladimir; Ogilvy, Stephen; Okhrimenko, Oleksandr; Oldeman, Rudolf; Onderwater, Gerco; Osorio Rodrigues, Bruno; Otalora Goicochea, Juan Martin; Otto, Adam; Owen, Patrick; Oyanguren, Maria Arantza; Palano, Antimo; Palombo, Fernando; Palutan, Matteo; Panman, Jacob; Papanestis, Antonios; Pappagallo, Marco; Pappalardo, Luciano; Parkes, Christopher; Passaleva, Giovanni; Patel, Girish; Patel, Mitesh; Patrignani, Claudia; Pearce, Alex; Pellegrino, Antonio; Penso, Gianni; Pepe Altarelli, Monica; Perazzini, Stefano; Perret, Pascal; Pescatore, Luca; Pesen, Erhan; Petridis, Konstantin; Petrolini, Alessandro; Picatoste Olloqui, Eduardo; Pietrzyk, Boleslaw; Pilař, Tomas; Pinci, Davide; Pistone, Alessandro; Playfer, Stephen; Plo Casasus, Maximo; Poikela, Tuomas; Polci, Francesco; Poluektov, Anton; Polyakov, Ivan; Polycarpo, Erica; Popov, Alexander; Popov, Dmitry; Popovici, Bogdan; Potterat, Cédric; Price, Eugenia; Price, Joseph David; Prisciandaro, Jessica; Pritchard, Adrian; Prouve, Claire; Pugatch, Valery; Puig Navarro, Albert; Punzi, Giovanni; Qian, Wenbin; Quagliani, Renato; Rachwal, Bartolomiej; Rademacker, Jonas; Rakotomiaramanana, Barinjaka; Rama, Matteo; Rangel, Murilo; Raniuk, Iurii; Rauschmayr, Nathalie; Raven, Gerhard; Redi, Federico; Reichert, Stefanie; Reid, Matthew; dos Reis, Alberto; Ricciardi, Stefania; Richards, Sophie; Rihl, Mariana; Rinnert, Kurt; Rives Molina, Vincente; Robbe, Patrick; Rodrigues, Ana Barbara; Rodrigues, Eduardo; Rodriguez Lopez, Jairo Alexis; Rodriguez Perez, Pablo; Roiser, Stefan; Romanovsky, Vladimir; Romero Vidal, Antonio; Rotondo, Marcello; Rouvinet, Julien; Ruf, Thomas; Ruiz, Hugo; Ruiz Valls, Pablo; Saborido Silva, Juan Jose; Sagidova, Naylya; Sail, Paul; Saitta, Biagio; Salustino Guimaraes, Valdir; Sanchez Mayordomo, Carlos; Sanmartin Sedes, Brais; Santacesaria, Roberta; Santamarina Rios, Cibran; Santovetti, Emanuele; Sarti, Alessio; Satriano, Celestina; Satta, Alessia; Saunders, Daniel Martin; Savrina, Darya; Schiller, Manuel; Schindler, Heinrich; Schlupp, Maximilian; Schmelling, Michael; Schmidt, Burkhard; Schneider, Olivier; Schopper, Andreas; Schune, Marie Helene; Schwemmer, Rainer; Sciascia, Barbara; Sciubba, Adalberto; Semennikov, Alexander; Sepp, Indrek; Serra, Nicola; Serrano, Justine; Sestini, Lorenzo; Seyfert, Paul; Shapkin, Mikhail; Shapoval, Illya; Shcheglov, Yury; Shears, Tara; Shekhtman, Lev; Shevchenko, Vladimir; Shires, Alexander; Silva Coutinho, Rafael; Simi, Gabriele; Sirendi, Marek; Skidmore, Nicola; Skillicorn, Ian; Skwarnicki, Tomasz; Smith, Anthony; Smith, Edmund; Smith, Eluned; Smith, Jackson; Smith, Mark; Snoek, Hella; Sokoloff, Michael; Soler, Paul; Soomro, Fatima; Souza, Daniel; Souza De Paula, Bruno; Spaan, Bernhard; Spradlin, Patrick; Sridharan, Srikanth; Stagni, Federico; Stahl, Marian; Stahl, Sascha; Steinkamp, Olaf; Stenyakin, Oleg; Sterpka, Christopher Francis; Stevenson, Scott; Stoica, Sabin; Stone, Sheldon; Storaci, Barbara; Stracka, Simone; Straticiuc, Mihai; Straumann, Ulrich; Stroili, Roberto; Sun, Liang; Sutcliffe, William; Swientek, Krzysztof; Swientek, Stefan; Syropoulos, Vasileios; Szczekowski, Marek; Szczypka, Paul; Szumlak, Tomasz; T'Jampens, Stephane; Teklishyn, Maksym; Tellarini, Giulia; Teubert, Frederic; Thomas, Christopher; Thomas, Eric; van Tilburg, Jeroen; Tisserand, Vincent; Tobin, Mark; Todd, Jacob; Tolk, Siim; Tomassetti, Luca; Tonelli, Diego; Topp-Joergensen, Stig; Torr, Nicholas; Tournefier, Edwige; Tourneur, Stephane; Trabelsi, Karim; Tran, Minh Tâm; Tresch, Marco; Trisovic, Ana; Tsaregorodtsev, Andrei; Tsopelas, Panagiotis; Tuning, Niels; Ukleja, Artur; Ustyuzhanin, Andrey; Uwer, Ulrich; Vacca, Claudia; Vagnoni, Vincenzo; Valenti, Giovanni; Vallier, Alexis; Vazquez Gomez, Ricardo; Vazquez Regueiro, Pablo; Vázquez Sierra, Carlos; Vecchi, Stefania; Velthuis, Jaap; Veltri, Michele; Veneziano, Giovanni; Vesterinen, Mika; Viana Barbosa, Joao Vitor; Viaud, Benoit; Vieira, Daniel; Vieites Diaz, Maria; Vilasis-Cardona, Xavier; Vollhardt, Achim; Volyanskyy, Dmytro; Voong, David; Vorobyev, Alexey; Vorobyev, Vitaly; Voß, Christian; de Vries, Jacco; Waldi, Roland; Wallace, Charlotte; Wallace, Ronan; Walsh, John; Wandernoth, Sebastian; Wang, Jianchun; Ward, David; Watson, Nigel; Websdale, David; Weiden, Andreas; Whitehead, Mark; Wiedner, Dirk; Wilkinson, Guy; Wilkinson, Michael; Williams, Mark Richard James; Williams, Matthew; Williams, Mike; Wilschut, Hans; Wilson, Fergus; Wimberley, Jack; Wishahi, Julian; Wislicki, Wojciech; Witek, Mariusz; Wormser, Guy; Wotton, Stephen; Wright, Simon; Wyllie, Kenneth; Xie, Yuehong; Xu, Zhirui; Yang, Zhenwei; Yuan, Xuhao; Yushchenko, Oleg; Zangoli, Maria; Zavertyaev, Mikhail; Zhang, Liming; Zhang, Yanxi; Zhelezov, Alexey; Zhokhov, Anatoly; Zhong, Liang

    2015-01-01

    In the Standard Model of particle physics, the strength of the couplings of the $b$ quark to the $u$ and $c$ quarks, $|V_{ub}|$ and $|V_{cb}|$, are governed by the coupling of the quarks to the Higgs boson. Using data from the LHCb experiment at the Large Hadron Collider, the probability for the $\\Lambda^0_b$ baryon to decay into the $p \\mu^- \\overline{\

  14. Plexcitons: The Role of Oscillator Strengths and Spectral Widths in Determining Strong Coupling

    Energy Technology Data Exchange (ETDEWEB)

    Thomas, Reshmi [School; Thomas, Anoop [School; Pullanchery, Saranya [School; Joseph, Linta [School; Somasundaran, Sanoop Mambully [School; Swathi, Rotti Srinivasamurthy [School; Gray, Stephen K. [Center; Thomas, K. George [School

    2018-01-05

    Strong coupling interactions between plasmon and exciton-based excitations have been proposed to be useful in the design of optoelectronic systems. However, the role of various optical parameters dictating the plasmon-exciton (plexciton) interactions is less understood. Herein, we propose an inequality for achieving strong coupling between plasmons and excitons through appropriate variation of their oscillator strengths and spectral widths. These aspects are found to be consistent with experiments on two sets of free-standing plexcitonic systems obtained by (i) linking fluorescein isothiocyanate on Ag nanoparticles of varying sizes through silane coupling and (ii) electrostatic binding of cyanine dyes on polystyrenesulfonate-coated Au nanorods of varying aspect ratios. Being covalently linked on Ag nanoparticles, fluorescein isothiocyanate remains in monomeric state, and its high oscillator strength and narrow spectral width enable us to approach the strong coupling limit. In contrast, in the presence of polystyrenesulfonate, monomeric forms of cyanine dyes exist in equilibrium with their aggregates: Coupling is not observed for monomers and H-aggregates whose optical parameters are unfavorable. The large aggregation number, narrow spectral width, and extremely high oscillator strength of J-aggregates of cyanines permit effective delocalization of excitons along the linear assembly of chromophores, which in turn leads to efficient coupling with the plasmons. Further, the results obtained from experiments and theoretical models are jointly employed to describe the plexcitonic states, estimate the coupling strengths, and rationalize the dispersion curves. The experimental results and the theoretical analysis presented here portray a way forward to the rational design of plexcitonic systems attaining the strong coupling limits.

  15. Globally exponential synchronization in an array of asymmetric coupled neural networks

    International Nuclear Information System (INIS)

    Lu Jianquan; Ho, Daniel W.C.; Liu Ming

    2007-01-01

    In this Letter, we study the globally exponential synchronization in an array of linearly coupled neural networks with delayed coupling. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the real-world network. The difficulty arising from the asymmetry of the coupling matrix has been overcame in this work. Some synchronization criteria are given in terms of strict linear matrix inequalities (LMIs), which can be efficiently solved by using interior point algorithm. Some previous synchronization results are generalized. Numerical simulation is also given to verify our theoretical analysis

  16. Distinct pathways of neural coupling for different basic emotions.

    Science.gov (United States)

    Tettamanti, Marco; Rognoni, Elena; Cafiero, Riccardo; Costa, Tommaso; Galati, Dario; Perani, Daniela

    2012-01-16

    Emotions are complex events recruiting distributed cortical and subcortical cerebral structures, where the functional integration dynamics within the involved neural circuits in relation to the nature of the different emotions are still unknown. Using fMRI, we measured the neural responses elicited by films representing basic emotions (fear, disgust, sadness, happiness). The amygdala and the associative cortex were conjointly activated by all basic emotions. Furthermore, distinct arrays of cortical and subcortical brain regions were additionally activated by each emotion, with the exception of sadness. Such findings informed the definition of three effective connectivity models, testing for the functional integration of visual cortex and amygdala, as regions processing all emotions, with domain-specific regions, namely: i) for fear, the frontoparietal system involved in preparing adaptive motor responses; ii) for disgust, the somatosensory system, reflecting protective responses against contaminating stimuli; iii) for happiness: medial prefrontal and temporoparietal cortices involved in understanding joyful interactions. Consistently with these domain-specific models, the results of the effective connectivity analysis indicate that the amygdala is involved in distinct functional integration effects with cortical networks processing sensorimotor, somatosensory, or cognitive aspects of basic emotions. The resulting effective connectivity networks may serve to regulate motor and cognitive behavior based on the quality of the induced emotional experience. Copyright © 2011. Published by Elsevier Inc.

  17. Noise Controlled Synchronization in Potassium Coupled Neural Models

    DEFF Research Database (Denmark)

    Postnov, D. E.; Ryazanova, L. S.; Zhirin, R. A.

    2007-01-01

    The paper applies biologically plausible models to investigate how noise input to small ensembles of neurons, coupled via the extracellular potassium concentration, can influence their firing patterns. Using the noise intensity and the volume of the extracellular space as control parameters, we......-temporal oscillations in neuronal ensembles....

  18. Prediction of zeolite-cement-sand unconfined compressive strength using polynomial neural network

    Science.gov (United States)

    MolaAbasi, H.; Shooshpasha, I.

    2016-04-01

    The improvement of local soils with cement and zeolite can provide great benefits, including strengthening slopes in slope stability problems, stabilizing problematic soils and preventing soil liquefaction. Recently, dosage methodologies are being developed for improved soils based on a rational criterion as it exists in concrete technology. There are numerous earlier studies showing the possibility of relating Unconfined Compressive Strength (UCS) and Cemented sand (CS) parameters (voids/cement ratio) as a power function fits. Taking into account the fact that the existing equations are incapable of estimating UCS for zeolite cemented sand mixture (ZCS) well, artificial intelligence methods are used for forecasting them. Polynomial-type neural network is applied to estimate the UCS from more simply determined index properties such as zeolite and cement content, porosity as well as curing time. In order to assess the merits of the proposed approach, a total number of 216 unconfined compressive tests have been done. A comparison is carried out between the experimentally measured UCS with the predictions in order to evaluate the performance of the current method. The results demonstrate that generalized polynomial-type neural network has a great ability for prediction of the UCS. At the end sensitivity analysis of the polynomial model is applied to study the influence of input parameters on model output. The sensitivity analysis reveals that cement and zeolite content have significant influence on predicting UCS.

  19. A New Metric for Land-Atmosphere Coupling Strength: Applications on Observations and Modeling

    Science.gov (United States)

    Tang, Q.; Xie, S.; Zhang, Y.; Phillips, T. J.; Santanello, J. A., Jr.; Cook, D. R.; Riihimaki, L.; Gaustad, K.

    2017-12-01

    A new metric is proposed to quantify the land-atmosphere (LA) coupling strength and is elaborated by correlating the surface evaporative fraction and impacting land and atmosphere variables (e.g., soil moisture, vegetation, and radiation). Based upon multiple linear regression, this approach simultaneously considers multiple factors and thus represents complex LA coupling mechanisms better than existing single variable metrics. The standardized regression coefficients quantify the relative contributions from individual drivers in a consistent manner, avoiding the potential inconsistency in relative influence of conventional metrics. Moreover, the unique expendable feature of the new method allows us to verify and explore potentially important coupling mechanisms. Our observation-based application of the new metric shows moderate coupling with large spatial variations at the U.S. Southern Great Plains. The relative importance of soil moisture vs. vegetation varies by location. We also show that LA coupling strength is generally underestimated by single variable methods due to their incompleteness. We also apply this new metric to evaluate the representation of LA coupling in the Accelerated Climate Modeling for Energy (ACME) V1 Contiguous United States (CONUS) regionally refined model (RRM). This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-734201

  20. Global synchronization in arrays of delayed neural networks with constant and delayed coupling

    International Nuclear Information System (INIS)

    Cao Jinde; Li Ping; Wang Weiwei

    2006-01-01

    This Letter investigates the global exponential synchronization in arrays of coupled identical delayed neural networks (DNNs) with constant and delayed coupling. By referring to Lyapunov functional method and Kronecker product technique, some sufficient conditions are derived for global synchronization of such systems. These new synchronization criteria offer some adjustable matrix parameters, which is of important significance in the design and applications of such coupled DNNs, and the results improve and extend the earlier works. Finally, an example is given to illustrate the theoretical results

  1. Magnetic-Field Dependence of Raman Coupling Strength in Ultracold "4"0K Atomic Fermi Gas

    International Nuclear Information System (INIS)

    Huang Liang-Hui; Wang Peng-Jun; Meng Zeng-Ming; Peng Peng; Chen Liang-Chao; Li Dong-Hao; Zhang Jing

    2016-01-01

    We experimentally demonstrate the relation of Raman coupling strength with the external bias magnetic field in degenerate Fermi gas of "4"0K atoms. Two Raman lasers couple two Zeeman energy levels, whose energy splitting depends on the external bias magnetic field. The Raman coupling strength is determined by measuring the Rabi oscillation frequency. The characteristics of the Rabi oscillation is to be damped after several periods due to Fermi atoms in different momentum states oscillating with different Rabi frequencies. The experimental results show that the Raman coupling strength will decrease as the external bias magnetic field increases, which is in good agreement with the theoretical prediction. (paper)

  2. The covariability of North American land-atmosphere coupling strength and rainfall characteristics in reanalyses

    Science.gov (United States)

    Ferguson, C. R.; Roundy, J. K.; Kim, W.

    2016-12-01

    The GEWEX North American Regional Hydroclimate Project (RHP): Water for the Food Baskets of the World initiative is aimed at: improving understanding of key processes—both natural and anthropogenic—that determine water availability, improving understanding of the independent and collective sensitivity of these processes to local and global change, and the integration of knowledge gained into the next model development cycle for the benefit of improved water availability forecasts. Considering that the agricultural sector accounts for three quarters of water withdrawals and suffers the brunt of drought-related financial damages, a rational RHP focal point is subseasonal-to-seasonal forecast skill. Forecasts on this timescale over the Great Plains food basket have shown particular sensitivity to land initial conditions (i.e., soil moisture, snow cover, and vegetative stress) and the realism of modeled land-atmosphere (L-A) coupling. L-A coupling strength denotes the degree to which the model's land scheme (i.e., soil column memory and surface flux partitioning) affect the atmospheric forecast scheme's daytime evolution of the convective boundary layer, including cloud development and precipitation. Prior studies have connected L-A coupling strength to the phase and amplitude of the diurnal precipitation cycle, as well as the evolution of heatwaves and drought. In this study, we apply three metrics of L-A coupling strength: soil moisture memory, the two-legged coupling metric, and the convective triggering potential-humidity index, to the 161-year NOAA-Cooperative Institute for Research in Environmental Sciences Twentieth Century Reanalysis (20CRV2c). Over the full period, we also analyze warm-season rainfall characteristics and subsequently perform statistical trend and change point analyses on both sets of results. We test the stationarity of both coupling and rainfall characteristics as well as the hypothesis that any detected shifts in coupling strength and

  3. Neural Models for the Broadside-Coupled V-Shaped Microshield Coplanar Waveguides

    Science.gov (United States)

    Guney, K.; Yildiz, C.; Kaya, S.; Turkmen, M.

    2006-09-01

    This article presents a new approach based on multilayered perceptron neural networks (MLPNNs) to calculate the odd-and even-mode characteristic impedances and effective permittivities of the broadside-coupled V-shaped microshield coplanar waveguides (BC-VSMCPWs). Six learning algorithms, bayesian regulation (BR), Levenberg-Marquardt (LM), quasi-Newton (QN), scaled conjugate gradient (SCG), resilient propagation (RP), and conjugate gradient of Fletcher-Powell (CGF), are used to train the MLPNNs. The neural results are in very good agreement with the results reported elsewhere. When the performances of neural models are compared with each other, the best and worst results are obtained from the MLPNNs trained by the BR and CGF algorithms, respectively.

  4. Strength Analysis of the Carbon-Fiber Reinforced Polymer Impeller Based on Fluid Solid Coupling Method

    Directory of Open Access Journals (Sweden)

    Jinbao Lin

    2014-01-01

    Full Text Available Carbon-fiber reinforced polymer material impeller is designed for the centrifugal pump to deliver corrosive, toxic, and abrasive media in the chemical and pharmaceutical industries. The pressure-velocity coupling fields in the pump are obtained from the CFD simulation. The stress distribution of the impeller couple caused by the flow water pressure and rotation centrifugal force of the blade is analyzed using one-way fluid-solid coupling method. Results show that the strength of the impeller can meet the requirement of the centrifugal pumps, and the largest stress occurred around the blades root on a pressure side of blade surface. Due to the existence of stress concentration at the blades root, the fatigue limit of the impeller would be reduced greatly. In the further structure optimal design, the blade root should be strengthened.

  5. Dependence of transmittance and group index on the coupling strength between constituents of a metamaterial

    International Nuclear Information System (INIS)

    Thuy Vu, Tran Thanh; Lee, Young Pak; Nguyen, Thanh Tung; Rhee, Joo Yull; Vu, Dinh Lam

    2011-01-01

    Recent studies on the coupling effects between constituent elements of metamaterials have opened up a new gateway to many fascinating electromagnetic properties and functionalities that cannot be explained by the uncoupled point of view. In this work, we numerically investigated, in a THz regime, the coupling between a cut wire and a split-ring resonator, which gives rise to an interesting phenomenon—the so-called electromagnetically induced transparency-like effect. The trade-off between the maximum transmittance of the transmission window and the group index, which depends on the coupling strength between constituent elements, was systematically studied. Furthermore, by characterizing this trade-off by the transmittance-delay product (figure of merit), a criterion for slow-light applications was provided

  6. Measures of Coupling between Neural Populations Based on Granger Causality Principle.

    Science.gov (United States)

    Kaminski, Maciej; Brzezicka, Aneta; Kaminski, Jan; Blinowska, Katarzyna J

    2016-01-01

    This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a "weak node." Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF) supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

  7. Measures of coupling between neural populations based on Granger causality principle

    Directory of Open Access Journals (Sweden)

    Maciej Kaminski

    2016-10-01

    Full Text Available This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a weak node. Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

  8. Dynamics in a Delayed Neural Network Model of Two Neurons with Inertial Coupling

    Directory of Open Access Journals (Sweden)

    Changjin Xu

    2012-01-01

    Full Text Available A delayed neural network model of two neurons with inertial coupling is dealt with in this paper. The stability is investigated and Hopf bifurcation is demonstrated. Applying the normal form theory and the center manifold argument, we derive the explicit formulas for determining the properties of the bifurcating periodic solutions. An illustrative example is given to demonstrate the effectiveness of the obtained results.

  9. A Study of the Solar Wind-Magnetosphere Coupling Using Neural Networks

    Science.gov (United States)

    Wu, Jian-Guo; Lundstedt, Henrik

    1996-12-01

    The interaction between solar wind plasma and interplanetary magnetic field (IMF) and Earth's magnetosphere induces geomagnetic activity. Geomagnetic storms can cause many adverse effects on technical systems in space and on the Earth. It is therefore of great significance to accurately predict geomagnetic activity so as to minimize the amount of disruption to these operational systems and to allow them to work as efficiently as possible. Dynamic neural networks are powerful in modeling the dynamics encoded in time series of data. In this study, we use partially recurrent neural networks to study the solar wind-magnetosphere coupling by predicting geomagnetic storms (as measured by the Dstindex) from solar wind measurements. The solar wind, the IMF and the geomagnetic index Dst data are hourly averaged and read from the National Space Science Data Center's OMNI database. We selected these data from the period 1963 to 1992, which cover 10552h and contain storm time periods 9552h and quiet time periods 1000h. The data are then categorized into three data sets: a training set (6634h), across-validation set (1962h), and a test set (1956h). The validation set is used to determine where the training should be stopped whereas the test set is used for neural networks to get the generalization capability (the out-of-sample performance). Based on the correlation analysis between the Dst index and various solar wind parameters (including various combinations of solar wind parameters), the best coupling functions can be found from the out-of-sample performance of trained neural networks. The coupling functions found are then used to forecast geomagnetic storms one to several hours in advance. The comparisons are made on iterating the single-step prediction several times and on making a non iterated, direct prediction. Thus, we will present the best solar wind-magnetosphere coupling functions and the corresponding prediction results. Interesting Links: Lund Space Weather and AI

  10. Analysis of Drug Design for a Selection of G Protein-Coupled Neuro-Receptors Using Neural Network Techniques

    DEFF Research Database (Denmark)

    Agerskov, Claus; Mortensen, Rasmus M.; Bohr, Henrik G.

    2015-01-01

    A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be corresponding to the G protein-coupled receptors...... computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques....... mu-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drug-design.......925. The performance of 8 category networks (8 output classes for binding strength) obtained a prediction accuracy of above 60 %. After training the networks, tests were done on how well the systems could be used as an aid in designing candidate drug molecules. Specifically, it was shown how a selection of chemical...

  11. The role of frictional strength on plate coupling at the subduction interface

    KAUST Repository

    Tan, Eh

    2012-10-01

    At a subduction zone the amount of friction between the incoming plate and the forearc is an important factor in controlling the dip angle of subduction and the structure of the forearc. In this paper, we investigate the role of the frictional strength of sediments and of the serpentinized peridotite on the evolution of convergent margins. In numerical models, we vary thickness of a serpentinized layer in the mantle wedge (15 to 25km) and the frictional strength of both the sediments and serpentinized mantle (friction angle 1 to 15, or static friction coefficient 0.017 to 0.27) to control the amount of frictional coupling between the plates. With plastic strain weakening in the lithosphere, our numerical models can attain stable subduction geometry over millions of years. We find that the frictional strength of the sediments and serpentinized peridotite exerts the largest control on the dip angle of the subduction interface at seismogenic depths. In the case of low sediment and serpentinite friction, the subduction interface has a shallow dip, while the subduction zone develops an accretionary prism, a broad forearc high, a deep forearc basin, and a shallow trench. In the high friction case, the subduction interface is steep, the trench is deeper, and the accretionary prism, forearc high and basin are all absent. The resultant free-air gravity and topographic signature of these subduction zone models are consistent with observations. We believe that the low-friction model produces a geometry and forearc structure similar to that of accretionary margins. Conversely, models with high friction angles in sediments and serpentinite develop characteristics of an erosional convergent margin. We find that the strength of the subduction interface is critical in controlling the amount of coupling at the seismogenic zone and perhaps ultimately the size of the largest earthquakes at subduction zones. © 2012. American Geophysical Union. All Rights Reserved.

  12. Non-linear auto-regressive models for cross-frequency coupling in neural time series

    Science.gov (United States)

    Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre

    2017-01-01

    We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989

  13. Prediction Of Tensile And Shear Strength Of Friction Surfaced Tool Steel Deposit By Using Artificial Neural Networks

    Science.gov (United States)

    Manzoor Hussain, M.; Pitchi Raju, V.; Kandasamy, J.; Govardhan, D.

    2018-04-01

    Friction surface treatment is well-established solid technology and is used for deposition, abrasion and corrosion protection coatings on rigid materials. This novel process has wide range of industrial applications, particularly in the field of reclamation and repair of damaged and worn engineering components. In this paper, we present the prediction of tensile and shear strength of friction surface treated tool steel using ANN for simulated results of friction surface treatment. This experiment was carried out to obtain tool steel coatings of low carbon steel parts by changing contribution process parameters essentially friction pressure, rotational speed and welding speed. The simulation is performed by a 33-factor design that takes into account the maximum and least limits of the experimental work performed with the 23-factor design. Neural network structures, such as the Feed Forward Neural Network (FFNN), were used to predict tensile and shear strength of tool steel sediments caused by friction.

  14. Probabilistic models for neural populations that naturally capture global coupling and criticality.

    Science.gov (United States)

    Humplik, Jan; Tkačik, Gašper

    2017-09-01

    Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system's state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.

  15. Optimizing the Flexural Strength of Beams Reinforced with Fiber Reinforced Polymer Bars Using Back-Propagation Neural Networks

    Directory of Open Access Journals (Sweden)

    Bahman O. Taha

    2015-06-01

    Full Text Available The reinforced concrete with fiber reinforced polymer (FRP bars (carbon, aramid, basalt and glass is used in places where a high ratio of strength to weight is required and corrosion is not acceptable. Behavior of structural members using (FRP bars is hard to be modeled using traditional methods because of the high non-linearity relationship among factors influencing the strength of structural members. Back-propagation neural network is a very effective method for modeling such complicated relationships. In this paper, back-propagation neural network is used for modeling the flexural behavior of beams reinforced with (FRP bars. 101 samples of beams reinforced with fiber bars were collected from literatures. Five important factors are taken in consideration for predicting the strength of beams. Two models of Multilayer Perceptron (MLP are created, first with single-hidden layer and the second with two-hidden layers. The two-hidden layer model showed better accuracy ratio than the single-hidden layer model. Parametric study has been done for two-hidden layer model only. Equations are derived to be used instead of the model and the importance of input factors is determined. Results showed that the neural network is successful in modeling the behavior of concrete beams reinforced with different types of (FRP bars.

  16. Cross-Coupled Eye Movement Supports Neural Origin of Pattern Strabismus

    Science.gov (United States)

    Ghasia, Fatema F.; Shaikh, Aasef G.; Jacobs, Jonathan; Walker, Mark F.

    2015-01-01

    Purpose. Pattern strabismus describes vertically incomitant horizontal strabismus. Conventional theories emphasized the role of orbital etiologies, such as abnormal fundus torsion and misaligned orbital pulleys as a cause of the pattern strabismus. Experiments in animal models, however, suggested the role of abnormal cross-connections between the neural circuits. We quantitatively assessed eye movements in patients with pattern strabismus with a goal to delineate the role of neural circuits versus orbital etiologies. Methods. We measured saccadic eye movements with high-precision video-oculography in 14 subjects with pattern strabismus, 5 with comitant strabismus, and 15 healthy controls. We assessed change in eye position in the direction orthogonal to that of the desired eye movement (cross-coupled responses). We used fundus photography to quantify the fundus torsion. Results. We found cross-coupling of saccades in all patients with pattern strabismus. The cross-coupled responses were in the same direction in both eyes, but larger in the nonviewing eye. All patients had clinically apparent inferior oblique overaction with abnormal excylotorsion. There was no correlation between the amount of the fundus torsion or the grade of oblique overaction and the severity of cross-coupling. The disconjugacy in the saccade direction and amplitude in pattern strabismics did not have characteristics predicted by clinically apparent inferior oblique overaction. Conclusions. Our results validated primate models of pattern strabismus in human patients. We found no correlation between ocular torsion or oblique overaction and cross-coupling. Therefore, we could not ascribe cross-coupling exclusively to the orbital etiology. Patients with pattern strabismus could have abnormalities in the saccade generators. PMID:26024072

  17. Effects of the network structure and coupling strength on the noise-induced response delay of a neuronal network

    International Nuclear Information System (INIS)

    Ozer, Mahmut; Uzuntarla, Muhammet

    2008-01-01

    The Hodgkin-Huxley (H-H) neuron model driven by stimuli just above threshold shows a noise-induced response delay with respect to time to the first spike for a certain range of noise strengths, an effect called 'noise delayed decay' (NDD). We study the response time of a network of coupled H-H neurons, and investigate how the NDD can be affected by the connection topology of the network and the coupling strength. We show that the NDD effect exists for weak and intermediate coupling strengths, whereas it disappears for strong coupling strength regardless of the connection topology. We also show that although the network structure has very little effect on the NDD for a weak coupling strength, the network structure plays a key role for an intermediate coupling strength by decreasing the NDD effect with the increasing number of random shortcuts, and thus provides an additional operating regime, that is absent in the regular network, in which the neurons may also exploit a spike time code

  18. Star-coupled Hindmarsh-Rose neural network with chemical synapses

    Science.gov (United States)

    Usha, K.; Subha, P. A.

    We analyze the patterns like synchrony, desynchrony, and Drum head mode in a network of Hindmarsh-Rose (HR) neurons interacting via chemical synapse in unidirectional and bidirectional star topology. A two-coupled system has been studied for synchronization by varying the coupling strength and the parameter describing the activation and inactivation of the fast ion channel. The transverse Lyapunov exponent spectrum is plotted to observe the point of transition from desynchrony to synchrony. The synchronized, desynchronized, and drum head mode regions are observed when the neurons are connected in unidirectional and bidirectional coupling configurations. A detailed analysis about the time evolution of membrane potential corresponding to each region is presented. The annihilation of synchronized region and the expansion of drum head mode region in bidirectional coupling is discussed using parameter space. Our work provides finer insight into the existence and stability of Drum head mode and is useful for designing communication networks.

  19. Copula Entropy coupled with Wavelet Neural Network Model for Hydrological Prediction

    Science.gov (United States)

    Wang, Yin; Yue, JiGuang; Liu, ShuGuang; Wang, Li

    2018-02-01

    Artificial Neural network(ANN) has been widely used in hydrological forecasting. in this paper an attempt has been made to find an alternative method for hydrological prediction by combining Copula Entropy(CE) with Wavelet Neural Network(WNN), CE theory permits to calculate mutual information(MI) to select Input variables which avoids the limitations of the traditional linear correlation(LCC) analysis. Wavelet analysis can provide the exact locality of any changes in the dynamical patterns of the sequence Coupled with ANN Strong non-linear fitting ability. WNN model was able to provide a good fit with the hydrological data. finally, the hybrid model(CE+WNN) have been applied to daily water level of Taihu Lake Basin, and compared with CE ANN, LCC WNN and LCC ANN. Results showed that the hybrid model produced better results in estimating the hydrograph properties than the latter models.

  20. Depotentiation from potentiated synaptic strength in a tristable system of coupled phosphatase and kinase

    Directory of Open Access Journals (Sweden)

    Mengjiao Chen

    2016-10-01

    Full Text Available Long-term potentiation (LTP of synaptic strength is strongly implicated in learning and memory. On the other hand, depotentiation, the reversal of synaptic strength from potentiated LTP state to the pre-LTP level, is required in extinction of the obsolete memory. A generic tristable system, which couples the phosphatase and kinase switches, exclusively explains how moderate and high elevation of intracellular calcium concentration triggers long-term depression (LTD and LTP, respectively. The present study, introducing calcium influx and calcium release from internal store into the tristable system, further show that significant elevation of cytoplasmic calcium concentration switches activation of both kinase and phosphatase to their basal states, thereby depotentiate the synaptic strength. A phase-plane analysis of the combined model was employed to explain the previously reported depotentiation in experiments and predict a threshold-like effect with calcium concentration. The results not only reveal a mechanism of NMDAR- and mGluR-dependent depotentiation, but also predict further experiments about the role of internal calcium store in induction of depotentiation and extinction of established memories.

  1. Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter.

    Science.gov (United States)

    Rigatos, Gerasimos

    2014-12-01

    A synchronizing control scheme for coupled neural oscillators of the FitzHugh-Nagumo type is proposed. Using differential flatness theory the dynamical model of two coupled neural oscillators is transformed into an equivalent model in the linear canonical (Brunovsky) form. A similar linearized description is succeeded using differential geometry methods and the computation of Lie derivatives. For such a model it becomes possible to design a state feedback controller that assures the synchronization of the membrane's voltage variations for the two neurons. To compensate for disturbances that affect the neurons' model as well as for parametric uncertainties and variations a disturbance observer is designed based on Kalman Filtering. This consists of implementation of the standard Kalman Filter recursion on the linearized equivalent model of the coupled neurons and computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. After estimating the disturbance terms in the neurons' model their compensation becomes possible. The performance of the synchronization control loop is tested through simulation experiments.

  2. Equivariant bifurcation in a coupled complex-valued neural network rings

    International Nuclear Information System (INIS)

    Zhang, Chunrui; Sui, Zhenzhang; Li, Hongpeng

    2017-01-01

    Highlights: • Complex value Hopfield-type network with Z4 × Z2 symmetry is discussed. • The spatio-temporal patterns of bifurcating periodic oscillations are obtained. • The oscillations can be in phase or anti-phase depending on the parameters and delay. - Abstract: Network with interacting loops and time delays are common in physiological systems. In the past few years, the dynamic behaviors of coupled interacting loops neural networks have been widely studied due to their extensive applications in classification of pattern recognition, signal processing, image processing, engineering optimization and animal locomotion, and other areas, see the references therein. In a large amount of applications, complex signals often occur and the complex-valued recurrent neural networks are preferable. In this paper, we study a complex value Hopfield-type network that consists of a pair of one-way rings each with four neurons and two-way coupling between each ring. We discuss the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay differential equations combined with representation theory of Lie groups. The existence of multiple branches of bifurcating periodic solution is obtained. We also found that the spatio-temporal patterns of bifurcating periodic oscillations alternate according to the change of the propagation time delay in the coupling, i.e., different ranges of delays correspond to different patterns of neural network oscillators. The oscillations of corresponding neurons in the two loops can be in phase or anti-phase depending on the parameters and delay. Some numerical simulations support our analysis results.

  3. Inductive coupling between overhead power lines and nearby metallic pipelines. A neural network approach

    Directory of Open Access Journals (Sweden)

    Levente Czumbil

    2015-12-01

    Full Text Available The current paper presents an artificial intelligence based technique applied in the investigation of electromagnetic interference problems between high voltage power lines (HVPL and nearby underground metallic pipelines (MP. An artificial neural network (NN solution has been implemented by the authors to evaluate the inductive coupling between HVPL and MP for different constructive geometries of an electromagnetic interference problem considering a multi-layer soil structure. Obtained results are compared to solutions provided by a finite element method (FEM based analysis and considered as reference. The advantage of the proposed method yields in a simplified computation model compared to FEM, and implicitly a lower computational time.

  4. Dynamics of delay-coupled FitzHugh-Nagumo neural rings

    Science.gov (United States)

    Mao, Xiaochen; Sun, Jianqiao; Li, Shaofan

    2018-01-01

    This paper studies the dynamical behaviors of a pair of FitzHugh-Nagumo neural networks with bidirectional delayed couplings. It presents a detailed analysis of delay-independent and delay-dependent stabilities and the existence of bifurcated oscillations. Illustrative examples are performed to validate the analytical results and to discover interesting phenomena. It is shown that the network exhibits a variety of complicated activities, such as multiple stability switches, the coexistence of periodic and quasi-periodic oscillations, the coexistence of periodic and chaotic orbits, and the coexisting chaotic attractors.

  5. Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images.

    Science.gov (United States)

    Johnson, J L

    1994-09-10

    The linking-field neural network model of Eckhorn et al. [Neural Comput. 2, 293-307 (1990)] was introduced to explain the experimentally observed synchronous activity among neural assemblies in the cat cortex induced by feature-dependent visual activity. The model produces synchronous bursts of pulses from neurons with similar activity, effectively grouping them by phase and pulse frequency. It gives a basic new function: grouping by similarity. The synchronous bursts are obtained in the limit of strong linking strengths. The linking-field model in the limit of moderate-to-weak linking characterized by few if any multiple bursts is investigated. In this limit dynamic, locally periodic traveling waves exist whose time signal encodes the geometrical structure of a two-dimensional input image. The signal can be made insensitive to translation, scale, rotation, distortion, and intensity. The waves transmit information beyond the physical interconnect distance. The model is implemented in an optical hybrid demonstration system. Results of the simulations and the optical system are presented.

  6. A PERFORMANCE COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL METHODS IN FORECASTING FINANCIAL STRENGTH RATING IN TURKISH BANKING SECTOR

    Directory of Open Access Journals (Sweden)

    MELEK ACAR BOYACIOĞLU

    2013-06-01

    Full Text Available Financial strength rating indicates the fundamental financial strength of a bank. The aim of financial strength rating is to measure a bank’s fundamental financial strength excluding the external factors. External factors can stem from the working environment or can be linked with the outside protective support mechanisms. With the evaluation, the rating of a bank free from outside supportive factors is being sought. Also the financial fundamental, franchise value, the variety of assets and working environment of a bank are being evaluated in this context. In this study, a model has been developed in order to predict the financial strength rating of Turkish banks. The methodology of this study is as follows: Selecting variables to be used in the model, creating a data set, choosing the techniques to be used and the evaluation of classification success of the techniques. It is concluded that the artificial neural network system shows a better performance in terms of classification of financial strength rating in comparison to multivariate statistical methods in the raining set. On the other hand, there is no meaningful difference could be found in the validation set in which the prediction performances of the employed techniques are tested.

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

    Science.gov (United States)

    Chandrasekar, A; Rakkiyappan, R; Cao, Jinde

    2015-10-01

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

  8. Unified synchronization criteria in an array of coupled neural networks with hybrid impulses.

    Science.gov (United States)

    Wang, Nan; Li, Xuechen; Lu, Jianquan; Alsaadi, Fuad E

    2018-05-01

    This paper investigates the problem of globally exponential synchronization of coupled neural networks with hybrid impulses. Two new concepts on average impulsive interval and average impulsive gain are proposed to deal with the difficulties coming from hybrid impulses. By employing the Lyapunov method combined with some mathematical analysis, some efficient unified criteria are obtained to guarantee the globally exponential synchronization of impulsive networks. Our method and criteria are proved to be effective for impulsively coupled neural networks simultaneously with synchronizing impulses and desynchronizing impulses, and we do not need to discuss these two kinds of impulses separately. Moreover, by using our average impulsive interval method, we can obtain an interesting and valuable result for the case of average impulsive interval T a =∞. For some sparse impulsive sequences with T a =∞, the impulses can happen for infinite number of times, but they do not have essential influence on the synchronization property of networks. Finally, numerical examples including scale-free networks are exploited to illustrate our theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. Super-pixel extraction based on multi-channel pulse coupled neural network

    Science.gov (United States)

    Xu, GuangZhu; Hu, Song; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun

    2018-04-01

    Super-pixel extraction techniques group pixels to form over-segmented image blocks according to the similarity among pixels. Compared with the traditional pixel-based methods, the image descripting method based on super-pixel has advantages of less calculation, being easy to perceive, and has been widely used in image processing and computer vision applications. Pulse coupled neural network (PCNN) is a biologically inspired model, which stems from the phenomenon of synchronous pulse release in the visual cortex of cats. Each PCNN neuron can correspond to a pixel of an input image, and the dynamic firing pattern of each neuron contains both the pixel feature information and its context spatial structural information. In this paper, a new color super-pixel extraction algorithm based on multi-channel pulse coupled neural network (MPCNN) was proposed. The algorithm adopted the block dividing idea of SLIC algorithm, and the image was divided into blocks with same size first. Then, for each image block, the adjacent pixels of each seed with similar color were classified as a group, named a super-pixel. At last, post-processing was adopted for those pixels or pixel blocks which had not been grouped. Experiments show that the proposed method can adjust the number of superpixel and segmentation precision by setting parameters, and has good potential for super-pixel extraction.

  10. Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples

    International Nuclear Information System (INIS)

    Soezen, Adnan; Arcaklioglu, Erol; Oezalp, Mehmet

    2005-01-01

    This paper presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion and use in the absorption thermal systems (ATSs). ANNs are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a 'black box' model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. In this study, in input layer, there are temperatures in the range of 298-498 K, pressures (0.1-40 MPa) and concentrations of 2%, 7%, 12% of the couples; specific volume is in output layer. The back-propagation learning algorithm with three different variants, namely scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM), and logistic sigmoid transfer function were used in the network so that the best approach can find. The most suitable algorithm and neuron number in the hidden layer are found as SCG with 8 neurons. For this number level, after the training, it is found that maximum error is less than 3%, average error is about 1% and R 2 value are 99.999%. As seen from the results obtained the thermodynamic equations for each pair by using the weights of network have been obviously predicted within acceptable errors. This paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations

  11. Effect of gender on strength gains after isometric exercise coupled with electromyographic biofeedback in knee osteoarthritis: a preliminary study.

    Science.gov (United States)

    Anwer, S; Equebal, A; Nezamuddin, M; Kumar, R; Lenka, P K

    2013-09-01

    The objective of this trial was to evaluate the effect of gender on strength gains after five week training programme that consisted of isometric exercise coupled with electromyographic biofeedback to the quadriceps muscle. Forty-three (20 men and 23 women) patients with knee osteoarthritis (OA), were placed into two groups based on their gender. Both groups performed isometric exercise coupled with electromyographic biofeedback for five days a week for five weeks. Both groups reported gains in muscle strength after five week training. However, the difference was found to be statistically insignificant between the two groups (P=0.224). The results suggest that gender did not affect gains in muscle strength by isometric exercise coupled with electromyographic biofeedback in patients with knee OA. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

  12. [Effect of amount of silane coupling agent on flexural strength of dental composite resins reinforced with aluminium borate whisker].

    Science.gov (United States)

    Zhu, Ming-yi; Zhang, Xiu-yin

    2015-06-01

    To evaluate the effect of amount of silane coupling agent on flexural strength of dental composite resins reinforced with aluminium borate whisker (ABW). ABW was surface-treated with 0%, 1%, 2%, 3% and 4% silan coupling agent (γ-MPS), and mixed with resin matrix to synthesize 5 groups of composite resins. After heat-cured at 120 degrees centigrade for 1 h, specimens were tested in three-point flexure to measure strength according to ISO-4049. One specimen was selected randomly from each group and observed under scanning electron microscope (SEM). The data was analyzed with SAS 9.2 software package. The flexural strength (117.93±11.9 Mpa) of the group treated with 2% silane coupling agent was the highest, and significantly different from that of the other 4 groups (α=0.01). The amount of silane coupling agent has impact on the flexural strength of dental composite resins reinforced with whiskers; The flexual strength will be reduced whenever the amount is higher or lower than the threshold. Supported by Research Fund of Science and Technology Committee of Shanghai Municipality (08DZ2271100).

  13. A new approach using artificial neural networks for determination of the thermodynamic properties of fluid couples

    International Nuclear Information System (INIS)

    Sencan, Arzu; Kalogirou, Soteris A.

    2005-01-01

    This paper presents a new approach using artificial neural networks (ANN) to determine the thermodynamic properties of two alternative refrigerant/absorbent couples (LiCl-H 2 O and LiBr + LiNO 3 + LiI + LiCl-H 2 O). These pairs can be used in absorption heat pump systems, and their main advantage is that they do not cause ozone depletion. In order to train the network, limited experimental measurements were used as training and test data. Two feedforward ANNs were trained, one for each pair, using the Levenberg-Marquardt algorithm. The training and validation were performed with good accuracy. The correlation coefficient obtained when unknown data were applied to the networks was 0.9997 and 0.9987 for the two pairs, respectively, which is very satisfactory. The present methodology proved to be much better than linear multiple regression analysis. Using the weights obtained from the trained network, a new formulation is presented for determination of the vapor pressures of the two refrigerant/absorbent couples. The use of this new formulation, which can be employed with any programming language or spreadsheet program for estimation of the vapor pressures of fluid couples, as described in this paper, may make the use of dedicated ANN software unnecessary

  14. Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations

    Directory of Open Access Journals (Sweden)

    Mahmood Akbari

    2017-12-01

    Full Text Available In recent years, numerous experimental tests were done on the concrete beams reinforced with the fiber-reinforced polymer (FRP. In this way, some equations were proposed to estimate the shear strength of the beams reinforced with FRP. The aim of this study is to explore the feasibility of using a feed-forward artificial neural network (ANN model to predict the ultimate shear strength of the beams strengthened with FRP composites. For this purpose, a database consists of 304 reinforced FRP concrete beams have been collected from the available articles on the analysis of shear behavior of these beams. The inputs to the ANN model consists of the 11 variables including the geometric dimensions of the section, steel reinforcement amount, FRP amount and the properties of the concrete, steel reinforcement and FRP materials while the output variable is the shear strength of the FRP beam. To assess the performance of the ANN model for estimating the shear strength of the reinforced beams, the outputs of the ANN are compared to those of equations of the Iranian code (Publication No. 345 and the American code (ACI 440. The comparisons between the outputs of Iran and American regulations with those of the proposed model indicates that the predictive power of this model is much better than the experimental codes. Specifically, for under study data, mean absolute relative error (MARE criteria is 13%, 34% and 39% for the ANN model, the American and the Iranian codes, respectively.

  15. 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.

  16. Neural representations of kinematic laws of motion: evidence for action-perception coupling.

    Science.gov (United States)

    Dayan, Eran; Casile, Antonino; Levit-Binnun, Nava; Giese, Martin A; Hendler, Talma; Flash, Tamar

    2007-12-18

    Behavioral and modeling studies have established that curved and drawing human hand movements obey the 2/3 power law, which dictates a strong coupling between movement curvature and velocity. Human motion perception seems to reflect this constraint. The functional MRI study reported here demonstrates that the brain's response to this law of motion is much stronger and more widespread than to other types of motion. Compliance with this law is reflected in the activation of a large network of brain areas subserving motor production, visual motion processing, and action observation functions. Hence, these results strongly support the notion of similar neural coding for motion perception and production. These findings suggest that cortical motion representations are optimally tuned to the kinematic and geometrical invariants characterizing biological actions.

  17. From neurovascular coupling to neurovascular cascade: a study on neural, autonomic and vascular transients in attention

    International Nuclear Information System (INIS)

    Bari, V; Calcagnile, P; Molteni, E; Cerutti, S; Bianchi, A M; Re, R; Contini, D; Caffini, M; Torricelli, A; Cubeddu, R; Spinelli, L

    2012-01-01

    Mental processes bring about neural, vascular and autonomic changes in the brain cortex. Due to the different nature of these modifications, their onsets show no synchrony and time dynamics is often strongly dissimilar. After acquiring data from a group of 16 subjects, we estimated temporal correlation between task and signals in order to assess possible influences induced by an attentive task on electroencephalographic (EEG), heart rate variability (HRV), oxy- and deoxy-haemoglobin concentration signals. We also investigated correlations and time delays between couples of different biological signals. This allowed for the isolation of a subgroup of subjects showing similar tracks. Cardiac frequency and deoxy-haemoglobin signals displayed a strong positive correlation with the task design, while EEG alpha rhythm and oxygenation showed a negative correlation. Neural electrical response was nearly instantaneous with respect to the task progression, and autonomic response showed a mean delay of about 15 s and a slower hemodynamic response (mean delay above 20 s) was finally induced. Globally, the task elicited a cascade of responses, in which delays can be quantified. (paper)

  18. Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networks

    International Nuclear Information System (INIS)

    Soezen, Adnan; Arcaklioglu, Erol; Oezalp, Mehmet

    2004-01-01

    Thermodynamic analysis of absorption thermal systems is too complex because the analytic functions calculating the thermodynamic properties of fluid couples involve the solution of complex differential equations and simulation programs. This study aims at easing this complex situation and consists of three cases: (i) A special ejector, located at the absorber inlet, instead of the common location at the condenser inlet, to increase overall performance was used in the ejector absorption heat pump (EAHP). The ejector has two functions: Firstly, it aids the pressure recovery from the evaporator and then upgrades the mixing process and pre-absorption by the weak solution of the methanol coming from the evaporator. (ii) Use of artificial neural networks (ANNs) has been proposed to determine the properties of the liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiCl), which does not cause ozone depletion. (iii) A comparative performance study of the EAHP was performed between the analytic functions and the values predicted by the ANN for the properties of the couple. The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. In the input layer, there are temperature, pressure and concentration of the couples. Specific volume is in the output layer. After training, it was found that the maximum error was less than 3%, the average error was less than 1.2% and the R 2 values were about 0.9999. Additionally, in comparison of the analysis results between analytic equations obtained by using experimental data and by means of the ANN, the deviations of the refrigeration effectiveness of the system for cooling (COP r ), exergetic coefficient of performance of the system for cooling (ECOP r ) and circulation ratio (F) for all working temperatures were

  19. Calculation of marine propeller static strength based on coupled BEM/FEM

    Directory of Open Access Journals (Sweden)

    YE Liyu

    2017-10-01

    Full Text Available [Objectives] The reliability of propeller stress has a great influence on the safe navigation of a ship. To predict propeller stress quickly and accurately,[Methods] a new numerical prediction model is developed by coupling the Boundary Element Method(BEMwith the Finite Element Method (FEM. The low order BEM is used to calculate the hydrodynamic load on the blades, and the Prandtl-Schlichting plate friction resistance formula is used to calculate the viscous load. Next, the calculated hydrodynamic load and viscous correction load are transmitted to the calculation of the Finite Element as surface loads. Considering the particularity of propeller geometry, a continuous contact detection algorithm is developed; an automatic method for generating the finite element mesh is developed for the propeller blade; a code based on the FEM is compiled for predicting blade stress and deformation; the DTRC 4119 propeller model is applied to validate the reliability of the method; and mesh independence is confirmed by comparing the calculated results with different sizes and types of mesh.[Results] The results show that the calculated blade stress and displacement distribution are reliable. This method avoids the process of artificial modeling and finite element mesh generation, and has the advantages of simple program implementation and high calculation efficiency.[Conclusions] The code can be embedded into the code of theoretical and optimized propeller designs, thereby helping to ensure the strength of designed propellers and improve the efficiency of propeller design.

  20. Dynamics of Entanglement in Jaynes–Cummings Nodes with Nonidentical Qubit-Field Coupling Strengths

    Directory of Open Access Journals (Sweden)

    Li-Tuo Shen

    2017-07-01

    Full Text Available How to analytically deal with the general entanglement dynamics of separate Jaynes–Cummings nodes with continuous-variable fields is still an open question, and few analytical approaches can be used to solve their general entanglement dynamics. Entanglement dynamics between two separate Jaynes–Cummings nodes are examined in this article. Both vacuum state and coherent state in the initial fields are considered through the numerical and analytical methods. The gap between two nonidentical qubit-field coupling strengths shifts the revival period and changes the revival amplitude of two-qubit entanglement. For vacuum-state fields, the maximal entanglement is fully revived after a gap-dependence period, within which the entanglement nonsmoothly decreases to zero and partly recovers without exhibiting sudden death phenomenon. For strong coherent-state fields, the two-qubit entanglement decays exponentially as the evolution time increases, exhibiting sudden death phenomenon, and the increasing gap accelerates the revival period and amplitude decay of the entanglement, where the numerical and analytical results have an excellent coincidence.

  1. Maximizing coupling strength of magnetically anchored surgical instruments: how thick can we go?

    Science.gov (United States)

    Best, Sara L; Bergs, Richard; Gedeon, Makram; Paramo, Juan; Fernandez, Raul; Cadeddu, Jeffrey A; Scott, Daniel J

    2011-01-01

    The Magnetic Anchoring and Guidance System (MAGS) includes an external magnet that controls intra-abdominal surgical instruments via magnetic attraction forces. We have performed NOTES (Natural Orifice Transluminal Endoscopic Surgery) and LESS (Laparoendoscopic Single Site) procedures using MAGS instruments in porcine models with up to 2.5-cm-thick abdominal walls, but this distance may not be sufficient in some humans. The purpose of this study was to determine the maximal abdominal wall thickness for which the current MAGS platform is suitable. Successive iterations of prototype instruments were developed; those evaluated in this study include external (134-583 g, 38-61 mm diameter) and internal (8-39 g, 10-22 mm diameter) components using various grades, diameters, thicknesses, and stacking/shielding/focusing configurations of permanent Neodymium-iron-boron (NdFeB) magnets. Nine configurations were tested for coupling strength across distances of 0.1-10 cm. The force-distance tests across an air medium were conducted at 0.5-mm increments using a robotic arm fitted with a force sensor. A minimum theoretical instrument drop-off (decoupling) threshold was defined as the separation distance at which force decreased below the weight of the heaviest internal component (39 g). Magnetic attraction forces decreased exponentially over distance. For the nine configurations tested, the average forces were 3,334 ± 1,239 gf at 0.1 cm, 158 ± 98 gf at 2.5 cm, and 8.7 ± 12 gf at 5 cm; the drop-off threshold was 3.64 ± 0.8 cm. The larger stacking configurations and magnets yielded up to a 592% increase in attraction force at 2.5 cm and extended the drop-off threshold distance by up to 107% over single-stack anchors. For the strongest configuration, coupling force ranged from 5,337 gf at 0.1 cm to 0 gf at 6.95 cm and yielded a drop-off threshold distance of 4.78 cm. This study suggests that the strongest configuration of currently available MAGS instruments is suitable for

  2. Fixed-time stability of dynamical systems and fixed-time synchronization of coupled discontinuous neural networks.

    Science.gov (United States)

    Hu, Cheng; Yu, Juan; Chen, Zhanheng; Jiang, Haijun; Huang, Tingwen

    2017-05-01

    In this paper, the fixed-time stability of dynamical systems and the fixed-time synchronization of coupled discontinuous neural networks are investigated under the framework of Filippov solution. Firstly, by means of reduction to absurdity, a theorem of fixed-time stability is established and a high-precision estimation of the settling-time is given. It is shown by theoretic proof that the estimation bound of the settling time given in this paper is less conservative and more accurate compared with the classical results. Besides, as an important application, the fixed-time synchronization of coupled neural networks with discontinuous activation functions is proposed. By designing a discontinuous control law and using the theory of differential inclusions, some new criteria are derived to ensure the fixed-time synchronization of the addressed coupled networks. Finally, two numerical examples are provided to show the effectiveness and validity of the theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Coupling a Neural Network with Atmospheric Flow Simulations to Locate and Quantify CH4 Emissions at Well Pads

    Science.gov (United States)

    Travis, B. J.; Sauer, J.; Dubey, M. K.

    2017-12-01

    Methane (CH4) leaks from oil and gas production fields are a potentially significant source of atmospheric methane. US DOE's ARPA-E office is supporting research to locate methane emissions at 10 m size well pads to within 1 m. A team led by Aeris Technologies, and that includes LANL, Planetary Science Institute and Rice University has developed an autonomous leak detection system (LDS) employing a compact laser absorption methane sensor, a sonic anemometer and multiport sampling. The LDS system analyzes monitoring data using a convolutional neural network (cNN) to locate and quantify CH4 emissions. The cNN was trained using three sources: (1) ultra-high-resolution simulations of methane transport provided by LANL's coupled atmospheric transport model HIGRAD, for numerous controlled methane release scenarios and methane sampling configurations under variable atmospheric conditions, (2) Field tests at the METEC site in Ft. Collins, CO., and (3) Field data from other sites where point-source surface methane releases were monitored downwind. A cNN learning algorithm is well suited to problems in which the training and observed data are noisy, or correspond to complex sensor data as is typical of meteorological and sensor data over a well pad. Recent studies with our cNN emphasize the importance of tracking wind speeds and directions at fine resolution ( 1 second), and accounting for variations in background CH4 levels. A few cases illustrate the importance of sufficiently long monitoring; short monitoring may not provide enough information to determine accurately a leak location or strength, mainly because of short-term unfavorable wind directions and choice of sampling configuration. Length of multiport duty cycle sampling and sample line flush time as well as number and placement of monitoring sensors can significantly impact ability to locate and quantify leaks. Source location error at less than 10% requires about 30 or more training cases.

  4. Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks.

    Science.gov (United States)

    Park, Jihoon; Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

    2017-01-01

    Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the "information networks" different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.

  5. Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks.

    Directory of Open Access Journals (Sweden)

    Jihoon Park

    Full Text Available Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random with a musculoskeletal model (i.e., a snake-like robot as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the "information networks" different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1 the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2 two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.

  6. Suppression of seizures based on the multi-coupled neural mass model.

    Science.gov (United States)

    Cao, Yuzhen; Ren, Kaili; Su, Fei; Deng, Bin; Wei, Xile; Wang, Jiang

    2015-10-01

    Epilepsy is one of the most common serious neurological disorders, which affects approximately 1% of population in the world. In order to effectively control the seizures, we propose a novel control methodology, which combines the feedback linearization control (FLC) with the underlying mechanism of epilepsy, to achieve the suppression of seizures. The three coupled neural mass model is constructed to study the property of the electroencephalographs (EEGs). Meanwhile, with the model we research on the propagation of epileptiform waves and the synchronization of populations, which are taken as the foundation of our control method. Results show that the proposed approach not only yields excellent performances in clamping the pathological spiking patterns to the reference signals derived under the normal state but also achieves the normalization of the pathological parameter, where the parameters are estimated from EEGs with Unscented Kalman Filter. The specific contribution of this paper is to treat the epilepsy from its pathogenesis with the FLC, which provides critical theoretical basis for the clinical treatment of neurological disorders.

  7. Optimization of a hardware implementation for pulse coupled neural networks for image applications

    Science.gov (United States)

    Gimeno Sarciada, Jesús; Lamela Rivera, Horacio; Warde, Cardinal

    2010-04-01

    Pulse Coupled Neural Networks are a very useful tool for image processing and visual applications, since it has the advantages of being invariant to image changes as rotation, scale, or certain distortion. Among other characteristics, the PCNN changes a given image input into a temporal representation which can be easily later analyzed for pattern recognition. The structure of a PCNN though, makes it necessary to determine all of its parameters very carefully in order to function optimally, so that the responses to the kind of inputs it will be subjected are clearly discriminated allowing for an easy and fast post-processing yielding useful results. This tweaking of the system is a taxing process. In this paper we analyze and compare two methods for modeling PCNNs. A purely mathematical model is programmed and a similar circuital model is also designed. Both are then used to determine the optimal values of the several parameters of a PCNN: gain, threshold, time constants for feed-in and threshold and linking leading to an optimal design for image recognition. The results are compared for usefulness, accuracy and speed, as well as the performance and time requirements for fast and easy design, thus providing a tool for future ease of management of a PCNN for different tasks.

  8. Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover

    Directory of Open Access Journals (Sweden)

    Yueru Ma

    2014-01-01

    Full Text Available The tendency of labor turnover in the Chinese enterprise shows the characteristics of seasonal fluctuations and irregular distribution of various factors, especially the Chinese traditional social and cultural characteristics. In this paper, we present a coupled model for the tendency prediction of labor turnover. In the model, a time series of tendency prediction of labor turnover was expressed as trend item and its random item. Trend item of tendency prediction of labor turnover is predicted using Grey theory. Random item of trend item is calculated by artificial neural network model (ANN. A case study is presented by the data of 24 months in a Chinese matured enterprise. The model uses the advantages of “accumulative generation” of a Grey prediction method, which weakens the original sequence of random disturbance factors and increases the regularity of data. It also takes full advantage of the ANN model approximation performance, which has a capacity to solve economic problems rapidly, describes the nonlinear relationship easily, and avoids the defects of Grey theory.

  9. Robustly Fitting and Forecasting Dynamical Data With Electromagnetically Coupled Artificial Neural Network: A Data Compression Method.

    Science.gov (United States)

    Wang, Ziyin; Liu, Mandan; Cheng, Yicheng; Wang, Rubin

    2017-06-01

    In this paper, a dynamical recurrent artificial neural network (ANN) is proposed and studied. Inspired from a recent research in neuroscience, we introduced nonsynaptic coupling to form a dynamical component of the network. We mathematically proved that, with adequate neurons provided, this dynamical ANN model is capable of approximating any continuous dynamic system with an arbitrarily small error in a limited time interval. Its extreme concise Jacobian matrix makes the local stability easy to control. We designed this ANN for fitting and forecasting dynamic data and obtained satisfied results in simulation. The fitting performance is also compared with those of both the classic dynamic ANN and the state-of-the-art models. Sufficient trials and the statistical results indicated that our model is superior to those have been compared. Moreover, we proposed a robust approximation problem, which asking the ANN to approximate a cluster of input-output data pairs in large ranges and to forecast the output of the system under previously unseen input. Our model and learning scheme proposed in this paper have successfully solved this problem, and through this, the approximation becomes much more robust and adaptive to noise, perturbation, and low-order harmonic wave. This approach is actually an efficient method for compressing massive external data of a dynamic system into the weight of the ANN.

  10. Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system

    Energy Technology Data Exchange (ETDEWEB)

    Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

    2008-07-01

    This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation of air-conditioning systems. So obtained models will help the system designer to realize this precondition. The most suitable algorithm and neuron number in the hidden layer are found as Levenberg-Marquardt (LM) with seven neurons for ANN model whereas the most suitable membership function and number of membership functions are found as Gauss and two, respectively, for ANFIS model. The root-mean squared (RMS) value and the coefficient of variation in percent (cov) value are 0.0047 and 0.1363, respectively. The absolute fraction of variance (R{sup 2}) is 0.9999 which can be considered as very promising. This paper shows the appropriateness of ANFIS for the quantitative modeling of GCHP systems. (author)

  11. An improved pulse coupled neural network with spectral residual for infrared pedestrian segmentation

    Science.gov (United States)

    He, Fuliang; Guo, Yongcai; Gao, Chao

    2017-12-01

    Pulse coupled neural network (PCNN) has become a significant tool for the infrared pedestrian segmentation, and a variety of relevant methods have been developed at present. However, these existing models commonly have several problems of the poor adaptability of infrared noise, the inaccuracy of segmentation results, and the fairly complex determination of parameters in current methods. This paper presents an improved PCNN model that integrates the simplified framework and spectral residual to alleviate the above problem. In this model, firstly, the weight matrix of the feeding input field is designed by the anisotropic Gaussian kernels (ANGKs), in order to suppress the infrared noise effectively. Secondly, the normalized spectral residual saliency is introduced as linking coefficient to enhance the edges and structural characteristics of segmented pedestrians remarkably. Finally, the improved dynamic threshold based on the average gray values of the iterative segmentation is employed to simplify the original PCNN model. Experiments on the IEEE OTCBVS benchmark and the infrared pedestrian image database built by our laboratory, demonstrate that the superiority of both subjective visual effects and objective quantitative evaluations in information differences and segmentation errors in our model, compared with other classic segmentation methods.

  12. Bonding Strength Effects in Hydro-Mechanical Coupling Transport in Granular Porous Media by Pore-Scale Modeling

    Directory of Open Access Journals (Sweden)

    Zhiqiang Chen

    2016-03-01

    Full Text Available The hydro-mechanical coupling transport process of sand production is numerically investigated with special attention paid to the bonding effect between sand grains. By coupling the lattice Boltzmann method (LBM and the discrete element method (DEM, we are able to capture particles movements and fluid flows simultaneously. In order to account for the bonding effects on sand production, a contact bond model is introduced into the LBM-DEM framework. Our simulations first examine the experimental observation of “initial sand production is evoked by localized failure” and then show that the bonding or cement plays an important role in sand production. Lower bonding strength will lead to more sand production than higher bonding strength. It is also found that the influence of flow rate on sand production depends on the bonding strength in cemented granular media, and for low bonding strength sample, the higher the flow rate is, the more severe the erosion found in localized failure zone becomes.

  13. High-Intensity Progressive Resistance Training Increases Strength With No Change in Cardiovascular Function and Autonomic Neural Regulation in Older Adults.

    Science.gov (United States)

    Kanegusuku, Hélcio; Queiroz, Andréia C; Silva, Valdo J; de Mello, Marco T; Ugrinowitsch, Carlos; Forjaz, Cláudia L

    2015-07-01

    The effects of high-intensity progressive resistance training (HIPRT) on cardiovascular function and autonomic neural regulation in older adults are unclear. To investigate this issue, 25 older adults were randomly divided into two groups: control (CON, N = 13, 63 ± 4 years; no training) and HIPRT (N = 12, 64 ± 4 years; 2 sessions/week, 7 exercises, 2–4 sets, 10–4 RM). Before and after four months, maximal strength, quadriceps cross-sectional area (QCSA), clinic and ambulatory blood pressures (BP), systemic hemodynamics, and cardiovascular autonomic modulation were measured. Maximal strength and QCSA increased in the HIPRT group and did not change in the CON group. Clinic and ambulatory BP, cardiac output, systemic vascular resistance, stroke volume, heart rate, and cardiac sympathovagal balance did not change in the HIPRT group or the CON group. In conclusion, HIPRT was effective at increasing muscle mass and strength without promoting changes in cardiovascular function or autonomic neural regulation.

  14. Dual Channel Pulse Coupled Neural Network Algorithm for Fusion of Multimodality Brain Images with Quality Analysis

    Directory of Open Access Journals (Sweden)

    Kavitha SRINIVASAN

    2014-09-01

    Full Text Available Background: In the review of medical imaging techniques, an important fact that emerged is that radiologists and physicians still are in a need of high-resolution medical images with complementary information from different modalities to ensure efficient analysis. This requirement should have been sorted out using fusion techniques with the fused image being used in image-guided surgery, image-guided radiotherapy and non-invasive diagnosis. Aim: This paper focuses on Dual Channel Pulse Coupled Neural Network (PCNN Algorithm for fusion of multimodality brain images and the fused image is further analyzed using subjective (human perception and objective (statistical measures for the quality analysis. Material and Methods: The modalities used in fusion are CT, MRI with subtypes T1/T2/PD/GAD, PET and SPECT, since the information from each modality is complementary to one another. The objective measures selected for evaluation of fused image were: Information Entropy (IE - image quality, Mutual Information (MI – deviation in fused to the source images and Signal to Noise Ratio (SNR – noise level, for analysis. Eight sets of brain images with different modalities (T2 with T1, T2 with CT, PD with T2, PD with GAD, T2 with GAD, T2 with SPECT-Tc, T2 with SPECT-Ti, T2 with PET are chosen for experimental purpose and the proposed technique is compared with existing fusion methods such as the Average method, the Contrast pyramid, the Shift Invariant Discrete Wavelet Transform (SIDWT with Harr and the Morphological pyramid, using the selected measures to ascertain relative performance. Results: The IE value and SNR value of the fused image derived from dual channel PCNN is higher than other fusion methods, shows that the quality is better with less noise. Conclusion: The fused image resulting from the proposed method retains the contrast, shape and texture as in source images without false information or information loss.

  15. Computational simulation of coupled material degradation processes for probabilistic lifetime strength of aerospace materials

    Science.gov (United States)

    Boyce, Lola; Bast, Callie C.

    1992-01-01

    The research included ongoing development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primative variables. These primative variable may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above described constitutive equation using actual experimental materials data together with linear regression of that data, thereby predicting values for the empirical material constraints for each effect or primative variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from the open literature for materials typically of interest to those studying aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

  16. Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Mohsen Shanbeh

    2011-01-01

    Full Text Available One of the main methods to reduce the production costs is waste recycling which is the most important challenge for the future. Cotton wastes collected from ginning process have desirable properties which could be used during spinning process. The purpose of this study was to develop predictive models of breaking strength and mass irregularity (CV% of cotton waste rotor-spun yarns containing cotton waste collected from ginning process by using the artificial neural network trained with backpropagation algorithm. Artificial neural network models have been developed based on rotor diameter, rotor speed, navel type, opener roller speed, ginning waste proportion and yarn linear density as input parameters. The parameters of artificial neural network model, namely, learning, and momentum rate, number of hidden layers and number of hidden processing elements (neurons were optimized to get the best predictive models. The findings showed that the breaking strength and mass irregularity of rotor spun yarns could be predicted satisfactorily by artificial neural network. The maximum error in predicting the breaking strength and mass irregularity of testing data was 8.34% and 6.65%, respectively.

  17. Strength training reduces arterial blood pressure but not sympathetic neural activity in young normotensive subjects

    Science.gov (United States)

    Carter, Jason R.; Ray, Chester A.; Downs, Emily M.; Cooke, William H.

    2003-01-01

    The effects of resistance training on arterial blood pressure and muscle sympathetic nerve activity (MSNA) at rest have not been established. Although endurance training is commonly recommended to lower arterial blood pressure, it is not known whether similar adaptations occur with resistance training. Therefore, we tested the hypothesis that whole body resistance training reduces arterial blood pressure at rest, with concomitant reductions in MSNA. Twelve young [21 +/- 0.3 (SE) yr] subjects underwent a program of whole body resistance training 3 days/wk for 8 wk. Resting arterial blood pressure (n = 12; automated sphygmomanometer) and MSNA (n = 8; peroneal nerve microneurography) were measured during a 5-min period of supine rest before and after exercise training. Thirteen additional young (21 +/- 0.8 yr) subjects served as controls. Resistance training significantly increased one-repetition maximum values in all trained muscle groups (P < 0.001), and it significantly decreased systolic (130 +/- 3 to 121 +/- 2 mmHg; P = 0.01), diastolic (69 +/- 3 to 61 +/- 2 mmHg; P = 0.04), and mean (89 +/- 2 to 81 +/- 2 mmHg; P = 0.01) arterial blood pressures at rest. Resistance training did not affect MSNA or heart rate. Arterial blood pressures and MSNA were unchanged, but heart rate increased after 8 wk of relative inactivity for subjects in the control group (61 +/- 2 to 67 +/- 3 beats/min; P = 0.01). These results indicate that whole body resistance exercise training might decrease the risk for development of cardiovascular disease by lowering arterial blood pressure but that reductions of pressure are not coupled to resistance exercise-induced decreases of sympathetic tone.

  18. Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

    Science.gov (United States)

    Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.

    2015-01-01

    Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

  19. Interpretation of bend strength increase of graphite by the couple-stress theory

    International Nuclear Information System (INIS)

    Tang, P.Y.

    1981-05-01

    This paper presents a continued evaluation of the applicability of the couple-stress constitutive theory to graphite. The evaluation is performed by examining four-point bend and uniaxial tensile data of various sized cylindrical and square specimens for three grades of graphites. These data are superficially inconsistent and, usually, at variance with the predictions of classical theories. Nevertheless, this evaluation finds that they can be consistently interpreted by the couple-stress theory. This is compatible with results of an initial evaluation that considered one size of cylindrical specimen for H-451 graphite

  20. The role of frictional strength on plate coupling at the subduction interface

    KAUST Repository

    Tan, Eh; Lavier, Luc L.; Van Avendonk, Harm J. A.; Heuret, Arnauld

    2012-01-01

    and serpentinized mantle (friction angle 1 to 15, or static friction coefficient 0.017 to 0.27) to control the amount of frictional coupling between the plates. With plastic strain weakening in the lithosphere, our numerical models can attain stable subduction

  1. Contribution of the active control to the measurement of fluid-elastic coupling strengths

    International Nuclear Information System (INIS)

    Legendre, S.

    1999-01-01

    A precise dimensioning of the tubes inside a steam generator requires a better knowledge of the fluid-elastic coupling phenomena. The direct method for the determination of fluid-elastic coupling coefficients allows to explore only a reduced range of flow velocities and is unsuitable for the low velocities and for velocities close to the critical instability velocity. The active damping control method has been validated both with air and water and offers the possibility to extend the range of flow velocities using an artificial supply of damping: 50% of increase in single-phase flow conditions with measurements performed beyond the critical instability velocity, a doubling of the explored range of velocities in two-phase flow conditions. For a 25% two-phase flow, a stabilization of the damping of the coupled fluid-structure system is observed beyond the critical instability velocity. Finally, the calculation of fluid-elastic dimensionless coefficients has permitted to show the influence of the reduced velocity on the fluid-elastic coupling in two-phase flow conditions. (J.S.)

  2. Classification of Antarctic algae by applying Kohonen neural network with 14 elements determined by inductively coupled plasma optical emission spectrometry

    Energy Technology Data Exchange (ETDEWEB)

    Balbinot, L. [Departamento de Quimica Analitica-Instituto de Quimica-Unicamp, PO Box 6154, CEP: 13083-971, Campinas, SP (Brazil); Smichowski, P. [Comision Nacional de Energia Atomica, Unidad de Actividad Quimica, Centro Atomico Constituyentes, Av. Gral Paz 1499, B1650KNA, San Martin, Provincia de Buenos Aires (Argentina); Farias, S. [Comision Nacional de Energia Atomica, Unidad de Actividad Quimica, Centro Atomico Constituyentes, Av. Gral Paz 1499, B1650KNA, San Martin, Provincia de Buenos Aires (Argentina); Arruda, M.A.Z. [Departamento de Quimica Analitica-Instituto de Quimica-Unicamp, PO Box 6154, CEP: 13083-971, Campinas, SP (Brazil); Vodopivez, C. [Instituto Antartico Argentino, Cerrito 1010, C1248AAZ, Buenos Aires (Argentina); Poppi, R.J. [Departamento de Quimica Analitica-Instituto de Quimica-Unicamp, PO Box 6154, CEP: 13083-971, Campinas, SP (Brazil)]. E-mail: ronei@iqm.unicamp.br

    2005-06-30

    Optical emission spectrometers can generate results, which sometimes are not easy to interpret, mainly when the analyses involve classifications. To make simultaneous data interpretation possible, the Kohonen neural network is used to classify different Antarctic algae according to their taxonomic groups from the determination of 14 analytes. The Kohonen neural network architecture used was 5x5 neurons, thus reducing 14-dimension input data to two-dimensional space. The input data were 14 analytes (As, Co, Cu, Fe, Mn, Sr, Zn, Cd, Cr, Mo, Ni, Pb, Se, V) with their concentrations, determined by inductively coupled plasma optical emission spectrometry in 11 different species of algae. Three taxonomic groups (Rhodophyta, Phaeophyta and Cholorophyta) can be differentiated and classified through only their Cu content.

  3. Classification of Antarctic algae by applying Kohonen neural network with 14 elements determined by inductively coupled plasma optical emission spectrometry

    International Nuclear Information System (INIS)

    Balbinot, L.; Smichowski, P.; Farias, S.; Arruda, M.A.Z.; Vodopivez, C.; Poppi, R.J.

    2005-01-01

    Optical emission spectrometers can generate results, which sometimes are not easy to interpret, mainly when the analyses involve classifications. To make simultaneous data interpretation possible, the Kohonen neural network is used to classify different Antarctic algae according to their taxonomic groups from the determination of 14 analytes. The Kohonen neural network architecture used was 5x5 neurons, thus reducing 14-dimension input data to two-dimensional space. The input data were 14 analytes (As, Co, Cu, Fe, Mn, Sr, Zn, Cd, Cr, Mo, Ni, Pb, Se, V) with their concentrations, determined by inductively coupled plasma optical emission spectrometry in 11 different species of algae. Three taxonomic groups (Rhodophyta, Phaeophyta and Cholorophyta) can be differentiated and classified through only their Cu content

  4. Stability Switches, Hopf Bifurcations, and Spatio-temporal Patterns in a Delayed Neural Model with Bidirectional Coupling

    Science.gov (United States)

    Song, Yongli; Zhang, Tonghua; Tadé, Moses O.

    2009-12-01

    The dynamical behavior of a delayed neural network with bi-directional coupling is investigated by taking the delay as the bifurcating parameter. Some parameter regions are given for conditional/absolute stability and Hopf bifurcations by using the theory of functional differential equations. As the propagation time delay in the coupling varies, stability switches for the trivial solution are found. Conditions ensuring the stability and direction of the Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. We also discuss the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay differential equations combined with representation theory of Lie groups. In particular, we obtain that the spatio-temporal patterns of bifurcating periodic oscillations will alternate according to the change of the propagation time delay in the coupling, i.e., different ranges of delays correspond to different patterns of neural activities. Numerical simulations are given to illustrate the obtained results and show the existence of bursts in some interval of the time for large enough delay.

  5. Ferromagnetic coupling strength and electron-doping effects in double perovskites

    International Nuclear Information System (INIS)

    Fontcuberta, J.; Rubi, D.; Frontera, C.; Garcia-Munoz, J.L.; Wojcik, M.; Jedryka, E.; Nadolski, S.; Izquierdo, M.; Avila, J.; Asensio, M.C.

    2005-01-01

    We review experiments and results on ferromagnetic and metallic A 2 FeMoO 6 double perovskites that made it possible to obtain a detailed understanding of the nature of the ferromagnetic coupling and paved the way for further enhancement of the Curie temperature. We show that appropriate chemical substitutions, combined with detailed structural, magnetotransport and spectroscopic data allow us to map quite a complete picture of the properties of these oxides

  6. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  7. Stability switches, oscillatory multistability, and spatio-temporal patterns of nonlinear oscillations in recurrently delay coupled neural networks.

    Science.gov (United States)

    Song, Yongli; Makarov, Valeri A; Velarde, Manuel G

    2009-08-01

    A model of time-delay recurrently coupled spatially segregated neural assemblies is here proposed. We show that it operates like some of the hierarchical architectures of the brain. Each assembly is a neural network with no delay in the local couplings between the units. The delay appears in the long range feedforward and feedback inter-assemblies communications. Bifurcation analysis of a simple four-units system in the autonomous case shows the richness of the dynamical behaviors in a biophysically plausible parameter region. We find oscillatory multistability, hysteresis, and stability switches of the rest state provoked by the time delay. Then we investigate the spatio-temporal patterns of bifurcating periodic solutions by using the symmetric local Hopf bifurcation theory of delay differential equations and derive the equation describing the flow on the center manifold that enables us determining the direction of Hopf bifurcations and stability of the bifurcating periodic orbits. We also discuss computational properties of the system due to the delay when an external drive of the network mimicks external sensory input.

  8. A PERFORMANCE COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL METHODS IN FORECASTING FINANCIAL STRENGTH RATING IN TURKISH BANKING SECTOR

    OpenAIRE

    MELEK ACAR BOYACIOĞLU; YAKUP KARA

    2013-01-01

    Financial strength rating indicates the fundamental financial strength of a bank. The aim of financial strength rating is to measure a bank’s fundamental financial strength excluding the external factors. External factors can stem from the working environment or can be linked with the outside protective support mechanisms. With the evaluation, the rating of a bank free from outside supportive factors is being sought. Also the financial fundamental, franchise value, the variety of assets and w...

  9. Effects of Coupling Distance on Synchronization and Coherence in Chaotic Neural Networks

    International Nuclear Information System (INIS)

    Wang Maosheng

    2009-01-01

    Effects of coupling distance on synchronization and coherence of chaotic neurons in complex networks are numerically investigated. We find that it is not beneficial to neurons synchronization if confining the coupling distance of random edges to a limit d max , but help to improve their coherence. Moreover, there is an optimal value of d max at which the coherence is maximum.

  10. Mechanical strength calculation of the disk type windings with elastic couplings by the finite element method

    International Nuclear Information System (INIS)

    Sivkova, G.N.; Spirchenko, Yu.V.; Chvartatskij, P.V.

    1981-01-01

    Stressed-deformed state of toroidal field coils of the disc type with elastic couplings of the tokamaks has been investigated with provision for the effect of the central core pliability by means of the two-dimensional version of the finite element method. Numerical solution of the finite element method is performed by means of the ES 1040 computer according to the computer code permitting taking account of boundary conditions of elastic support. The calculation has been performed using as the example the project of T-20 facility coil of the disc type. Consideration of pliability of the central core of the facility inductor is accomplished by the introduction of additional rigidities to the complete matrix of rigidity. Scheme of the structure distretization includes 141 units, 211 elements. The accuracy of solution depends on the reduction accuracy of the volume load to unit forces and on the number of finite elements. Analysis of the solution convergence is performed by the comparison of solutions obtained for three different schemes of the disk discretization without regard for the inductor pliability. The comparative analysis of the results shows that transfer epures for all the three discretization versions practically coincide and stresses differ not more than by 10%. On the whole the above investigation has demonstrated good convergence of the problem solution [ru

  11. Relativistic hadrodynamics with field-strength dependent coupling of the scalar fields in Hartree and Hartree-Fock approximation

    International Nuclear Information System (INIS)

    Lindner, J.

    1992-09-01

    In this thesis in the framework of our model of the field-strength dependent coupling the properties of infinitely extended, homogeneous, static, spin- and isospin-saturated nuclear matter are studied. Thereby we use the Hartree-Mean-Field and the Hartree-Fock approximation, whereby the influence of the antiparticle states in the Fermi sea is neglected. In chapter 2 the Lagrangian density basing to our model is fixed. Starting from the Walecka model we modify in the Lagrangian density the Linear coupling of the scalar field to the scalar density as follows g S φanti ψψ→g S f(φ) anti ψψ. In chapter 3 we fix three different functions f(φ). For these three cases and for the Walecka model with f(φ)=φ nuclear-matter calculations are performed. In chapter 4 for the Hartree-Fock calculations, but also very especially regarding the molecular-dynamics calculations, the properties of the Dirac spinors in the plane-wave representation are intensively studied. (orig.)

  12. The hyperbolic effect of density and strength of inter beta-cell coupling on islet bursting: a theoretical investigation

    Directory of Open Access Journals (Sweden)

    Wang Xujing

    2008-08-01

    Full Text Available Abstract Background Insulin, the principal regulating hormone of blood glucose, is released through the bursting of the pancreatic islets. Increasing evidence indicates the importance of islet morphostructure in its function, and the need of a quantitative investigation. Recently we have studied this problem from the perspective of islet bursting of insulin, utilizing a new 3D hexagonal closest packing (HCP model of islet structure that we have developed. Quantitative non-linear dependence of islet function on its structure was found. In this study, we further investigate two key structural measures: the number of neighboring cells that each β-cell is coupled to, nc, and the coupling strength, gc. Results β-cell clusters of different sizes with number of β-cells nβ ranging from 1–343, nc from 0–12, and gc from 0–1000 pS, were simulated. Three functional measures of islet bursting characteristics – fraction of bursting β-cells fb, synchronization index λ, and bursting period Tb, were quantified. The results revealed a hyperbolic dependence on the combined effect of nc and gc. From this we propose to define a dimensionless cluster coupling index or CCI, as a composite measure for islet morphostructural integrity. We show that the robustness of islet oscillatory bursting depends on CCI, with all three functional measures fb, λ and Tb increasing monotonically with CCI when it is small, and plateau around CCI = 1. Conclusion CCI is a good islet function predictor. It has the potential of linking islet structure and function, and providing insight to identify therapeutic targets for the preservation and restoration of islet β-cell mass and function.

  13. A combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium-rare earth alloys

    Energy Technology Data Exchange (ETDEWEB)

    Birbilis, N., E-mail: nick.birbilis@monash.ed [ARC Centre of Excellence for Design in Light Metals, Monash University (Australia); CAST Co-operative Research Centre, Monash University (Australia); Cavanaugh, M.K. [Department of Materials Science and Engineering, The Ohio State University (United States); Sudholz, A.D. [ARC Centre of Excellence for Design in Light Metals, Monash University (Australia); Zhu, S.M.; Easton, M.A. [CAST Co-operative Research Centre, Monash University (Australia); Gibson, M.A. [CSIRO Division of Process Science and Engineering (Australia)

    2011-01-15

    Research highlights: This study presents a body of corrosion data for a set of custom alloys and displays this in multivariable space. These alloys represent the next generation of Mg alloys for auto applications. The data is processed using an ANN model, which makes it possible to yield a single expression for prediction of corrosion rate (and strength) as a function of any input composition (of Ce, La or Nd between 0 and 6 wt.%). The relative influence of the various RE elements on corrosion is assessed, with the outcome that Nd additions can offer comparable strength with minimal rise in corrosion rate. The morphology and solute present in the eutectic region itself (as opposed to just the intermetallic presence) was shown - for the first time - to also be a key contributor to corrosion. The above approach sets the foundation for rational alloy design of alloys with corrosion performance in mind. - Abstract: Additions of Ce, La and Nd to Mg were made in binary, ternary and quaternary combinations up to {approx}6 wt.%. This provided a dataset that was used in developing a neural network model for predicting corrosion rate and yield strength. Whilst yield strength increased with RE additions, corrosion rates also systematically increased, however, this depended on the type of RE element added and the combination of elements added (along with differences in intermetallic morphology). This work is permits an understanding of Mg-RE alloy performance, and can be exploited in Mg alloy design for predictable combinations of strength and corrosion resistance.

  14. Image processing using pulse-coupled neural networks applications in Python

    CERN Document Server

    Lindblad, Thomas

    2013-01-01

    Image processing algorithms based on the mammalian visual cortex are powerful tools for extraction information and manipulating images. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation, image fusion and information extraction. The third edition reflects renewed international interest in pulse image processing with updated sections presenting several newly developed applications. This edition also introduces a suite of Python scripts that assist readers in replicating results presented in the text and to further develop their own applications.

  15. Efficiency of conscious access improves with coupling of slow and fast neural oscillations.

    Science.gov (United States)

    Nakatani, Chie; Raffone, Antonino; van Leeuwen, Cees

    2014-05-01

    Global workspace access is considered as a critical factor for the ability to report a visual target. A plausible candidate mechanism for global workspace access is coupling of slow and fast brain activity. We studied coupling in EEG data using cross-frequency phase-amplitude modulation measurement between delta/theta phases and beta/gamma amplitudes from two experimental sessions, held on different days, of a typical attentional blink (AB) task, implying conscious access to targets. As the AB effect improved with practice between sessions, theta-gamma and theta-beta coupling increased generically. Most importantly, practice effects observed in delta-gamma and delta-beta couplings were specific to performance on the AB task. In particular, delta-gamma coupling showed the largest increase in cases of correct target detection in the most challenging AB conditions. All these practice effects were observed in the right temporal region. Given that the delta band is the main frequency of the P3 ERP, which is a marker of global workspace activity for conscious access, and because the gamma band is involved in visual object processing, the current results substantiate the role of phase-amplitude modulation in conscious access to visual target representations.

  16. The Exercising Together project: design and recruitment for a randomized, controlled trial to determine the benefits of partnered strength training for couples coping with prostate cancer.

    Science.gov (United States)

    Winters-Stone, Kerri M; Lyons, Karen S; Nail, Lillian M; Beer, Tomasz M

    2012-03-01

    Prostate cancer can threaten quality of life for the patient and his spouse and the quality of his marital relationship. The purpose of our study is to evaluate the effects of "Exercising Together" - a partnered strength training program for married couples coping with prostate cancer - on the physical and emotional health of prostate cancer survivors (PCS) and their spouses and on marital quality. We are conducting a 6-month randomized controlled trial with two groups: 1) Exercising Together - a progressive, supervised strength training program and 2) a usual care control condition. The primary aims of this exploratory study are to: 1) Determine the effect of partnered strength training on physical and emotional health (muscle strength, physical function, body composition and self-report physical and mental health) in PCS, 2) Determine the effect of partnered strength training on physical and emotional health in spouses and 3) Explore the effect of partnered strength training on marital quality (incongruence, communication, relationship quality, intimacy) of the PCS and spouse. Target accrual has been met in this study with 64 couples enrolled and randomized to exercise (n=32) or usual care (n=32) groups. This study is the first to examine the feasibility of this exercise format in both the chronically ill patient and spouse and explore benefits at the individual and couple level. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Dispersion of the intrinsic neuronal periods affects the relationship of the entrainment range to the coupling strength in the suprachiasmatic nucleus

    Science.gov (United States)

    Gu, Changgui; Yang, Huijie; Wang, Man

    2017-11-01

    Living beings on the Earth are subjected to and entrained (synchronized) to the natural 24-h light-dark cycle. Interestingly, they can also be entrained to an external artificial cycle of non-24-h periods. The range of these periods is called the entrainment range and it differs among species. In mammals, the entrainment range is regulated by a main clock located in the suprachiasmatic nucleus (SCN) which is composed of 10 000 neurons in the brain. Previous works have found that the entrainment range depends on the cellular coupling strength in the SCN. In particular, the entrainment range decreases with the increase of the cellular coupling strength, provided that all the neuronal oscillators are identical. However, the SCN neurons differ in the intrinsic periods that follow a normal distribution in a range from 22 to 28 h. In the present study, taking the dispersion of the intrinsic neuronal periods into account, we examined the relationship between the entrainment range and the coupling strength. Results from numerical simulations and theoretical analyses both show that the relationship is altered to be paraboliclike if the intrinsic neuronal periods are nonidentical, and the maximal entrainment range is obtained with a suitable coupling strength. Our results shed light on the role of the cellular coupling in the entrainment ability of the SCN network.

  18. Bifurcation analysis and spatio-temporal patterns of nonlinear oscillations in a delayed neural network with unidirectional coupling

    International Nuclear Information System (INIS)

    Song Yongli; Tadé, Moses O; Zhang Tonghua

    2009-01-01

    In this paper, a delayed neural network with unidirectional coupling is considered which consists of two two-dimensional nonlinear differential equation systems with exponential decay where one system receives a delayed input from the other system. Some parameter regions are given for conditional/absolute stability and Hopf bifurcations by using the theory of functional differential equations. Conditions ensuring the stability and direction of the Hopf bifurcation are determined by applying the normal form theory and the centre manifold theorem. We also investigate the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay-differential equations combined with representation theory of Lie groups. Then the global continuation of phase-locked periodic solutions is investigated. Numerical simulations are given to illustrate the results obtained

  19. Bilingualism increases neural response consistency and attentional control: evidence for sensory and cognitive coupling.

    Science.gov (United States)

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

    2014-01-01

    Auditory processing is presumed to be influenced by cognitive processes - including attentional control - in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] in adolescent Spanish-English bilinguals and English monolinguals and separately obtained measures of attentional control and language ability. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. Copyright © 2013 Elsevier Inc. All rights reserved.

  20. Modeling of thermal coupling in VO2-based oscillatory neural networks

    Science.gov (United States)

    Velichko, Andrey; Belyaev, Maksim; Putrolaynen, Vadim; Perminov, Valentin; Pergament, Alexander

    2018-01-01

    In this study, we have demonstrated the possibility of using the thermal coupling to control the dynamics of operation of coupled VO2 oscillators. Based on the example of a 'switch-microheater' pair, we have explored the synchronization and dissynchronization modes of a single oscillator with respect to an external harmonic heat impact. The features of changes in the spectra are shown, in particular, the effect of the natural frequency attraction to the affecting signal frequency and the self-oscillation noise reduction effects at synchronization. The time constant of the temperature effect for the considered system configuration is in the range 7-140 μs, which allows operation in the oscillation frequency range of up to ∼70 kHz. A model estimate of the minimum temperature sensitivity of the switch is δTswitch ∼ 0.2 K, and the effective action radius RTC of the switch-to-switch thermal coupling is not less than 25 μm. Nevertheless, as the simulation shows, the frequency range can be significantly extended up to the values of 1-30 GHz if using nanometer-scale switches (heaters). article>

  1. A combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium-rare earth alloys

    International Nuclear Information System (INIS)

    Birbilis, N.; Cavanaugh, M.K.; Sudholz, A.D.; Zhu, S.M.; Easton, M.A.; Gibson, M.A.

    2011-01-01

    Research highlights: → This study presents a body of corrosion data for a set of custom alloys and displays this in multivariable space. These alloys represent the next generation of Mg alloys for auto applications. → The data is processed using an ANN model, which makes it possible to yield a single expression for prediction of corrosion rate (and strength) as a function of any input composition (of Ce, La or Nd between 0 and 6 wt.%). → The relative influence of the various RE elements on corrosion is assessed, with the outcome that Nd additions can offer comparable strength with minimal rise in corrosion rate. → The morphology and solute present in the eutectic region itself (as opposed to just the intermetallic presence) was shown - for the first time - to also be a key contributor to corrosion. → The above approach sets the foundation for rational alloy design of alloys with corrosion performance in mind. - Abstract: Additions of Ce, La and Nd to Mg were made in binary, ternary and quaternary combinations up to ∼6 wt.%. This provided a dataset that was used in developing a neural network model for predicting corrosion rate and yield strength. Whilst yield strength increased with RE additions, corrosion rates also systematically increased, however, this depended on the type of RE element added and the combination of elements added (along with differences in intermetallic morphology). This work is permits an understanding of Mg-RE alloy performance, and can be exploited in Mg alloy design for predictable combinations of strength and corrosion resistance.

  2. Premature Ventricular Contraction Coupling Interval Variability Destabilizes Cardiac Neuronal and Electrophysiological Control: Insights from Simultaneous Cardio-Neural Mapping

    Science.gov (United States)

    Hamon, David; Rajendran, Pradeep S.; Chui, Ray W.; Ajijola, Olujimi A.; Irie, Tadanobu; Talebi, Ramin; Salavatian, Siamak; Vaseghi, Marmar; Bradfield, Jason S.; Armour, J. Andrew; Ardell, Jeffrey L.; Shivkumar, Kalyanam

    2017-01-01

    Background Variability in premature ventricular contraction (PVC) coupling interval (CI) increases the risk of cardiomyopathy and sudden death. The autonomic nervous system regulates cardiac electrical and mechanical indices, and its dysregulation plays an important role in cardiac disease pathogenesis. The impact of PVCs on the intrinsic cardiac nervous system (ICNS), a neural network on the heart, remains unknown. The objective was to determine the effect of PVCs and CI on ICNS function in generating cardiac neuronal and electrical instability using a novel cardio-neural mapping approach. Methods and Results In a porcine model (n=8) neuronal activity was recorded from a ventricular ganglion using a microelectrode array, and cardiac electrophysiological mapping was performed. Neurons were functionally classified based on their response to afferent and efferent cardiovascular stimuli, with neurons that responded to both defined as convergent (local reflex processors). Dynamic changes in neuronal activity were then evaluated in response to right ventricular outflow tract PVCs with fixed short, fixed long, and variable CI. PVC delivery elicited a greater neuronal response than all other stimuli (P<0.001). Compared to fixed short and long CI, PVCs with variable CI had a greater impact on neuronal response (P<0.05 versus short CI), particularly on convergent neurons (P<0.05), as well as neurons receiving sympathetic (P<0.05) and parasympathetic input (P<0.05). The greatest cardiac electrical instability was also observed following variable (short) CI PVCs. Conclusions Variable CI PVCs affect critical populations of ICNS neurons and alter cardiac repolarization. These changes may be critical for arrhythmogenesis and remodeling leading to cardiomyopathy. PMID:28408652

  3. Functional Strength Training and Movement Performance Therapy for Upper Limb Recovery Early Poststroke—Efficacy, Neural Correlates, Predictive Markers, and Cost-Effectiveness: FAST-INdiCATE Trial

    Directory of Open Access Journals (Sweden)

    Susan M. Hunter

    2018-01-01

    Full Text Available BackgroundVariation in physiological deficits underlying upper limb paresis after stroke could influence how people recover and to which physical therapy they best respond.ObjectivesTo determine whether functional strength training (FST improves upper limb recovery more than movement performance therapy (MPT. To identify: (a neural correlates of response and (b whether pre-intervention neural characteristics predict response.DesignExplanatory investigations within a randomised, controlled, observer-blind, and multicentre trial. Randomisation was computer-generated and concealed by an independent facility until baseline measures were completed. Primary time point was outcome, after the 6-week intervention phase. Follow-up was at 6 months after stroke.ParticipantsWith some voluntary muscle contraction in the paretic upper limb, not full dexterity, when recruited up to 60 days after an anterior cerebral circulation territory stroke.InterventionsConventional physical therapy (CPT plus either MPT or FST for up to 90 min-a-day, 5 days-a-week for 6 weeks. FST was “hands-off” progressive resistive exercise cemented into functional task training. MPT was “hands-on” sensory/facilitation techniques for smooth and accurate movement.OutcomesThe primary efficacy measure was the Action Research Arm Test (ARAT. Neural measures: fractional anisotropy (FA corpus callosum midline; asymmetry of corticospinal tracts FA; and resting motor threshold (RMT of motor-evoked potentials.AnalysisCovariance models tested ARAT change from baseline. At outcome: correlation coefficients assessed relationship between change in ARAT and neural measures; an interaction term assessed whether baseline neural characteristics predicted response.Results288 Participants had: mean age of 72.2 (SD 12.5 years and mean ARAT 25.5 (18.2. For 240 participants with ARAT at baseline and outcome the mean change was 9.70 (11.72 for FST + CPT and 7.90 (9.18 for MPT

  4. The application of a coupled artificial neural network and fault tree analysis model to predict coal and gas outbursts

    Energy Technology Data Exchange (ETDEWEB)

    Ruilin, Zhang [School of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo, Henan Province, 454003, PR (China); Lowndes, Ian S. [Process and Environmental Research Division, Faculty of Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD (United Kingdom)

    2010-11-01

    This paper proposes the use of a coupled fault tree analysis (FTA) and artificial neural network (ANN) model to improve the prediction of the potential risk of coal and gas outburst events during the underground mining of thick and deep Chinese coal seams. The model developed has been used to investigate the gas emission characteristics and the geological conditions that exist within the Huaibei coal mining region, Anhui province, China. The coal seams in this region exhibit a high incidence of coal and gas outbursts. An analysis of the results obtained from an initial application of an FTA model, identified eight dominant model parameters related to the gas content or geological conditions of the coal seams, which characterize the potential risk of in situ coal and gas outbursts. The eight dominant model parameters identified by the FTA method were subsequently used as input variables to an ANN model. The results produced by the ANN model were used to develop a qualitative risk index to characterize the potential risk level of occurrence of coal and gas outburst events. Four different potential risk alarm levels were defined: SAFE, POTENTIAL, HIGH and STRONG. Solutions to the prediction model were obtained using a combination of quantitative and qualitative data including the gas content or gas pressure and the geological and geotechnical conditions of coal seams. The application of this combined solution method identified more explicit and accurate model relationships between the in situ geological conditions and the potential risk of coal and gas outbursts. An analysis of the model solutions concluded that the coupled FTA and ANN model may offer a reliable alternative method to forecast the potential risk of coal and gas outbursts. (author)

  5. Back-propagation neural network-based approximate analysis of true stress-strain behaviors of high-strength metallic material

    International Nuclear Information System (INIS)

    Doh, Jaeh Yeok; Lee, Jong Soo; Lee, Seung Uk

    2016-01-01

    In this study, a Back-propagation neural network (BPN) is employed to conduct an approximation of a true stress-strain curve using the load-displacement experimental data of DP590, a high-strength material used in automobile bodies and chassis. The optimized interconnection weights are obtained with hidden layers and output layers of the BPN through intelligent learning and training of the experimental data; by using these weights, a mathematical model of the material's behavior is suggested through this feed-forward neural network. Generally, the material properties from the tensile test cannot be acquired until the fracture regions, since it is difficult to measure the cross-section area of a specimen after diffusion necking. For this reason, the plastic properties of the true stress-strain are extrapolated using the weighted-average method after diffusion necking. The accuracies of BPN-based meta-models for predicting material properties are validated in terms of the Root mean square error (RMSE). By applying the approximate material properties, the reliable finite element solution can be obtained to realize the different shapes of the finite element models. Furthermore, the sensitivity analysis of the approximate meta-model is performed using the first-order approximate derivatives of the BPN and is compared with the results of the finite difference method. In addition, we predict the tension velocity's effect on the material property through a first-order sensitivity analysis.

  6. Application of Multiphysics Coupling FEM on Open Wellbore Shrinkage and Casing Remaining Strength in an Incomplete Borehole in Deep Salt Formation

    OpenAIRE

    Tong, Hua; Guo, Daqiang; Zhu, Xiaohua

    2015-01-01

    Drilling and completing wells in deep salt stratum are technically challenging and costing, as when serving in an incomplete borehole in deep salt formation, well casing runs a high risk of collapse. To quantitatively calculate casing remaining strength under this harsh condition, a three-dimensional mechanical model is developed; then a computational model coupled with interbed salt rock-defective cement-casing and HPHT (high pressure and high temperature) is established and analyzed using m...

  7. Couplings

    Science.gov (United States)

    Stošić, Dušan; Auroux, Aline

    Basic principles of calorimetry coupled with other techniques are introduced. These methods are used in heterogeneous catalysis for characterization of acidic, basic and red-ox properties of solid catalysts. Estimation of these features is achieved by monitoring the interaction of various probe molecules with the surface of such materials. Overview of gas phase, as well as liquid phase techniques is given. Special attention is devoted to coupled calorimetry-volumetry method. Furthermore, the influence of different experimental parameters on the results of these techniques is discussed, since it is known that they can significantly influence the evaluation of catalytic properties of investigated materials.

  8. Effect of yttrium on electron–phonon coupling strength of 5d state of Ce3+ ion in LYSO:Ce crystals

    International Nuclear Information System (INIS)

    Ding, Dongzhou; Liu, Bo; Wu, Yuntao; Yang, Jianhua; Ren, Guohao; Chen, Junfeng

    2014-01-01

    This paper aims at an improved understanding of luminescence properties of (Lu 1−x Y x ) 2 SiO 5 :Ce (x=0 at%, 26 at%, 45 at%, 66 at% and 100 at%). Photoluminescence emission and excitation spectra as well as Raman spectra of (Lu 1−x Y x ) 2 SiO 5 :Ce were investigated as a function of yttrium (shortened as Y) content in it. Obtained Huang–Rhys factor S indicates that the coupling between Ce1 (7-oxygen-coordinated), Ce2 (6-oxygen-coordinated) and LYSO lattice is intermediate and strong, respectively. Besides, it was found that: with the increase of Y content, crystal field strength around Ce1 and Ce2 decreases, Stokes shift of Ce1 and Ce2 presents an increase trend, and S of Ce2 tends to decrease. These phenomena were explained by geometrical influence of Y 3+ /Lu 3+ on the crystal field splitting of the 5d levels of Ce 3+ and coupling strength. With the increase of Y content, the evolution of S and coupling energy ħω of Ce1 present a slight increase and decrease trend respectively, while S and coupling energy ħω of Ce2 present an obvious decrease and increase trend, respectively. - Highlights: • Crystal field strength around Ce1 decreases with increase of Y content in LYSO:Ce. • A diagram of Huang–Rhys factor S against Y content in LYSO:Ce was constructed. • A diagram of coupling energy ħω against Y content in LYSO:Ce was constructed. • A diagram of Stokes shift against Y content in LYSO:Ce was constructed

  9. Assessment of covalent bond formation between coupling agents and wood by FTIR spectroscopy and pull strength tests

    DEFF Research Database (Denmark)

    Rasmussen, Jonas Stensgaard; Barsberg, Søren Talbro; Venås, Thomas Mark

    2014-01-01

    In the focus was the question whether metal alkoxide coupling agents – titanium, silane, and zirconium – form covalent bonds to wood and how they improve coating adhesion. In a previous work, a downshift of the lignin infrared (IR) band ∼1600 cm-1 was shown to be consistent with the formation...... of ether linkages between lignin and titanium coupling agent. In the present work, changes were found in the attenuated total reflectance-Fourier transform IR (ATR-FTIR) spectra of lignin and wood mixed with silane, and titanium coupling agents, and to a lesser extent for a zirconium coupling agent...

  10. Ab initio characterization of coupling strength for all types of dangling-bond pairs on the hydrogen-terminated Si(100)-2 × 1 surface

    Science.gov (United States)

    Shaterzadeh-Yazdi, Zahra; Sanders, Barry C.; DiLabio, Gino A.

    2018-04-01

    Recent work has suggested that coupled silicon dangling bonds sharing an excess electron may serve as building blocks for quantum-cellular-automata cells and quantum computing schemes when constructed on hydrogen-terminated silicon surfaces. In this work, we employ ab initio density-functional theory to examine the details associated with the coupling between two dangling bonds sharing one excess electron and arranged in various configurations on models of phosphorous-doped hydrogen-terminated silicon (100) surfaces. Our results show that the coupling strength depends strongly on the relative orientation of the dangling bonds on the surface and on the separation between them. The orientation of dangling bonds is determined by the anisotropy of the silicon (100) surface, so this feature of the surface is a significant contributing factor to variations in the strength of coupling between dangling bonds. The results demonstrate that simple models for approximating tunneling, such as the Wentzel-Kramer-Brillouin method, which do not incorporate the details of surface structure, are incapable of providing reasonable estimates of tunneling rates between dangling bonds. The results provide guidance to efforts related to the development of dangling-bond based computing elements.

  11. Application of CMAC Neural Network Coupled with Active Disturbance Rejection Control Strategy on Three-motor Synchronization Control System

    Directory of Open Access Journals (Sweden)

    Hui Li

    2014-04-01

    Full Text Available Three-motor synchronous coordination system is a MI-MO nonlinear and complex control system. And it often works in poor working condition. Advanced control strategies are required to improve the control performance of the system and to achieve the decoupling between main motor speed and tension. Cerebellar Model Articulation Controller coupled with Active Disturbance Rejection Control (CMAC-ADRC control strategy is proposed. The speed of the main motor and tensions between two motors is decoupled by extended state observer (ESO in ADRC. ESO in ADRC is used to compensate internal and external disturbances of the system online. And the anti interference of the system is improved by ESO. And the same time the control model is optimized. Feedforward control is implemented by the adoption of CMAC neural network controller. And control precision of the system is improved in reason of CMAC. The overshoot of the system can be reduced without affecting the dynamic response of the system by the use of CMAC-ADRC. The simulation results show that: the CMAC- ADRC control strategy is better than the traditional PID control strategy. And CMAC-ADRC control strategy can achieve the decoupling between speed and tension. The control system using CMAC-ADRC have strong anti-interference ability and small regulate time and small overshoot. The magnitude of the system response incited by the interference using CMAC-ADRC is smaller than the system using conventional PID control 6.43 %. And the recovery time of the system with CMAC-ADRC is shorter than the system with traditional PID control 0.18 seconds. And the triangular wave tracking error of the system with CMAC-ADRC is smaller than the system with conventional PID control 0.24 rad/min. Thus the CMAC-ADRC control strategy is a good control strategy and is able to fit three-motor synchronous coordinated control.

  12. Coupling Effect of Intruding Water and Inherent Gas on Coal Strength Based on the Improved (Mohr-Coulomb Failure Criterion

    Directory of Open Access Journals (Sweden)

    Yiyu Lu

    2016-11-01

    Full Text Available When employing hydraulic processes to increase gas drainage efficiency in underground coal mines, coal seams become a three-phase medium, containing water intruding into the coal pores with the inherent occurrence of gas. This can change the stress state of the coal and cause instability. This work studied the mechanical properties of coal containing water and gas and derived an appropriate failure criterion. Based on mixture theory of unsaturated porous media, the effective stress of coal, considering the interaction of water and gas, was analyzed, and the failure criterion established by combining this with the Mohr–Coulomb criterion. By introducing the stress factor of matrix suction and using fitted curves of experimentally determined matrix suction and moisture content, the relationships between coal strength, gas pressure, and moisture content were determined. To verify the established strength theory, a series of triaxial compression strength tests of coal containing water and gas were carried out on samples taken from the Songzao, Pingdingshan, and Tashan mines in China. The experimental results correlated well with the theoretical predictions. The results showed a linear decrease in the peak strength of coal with increasing gas pressure and an exponential reduction in peak strength with increasing moisture content. The strength theory of coal containing water and gas can become an important part of multiphase medium damage theory.

  13. The effect of temperature, matrix alloying and substrate coatings on wettability and shear strength of Al/Al2O3 couples

    Science.gov (United States)

    Sobczak, N.; Ksiazek, M.; Radziwill, W.; Asthana, R.; Mikulowski, B.

    2004-03-01

    A fresh approach has been advanced to examine in the Al/Al2O3 system the effects of temperature, alloying of Al with Ti or Sn, and Ti and Sn coatings on the substrate, on contact angles measured using a sessile-drop test, and on interface strength measured using a modified push-off test that allows shearing of solidified droplets with less than 90 deg contact angle. In the modified test, the solidified sessile-drop samples are bisected perpendicular to the drop/Al2O3 interface at the midplane of the contact circle to obtain samples that permit bond strength measurement by stress application to the flat surface of the bisected couple. The test results show that interface strength is strongly influenced by the wetting properties; low contact angles correspond to high interface strength, which also exhibits a strong temperature dependence. An increase in the wettability test temperature led to an increase in the interface strength in the low-temperature range where contact angles were large and wettability was poor. The room-temperature shear tests conducted on thermally cycled sessile-drop test specimens revealed the effect of chemically formed interfacial oxides; a weakening of the thermally cycled Al/Al2O3 interface was caused under the following conditions: (1) slow contact heating and short contact times in the wettability test, and (2) fast contact heating and longer contact times. The addition of 6 wt pct Ti or 7 wt pct Sn to Al only marginally influenced the contact angle and interfacial shear strength. However, Al2O3 substrates having thin (<1 µm) Ti coatings yielded relatively low contact angles and high bond strength, which appears to be related to the dissolution of the coating in Al and formation of a favorable interface structure.

  14. The strength of a remorseful heart: psychological and neural basis of how apology emolliates reactive aggression and promotes forgiveness.

    Science.gov (United States)

    Beyens, Urielle; Yu, Hongbo; Han, Ting; Zhang, Li; Zhou, Xiaolin

    2015-01-01

    Apology from the offender facilitates forgiveness and thus has the power to restore a broken relationship. Here we showed that apology from the offender not only reduces the victim's propensity to react aggressively but also alters the victim's implicit attitude and neural responses toward the offender. We adopted an interpersonal competitive game which consisted of two phases. In the first, "passive" phase, participants were punished by high or low pain stimulation chosen by the opponents when losing a trial. During the break, participants received a note from each of the opponents, one apologizing and the other not. The second, "active" phase, involved a change of roles where participants could punish the two opponents after winning. Experiment 1 included an Implicit Association Test (IAT) in between the reception of notes and the second phase. Experiment 2 recorded participants' brain potentials in the second phase. We found that participants reacted less aggressively toward the apologizing opponent than the non-apologizing opponent in the active phase. Moreover, female, but not male, participants responded faster in the IAT when positive and negative words were associated with the apologizing and the non-apologizing opponents, respectively, suggesting that female participants had enhanced implicit attitude toward the apologizing opponent. Furthermore, the late positive potential (LPP), a component in brain potentials associated with affective/motivational reactions, was larger when viewing the portrait of the apologizing than the non-apologizing opponent when participants subsequently selected low punishment. Additionally, the LPP elicited by the apologizing opponents' portrait was larger in the female than in the male participants. These findings confirm the apology's role in reducing reactive aggression and further reveal that this forgiveness process engages, at least in female, an enhancement of the victim's implicit attitude and a prosocial motivational

  15. Effect of cervical vs. thoracic spinal manipulation on peripheral neural features and grip strength in subjects with chronic mechanical neck pain: a randomized controlled trial.

    Science.gov (United States)

    Bautista-Aguirre, Francisco; Oliva-Pascual-Vaca, Ángel; Heredia-Rizo, Alberto M; Boscá-Gandía, Juan J; Ricard, François; Rodriguez-Blanco, Cleofás

    2017-06-01

    treatment session using cervical or thoracic thrust techniques is not enough to achieve clinically relevant changes on neural mechanosensitivity and grip strength in chronic non-specific mechanical neck pain.

  16. The couplings of the Higgs boson and its CP properties from fits of the signal strengths and their ratios at the 7+8 TeV LHC

    International Nuclear Information System (INIS)

    Djouadi, Abdelhak; Moreau, Gregory

    2013-01-01

    Using the full set of the LHC Higgs data from the runs at 7 and 8 TeV center of mass energies that have been released by the ATLAS and CMS collaborations, we determine the couplings of the Higgs particle to fermions and gauge bosons as well as its parity or CP composition. We consider ratios of production cross sections times decay branching fractions in which the theoretical (and some experimental) uncertainties as well as some ambiguities from new physics cancel out. A fit of both the signal strengths in the various search channels that have been conducted, H→ZZ,WW,γγ,ττ and b anti b, and their ratios shows that the observed ∝ 126 GeV particle has couplings to fermions and gauge bosons that are Standard Model-like already at the 68 % confidence level (CL). From the signal strengths in which the theoretical uncertainty is taken to be a bias, the particle is shown to be at most 68 % CP-odd at the 99 %CL and the possibility that it is a pure pseudoscalar state is excluded at the 4σ level when including both the experimental and theoretical uncertainties. The signal strengths also measure the invisible Higgs decay width which, with the same type of uncertainty analysis, is shown to be Γ H inv / Γ H SM ≤ 0.52 at the 68 %CL. (orig.)

  17. Enhancement of mechanical strength of TiO2/high-density polyethylene composites for bone repair with silane-coupling treatment

    International Nuclear Information System (INIS)

    Hashimoto, Masami; Takadama, Hiroaki; Mizuno, Mineo; Kokubo, Tadashi

    2006-01-01

    Mechanical properties of composites made up of high-density polyethylene (HDPE) and silanated TiO 2 particles for use as a bone-repairing material were investigated in comparison with those of the composites of HDPE with unsilanized TiO 2 particles. The interfacial morphology and interaction between silanated TiO 2 and HDPE were analyzed by means of Fourier transform infrared (FT-IR) spectroscopy and scanning electron microscopy (SEM). The absorption in spectral bands related to the carboxyl bond in the silane-coupling agent, the vinyl group in the HDPE, and the formation of the ether bond was studied in order to assess the influence of the silane-coupling agent. The SEM micrograph showed that the 'bridging effect' between HDPE and TiO 2 was brought about by the silane-coupling agent. The use of the silane-coupling agent and the increase of the hot-pressing pressure for shaping the composites facilitated the penetration of polymer into cavities between individual TiO 2 particles, which increased the density of the composite. Therefore, mechanical properties such as bending yield strength and Young's modulus increased from 49 MPa and 7.5 GPa to 65 MPa and 10 GPa, respectively, after the silane-coupling treatment and increase in the hot-pressing pressure

  18. Reinterpretation of searches for supersymmetry in models with variable $R$-parity-violating coupling strength and long-lived $R$-hadrons

    CERN Document Server

    The ATLAS collaboration

    2018-01-01

    A selection of ATLAS searches for supersymmetry (SUSY), optimized for $R$-parity-conserving and $R$-parity-violating (RPV) models, are reinterpreted in SUSY models with variable RPV-coupling strength. Depending on the coupling strength the lightest supersymmetric particle is stable at collider scales, is long-lived and decays away from the interaction point, or decays promptly. Limits are placed on simplified models of pair-produced gluinos decaying to final states enhanced or depleted with top quarks, and models of pair-produced top squarks. In a model of pair-produced gluinos decaying to final states enhanced with top quarks, a lower limit of 1.8 TeV on the gluino mass is set at 95% confidence level regardless of the RPV coupling value. Limits are set on models of gluino pair production decaying to light-flavor quarks, and models of top squark production. Limits are also placed on meta-stable gluinos decaying within the detector volume.

  19. Initial and long-term bond strengths of one-step self-etch adhesives with silane coupling agent to enamel-dentin-composite in combined situation.

    Science.gov (United States)

    Mamanee, Teerapong; Takahashi, Masahiro; Nakajima, Masatoshi; Foxton, Richard M; Tagami, Junji

    2015-01-01

    This study evaluated the effect of adding silane coupling agent on initial and long-term bond strengths of one-step self-etch adhesives to enamel-dentin-composite in combined situation. Cervical cavities were prepared on extracted molars and filled with Clearfil AP-X. After water-storage for one-week, the filled teeth were sectioned in halves to expose enamel, dentin and composite surfaces and then enamel-dentin-composite surface was totally applied with one of adhesive treatments (Clearfil SE One, Clearfil SE One with Clearfil Porcelain Bond Activator, Beautibond Multi, Beautibond Multi with Beautibond Multi PR Plus and Scotchbond Universal). After designed period, micro-shear bond strengths (µSBSs) to each substrate were determined. For each period of water-storage, additive silane treatments significantly increased µSBS to composite (penamel (p>0.05). Moreover, the stability of µSBS was depended on materials and substrates used.

  20. The Coupled Effect of Loading Rate and Grain Size on Tensile Strength of Sandstones under Dynamic Disturbance

    Directory of Open Access Journals (Sweden)

    Miao Yu

    2017-01-01

    Full Text Available It is of significance to comprehend the effects of rock microstructure on the tensile strength under different loading rates caused by mining disturbance. So, in this paper, three kinds of sandstones drilled from surrounding rocks in Xiao Jihan Coal to simulate the in situ stress state, whose average grain size is 30 μm (fine grain, FG, 105 μm (medium grain, MG, and 231 μm (Coarse grain, CG, are selected with the calculation of optical microscopic technique and moreover processed to Brazilian disc (BD to study the mechanical response of samples. The dynamic Brazilian tests of samples with three kinds of grain sizes are conducted with the Split Hopkinson Pressure Bar (SHPB driven by pendulum hammer, which can produce four different velocities (V=2.0 m/s, 2.5 m/s, 3.3 m/s, and 4.2 m/s when the incident bar is impacted by pendulum hammer. The incident wave produced by pendulum hammer is a slowly rising stress wave, which allows gradual stress accumulation in the specimen and maintains the load at both ends of the specimen in an equilibrium state. The results show that the dynamic strength of three kinds of BD samples represented loading rates dependence, and FG sandstones are more sensitive for loading rates than MG and CG samples. Moreover, the peak strength is observed to increase linearly with an increasing stress rates, and the relationship between the dynamic BD strength and stress rates can be built through a linear equation. Finally, the failure modes of different grain sizes are discussed and explained by microfailure mechanism.

  1. Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

    Directory of Open Access Journals (Sweden)

    Ali Ghorbani

    2017-01-01

    Full Text Available Coupled Piled Raft Foundations (CPRFs are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using three-dimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile’s configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

  2. Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

    Science.gov (United States)

    Huang, Daizheng; Wu, Zhihui

    2017-01-01

    Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

  3. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Science.gov (United States)

    Ziv, Omer; Zaritsky, Assaf; Yaffe, Yakey; Mutukula, Naresh; Edri, Reuven; Elkabetz, Yechiel

    2015-10-01

    Neural stem cells (NSCs) are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture) and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM)--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII) reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  4. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Directory of Open Access Journals (Sweden)

    Omer Ziv

    2015-10-01

    Full Text Available Neural stem cells (NSCs are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  5. 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.

  6. Relativistic model-potential oscillator strengths and transition probabilities for 4fsup(n)6s-4fsup(n)6p transitions in Eu(II), Tb(II), and Ho(II) in J1j coupling

    International Nuclear Information System (INIS)

    Migdalek, J.

    1984-01-01

    The lowest 4fsup(n)6s-4fsup(n)6p transitions are studied for the Eu(II) (n=7), Tb(II) (n=9), and Ho(II) (n=11) spectra, where the J 1 J coupling is an acceptable approximation. The relativistic radial integrals, required to evaluate the oscillator strengths and transition probabilities, are calculated with the model-potential method, which includes also core-polarization effects. The similarities observed in oscillator strengths for transitions with given ΔJ but different J values are discussed and explained. The computed oscillator strengths are compared with those obtained with the Coulomb approximation and it is found that the latter are only 11-12% lower. The core polarization influence on oscillator strengths is also investigated and the 19-21% decrease in oscillator strengths due to this effect is predicted. This result may, however, be overestimated because of some deficiencies in our procedure. (author)

  7. Firing patterns transition and desynchronization induced by time delay in neural networks

    Science.gov (United States)

    Huang, Shoufang; Zhang, Jiqian; Wang, Maosheng; Hu, Chin-Kun

    2018-06-01

    We used the Hindmarsh-Rose (HR) model (Hindmarsh and Rose, 1984) to study the effect of time delay on the transition of firing behaviors and desynchronization in neural networks. As time delay is increased, neural networks exhibit diversity of firing behaviors, including regular spiking or bursting and firing patterns transitions (FPTs). Meanwhile, the desynchronization of firing and unstable bursting with decreasing amplitude in neural system, are also increasingly enhanced with the increase of time delay. Furthermore, we also studied the effect of coupling strength and network randomness on these phenomena. Our results imply that time delays can induce transition and desynchronization of firing behaviors in neural networks. These findings provide new insight into the role of time delay in the firing activities of neural networks, and can help to better understand the firing phenomena in complex systems of neural networks. A possible mechanism in brain that can cause the increase of time delay is discussed.

  8. The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

    Science.gov (United States)

    Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

    2018-01-01

    Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

  9. Forecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processing

    Energy Technology Data Exchange (ETDEWEB)

    Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

    2008-04-15

    The objective of this work is to improve the performance of an artificial neural network (ANN) with a statistical weighted pre-processing (SWP) method to learn to predict ground source heat pump (GCHP) systems with the minimum data set. Experimental studies were completed to obtain training and test data. Air temperatures entering/leaving condenser unit, water-antifreeze solution entering/leaving the horizontal ground heat exchangers and ground temperatures (1 and 2 m) were used as input layer, while the output is coefficient of performance (COP) of system. Some statistical methods, such as the root-mean squared (RMS), the coefficient of multiple determinations (R{sup 2}) and the coefficient of variation (cov) is used to compare predicted and actual values for model validation. It is found that RMS value is 0.074, R{sup 2} value is 0.9999 and cov value is 2.22 for SCG6 algorithm of only ANN structure. It is also found that RMS value is 0.002, R{sup 2} value is 0.9999 and cov value is 0.076 for SCG6 algorithm of SWP-ANN structure. The simulation results show that the SWP based networks can be used an alternative way in these systems. Therefore, instead of limited experimental data found in literature, faster and simpler solutions are obtained using hybridized structures such as SWP-ANN. (author)

  10. Nonlinear dynamics of a pulse-coupled neural oscillator model of orientation tuning in the visual cortex

    International Nuclear Information System (INIS)

    Bressloff, P.C.; Bressloff, N.W.

    2000-01-01

    Orientation tuning in a ring of pulse-coupled integrate-and-fire (IF) neurons is analyzed in terms of spontaneous pattern formation. It is shown how the ring bifurcates from a synchronous state to a non-phase-locked state whose spike trains are characterized by quasiperiodic variations of the inter-spike intervals (ISIs) on closed invariant circles. The separation of these invariant circles in phase space results in a localized peak of activity as measured by the time-averaged firing rate of the neurons. This generates a sharp orientation tuning curve that can lock to a slowly rotating, weakly tuned external stimulus. For fast synapses, breakup of the quasiperiodic orbits occurs leading to high spike time variability suggestive of chaos

  11. Nonlinear dynamics of a pulse-coupled neural oscillator model of orientation tuning in the visual cortex

    Science.gov (United States)

    Bressloff, P. C.; Bressloff, N. W.

    2000-02-01

    Orientation tuning in a ring of pulse-coupled integrate-and-fire (IF) neurons is analyzed in terms of spontaneous pattern formation. It is shown how the ring bifurcates from a synchronous state to a non-phase-locked state whose spike trains are characterized by quasiperiodic variations of the inter-spike intervals (ISIs) on closed invariant circles. The separation of these invariant circles in phase space results in a localized peak of activity as measured by the time-averaged firing rate of the neurons. This generates a sharp orientation tuning curve that can lock to a slowly rotating, weakly tuned external stimulus. For fast synapses, breakup of the quasiperiodic orbits occurs leading to high spike time variability suggestive of chaos.

  12. Coupled Heuristic Prediction of Long Lead-Time Accumulated Total Inflow of a Reservoir during Typhoons Using Deterministic Recurrent and Fuzzy Inference-Based Neural Network

    Directory of Open Access Journals (Sweden)

    Chien-Lin Huang

    2015-11-01

    Full Text Available This study applies Real-Time Recurrent Learning Neural Network (RTRLNN and Adaptive Network-based Fuzzy Inference System (ANFIS with novel heuristic techniques to develop an advanced prediction model of accumulated total inflow of a reservoir in order to solve the difficulties of future long lead-time highly varied uncertainty during typhoon attacks while using a real-time forecast. For promoting the temporal-spatial forecasted precision, the following original specialized heuristic inputs were coupled: observed-predicted inflow increase/decrease (OPIID rate, total precipitation, and duration from current time to the time of maximum precipitation and direct runoff ending (DRE. This study also investigated the temporal-spatial forecasted error feature to assess the feasibility of the developed models, and analyzed the output sensitivity of both single and combined heuristic inputs to determine whether the heuristic model is susceptible to the impact of future forecasted uncertainty/errors. Validation results showed that the long lead-time–predicted accuracy and stability of the RTRLNN-based accumulated total inflow model are better than that of the ANFIS-based model because of the real-time recurrent deterministic routing mechanism of RTRLNN. Simulations show that the RTRLNN-based model with coupled heuristic inputs (RTRLNN-CHI, average error percentage (AEP/average forecast lead-time (AFLT: 6.3%/49 h can achieve better prediction than the model with non-heuristic inputs (AEP of RTRLNN-NHI and ANFIS-NHI: 15.2%/31.8% because of the full consideration of real-time hydrological initial/boundary conditions. Besides, the RTRLNN-CHI model can promote the forecasted lead-time above 49 h with less than 10% of AEP which can overcome the previous forecasted limits of 6-h AFLT with above 20%–40% of AEP.

  13. A Visible and Passive Millimeter Wave Image Fusion Algorithm Based on Pulse-Coupled Neural Network in Tetrolet Domain for Early Risk Warning

    Directory of Open Access Journals (Sweden)

    Yuanjiang Li

    2018-01-01

    Full Text Available An algorithm based on pulse-coupled neural network (PCNN constructed in the Tetrolet transform domain is proposed for the fusion of the visible and passive millimeter wave images in order to effectively identify concealed targets. The Tetrolet transform is applied to build the framework of the multiscale decomposition due to its high sparse degree. Meanwhile, a Laplacian pyramid is used to decompose the low-pass band of the Tetrolet transform for improving the approximation performance. In addition, the maximum criterion based on regional average gradient is applied to fuse the top layers along with selecting the maximum absolute values of the other layers. Furthermore, an improved PCNN model is employed to enhance the contour feature of the hidden targets and obtain the fusion results of the high-pass band based on the firing time. Finally, the inverse transform of Tetrolet is exploited to obtain the fused results. Some objective evaluation indexes, such as information entropy, mutual information, and QAB/F, are adopted for evaluating the quality of the fused images. The experimental results show that the proposed algorithm is superior to other image fusion algorithms.

  14. Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database

    Directory of Open Access Journals (Sweden)

    Mihail Lucian Birsa

    2011-10-01

    Full Text Available In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen, or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry and GC-MS (gas chromatography-mass spectrometry spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA. The scores of the forensic compounds on the main principal components (PCs were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%, as well as a good selectivity (a rate of true negatives TN

  15. Locally optimal extracellular stimulation for chaotic desynchronization of neural populations.

    Science.gov (United States)

    Wilson, Dan; Moehlis, Jeff

    2014-10-01

    We use optimal control theory to design a methodology to find locally optimal stimuli for desynchronization of a model of neurons with extracellular stimulation. This methodology yields stimuli which lead to positive Lyapunov exponents, and hence desynchronizes a neural population. We analyze this methodology in the presence of interneuron coupling to make predictions about the strength of stimulation required to overcome synchronizing effects of coupling. This methodology suggests a powerful alternative to pulsatile stimuli for deep brain stimulation as it uses less energy than pulsatile stimuli, and could eliminate the time consuming tuning process.

  16. Individual differences in sound-in-noise perception are related to the strength of short-latency neural responses to noise.

    Directory of Open Access Journals (Sweden)

    Ekaterina Vinnik

    2011-02-01

    Full Text Available Important sounds can be easily missed or misidentified in the presence of extraneous noise. We describe an auditory illusion in which a continuous ongoing tone becomes inaudible during a brief, non-masking noise burst more than one octave away, which is unexpected given the frequency resolution of human hearing. Participants strongly susceptible to this illusory discontinuity did not perceive illusory auditory continuity (in which a sound subjectively continues during a burst of masking noise when the noises were short, yet did so at longer noise durations. Participants who were not prone to illusory discontinuity showed robust early electroencephalographic responses at 40-66 ms after noise burst onset, whereas those prone to the illusion lacked these early responses. These data suggest that short-latency neural responses to auditory scene components reflect subsequent individual differences in the parsing of auditory scenes.

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

    Science.gov (United States)

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

    2018-02-01

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

  18. Measurements of the Higgs boson production and decay rates and coupling strengths using pp collision data at [Formula: see text] and 8 TeV in the ATLAS experiment.

    Science.gov (United States)

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Simmons, B; Simon, D; Simoniello, R; Sinervo, P; Sinev, N B; Siragusa, G; Sisakyan, A N; Sivoklokov, S Yu; Sjölin, J; Sjursen, T B; Skinner, M B; Skottowe, H P; Skubic, P; Slater, M; Slavicek, T; Slawinska, M; Sliwa, K; Smakhtin, V; Smart, B H; Smestad, L; Smirnov, S Yu; Smirnov, Y; Smirnova, L N; Smirnova, O; Smith, M N K; Smith, R W; Smizanska, M; Smolek, K; Snesarev, A A; Snidero, G; Snyder, S; Sobie, R; Socher, F; Soffer, A; Soh, D A; Solans, C A; Solar, M; Solc, J; Soldatov, E Yu; Soldevila, U; Solodkov, A A; Soloshenko, A; Solovyanov, O V; Solovyev, V; Sommer, P; Song, H Y; Soni, N; Sood, A; Sopczak, A; Sopko, B; Sopko, V; Sorin, V; Sosa, D; Sosebee, M; Sotiropoulou, C L; Soualah, R; Soueid, P; Soukharev, A M; South, D; Sowden, B C; Spagnolo, S; Spalla, M; Spanò, F; Spearman, W R; Spettel, F; Spighi, R; Spigo, G; Spiller, L A; Spousta, M; Spreitzer, T; St Denis, R D; Staerz, S; Stahlman, J; Stamen, R; Stamm, S; Stanecka, E; Stanescu, C; Stanescu-Bellu, M; Stanitzki, M M; Stapnes, S; Starchenko, E A; Stark, J; Staroba, P; Starovoitov, P; Staszewski, R; Stavina, P; Steinberg, P; Stelzer, B; Stelzer, H J; Stelzer-Chilton, O; Stenzel, H; Stern, S; Stewart, G A; Stillings, J A; Stockton, M C; Stoebe, M; Stoicea, G; Stolte, P; Stonjek, S; Stradling, A R; Straessner, A; Stramaglia, M E; Strandberg, J; Strandberg, S; Strandlie, A; Strauss, E; Strauss, M; Strizenec, P; Ströhmer, R; Strom, D M; Stroynowski, R; Strubig, A; Stucci, S A; Stugu, B; Styles, N A; Su, D; Su, J; Subramaniam, R; Succurro, A; Sugaya, Y; Suhr, C; Suk, M; Sulin, V V; Sultansoy, S; Sumida, T; Sun, S; Sun, X; Sundermann, J E; Suruliz, K; Susinno, G; Sutton, M R; Suzuki, S; Suzuki, Y; Svatos, M; Swedish, S; Swiatlowski, M; Sykora, I; Sykora, T; Ta, D; Taccini, C; Tackmann, K; Taenzer, J; Taffard, A; Tafirout, R; Taiblum, N; Takai, H; Takashima, R; Takeda, H; Takeshita, T; Takubo, Y; Talby, M; Talyshev, A A; Tam, J Y C; Tan, K G; Tanaka, J; Tanaka, R; Tanaka, S; Tannenwald, B B; Tannoury, N; Tapprogge, S; Tarem, S; Tarrade, F; Tartarelli, G F; Tas, P; Tasevsky, M; Tashiro, T; Tassi, E; Tavares Delgado, A; Tayalati, Y; Taylor, F E; Taylor, G N; Taylor, W; Teischinger, F A; Teixeira Dias Castanheira, M; Teixeira-Dias, P; Temming, K K; Ten Kate, H; Teng, P K; Teoh, J J; Tepel, F; Terada, S; Terashi, K; Terron, J; Terzo, S; Testa, M; Teuscher, R J; Therhaag, J; Theveneaux-Pelzer, T; Thomas, J P; Thomas-Wilsker, J; Thompson, E N; Thompson, P D; Thompson, R J; Thompson, A S; Thomsen, L A; Thomson, E; Thomson, M; Thun, R P; Tibbetts, M J; Ticse Torres, R E; Tikhomirov, V O; Tikhonov, Yu A; Timoshenko, S; Tiouchichine, E; Tipton, P; Tisserant, S; Todorov, T; Todorova-Nova, S; Tojo, J; Tokár, S; Tokushuku, K; Tollefson, K; Tolley, E; Tomlinson, L; Tomoto, M; Tompkins, L; Toms, K; Torrence, E; Torres, H; Torró Pastor, E; Toth, J; Touchard, F; Tovey, D R; Trefzger, T; Tremblet, L; Tricoli, A; Trigger, I M; Trincaz-Duvoid, S; Tripiana, M F; Trischuk, W; Trocmé, B; Troncon, C; Trottier-McDonald, M; Trovatelli, M; True, P; Truong, L; Trzebinski, M; Trzupek, A; Tsarouchas, C; Tseng, J C-L; Tsiareshka, P V; Tsionou, D; Tsipolitis, G; Tsirintanis, N; Tsiskaridze, S; Tsiskaridze, V; Tskhadadze, E G; Tsukerman, I I; Tsulaia, V; Tsuno, S; Tsybychev, D; Tudorache, A; Tudorache, V; Tuna, A N; Tupputi, S A; Turchikhin, S; Turecek, D; Turra, R; Turvey, A J; Tuts, P M; Tykhonov, A; Tylmad, M; Tyndel, M; Ueda, I; Ueno, R; Ughetto, M; Ugland, M; Uhlenbrock, M; Ukegawa, F; Unal, G; Undrus, A; Unel, G; Ungaro, F C; Unno, Y; Unverdorben, C; Urban, J; Urquijo, P; Urrejola, P; Usai, G; Usanova, A; Vacavant, L; Vacek, V; Vachon, B; Valderanis, C; Valencic, N; Valentinetti, S; Valero, A; Valery, L; Valkar, S; Valladolid Gallego, E; Vallecorsa, S; Valls Ferrer, J A; Van Den Wollenberg, W; Van Der Deijl, P C; van der Geer, R; van der Graaf, H; Van Der Leeuw, R; van Eldik, N; van Gemmeren, P; Van Nieuwkoop, J; van Vulpen, I; van Woerden, M C; Vanadia, M; Vandelli, W; Vanguri, R; Vaniachine, A; Vannucci, F; Vardanyan, G; Vari, R; Varnes, E W; Varol, T; Varouchas, D; Vartapetian, A; Varvell, K E; Vazeille, F; Vazquez Schroeder, T; Veatch, J; Veloce, L M; Veloso, F; Velz, T; Veneziano, S; Ventura, A; Ventura, D; Venturi, M; Venturi, N; Venturini, A; Vercesi, V; Verducci, M; Verkerke, W; Vermeulen, J C; Vest, A; Vetterli, M C; Viazlo, O; Vichou, I; Vickey, T; Vickey Boeriu, O E; Viehhauser, G H A; Viel, S; Vigne, R; Villa, M; Villaplana Perez, M; Vilucchi, E; Vincter, M G; Vinogradov, V B; Vivarelli, I; Vives Vaque, F; Vlachos, S; Vladoiu, D; Vlasak, M; Vogel, M; Vokac, P; Volpi, G; Volpi, M; von der Schmitt, H; von Radziewski, H; von Toerne, E; Vorobel, V; Vorobev, K; Vos, M; Voss, R; Vossebeld, J H; Vranjes, N; Vranjes Milosavljevic, M; Vrba, V; Vreeswijk, M; Vuillermet, R; Vukotic, I; Vykydal, Z; Wagner, P; Wagner, W; Wahlberg, H; Wahrmund, S; Wakabayashi, J; Walder, J; Walker, R; Walkowiak, W; Wang, C; Wang, F; Wang, H; Wang, H; Wang, J; Wang, J; Wang, K; Wang, R; Wang, S M; Wang, T; Wang, X; Wanotayaroj, C; Warburton, A; Ward, C P; Wardrope, D R; Warsinsky, M; Washbrook, A; Wasicki, C; Watkins, P M; Watson, A T; Watson, I J; Watson, M F; Watts, G; Watts, S; Waugh, B M; Webb, S; Weber, M S; Weber, S W; Webster, J S; Weidberg, A R; Weinert, B; Weingarten, J; Weiser, C; Weits, H; Wells, P S; Wenaus, T; Wengler, T; Wenig, S; Wermes, N; Werner, M; Werner, P; Wessels, M; Wetter, J; Whalen, K; Wharton, A M; White, A; White, M J; White, R; White, S; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Wienemann, P; Wiglesworth, C; Wiik-Fuchs, L A M; Wildauer, A; Wilkens, H G; Williams, H H; Williams, S; Willis, C; Willocq, S; Wilson, A; Wilson, J A; Wingerter-Seez, I; Winklmeier, F; Winter, B T; Wittgen, M; Wittkowski, J; Wollstadt, S J; Wolter, M W; Wolters, H; Wosiek, B K; Wotschack, J; Woudstra, M J; Wozniak, K W; Wu, M; Wu, M; Wu, S L; Wu, X; Wu, Y; Wyatt, T R; Wynne, B M; Xella, S; Xu, D; Xu, L; Yabsley, B; Yacoob, S; Yakabe, R; Yamada, M; Yamaguchi, Y; Yamamoto, A; Yamamoto, S; Yamanaka, T; Yamauchi, K; Yamazaki, Y; Yan, Z; Yang, H; Yang, H; Yang, Y; Yao, L; Yao, W-M; Yasu, Y; Yatsenko, E; Yau Wong, K H; Ye, J; Ye, S; Yeletskikh, I; Yen, A L; Yildirim, E; Yorita, K; Yoshida, R; Yoshihara, K; Young, C; Young, C J S; Youssef, S; Yu, D R; Yu, J; Yu, J M; Yu, J; Yuan, L; Yurkewicz, A; Yusuff, I; Zabinski, B; Zaidan, R; Zaitsev, A M; Zalieckas, J; Zaman, A; Zambito, S; Zanello, L; Zanzi, D; Zeitnitz, C; Zeman, M; Zemla, A; Zengel, K; Zenin, O; Ženiš, T; Zerwas, D; Zhang, D; Zhang, F; Zhang, J; Zhang, L; Zhang, R; Zhang, X; Zhang, Z; Zhao, X; Zhao, Y; Zhao, Z; Zhemchugov, A; Zhong, J; Zhou, B; Zhou, C; Zhou, L; Zhou, L; Zhou, N; Zhu, C G; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhukov, K; Zibell, A; Zieminska, D; Zimine, N I; Zimmermann, C; Zimmermann, S; Zinonos, Z; Zinser, M; Ziolkowski, M; Živković, L; Zobernig, G; Zoccoli, A; Zur Nedden, M; Zurzolo, G; Zwalinski, L

    Combined analyses of the Higgs boson production and decay rates as well as its coupling strengths to vector bosons and fermions are presented. The combinations include the results of the analyses of the [Formula: see text] and [Formula: see text] decay modes, and the constraints on the associated production with a pair of top quarks and on the off-shell coupling strengths of the Higgs boson. The results are based on the LHC proton-proton collision datasets, with integrated luminosities of up to 4.7 [Formula: see text] at [Formula: see text] TeV and 20.3 [Formula: see text] at [Formula: see text] TeV, recorded by the ATLAS detector in 2011 and 2012. Combining all production modes and decay channels, the measured signal yield, normalised to the Standard Model expectation, is [Formula: see text]. The observed Higgs boson production and decay rates are interpreted in a leading-order coupling framework, exploring a wide range of benchmark coupling models both with and without assumptions on the Higgs boson width and on the Standard Model particle content in loop processes. The data are found to be compatible with the Standard Model expectations for a Higgs boson at a mass of 125.36 GeV for all models considered.

  19. Measurements of the Higgs boson production and decay rates and coupling strengths using pp collision data at √s=7 and 8 TeV in the ATLAS experiment

    Energy Technology Data Exchange (ETDEWEB)

    Aad, G. [CPPM, Aix-Marseille Université and CNRS/IN2P3, Marseille (France); Abbott, B. [Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK (United States); Abdallah, J. [Institute of Physics, Academia Sinica, Taipei, Taiwan (China); Abdinov, O. [Institute of Physics, Azerbaijan Academy of Sciences, Baku (Azerbaijan); Aben, R. [Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam (Netherlands); and others

    2016-01-05

    Combined analyses of the Higgs boson production and decay rates as well as its coupling strengths to vector bosons and fermions are presented. The combinations include the results of the analyses of the H→γγ, ZZ{sup ∗}, WW{sup ∗}, Zγ, bb{sup -bar}, ττ and μμ decay modes, and the constraints on the associated production with a pair of top quarks and on the off-shell coupling strengths of the Higgs boson. The results are based on the LHC proton-proton collision datasets, with integrated luminosities of up to 4.7 fb{sup -1} at √s=7 TeV and 20.3 fb{sup -1} at √s=8 TeV, recorded by the ATLAS detector in 2011 and 2012. Combining all production modes and decay channels, the measured signal yield, normalised to the Standard Model expectation, is 1.18{sub -0.14}{sup +0.15}. The observed Higgs boson production and decay rates are interpreted in a leading-order coupling framework, exploring a wide range of benchmark coupling models both with and without assumptions on the Higgs boson width and on the Standard Model particle content in loop processes. The data are found to be compatible with the Standard Model expectations for a Higgs boson at a mass of 125.36 GeV for all models considered.

  20. Measurements of the Higgs boson production and decay rates and coupling strengths using pp collision data at √(s) = 7 and 8 TeV in the ATLAS experiment

    International Nuclear Information System (INIS)

    Aad, G.; Abbott, B.; Abdallah, J.

    2016-01-01

    Combined analyses of the Higgs boson production and decay rates as well as its coupling strengths to vector bosons and fermions are presented. The combinations include the results of the analyses of the H → γγ, ZZ*, WW*, Zγ, b anti b, ττ and μμ decay modes, and the constraints on the associated production with a pair of top quarks and on the off-shell coupling strengths of the Higgs boson. The results are based on the LHC proton-proton collision datasets, with integrated luminosities of up to 4.7 fb -1 at √(s) = 7 TeV and 20.3 fb -1 at √(s) = 8 TeV, recorded by the ATLAS detector in 2011 and 2012. Combining all production modes and decay channels, the measured signal yield, normalised to the Standard Model expectation, is 1.18 -0.14 +0.15 . The observed Higgs boson production and decay rates are interpreted in a leading-order coupling framework, exploring a wide range of benchmark coupling models both with and without assumptions on the Higgs boson width and on the Standard Model particle content in loop processes. The data are found to be compatible with the Standard Model expectations for a Higgs boson at a mass of 125.36 GeV for all models considered. (orig.)

  1. Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network.

    Science.gov (United States)

    De Filippis, Luigi Alberto Ciro; Serio, Livia Maria; Facchini, Francesco; Mummolo, Giovanni; Ludovico, Antonio Domenico

    2016-11-10

    A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

  2. Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Luigi Alberto Ciro De Filippis

    2016-11-01

    Full Text Available A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable and the mechanical properties (output responses of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls. The simulation model was based on the adoption of the Artificial Neural Networks (ANNs characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

  3. Death and rebirth of neural activity in sparse inhibitory networks

    Science.gov (United States)

    Angulo-Garcia, David; Luccioli, Stefano; Olmi, Simona; Torcini, Alessandro

    2017-05-01

    Inhibition is a key aspect of neural dynamics playing a fundamental role for the emergence of neural rhythms and the implementation of various information coding strategies. Inhibitory populations are present in several brain structures, and the comprehension of their dynamics is strategical for the understanding of neural processing. In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of neural activity, as expected, but can also promote neural re-activation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neuronal death). However, the random pruning of connections is able to reverse the action of inhibition, i.e. in a random sparse network a sufficiently strong synaptic strength can surprisingly promote, rather than depress, the activity of neurons (neuronal rebirth). Thus, the number of firing neurons reaches a minimum value at some intermediate synaptic strength. We show that this minimum signals a transition from a regime dominated by neurons with a higher firing activity to a phase where all neurons are effectively sub-threshold and their irregular firing is driven by current fluctuations. We explain the origin of the transition by deriving a mean field formulation of the problem able to provide the fraction of active neurons as well as the first two moments of their firing statistics. The introduction of a synaptic time scale does not modify the main aspects of the reported phenomenon. However, for sufficiently slow synapses the transition becomes dramatic, and the system passes from a perfectly regular evolution to irregular bursting dynamics. In this latter regime the model provides predictions consistent with experimental findings for a specific class of neurons, namely the medium spiny neurons in the striatum.

  4. Effects of hydrodynamic mixing intensity coupled with ionic strength on the initial stage dynamics of bridging flocculation of polystyrene latex particles with polyelectrolyte

    NARCIS (Netherlands)

    Adachi, Y.; Matsumoto, T.; Cohen Stuart, M.A.

    2002-01-01

    Effects of hydrodynamic mixing intensity on the initial stage dynamics of bridging flocculation induced by adsorbing polyelectrolyte were analyzed as an extension of previous report on the effect of ionic strength (J. Coll. Int. Sci. 204 (1998) 328). Mixing condition were changed by adopting forked

  5. Transitions to Synchrony in Coupled Bursting Neurons

    Science.gov (United States)

    Dhamala, Mukeshwar; Jirsa, Viktor K.; Ding, Mingzhou

    2004-01-01

    Certain cells in the brain, for example, thalamic neurons during sleep, show spike-burst activity. We study such spike-burst neural activity and the transitions to a synchronized state using a model of coupled bursting neurons. In an electrically coupled network, we show that the increase of coupling strength increases incoherence first and then induces two different transitions to synchronized states, one associated with bursts and the other with spikes. These sequential transitions to synchronized states are determined by the zero crossings of the maximum transverse Lyapunov exponents. These results suggest that synchronization of spike-burst activity is a multi-time-scale phenomenon and burst synchrony is a precursor to spike synchrony.

  6. Transitions to synchrony in coupled bursting neurons

    International Nuclear Information System (INIS)

    Dhamala, Mukeshwar; Jirsa, Viktor K.; Ding Mingzhou

    2004-01-01

    Certain cells in the brain, for example, thalamic neurons during sleep, show spike-burst activity. We study such spike-burst neural activity and the transitions to a synchronized state using a model of coupled bursting neurons. In an electrically coupled network, we show that the increase of coupling strength increases incoherence first and then induces two different transitions to synchronized states, one associated with bursts and the other with spikes. These sequential transitions to synchronized states are determined by the zero crossings of the maximum transverse Lyapunov exponents. These results suggest that synchronization of spike-burst activity is a multi-time-scale phenomenon and burst synchrony is a precursor to spike synchrony

  7. Adaptations in athletic performance after ballistic power versus strength training.

    Science.gov (United States)

    Cormie, Prue; McGuigan, Michael R; Newton, Robert U

    2010-08-01

    To determine whether the magnitude of improvement in athletic performance and the mechanisms driving these adaptations differ in relatively weak individuals exposed to either ballistic power training or heavy strength training. Relatively weak men (n = 24) who could perform the back squat with proficient technique were randomized into three groups: strength training (n = 8; ST), power training (n = 8; PT), or control (n = 8). Training involved three sessions per week for 10 wk in which subjects performed back squats with 75%-90% of one-repetition maximum (1RM; ST) or maximal-effort jump squats with 0%-30% 1RM (PT). Jump and sprint performances were assessed as well as measures of the force-velocity relationship, jumping mechanics, muscle architecture, and neural drive. Both experimental groups showed significant (P training with no significant between-group differences evident in either jump (peak power: ST = 17.7% +/- 9.3%, PT = 17.6% +/- 4.5%) or sprint performance (40-m sprint: ST = 2.2% +/- 1.9%, PT = 3.6% +/- 2.3%). ST also displayed a significant increase in maximal strength that was significantly greater than the PT group (squat 1RM: ST = 31.2% +/- 11.3%, PT = 4.5% +/- 7.1%). The mechanisms driving these improvements included significant (P force-velocity relationship, jump mechanics, muscle architecture, and neural activation that showed a degree of specificity to the different training stimuli. Improvements in athletic performance were similar in relatively weak individuals exposed to either ballistic power training or heavy strength training for 10 wk. These performance improvements were mediated through neuromuscular adaptations specific to the training stimulus. The ability of strength training to render similar short-term improvements in athletic performance as ballistic power training, coupled with the potential long-term benefits of improved maximal strength, makes strength training a more effective training modality for relatively weak individuals.

  8. Contribution of the active control to the measurement of fluid-elastic coupling strengths; Apport du controle actif pour la mesure des forces de couplage fluide-elastique

    Energy Technology Data Exchange (ETDEWEB)

    Legendre, S

    1999-06-30

    A precise dimensioning of the tubes inside a steam generator requires a better knowledge of the fluid-elastic coupling phenomena. The direct method for the determination of fluid-elastic coupling coefficients allows to explore only a reduced range of flow velocities and is unsuitable for the low velocities and for velocities close to the critical instability velocity. The active damping control method has been validated both with air and water and offers the possibility to extend the range of flow velocities using an artificial supply of damping: 50% of increase in single-phase flow conditions with measurements performed beyond the critical instability velocity, a doubling of the explored range of velocities in two-phase flow conditions. For a 25% two-phase flow, a stabilization of the damping of the coupled fluid-structure system is observed beyond the critical instability velocity. Finally, the calculation of fluid-elastic dimensionless coefficients has permitted to show the influence of the reduced velocity on the fluid-elastic coupling in two-phase flow conditions. (J.S.)

  9. Coherence Resonance and Noise-Induced Synchronization in Hindmarsh-Rose Neural Network with Different Topologies

    International Nuclear Information System (INIS)

    Wei Duqu; Luo Xiaoshu

    2007-01-01

    In this paper, we investigate coherence resonance (CR) and noise-induced synchronization in Hindmarsh-Rose (HR) neural network with three different types of topologies: regular, random, and small-world. It is found that the additive noise can induce CR in HR neural network with different topologies and its coherence is optimized by a proper noise level. It is also found that as coupling strength increases the plateau in the measure of coherence curve becomes broadened and the effects of network topology is more pronounced simultaneously. Moreover, we find that increasing the probability p of the network topology leads to an enhancement of noise-induced synchronization in HR neurons network.

  10. Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks.

    Science.gov (United States)

    Arbabi, Vahid; Pouran, Behdad; Campoli, Gianni; Weinans, Harrie; Zadpoor, Amir A

    2016-03-21

    One of the most widely used techniques to determine the mechanical properties of cartilage is based on indentation tests and interpretation of the obtained force-time or displacement-time data. In the current computational approaches, one needs to simulate the indentation test with finite element models and use an optimization algorithm to estimate the mechanical properties of cartilage. The modeling procedure is cumbersome, and the simulations need to be repeated for every new experiment. For the first time, we propose a method for fast and accurate estimation of the mechanical and physical properties of cartilage as a poroelastic material with the aid of artificial neural networks. In our study, we used finite element models to simulate the indentation for poroelastic materials with wide combinations of mechanical and physical properties. The obtained force-time curves are then divided into three parts: the first two parts of the data is used for training and validation of an artificial neural network, while the third part is used for testing the trained network. The trained neural network receives the force-time curves as the input and provides the properties of cartilage as the output. We observed that the trained network could accurately predict the properties of cartilage within the range of properties for which it was trained. The mechanical and physical properties of cartilage could therefore be estimated very fast, since no additional finite element modeling is required once the neural network is trained. The robustness of the trained artificial neural network in determining the properties of cartilage based on noisy force-time data was assessed by introducing noise to the simulated force-time data. We found that the training procedure could be optimized so as to maximize the robustness of the neural network against noisy force-time data. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Measurements of the Higgs boson production and decay rates and coupling strengths using pp collision data at √s = 7 and 8 TeV in the ATLAS experiment

    CERN Document Server

    The ATLAS collaboration

    2015-01-01

    Combined analyses of the Higgs boson production and decay rates as well as of its coupling strengths to vector bosons and fermions are presented. Included in the combinations are the results of the decay modes H → γγ, ZZ∗, WW∗, Zγ, bb ̄, ττ and μμ, and the constraints on the associated production with a pair of top quarks and on the off-shell coupling strengths of the Higgs boson. The results are based on the LHC proton-proton collision datasets, with integrated luminosities of up to 4.7 fb−1 at √s = 7 TeV and 20.3 fb−1 at √s = 8 TeV, recorded by the ATLAS detector in 2011 and 2012. Combining all production modes and decay channels, the measured signal yield, normalised to the Standard Model expectation, is 1.18 ± 0.10 ± 0.07±0.08, where the first error reflects the statistical uncertainty and 0.07 the second and third errors reflect respectively the experimental and theoretical systematic uncertainties. Strong evidence is found for the vector boson fusion process with a signifi...

  12. Measurements of the Higgs boson production and decay rates and coupling strengths using $pp$ collision data at $\\sqrt{s}=7$ and $8$ TeV in the ATLAS experiment

    CERN Document Server

    Aad, Georges; Abdallah, Jalal; Abdinov, Ovsat; Aben, Rosemarie; Abolins, Maris; AbouZeid, Ossama; Abramowicz, Halina; Abreu, Henso; Abreu, Ricardo; Abulaiti, Yiming; Acharya, Bobby Samir; Adamczyk, Leszek; Adams, David; Adelman, Jahred; Adomeit, Stefanie; Adye, Tim; Affolder, Tony; Agatonovic-Jovin, Tatjana; Aguilar-Saavedra, Juan Antonio; Ahlen, Steven; Ahmadov, Faig; Aielli, Giulio; Akerstedt, Henrik; Åkesson, Torsten Paul Ake; Akimoto, Ginga; Akimov, Andrei; Alberghi, Gian Luigi; Albert, Justin; Albrand, Solveig; Alconada Verzini, Maria Josefina; Aleksa, Martin; Aleksandrov, Igor; Alexa, Calin; Alexander, Gideon; Alexopoulos, Theodoros; Alhroob, Muhammad; Alimonti, Gianluca; Alio, Lion; Alison, John; Alkire, Steven Patrick; Allbrooke, Benedict; Allport, Phillip; Aloisio, Alberto; Alonso, Alejandro; Alonso, Francisco; Alpigiani, Cristiano; Altheimer, Andrew David; Alvarez Gonzalez, Barbara; Άlvarez Piqueras, Damián; Alviggi, Mariagrazia; Amadio, Brian Thomas; Amako, Katsuya; Amaral Coutinho, Yara; Amelung, Christoph; Amidei, Dante; Amor Dos Santos, Susana Patricia; Amorim, Antonio; Amoroso, Simone; Amram, Nir; Amundsen, Glenn; Anastopoulos, Christos; Ancu, Lucian Stefan; Andari, Nansi; Andeen, Timothy; Anders, Christoph Falk; Anders, Gabriel; Anders, John Kenneth; Anderson, Kelby; Andreazza, Attilio; Andrei, George Victor; Angelidakis, Stylianos; Angelozzi, Ivan; Anger, Philipp; Angerami, Aaron; Anghinolfi, Francis; Anisenkov, Alexey; Anjos, Nuno; Annovi, Alberto; Antonelli, Mario; Antonov, Alexey; Antos, Jaroslav; Anulli, Fabio; Aoki, Masato; Aperio Bella, Ludovica; Arabidze, Giorgi; Arai, Yasuo; Araque, Juan Pedro; Arce, Ayana; Arduh, Francisco Anuar; Arguin, Jean-Francois; Argyropoulos, Spyridon; Arik, Metin; Armbruster, Aaron James; Arnaez, Olivier; Arnal, Vanessa; Arnold, Hannah; Arratia, Miguel; Arslan, Ozan; Artamonov, Andrei; Artoni, Giacomo; Asai, Shoji; Asbah, Nedaa; Ashkenazi, Adi; Åsman, Barbro; Asquith, Lily; Assamagan, Ketevi; Astalos, Robert; Atkinson, Markus; Atlay, Naim Bora; Auerbach, Benjamin; Augsten, Kamil; Aurousseau, Mathieu; Avolio, Giuseppe; Axen, Bradley; Ayoub, Mohamad Kassem; Azuelos, Georges; Baak, Max; Baas, Alessandra; Bacci, Cesare; Bachacou, Henri; Bachas, Konstantinos; Backes, Moritz; Backhaus, Malte; Bagiacchi, Paolo; Bagnaia, Paolo; Bai, Yu; Bain, Travis; Baines, John; Baker, Oliver Keith; Balek, Petr; Balestri, Thomas; Balli, Fabrice; Banas, Elzbieta; Banerjee, Swagato; Bannoura, Arwa A E; Bansil, Hardeep Singh; Barak, Liron; Barberio, Elisabetta Luigia; Barberis, Dario; Barbero, Marlon; Barillari, Teresa; Barisonzi, Marcello; Barklow, Timothy; Barlow, Nick; Barnes, Sarah Louise; Barnett, Bruce; Barnett, Michael; Barnovska, Zuzana; Baroncelli, Antonio; Barone, Gaetano; Barr, Alan; Barreiro, Fernando; Barreiro Guimarães da Costa, João; Bartoldus, Rainer; Barton, Adam Edward; Bartos, Pavol; Basalaev, Artem; Bassalat, Ahmed; Basye, Austin; Bates, Richard; Batista, Santiago Juan; Batley, Richard; Battaglia, Marco; Bauce, Matteo; Bauer, Florian; Bawa, Harinder Singh; Beacham, James Baker; Beattie, Michael David; Beau, Tristan; Beauchemin, Pierre-Hugues; Beccherle, Roberto; Bechtle, Philip; Beck, Hans Peter; Becker, Anne Kathrin; Becker, Maurice; Becker, Sebastian; Beckingham, Matthew; Becot, Cyril; Beddall, Andrew; Beddall, Ayda; Bednyakov, Vadim; Bee, Christopher; Beemster, Lars; Beermann, Thomas; Begel, Michael; Behr, Janna Katharina; Belanger-Champagne, Camille; Bell, William; Bella, Gideon; Bellagamba, Lorenzo; Bellerive, Alain; Bellomo, Massimiliano; Belotskiy, Konstantin; Beltramello, Olga; Benary, Odette; Benchekroun, Driss; Bender, Michael; Bendtz, Katarina; Benekos, Nektarios; Benhammou, Yan; Benhar Noccioli, Eleonora; Benitez Garcia, Jorge-Armando; Benjamin, Douglas; Bensinger, James; Bentvelsen, Stan; Beresford, Lydia; Beretta, Matteo; Berge, David; Bergeaas Kuutmann, Elin; Berger, Nicolas; Berghaus, Frank; Beringer, Jürg; Bernard, Clare; Bernard, Nathan Rogers; Bernius, Catrin; Bernlochner, Florian Urs; Berry, Tracey; Berta, Peter; Bertella, Claudia; Bertoli, Gabriele; Bertolucci, Federico; Bertsche, Carolyn; Bertsche, David; Besana, Maria Ilaria; Besjes, Geert-Jan; Bessidskaia Bylund, Olga; Bessner, Martin Florian; Besson, Nathalie; Betancourt, Christopher; Bethke, Siegfried; Bevan, Adrian John; Bhimji, Wahid; Bianchi, Riccardo-Maria; Bianchini, Louis; Bianco, Michele; Biebel, Otmar; Bieniek, Stephen Paul; Biglietti, Michela; Bilbao De Mendizabal, Javier; Bilokon, Halina; Bindi, Marcello; Binet, Sebastien; Bingul, Ahmet; Bini, Cesare; Black, Curtis; Black, James; Black, Kevin; Blackburn, Daniel; Blair, Robert; Blanchard, Jean-Baptiste; Blanco, Jacobo Ezequiel; Blazek, Tomas; Bloch, Ingo; Blocker, Craig; Blum, Walter; Blumenschein, Ulrike; Bobbink, Gerjan; Bobrovnikov, Victor; Bocchetta, Simona Serena; Bocci, Andrea; Bock, Christopher; Boehler, Michael; Bogaerts, Joannes Andreas; Bogdanchikov, Alexander; Bohm, Christian; Boisvert, Veronique; Bold, Tomasz; Boldea, Venera; Boldyrev, Alexey; Bomben, Marco; Bona, Marcella; Boonekamp, Maarten; Borisov, Anatoly; Borissov, Guennadi; Borroni, Sara; Bortfeldt, Jonathan; Bortolotto, Valerio; Bos, Kors; Boscherini, Davide; Bosman, Martine; Boudreau, Joseph; Bouffard, Julian; Bouhova-Thacker, Evelina Vassileva; Boumediene, Djamel Eddine; Bourdarios, Claire; Bousson, Nicolas; Boveia, Antonio; Boyd, James; Boyko, Igor; Bozic, Ivan; Bracinik, Juraj; Brandt, Andrew; Brandt, Gerhard; Brandt, Oleg; Bratzler, Uwe; Brau, Benjamin; Brau, James; Braun, Helmut; Brazzale, Simone Federico; Brendlinger, Kurt; Brennan, Amelia Jean; Brenner, Lydia; Brenner, Richard; Bressler, Shikma; Bristow, Kieran; Bristow, Timothy Michael; Britton, Dave; Britzger, Daniel; Brochu, Frederic; Brock, Ian; Brock, Raymond; Bronner, Johanna; Brooijmans, Gustaaf; Brooks, Timothy; Brooks, William; Brosamer, Jacquelyn; Brost, Elizabeth; Brown, Jonathan; Bruckman de Renstrom, Pawel; Bruncko, Dusan; Bruneliere, Renaud; Bruni, Alessia; Bruni, Graziano; Bruschi, Marco; Bryngemark, Lene; Buanes, Trygve; Buat, Quentin; Buchholz, Peter; Buckley, Andrew; Buda, Stelian Ioan; Budagov, Ioulian; Buehrer, Felix; Bugge, Lars; Bugge, Magnar Kopangen; Bulekov, Oleg; Bullock, Daniel; Burckhart, Helfried; Burdin, Sergey; Burghgrave, Blake; Burke, Stephen; Burmeister, Ingo; Busato, Emmanuel; Büscher, Daniel; Büscher, Volker; Bussey, Peter; Butler, John; Butt, Aatif Imtiaz; Buttar, Craig; Butterworth, Jonathan; Butti, Pierfrancesco; Buttinger, William; Buzatu, Adrian; Buzykaev, Aleksey; Cabrera Urbán, Susana; Caforio, Davide; Cairo, Valentina; Cakir, Orhan; Calafiura, Paolo; Calandri, Alessandro; Calderini, Giovanni; Calfayan, Philippe; Caloba, Luiz; Calvet, David; Calvet, Samuel; Camacho Toro, Reina; Camarda, Stefano; Camarri, Paolo; Cameron, David; Caminada, Lea Michaela; Caminal Armadans, Roger; Campana, Simone; Campanelli, Mario; Campoverde, Angel; Canale, Vincenzo; Canepa, Anadi; Cano Bret, Marc; Cantero, Josu; Cantrill, Robert; Cao, Tingting; Capeans Garrido, Maria Del Mar; Caprini, Irinel; Caprini, Mihai; Capua, Marcella; Caputo, Regina; Cardarelli, Roberto; Carli, Tancredi; Carlino, Gianpaolo; Carminati, Leonardo; Caron, Sascha; Carquin, Edson; Carrillo-Montoya, German D; Carter, Janet; Carvalho, João; Casadei, Diego; Casado, Maria Pilar; Casolino, Mirkoantonio; Castaneda-Miranda, Elizabeth; Castelli, Angelantonio; Castillo Gimenez, Victoria; Castro, Nuno Filipe; Catastini, Pierluigi; Catinaccio, Andrea; Catmore, James; Cattai, Ariella; Caudron, Julien; Cavaliere, Viviana; Cavalli, Donatella; Cavalli-Sforza, Matteo; Cavasinni, Vincenzo; Ceradini, Filippo; Cerio, Benjamin; Cerny, Karel; Santiago Cerqueira, Augusto; Cerri, Alessandro; Cerrito, Lucio; Cerutti, Fabio; Cerv, Matevz; Cervelli, Alberto; Cetin, Serkant Ali; Chafaq, Aziz; Chakraborty, Dhiman; Chalupkova, Ina; Chang, Philip; Chapleau, Bertrand; Chapman, John Derek; Charlton, Dave; Chau, Chav Chhiv; Chavez Barajas, Carlos Alberto; Cheatham, Susan; Chegwidden, Andrew; Chekanov, Sergei; Chekulaev, Sergey; Chelkov, Gueorgui; Chelstowska, Magda Anna; Chen, Chunhui; Chen, Hucheng; Chen, Karen; Chen, Liming; Chen, Shenjian; Chen, Xin; Chen, Ye; Cheng, Hok Chuen; Cheng, Yangyang; Cheplakov, Alexander; Cheremushkina, Evgenia; Cherkaoui El Moursli, Rajaa; Chernyatin, Valeriy; Cheu, Elliott; Chevalier, Laurent; Chiarella, Vitaliano; Childers, John Taylor; Chiodini, Gabriele; Chisholm, Andrew; Chislett, Rebecca Thalatta; Chitan, Adrian; Chizhov, Mihail; Choi, Kyungeon; Chouridou, Sofia; Chow, Bonnie Kar Bo; Christodoulou, Valentinos; Chromek-Burckhart, Doris; Chu, Ming-Lee; Chudoba, Jiri; Chuinard, Annabelle Julia; Chwastowski, Janusz; Chytka, Ladislav; Ciapetti, Guido; Ciftci, Abbas Kenan; Cinca, Diane; Cindro, Vladimir; Cioara, Irina Antonela; Ciocio, Alessandra; Citron, Zvi Hirsh; Ciubancan, Mihai; Clark, Allan G; Clark, Brian Lee; Clark, Philip James; Clarke, Robert; Cleland, Bill; Clement, Christophe; Coadou, Yann; Cobal, Marina; Coccaro, Andrea; Cochran, James H; Coffey, Laurel; Cogan, Joshua Godfrey; Cole, Brian; Cole, Stephen; Colijn, Auke-Pieter; Collot, Johann; Colombo, Tommaso; Compostella, Gabriele; Conde Muiño, Patricia; Coniavitis, Elias; Connell, Simon Henry; Connelly, Ian; Consonni, Sofia Maria; Consorti, Valerio; Constantinescu, Serban; Conta, Claudio; Conti, Geraldine; Conventi, Francesco; Cooke, Mark; Cooper, Ben; Cooper-Sarkar, Amanda; Cornelissen, Thijs; Corradi, Massimo; Corriveau, Francois; Corso-Radu, Alina; Cortes-Gonzalez, Arely; Cortiana, Giorgio; Costa, Giuseppe; Costa, María José; Costanzo, Davide; Côté, David; Cottin, Giovanna; Cowan, Glen; Cox, Brian; Cranmer, Kyle; Cree, Graham; Crépé-Renaudin, Sabine; Crescioli, Francesco; Cribbs, Wayne Allen; Crispin Ortuzar, Mireia; Cristinziani, Markus; Croft, Vince; Crosetti, Giovanni; Cuhadar Donszelmann, Tulay; Cummings, Jane; Curatolo, Maria; Cuthbert, Cameron; Czirr, Hendrik; Czodrowski, Patrick; D'Auria, Saverio; D'Onofrio, Monica; Da Cunha Sargedas De Sousa, Mario Jose; Da Via, Cinzia; Dabrowski, Wladyslaw; Dafinca, Alexandru; Dai, Tiesheng; Dale, Orjan; Dallaire, Frederick; Dallapiccola, Carlo; Dam, Mogens; Dandoy, Jeffrey Rogers; Dang, Nguyen Phuong; Daniells, Andrew Christopher; Danninger, Matthias; Dano Hoffmann, Maria; Dao, Valerio; Darbo, Giovanni; Darmora, Smita; Dassoulas, James; Dattagupta, Aparajita; Davey, Will; David, Claire; Davidek, Tomas; Davies, Eleanor; Davies, Merlin; Davison, Peter; Davygora, Yuriy; Dawe, Edmund; Dawson, Ian; Daya-Ishmukhametova, Rozmin; De, Kaushik; de Asmundis, Riccardo; De Castro, Stefano; De Cecco, Sandro; De Groot, Nicolo; de Jong, Paul; De la Torre, Hector; De Lorenzi, Francesco; De Nooij, Lucie; De Pedis, Daniele; De Salvo, Alessandro; De Sanctis, Umberto; De Santo, Antonella; De Vivie De Regie, Jean-Baptiste; Dearnaley, William James; Debbe, Ramiro; Debenedetti, Chiara; Dedovich, Dmitri; Deigaard, Ingrid; Del Peso, Jose; Del Prete, Tarcisio; Delgove, David; Deliot, Frederic; Delitzsch, Chris Malena; Deliyergiyev, Maksym; Dell'Acqua, Andrea; Dell'Asta, Lidia; Dell'Orso, Mauro; Della Pietra, Massimo; della Volpe, Domenico; Delmastro, Marco; Delsart, Pierre-Antoine; Deluca, Carolina; DeMarco, David; Demers, Sarah; Demichev, Mikhail; Demilly, Aurelien; Denisov, Sergey; Derendarz, Dominik; Derkaoui, Jamal Eddine; Derue, Frederic; Dervan, Paul; Desch, Klaus Kurt; Deterre, Cecile; Deviveiros, Pier-Olivier; Dewhurst, Alastair; Dhaliwal, Saminder; Di Ciaccio, Anna; Di Ciaccio, Lucia; Di Domenico, Antonio; Di Donato, Camilla; Di Girolamo, Alessandro; Di Girolamo, Beniamino; Di Mattia, Alessandro; Di Micco, Biagio; Di Nardo, Roberto; Di Simone, Andrea; Di Sipio, Riccardo; Di Valentino, David; Diaconu, Cristinel; Diamond, Miriam; Dias, Flavia; Diaz, Marco Aurelio; Diehl, Edward; Dietrich, Janet; Diglio, Sara; Dimitrievska, Aleksandra; Dingfelder, Jochen; Dita, Petre; Dita, Sanda; Dittus, Fridolin; Djama, Fares; Djobava, Tamar; Djuvsland, Julia Isabell; Barros do Vale, Maria Aline; Dobos, Daniel; Dobre, Monica; Doglioni, Caterina; Dohmae, Takeshi; Dolejsi, Jiri; Dolezal, Zdenek; Dolgoshein, Boris; Donadelli, Marisilvia; Donati, Simone; Dondero, Paolo; Donini, Julien; Dopke, Jens; Doria, Alessandra; Dova, Maria-Teresa; Doyle, Tony; Drechsler, Eric; Dris, Manolis; Dubreuil, Emmanuelle; Duchovni, Ehud; Duckeck, Guenter; Ducu, Otilia Anamaria; Duda, Dominik; Dudarev, Alexey; Duflot, Laurent; Duguid, Liam; Dührssen, Michael; Dunford, Monica; Duran Yildiz, Hatice; Düren, Michael; Durglishvili, Archil; Duschinger, Dirk; Dyndal, Mateusz; Eckardt, Christoph; Ecker, Katharina Maria; Edgar, Ryan Christopher; Edson, William; Edwards, Nicholas Charles; Ehrenfeld, Wolfgang; Eifert, Till; Eigen, Gerald; Einsweiler, Kevin; Ekelof, Tord; El Kacimi, Mohamed; Ellert, Mattias; Elles, Sabine; Ellinghaus, Frank; Elliot, Alison; Ellis, Nicolas; Elmsheuser, Johannes; Elsing, Markus; Emeliyanov, Dmitry; Enari, Yuji; Endner, Oliver Chris; Endo, Masaki; Erdmann, Johannes; Ereditato, Antonio; Ernis, Gunar; Ernst, Jesse; Ernst, Michael; Errede, Steven; Ertel, Eugen; Escalier, Marc; Esch, Hendrik; Escobar, Carlos; Esposito, Bellisario; Etienvre, Anne-Isabelle; Etzion, Erez; Evans, Hal; Ezhilov, Alexey; Fabbri, Laura; Facini, Gabriel; Fakhrutdinov, Rinat; Falciano, Speranza; Falla, Rebecca Jane; Faltova, Jana; Fang, Yaquan; Fanti, Marcello; Farbin, Amir; Farilla, Addolorata; Farooque, Trisha; Farrell, Steven; Farrington, Sinead; Farthouat, Philippe; Fassi, Farida; Fassnacht, Patrick; Fassouliotis, Dimitrios; Faucci Giannelli, Michele; Favareto, Andrea; Fayard, Louis; Federic, Pavol; Fedin, Oleg; Fedorko, Wojciech; Feigl, Simon; Feligioni, Lorenzo; Feng, Cunfeng; Feng, Eric; Feng, Haolu; Fenyuk, Alexander; Fernandez Martinez, Patricia; Fernandez Perez, Sonia; Ferrando, James; Ferrari, Arnaud; Ferrari, Pamela; Ferrari, Roberto; Ferreira de Lima, Danilo Enoque; Ferrer, Antonio; Ferrere, Didier; Ferretti, Claudio; Ferretto Parodi, Andrea; Fiascaris, Maria; Fiedler, Frank; Filipčič, Andrej; Filipuzzi, Marco; Filthaut, Frank; Fincke-Keeler, Margret; Finelli, Kevin Daniel; Fiolhais, Miguel; Fiorini, Luca; Firan, Ana; Fischer, Adam; Fischer, Cora; Fischer, Julia; Fisher, Wade Cameron; Fitzgerald, Eric Andrew; Flechl, Martin; Fleck, Ivor; Fleischmann, Philipp; Fleischmann, Sebastian; Fletcher, Gareth Thomas; Fletcher, Gregory; Flick, Tobias; Floderus, Anders; Flores Castillo, Luis; Flowerdew, Michael; Formica, Andrea; Forti, Alessandra; Fournier, Daniel; Fox, Harald; Fracchia, Silvia; Francavilla, Paolo; Franchini, Matteo; Francis, David; Franconi, Laura; Franklin, Melissa; Fraternali, Marco; Freeborn, David; French, Sky; Friedrich, Felix; Froidevaux, Daniel; Frost, James; Fukunaga, Chikara; Fullana Torregrosa, Esteban; Fulsom, Bryan Gregory; Fuster, Juan; Gabaldon, Carolina; Gabizon, Ofir; Gabrielli, Alessandro; Gabrielli, Andrea; Gadatsch, Stefan; Gadomski, Szymon; Gagliardi, Guido; Gagnon, Pauline; Galea, Cristina; Galhardo, Bruno; Gallas, Elizabeth; Gallop, Bruce; Gallus, Petr; Galster, Gorm Aske Gram Krohn; Gan, KK; Gao, Jun; Gao, Yanyan; Gao, Yongsheng; Garay Walls, Francisca; Garberson, Ford; García, Carmen; García Navarro, José Enrique; Garcia-Sciveres, Maurice; Gardner, Robert; Garelli, Nicoletta; Garonne, Vincent; Gatti, Claudio; Gaudiello, Andrea; Gaudio, Gabriella; Gaur, Bakul; Gauthier, Lea; Gauzzi, Paolo; Gavrilenko, Igor; Gay, Colin; Gaycken, Goetz; Gazis, Evangelos; Ge, Peng; Gecse, Zoltan; Gee, Norman; Geerts, Daniël Alphonsus Adrianus; Geich-Gimbel, Christoph; Geisler, Manuel Patrice; Gemme, Claudia; Genest, Marie-Hélène; Gentile, Simonetta; George, Matthias; George, Simon; Gerbaudo, Davide; Gershon, Avi; Ghazlane, Hamid; Giacobbe, Benedetto; Giagu, Stefano; Giangiobbe, Vincent; Giannetti, Paola; Gibbard, Bruce; Gibson, Stephen; Gilchriese, Murdock; Gillam, Thomas; Gillberg, Dag; Gilles, Geoffrey; Gingrich, Douglas; Giokaris, Nikos; Giordani, MarioPaolo; Giorgi, Filippo Maria; Giorgi, Francesco Michelangelo; Giraud, Pierre-Francois; Giromini, Paolo; Giugni, Danilo; Giuliani, Claudia; Giulini, Maddalena; Gjelsten, Børge Kile; Gkaitatzis, Stamatios; Gkialas, Ioannis; Gkougkousis, Evangelos Leonidas; Gladilin, Leonid; Glasman, Claudia; Glatzer, Julian; Glaysher, Paul; Glazov, Alexandre; Goblirsch-Kolb, Maximilian; Goddard, Jack Robert; Godlewski, Jan; Goldfarb, Steven; Golling, Tobias; Golubkov, Dmitry; Gomes, Agostinho; Gonçalo, Ricardo; Goncalves Pinto Firmino Da Costa, Joao; Gonella, Laura; González de la Hoz, Santiago; Gonzalez Parra, Garoe; Gonzalez-Sevilla, Sergio; Goossens, Luc; Gorbounov, Petr Andreevich; Gordon, Howard; Gorelov, Igor; Gorini, Benedetto; Gorini, Edoardo; Gorišek, Andrej; Gornicki, Edward; Goshaw, Alfred; Gössling, Claus; Gostkin, Mikhail Ivanovitch; Goujdami, Driss; Goussiou, Anna; Govender, Nicolin; Grabas, Herve Marie Xavier; Graber, Lars; Grabowska-Bold, Iwona; Grafström, Per; Grahn, Karl-Johan; Gramling, Johanna; Gramstad, Eirik; Grancagnolo, Sergio; Grassi, Valerio; Gratchev, Vadim; Gray, Heather; Graziani, Enrico; Greenwood, Zeno Dixon; Gregersen, Kristian; Gregor, Ingrid-Maria; Grenier, Philippe; Griffiths, Justin; Grillo, Alexander; Grimm, Kathryn; Grinstein, Sebastian; Gris, Philippe Luc Yves; Grivaz, Jean-Francois; Grohs, Johannes Philipp; Grohsjean, Alexander; Gross, Eilam; Grosse-Knetter, Joern; Grossi, Giulio Cornelio; Grout, Zara Jane; Guan, Liang; Guenther, Jaroslav; Guescini, Francesco; Guest, Daniel; Gueta, Orel; Guido, Elisa; Guillemin, Thibault; Guindon, Stefan; Gul, Umar; Gumpert, Christian; Guo, Jun; Gupta, Shaun; Gutierrez, Phillip; Gutierrez Ortiz, Nicolas Gilberto; Gutschow, Christian; Guyot, Claude; Gwenlan, Claire; Gwilliam, Carl; Haas, Andy; Haber, Carl; Hadavand, Haleh Khani; Haddad, Nacim; Haefner, Petra; Hageböck, Stephan; Hajduk, Zbigniew; Hakobyan, Hrachya; Haleem, Mahsana; Haley, Joseph; Hall, David; Halladjian, Garabed; Hallewell, Gregory David; Hamacher, Klaus; Hamal, Petr; Hamano, Kenji; Hamer, Matthias; Hamilton, Andrew; Hamity, Guillermo Nicolas; Hamnett, Phillip George; Han, Liang; Hanagaki, Kazunori; Hanawa, Keita; Hance, Michael; Hanke, Paul; Hanna, Remie; Hansen, Jørgen Beck; Hansen, Jorn Dines; Hansen, Maike Christina; Hansen, Peter Henrik; Hara, Kazuhiko; Hard, Andrew; Harenberg, Torsten; Hariri, Faten; Harkusha, Siarhei; Harrington, Robert; Harrison, Paul Fraser; Hartjes, Fred; Hasegawa, Makoto; Hasegawa, Satoshi; Hasegawa, Yoji; Hasib, A; Hassani, Samira; Haug, Sigve; Hauser, Reiner; Hauswald, Lorenz; Havranek, Miroslav; Hawkes, Christopher; Hawkings, Richard John; Hawkins, Anthony David; Hayashi, Takayasu; Hayden, Daniel; Hays, Chris; Hays, Jonathan Michael; Hayward, Helen; Haywood, Stephen; Head, Simon; Heck, Tobias; Hedberg, Vincent; Heelan, Louise; Heim, Sarah; Heim, Timon; Heinemann, Beate; Heinrich, Lukas; Hejbal, Jiri; Helary, Louis; Hellman, Sten; Hellmich, Dennis; Helsens, Clement; Henderson, James; Henderson, Robert; Heng, Yang; Hengler, Christopher; Henrichs, Anna; Henriques Correia, Ana Maria; Henrot-Versille, Sophie; Herbert, Geoffrey Henry; Hernández Jiménez, Yesenia; Herrberg-Schubert, Ruth; Herten, Gregor; Hertenberger, Ralf; Hervas, Luis; Hesketh, Gavin Grant; Hessey, Nigel; Hetherly, Jeffrey Wayne; Hickling, Robert; Higón-Rodriguez, Emilio; Hill, Ewan; Hill, John; Hiller, Karl Heinz; Hillier, Stephen; Hinchliffe, Ian; Hines, Elizabeth; Hinman, Rachel Reisner; Hirose, Minoru; Hirschbuehl, Dominic; Hobbs, John; Hod, Noam; Hodgkinson, Mark; Hodgson, Paul; Hoecker, Andreas; Hoeferkamp, Martin; Hoenig, Friedrich; Hohlfeld, Marc; Hohn, David; Holmes, Tova Ray; Homann, Michael; Hong, Tae Min; Hooft van Huysduynen, Loek; Hopkins, Walter; Horii, Yasuyuki; Horton, Arthur James; Hostachy, Jean-Yves; Hou, Suen; Hoummada, Abdeslam; Howard, Jacob; Howarth, James; Hrabovsky, Miroslav; Hristova, Ivana; Hrivnac, Julius; Hryn'ova, Tetiana; Hrynevich, Aliaksei; Hsu, Catherine; Hsu, Pai-hsien Jennifer; Hsu, Shih-Chieh; Hu, Diedi; Hu, Qipeng; Hu, Xueye; Huang, Yanping; Hubacek, Zdenek; Hubaut, Fabrice; Huegging, Fabian; Huffman, Todd Brian; Hughes, Emlyn; Hughes, Gareth; Huhtinen, Mika; Hülsing, Tobias Alexander; Huseynov, Nazim; Huston, Joey; Huth, John; Iacobucci, Giuseppe; Iakovidis, Georgios; Ibragimov, Iskander; Iconomidou-Fayard, Lydia; Ideal, Emma; Idrissi, Zineb; Iengo, Paolo; Igonkina, Olga; Iizawa, Tomoya; Ikegami, Yoichi; Ikematsu, Katsumasa; Ikeno, Masahiro; Ilchenko, Iurii; Iliadis, Dimitrios; Ilic, Nikolina; Inamaru, Yuki; Ince, Tayfun; Ioannou, Pavlos; Iodice, Mauro; Iordanidou, Kalliopi; Ippolito, Valerio; Irles Quiles, Adrian; Isaksson, Charlie; Ishino, Masaya; Ishitsuka, Masaki; Ishmukhametov, Renat; Issever, Cigdem; Istin, Serhat; Iturbe Ponce, Julia Mariana; Iuppa, Roberto; Ivarsson, Jenny; Iwanski, Wieslaw; Iwasaki, Hiroyuki; Izen, Joseph; Izzo, Vincenzo; Jabbar, Samina; Jackson, Brett; Jackson, Matthew; Jackson, Paul; Jaekel, Martin; Jain, Vivek; Jakobs, Karl; Jakobsen, Sune; Jakoubek, Tomas; Jakubek, Jan; Jamin, David Olivier; Jana, Dilip; Jansen, Eric; Jansky, Roland; Janssen, Jens; Janus, Michel; Jarlskog, Göran; Javadov, Namig; Javůrek, Tomáš; Jeanty, Laura; Jejelava, Juansher; Jeng, Geng-yuan; Jennens, David; Jenni, Peter; Jentzsch, Jennifer; Jeske, Carl; Jézéquel, Stéphane; Ji, Haoshuang; Jia, Jiangyong; Jiang, Yi; Jiggins, Stephen; Jimenez Pena, Javier; Jin, Shan; Jinaru, Adam; Jinnouchi, Osamu; Joergensen, Morten Dam; Johansson, Per; Johns, Kenneth; Jon-And, Kerstin; Jones, Graham; Jones, Roger; Jones, Tim; Jongmanns, Jan; Jorge, Pedro; Joshi, Kiran Daniel; Jovicevic, Jelena; Ju, Xiangyang; Jung, Christian; Jussel, Patrick; Juste Rozas, Aurelio; Kaci, Mohammed; Kaczmarska, Anna; Kado, Marumi; Kagan, Harris; Kagan, Michael; Kahn, Sebastien Jonathan; Kajomovitz, Enrique; Kalderon, Charles William; Kama, Sami; Kamenshchikov, Andrey; Kanaya, Naoko; Kaneda, Michiru; Kaneti, Steven; Kantserov, Vadim; Kanzaki, Junichi; Kaplan, Benjamin; Kapliy, Anton; Kar, Deepak; Karakostas, Konstantinos; Karamaoun, Andrew; Karastathis, Nikolaos; Kareem, Mohammad Jawad; Karnevskiy, Mikhail; Karpov, Sergey; Karpova, Zoya; Karthik, Krishnaiyengar; Kartvelishvili, Vakhtang; Karyukhin, Andrey; Kashif, Lashkar; Kass, Richard; Kastanas, Alex; Kataoka, Yousuke; Katre, Akshay; Katzy, Judith; Kawagoe, Kiyotomo; Kawamoto, Tatsuo; Kawamura, Gen; Kazama, Shingo; Kazanin, Vassili; Kazarinov, Makhail; Keeler, Richard; Kehoe, Robert; Keller, John; Kempster, Jacob Julian; Keoshkerian, Houry; Kepka, Oldrich; Kerševan, Borut Paul; Kersten, Susanne; Keyes, Robert; Khalil-zada, Farkhad; Khandanyan, Hovhannes; Khanov, Alexander; Kharlamov, Alexey; Khoo, Teng Jian; Khovanskiy, Valery; Khramov, Evgeniy; Khubua, Jemal; Kim, Hee Yeun; Kim, Hyeon Jin; Kim, Shinhong; Kim, Young-Kee; Kimura, Naoki; Kind, Oliver Maria; King, Barry; King, Matthew; King, Robert Steven Beaufoy; King, Samuel Burton; Kirk, Julie; Kiryunin, Andrey; Kishimoto, Tomoe; Kisielewska, Danuta; Kiss, Florian; Kiuchi, Kenji; Kivernyk, Oleh; Kladiva, Eduard; Klein, Matthew Henry; Klein, Max; Klein, Uta; Kleinknecht, Konrad; Klimek, Pawel; Klimentov, Alexei; Klingenberg, Reiner; Klinger, Joel Alexander; Klioutchnikova, Tatiana; Kluge, Eike-Erik; Kluit, Peter; Kluth, Stefan; Kneringer, Emmerich; Knoops, Edith; Knue, Andrea; Kobayashi, Aine; Kobayashi, Dai; Kobayashi, Tomio; Kobel, Michael; Kocian, Martin; Kodys, Peter; Koffas, Thomas; Koffeman, Els; Kogan, Lucy Anne; Kohlmann, Simon; Kohout, Zdenek; Kohriki, Takashi; Koi, Tatsumi; Kolanoski, Hermann; Koletsou, Iro; Komar, Aston; Komori, Yuto; Kondo, Takahiko; Kondrashova, Nataliia; Köneke, Karsten; König, Adriaan; König, Sebastian; Kono, Takanori; Konoplich, Rostislav; Konstantinidis, Nikolaos; Kopeliansky, Revital; Koperny, Stefan; Köpke, Lutz; Kopp, Anna Katharina; Korcyl, Krzysztof; Kordas, Kostantinos; Korn, Andreas; Korol, Aleksandr; Korolkov, Ilya; Korolkova, Elena; Kortner, Oliver; Kortner, Sandra; Kosek, Tomas; Kostyukhin, Vadim; Kotov, Vladislav; Kotwal, Ashutosh; Kourkoumeli-Charalampidi, Athina; Kourkoumelis, Christine; Kouskoura, Vasiliki; Koutsman, Alex; Kowalewski, Robert Victor; Kowalski, Tadeusz; Kozanecki, Witold; Kozhin, Anatoly; Kramarenko, Viktor; Kramberger, Gregor; Krasnopevtsev, Dimitriy; Krasznahorkay, Attila; Kraus, Jana; Kravchenko, Anton; Kreiss, Sven; Kretz, Moritz; Kretzschmar, Jan; Kreutzfeldt, Kristof; Krieger, Peter; Krizka, Karol; Kroeninger, Kevin; Kroha, Hubert; Kroll, Joe; Kroseberg, Juergen; Krstic, Jelena; Kruchonak, Uladzimir; Krüger, Hans; Krumnack, Nils; Krumshteyn, Zinovii; Kruse, Amanda; Kruse, Mark; Kruskal, Michael; Kubota, Takashi; Kucuk, Hilal; Kuday, Sinan; Kuehn, Susanne; Kugel, Andreas; Kuger, Fabian; Kuhl, Andrew; Kuhl, Thorsten; Kukhtin, Victor; Kulchitsky, Yuri; Kuleshov, Sergey; Kuna, Marine; Kunigo, Takuto; Kupco, Alexander; Kurashige, Hisaya; Kurochkin, Yurii; Kurumida, Rie; Kus, Vlastimil; Kuwertz, Emma Sian; Kuze, Masahiro; Kvita, Jiri; Kwan, Tony; Kyriazopoulos, Dimitrios; La Rosa, Alessandro; La Rosa Navarro, Jose Luis; La Rotonda, Laura; Lacasta, Carlos; Lacava, Francesco; Lacey, James; Lacker, Heiko; Lacour, Didier; Lacuesta, Vicente Ramón; Ladygin, Evgueni; Lafaye, Remi; Laforge, Bertrand; Lagouri, Theodota; Lai, Stanley; Lambourne, Luke; Lammers, Sabine; Lampen, Caleb; Lampl, Walter; Lançon, Eric; Landgraf, Ulrich; Landon, Murrough; Lang, Valerie Susanne; Lange, J örn Christian; Lankford, Andrew; Lanni, Francesco; Lantzsch, Kerstin; Laplace, Sandrine; Lapoire, Cecile; Laporte, Jean-Francois; Lari, Tommaso; Lasagni Manghi, Federico; Lassnig, Mario; Laurelli, Paolo; Lavrijsen, Wim; Law, Alexander; Laycock, Paul; Lazovich, Tomo; Le Dortz, Olivier; Le Guirriec, Emmanuel; Le Menedeu, Eve; LeBlanc, Matthew Edgar; LeCompte, Thomas; Ledroit-Guillon, Fabienne Agnes Marie; Lee, Claire Alexandra; Lee, Shih-Chang; Lee, Lawrence; Lefebvre, Guillaume; Lefebvre, Michel; Legger, Federica; Leggett, Charles; Lehan, Allan; Lehmann Miotto, Giovanna; Lei, Xiaowen; Leight, William Axel; Leisos, Antonios; Leister, Andrew Gerard; Leite, Marco Aurelio Lisboa; Leitner, Rupert; Lellouch, Daniel; Lemmer, Boris; Leney, Katharine; Lenz, Tatjana; Lenzi, Bruno; Leone, Robert; Leone, Sandra; Leonidopoulos, Christos; Leontsinis, Stefanos; Leroy, Claude; Lester, Christopher; Levchenko, Mikhail; Levêque, Jessica; Levin, Daniel; Levinson, Lorne; Levy, Mark; Lewis, Adrian; Leyko, Agnieszka; Leyton, Michael; Li, Bing; Li, Haifeng; Li, Ho Ling; Li, Lei; Li, Liang; Li, Shu; Li, Yichen; Liang, Zhijun; Liao, Hongbo; Liberti, Barbara; Liblong, Aaron; Lichard, Peter; Lie, Ki; Liebal, Jessica; Liebig, Wolfgang; Limbach, Christian; Limosani, Antonio; Lin, Simon; Lin, Tai-Hua; Linde, Frank; Lindquist, Brian Edward; Linnemann, James; Lipeles, Elliot; Lipniacka, Anna; Lisovyi, Mykhailo; Liss, Tony; Lissauer, David; Lister, Alison; Litke, Alan; Liu, Bo; Liu, Dong; Liu, Jian; Liu, Jianbei; Liu, Kun; Liu, Lulu; Liu, Miaoyuan; Liu, Minghui; Liu, Yanwen; Livan, Michele; Lleres, Annick; Llorente Merino, Javier; Lloyd, Stephen; Lo Sterzo, Francesco; Lobodzinska, Ewelina; Loch, Peter; Lockman, William; Loebinger, Fred; Loevschall-Jensen, Ask Emil; Loginov, Andrey; Lohse, Thomas; Lohwasser, Kristin; Lokajicek, Milos; Long, Brian Alexander; Long, Jonathan; Long, Robin Eamonn; Looper, Kristina Anne; Lopes, Lourenco; Lopez Mateos, David; Lopez Paredes, Brais; Lopez Paz, Ivan; Lorenz, Jeanette; Lorenzo Martinez, Narei; Losada, Marta; Loscutoff, Peter; Lösel, Philipp Jonathan; Lou, XinChou; Lounis, Abdenour; Love, Jeremy; Love, Peter; Lu, Nan; Lubatti, Henry; Luci, Claudio; Lucotte, Arnaud; Luehring, Frederick; Lukas, Wolfgang; Luminari, Lamberto; Lundberg, Olof; Lund-Jensen, Bengt; Lynn, David; Lysak, Roman; Lytken, Else; Ma, Hong; Ma, Lian Liang; Maccarrone, Giovanni; Macchiolo, Anna; Macdonald, Calum Michael; Machado Miguens, Joana; Macina, Daniela; Madaffari, Daniele; Madar, Romain; Maddocks, Harvey Jonathan; Mader, Wolfgang; Madsen, Alexander; Maeland, Steffen; Maeno, Tadashi; Maevskiy, Artem; Magradze, Erekle; Mahboubi, Kambiz; Mahlstedt, Joern; Maiani, Camilla; Maidantchik, Carmen; Maier, Andreas Alexander; Maier, Thomas; Maio, Amélia; Majewski, Stephanie; Makida, Yasuhiro; Makovec, Nikola; Malaescu, Bogdan; Malecki, Pawel; Maleev, Victor; Malek, Fairouz; Mallik, Usha; Malon, David; Malone, Caitlin; Maltezos, Stavros; Malyshev, Vladimir; Malyukov, Sergei; Mamuzic, Judita; Mancini, Giada; Mandelli, Beatrice; Mandelli, Luciano; Mandić, Igor; Mandrysch, Rocco; Maneira, José; Manfredini, Alessandro; Manhaes de Andrade Filho, Luciano; Manjarres Ramos, Joany; Mann, Alexander; Manning, Peter; Manousakis-Katsikakis, Arkadios; Mansoulie, Bruno; Mantifel, Rodger; Mantoani, Matteo; Mapelli, Livio; March, Luis; Marchiori, Giovanni; Marcisovsky, Michal; Marino, Christopher; Marjanovic, Marija; Marroquim, Fernando; Marsden, Stephen Philip; Marshall, Zach; Marti, Lukas Fritz; Marti-Garcia, Salvador; Martin, Brian Thomas; Martin, Tim; Martin, Victoria Jane; Martin dit Latour, Bertrand; Martinez, Mario; Martin-Haugh, Stewart; Martoiu, Victor Sorin; Martyniuk, Alex; Marx, Marilyn; Marzano, Francesco; Marzin, Antoine; Masetti, Lucia; Mashimo, Tetsuro; Mashinistov, Ruslan; Masik, Jiri; Maslennikov, Alexey; Massa, Ignazio; Massa, Lorenzo; Massol, Nicolas; Mastrandrea, Paolo; Mastroberardino, Anna; Masubuchi, Tatsuya; Mättig, Peter; Mattmann, Johannes; Maurer, Julien; Maxfield, Stephen; Maximov, Dmitriy; Mazini, Rachid; Mazza, Simone Michele; Mazzaferro, Luca; Mc Goldrick, Garrin; Mc Kee, Shawn Patrick; McCarn, Allison; McCarthy, Robert; McCarthy, Tom; McCubbin, Norman; McFarlane, Kenneth; Mcfayden, Josh; Mchedlidze, Gvantsa; McMahon, Steve; McPherson, Robert; Medinnis, Michael; Meehan, Samuel; Mehlhase, Sascha; Mehta, Andrew; Meier, Karlheinz; Meineck, Christian; Meirose, Bernhard; Mellado Garcia, Bruce Rafael; Meloni, Federico; Mengarelli, Alberto; Menke, Sven; Meoni, Evelin; Mercurio, Kevin Michael; Mergelmeyer, Sebastian; Mermod, Philippe; Merola, Leonardo; Meroni, Chiara; Merritt, Frank; Messina, Andrea; Metcalfe, Jessica; Mete, Alaettin Serhan; Meyer, Carsten; Meyer, Christopher; Meyer, Jean-Pierre; Meyer, Jochen; Middleton, Robin; Miglioranzi, Silvia; Mijović, Liza; Mikenberg, Giora; Mikestikova, Marcela; Mikuž, Marko; Milesi, Marco; Milic, Adriana; Miller, David; Mills, Corrinne; Milov, Alexander; Milstead, David; Minaenko, Andrey; Minami, Yuto; Minashvili, Irakli; Mincer, Allen; Mindur, Bartosz; Mineev, Mikhail; Ming, Yao; Mir, Lluisa-Maria; Mitani, Takashi; Mitrevski, Jovan; Mitsou, Vasiliki A; Miucci, Antonio; Miyagawa, Paul; Mjörnmark, Jan-Ulf; Moa, Torbjoern; Mochizuki, Kazuya; Mohapatra, Soumya; Mohr, Wolfgang; Molander, Simon; Moles-Valls, Regina; Mönig, Klaus; Monini, Caterina; Monk, James; Monnier, Emmanuel; Montejo Berlingen, Javier; Monticelli, Fernando; Monzani, Simone; Moore, Roger; Morange, Nicolas; Moreno, Deywis; Moreno Llácer, María; Morettini, Paolo; Morgenstern, Marcus; Morii, Masahiro; Morinaga, Masahiro; Morisbak, Vanja; Moritz, Sebastian; Morley, Anthony Keith; Mornacchi, Giuseppe; Morris, John; Mortensen, Simon Stark; Morton, Alexander; Morvaj, Ljiljana; Mosidze, Maia; Moss, Josh; Motohashi, Kazuki; Mount, Richard; Mountricha, Eleni; Mouraviev, Sergei; Moyse, Edward; Muanza, Steve; Mudd, Richard; Mueller, Felix; Mueller, James; Mueller, Klemens; Mueller, Ralph Soeren Peter; Mueller, Thibaut; Muenstermann, Daniel; Mullen, Paul; Munwes, Yonathan; Murillo Quijada, Javier Alberto; Murray, Bill; Musheghyan, Haykuhi; Musto, Elisa; Myagkov, Alexey; Myska, Miroslav; Nackenhorst, Olaf; Nadal, Jordi; Nagai, Koichi; Nagai, Ryo; Nagai, Yoshikazu; Nagano, Kunihiro; Nagarkar, Advait; Nagasaka, Yasushi; Nagata, Kazuki; Nagel, Martin; Nagy, Elemer; Nairz, Armin Michael; Nakahama, Yu; Nakamura, Koji; Nakamura, Tomoaki; Nakano, Itsuo; Namasivayam, Harisankar; Naranjo Garcia, Roger Felipe; Narayan, Rohin; Naumann, Thomas; Navarro, Gabriela; Nayyar, Ruchika; Neal, Homer; Nechaeva, Polina; Neep, Thomas James; Nef, Pascal Daniel; Negri, Andrea; Negrini, Matteo; Nektarijevic, Snezana; Nellist, Clara; Nelson, Andrew; Nemecek, Stanislav; Nemethy, Peter; Nepomuceno, Andre Asevedo; Nessi, Marzio; Neubauer, Mark; Neumann, Manuel; Neves, Ricardo; Nevski, Pavel; Newman, Paul; Nguyen, Duong Hai; Nickerson, Richard; Nicolaidou, Rosy; Nicquevert, Bertrand; Nielsen, Jason; Nikiforou, Nikiforos; Nikiforov, Andriy; Nikolaenko, Vladimir; Nikolic-Audit, Irena; Nikolopoulos, Konstantinos; Nilsen, Jon Kerr; Nilsson, Paul; Ninomiya, Yoichi; Nisati, Aleandro; Nisius, Richard; Nobe, Takuya; Nomachi, Masaharu; Nomidis, Ioannis; Nooney, Tamsin; Norberg, Scarlet; Nordberg, Markus; Novgorodova, Olga; Nowak, Sebastian; Nozaki, Mitsuaki; Nozka, Libor; Ntekas, Konstantinos; Nunes Hanninger, Guilherme; Nunnemann, Thomas; Nurse, Emily; Nuti, Francesco; O'Brien, Brendan Joseph; O'grady, Fionnbarr; O'Neil, Dugan; O'Shea, Val; Oakham, Gerald; Oberlack, Horst; Obermann, Theresa; Ocariz, Jose; Ochi, Atsuhiko; Ochoa, Ines; Ochoa-Ricoux, Juan Pedro; Oda, Susumu; Odaka, Shigeru; Ogren, Harold; Oh, Alexander; Oh, Seog; Ohm, Christian; Ohman, Henrik; Oide, Hideyuki; Okamura, Wataru; Okawa, Hideki; Okumura, Yasuyuki; Okuyama, Toyonobu; Olariu, Albert; Olivares Pino, Sebastian Andres; Oliveira Damazio, Denis; Oliver Garcia, Elena; Olszewski, Andrzej; Olszowska, Jolanta; Onofre, António; Onyisi, Peter; Oram, Christopher; Oreglia, Mark; Oren, Yona; Orestano, Domizia; Orlando, Nicola; Oropeza Barrera, Cristina; Orr, Robert; Osculati, Bianca; Ospanov, Rustem; Otero y Garzon, Gustavo; Otono, Hidetoshi; Ouchrif, Mohamed; Ouellette, Eric; Ould-Saada, Farid; Ouraou, Ahmimed; Oussoren, Koen Pieter; Ouyang, Qun; Ovcharova, Ana; Owen, Mark; Owen, Rhys Edward; Ozcan, Veysi Erkcan; Ozturk, Nurcan; Pachal, Katherine; Pacheco Pages, Andres; Padilla Aranda, Cristobal; Pagáčová, Martina; Pagan Griso, Simone; Paganis, Efstathios; Pahl, Christoph; Paige, Frank; Pais, Preema; Pajchel, Katarina; Palacino, Gabriel; Palestini, Sandro; Palka, Marek; Pallin, Dominique; Palma, Alberto; Pan, Yibin; Panagiotopoulou, Evgenia; Pandini, Carlo Enrico; Panduro Vazquez, William; Pani, Priscilla; Panitkin, Sergey; Pantea, Dan; Paolozzi, Lorenzo; Papadopoulou, Theodora; Papageorgiou, Konstantinos; Paramonov, Alexander; Paredes Hernandez, Daniela; Parker, Michael Andrew; Parker, Kerry Ann; Parodi, Fabrizio; Parsons, John; Parzefall, Ulrich; Pasqualucci, Enrico; Passaggio, Stefano; Pastore, Fernanda; Pastore, Francesca; Pásztor, Gabriella; Pataraia, Sophio; Patel, Nikhul; Pater, Joleen; Pauly, Thilo; Pearce, James; Pearson, Benjamin; Pedersen, Lars Egholm; Pedersen, Maiken; Pedraza Lopez, Sebastian; Pedro, Rute; Peleganchuk, Sergey; Pelikan, Daniel; Peng, Haiping; Penning, Bjoern; Penwell, John; Perepelitsa, Dennis; Perez Codina, Estel; Pérez García-Estañ, María Teresa; Perini, Laura; Pernegger, Heinz; Perrella, Sabrina; Peschke, Richard; Peshekhonov, Vladimir; Peters, Krisztian; Peters, Yvonne; Petersen, Brian; Petersen, Troels; Petit, Elisabeth; Petridis, Andreas; Petridou, Chariclia; Petrolo, Emilio; Petrucci, Fabrizio; Pettersson, Nora Emilia; Pezoa, Raquel; Phillips, Peter William; Piacquadio, Giacinto; Pianori, Elisabetta; Picazio, Attilio; Piccaro, Elisa; Piccinini, Maurizio; Pickering, Mark Andrew; Piegaia, Ricardo; Pignotti, David; Pilcher, James; Pilkington, Andrew; Pina, João Antonio; Pinamonti, Michele; Pinfold, James; Pingel, Almut; Pinto, Belmiro; Pires, Sylvestre; Pitt, Michael; Pizio, Caterina; Plazak, Lukas; Pleier, Marc-Andre; Pleskot, Vojtech; Plotnikova, Elena; Plucinski, Pawel; Pluth, Daniel; Poettgen, Ruth; Poggioli, Luc; Pohl, David-leon; Polesello, Giacomo; Policicchio, Antonio; Polifka, Richard; Polini, Alessandro; Pollard, Christopher Samuel; Polychronakos, Venetios; Pommès, Kathy; Pontecorvo, Ludovico; Pope, Bernard; Popeneciu, Gabriel Alexandru; Popovic, Dragan; Poppleton, Alan; Pospisil, Stanislav; Potamianos, Karolos; Potrap, Igor; Potter, Christina; Potter, Christopher; Poulard, Gilbert; Poveda, Joaquin; Pozdnyakov, Valery; Pralavorio, Pascal; Pranko, Aliaksandr; Prasad, Srivas; Prell, Soeren; Price, Darren; Price, Lawrence; Primavera, Margherita; Prince, Sebastien; Proissl, Manuel; Prokofiev, Kirill; Prokoshin, Fedor; Protopapadaki, Eftychia-sofia; Protopopescu, Serban; Proudfoot, James; Przybycien, Mariusz; Ptacek, Elizabeth; Puddu, Daniele; Pueschel, Elisa; Puldon, David; Purohit, Milind; Puzo, Patrick; Qian, Jianming; Qin, Gang; Qin, Yang; Quadt, Arnulf; Quarrie, David; Quayle, William; Queitsch-Maitland, Michaela; Quilty, Donnchadha; Raddum, Silje; Radeka, Veljko; Radescu, Voica; Radhakrishnan, Sooraj Krishnan; Radloff, Peter; Rados, Pere; Ragusa, Francesco; Rahal, Ghita; Rajagopalan, Srinivasan; Rammensee, Michael; Rangel-Smith, Camila; Rauscher, Felix; Rave, Stefan; Ravenscroft, Thomas; Raymond, Michel; Read, Alexander Lincoln; Readioff, Nathan Peter; Rebuzzi, Daniela; Redelbach, Andreas; Redlinger, George; Reece, Ryan; Reeves, Kendall; Rehnisch, Laura; Reisin, Hernan; Relich, Matthew; Rembser, Christoph; Ren, Huan; Renaud, Adrien; Rescigno, Marco; Resconi, Silvia; Rezanova, Olga; Reznicek, Pavel; Rezvani, Reyhaneh; Richter, Robert; Richter, Stefan; Richter-Was, Elzbieta; Ricken, Oliver; Ridel, Melissa; Rieck, Patrick; Riegel, Christian Johann; Rieger, Julia; Rijssenbeek, Michael; Rimoldi, Adele; Rinaldi, Lorenzo; Ristić, Branislav; Ritsch, Elmar; Riu, Imma; Rizatdinova, Flera; Rizvi, Eram; Robertson, Steven; Robichaud-Veronneau, Andree; Robinson, Dave; Robinson, James; Robson, Aidan; Roda, Chiara; Roe, Shaun; Røhne, Ole; Rolli, Simona; Romaniouk, Anatoli; Romano, Marino; Romano Saez, Silvestre Marino; Romero Adam, Elena; Rompotis, Nikolaos; Ronzani, Manfredi; Roos, Lydia; Ros, Eduardo; Rosati, Stefano; Rosbach, Kilian; Rose, Peyton; Rosendahl, Peter Lundgaard; Rosenthal, Oliver; Rossetti, Valerio; Rossi, Elvira; Rossi, Leonardo Paolo; Rosten, Rachel; Rotaru, Marina; Roth, Itamar; Rothberg, Joseph; Rousseau, David; Royon, Christophe; Rozanov, Alexandre; Rozen, Yoram; Ruan, Xifeng; Rubbo, Francesco; Rubinskiy, Igor; Rud, Viacheslav; Rudolph, Christian; Rudolph, Matthew Scott; Rühr, Frederik; Ruiz-Martinez, Aranzazu; Rurikova, Zuzana; Rusakovich, Nikolai; Ruschke, Alexander; Russell, Heather; Rutherfoord, John; Ruthmann, Nils; Ryabov, Yury; Rybar, Martin; Rybkin, Grigori; Ryder, Nick; Saavedra, Aldo; Sabato, Gabriele; Sacerdoti, Sabrina; Saddique, Asif; Sadrozinski, Hartmut; Sadykov, Renat; Safai Tehrani, Francesco; Saimpert, Matthias; Sakamoto, Hiroshi; Sakurai, Yuki; Salamanna, Giuseppe; Salamon, Andrea; Saleem, Muhammad; Salek, David; Sales De Bruin, Pedro Henrique; Salihagic, Denis; Salnikov, Andrei; Salt, José; Salvatore, Daniela; Salvatore, Pasquale Fabrizio; Salvucci, Antonio; Salzburger, Andreas; Sampsonidis, Dimitrios; Sanchez, Arturo; Sánchez, Javier; Sanchez Martinez, Victoria; Sandaker, Heidi; Sandbach, Ruth Laura; Sander, Heinz Georg; Sanders, Michiel; Sandhoff, Marisa; Sandoval, Carlos; Sandstroem, Rikard; Sankey, Dave; Sannino, Mario; Sansoni, Andrea; Santoni, Claudio; Santonico, Rinaldo; Santos, Helena; Santoyo Castillo, Itzebelt; Sapp, Kevin; Sapronov, Andrey; Saraiva, João; Sarrazin, Bjorn; Sasaki, Osamu; Sasaki, Yuichi; Sato, Koji; Sauvage, Gilles; Sauvan, Emmanuel; Savage, Graham; Savard, Pierre; Sawyer, Craig; Sawyer, Lee; Saxon, James; Sbarra, Carla; Sbrizzi, Antonio; Scanlon, Tim; Scannicchio, Diana; Scarcella, Mark; Scarfone, Valerio; 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Solovyev, Victor; Sommer, Philip; Song, Hong Ye; Soni, Nitesh; Sood, Alexander; Sopczak, Andre; Sopko, Bruno; Sopko, Vit; Sorin, Veronica; Sosa, David; Sosebee, Mark; Sotiropoulou, Calliope Louisa; Soualah, Rachik; Soueid, Paul; Soukharev, Andrey; South, David; Sowden, Benjamin; Spagnolo, Stefania; Spalla, Margherita; Spanò, Francesco; Spearman, William Robert; Spettel, Fabian; Spighi, Roberto; Spigo, Giancarlo; Spiller, Laurence Anthony; Spousta, Martin; Spreitzer, Teresa; St Denis, Richard Dante; Staerz, Steffen; Stahlman, Jonathan; Stamen, Rainer; Stamm, Soren; Stanecka, Ewa; Stanescu, Cristian; Stanescu-Bellu, Madalina; Stanitzki, Marcel Michael; Stapnes, Steinar; Starchenko, Evgeny; Stark, Jan; Staroba, Pavel; Starovoitov, Pavel; Staszewski, Rafal; Stavina, Pavel; Steinberg, Peter; Stelzer, Bernd; Stelzer, Harald Joerg; Stelzer-Chilton, Oliver; Stenzel, Hasko; Stern, Sebastian; Stewart, Graeme; Stillings, Jan Andre; Stockton, Mark; Stoebe, Michael; Stoicea, Gabriel; Stolte, Philipp; 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Tannenwald, Benjamin Bordy; Tannoury, Nancy; Tapprogge, Stefan; Tarem, Shlomit; Tarrade, Fabien; Tartarelli, Giuseppe Francesco; Tas, Petr; Tasevsky, Marek; Tashiro, Takuya; Tassi, Enrico; Tavares Delgado, Ademar; Tayalati, Yahya; Taylor, Frank; Taylor, Geoffrey; Taylor, Wendy; Teischinger, Florian Alfred; Teixeira Dias Castanheira, Matilde; Teixeira-Dias, Pedro; Temming, Kim Katrin; Ten Kate, Herman; Teng, Ping-Kun; Teoh, Jia Jian; Tepel, Fabian-Phillipp; Terada, Susumu; Terashi, Koji; Terron, Juan; Terzo, Stefano; Testa, Marianna; Teuscher, Richard; Therhaag, Jan; Theveneaux-Pelzer, Timothée; Thomas, Juergen; Thomas-Wilsker, Joshuha; Thompson, Emily; Thompson, Paul; Thompson, Ray; Thompson, Stan; Thomsen, Lotte Ansgaard; Thomson, Evelyn; Thomson, Mark; Thun, Rudolf; Tibbetts, Mark James; Ticse Torres, Royer Edson; Tikhomirov, Vladimir; Tikhonov, Yury; Timoshenko, Sergey; Tiouchichine, Elodie; Tipton, Paul; Tisserant, Sylvain; Todorov, Theodore; Todorova-Nova, Sharka; Tojo, Junji; Tokár, Stanislav; 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Walker, Rodney; Walkowiak, Wolfgang; Wang, Chao; Wang, Fuquan; Wang, Haichen; Wang, Hulin; Wang, Jike; Wang, Jin; Wang, Kuhan; Wang, Rui; Wang, Song-Ming; Wang, Tan; Wang, Xiaoxiao; Wanotayaroj, Chaowaroj; Warburton, Andreas; Ward, Patricia; Wardrope, David Robert; Warsinsky, Markus; Washbrook, Andrew; Wasicki, Christoph; Watkins, Peter; Watson, Alan; Watson, Ian; Watson, Miriam; Watts, Gordon; Watts, Stephen; Waugh, Ben; Webb, Samuel; Weber, Michele; Weber, Stefan Wolf; Webster, Jordan S; Weidberg, Anthony; Weinert, Benjamin; Weingarten, Jens; Weiser, Christian; Weits, Hartger; Wells, Phillippa; Wenaus, Torre; Wengler, Thorsten; Wenig, Siegfried; Wermes, Norbert; Werner, Matthias; Werner, Per; Wessels, Martin; Wetter, Jeffrey; Whalen, Kathleen; Wharton, Andrew Mark; White, Andrew; White, Martin; White, Ryan; White, Sebastian; Whiteson, Daniel; Wickens, Fred; Wiedenmann, Werner; Wielers, Monika; Wienemann, Peter; Wiglesworth, Craig; Wiik-Fuchs, Liv Antje Mari; Wildauer, Andreas; Wilkens, Henric George; Williams, Hugh; Williams, Sarah; Willis, Christopher; Willocq, Stephane; Wilson, Alan; Wilson, John; Wingerter-Seez, Isabelle; Winklmeier, Frank; Winter, Benedict Tobias; Wittgen, Matthias; Wittkowski, Josephine; Wollstadt, Simon Jakob; Wolter, Marcin Wladyslaw; Wolters, Helmut; Wosiek, Barbara; Wotschack, Jorg; Woudstra, Martin; Wozniak, Krzysztof; Wu, Mengqing; Wu, Miles; Wu, Sau Lan; Wu, Xin; Wu, Yusheng; Wyatt, Terry Richard; Wynne, Benjamin; Xella, Stefania; Xu, Da; Xu, Lailin; Yabsley, Bruce; Yacoob, Sahal; Yakabe, Ryota; Yamada, Miho; Yamaguchi, Yohei; Yamamoto, Akira; Yamamoto, Shimpei; Yamanaka, Takashi; Yamauchi, Katsuya; Yamazaki, Yuji; Yan, Zhen; Yang, Haijun; Yang, Hongtao; Yang, Yi; Yao, Liwen; Yao, Weiming; Yasu, Yoshiji; Yatsenko, Elena; Yau Wong, Kaven Henry; Ye, Jingbo; Ye, Shuwei; Yeletskikh, Ivan; Yen, Andy L; Yildirim, Eda; Yorita, Kohei; Yoshida, Rikutaro; Yoshihara, Keisuke; Young, Charles; Young, Christopher John; Youssef, Saul; Yu, David Ren-Hwa; Yu, Jaehoon; Yu, Jiaming; Yu, Jie; Yuan, Li; Yurkewicz, Adam; Yusuff, Imran; Zabinski, Bartlomiej; Zaidan, Remi; Zaitsev, Alexander; Zalieckas, Justas; Zaman, Aungshuman; Zambito, Stefano; Zanello, Lucia; Zanzi, Daniele; Zeitnitz, Christian; Zeman, Martin; Zemla, Andrzej; Zengel, Keith; Zenin, Oleg; Ženiš, Tibor; Zerwas, Dirk; Zhang, Dongliang; Zhang, Fangzhou; Zhang, Jinlong; Zhang, Lei; Zhang, Ruiqi; Zhang, Xueyao; Zhang, Zhiqing; Zhao, Xiandong; Zhao, Yongke; Zhao, Zhengguo; Zhemchugov, Alexey; Zhong, Jiahang; Zhou, Bing; Zhou, Chen; Zhou, Lei; Zhou, Li; Zhou, Ning; Zhu, Cheng Guang; Zhu, Hongbo; Zhu, Junjie; Zhu, Yingchun; Zhuang, Xuai; Zhukov, Konstantin; Zibell, Andre; Zieminska, Daria; Zimine, Nikolai; Zimmermann, Christoph; Zimmermann, Stephanie; Zinonos, Zinonas; Zinser, Markus; Ziolkowski, Michael; Živković, Lidija; Zobernig, Georg; Zoccoli, Antonio; zur Nedden, Martin; Zurzolo, Giovanni; Zwalinski, Lukasz

    2016-01-05

    Combined analyses of the Higgs boson production and decay rates as well as its coupling strengths to vector bosons and fermions are presented. The combinations include the results of the analyses of the $H\\to\\gamma\\gamma,\\, ZZ^*,\\, WW^*,\\, Z\\gamma,\\, b\\bar{b},\\, \\tau\\tau$ and $\\mu\\mu$ decay modes, and the constraints on the associated production with a pair of top quarks and on the off-shell coupling strengths of the Higgs boson. The results are based on the LHC proton--proton collision datasets, with integrated luminosities of up to 4.7 fb$^{-1}$ at $\\sqrt{s}=7$ TeV and 20.3 fb$^{-1}$ at $\\sqrt{s}=8$ TeV, recorded by the ATLAS detector in 2011 and 2012. Combining all production modes and decay channels, the measured signal yield, normalised to the Standard Model expectation, is $1.18^{+0.15}_{-0.14}$. The data provide unequivocal confirmation of the gluon fusion production of the Higgs boson, strong evidence of vector-boson fusion production and support Standard Model assumptions of production in association...

  13. Raman dispersion spectroscopy on the highly saddled nickel(II)-octaethyltetraphenylporphyrin reveals the symmetry of nonplanar distortions and the vibronic coupling strength of normal modes

    International Nuclear Information System (INIS)

    Schweitzer-Stenner, R.; Stichternath, A.; Dreybrodt, W.; Jentzen, W.; Song, X.; Shelnutt, J.A.; Nielsen, O.F.; Medforth, C.J.; Smith, K.M.

    1997-01-01

    We have measured the polarized Raman cross sections and depolarization ratios of 16 fundamental modes of nickel octaethyltetraphenylporphyrin in a CS 2 solution for 16 fundamental modes, i.e., the A 1g -type vibrations ν 1 , ν 2 , ν 3 , ν 4 , ν 5 , and φ 8 , the B 1g vibrations ν 11 and ν 14 , the B 2g vibrations ν 28 , ν 29 , and ν 30 and the antisymmetric A 2g modes ν 19 , ν 20 , ν 22 , and ν 23 as function of the excitation wavelength. The data cover the entire resonant regions of the Q- and B-bands. They were analyzed by use of a theory which describes intra- and intermolecular coupling in terms of a time-independent nonadiabatic perturbation theory [E. Unger, U. Bobinger, W. Dreybrodt, and R. Schweitzer-Stenner, J. Phys. Chem. 97, 9956 (1993)]. This approach explicitly accounts in a self-consistent way for multimode mixing with all Raman modes investigated. The vibronic coupling parameters obtained from this procedure were then used to successfully fit the vibronic side bands of the absorption spectrum and to calculate the resonance excitation profiles in absolute units. Our results show that the porphyrin macrocycle is subject to B 2u -(saddling) and B 1u -(ruffling) distortions which lower its symmetry to S 4 . Thus, evidence is provided that the porphyrin molecule maintains the nonplanar structure of its crystal phase in an organic solvent. The vibronic coupling parameters indicate a breakdown of the four-orbital model. This notion is corroborated by (ZINDO/S) calculations which reveal that significant configurational interaction occurs between the electronic transitions into |Q right-angle- and |1B right-angle-states and various porphyrin→porphyrin, metal→porphyrin, and porphyrin→metal transitions. (Abstract Truncated)

  14. Potential Mechanisms and Functions of Intermittent Neural Synchronization

    Directory of Open Access Journals (Sweden)

    Sungwoo Ahn

    2017-05-01

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

  15. Autaptic effects on synchrony of neurons coupled by electrical synapses

    Science.gov (United States)

    Kim, Youngtae

    2017-07-01

    In this paper, we numerically study the effects of a special synapse known as autapse on synchronization of population of Morris-Lecar (ML) neurons coupled by electrical synapses. Several configurations of the ML neuronal populations such as a pair or a ring or a globally coupled network with and without autapses are examined. While most of the papers on the autaptic effects on synchronization have used networks of neurons of same spiking rate, we use the network of neurons of different spiking rates. We find that the optimal autaptic coupling strength and the autaptic time delay enhance synchronization in our neural networks. We use the phase response curve analysis to explain the enhanced synchronization by autapses. Our findings reveal the important relationship between the intraneuronal feedback loop and the interneuronal coupling.

  16. Firing Patterns and Transitions in Coupled Neurons Controlled by a Pacemaker

    International Nuclear Information System (INIS)

    Mei-Sheng, Li; Qi-Shao, Lu; Li-Xia, Duan; Qing-Yun, Wang

    2008-01-01

    To reveal the dynamics of neuronal networks with pacemakers, the firing patterns and their transitions are investigated in a ring HR neuronal network with gap junctions under the control of a pacemaker. Compared with the situation without pacemaker, the neurons in the network can exhibit various firing patterns as the external current is applied or the coupling strength of pacemaker varies. The results are beneficial for understanding the complex cooperative behaviour of large neural assemblies with pacemaker control

  17. Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks.

    Science.gov (United States)

    Gong, Yubing; Xu, Bo; Wu, Ya'nan

    2013-09-01

    In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.

  18. Optimizing the wind power generation in low wind speed areas using an advanced hybrid RBF neural network coupled with the HGA-GSA optimization method

    Energy Technology Data Exchange (ETDEWEB)

    Assareh, Ehsanolah; Poultangari, Iman [Dezful Branch, Islamic Azad University, Dezful (Iran, Islamic Republic of); Tandis, Emad [Mechanical Engineering Department, University of Jundi Shapor, Dezful (Iran, Islamic Republic of); Nedael, Mojtaba [Dept. of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran (Iran, Islamic Republic of)

    2016-10-15

    Enhancing the energy production from wind power in low-wind areas has always been a fundamental subject of research in the field of wind energy industry. In the first phase of this research, an initial investigation was performed to evaluate the potential of wind in south west of Iran. The initial results indicate that the wind potential in the studied location is not sufficient enough and therefore the investigated region is identified as a low wind speed area. In the second part of this study, an advanced optimization model was presented to regulate the torque in the wind generators. For this primary purpose, the torque of wind turbine is adjusted using a Proportional and integral (PI) control system so that at lower speeds of the wind, the power generated by generator is enhanced significantly. The proposed model uses the RBF neural network to adjust the net obtained gains of the PI controller for the purpose of acquiring the utmost electricity which is produced through the generator. Furthermore, in order to edify and instruct the neural network, the optimal data set is obtained by a Hybrid genetic algorithm along with a gravitational search algorithm (HGA-GSA). The proposed method is evaluated by using a 5MW wind turbine manufactured by National Renewable Energy Laboratory (NREL). Final results of this study are indicative of the satisfactory and successful performance of the proposed investigated model.

  19. Synchronization of hypernetworks of coupled dynamical systems

    International Nuclear Information System (INIS)

    Sorrentino, Francesco

    2012-01-01

    We consider the synchronization of coupled dynamical systems when different types of interactions are simultaneously present. We assume that a set of dynamical systems is coupled through the connections of two or more distinct networks (each of which corresponds to a distinct type of interaction), and we refer to such a system as a dynamical hypernetwork. Applications include neural networks made up of both electrical gap junctions and chemical synapses, the coordinated motion of shoals of fish communicating through both vision and flow sensing, and hypernetworks of coupled chaotic oscillators. We first analyze the case of a hypernetwork made up of m = 2 networks. We look for the necessary and sufficient conditions for synchronization. We attempt to reduce the linear stability problem to a master stability function (MSF) form, i.e. decoupling the effects of the coupling functions from the structure of the networks. Unfortunately, we are unable to obtain a reduction in an MSF form for the general case. However, we show that such a reduction is possible in three cases of interest: (i) the Laplacian matrices associated with the two networks commute; (ii) one of the two networks is unweighted and fully connected; and (iii) one of the two networks is such that the coupling strength from node i to node j is a function of j but not of i. Furthermore, we define a class of networks such that if either one of the two coupling networks belongs to this class, the reduction can be obtained independently of the other network. As an example of interest, we study synchronization of a neural hypernetwork for which the connections can be either chemical synapses or electrical gap junctions. We propose a generalization of our stability results to the case of hypernetworks formed of m ⩾ 2 networks. (paper)

  20. 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.

  1. Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model

    Directory of Open Access Journals (Sweden)

    Yaojie Yue

    2016-12-01

    Full Text Available Crop frost, one kind of agro-meteorological disaster, often causes significant loss to agriculture. Thus, evaluating the risk of wheat frost aids scientific response to such disasters, which will ultimately promote food security. Therefore, this paper aims to propose an integrated risk assessment model of wheat frost, based on meteorological data and a hybrid fuzzy neural network model, taking China as an example. With the support of a geographic information system (GIS, a comprehensive method was put forward. Firstly, threshold temperatures of wheat frost at three growth stages were proposed, referring to phenology in different wheat growing areas and the meteorological standard of Degree of Crop Frost Damage (QX/T 88-2008. Secondly, a vulnerability curve illustrating the relationship between frost hazard intensity and wheat yield loss was worked out using hybrid fuzzy neural network model. Finally, the wheat frost risk was assessed in China. Results show that our proposed threshold temperatures are more suitable than using 0 °C in revealing the spatial pattern of frost occurrence, and hybrid fuzzy neural network model can further improve the accuracy of the vulnerability curve of wheat subject to frost with limited historical hazard records. Both these advantages ensure the precision of wheat frost risk assessment. In China, frost widely distributes in 85.00% of the total winter wheat planting area, but mainly to the north of 35°N; the southern boundary of wheat frost has moved northward, potentially because of the warming climate. There is a significant trend that suggests high risk areas will enlarge and gradually expand to the south, with the risk levels increasing from a return period of 2 years to 20 years. Among all wheat frost risk levels, the regions with loss rate ranges from 35.00% to 45.00% account for the largest area proportion, ranging from 58.60% to 63.27%. We argue that for wheat and other frost-affected crops, it is

  2. Fractional dynamical model for neurovascular coupling

    KAUST Repository

    Belkhatir, Zehor; Laleg-Kirati, Taous-Meriem

    2014-01-01

    The neurovascular coupling is a key mechanism linking the neural activity to the hemodynamic behavior. Modeling of this coupling is very important to understand the brain function but it is at the same time very complex due to the complexity

  3. Phase-flip bifurcation in a coupled Josephson junction neuron system

    Energy Technology Data Exchange (ETDEWEB)

    Segall, Kenneth, E-mail: ksegall@colgate.edu [Department of Physics and Astronomy, Colgate University, Hamilton, NY 13346 (United States); Guo, Siyang; Crotty, Patrick [Department of Physics and Astronomy, Colgate University, Hamilton, NY 13346 (United States); Schult, Dan [Department of Mathematics, Colgate University, Hamilton, NY 13346 (United States); Miller, Max [Department of Physics and Astronomy, Colgate University, Hamilton, NY 13346 (United States)

    2014-12-15

    Aiming to understand group behaviors and dynamics of neural networks, we have previously proposed the Josephson junction neuron (JJ neuron) as a fast analog model that mimics a biological neuron using superconducting Josephson junctions. In this study, we further analyze the dynamics of the JJ neuron numerically by coupling one JJ neuron to another. In this coupled system we observe a phase-flip bifurcation, where the neurons synchronize out-of-phase at weak coupling and in-phase at strong coupling. We verify this by simulation of the circuit equations and construct a bifurcation diagram for varying coupling strength using the phase response curve and spike phase difference map. The phase-flip bifurcation could be observed experimentally using standard digital superconducting circuitry.

  4. Phase-flip bifurcation in a coupled Josephson junction neuron system

    International Nuclear Information System (INIS)

    Segall, Kenneth; Guo, Siyang; Crotty, Patrick; Schult, Dan; Miller, Max

    2014-01-01

    Aiming to understand group behaviors and dynamics of neural networks, we have previously proposed the Josephson junction neuron (JJ neuron) as a fast analog model that mimics a biological neuron using superconducting Josephson junctions. In this study, we further analyze the dynamics of the JJ neuron numerically by coupling one JJ neuron to another. In this coupled system we observe a phase-flip bifurcation, where the neurons synchronize out-of-phase at weak coupling and in-phase at strong coupling. We verify this by simulation of the circuit equations and construct a bifurcation diagram for varying coupling strength using the phase response curve and spike phase difference map. The phase-flip bifurcation could be observed experimentally using standard digital superconducting circuitry

  5. Investigation of co-combustion characteristics of sewage sludge and coffee grounds mixtures using thermogravimetric analysis coupled to artificial neural networks modeling.

    Science.gov (United States)

    Chen, Jiacong; Liu, Jingyong; He, Yao; Huang, Limao; Sun, Shuiyu; Sun, Jian; Chang, KenLin; Kuo, Jiahong; Huang, Shaosong; Ning, Xunan

    2017-02-01

    Artificial neural network (ANN) modeling was applied to thermal data obtained by non-isothermal thermogravimetric analysis (TGA) from room temperature to 1000°C at three different heating rates in air to predict the TG curves of sewage sludge (SS) and coffee grounds (CG) mixtures. A good agreement between experimental and predicted data verified the accuracy of the ANN approach. The results of co-combustion showed that there were interactions between SS and CG, and the impacts were mostly positive. With the addition of CG, the mass loss rate and the reactivity of SS were increased while charring was reduced. Measured activation energies (E a ) determined by the Kissinger-Akahira-Sunose (KAS) and Ozawa-Flynn-Wall (OFW) methods deviated by <5%. The average value of E a (166.8kJ/mol by KAS and 168.8kJ/mol by OFW, respectively) was the lowest when the fraction of CG in the mixture was 40%. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. 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

  7. Dark coupling

    International Nuclear Information System (INIS)

    Gavela, M.B.; Hernández, D.; Honorez, L. Lopez; Mena, O.; Rigolin, S.

    2009-01-01

    The two dark sectors of the universe—dark matter and dark energy—may interact with each other. Background and linear density perturbation evolution equations are developed for a generic coupling. We then establish the general conditions necessary to obtain models free from non-adiabatic instabilities. As an application, we consider a viable universe in which the interaction strength is proportional to the dark energy density. The scenario does not exhibit ''phantom crossing'' and is free from instabilities, including early ones. A sizeable interaction strength is compatible with combined WMAP, HST, SN, LSS and H(z) data. Neutrino mass and/or cosmic curvature are allowed to be larger than in non-interacting models. Our analysis sheds light as well on unstable scenarios previously proposed

  8. Sequentially firing neurons confer flexible timing in neural pattern generators

    International Nuclear Information System (INIS)

    Urban, Alexander; Ermentrout, Bard

    2011-01-01

    Neuronal networks exhibit a variety of complex spatiotemporal patterns that include sequential activity, synchrony, and wavelike dynamics. Inhibition is the primary means through which such patterns are implemented. This behavior is dependent on both the intrinsic dynamics of the individual neurons as well as the connectivity patterns. Many neural circuits consist of networks of smaller subcircuits (motifs) that are coupled together to form the larger system. In this paper, we consider a particularly simple motif, comprising purely inhibitory interactions, which generates sequential periodic dynamics. We first describe the dynamics of the single motif both for general balanced coupling (all cells receive the same number and strength of inputs) and then for a specific class of balanced networks: circulant systems. We couple these motifs together to form larger networks. We use the theory of weak coupling to derive phase models which, themselves, have a certain structure and symmetry. We show that this structure endows the coupled system with the ability to produce arbitrary timing relationships between symmetrically coupled motifs and that the phase relationships are robust over a wide range of frequencies. The theory is applicable to many other systems in biology and physics.

  9. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  10. Reduction in LFP cross-frequency coupling between theta and gamma rhythms associated with impaired STP and LTP in a rat model of brain ischemia

    Directory of Open Access Journals (Sweden)

    Tao eZhang

    2013-04-01

    Full Text Available The theta-gamma cross-frequency coupling (CFC in hippocampus was reported to reflect memory process. In this study, we measured the CFC of hippocampal local field potentials (LFPs in a two-vessel occlusion (2VO rat model, combined with both amplitude and phase properties and associated with short and long-term plasticity indicating the memory function. Male Wistar rats were used and a 2VO model was established. STP and LTP were recorded in hippocampal CA3-CA1 pathway after LFPs were collected in both CA3 and CA1. Based on the data of relative power spectra and phase synchronization, it suggested that both the amplitude and phase coupling of either theta or gamma rhythm were involved in modulating the neural network in 2VO rats. In order to determine whether the CFC was also implicated in neural impairment in 2VO rats, the coupling of CA3 theta–CA1 gamma was measured by both phase-phase coupling (n:m phase synchronization and phase-amplitude coupling. The attenuated CFC strength in 2VO rats implied the impaired neural communication in the coordination of theta-gamma entraining process. Moreover, compared with modulation index (MI a novel algorithm named cross frequency conditional mutual information (CF-CMI, was developed to focus on the coupling between theta phase and the phase of gamma amplitude. The results suggest that the reduced CFC strength probably attributed to the disruption of the phase of CA1 gamma envelop. In conclusion, it implied that the phase coupling and CFC of hippocampal theta and gamma played an important role in supporting functions of neural network. Furthermore, synaptic plasticity on CA3-CA1 pathway was reduced in line with the decreased CFC strength from CA3 to CA1. It partly supported our hypothesis that directional CFC indicator might probably be used as a measure of synaptic plasticity.

  11. Electronic properties of quasi one-dimensional quantum wire models under equal coupling strength superpositions of Rashba and Dresselhaus spin-orbit interactions in the presence of an in-plane magnetic field

    International Nuclear Information System (INIS)

    Papp, E.; Micu, C.; Racolta, D.

    2013-01-01

    In this paper one deals with the theoretical derivation of energy bands and of related wavefunctions characterizing quasi 1D semiconductor heterostructures, such as InAs quantum wire models. Such models get characterized this time by equal coupling strength superpositions of Rashba and Dresselhaus spin-orbit interactions of dimensionless magnitude a under the influence of in-plane magnetic fields of magnitude B. We found that the orientations of the field can be selected by virtue of symmetry requirements. For this purpose one resorts to spin conservations, but alternative conditions providing sensible simplifications of the energy-band formula can be reasonably accounted for. Besides the wavenumber k relying on the 1D electron, one deals with the spin-like s=±1 factors in the front of the square root term of the energy. Having obtained the spinorial wavefunction, opens the way to the derivation of spin precession effects. For this purpose one resorts to the projections of the wavenumber operator on complementary spin states. Such projections are responsible for related displacements proceeding along the Ox-axis. This results in a 2D rotation matrix providing both the precession angle as well as the precession axis

  12. Optimization of the Production of Extracellular Polysaccharide from the Shiitake Medicinal Mushroom Lentinus edodes (Agaricomycetes) Using Mutation and a Genetic Algorithm-Coupled Artificial Neural Network (GA-ANN).

    Science.gov (United States)

    Adeeyo, Adeyemi Ojutalayo; Lateef, Agbaje; Gueguim-Kana, Evariste Bosco

    2016-01-01

    Exopolysaccharide (EPS) production by a strain of Lentinus edodes was studied via the effects of treatments with ultraviolet (UV) irradiation and acridine orange. Furthermore, optimization of EPS production was studied using a genetic algorithm coupled with an artificial neural network in submerged fermentation. Exposure to irradiation and acridine orange resulted in improved EPS production (2.783 and 5.548 g/L, respectively) when compared with the wild strain (1.044 g/L), whereas optimization led to improved productivity (23.21 g/L). The EPS produced by various strains also demonstrated good DPPH scavenging activities of 45.40-88.90%, and also inhibited the growth of Escherichia coli and Klebsiella pneumoniae. This study shows that multistep optimization schemes involving physical-chemical mutation and media optimization can be an attractive strategy for improving the yield of bioactives from medicinal mushrooms. To the best of our knowledge, this report presents the first reference of a multistep approach to optimizing EPS production in L. edodes.

  13. A novel method combining cellular neural networks and the coupled nonlinear oscillators' paradigm involving a related bifurcation analysis for robust image contrast enhancement in dynamically changing difficult visual environments

    International Nuclear Information System (INIS)

    Chedjou, Jean Chamberlain; Kyamakya, Kyandoghere

    2010-01-01

    It is well known that a machine vision-based analysis of a dynamic scene, for example in the context of advanced driver assistance systems (ADAS), does require real-time processing capabilities. Therefore, the system used must be capable of performing both robust and ultrafast analyses. Machine vision in ADAS must fulfil the above requirements when dealing with a dynamically changing visual context (i.e. driving in darkness or in a foggy environment, etc). Among the various challenges related to the analysis of a dynamic scene, this paper focuses on contrast enhancement, which is a well-known basic operation to improve the visual quality of an image (dynamic or static) suffering from poor illumination. The key objective is to develop a systematic and fundamental concept for image contrast enhancement that should be robust despite a dynamic environment and that should fulfil the real-time constraints by ensuring an ultrafast analysis. It is demonstrated that the new approach developed in this paper is capable of fulfilling the expected requirements. The proposed approach combines the good features of the 'coupled oscillators'-based signal processing paradigm with the good features of the 'cellular neural network (CNN)'-based one. The first paradigm in this combination is the 'master system' and consists of a set of coupled nonlinear ordinary differential equations (ODEs) that are (a) the so-called 'van der Pol oscillator' and (b) the so-called 'Duffing oscillator'. It is then implemented or realized on top of a 'slave system' platform consisting of a CNN-processors platform. An offline bifurcation analysis is used to find out, a priori, the windows of parameter settings in which the coupled oscillator system exhibits the best and most appropriate behaviours of interest for an optimal resulting image processing quality. In the frame of the extensive bifurcation analysis carried out, analytical formulae have been derived, which are capable of determining the various

  14. Stability analysis and synchronization in discrete-time complex networks with delayed coupling

    Science.gov (United States)

    Cheng, Ranran; Peng, Mingshu; Yu, Weibin; Sun, Bo; Yu, Jinchen

    2013-12-01

    A new network of coupled maps is proposed in which the connections between units involve no delays but the intra-neural communication does, whereas in the work of Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], the focus is on information processing delayed by the inter-neural communication. We show that the synchronization of the network depends on not only the intrinsic dynamical features and inter-connection topology (characterized by the spectrum of the graph Laplacian) but also the delays and the coupling strength. There are two main findings: (i) the more neighbours, the easier to be synchronized; (ii) odd delays are easier to be synchronized than even ones. In addition, compared with those discussed by Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], our model has a better synchronizability for regular networks and small-world variants.

  15. Attitude Strength.

    Science.gov (United States)

    Howe, Lauren C; Krosnick, Jon A

    2017-01-03

    Attitude strength has been the focus of a huge volume of research in psychology and related sciences for decades. The insights offered by this literature have tremendous value for understanding attitude functioning and structure and for the effective application of the attitude concept in applied settings. This is the first Annual Review of Psychology article on the topic, and it offers a review of theory and evidence regarding one of the most researched strength-related attitude features: attitude importance. Personal importance is attached to an attitude when the attitude is perceived to be relevant to self-interest, social identification with reference groups or reference individuals, and values. Attaching personal importance to an attitude causes crystallizing of attitudes (via enhanced resistance to change), effortful gathering and processing of relevant information, accumulation of a large store of well-organized relevant information in long-term memory, enhanced attitude extremity and accessibility, enhanced attitude impact on the regulation of interpersonal attraction, energizing of emotional reactions, and enhanced impact of attitudes on behavioral intentions and action. Thus, important attitudes are real and consequential psychological forces, and their study offers opportunities for addressing behavioral change.

  16. 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

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

    NARCIS (Netherlands)

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

    2010-01-01

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

  18. Hierarchical modular structure enhances the robustness of self-organized criticality in neural networks

    International Nuclear Information System (INIS)

    Wang Shengjun; Zhou Changsong

    2012-01-01

    One of the most prominent architecture properties of neural networks in the brain is the hierarchical modular structure. How does the structure property constrain or improve brain function? It is thought that operating near criticality can be beneficial for brain function. Here, we find that networks with modular structure can extend the parameter region of coupling strength over which critical states are reached compared to non-modular networks. Moreover, we find that one aspect of network function—dynamical range—is highest for the same parameter region. Thus, hierarchical modularity enhances robustness of criticality as well as function. However, too much modularity constrains function by preventing the neural networks from reaching critical states, because the modular structure limits the spreading of avalanches. Our results suggest that the brain may take advantage of the hierarchical modular structure to attain criticality and enhanced function. (paper)

  19. Chimera States in Neural Oscillators

    Science.gov (United States)

    Bahar, Sonya; Glaze, Tera

    2014-03-01

    Chimera states have recently been explored both theoretically and experimentally, in various coupled nonlinear oscillators, ranging from phase-oscillator models to coupled chemical reactions. In a chimera state, both coherent and incoherent (or synchronized and desynchronized) states occur simultaneously in populations of identical oscillators. We investigate chimera behavior in a population of neural oscillators using the Huber-Braun model, a Hodgkin-Huxley-like model originally developed to characterize the temperature-dependent bursting behavior of mammalian cold receptors. One population of neurons is allowed to synchronize, with each neuron receiving input from all the others in its group (global within-group coupling). Subsequently, a second population of identical neurons is placed under an identical global within-group coupling, and the two populations are also coupled to each other (between-group coupling). For certain values of the coupling constants, the neurons in the two populations exhibit radically different synchronization behavior. We will discuss the range of chimera activity in the model, and discuss its implications for actual neural activity, such as unihemispheric sleep.

  20. Bond strength of masonry

    NARCIS (Netherlands)

    Pluijm, van der R.; Vermeltfoort, A.Th.

    1992-01-01

    Bond strength is not a well defined property of masonry. Normally three types of bond strength can be distinguished: - tensile bond strength, - shear (and torsional) bond strength, - flexural bond strength. In this contribution the behaviour and strength of masonry in deformation controlled uniaxial

  1. 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

  2. predicting the compressive strength of concretes made with granite

    African Journals Online (AJOL)

    2013-03-01

    Mar 1, 2013 ... computational model based on artificial neural networks for the determination of the compressive strength of concrete ... Strength being the most important property of con- ... to cut corners use low quality concrete materials in .... manner of operation of natural neurons in the human body. ... the output ai.

  3. The Importance of Muscular Strength: Training Considerations.

    Science.gov (United States)

    Suchomel, Timothy J; Nimphius, Sophia; Bellon, Christopher R; Stone, Michael H

    2018-04-01

    This review covers underlying physiological characteristics and training considerations that may affect muscular strength including improving maximal force expression and time-limited force expression. Strength is underpinned by a combination of morphological and neural factors including muscle cross-sectional area and architecture, musculotendinous stiffness, motor unit recruitment, rate coding, motor unit synchronization, and neuromuscular inhibition. Although single- and multi-targeted block periodization models may produce the greatest strength-power benefits, concepts within each model must be considered within the limitations of the sport, athletes, and schedules. Bilateral training, eccentric training and accentuated eccentric loading, and variable resistance training may produce the greatest comprehensive strength adaptations. Bodyweight exercise, isolation exercises, plyometric exercise, unilateral exercise, and kettlebell training may be limited in their potential to improve maximal strength but are still relevant to strength development by challenging time-limited force expression and differentially challenging motor demands. Training to failure may not be necessary to improve maximum muscular strength and is likely not necessary for maximum gains in strength. Indeed, programming that combines heavy and light loads may improve strength and underpin other strength-power characteristics. Multiple sets appear to produce superior training benefits compared to single sets; however, an athlete's training status and the dose-response relationship must be considered. While 2- to 5-min interset rest intervals may produce the greatest strength-power benefits, rest interval length may vary based an athlete's training age, fiber type, and genetics. Weaker athletes should focus on developing strength before emphasizing power-type training. Stronger athletes may begin to emphasize power-type training while maintaining/improving their strength. Future research should

  4. Endogenous field feedback promotes the detectability for exogenous electric signal in the hybrid coupled population

    Energy Technology Data Exchange (ETDEWEB)

    Wei, Xile; Zhang, Danhong; Wang, Jiang; Yu, Haitao, E-mail: htyu@tju.edu.cn [Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Lu, Meili [School of Informational Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222 (China); Che, Yanqiu [School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222 (China)

    2015-01-15

    This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance.

  5. Endogenous field feedback promotes the detectability for exogenous electric signal in the hybrid coupled population

    International Nuclear Information System (INIS)

    Wei, Xile; Zhang, Danhong; Wang, Jiang; Yu, Haitao; Lu, Meili; Che, Yanqiu

    2015-01-01

    This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance

  6. Endogenous field feedback promotes the detectability for exogenous electric signal in the hybrid coupled population.

    Science.gov (United States)

    Wei, Xile; Zhang, Danhong; Lu, Meili; Wang, Jiang; Yu, Haitao; Che, Yanqiu

    2015-01-01

    This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance.

  7. Synchrony, waves and ripple in spatially coupled Kuramoto oscillators with Mexican hat connectivity.

    Science.gov (United States)

    Heitmann, Stewart; Ermentrout, G Bard

    2015-06-01

    Spatiotemporal waves of synchronized activity are known to arise in oscillatory neural networks with lateral inhibitory coupling. How such patterns respond to dynamic changes in coupling strength is largely unexplored. The present study uses analysis and simulation to investigate the evolution of wave patterns when the strength of lateral inhibition is varied dynamically. Neural synchronization was modeled by a spatial ring of Kuramoto oscillators with Mexican hat lateral coupling. Broad bands of coexisting stable wave solutions were observed at all levels of inhibition. The stability of these waves was formally analyzed in both the infinite ring and the finite ring. The broad range of multi-stability predicted hysteresis in transitions between neighboring wave solutions when inhibition is slowly varied. Numerical simulation confirmed the predicted transitions when inhibition was ramped down from a high initial value. However, non-wave solutions emerged from the uniform solution when inhibition was ramped upward from zero. These solutions correspond to spatially periodic deviations of phase that we call ripple states. Numerical continuation showed that stable ripple states emerge from synchrony via a supercritical pitchfork bifurcation. The normal form of this bifurcation was derived analytically, and its predictions compared against the numerical results. Ripple states were also found to bifurcate from wave solutions, but these were locally unstable. Simulation also confirmed the existence of hysteresis and ripple states in two spatial dimensions. Our findings show that spatial synchronization patterns can remain structurally stable despite substantial changes in network connectivity.

  8. Chaotic Motion of Nonlinearly Coupled Quintic Oscillators | Adeloye ...

    African Journals Online (AJOL)

    With a fixed energy, we investigate the motion of two nonlinearly coupled quintic oscillators for various values of the coupling strength with the aid of the Poincare surface of section. It is observed that chaotic motion sets in for coupling strength as low as 0.001. The degree of chaoticity generally increases as the coupling ...

  9. Signal Processing and Neural Network Simulator

    Science.gov (United States)

    Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

    1995-04-01

    The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.

  10. 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.

  11. A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia.

    Science.gov (United States)

    Floares, Alexandru George

    2008-01-01

    Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.

  12. Synaptic energy drives the information processing mechanisms in spiking neural networks.

    Science.gov (United States)

    El Laithy, Karim; Bogdan, Martin

    2014-04-01

    Flow of energy and free energy minimization underpins almost every aspect of naturally occurring physical mechanisms. Inspired by this fact this work establishes an energy-based framework that spans the multi-scale range of biological neural systems and integrates synaptic dynamic, synchronous spiking activity and neural states into one consistent working paradigm. Following a bottom-up approach, a hypothetical energy function is proposed for dynamic synaptic models based on the theoretical thermodynamic principles and the Hopfield networks. We show that a synapse exposes stable operating points in terms of its excitatory postsynaptic potential as a function of its synaptic strength. We postulate that synapses in a network operating at these stable points can drive this network to an internal state of synchronous firing. The presented analysis is related to the widely investigated temporal coherent activities (cell assemblies) over a certain range of time scales (binding-by-synchrony). This introduces a novel explanation of the observed (poly)synchronous activities within networks regarding the synaptic (coupling) functionality. On a network level the transitions from one firing scheme to the other express discrete sets of neural states. The neural states exist as long as the network sustains the internal synaptic energy.

  13. Nonlinear programming with feedforward neural networks.

    Energy Technology Data Exchange (ETDEWEB)

    Reifman, J.

    1999-06-02

    We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.

  14. Autonomous Navigation Apparatus With Neural Network for a Mobile Vehicle

    Science.gov (United States)

    Quraishi, Naveed (Inventor)

    1996-01-01

    An autonomous navigation system for a mobile vehicle arranged to move within an environment includes a plurality of sensors arranged on the vehicle and at least one neural network including an input layer coupled to the sensors, a hidden layer coupled to the input layer, and an output layer coupled to the hidden layer. The neural network produces output signals representing respective positions of the vehicle, such as the X coordinate, the Y coordinate, and the angular orientation of the vehicle. A plurality of patch locations within the environment are used to train the neural networks to produce the correct outputs in response to the distances sensed.

  15. Neural Global Pattern Similarity Underlies True and False Memories.

    Science.gov (United States)

    Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

    2016-06-22

    The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

  16. The Strength Compass

    DEFF Research Database (Denmark)

    Ledertoug, Mette Marie

    In the Ph.D-project ͚Strengths-based Learning - Children͛s character strengths as a means to their learning potential͛ 750 Danish children have assessed ͚The Strength Compass͛ in order to identify their strengths and to create awareness of strengths. This was followed by a strengths......-based intervention program in order to explore the strengths. Finally different methods to apply the strength in everyday life at school were applied. The paper presentation will show the results for strengths display for children aged 6-16 in different categories: Different age groups: Are the same strengths...... present in both small children and youths? Gender: Do the results show differences between the two genders? Danish as a mother- tongue language: Do the results show any differences in the strengths display when considering different language and cultural backgrounds? Children with Special Needs: Do...

  17. Effects of Mental Imagery on Muscular Strength in Healthy and Patient Participants: A Systematic Review

    Science.gov (United States)

    Slimani, Maamer; Tod, David; Chaabene, Helmi; Miarka, Bianca; Chamari, Karim

    2016-01-01

    sensorimotor activation and physiological responses such as blood pressure, heart rate, and respiration rate. Key points Coupling mental imagery with physical training is the best suited intervention for improving strength performance. An examination of potential moderator variables revealed that the effectiveness of mental imagery on strength performance may vary depending on the appropriate matching of muscular groups, the characteristics of mental imagery interventions, training duration, and type of skills. Self-efficacy, motivation, and imagery ability were the mediator variables in the mental imagery-strength performance relationship. Greater effects of internal imagery perspective on strength performance than those of external imagery could be explained in terms of neural adaptations, stronger brain activation, higher muscles excitation, greater somatic and sensorimotor activation, and higher physiological responses such as blood pressure, heart rate, and respiration rate. Mental imagery prevention interventions may provide a valuable tool to improve the functional recovery after short-term muscle immobilization and anterior cruciate ligament in patients. PMID:27803622

  18. Active voltammetric microsensors with neural signal processing.

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, M. C.

    1998-12-11

    Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical ''signatures'' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration, the calibration, sensing, and processing methods of these active voltammetric microsensors can

  19. Active voltammetric microsensors with neural signal processing

    Science.gov (United States)

    Vogt, Michael C.; Skubal, Laura R.

    1999-02-01

    Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical 'signatures' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration; the calibration, sensing, and processing methods of these active voltammetric microsensors can detect, recognize, and

  20. Deep Neural Network Detects Quantum Phase Transition

    Science.gov (United States)

    Arai, Shunta; Ohzeki, Masayuki; Tanaka, Kazuyuki

    2018-03-01

    We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network. In the present study, we utilized the simplest case of a quantum many-body system, namely a one-dimensional chain of Ising spins with the transverse Ising model. We prepared several spin configurations, which were obtained using repeated observations of the model for a particular strength of the transverse field, as input data for the neural network. Although the proposed method can be employed using experimental observations of quantum many-body systems, we tested our technique with spin configurations generated by a quantum Monte Carlo simulation without initial relaxation. The neural network successfully identified the strength of transverse field only from the spin configurations, leading to consistent estimations of the critical point of our model Γc = J.

  1. Fiber optically coupled radioluminescence detectors: A short review of key strengths and weaknesses of BCF-60 and Al2O3:C scintillating-material based systems in radiotherapy dosimetry applications

    DEFF Research Database (Denmark)

    Buranurak, Siritorn; Andersen, Claus E.

    2017-01-01

    the years, developments and research of the fiber detector systems have undergone in several groups worldwide. In this article, the in-house developed fiber detector systems based on two luminescence phosphors of (i) BCF-60 polystyrene-based organic plastic scintillator and (ii) carbon-doped aluminum oxide...... in the new hybrid MRI LINAC/cobalt systems, and (iii) in vivo measurements due to safety-issues related to the high operating voltage. Fiber optically coupled luminescence detectors provide a promising supplement to ionization chambers by offering the capability of real-time in vivo dose monitoring with high...... time resolution. In particular, the all-optical nature of these detectors is an advantage for in vivo measurements due to the absence of high voltage supply or electrical wire that could cause harm to the patient or disturb the treatment. Basically, fiber-coupled luminescence detector systems function...

  2. Gestural coupling and social cognition

    DEFF Research Database (Denmark)

    Michael, John; Krueger, Joel William

    2012-01-01

    Social cognition researchers have become increasingly interested in the ways that behavioral, physiological, and neural coupling facilitate social interaction and interpersonal understanding. We distinguish two ways of conceptualizing the role of such coupling processes in social cognition: strong...... an essential enabling feature for social interaction and interpersonal understanding more generally and thus ought to exhibit severe deficits in these areas. We challenge SI's prediction and show how MS cases offer compelling reasons for instead adopting MI's pluralistic model of social interaction...... and interpersonal understanding. We conclude that investigations of coupling processes within social interaction should inform rather than marginalize or eliminate investigation of higher-level individual cognition...

  3. Binocular Rivalry in a Competitive Neural Network with Synaptic Depression

    KAUST Repository

    Kilpatrick, Zachary P.; Bressloff, Paul C.

    2010-01-01

    We study binocular rivalry in a competitive neural network with synaptic depression. In particular, we consider two coupled hypercolums within primary visual cortex (V1), representing orientation selective cells responding to either left or right

  4. The strength compass

    DEFF Research Database (Denmark)

    Ledertoug, Mette Marie

    of agreement/disagreement. Also the child/teacher is asked whether the actual strength is important and if he or she has the possibilities to apply the strength in the school. In a PhDproject ‘Strengths-based Learning - Children’s Character Strengths as Means to their Learning Potential’ 750 Danish children......Individual paper presentation: The ‘Strength Compass’. The results of a PhDresearch project among schoolchildren (age 6-16) identifying VIAstrengths concerning age, gender, mother-tongue-langue and possible child psychiatric diagnosis. Strengths-based interventions in schools have a theoretical...... Psychological Publishing Company. ‘The Strength Compass’ is a computer/Ipad based qualitative tool to identify the strengths of a child by a self-survey or a teacher’s survey. It is designed as a visual analogue scale with a statement of the strength in which the child/teacher may declare the degree...

  5. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  6. Neural attractor network for application in visual field data classification

    International Nuclear Information System (INIS)

    Fink, Wolfgang

    2004-01-01

    The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a ' counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available

  7. Electric quadrupole strength in nuclei

    International Nuclear Information System (INIS)

    Kirson, M.W.

    1979-01-01

    Isoscalar electric quadrupole strength distributions in nuclei are surveyed, and it is concluded that the strength is shared, in most cases, roughly equally between low-lying transitions and the giant quadrupole state. The same is not true of the isovector case. A simple extension of the schematic model gives a remarkably successul description of the data, and emphasizes the vital importance of the coupling between high-lying and low-lying quadrupole modes. The standadrd simple representation of the giant quadrupole resonance as produced by operating on the nuclear ground state with the quadrupole transition operator is not applicable to the isoscalar case. It is suggested that giant resonances fall into broad classes of similar states, with considerable qualitative differences between the distinct classes. (author)

  8. Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function.

    Science.gov (United States)

    Lerman, Caryn; Gu, Hong; Loughead, James; Ruparel, Kosha; Yang, Yihong; Stein, Elliot A

    2014-05-01

    Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. To test the hypothesis that the strength of coupling among 3 large-scale brain networks--salience, executive control, and default mode--will reflect the state of nicotine withdrawal (vs smoking satiety) and will predict abstinence-induced craving and cognitive deficits and to develop a resource allocation index (RAI) that reflects the combined strength of interactions among the 3 large-scale networks. A within-subject functional magnetic resonance imaging study in an academic medical center compared resting-state functional connectivity coherence strength after 24 hours of abstinence and after smoking satiety. We examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural functions. We included 37 healthy smoking volunteers, aged 19 to 61 years, for analyses. Twenty-four hours of abstinence vs smoking satiety. Inter-network connectivity strength (primary) and the relationship with subjective, behavioral, and neural measures of nicotine withdrawal during abstinence vs smoking satiety states (secondary). The RAI was significantly lower in the abstinent compared with the smoking satiety states (left RAI, P = .002; right RAI, P = .04), suggesting weaker inhibition between the default mode and salience networks. Weaker inter-network connectivity (reduced RAI) predicted abstinence-induced cravings to smoke (r = -0.59; P = .007) and less suppression of default mode activity during performance of a subsequent working memory task (ventromedial prefrontal cortex, r = -0.66, P = .003; posterior cingulate cortex, r = -0.65, P = .001). Alterations in coupling of the salience and default mode networks and the inability to disengage from the default mode network may be critical in cognitive/affective alterations that underlie nicotine dependence.

  9. Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy

    Science.gov (United States)

    Maleki, E.

    2015-12-01

    Friction stir welding (FSW) is a relatively new solid-state joining technique that is widely adopted in manufacturing and industry fields to join different metallic alloys that are hard to weld by conventional fusion welding. Friction stir welding is a very complex process comprising several highly coupled physical phenomena. The complex geometry of some kinds of joints makes it difficult to develop an overall governing equations system for theoretical behavior analyse of the friction stir welded joints. Weld quality is predominantly affected by welding effective parameters, and the experiments are often time consuming and costly. On the other hand, employing artificial intelligence (AI) systems such as artificial neural networks (ANNs) as an efficient approach to solve the science and engineering problems is considerable. In present study modeling of FSW effective parameters by ANNs is investigated. To train the networks, experimental test results on thirty AA-7075-T6 specimens are considered, and the networks are developed based on back propagation (BP) algorithm. ANNs testing are carried out using different experimental data that they are not used during networks training. In this paper, rotational speed of tool, welding speed, axial force, shoulder diameter, pin diameter and tool hardness are regarded as inputs of the ANNs. Yield strength, tensile strength, notch-tensile strength and hardness of welding zone are gathered as outputs of neural networks. According to the obtained results, predicted values for the hardness of welding zone, yield strength, tensile strength and notch-tensile strength have the least mean relative error (MRE), respectively. Comparison of the predicted and the experimental results confirms that the networks are adjusted carefully, and the ANN can be used for modeling of FSW effective parameters.

  10. Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy

    International Nuclear Information System (INIS)

    Maleki, E

    2015-01-01

    Friction stir welding (FSW) is a relatively new solid-state joining technique that is widely adopted in manufacturing and industry fields to join different metallic alloys that are hard to weld by conventional fusion welding. Friction stir welding is a very complex process comprising several highly coupled physical phenomena. The complex geometry of some kinds of joints makes it difficult to develop an overall governing equations system for theoretical behavior analyse of the friction stir welded joints. Weld quality is predominantly affected by welding effective parameters, and the experiments are often time consuming and costly. On the other hand, employing artificial intelligence (AI) systems such as artificial neural networks (ANNs) as an efficient approach to solve the science and engineering problems is considerable. In present study modeling of FSW effective parameters by ANNs is investigated. To train the networks, experimental test results on thirty AA-7075-T6 specimens are considered, and the networks are developed based on back propagation (BP) algorithm. ANNs testing are carried out using different experimental data that they are not used during networks training. In this paper, rotational speed of tool, welding speed, axial force, shoulder diameter, pin diameter and tool hardness are regarded as inputs of the ANNs. Yield strength, tensile strength, notch-tensile strength and hardness of welding zone are gathered as outputs of neural networks. According to the obtained results, predicted values for the hardness of welding zone, yield strength, tensile strength and notch-tensile strength have the least mean relative error (MRE), respectively. Comparison of the predicted and the experimental results confirms that the networks are adjusted carefully, and the ANN can be used for modeling of FSW effective parameters. (paper)

  11. Machine Learning Topological Invariants with Neural Networks

    Science.gov (United States)

    Zhang, Pengfei; Shen, Huitao; Zhai, Hui

    2018-02-01

    In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

  12. Desynchronization in diluted neural networks

    International Nuclear Information System (INIS)

    Zillmer, Ruediger; Livi, Roberto; Politi, Antonio; Torcini, Alessandro

    2006-01-01

    The dynamical behavior of a weakly diluted fully inhibitory network of pulse-coupled spiking neurons is investigated. Upon increasing the coupling strength, a transition from regular to stochasticlike regime is observed. In the weak-coupling phase, a periodic dynamics is rapidly approached, with all neurons firing with the same rate and mutually phase locked. The strong-coupling phase is characterized by an irregular pattern, even though the maximum Lyapunov exponent is negative. The paradox is solved by drawing an analogy with the phenomenon of 'stable chaos', i.e., by observing that the stochasticlike behavior is 'limited' to an exponentially long (with the system size) transient. Remarkably, the transient dynamics turns out to be stationary

  13. A computational study on altered theta-gamma coupling during learning and phase coding.

    Directory of Open Access Journals (Sweden)

    Xuejuan Zhang

    Full Text Available There is considerable interest in the role of coupling between theta and gamma oscillations in the brain in the context of learning and memory. Here we have used a neural network model which is capable of producing coupling of theta phase to gamma amplitude firstly to explore its ability to reproduce reported learning changes and secondly to memory-span and phase coding effects. The spiking neural network incorporates two kinetically different GABA(A receptor-mediated currents to generate both theta and gamma rhythms and we have found that by selective alteration of both NMDA receptors and GABA(A,slow receptors it can reproduce learning-related changes in the strength of coupling between theta and gamma either with or without coincident changes in theta amplitude. When the model was used to explore the relationship between theta and gamma oscillations, working memory capacity and phase coding it showed that the potential storage capacity of short term memories, in terms of nested gamma-subcycles, coincides with the maximal theta power. Increasing theta power is also related to the precision of theta phase which functions as a potential timing clock for neuronal firing in the cortex or hippocampus.

  14. Strengths-based Learning

    DEFF Research Database (Denmark)

    Ledertoug, Mette Marie

    -being. The Ph.D.-project in Strength-based learning took place in a Danish school with 750 pupils age 6-16 and a similar school was functioning as a control group. The presentation will focus on both the aware-explore-apply processes and the practical implications for the schools involved, and on measurable......Strength-based learning - Children͛s Character Strengths as Means to their Learning Potential͛ is a Ph.D.-project aiming to create a strength-based mindset in school settings and at the same time introducing strength-based interventions as specific tools to improve both learning and well...

  15. Hindcasting of storm waves using neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Rao, S.; Mandal, S.

    Department NN neural network net i weighted sum of the inputs of neuron i o k network output at kth output node P total number of training pattern s i output of neuron i t k target output at kth output node 1. Introduction Severe storms occur in Bay of Bengal...), forecasting of runoff (Crespo and Mora, 1993), concrete strength (Kasperkiewicz et al., 1995). The uses of neural network in the coastal the wave conditions will change from year to year, thus a proper statistical and climatological treatment requires several...

  16. Strength functions for fragmented doorway states

    International Nuclear Information System (INIS)

    MacDonald, W.M.

    1980-01-01

    Coupling a strongly excited ''doorway state'' to weak ''hallway states'' distributes its strength into micro-resonances seen in differential cross sections taken with very good energy resolution. The distribution of strength is shown to be revealed by reduced widths of the K-matrix rather than by the imaginary part of poles of the S-matrix. Different strength functions (SF) constructed by averaging the K-matrix widths are then investigated to determine their dependences on energy and on parameters related to averages of microscopic matrix elements. A new sum rule on the integrated strength of these SF is derived and used to show that different averaging procedures actually distribute the strength differently. Finally, it is shown that the discontinuous summed strength defines spreading parameters for the doorway state only in strong coupling, where it approximates the idefinite integral of the continuous SF of MacDonald-Mekjian-Kerman-De Toledo Piza. A new method of ''parametric continuation'' is used to relate a discontinuous sliding box-average, or a finite sum, of discrete terms to a continous function

  17. Brain Structure-function Couplings (FY11)

    Science.gov (United States)

    2012-01-01

    importance of context in the differential engagement of neural resources during task performance (Foerde et al., 2006), and further stressed that to better...sigmoid function. 4.3.3.1 Neural Mass Region State Space Formally, the NMM el is a dynamical system composed of six coupled first-order differential ...8, 1148–1150. Berger, H. Uber das elektrenkephalogramm des menschen. Archiv fur Psychiatrie und Nervenkrankheiten 1929, 87, 527. Börner, K

  18. Pulsed neural networks consisting of single-flux-quantum spiking neurons

    International Nuclear Information System (INIS)

    Hirose, T.; Asai, T.; Amemiya, Y.

    2007-01-01

    An inhibitory pulsed neural network was developed for brain-like information processing, by using single-flux-quantum (SFQ) circuits. It consists of spiking neuron devices that are coupled to each other through all-to-all inhibitory connections. The network selects neural activity. The operation of the neural network was confirmed by computer simulation. SFQ neuron devices can imitate the operation of the inhibition phenomenon of neural networks

  19. Directed coupling in local field potentials of macaque V4 during visual short-term memory revealed by multivariate autoregressive models

    Directory of Open Access Journals (Sweden)

    Gregor M Hoerzer

    2010-05-01

    Full Text Available Processing and storage of sensory information is based on the interaction between different neural populations rather than the isolated activity of single neurons. In order to characterize the dynamic interaction and transient cooperation of sub-circuits within a neural network, multivariate autoregressive (MVAR models have proven to be an important analysis tool. In this study, we apply directed functional coupling based on MVAR models and describe the temporal and spatial changes of functional coupling between simultaneously recorded local field potentials (LFP in extrastriate area V4 during visual memory. Specifically, we compare the strength and directional relations of coupling based on Generalized Partial Directed Coherence (GDPC measures while two rhesus monkeys perform a visual short-term memory task. In both monkeys we find increases in theta power during the memory period that are accompanied by changes in directed coupling. These interactions are most prominent in the low frequency range encompassing the theta band (3-12~Hz and, more importantly, are asymmetric between pairs of recording sites. Furthermore, we find that the degree of interaction decreases as a function of distance between electrode positions, suggesting that these interactions are a predominantly local phenomenon. Taken together, our results show that directed coupling measures based on MVAR models are able to provide important insights into the spatial and temporal formation of local functionally coupled ensembles during visual memory in V4. Moreover, our findings suggest that visual memory is accompanied not only by a temporary increase of oscillatory activity in the theta band, but by a direction-dependent change in theta coupling, which ultimately represents a change in functional connectivity within the neural circuit.

  20. Synchronizability of coupled PWL maps

    International Nuclear Information System (INIS)

    Polynikis, A.; Di Bernardo, M.; Hogan, S.J.

    2009-01-01

    In this paper we discuss the phenomenon of synchronization of chaotic systems in the case of coupled piecewise linear (PWL) continuous and discontinuous one-dimensional maps. We present numerical results for two examples of coupled systems consisting of two PWL maps. We illustrate how the coupled system can achieve synchronization and discuss the nature of the bifurcation that occurs at a critical value of the coupling strength. We then determine this critical coupling using linear stability analysis. We discuss the effects of variation of the parameters of the PWL maps on the critical coupling and present different bifurcation scenarios obtained for different sets of values of these parameters. Finally, we discuss an extension of our work to the synchronizability of networks consisting of two or more PWL maps. We show how the synchronizability of a network of PWL maps can be improved by tuning the map parameters.

  1. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  2. Strength and power of knee extensor muscles

    Directory of Open Access Journals (Sweden)

    Knežević Olivera

    2011-01-01

    Full Text Available In the studies of human neuromuscular function, the function of leg muscles has been most often measured, particularly the function of the knee extensors. Therefore, this review will be focused on knee extensors, methods for assessment of its function, the interdependence of strength and power, relations that describe these two abilities and the influence of various factors on their production (resistance training, stretching, movement tasks, age, etc.. Given that it consists of four separate muscles, the variability of their anatomical characteristics affects their participation in strength and power production, depending on the type of movement and motion that is performed. Since KE is active in a variety of activities it must be able to generate great strength in a large and diverse range of muscle lengths and high shortening velocities, in respect to different patterns of strength production, and thus different generation capacities within the muscle (Blazevich et al., 2006. It has been speculated that KE exerts its Pmax at workloads close to subject's own body weight or lower (Rahmani et al., 2001, which is very close to the maximum dynamic output hypothesis (MDI of Jaric and Markovic (2009. Changes under the influence of resistance training or biological age are variously manifested in muscle's morphological, physiological and neural characteristics, and thus in strength and power. Understanding the issues related to strength and power as abilities of great importance for daily activities, is also important for sports and rehabilitation. Performances improvement in sports in which leg muscles strength and power are crucial, as well as recovery after the injuries, are largely dependent on the research results regarding KE function. Also, the appropriate strength balance between knee flexors and extensors is important for the knee joint stability, so that the presence of imbalance between these two muscle groups might be a risk factor for

  3. 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 ...

  4. Dynamics of unidirectionally coupled bistable Henon maps

    International Nuclear Information System (INIS)

    Sausedo-Solorio, J.M.; Pisarchik, A.N.

    2011-01-01

    We study dynamics of two bistable Henon maps coupled in a master-slave configuration. In the case of coexistence of two periodic orbits, the slave map evolves into the master map state after transients, which duration determines synchronization time and obeys a -1/2 power law with respect to the coupling strength. This scaling law is almost independent of the map parameter. In the case of coexistence of chaotic and periodic attractors, very complex dynamics is observed, including the emergence of new attractors as the coupling strength is increased. The attractor of the master map always exists in the slave map independently of the coupling strength. For a high coupling strength, complete synchronization can be achieved only for the attractor similar to that of the master map. -- Highlights: → We study dynamics of two bistable Henon maps coupled in a master-slave configuration. → Synchronization time for periodic orbits obeys a -1/2 power law with respect to coupling. → For a high coupling strength, the slave map remains bistable. → Complete synchronization can be achieved only when both maps stay at the same attractor.

  5. Give Me Strength.

    Institute of Scientific and Technical Information of China (English)

    维拉

    1996-01-01

    Mort had an absolutely terrible day at the office.Everythingthat could go wrong did go wrong.As he walked home he could beheard muttering strange words to himself:“Oh,give me strength,give me strength.”Mort isn’t asking for the kind of strength thatbuilds strong muscles:he’s asking for the courage or ability to

  6. 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...

  7. Entrepreneurial Couples

    DEFF Research Database (Denmark)

    Dahl, Michael S.; Van Praag, Mirjam; Thompson, Peter

    2015-01-01

    We study possible motivations for co-entreprenurial couples to start up a joint firm, using a sample of 1,069 Danish couples that established a joint enterprise between 2001 and 2010. We compare their pre-entry characteristics, firm performance and post-dissolution private and financial outcomes...

  8. Slower speed and stronger coupling: adaptive mechanisms of chaos synchronization.

    Science.gov (United States)

    Wang, Xiao Fan

    2002-06-01

    We show that two initially weakly coupled chaotic systems can achieve synchronization by adaptively reducing their speed and/or enhancing the coupling strength. Explicit adaptive algorithms for speed reduction and coupling enhancement are provided. We apply these algorithms to the synchronization of two coupled Lorenz systems. It is found that after a long-time adaptive process, the two coupled chaotic systems can achieve synchronization with almost the minimum required coupling-speed ratio.

  9. Complete synchronization in coupled type-I neurons

    Indian Academy of Sciences (India)

    neural activity has elicited a great deal of interest since it is believed that such ... mechanism in the networks of excitatory (E) and inhibitory (I) neurons both in the ... excitatory–excitatory (EE) and inhibitory–excitatory (IE) bidirectional couplings.

  10. 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.

  11. Anisotropic Concrete Compressive Strength

    DEFF Research Database (Denmark)

    Gustenhoff Hansen, Søren; Jørgensen, Henrik Brøner; Hoang, Linh Cao

    2017-01-01

    When the load carrying capacity of existing concrete structures is (re-)assessed it is often based on compressive strength of cores drilled out from the structure. Existing studies show that the core compressive strength is anisotropic; i.e. it depends on whether the cores are drilled parallel...

  12. High strength ferritic alloy

    International Nuclear Information System (INIS)

    1977-01-01

    A high strength ferritic steel is specified in which the major alloying elements are chromium and molybdenum, with smaller quantities of niobium, vanadium, silicon, manganese and carbon. The maximum swelling is specified for various irradiation conditions. Rupture strength is also specified. (U.K.)

  13. Photon strength functions

    International Nuclear Information System (INIS)

    Bergqvist, I.

    1976-01-01

    Methods for extracting photon strength functions are briefly discussed. We follow the Brink-Axel approach to relate the strength functions to the giant resonances observed in photonuclear work and summarize the available data on the E1, E2 and M1 resonances. Some experimental and theoretical problems are outlined. (author)

  14. Interviewing to Understand Strengths

    Science.gov (United States)

    Hass, Michael R.

    2018-01-01

    Interviewing clients about their strengths is an important part of developing a complete understanding of their lives and has several advantages over simply focusing on problems and pathology. Prerequisites for skillfully interviewing for strengths include the communication skills that emerge from a stance of not knowing, developing a vocabulary…

  15. 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)

  16. Forex Market Prediction Using NARX Neural Network with Bagging

    Directory of Open Access Journals (Sweden)

    Shahbazi Nima

    2016-01-01

    Full Text Available We propose a new methodfor predicting movements in Forex market based on NARX neural network withtime shifting bagging techniqueand financial indicators, such as relative strength index and stochastic indicators. Neural networks have prominent learning ability but they often exhibit bad and unpredictable performance for noisy data. When compared with the static neural networks, our method significantly reducesthe error rate of the responseandimproves the performance of the prediction. We tested three different types ofarchitecture for predicting the response and determined the best network approach. We applied our method to prediction the hourly foreign exchange rates and found remarkable predictability in comprehensive experiments with 2 different foreign exchange rates (GBPUSD and EURUSD.

  17. Cut-loading: a useful tool for examining the extent of gap junction tracer coupling between retinal neurons.

    Science.gov (United States)

    Choi, Hee Joo; Ribelayga, Christophe P; Mangel, Stuart C

    2012-01-12

    In addition to chemical synaptic transmission, neurons that are connected by gap junctions can also communicate rapidly via electrical synaptic transmission. Increasing evidence indicates that gap junctions not only permit electrical current flow and synchronous activity between interconnected or coupled cells, but that the strength or effectiveness of electrical communication between coupled cells can be modulated to a great extent(1,2). In addition, the large internal diameter (~1.2 nm) of many gap junction channels permits not only electric current flow, but also the diffusion of intracellular signaling molecules and small metabolites between interconnected cells, so that gap junctions may also mediate metabolic and chemical communication. The strength of gap junctional communication between neurons and its modulation by neurotransmitters and other factors can be studied by simultaneously electrically recording from coupled cells and by determining the extent of diffusion of tracer molecules, which are gap junction permeable, but not membrane permeable, following iontophoretic injection into single cells. However, these procedures can be extremely difficult to perform on neurons with small somata in intact neural tissue. Numerous studies on electrical synapses and the modulation of electrical communication have been conducted in the vertebrate retina, since each of the five retinal neuron types is electrically connected by gap junctions(3,4). Increasing evidence has shown that the circadian (24-hour) clock in the retina and changes in light stimulation regulate gap junction coupling(3-8). For example, recent work has demonstrated that the retinal circadian clock decreases gap junction coupling between rod and cone photoreceptor cells during the day by increasing dopamine D2 receptor activation, and dramatically increases rod-cone coupling at night by reducing D2 receptor activation(7,8). However, not only are these studies extremely difficult to perform on

  18. Entrepreneurial Couples

    DEFF Research Database (Denmark)

    Dahl, Michael S.; Van Praag, Mirjam; Thompson, Peter

    with a selected set of comparable firms and couples. We find evidence that couples often establish a business together because one spouse – most commonly the female – has limited outside opportunities in the labor market. However, the financial benefits for each of the spouses, and especially the female......We study possible motivations for co-entrepenurial couples to start up a joint firm, using a sample of 1,069 Danish couples that established a joint enterprise between 2001 and 2010. We compare their pre-entry characteristics, firm performance and postdissolution private and financial outcomes......, are larger in co-entrepreneurial firms, both during the life of the business and post-dissolution. The start-up of co-entrepreneurial firms seems therefore a sound investment in the human capital of both spouses as well as in the reduction of income inequality in the household. We find no evidence of non...

  19. Entrepreneurial Couples

    DEFF Research Database (Denmark)

    Dahl, Michael S.; Van Praag, Mirjam; Thompson, Peter

    with a selected set of comparable firms and couples. We find evidence that couples often establish a business together because one spouse - most commonly the female - has limited outside opportunities in the labor market. However, the financial benefits for each of the spouses, and especially the female......We study possible motivations for co-entrepenurial couples to start up a joint firm, us-ing a sample of 1,069 Danish couples that established a joint enterprise between 2001 and 2010. We compare their pre-entry characteristics, firm performance and post-dissolution private and financial outcomes......, are larger in co-entrepreneurial firms, both during the life of the business and post-dissolution. The start-up of co-entrepreneurial firms seems therefore a sound in-vestment in the human capital of both spouses as well as in the reduction of income inequality in the household. We find no evidence of non...

  20. Knee Pain during Strength Training Shortly following Fast-Track Total Knee Arthroplasty

    DEFF Research Database (Denmark)

    Bandholm, Thomas; Thorborg, Kristian; Lunn, Troels Haxholdt

    2014-01-01

    BACKGROUND: Loading and contraction failure (muscular exhaustion) are strength training variables known to influence neural activation of the exercising muscle in healthy subjects, which may help reduce neural inhibition of the quadriceps muscle following total knee arthroplasty (TKA). It is unkn......BACKGROUND: Loading and contraction failure (muscular exhaustion) are strength training variables known to influence neural activation of the exercising muscle in healthy subjects, which may help reduce neural inhibition of the quadriceps muscle following total knee arthroplasty (TKA......). It is unknown how these exercise variables influence knee pain after TKA. OBJECTIVE: To investigate the effect of loading and contraction failure on knee pain during strength training, shortly following TKA. DESIGN: Cross-sectional study. SETTING: Consecutive sample of patients from the Copenhagen area, Denmark...... TKA. However, only the increase in pain during repetitions to contraction failure exceeded that defined as clinically relevant, and was very short-lived. TRIAL REGISTRATION: ClinicalTrials.gov NCT01729520....

  1. Gamma activity coupled to alpha phase as a mechanism for top-down controlled gating

    NARCIS (Netherlands)

    Bonnefond, M.; Jensen, O.

    2015-01-01

    Coupling between neural oscillations in different frequency bands has been proposed to coordinate neural processing. In particular, gamma power coupled to alpha phase is proposed to reflect gating of information in the visual system but the existence of such a mechanism remains untested. Here, we

  2. 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.

  3. Bidirectional communication using delay coupled chaotic directly ...

    Indian Academy of Sciences (India)

    A symmetric bidirectional coupling is identified as a suitable method for isochronal synchronization of such lasers. The optimum values of coupling and feedback strength that can provide maximum quality of synchronization are identified. This method is successfully employed for encoding/decoding both analog and digital ...

  4. Prediction of properties of polymer concrete composite with tire rubber using neural networks

    International Nuclear Information System (INIS)

    Diaconescu, Rodica-Mariana; Barbuta, Marinela; Harja, Maria

    2013-01-01

    Highlights: ► Using waste a new composite material was obtained with specific characteristics. ► The objective was to maximize tire powder content with the minimum resin content. ► By direct modeling, the maximum compressive strength was obtained for 30% tire powder. ► Inverse neural modeling was used for obtaining maximum values of strengths. -- Abstract: The neural network method was used to investigate the influence of filler and resin content on the mechanical properties of polymer concrete with powdered tire waste. The mechanical strengths of 10 experimentally determined combinations using mixed epoxy resin, aggregates and tire powder as filler were optimized using direct neural modeling and inverse neural modeling, by imposing a minimum cost (content in resin). Direct neural modeling gave the optimum composition for obtaining maximum values for compressive strength, flexural strength and split tensile strength. Inverse neural modeling analyzed the possibility of obtaining maximum values of mechanical properties by variations in the dosages of the epoxy resin and tire powder. Neural network modeling generated the mixes with the lowest cost and maximum strength. The modeling method has shown that two mechanical properties can be simultaneously optimized in the investigation domain. From direct modeling, the maximum compressive strength was obtained for a composition with 0.215 (fraction weight) epoxy resin and 0.3 (fraction weight) tire powder. Maximum flexural strength was obtained for experimental values of 0.23 epoxy resin and 0.17 tire powder with a severe reduction noted for smaller resin dosages. The maximum split tensile strength was obtained for a resin dosage of 0.24 and tire powder dosage of 0.17

  5. Metabolic neural mapping in neonatal rats

    International Nuclear Information System (INIS)

    DiRocco, R.J.; Hall, W.G.

    1981-01-01

    Functional neural mapping by 14 C-deoxyglucose autoradiography in adult rats has shown that increases in neural metabolic rate that are coupled to increased neurophysiological activity are more evident in axon terminals and dendrites than neuron cell bodies. Regions containing architectonically well-defined concentrations of terminals and dendrites (neuropil) have high metabolic rates when the neuropil is physiologically active. In neonatal rats, however, we find that regions containing well-defined groupings of neuron cell bodies have high metabolic rates in 14 C-deoxyglucose autoradiograms. The striking difference between the morphological appearance of 14 C-deoxyglucose autoradiograms obtained from neonatal and adult rats is probably related to developmental changes in morphometric features of differentiating neurons, as well as associated changes in type and locus of neural work performed

  6. A new measure for the strength of electrical synapses

    Directory of Open Access Journals (Sweden)

    Julie S Haas

    2015-09-01

    Full Text Available Electrical synapses, like chemical synapses, mediate intraneuronal communication. Electrical synapses are typically quantified by subthreshold measurements of coupling, which fall short in describing their impact on spiking activity in coupled neighbors. Here we describe a novel measurement for electrical synapse strength that directly evaluates the effect of synaptically transmitted activity on spike timing. This method, also applicable to neurotransmitter-based synapses, communicates the considerable strength of electrical synapses. For electrical synapses measured in rodent slices of the thalamic reticular nucleus, spike timing is modulated by tens of ms by activity in a coupled neighbor.

  7. Collectively-enhanced optomechanical coupling in periodic arrays of scatterers

    DEFF Research Database (Denmark)

    Xuereb, André; Genes, Claudiu; Dantan, Aurelien Romain

    2013-01-01

    in linear optomechanical coupling strengths between the cavity field and collective motional modes of the array that may be several orders of magnitude larger than is possible with an equivalent reflective ensemble. We describe and interpret these effects in detail and investigate the nature of the scaling...... laws of the coupling strengths for the different transmissive points in various regimes....

  8. Semanticized autobiographical memory and the default - executive coupling hypothesis of aging.

    Science.gov (United States)

    Spreng, R Nathan; Lockrow, Amber W; DuPre, Elizabeth; Setton, Roni; Spreng, Karen A P; Turner, Gary R

    2018-02-01

    As we age, the architecture of cognition undergoes a fundamental transition. Fluid intellectual abilities decline while crystalized abilities remain stable or increase. This shift has a profound impact across myriad cognitive and functional domains, yet the neural mechanisms remain under-specified. We have proposed that greater connectivity between the default network and executive control regions in lateral prefrontal cortex may underlie this shift, as older adults increasingly rely upon accumulated knowledge to support goal-directed behavior. Here we provide direct evidence for this mechanism within the domain of autobiographical memory. In a large sample of healthy adult participants (n = 103 Young; n = 80 Old) the strength of default - executive coupling reliably predicted more semanticized, or knowledge-based, recollection of autobiographical memories in the older adult cohort. The findings are consistent with the default - executive coupling hypothesis of aging and identify this shift in network dynamics as a candidate neural mechanism associated with crystalized cognition in later life that may signal adaptive capacity in the context of declining fluid cognitive abilities. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Coupling correction using closed orbit measurements

    International Nuclear Information System (INIS)

    Safranek, J.; Krinsky, S.

    1994-01-01

    The authors describe a coupling correction scheme they have developed and used to successfully reduce the vertical emittance of the NSLS X-Ray ring by a factor of 6 to below 2 A. This gives a vertical to horizontal emittance ratio of less than 0.2%. They find the strengths of 17 skew quadrupoles to simultaneously minimize the vertical dispersion and the coupling. As a measure of coupling they utilize the shift in vertical closed orbit resulting from a change in strength of a horizontal steering magnet. Experimental measurements confirm the reduced emittance

  10. 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...

  11. 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...

  12. Neural correlates of treatment outcome in major depression.

    LENUS (Irish Health Repository)

    Lisiecka, Danuta

    2012-02-01

    There is a need to identify clinically useful biomarkers in major depressive disorder (MDD). In this context the functional connectivity of the orbitofrontal cortex (OFC) to other areas of the affect regulation circuit is of interest. The aim of this study was to identify neural changes during antidepressant treatment and correlates associated with the treatment outcome. In an exploratory analysis it was investigated whether functional connectivity measures moderated a response to mirtazapine and venlafaxine. Twenty-three drug-free patients with MDD were recruited from the Department of Psychiatry and Psychotherapy of the Ludwig-Maximilians University in Munich. The patients were subjected to a 4-wk randomized clinical trial with two common antidepressants, venlafaxine or mirtazapine. Functional connectivity of the OFC, derived from functional magnetic resonance imaging with an emotional face-matching task, was measured before and after the trial. Higher OFC connectivity with the left motor areas and the OFC regions prior to the trial characterized responders (p<0.05, false discovery rate). The treatment non-responders were characterized by higher OFC-cerebellum connectivity. The strength of response was positively correlated with functional coupling between left OFC and the caudate nuclei and thalami. Differences in longitudinal changes were detected between venlafaxine and mirtazapine treatment in the motor areas, cerebellum, cingulate gyrus and angular gyrus. These results indicate that OFC functional connectivity might be useful as a marker for therapy response to mirtazapine and venlafaxine and to reconstruct the differences in their mechanism of action.

  13. Implicitly Defined Neural Networks for Sequence Labeling

    Science.gov (United States)

    2017-07-31

    ularity has soared for the Long Short - Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) and vari- ants such as Gated Recurrent Unit (GRU) (Cho et...610. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short - term memory . Neural computation 9(8):1735– 1780. Zhiheng Huang, Wei Xu, and Kai Yu. 2015...network are coupled together, in order to improve perfor- mance on complex, long -range dependencies in either direction of a sequence. We contrast our

  14. Redox potentials of PuO{sub 2}{sup 2+}/PuO{sub 2}{sup +} and Pu{sup 4+}/Pu{sup 3+} at different ionic strengths and temperatures; entropy and heat capacity; Potentiels Redox des couples PuO{sub 2}{sup 2+}/PuO{sub 2}{sup +} et Pu{sup 4+}/Pu{sup 3+} a force ionique et temperature variables. Entropie et capacite calorifique

    Energy Technology Data Exchange (ETDEWEB)

    Capdevila, H.; Vitorge, P.

    1994-05-01

    The reversible redox potentials of the Plutonium couples are measured by using cyclic voltammetry, in perchloric media at ionic strength, I from 0,5 M to 3M, and temperature, T, from 5 deg C to 65 deg C. At each T, experimental results, E(T,I), are extrapolated to I = O by applying the Specific Interaction Theory (S.I.T.) to get interaction coefficients, {Delta} is element of (T), and E(T,O) (e.g. standard potentials when T = 25 deg C). At T = 25 deg C the numerical values of the potentials of all the Pu couples are nearly the same. It is then not easy to detect a systematic error due to disproportionation or redox impurity. This can explain some discrepancy on numerical values already published. We finally propose ``recommended values`` of the reversible redox potentials. As a first approximation, the variations of these potentials seem to be quite linear versus temperature: entropy variation versus T is small. But taking into account heat capacity that is involved in the E(T,I) second order derivative, usually improves the fitting. A second order expansion of {epsilon}(T) and of the Debye Huckel term, D(T) are used to propose equations that account for simultaneous ionic strength and temperature influences on G, S, Cp, H, and lg K. These equations, in particular those modelling the ionic strength influence on {Delta}S, {Delta}Cp, and {Delta}H are first checked for published mean activity coefficients of HCI and NaCI. Small discrepancy between the numerical values of entropy changes of actinides redox couples, deduced from electrochemical and calorimetric techniques are discussed. (authors). 27 refs., 6 tabs., 10 figs.

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

    Science.gov (United States)

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

    2014-01-01

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

  16. Qualitative analysis and control of complex neural networks with delays

    CERN Document Server

    Wang, Zhanshan; Zheng, Chengde

    2016-01-01

    This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.

  17. 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

  18. Short-term Periodization Models: Effects on Strength and Speed-strength Performance.

    Science.gov (United States)

    Hartmann, Hagen; Wirth, Klaus; Keiner, Michael; Mickel, Christoph; Sander, Andre; Szilvas, Elena

    2015-10-01

    Dividing training objectives into consecutive phases to gain morphological adaptations (hypertrophy phase) and neural adaptations (strength and power phases) is called strength-power periodization (SPP). These phases differ in program variables (volume, intensity, and exercise choice or type) and use stepwise intensity progression and concomitant decreasing volume, converging to peak intensity (peaking phase). Undulating periodization strategies rotate these program variables in a bi-weekly, weekly, or daily fashion. The following review addresses the effects of different short-term periodization models on strength and speed-strength both with subjects of different performance levels and with competitive athletes from different sports who use a particular periodization model during off-season, pre-season, and in-season conditioning. In most periodization studies, it is obvious that the strength endurance sessions are characterized by repetition zones (12-15 repetitions) that induce muscle hypertrophy in persons with a low performance level. Strictly speaking, when examining subjects with a low training level, many periodization studies include mainly hypertrophy sessions interspersed with heavy strength/power sessions. Studies have demonstrated equal or statistically significant higher gains in maximal strength for daily undulating periodization compared with SPP in subjects with a low to moderate performance level. The relatively short intervention period and the lack of concomitant sports conditioning call into question the practical value of these findings for competitive athletes. Possibly owing to differences in mesocycle length, conditioning programs, and program variables, competitive athletes either maintained or improved strength and/or speed-strength performance by integrating daily undulating periodization and SPP during off-season, pre-season and in-season conditioning. In high-performance sports, high-repetition strength training (>15) should be

  19. 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…

  20. 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...

  1. Strength of Fibrous Composites

    CERN Document Server

    Huang, Zheng-Ming

    2012-01-01

    "Strength of Fibrous Composites" addresses evaluation of the strength of a fibrous composite by using its constituent material properties and its fiber architecture parameters. Having gone through the book, a reader is able to predict the progressive failure behavior and ultimate strength of a fibrous laminate subjected to an arbitrary load condition in terms of the constituent fiber and matrix properties, as well as fiber geometric parameters. The book is useful to researchers and engineers working on design and analysis for composite materials. Dr. Zheng-Ming Huang is a professor at the School of Aerospace Engineering & Applied Mechanics, Tongji University, China. Mr. Ye-Xin Zhou is a PhD candidate at the Department of Mechanical Engineering, the University of Hong Kong, China.

  2. Individual differences in speech-in-noise perception parallel neural speech processing and attention in preschoolers

    Science.gov (United States)

    Thompson, Elaine C.; Carr, Kali Woodruff; White-Schwoch, Travis; Otto-Meyer, Sebastian; Kraus, Nina

    2016-01-01

    From bustling classrooms to unruly lunchrooms, school settings are noisy. To learn effectively in the unwelcome company of numerous distractions, children must clearly perceive speech in noise. In older children and adults, speech-in-noise perception is supported by sensory and cognitive processes, but the correlates underlying this critical listening skill in young children (3–5 year olds) remain undetermined. Employing a longitudinal design (two evaluations separated by ~12 months), we followed a cohort of 59 preschoolers, ages 3.0–4.9, assessing word-in-noise perception, cognitive abilities (intelligence, short-term memory, attention), and neural responses to speech. Results reveal changes in word-in-noise perception parallel changes in processing of the fundamental frequency (F0), an acoustic cue known for playing a role central to speaker identification and auditory scene analysis. Four unique developmental trajectories (speech-in-noise perception groups) confirm this relationship, in that improvements and declines in word-in-noise perception couple with enhancements and diminishments of F0 encoding, respectively. Improvements in word-in-noise perception also pair with gains in attention. Word-in-noise perception does not relate to strength of neural harmonic representation or short-term memory. These findings reinforce previously-reported roles of F0 and attention in hearing speech in noise in older children and adults, and extend this relationship to preschool children. PMID:27864051

  3. High strength alloys

    Science.gov (United States)

    Maziasz, Phillip James [Oak Ridge, TN; Shingledecker, John Paul [Knoxville, TN; Santella, Michael Leonard [Knoxville, TN; Schneibel, Joachim Hugo [Knoxville, TN; Sikka, Vinod Kumar [Oak Ridge, TN; Vinegar, Harold J [Bellaire, TX; John, Randy Carl [Houston, TX; Kim, Dong Sub [Sugar Land, TX

    2010-08-31

    High strength metal alloys are described herein. At least one composition of a metal alloy includes chromium, nickel, copper, manganese, silicon, niobium, tungsten and iron. System, methods, and heaters that include the high strength metal alloys are described herein. At least one heater system may include a canister at least partially made from material containing at least one of the metal alloys. At least one system for heating a subterranean formation may include a tubular that is at least partially made from a material containing at least one of the metal alloys.

  4. Hand grip strength

    DEFF Research Database (Denmark)

    Frederiksen, Henrik; Gaist, David; Petersen, Hans Christian

    2002-01-01

    in life is a major problem in terms of prevalence, morbidity, functional limitations, and quality of life. It is therefore of interest to find a phenotype reflecting physical functioning which has a relatively high heritability and which can be measured in large samples. Hand grip strength is known......-55%). A powerful design to detect genes associated with a phenotype is obtained using the extreme discordant and concordant sib pairs, of whom 28 and 77 dizygotic twin pairs, respectively, were found in this study. Hence grip strength is a suitable phenotype for identifying genetic variants of importance to mid...

  5. Cotton genotypes selection through artificial neural networks.

    Science.gov (United States)

    Júnior, E G Silva; Cardoso, D B O; Reis, M C; Nascimento, A F O; Bortolin, D I; Martins, M R; Sousa, L B

    2017-09-27

    Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

  6. Artificial neural networks for prediction of percentage of water ...

    Indian Academy of Sciences (India)

    have high compressive strengths in comparison with con- crete specimens ... presenting suitable model based on artificial neural networks. (ANNs) to ... by experimental ones to evaluate the software power for pre- dicting the ..... Figure 7. Correlation of measured and predicted percentage of water absorption values of.

  7. Weak correlations between hemodynamic signals and ongoing neural activity during the resting state

    Science.gov (United States)

    Winder, Aaron T.; Echagarruga, Christina; Zhang, Qingguang; Drew, Patrick J.

    2017-01-01

    Spontaneous fluctuations in hemodynamic signals in the absence of a task or overt stimulation are used to infer neural activity. We tested this coupling by simultaneously measuring neural activity and changes in cerebral blood volume (CBV) in the somatosensory cortex of awake, head-fixed mice during periods of true rest, and during whisker stimulation and volitional whisking. Here we show that neurovascular coupling was similar across states, and large spontaneous CBV changes in the absence of sensory input were driven by volitional whisker and body movements. Hemodynamic signals during periods of rest were weakly correlated with neural activity. Spontaneous fluctuations in CBV and vessel diameter persisted when local neural spiking and glutamatergic input was blocked, and during blockade of noradrenergic receptors, suggesting a non-neuronal origin for spontaneous CBV fluctuations. Spontaneous hemodynamic signals reflect a combination of behavior, local neural activity, and putatively non-neural processes. PMID:29184204

  8. Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control.

    Science.gov (United States)

    Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen

    2017-10-01

    This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.

  9. Noise-enhanced categorization in a recurrently reconnected neural network

    International Nuclear Information System (INIS)

    Monterola, Christopher; Zapotocky, Martin

    2005-01-01

    We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails

  10. Noise-enhanced categorization in a recurrently reconnected neural network

    Science.gov (United States)

    Monterola, Christopher; Zapotocky, Martin

    2005-03-01

    We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails.

  11. Superstrong coupling of thin film magnetostatic waves with microwave cavity

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Xufeng; Tang, Hong X., E-mail: hong.tang@yale.edu [Department of Electrical Engineering, Yale University, New Haven, Connecticut 06511 (United States); Zou, Changling [Department of Electrical Engineering, Yale University, New Haven, Connecticut 06511 (United States); Department of Applied Physics, Yale University, New Haven, Connecticut 06511 (United States); Jiang, Liang [Department of Applied Physics, Yale University, New Haven, Connecticut 06511 (United States)

    2016-01-14

    We experimentally demonstrated the strong coupling between a microwave cavity and standing magnetostatic magnon modes in a yttrium iron garnet film. Such strong coupling can be observed for various spin wave modes under different magnetic field bias configurations, with a coupling strength inversely proportional to the transverse mode number. A comb-like spectrum can be obtained from these high order modes. The collectively enhanced magnon-microwave photon coupling strength is comparable with the magnon free spectral range and therefore leads to the superstrong coupling regime. Our findings pave the road towards designing a new type of strongly hybridized magnon-photon system.

  12. Neural dynamics in reconfigurable silicon.

    Science.gov (United States)

    Basu, A; Ramakrishnan, S; Petre, C; Koziol, S; Brink, S; Hasler, P E

    2010-10-01

    A neuromorphic analog chip is presented that is capable of implementing massively parallel neural computations while retaining the programmability of digital systems. We show measurements from neurons with Hopf bifurcations and integrate and fire neurons, excitatory and inhibitory synapses, passive dendrite cables, coupled spiking neurons, and central pattern generators implemented on the chip. This chip provides a platform for not only simulating detailed neuron dynamics but also uses the same to interface with actual cells in applications such as a dynamic clamp. There are 28 computational analog blocks (CAB), each consisting of ion channels with tunable parameters, synapses, winner-take-all elements, current sources, transconductance amplifiers, and capacitors. There are four other CABs which have programmable bias generators. The programmability is achieved using floating gate transistors with on-chip programming control. The switch matrix for interconnecting the components in CABs also consists of floating-gate transistors. Emphasis is placed on replicating the detailed dynamics of computational neural models. Massive computational area efficiency is obtained by using the reconfigurable interconnect as synaptic weights, resulting in more than 50 000 possible 9-b accurate synapses in 9 mm(2).

  13. How the self-coupled neuron can affect the chaotic synchronization of network

    International Nuclear Information System (INIS)

    Jia Chenhui; Wang Jiang; Deng, Bin

    2009-01-01

    We have calculated 34 kinds of three-cell neuron networks' minimum coupling strength, from the result; we find that a self-coupled neuron can have some effect on the synchronization of the network. The reason is the self-coupled neurons make the number of neurons looks 'decrease', and they decrease the coupling strength of the other neurons which are coupled with them.

  14. Temporal-spatial characteristics of phase-amplitude coupling in electrocorticogram for human temporal lobe epilepsy.

    Science.gov (United States)

    Zhang, Ruihua; Ren, Ye; Liu, Chunyan; Xu, Na; Li, Xiaoli; Cong, Fengyu; Ristaniemi, Tapani; Wang, YuPing

    2017-09-01

    Neural activity of the epileptic human brain contains low- and high-frequency oscillations in different frequency bands, some of which have been used as reliable biomarkers of the epileptogenic brain areas. However, the relationship between the low- and high-frequency oscillations in different cortical areas during the period from pre-seizure to post-seizure has not been completely clarified. We recorded electrocorticogram data from the temporal lobe and hippocampus of seven patients with temporal lobe epilepsy. The modulation index based on the Kullback-Leibler distance and the phase-amplitude coupling co-modulogram were adopted to quantify the coupling strength between the phase of low-frequency oscillations (0.2-10Hz) and the amplitude of high-frequency oscillations (11-400Hz) in different seizure epochs. The time-varying phase-amplitude modulogram was used to analyze the phase-amplitude coupling pattern during the entire period from pre-seizure to post-seizure in both the left and right temporal lobe and hippocampus. Channels with strong modulation index were compared with the seizure onset channels identified by the neurosurgeons and the resection channels in the clinical surgery. The phase-amplitude coupling strength (modulation index) increased significantly in the mid-seizure epoch and decrease significantly in seizure termination and post-seizure epochs (ptemporal cortex and hippocampus. The "fall-max" phase-amplitude modulation pattern, i.e., high-frequency amplitudes were largest in the low-frequency phase range [-π, 0], which corresponded to the falling edges of low-frequency oscillations, appeared in the middle period of the seizures at epileptic focus channels. Channels with strong modulation index appeared on the corresponding left or right temporal cortex of surgical resection and overlapped with the clinical resection zones in all patients. The "fall-max" pattern between the phase of low-frequency oscillation and amplitude of high

  15. Probe tests microweld strength

    Science.gov (United States)

    1965-01-01

    Probe is developed to test strength of soldered, brazed or microwelded joints. It consists of a spring which may be adjusted to the desired test pressure by means of a threaded probe head, and an indicator lamp. Device may be used for electronic equipment testing.

  16. Localized chaoticity in two linearly coupled inverted double-well ...

    African Journals Online (AJOL)

    Two linearly coupled inverted double-well oscillators for a fixed energy and varying coupling strength were studied. The dynamics yielded a chaotic system in which the Poincare surface was characterised by two non-mixing regions, one of regular motion and the other region that became chaotic as the coupling increased.

  17. Synchronization of three electrochemical oscillators: From local to global coupling

    Science.gov (United States)

    Liu, Yifan; Sebek, Michael; Mori, Fumito; Kiss, István Z.

    2018-04-01

    We investigate the formation of synchronization patterns in an oscillatory nickel electrodissolution system in a network obtained by superimposing local and global coupling with three electrodes. We explored the behavior through numerical simulations using kinetic ordinary differential equations, Kuramoto type phase models, and experiments, in which the local to global coupling could be tuned by cross resistances between the three nickel wires. At intermediate coupling strength with predominant global coupling, two of the three oscillators, whose natural frequencies are closer, can synchronize. By adding even a relatively small amount of local coupling (about 9%-25%), a spatially organized partially synchronized state can occur where one of the two synchronized elements is in the center. A formula was derived for predicting the critical coupling strength at which full synchronization will occur independent of the permutation of the natural frequencies of the oscillators over the network. The formula correctly predicts the variation of the critical coupling strength as a function of the global coupling fraction, e.g., with local coupling the critical coupling strength is about twice than that required with global coupling. The results show the importance of the topology of the network on the synchronization properties in a simple three-oscillator setup and could provide guidelines for decrypting coupling topology from identification of synchronization patterns.

  18. Frontal delta-beta cross-frequency coupling in high and low social anxiety: An index of stress regulation?

    Science.gov (United States)

    Poppelaars, Eefje S; Harrewijn, Anita; Westenberg, P Michiel; van der Molen, Melle J W

    2018-05-17

    Cross-frequency coupling (CFC) between frontal delta (1-4 Hz) and beta (14-30 Hz) oscillations has been suggested as a candidate neural correlate of social anxiety disorder, a disorder characterized by fear and avoidance of social and performance situations. Prior studies have used amplitude-amplitude correlation (AAC) as a CFC measure and hypothesized it as a candidate neural mechanism of affective control. However, using this metric has yielded inconsistent results regarding the direction of CFC, and the functional significance of coupling strength is uncertain. To offer a better understanding of CFC in social anxiety, we compared frontal delta-beta AAC with phase-amplitude coupling (PAC) - a mechanism for information transfer through neural circuits. Twenty high socially anxious (HSA) and 32 low socially anxious (LSA) female undergraduates participated in a social performance task (SPT). Delta-beta PAC and AAC were estimated during the resting state, as well as the anticipation and recovery conditions. Results showed significantly more AAC in LSA than HSA participants during early anticipation, as well as significant values during all conditions in LSA participants only. PAC did not distinguish between LSA and HSA participants, and instead was found to correlate with state nervousness during early anticipation, but in LSA participants only. Together, these findings are interpreted to suggest that delta-beta AAC is a plausible neurobiological index of adaptive stress regulation and can distinguish between trait high and low social anxiety during stress, while delta-beta PAC might be sensitive enough to reflect mild state anxiety in LSA participants.

  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. Circuit electromechanics with single photon strong coupling

    Energy Technology Data Exchange (ETDEWEB)

    Xue, Zheng-Yuan, E-mail: zyxue@scnu.edu.cn; Yang, Li-Na [Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, and School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006 (China); Zhou, Jian, E-mail: jianzhou8627@163.com [Department of Electronic Communication Engineering, Anhui Xinhua University, Hefei 230088 (China); Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, and School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006 (China)

    2015-07-13

    In circuit electromechanics, the coupling strength is usually very small. Here, replacing the capacitor in circuit electromechanics by a superconducting flux qubit, we show that the coupling among the qubit and the two resonators can induce effective electromechanical coupling which can attain the strong coupling regime at the single photon level with feasible experimental parameters. We use dispersive couplings among two resonators and the qubit while the qubit is also driven by an external classical field. These couplings form a three-wave mixing configuration among the three elements where the qubit degree of freedom can be adiabatically eliminated, and thus results in the enhanced coupling between the two resonators. Therefore, our work constitutes the first step towards studying quantum nonlinear effect in circuit electromechanics.

  3. An Adaptive Neural Mechanism with a Lizard Ear Model for Binaural Acoustic Tracking

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2016-01-01

    expensive algorithms. We present a novel bioinspired solution to acoustic tracking that uses only two microphones. The system is based on a neural mechanism coupled with a model of the peripheral auditory system of lizards. The peripheral auditory model provides sound direction information which the neural...

  4. 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.

  5. 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

  6. [A Structural Equation Model on Family Strength of Married Working Women].

    Science.gov (United States)

    Hong, Yeong Seon; Han, Kuem Sun

    2015-12-01

    The purpose of this study was to identify the effect of predictive factors related to family strength and develop a structural equation model that explains family strength among married working women. A hypothesized model was developed based on literature reviews and predictors of family strength by Yoo. This constructed model was built of an eight pathway form. Two exogenous variables included in this model were ego-resilience and family support. Three endogenous variables included in this model were functional couple communication, family stress and family strength. Data were collected using a self-report questionnaire from 319 married working women who were 30~40 of age and lived in cities of Chungnam province in Korea. Data were analyzed with PASW/WIN 18.0 and AMOS 18.0 programs. Family support had a positive direct, indirect and total effect on family strength. Family stress had a negative direct, indirect and total effect on family strength. Functional couple communication had a positive direct and total effect on family strength. These predictive variables of family strength explained 61.8% of model. The results of the study show a structural equation model for family strength of married working women and that predicting factors for family strength are family support, family stress, and functional couple communication. To improve family strength of married working women, the results of this study suggest nursing access and mediative programs to improve family support and functional couple communication, and reduce family stress.

  7. Criteria for robustness of heteroclinic cycles in neural microcircuits

    Science.gov (United States)

    2011-01-01

    We introduce a test for robustness of heteroclinic cycles that appear in neural microcircuits modeled as coupled dynamical cells. Robust heteroclinic cycles (RHCs) can appear as robust attractors in Lotka-Volterra-type winnerless competition (WLC) models as well as in more general coupled and/or symmetric systems. It has been previously suggested that RHCs may be relevant to a range of neural activities, from encoding and binding to spatio-temporal sequence generation. The robustness or otherwise of such cycles depends both on the coupling structure and the internal structure of the neurons. We verify that robust heteroclinic cycles can appear in systems of three identical cells, but only if we require perturbations to preserve some invariant subspaces for the individual cells. On the other hand, heteroclinic attractors can appear robustly in systems of four or more identical cells for some symmetric coupling patterns, without restriction on the internal dynamics of the cells. PMID:22656192

  8. The theoretical tensile strength of fcc crystals predicted from shear strength calculations

    International Nuclear Information System (INIS)

    Cerny, M; Pokluda, J

    2009-01-01

    This work presents a simple way of estimating uniaxial tensile strength on the basis of theoretical shear strength calculations, taking into account its dependence on a superimposed normal stress. The presented procedure enables us to avoid complicated and time-consuming analyses of elastic stability of crystals under tensile loading. The atomistic simulations of coupled shear and tensile deformations in cubic crystals are performed using first principles computational code based on pseudo-potentials and the plane wave basis set. Six fcc crystals are subjected to shear deformations in convenient slip systems and a special relaxation procedure controls the stress tensor. The obtained dependence of the ideal shear strength on the normal tensile stress seems to be almost linearly decreasing for all investigated crystals. Taking these results into account, the uniaxial tensile strength values in three crystallographic directions were evaluated by assuming a collapse of the weakest shear system. Calculated strengths for and loading were found to be mostly lower than previously calculated stresses related to tensile instability but rather close to those obtained by means of the shear instability analysis. On the other hand, the strengths for loading almost match the stresses related to tensile instability.

  9. Redox Buffer Strength

    Science.gov (United States)

    de Levie, Robert

    1999-04-01

    The proper functioning of enzymes in bodily fluids requires that the pH be maintained within rather narrow limits. The first line of defense against large pH fluctuations in such fluids is the passive control provided by the presence of pH buffers. The ability of pH buffers to stabilize the pH is indicated by the buffer value b introduced in 1922 by van Slyke. It is equally important for many enzymes that the redox potential is kept within a narrow range. In that case, stability of the potential is most readily achieved with a redox buffer. In this communication we define the redox buffer strength by analogy with acid-base buffer strength.

  10. Corium crust strength measurements

    Energy Technology Data Exchange (ETDEWEB)

    Lomperski, S. [Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439-4840 (United States)], E-mail: lomperski@anl.gov; Farmer, M.T. [Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439-4840 (United States)], E-mail: farmer@anl.gov

    2009-11-15

    Corium strength is of interest in the context of a severe reactor accident in which molten core material melts through the reactor vessel and collects on the containment basemat. Some accident management strategies involve pouring water over the melt to solidify it and halt corium/concrete interactions. The effectiveness of this method could be influenced by the strength of the corium crust at the interface between the melt and coolant. A strong, coherent crust anchored to the containment walls could allow the yet-molten corium to fall away from the crust as it erodes the basemat, thereby thermally decoupling the melt from the coolant and sharply reducing the cooling rate. This paper presents a diverse collection of measurements of the mechanical strength of corium. The data is based on load tests of corium samples in three different contexts: (1) small blocks cut from the debris of the large-scale MACE experiments, (2) 30 cm-diameter, 75 kg ingots produced by SSWICS quench tests, and (3) high temperature crusts loaded during large-scale corium/concrete interaction (CCI) tests. In every case the corium consisted of varying proportions of UO{sub 2}, ZrO{sub 2}, and the constituents of concrete to represent a LWR melt at different stages of a molten core/concrete interaction. The collection of data was used to assess the strength and stability of an anchored, plant-scale crust. The results indicate that such a crust is likely to be too weak to support itself above the melt. It is therefore improbable that an anchored crust configuration could persist and the melt become thermally decoupled from the water layer to restrict cooling and prolong an attack of the reactor cavity concrete.

  11. 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...

  12. Strength capability while kneeling.

    Science.gov (United States)

    Haslegrave, C M; Tracy, M F; Corlett, E N

    1997-12-01

    Work sometimes has to be carried out kneeling, particularly where jobs are performed in confined spaces as is common for miners, aircraft baggage handlers and maintenance workers. In order to assess the risks in performing forceful tasks under such conditions, data is needed on strength capabilities of kneeling subjects. A study was undertaken to measure isometric strength in single-handed exertions for male subjects and to investigate the effects on this of task layout factors (direction of force exertion, reach distance, height of the workpiece and orientation relative to the subject's sagittal plane). The data has been tabulated to show the degree to which strength may be reduced in different situations and analysis of the task factors showed their influence to be complex with direction of exertion and reach distance having the greatest effect. The results also suggest that exertions are weaker when subjects are kneeling on two knees than when kneeling on one knee, although this needs to be confirmed by direct experimental comparison.

  13. 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.

  14. 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...

  15. 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.

  16. Neural networks in continuous optical media

    International Nuclear Information System (INIS)

    Anderson, D.Z.

    1987-01-01

    The authors' interest is to see to what extent neural models can be implemented using continuous optical elements. Thus these optical networks represent a continuous distribution of neuronlike processors rather than a discrete collection. Most neural models have three characteristic features: interconnections; adaptivity; and nonlinearity. In their optical representation the interconnections are implemented with linear one- and two-port optical elements such as lenses and holograms. Real-time holographic media allow these interconnections to become adaptive. The nonlinearity is achieved with gain, for example, from two-beam coupling in photorefractive media or a pumped dye medium. Using these basic optical elements one can in principle construct continuous representations of a number of neural network models. The authors demonstrated two devices based on continuous optical elements: an associative memory which recalls an entire object when addressed with a partial object and a tracking novelty filter which identifies time-dependent features in an optical scene. These devices demonstrate the potential of distributed optical elements to implement more formal models of neural networks

  17. Strengths only or strengths and relative weaknesses? A preliminary study.

    Science.gov (United States)

    Rust, Teri; Diessner, Rhett; Reade, Lindsay

    2009-10-01

    Does working on developing character strengths and relative character weaknesses cause lower life satisfaction than working on developing character strengths only? The present study provides a preliminary answer. After 76 college students completed the Values in Action Inventory of Strengths (C. Peterson & M. E. P. Seligman, 2004), the authors randomly assigned them to work on 2 character strengths or on 1 character strength and 1 relative weakness. Combined, these groups showed significant gains on the Satisfaction With Life Scale (E. Diener, R. A. Emmons, R. J. Larsen, & S. Griffin, 1985), compared with a 32-student no-treatment group. However, there was no significant difference in gain scores between the 2-strengths group and the 1-character-strength-and-1-relative-character-weakness group. The authors discuss how focusing on relative character weaknesses (along with strengths) does not diminish-and may assist in increasing-life satisfaction.

  18. Search for anomalous Wtb couplings and top FCNC in t-channel single-top-quark events

    CERN Document Server

    CMS Collaboration

    2014-01-01

    Single-top-quark events in the $t$-channel are used to probe Wtb anomalous couplings and to search for top quark Flavor Changing Neutral Current (FCNC) interactions in proton-proton collisions at $\\sqrt{s}=7$ TeV. The analyzed data correspond to an integrated luminosity of 5~fb$^{-1}$. Events with the top quark decaying into a muon, neutrino and b-quark are selected. A Bayesian neural network is used to discriminate between signal and backgrounds. The observed event yields are consistent with SM prediction, and exclusion limits at 95\\% C.L. are determined. The exclusion limits on anomalous right vector and left tensor couplings of the Wtb vertex are found to be $|f_{V}^{R}|< 0.34$ and $|f_{T}^{L}|<0.09$. In the scenarios with FCNC tcg and tug couplings, limits on the coupling strengths are found to be $\\kappa_{tug}/\\Lambda < 1.8 \\cdot 10^{-2}~ \\mathrm{TeV^{-1}},\\ \\kappa_{tcg}/\\Lambda < 5.6 \\cdot 10^{-2} ~ \\mathrm{TeV^{-1}}$ which corresponds to limits on the branching ratios $Br(t~\\rightarrow~u+g)...

  19. Optimal capacity of Ashkin-Teller neural networks

    International Nuclear Information System (INIS)

    Kozleowski, P.; Bolle, D.

    2001-01-01

    The maximal capacity per number of couplings is calculated for Ashkin-Teller type neural networks using a replica-symmetric Gardner approach. The results are compared with numerical simulations. The best value obtained at κ=0 is α c =2.26±0.01

  20. Noise Analysis studies with neural networks

    International Nuclear Information System (INIS)

    Seker, S.; Ciftcioglu, O.

    1996-01-01

    Noise analysis studies with neural network are aimed. Stochastic signals at the input of the network are used to obtain an algorithmic multivariate stochastic signal modeling. To this end, lattice modeling of a stochastic signal is performed to obtain backward residual noise sources which are uncorrelated among themselves. There are applied together with an additional input to the network to obtain an algorithmic model which is used for signal detection for early failure in plant monitoring. The additional input provides the information to the network to minimize the difference between the signal and the network's one-step-ahead prediction. A stochastic algorithm is used for training where the errors reflecting the measurement error during the training are also modelled so that fast and consistent convergence of network's weights is obtained. The lattice structure coupled to neural network investigated with measured signals from an actual power plant. (authors)

  1. Strong Coupling Holography

    CERN Document Server

    Dvali, Gia

    2009-01-01

    We show that whenever a 4-dimensional theory with N particle species emerges as a consistent low energy description of a 3-brane embedded in an asymptotically-flat (4+d)-dimensional space, the holographic scale of high-dimensional gravity sets the strong coupling scale of the 4D theory. This connection persists in the limit in which gravity can be consistently decoupled. We demonstrate this effect for orbifold planes, as well as for the solitonic branes and string theoretic D-branes. In all cases the emergence of a 4D strong coupling scale from bulk holography is a persistent phenomenon. The effect turns out to be insensitive even to such extreme deformations of the brane action that seemingly shield 4D theory from the bulk gravity effects. A well understood example of such deformation is given by large 4D Einstein term in the 3-brane action, which is known to suppress the strength of 5D gravity at short distances and change the 5D Newton's law into the four-dimensional one. Nevertheless, we observe that the ...

  2. Phase coupling in the cardiorespiratory interaction.

    Science.gov (United States)

    Bahraminasab, A; Kenwright, D; Stefanovska, A; Ghasemi, F; McClintock, P V E

    2008-01-01

    Markovian analysis is applied to derive nonlinear stochastic equations for the reconstruction of heart rate and respiration rate variability data. A model of their 'phase' interactions is obtained for the first time, thereby gaining new insights into the strength and direction of the cardiorespiratory phase coupling. The reconstructed model can reproduce synchronisation phenomena between the cardiac and the respiratory systems, including switches in synchronisation ratio. The technique is equally applicable to the extraction of the multi-dimensional couplings between many interacting subsystems.

  3. B-L violating supersymmetric couplings

    International Nuclear Information System (INIS)

    Ramond, P.

    1983-01-01

    We consider two problems: one is the possible effect of the breaking of Peccei-Quinn symmetry on the inflationary universe scenario; the other is the remark that even the minimal supersymmetric SU 5 theory contains B-L violating couplings which give rise to neutrino masses and family-diagonal proton decay. However the strength of these couplings is limited by the gauge hierarchy

  4. Strength Training: For Overall Fitness

    Science.gov (United States)

    Healthy Lifestyle Fitness Strength training is an important part of an overall fitness program. Here's what strength training can do for ... is a key component of overall health and fitness for everyone. Lean muscle mass naturally diminishes with ...

  5. Intersegmental coordination of cockroach locomotion: adaptive control of centrally coupled pattern generator circuits

    Directory of Open Access Journals (Sweden)

    Einat eFuchs

    2011-01-01

    Full Text Available Animals’ ability to demonstrate both stereotyped and adaptive locomotor behavior is largely dependent on the interplay between centrally-generated motor patterns and the sensory inputs that shape them. We utilized a combined experimental and theoretical approach to investigate the relative importance of CPG interconnections vs. intersegmental afferents in the cockroach: an animal that is renowned for rapid and stable locomotion. We simultaneously recorded coxal levator and depressor motor neurons (MN in the thoracic ganglia of Periplaneta americana, while sensory feedback was completely blocked or allowed only from one intact stepping leg. In the absence of sensory feedback, we observed a coordination pattern with consistent phase relationship that shares similarities with a double tripod gait, suggesting central, feedforward control. This intersegmental coordination pattern was then reinforced in the presence of sensory feedback from a single stepping leg. Specifically, we report on transient stabilization of phase differences between activity recorded in the middle and hind thoracic MN following individual front-leg steps, suggesting a role for afferent phasic information in the coordination of motor circuits at the different hemiganglia. Data were further analyzed using stochastic models of coupled oscillators and maximum likelihood techniques to estimate underlying physiological parameters, such as uncoupled endogenous frequencies of hemisegmental oscillators and coupling strengths and directions. We found that descending ipsilateral coupling is stronger than ascending coupling, while left-right coupling in both the meso- and meta-thoracic ganglia appear to be symmetrical. We discuss our results in comparison with recent findings in stick insects that share similar neural and body architectures, and argue that the two species may exemplify opposite extremes of a fast-slow locomotion continuum, mediated through different intersegmental

  6. Neural principles of memory and a neural theory of analogical insight

    Science.gov (United States)

    Lawson, David I.; Lawson, Anton E.

    1993-12-01

    Grossberg's principles of neural modeling are reviewed and extended to provide a neural level theory to explain how analogies greatly increase the rate of learning and can, in fact, make learning and retention possible. In terms of memory, the key point is that the mind is able to recognize and recall when it is able to match sensory input from new objects, events, or situations with past memory records of similar objects, events, or situations. When a match occurs, an adaptive resonance is set up in which the synaptic strengths of neurons are increased; thus a long term record of the new input is formed in memory. Systems of neurons called outstars and instars are presumably the underlying units that enable this to occur. Analogies can greatly facilitate learning and retention because they activate the outstars (i.e., the cells that are sampling the to-be-learned pattern) and cause the neural activity to grow exponentially by forming feedback loops. This increased activity insures the boost in synaptic strengths of neurons, thus causing storage and retention in long-term memory (i.e., learning).

  7. Delay-induced cluster patterns in coupled Cayley tree networks

    Science.gov (United States)

    Singh, A.; Jalan, S.

    2013-07-01

    We study effects of delay in diffusively coupled logistic maps on the Cayley tree networks. We find that smaller coupling values exhibit sensitiveness to value of delay, and lead to different cluster patterns of self-organized and driven types. Whereas larger coupling strengths exhibit robustness against change in delay values, and lead to stable driven clusters comprising nodes from last generation of the Cayley tree. Furthermore, introduction of delay exhibits suppression as well as enhancement of synchronization depending upon coupling strength values. To the end we discuss the importance of results to understand conflicts and cooperations observed in family business.

  8. 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.

  9. Artificial neural network coupled with wavelet transform for ...

    Indian Academy of Sciences (India)

    WANN) is .... study, ground and satellite measured data are pre- .... cess information and predict outputs based on net- ... Comparison of correlation coefficients between data from ground stations and .... k-fold may or may not be higher in evaluation.

  10. 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

  11. coupling the image analysis and the artificial neural networks to ...

    African Journals Online (AJOL)

    Y. Mahdi, L. Mouhi, N. Guemras and K. Daoud

    mixture, we use the method of Image processing, figure 2. Fig. 2. Schematic diagram of the image acquisition setup. The pictures taken during the processes of mixture at the wall of the blenders have been analyzed using ImageJ software and have been validated with the results obtained by the. UV-vis spectrophotometer ...

  12. CANDiS: Coupled & Attention-Driven Neural Distant Supervision

    OpenAIRE

    Nagarajan, Tushar; Sharmistha; Talukdar, Partha

    2017-01-01

    Distant Supervision for Relation Extraction uses heuristically aligned text data with an existing knowledge base as training data. The unsupervised nature of this technique allows it to scale to web-scale relation extraction tasks, at the expense of noise in the training data. Previous work has explored relationships among instances of the same entity-pair to reduce this noise, but relationships among instances across entity-pairs have not been fully exploited. We explore the use of inter-ins...

  13. Micro-tensile strength of a welded turbine disc superalloy

    Energy Technology Data Exchange (ETDEWEB)

    Oluwasegun, K.M.; Cooper, C.; Chiu, Y.L.; Jones, I.P. [School of Metallurgy and Materials, University of Birmingham, B15 2TT (United Kingdom); Li, H.Y., E-mail: h.y.li.1@bham.ac.uk [School of Metallurgy and Materials, University of Birmingham, B15 2TT (United Kingdom); Baxter, G. [Rolls-Royce plc., P.O. Box 31, Derby DE24 8BJ (United Kingdom)

    2014-02-24

    A micro-tensile testing system coupled with focussed ion beam (FIB) machining was used to characterise the micro-mechanical properties of the weld from a turbine disc alloy. The strength variations between the weld and the base alloy are rationalised via the microstructure obtained.

  14. Distorted Character Recognition Via An Associative Neural Network

    Science.gov (United States)

    Messner, Richard A.; Szu, Harold H.

    1987-03-01

    The purpose of this paper is two-fold. First, it is intended to provide some preliminary results of a character recognition scheme which has foundations in on-going neural network architecture modeling, and secondly, to apply some of the neural network results in a real application area where thirty years of effort has had little effect on providing the machine an ability to recognize distorted objects within the same object class. It is the author's belief that the time is ripe to start applying in ernest the results of over twenty years of effort in neural modeling to some of the more difficult problems which seem so hard to solve by conventional means. The character recognition scheme proposed utilizes a preprocessing stage which performs a 2-dimensional Walsh transform of an input cartesian image field, then sequency filters this spectrum into three feature bands. Various features are then extracted and organized into three sets of feature vectors. These vector patterns that are stored and recalled associatively. Two possible associative neural memory models are proposed for further investigation. The first being an outer-product linear matrix associative memory with a threshold function controlling the strength of the output pattern (similar to Kohonen's crosscorrelation approach [1]). The second approach is based upon a modified version of Grossberg's neural architecture [2] which provides better self-organizing properties due to its adaptive nature. Preliminary results of the sequency filtering and feature extraction preprocessing stage and discussion about the use of the proposed neural architectures is included.

  15. Therapeutic physical exercise in neural injury: friend or foe?

    Science.gov (United States)

    Park, Kanghui; Lee, Seunghoon; Hong, Yunkyung; Park, Sookyoung; Choi, Jeonghyun; Chang, Kyu-Tae; Kim, Joo-Heon; Hong, Yonggeun

    2015-12-01

    [Purpose] The intensity of therapeutic physical exercise is complex and sometimes controversial in patients with neural injuries. This review assessed whether therapeutic physical exercise is beneficial according to the intensity of the physical exercise. [Methods] The authors identified clinically or scientifically relevant articles from PubMed that met the inclusion criteria. [Results] Exercise training can improve body strength and lead to the physiological adaptation of skeletal muscles and the nervous system after neural injuries. Furthermore, neurophysiological and neuropathological studies show differences in the beneficial effects of forced therapeutic exercise in patients with severe or mild neural injuries. Forced exercise alters the distribution of muscle fiber types in patients with neural injuries. Based on several animal studies, forced exercise may promote functional recovery following cerebral ischemia via signaling molecules in ischemic brain regions. [Conclusions] This review describes several types of therapeutic forced exercise and the controversy regarding the therapeutic effects in experimental animals versus humans with neural injuries. This review also provides a therapeutic strategy for physical therapists that grades the intensity of forced exercise according to the level of neural injury.

  16. 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.

  17. Convolutional neural networks and face recognition task

    Science.gov (United States)

    Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

    2017-09-01

    Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

  18. Gaussian discriminating strength

    Science.gov (United States)

    Rigovacca, L.; Farace, A.; De Pasquale, A.; Giovannetti, V.

    2015-10-01

    We present a quantifier of nonclassical correlations for bipartite, multimode Gaussian states. It is derived from the Discriminating Strength measure, introduced for finite dimensional systems in Farace et al., [New J. Phys. 16, 073010 (2014), 10.1088/1367-2630/16/7/073010]. As the latter the new measure exploits the quantum Chernoff bound to gauge the susceptibility of the composite system with respect to local perturbations induced by unitary gates extracted from a suitable set of allowed transformations (the latter being identified by posing some general requirements). Closed expressions are provided for the case of two-mode Gaussian states obtained by squeezing or by linearly mixing via a beam splitter a factorized two-mode thermal state. For these density matrices, we study how nonclassical correlations are related with the entanglement present in the system and with its total photon number.

  19. Synchronization of hyperchaotic oscillators via single unidirectional chaotic-coupling

    International Nuclear Information System (INIS)

    Zou Yanli; Zhu Jie; Chen Guanrong; Luo Xiaoshu

    2005-01-01

    In this paper, synchronization of two hyperchaotic oscillators via a single variable's unidirectional coupling is studied. First, the synchronizability of the coupled hyperchaotic oscillators is proved mathematically. Then, the convergence speed of this synchronization scheme is analyzed. In order to speed up the response with a relatively large coupling strength, two kinds of chaotic coupling synchronization schemes are proposed. In terms of numerical simulations and the numerical calculation of the largest conditional Lyapunov exponent, it is shown that in a given range of coupling strengths, chaotic-coupling synchronization is quicker than the typical continuous-coupling synchronization. Furthermore, A circuit realization based on the chaotic synchronization scheme is designed and Pspice circuit simulation validates the simulated hyperchaos synchronization mechanism

  20. Task-dependent modulation of oscillatory neural activity during movements

    DEFF Research Database (Denmark)

    Herz, D. M.; Christensen, M. S.; Reck, C.

    2011-01-01

    connectivity was strongest between central and cerebellar regions. Our results show that neural coupling within motor networks is modulated in distinct frequency bands depending on the motor task. They provide evidence that dynamic causal modeling in combination with EEG source analysis is a valuable tool......Neural oscillations in different frequency bands have been observed in a range of sensorimotor tasks and have been linked to coupling of spatially distinct neurons. The goal of this study was to detect a general motor network that is activated during phasic and tonic movements and to study the task......-dependent modulation of frequency coupling within this network. To this end we recorded 122-multichannel EEG in 13 healthy subjects while they performed three simple motor tasks. EEG data source modeling using individual MR images was carried out with a multiple source beamformer approach. A bilateral motor network...

  1. 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....

  2. 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.

  3. 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....

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

    International Nuclear Information System (INIS)

    Takiyama, Ken

    2017-01-01

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

  5. Bimanual training in stroke: How do coupling and symmetry-breaking matter?

    Directory of Open Access Journals (Sweden)

    Berton Eric

    2011-01-01

    Full Text Available Abstract Background The dramatic consequences of stroke on patient autonomy in daily living activities urged the need for new reliable therapeutic strategies. Recently, bimanual training has emerged as a promising tool to improve the functional recovery of upper-limbs in stroke patients. However, who could benefit from bimanual therapy and how it could be used as a part of a more complete rehabilitation protocol remain largely unknown. A possible reason explaining this situation is that coupling and symmetry-breaking mechanisms, two fundamental principles governing bimanual behaviour, have been largely under-explored in both research and rehabilitation in stroke. Discussion Bimanual coordination emerges as an active, task-specific assembling process where the limbs are constrained to act as a single unit by virtue of mutual coupling. Consequently, exploring, assessing, re-establishing and exploiting functional bimanual synergies following stroke, require moving beyond the classical characterization of performance of each limb in separate and isolated fashion, to study coupling signatures at both neural and behavioural levels. Grounded on the conceptual framework of the dynamic system approach to bimanual coordination, we debated on two main assumptions: 1 stroke-induced impairment of bimanual coordination might be anticipated/understood by comparing, in join protocols, changes in coupling strength and asymmetry of bimanual discrete movements observed in healthy people and those observed in stroke; 2 understanding/predicting behavioural manifestations of decrease in bimanual coupling strength and/or increase in interlimb asymmetry might constitute an operational prerequisite to adapt therapy and better target training at the specific needs of each patient. We believe that these statements draw new directions for experimental and clinical studies and contribute in promoting bimanual training as an efficient and adequate tool to facilitate the

  6. A neural circuit covarying with social hierarchy in macaques.

    Science.gov (United States)

    Noonan, MaryAnn P; Sallet, Jerome; Mars, Rogier B; Neubert, Franz X; O'Reilly, Jill X; Andersson, Jesper L; Mitchell, Anna S; Bell, Andrew H; Miller, Karla L; Rushworth, Matthew F S

    2014-09-01

    Despite widespread interest in social dominance, little is known of its neural correlates in primates. We hypothesized that social status in primates might be related to individual variation in subcortical brain regions implicated in other aspects of social and emotional behavior in other mammals. To examine this possibility we used magnetic resonance imaging (MRI), which affords the taking of quantitative measurements noninvasively, both of brain structure and of brain function, across many regions simultaneously. We carried out a series of tests of structural and functional MRI (fMRI) data in 25 group-living macaques. First, a deformation-based morphometric (DBM) approach was used to show that gray matter in the amygdala, brainstem in the vicinity of the raphe nucleus, and reticular formation, hypothalamus, and septum/striatum of the left hemisphere was correlated with social status. Second, similar correlations were found in the same areas in the other hemisphere. Third, similar correlations were found in a second data set acquired several months later from a subset of the same animals. Fourth, the strength of coupling between fMRI-measured activity in the same areas was correlated with social status. The network of subcortical areas, however, had no relationship with the sizes of individuals' social networks, suggesting the areas had a simple and direct relationship with social status. By contrast a second circuit in cortex, comprising the midsuperior temporal sulcus and anterior and dorsal prefrontal cortex, covaried with both individuals' social statuses and the social network sizes they experienced. This cortical circuit may be linked to the social cognitive processes that are taxed by life in more complex social networks and that must also be used if an animal is to achieve a high social status.

  7. A neural circuit covarying with social hierarchy in macaques.

    Directory of Open Access Journals (Sweden)

    MaryAnn P Noonan

    2014-09-01

    Full Text Available Despite widespread interest in social dominance, little is known of its neural correlates in primates. We hypothesized that social status in primates might be related to individual variation in subcortical brain regions implicated in other aspects of social and emotional behavior in other mammals. To examine this possibility we used magnetic resonance imaging (MRI, which affords the taking of quantitative measurements noninvasively, both of brain structure and of brain function, across many regions simultaneously. We carried out a series of tests of structural and functional MRI (fMRI data in 25 group-living macaques. First, a deformation-based morphometric (DBM approach was used to show that gray matter in the amygdala, brainstem in the vicinity of the raphe nucleus, and reticular formation, hypothalamus, and septum/striatum of the left hemisphere was correlated with social status. Second, similar correlations were found in the same areas in the other hemisphere. Third, similar correlations were found in a second data set acquired several months later from a subset of the same animals. Fourth, the strength of coupling between fMRI-measured activity in the same areas was correlated with social status. The network of subcortical areas, however, had no relationship with the sizes of individuals' social networks, suggesting the areas had a simple and direct relationship with social status. By contrast a second circuit in cortex, comprising the midsuperior temporal sulcus and anterior and dorsal prefrontal cortex, covaried with both individuals' social statuses and the social network sizes they experienced. This cortical circuit may be linked to the social cognitive processes that are taxed by life in more complex social networks and that must also be used if an animal is to achieve a high social status.

  8. A Neural Circuit Covarying with Social Hierarchy in Macaques

    Science.gov (United States)

    Neubert, Franz X.; O'Reilly, Jill X.; Andersson, Jesper L.; Mitchell, Anna S.; Bell, Andrew H.; Miller, Karla L.; Rushworth, Matthew F. S.

    2014-01-01

    Despite widespread interest in social dominance, little is known of its neural correlates in primates. We hypothesized that social status in primates might be related to individual variation in subcortical brain regions implicated in other aspects of social and emotional behavior in other mammals. To examine this possibility we used magnetic resonance imaging (MRI), which affords the taking of quantitative measurements noninvasively, both of brain structure and of brain function, across many regions simultaneously. We carried out a series of tests of structural and functional MRI (fMRI) data in 25 group-living macaques. First, a deformation-based morphometric (DBM) approach was used to show that gray matter in the amygdala, brainstem in the vicinity of the raphe nucleus, and reticular formation, hypothalamus, and septum/striatum of the left hemisphere was correlated with social status. Second, similar correlations were found in the same areas in the other hemisphere. Third, similar correlations were found in a second data set acquired several months later from a subset of the same animals. Fourth, the strength of coupling between fMRI-measured activity in the same areas was correlated with social status. The network of subcortical areas, however, had no relationship with the sizes of individuals' social networks, suggesting the areas had a simple and direct relationship with social status. By contrast a second circuit in cortex, comprising the midsuperior temporal sulcus and anterior and dorsal prefrontal cortex, covaried with both individuals' social statuses and the social network sizes they experienced. This cortical circuit may be linked to the social cognitive processes that are taxed by life in more complex social networks and that must also be used if an animal is to achieve a high social status. PMID:25180883

  9. 3D silicon neural probe with integrated optical fibers for optogenetic modulation.

    Science.gov (United States)

    Kim, Eric G R; Tu, Hongen; Luo, Hao; Liu, Bin; Bao, Shaowen; Zhang, Jinsheng; Xu, Yong

    2015-07-21

    Optogenetics is a powerful modality for neural modulation that can be useful for a wide array of biomedical studies. Penetrating microelectrode arrays provide a means of recording neural signals with high spatial resolution. It is highly desirable to integrate optics with neural probes to allow for functional study of neural tissue by optogenetics. In this paper, we report the development of a novel 3D neural probe coupled simply and robustly to optical fibers using a hollow parylene tube structure. The device shanks are hollow tubes with rigid silicon tips, allowing the insertion and encasement of optical fibers within the shanks. The position of the fiber tip can be precisely controlled relative to the electrodes on the shank by inherent design features. Preliminary in vivo rat studies indicate that these devices are capable of optogenetic modulation simultaneously with 3D neural signal recording.

  10. Analysis of Synchronization for Coupled Hybrid Systems

    DEFF Research Database (Denmark)

    Li, Zheng; Wisniewski, Rafal

    2006-01-01

    In the control systems with coupled multi-subsystem, the subsystems might be synchronized (i.e. all the subsystems have the same operation states), which results in negative influence to the whole system. For example, in the supermarket refrigeration systems, the synchronized switch of each...... subsystem will cause low efficiency, inferior control performance and a high wear on the compressor. This paper takes the supermarket refrigeration systems as an example to analyze the synchronization and its coupling strengths of coupled hybrid systems, which may provide a base for further research...... of control strategies. This paper combines topology and section mapping theories together to show a new way of analyzing hybrid systems...

  11. Trail making test performance in youth varies as a function of anatomical coupling between the prefrontal cortex and distributed cortical regions

    Directory of Open Access Journals (Sweden)

    Nancy Raitano Lee

    2014-07-01

    Full Text Available While researchers have gained a richer understanding of the neural correlates of executive function in adulthood, much less is known about how these abilities are represented in the developing brain and what structural brain networks underlie them. Thus, the current study examined how individual differences in executive function, as measured by the Trail Making Test (TMT, relate to structural covariance in the pediatric brain. The sample included 146 unrelated, typically developing youth (80 females, ages 9-14 years, who completed a structural MRI scan of the brain and the Halstead-Reitan TMT (intermediate form. TMT scores used to index executive function included those that evaluated set-shifting ability: Trails B time (number-letter sequencing and the difference in time between Trails B and A (number sequencing only. Anatomical coupling was measured by examining correlations between mean cortical thickness (MCT across the entire cortical ribbon and individual vertex thickness measured at ~81,000 vertices. To examine how TMT scores related to anatomical coupling strength, linear regression was utilized and the interaction between age-normed TMT scores and both age and sex-normed MCT was used to predict vertex thickness. Results revealed that stronger Trails B scores were associated with greater anatomical coupling between a large swath of prefrontal cortex and the rest of cortex. For the difference between Trails B and A, a network of regions in the frontal, temporal and parietal lobes was found to be more tightly coupled with the rest of cortex in stronger performers. This study is the first to highlight the importance of structural covariance in the prediction of individual differences in executive function skills in youth. Thus, it adds to the growing literature on the neural correlates of childhood executive functions and identifies neuroanatomic coupling as a biological substrate that may contribute to executive function and dysfunction in

  12. Trail making test performance in youth varies as a function of anatomical coupling between the prefrontal cortex and distributed cortical regions

    Science.gov (United States)

    Lee, Nancy Raitano; Wallace, Gregory L.; Raznahan, Armin; Clasen, Liv S.; Giedd, Jay N.

    2014-01-01

    While researchers have gained a richer understanding of the neural correlates of executive function in adulthood, much less is known about how these abilities are represented in the developing brain and what structural brain networks underlie them. Thus, the current study examined how individual differences in executive function, as measured by the Trail Making Test (TMT), relate to structural covariance in the pediatric brain. The sample included 146 unrelated, typically developing youth (80 females), ages 9–14 years, who completed a structural MRI scan of the brain and the Halstead-Reitan TMT (intermediate form). TMT scores used to index executive function included those that evaluated set-shifting ability: Trails B time (number-letter sequencing) and the difference in time between Trails B and A (number sequencing only). Anatomical coupling was measured by examining correlations between mean cortical thickness (MCT) across the entire cortical ribbon and individual vertex thickness measured at ~81,000 vertices. To examine how TMT scores related to anatomical coupling strength, linear regression was utilized and the interaction between age-normed TMT scores and both age and sex-normed MCT was used to predict vertex thickness. Results revealed that stronger Trails B scores were associated with greater anatomical coupling between a large swath of prefrontal cortex and the rest of cortex. For the difference between Trails B and A, a network of regions in the frontal, temporal, and parietal lobes was found to be more tightly coupled with the rest of cortex in stronger performers. This study is the first to highlight the importance of structural covariance in in the prediction of individual differences in executive function skills in youth. Thus, it adds to the growing literature on the neural correlates of childhood executive functions and identifies neuroanatomic coupling as a biological substrate that may contribute to executive function and dysfunction in

  13. Compressive and flexural strength of high strength phase change mortar

    Science.gov (United States)

    Qiao, Qingyao; Fang, Changle

    2018-04-01

    High-strength cement produces a lot of hydration heat when hydrated, it will usually lead to thermal cracks. Phase change materials (PCM) are very potential thermal storage materials. Utilize PCM can help reduce the hydration heat. Research shows that apply suitable amount of PCM has a significant effect on improving the compressive strength of cement mortar, and can also improve the flexural strength to some extent.

  14. 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....

  15. 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 ...

  16. 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.

  17. 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

  18. 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.

  19. 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...

  20. 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...

  1. Electric Monopole Transition Strengths in 62Ni

    Directory of Open Access Journals (Sweden)

    Evitts L. J.

    2016-01-01

    Full Text Available Excited states in 62Ni were populated with a (p, p’ reaction using the 14UD Pelletron accelerator at the Australian National University. Electric monopole transition strengths, ρ2(E0, were measured through simultaneous detection of the internal conversion electrons and γ rays emitted from the de-excitation of populated states, using the Super-e spectrometer coupled with a germanium detector. The strength of the 02+ to 01+ transition has been measured to be 77−34+23 × 10−3 and agrees with previously reported values. Upper limits have been placed on the 03+ to 01+ and 03+ to 02+ transitions. The measured ρ2(E0 value of the 22+ to 21+ transition in 62Ni has been measured for the first time and found to be one of the largest ρ2(E0 values measured to date in nuclei heavier than Ca. The low-lying states of 62Ni have previously been classified as one- and two-phonon vibrational states based on level energies. The measured electric quadrupole transition strengths are consistent with this interpretation. However as electric monopole transitions are forbidden between states which differ by one phonon number, the simple harmonic quadrupole vibrational picture is not suffcient to explain the large ρ2(E0 value for the 22+ to 21+ transition.

  2. Electric Monopole Transition Strengths in 62Ni

    Science.gov (United States)

    Evitts, L. J.; Garnsworthy, A. B.; Kibédi, T.; Moukaddam, M.; Alshahrani, B.; Eriksen, T. K.; Holt, J. D.; Hota, S. S.; Lane, G. J.; Lee, B. Q.; McCormick, B. P.; Palalani, N.; Reed, M. W.; Stroberg, S. R.; Stuchbery, A. E.

    2016-09-01

    Excited states in 62Ni were populated with a (p, p') reaction using the 14UD Pelletron accelerator at the Australian National University. Electric monopole transition strengths, ρ2(E0), were measured through simultaneous detection of the internal conversion electrons and γ rays emitted from the de-excitation of populated states, using the Super-e spectrometer coupled with a germanium detector. The strength of the 02+ to 01+ transition has been measured to be 77-34+23 × 10-3 and agrees with previously reported values. Upper limits have been placed on the 03+ to 01+ and 03+ to 02+ transitions. The measured ρ2(E0) value of the 22+ to 21+ transition in 62Ni has been measured for the first time and found to be one of the largest ρ2(E0) values measured to date in nuclei heavier than Ca. The low-lying states of 62Ni have previously been classified as one- and two-phonon vibrational states based on level energies. The measured electric quadrupole transition strengths are consistent with this interpretation. However as electric monopole transitions are forbidden between states which differ by one phonon number, the simple harmonic quadrupole vibrational picture is not suffcient to explain the large ρ2(E0) value for the 22+ to 21+ transition.

  3. Dynamic artificial neural networks with affective systems.

    Directory of Open Access Journals (Sweden)

    Catherine D Schuman

    Full Text Available Artificial neural networks (ANNs are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP and long term depression (LTD, and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  4. Modeling of quasistatic magnetic hysteresis with feed-forward neural networks

    International Nuclear Information System (INIS)

    Makaveev, Dimitre; Dupre, Luc; De Wulf, Marc; Melkebeek, Jan

    2001-01-01

    A modeling technique for rate-independent (quasistatic) scalar magnetic hysteresis is presented, using neural networks. Based on the theory of dynamic systems and the wiping-out and congruency properties of the classical scalar Preisach hysteresis model, the choice of a feed-forward neural network model is motivated. The neural network input parameters at each time step are the corresponding magnetic field strength and memory state, thereby assuring accurate prediction of the change of magnetic induction. For rate-independent hysteresis, the current memory state can be determined by the last extreme magnetic field strength and induction values, kept in memory. The choice of a network training set is motivated and the performance of the network is illustrated for a test set not used during training. Very accurate prediction of both major and minor hysteresis loops is observed, proving that the neural network technique is suitable for hysteresis modeling. [copyright] 2001 American Institute of Physics

  5. Institutional Strength in Depth

    International Nuclear Information System (INIS)

    Weightman, M.

    2016-01-01

    Much work has been undertaken in order to identify, learn and implement the lessons from the TEPCO Fukushima Daiichi nuclear accident. These have mainly targeted on engineering or operational lessons. Less attention has been paid to the institutional lessons, although there have been some measures to improve individual peer reviews, particularly by the World Association of Nuclear Operators, and the authoritative IAEA report published in 2015 brought forward several important lessons for regulators and advocated a system approach. The report noted that one of the contributing factors the accident was the tendency of stakeholders not to challenge. Additionally, it reported deficiencies in the regulatory authority and system. Earlier, the root cause of the accident was identified by a Japanese independent parliamentary report as being cultural and institutional. The sum total of the institutions, the safety system, was ineffective. While it is important to address the many technical and operational lessons these may not necessary address this more fundamental lesson, and may not serve to provide robust defences against human or institutional failings over a wide variety of possible events and combinations. The overall lesson is that we can have rigorous and comprehensive safety standards and other tools in place to deliver high levels of safety, but ultimately what is important is the ability of the nuclear safety system to ensure that the relevant institutions diligently and effectively apply those standards and tools — to be robust and resilient. This has led to the consideration of applying the principles of the strength in depth philosophy to a nuclear safety system as a way of providing a framework for developing, assessing, reviewing and improving the system. At an IAEA conference in October 2013, a model was presented for a robust national nuclear safety system based on strength in depth philosophy. The model highlighted three main layers: industry, the

  6. Strong Coupling Corrections in Quantum Thermodynamics

    Science.gov (United States)

    Perarnau-Llobet, M.; Wilming, H.; Riera, A.; Gallego, R.; Eisert, J.

    2018-03-01

    Quantum systems strongly coupled to many-body systems equilibrate to the reduced state of a global thermal state, deviating from the local thermal state of the system as it occurs in the weak-coupling limit. Taking this insight as a starting point, we study the thermodynamics of systems strongly coupled to thermal baths. First, we provide strong-coupling corrections to the second law applicable to general systems in three of its different readings: As a statement of maximal extractable work, on heat dissipation, and bound to the Carnot efficiency. These corrections become relevant for small quantum systems and vanish in first order in the interaction strength. We then move to the question of power of heat engines, obtaining a bound on the power enhancement due to strong coupling. Our results are exemplified on the paradigmatic non-Markovian quantum Brownian motion.

  7. Linking Neural and Symbolic Representation and Processing of Conceptual Structures

    Directory of Open Access Journals (Sweden)

    Frank van der Velde

    2017-08-01

    Full Text Available We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like structures. First is the Neural Blackboard Architecture (NBA, which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking, which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.

  8. Genetic optimization of neural network architecture

    International Nuclear Information System (INIS)

    Harp, S.A.; Samad, T.

    1994-03-01

    Neural networks are now a popular technology for a broad variety of application domains, including the electric utility industry. Yet, as the technology continues to gain increasing acceptance, it is also increasingly apparent that the power that neural networks provide is not an unconditional blessing. Considerable care must be exercised during application development if the full benefit of the technology is to be realized. At present, no fully general theory or methodology for neural network design is available, and application development is a trial-and-error process that is time-consuming and expertise-intensive. Each application demands appropriate selections of the network input space, the network structure, and values of learning algorithm parameters-design choices that are closely coupled in ways that largely remain a mystery. This EPRI-funded exploratory research project was initiated to take the key next step in this research program: the validation of the approach on a realistic problem. We focused on the problem of modeling the thermal performance of the TVA Sequoyah nuclear power plant (units 1 and 2)

  9. On the disordered fermion couplings

    International Nuclear Information System (INIS)

    Bernaschi, M.; Cabasino, S.; Marinari, E.; Rome-2 Univ.; Sarno, R.; Rome-1 Univ.

    1989-01-01

    We study the possibility of avoiding the fermion doubling problem by using a random coupling. We use numerical simulations in order to study the theory in the strong disorder region. We find a sharp crossover as a function of the strength of the disorder. For weak quenched disorder we find that the species doubling survives, while for strong quenched disorder only with a particular choice of the random term (antihermitian) it is possible to get a theory that seems to avoid fermion doubling. (orig.)

  10. Beam echoes in the presence of coupling

    Energy Technology Data Exchange (ETDEWEB)

    Gross, Axel [Case Western Reserve U.

    2017-10-03

    Transverse beam echoes could provide a new technique of measuring diusion characteristics orders of magnitude faster than the current methods; however, their interaction with many accelerator parameters is poorly understood. Using a program written in C, we explored the relationship between coupling and echo strength. We found that echoes could be generated in both dimensions, even with a dipole kick in only one dimension. We found that the echo eects are not destroyed even when there is strong coupling, falling o only at extremely high coupling values. We found that at intermediate values of skew quadrupole strength, the decoherence time of the beam is greatly increased, causing a destruction of the echo eects. We found that this is caused by a narrowing of the tune width of the particles. Results from this study will help to provide recommendations to IOTA (Integrable Optics Test Accelerator) for their upcoming echo experiment.

  11. Stochastic Nonlinear Evolutional Model of the Large-Scaled Neuronal Population and Dynamic Neural Coding Subject to Stimulation

    International Nuclear Information System (INIS)

    Wang Rubin; Yu Wei

    2005-01-01

    In this paper, we investigate how the population of neuronal oscillators deals with information and the dynamic evolution of neural coding when the external stimulation acts on it. Numerically computing method is used to describe the evolution process of neural coding in three-dimensioned space. The numerical result proves that only the suitable stimulation can change the coupling structure and plasticity of neurons

  12. 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.

  13. Cluster synchronization in community network with hybrid coupling

    International Nuclear Information System (INIS)

    Yang, Lixin; Jiang, Jun; Liu, Xiaojun

    2016-01-01

    Highlights: • A community network model with hybrid coupling is proposed. • Control scheme is designed via combining adaptive external coupling strength and feedback control. • The influence of topology structure on synchronization of community network is discussed. - Abstract: A general model of community network with hybrid coupling is proposed in this paper. In the community network model with hybrid coupling, the inner connections are in the same type of coupling within the same community and in different types of coupling in different communities. The connections between different pair of communities are also nonidentical. Cluster synchronization of community network with hybrid coupling is investigated via adaptive couplings control scheme. Effective controllers are designed for constructing an effective control scheme and adjusting automatically the adaptive external coupling strength by taking external coupling strength as adaptive variables on a small fraction of network edges. Moreover, the impact of the topology on the synchronizability of community network is investigated. The numerical results reveal that the number of links between communities and the degree of the connector nodes have significant effects on the synchronization performance.

  14. Wireless Concrete Strength Monitoring of Wind Turbine Foundations

    Directory of Open Access Journals (Sweden)

    Marcus Perry

    2017-12-01

    Full Text Available Wind turbine foundations are typically cast in place, leaving the concrete to mature under environmental conditions that vary in time and space. As a result, there is uncertainty around the concrete’s initial performance, and this can encourage both costly over-design and inaccurate prognoses of structural health. Here, we demonstrate the field application of a dense, wireless thermocouple network to monitor the strength development of an onshore, reinforced-concrete wind turbine foundation. Up-to-date methods in fly ash concrete strength and maturity modelling are used to estimate the distribution and evolution of foundation strength over 29 days of curing. Strength estimates are verified by core samples, extracted from the foundation base. In addition, an artificial neural network, trained using temperature data, is exploited to demonstrate that distributed concrete strengths can be estimated for foundations using only sparse thermocouple data. Our techniques provide a practical alternative to computational models, and could assist site operators in making more informed decisions about foundation design, construction, operation and maintenance.

  15. Wireless Concrete Strength Monitoring of Wind Turbine Foundations.

    Science.gov (United States)

    Perry, Marcus; Fusiek, Grzegorz; Niewczas, Pawel; Rubert, Tim; McAlorum, Jack

    2017-12-16

    Wind turbine foundations are typically cast in place, leaving the concrete to mature under environmental conditions that vary in time and space. As a result, there is uncertainty around the concrete's initial performance, and this can encourage both costly over-design and inaccurate prognoses of structural health. Here, we demonstrate the field application of a dense, wireless thermocouple network to monitor the strength development of an onshore, reinforced-concrete wind turbine foundation. Up-to-date methods in fly ash concrete strength and maturity modelling are used to estimate the distribution and evolution of foundation strength over 29 days of curing. Strength estimates are verified by core samples, extracted from the foundation base. In addition, an artificial neural network, trained using temperature data, is exploited to demonstrate that distributed concrete strengths can be estimated for foundations using only sparse thermocouple data. Our techniques provide a practical alternative to computational models, and could assist site operators in making more informed decisions about foundation design, construction, operation and maintenance.

  16. poincare surface analysis of two coupled quintic oscillators in a ...

    African Journals Online (AJOL)

    DJFLEX

    We have investigated the chaotic dynamics of two coupled quintic oscillators in a single well potential as the energy of the oscillator increases, keeping the coupling strength constant. The degree of chaoticity does not increase monotonously with the energy as regular regions reappear within chaotic seas as the energy ...

  17. Poincare surface analysis of two coupled quintic oscillators in a ...

    African Journals Online (AJOL)

    We have investigated the chaotic dynamics of two coupled quintic oscillators in a single well potential as the energy of the oscillator increases, keeping the coupling strength constant. The degree of chaoticity does not increase monotonously with the energy as regular regions reappear within chaotic seas as the energy ...

  18. Incorporating Ideological Context in Counseling Couples Experiencing Infertility

    Science.gov (United States)

    Burnett, Judith A.; Panchal, Krishna

    2008-01-01

    This article describes the influence of ideological values on couples' experience of infertility. Contextual issues are considered in terms of how they influence medical decision making as well as emotional factors. Strength-based counseling interventions that attend to couples' diverse values are described. Last, implications for counselors,…

  19. Dynamics of chaotic oscillations in mutually coupled microchip lasers

    CERN Document Server

    Uchida, A; Kinugawa, S; Yoshimori, S

    2003-01-01

    We have numerically and experimentally investigated the dynamics of mutually coupled microchip lasers. Chaotic oscillations are observed in the vicinity of the boundary of the injection-locking range when the coupling strength and the difference of the optical frequencies are varied. Synchronization of chaos is always achieved under the condition to generate chaos.

  20. Effect of spatially correlated noise on stochastic synchronization in globally coupled FitzHugh-Nagumo neuron systems

    Directory of Open Access Journals (Sweden)

    Yange Shao

    2014-01-01

    Full Text Available The phenomenon of stochastic synchronization in globally coupled FitzHugh-Nagumo (FHN neuron system subjected to spatially correlated Gaussian noise is investigated based on dynamical mean-field approximation (DMA and direct simulation (DS. Results from DMA are in good quantitative or qualitative agreement with those from DS for weak noise intensity and larger system size. Whether the consisting single FHN neuron is staying at the resting state, subthreshold oscillatory regime, or the spiking state, our investigation shows that the synchronization ratio of the globally coupled system becomes higher as the noise correlation coefficient increases, and thus we conclude that spatial correlation has an active effect on stochastic synchronization, and the neurons can achieve complete synchronization in the sense of statistics when the noise correlation coefficient tends to one. Our investigation also discloses that the noise spatial correlation plays the same beneficial role as the global coupling strength in enhancing stochastic synchronization in the ensemble. The result might be useful in understanding the information coding mechanism in neural systems.

  1. COLLISION STRENGTHS AND EFFECTIVE COLLISION STRENGTHS FOR TRANSITIONS WITHIN THE GROUND-STATE CONFIGURATION OF S III

    Energy Technology Data Exchange (ETDEWEB)

    Hudson, C. E.; Ramsbottom, C. A.; Scott, M. P., E-mail: c.hudson@qub.ac.uk, E-mail: c.ramsbottom@qub.ac.uk, E-mail: p.scott@qub.ac.uk [Department of Applied Maths and Theoretical Physics, The Queen' s University of Belfast, Belfast BT7 1NN (United Kingdom)

    2012-05-01

    We have carried out a 29-state R-matrix calculation in order to calculate collision strengths and effective collision strengths for the electron impact excitation of S III. The recently developed parallel RMATRX II suite of codes have been used, which perform the calculation in intermediate coupling. Collision strengths have been generated over an electron energy range of 0-12 Ryd, and effective collision strength data have been calculated from these at electron temperatures in the range 1000-100,000 K. Results are here presented for the fine-structure transitions between the ground-state configurations of 3s {sup 2}3p {sup 23} P{sub 0,1,2}, {sup 1}D{sub 2}, and {sup 1} S{sub 0}, and the values given resolve a discrepancy between two previous R-matrix calculations.

  2. Synchronous behavior of two coupled electronic neurons

    International Nuclear Information System (INIS)

    Pinto, R. D.; Varona, P.; Volkovskii, A. R.; Szuecs, A.; Abarbanel, Henry D. I.; Rabinovich, M. I.

    2000-01-01

    We report on experimental studies of synchronization phenomena in a pair of analog electronic neurons (ENs). The ENs were designed to reproduce the observed membrane voltage oscillations of isolated biological neurons from the stomatogastric ganglion of the California spiny lobster Panulirus interruptus. The ENs are simple analog circuits which integrate four-dimensional differential equations representing fast and slow subcellular mechanisms that produce the characteristic regular/chaotic spiking-bursting behavior of these cells. In this paper we study their dynamical behavior as we couple them in the same configurations as we have done for their counterpart biological neurons. The interconnections we use for these neural oscillators are both direct electrical connections and excitatory and inhibitory chemical connections: each realized by analog circuitry and suggested by biological examples. We provide here quantitative evidence that the ENs and the biological neurons behave similarly when coupled in the same manner. They each display well defined bifurcations in their mutual synchronization and regularization. We report briefly on an experiment on coupled biological neurons and four-dimensional ENs, which provides further ground for testing the validity of our numerical and electronic models of individual neural behavior. Our experiments as a whole present interesting new examples of regularization and synchronization in coupled nonlinear oscillators. (c) 2000 The American Physical Society

  3. Fractional dynamical model for neurovascular coupling

    KAUST Repository

    Belkhatir, Zehor

    2014-08-01

    The neurovascular coupling is a key mechanism linking the neural activity to the hemodynamic behavior. Modeling of this coupling is very important to understand the brain function but it is at the same time very complex due to the complexity of the involved phenomena. Many studies have reported a time delay between the neural activity and the cerebral blood flow, which has been described by adding a delay parameter in some of the existing models. An alternative approach is proposed in this paper, where a fractional system is used to model the neurovascular coupling. Thanks to its nonlocal property, a fractional derivative is suitable for modeling the phenomena with delay. The proposed model is coupled with the first version of the well-known balloon model, which relates the cerebral blood flow to the Blood Oxygen Level Dependent (BOLD) signal measured using functional Magnetic Resonance Imaging (fMRI). Through some numerical simulations, the properties of the fractional model are explained and some preliminary comparisons to a real BOLD data set are provided. © 2014 IEEE.

  4. Feedback coupling in dynamical systems

    Science.gov (United States)

    Trimper, Steffen; Zabrocki, Knud

    2003-05-01

    Different evolution models are considered with feedback-couplings. In particular, we study the Lotka-Volterra system under the influence of a cumulative term, the Ginzburg-Landau model with a convolution memory term and chemical rate equations with time delay. The memory leads to a modified dynamical behavior. In case of a positive coupling the generalized Lotka-Volterra system exhibits a maximum gain achieved after a finite time, but the population will die out in the long time limit. In the opposite case, the time evolution is terminated in a crash. Due to the nonlinear feedback coupling the two branches of a bistable model are controlled by the the strength and the sign of the memory. For a negative coupling the system is able to switch over between both branches of the stationary solution. The dynamics of the system is further controlled by the initial condition. The diffusion-limited reaction is likewise studied in case the reacting entities are not available simultaneously. Whereas for an external feedback the dynamics is altered, but the stationary solution remain unchanged, a self-organized internal feedback leads to a time persistent solution.

  5. Stirring Strongly Coupled Plasma

    CERN Document Server

    Fadafan, Kazem Bitaghsir; Rajagopal, Krishna; Wiedemann, Urs Achim

    2009-01-01

    We determine the energy it takes to move a test quark along a circle of radius L with angular frequency w through the strongly coupled plasma of N=4 supersymmetric Yang-Mills (SYM) theory. We find that for most values of L and w the energy deposited by stirring the plasma in this way is governed either by the drag force acting on a test quark moving through the plasma in a straight line with speed v=Lw or by the energy radiated by a quark in circular motion in the absence of any plasma, whichever is larger. There is a continuous crossover from the drag-dominated regime to the radiation-dominated regime. In the crossover regime we find evidence for significant destructive interference between energy loss due to drag and that due to radiation as if in vacuum. The rotating quark thus serves as a model system in which the relative strength of, and interplay between, two different mechanisms of parton energy loss is accessible via a controlled classical gravity calculation. We close by speculating on the implicati...

  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. Loading Conditions and Longitudinal Strength

    DEFF Research Database (Denmark)

    Sørensen, Herman

    1995-01-01

    Methods for the calculation of the lightweight of the ship.Loading conditions satisfying draught, trim and intact stability requirements and analysis of the corresponding stillwater longitudinal strength.......Methods for the calculation of the lightweight of the ship.Loading conditions satisfying draught, trim and intact stability requirements and analysis of the corresponding stillwater longitudinal strength....

  8. Oscillator strengths for neutral technetium

    International Nuclear Information System (INIS)

    Garstang, R.H.

    1981-01-01

    Oscillator strengths have been calculated for most of the spectral lines of TcI which are of interest in the study of stars of spectral type S. Oscillator strengths have been computed for the corresponding transitions in MnI as a partial check of the technetium calculations

  9. Failing to learn from negative prediction errors: Obesity is associated with alterations in a fundamental neural learning mechanism.

    Science.gov (United States)

    Mathar, David; Neumann, Jane; Villringer, Arno; Horstmann, Annette

    2017-10-01

    Prediction errors (PEs) encode the difference between expected and actual action outcomes in the brain via dopaminergic modulation. Integration of these learning signals ensures efficient behavioral adaptation. Obesity has recently been linked to altered dopaminergic fronto-striatal circuits, thus implying impairments in cognitive domains that rely on its integrity. 28 obese and 30 lean human participants performed an implicit stimulus-response learning paradigm inside an fMRI scanner. Computational modeling and psycho-physiological interaction (PPI) analysis was utilized for assessing PE-related learning and associated functional connectivity. We show that human obesity is associated with insufficient incorporation of negative PEs into behavioral adaptation even in a non-food context, suggesting differences in a fundamental neural learning mechanism. Obese subjects were less efficient in using negative PEs to improve implicit learning performance, despite proper coding of PEs in striatum. We further observed lower functional coupling between ventral striatum and supplementary motor area in obese subjects subsequent to negative PEs. Importantly, strength of functional coupling predicted task performance and negative PE utilization. These findings show that obesity is linked to insufficient behavioral adaptation specifically in response to negative PEs, and to associated alterations in function and connectivity within the fronto-striatal system. Recognition of neural differences as a central characteristic of obesity hopefully paves the way to rethink established intervention strategies: Differential behavioral sensitivity to negative and positive PEs should be considered when designing intervention programs. Measures relying on penalization of unwanted behavior may prove less effective in obese subjects than alternative approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. 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...

  11. 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

  12. 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. .

  13. 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...

  14. 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...

  15. LTE modem power consumption, SAR and RF signal strength emulation

    DEFF Research Database (Denmark)

    Musiige, Deogratius; Vincent, Laulagnet; Anton, François

    2012-01-01

    This paper presents a new methodology for emulating the LTE modem power consumption, emitted SAR and RF signal strength when transmitting an LTE signal. The inputs of the methodology are: modem logical/protocol commands, time advance, near-field specifier, and antenna characteristics. The power...... emulation model(s) are computed by a two layer 451 neural network based on physical power measurements. SAR is emulated by polynomial interpolation models based on FDTD simulations. The accuracies of the mathematical function approximations for the emulation models of power and SAR are 5.19% and 3...

  16. Synchronization and chaotic dynamics of coupled mechanical metronomes

    Science.gov (United States)

    Ulrichs, Henning; Mann, Andreas; Parlitz, Ulrich

    2009-12-01

    Synchronization scenarios of coupled mechanical metronomes are studied by means of numerical simulations showing the onset of synchronization for two, three, and 100 globally coupled metronomes in terms of Arnol'd tongues in parameter space and a Kuramoto transition as a function of coupling strength. Furthermore, we study the dynamics of metronomes where overturning is possible. In this case hyperchaotic dynamics associated with some diffusion process in configuration space is observed, indicating the potential complexity of metronome dynamics.

  17. Order in the turbulent phase of globally coupled maps

    International Nuclear Information System (INIS)

    Perez, G.; Sinha, S.; Cerdeira, H.A.

    1991-04-01

    The very surprising broad peaks seen in the power spectra of the mean field in a globally coupled map system, indicating subtle coherences between the elements even in the ''turbulent'' phase, are investigated in detail with respect to number of elements coupled, nonlinearity and global coupling strength. We find that the peaks are determined by two distinct components: effective renormalization of the nonlinearity parameter in the local mapping and the strength of the mean field iteration term. We also demonstrate the influence of background noise on the peaks - which is quite counterintuitive, as the peaks become sharper with increase in strength of noise, up to a certain critical noise strength. (author). 11 refs, 10 figs

  18. STRENGTH OF NANOMODIFIED HIGH-STRENGTH LIGHTWEIGHT CONCRETES

    Directory of Open Access Journals (Sweden)

    NOZEMTСEV Alexandr Sergeevich

    2013-02-01

    Full Text Available The paper presents the results of research aimed at development of nanomodified high-strength lightweight concrete for construction. The developed concretes are of low average density and high ultimate compressive strength. It is shown that to produce this type of concrete one need to use hollow glass and aluminosilicate microspheres. To increase the durability of adhesion between cement stone and fine filler the authors offer to use complex nanodimensinal modifier based on iron hydroxide sol and silica sol as a surface nanomodifier for hollow microspheres. It is hypothesized that the proposed modifier has complex effect on the activity of the cement hydration and, at the same time increases bond strength between filler and cement-mineral matrix. The compositions for energy-efficient nanomodified high-strength lightweight concrete which density is 1300…1500 kg/m³ and compressive strength is 40…65 MPa have been developed. The approaches to the design of high-strength lightweight concrete with density of less than 2000 kg/m³ are formulated. It is noted that the proposed concretes possess dense homogeneous structure and moderate mobility. Thus, they allow processing by vibration during production. The economic and practical implications for realization of high-strength lightweight concrete in industrial production have been justified.

  19. 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

  20. The two-qubit quantum Rabi model: inhomogeneous coupling

    International Nuclear Information System (INIS)

    Mao, Lijun; Huai, Sainan; Zhang, Yunbo

    2015-01-01

    We revisit the analytic solution of the two-qubit quantum Rabi model with inhomogeneous coupling and transition frequencies using a displaced oscillator basis. This approach enables us to apply the same truncation rules and techniques adopted in the Rabi model to the two qubits system. The derived analytical spectra match perfectly with the numerical solutions in the parameter regime where the qubits’ transition frequencies are far off-resonance with the field frequency and the interaction strengths reach the ultrastrong coupling regime. We further explore the dynamical behavior of the two qubits as well as the evolution of entanglement. The analytical methods provide unexpectedly accurate results in describing the dynamics of the two qubits in the present experimentally accessible coupling regime. The time evolutions of the probability for the qubits show that the collapse-revival phenomena emerge, survive and finally disappear when one coupling strength increases from weak to strong coupling regimes and the other coupling strength is well into the ultrastrong coupling regime. The inhomogeneous coupling system exhibits new dynamics, which are different from the homogeneous coupling case. (paper)

  1. Fractal properties of percolation clusters in Euclidian neural networks

    International Nuclear Information System (INIS)

    Franovic, Igor; Miljkovic, Vladimir

    2009-01-01

    The process of spike packet propagation is observed in two-dimensional recurrent networks, consisting of locally coupled neuron pools. Local population dynamics is characterized by three key parameters - probability for pool connectedness, synaptic strength and neuron refractoriness. The formation of dynamic attractors in our model, synfire chains, exhibits critical behavior, corresponding to percolation phase transition, with probability for non-zero synaptic strength values representing the critical parameter. Applying the finite-size scaling method, we infer a family of critical lines for various synaptic strengths and refractoriness values, and determine the Hausdorff-Besicovitch fractal dimension of the percolation clusters.

  2. Optical switching of nuclear spin-spin couplings in semiconductors.

    Science.gov (United States)

    Goto, Atsushi; Ohki, Shinobu; Hashi, Kenjiro; Shimizu, Tadashi

    2011-07-05

    Two-qubit operation is an essential part of quantum computation. However, solid-state nuclear magnetic resonance quantum computing has not been able to fully implement this functionality, because it requires a switchable inter-qubit coupling that controls the time evolutions of entanglements. Nuclear dipolar coupling is beneficial in that it is present whenever nuclear-spin qubits are close to each other, while it complicates two-qubit operation because the qubits must remain decoupled to prevent unwanted couplings. Here we introduce optically controllable internuclear coupling in semiconductors. The coupling strength can be adjusted externally through light power and even allows on/off switching. This feature provides a simple way of switching inter-qubit couplings in semiconductor-based quantum computers. In addition, its long reach compared with nuclear dipolar couplings allows a variety of options for arranging qubits, as they need not be next to each other to secure couplings.

  3. Optical switching of nuclear spin–spin couplings in semiconductors

    Science.gov (United States)

    Goto, Atsushi; Ohki, Shinobu; Hashi, Kenjiro; Shimizu, Tadashi

    2011-01-01

    Two-qubit operation is an essential part of quantum computation. However, solid-state nuclear magnetic resonance quantum computing has not been able to fully implement this functionality, because it requires a switchable inter-qubit coupling that controls the time evolutions of entanglements. Nuclear dipolar coupling is beneficial in that it is present whenever nuclear–spin qubits are close to each other, while it complicates two-qubit operation because the qubits must remain decoupled to prevent unwanted couplings. Here we introduce optically controllable internuclear coupling in semiconductors. The coupling strength can be adjusted externally through light power and even allows on/off switching. This feature provides a simple way of switching inter-qubit couplings in semiconductor-based quantum computers. In addition, its long reach compared with nuclear dipolar couplings allows a variety of options for arranging qubits, as they need not be next to each other to secure couplings. PMID:21730962

  4. Action Potential Modulation of Neural Spin Networks Suggests Possible Role of Spin

    CERN Document Server

    Hu, H P

    2004-01-01

    In this paper we show that nuclear spin networks in neural membranes are modulated by action potentials through J-coupling, dipolar coupling and chemical shielding tensors and perturbed by microscopically strong and fluctuating internal magnetic fields produced largely by paramagnetic oxygen. We suggest that these spin networks could be involved in brain functions since said modulation inputs information carried by the neural spike trains into them, said perturbation activates various dynamics within them and the combination of the two likely produce stochastic resonance thus synchronizing said dynamics to the neural firings. Although quantum coherence is desirable and may indeed exist, it is not required for these spin networks to serve as the subatomic components for the conventional neural networks.

  5. BWR fuel cycle optimization using neural networks

    International Nuclear Information System (INIS)

    Ortiz-Servin, Juan Jose; Castillo, Jose Alejandro; Pelta, David Alejandro

    2011-01-01

    Highlights: → OCONN a new system to optimize all nuclear fuel management steps in a coupled way. → OCON is based on an artificial recurrent neural network to find the best combination of partial solutions to each fuel management step. → OCONN works with a fuel lattices' stock, a fuel reloads' stock and a control rod patterns' stock, previously obtained with different heuristic techniques. → Results show OCONN is able to find good combinations according the global objective function. - Abstract: In nuclear fuel management activities for BWRs, four combinatorial optimization problems are solved: fuel lattice design, axial fuel bundle design, fuel reload design and control rod patterns design. Traditionally, these problems have been solved in separated ways due to their complexity and the required computational resources. In the specialized literature there are some attempts to solve fuel reloads and control rod patterns design or fuel lattice and axial fuel bundle design in a coupled way. In this paper, the system OCONN to solve all of these problems in a coupled way is shown. This system is based on an artificial recurrent neural network to find the best combination of partial solutions to each problem, in order to maximize a global objective function. The new system works with a fuel lattices' stock, a fuel reloads' stock and a control rod patterns' stock, previously obtained with different heuristic techniques. The system was tested to design an equilibrium cycle with a cycle length of 18 months. Results show that the new system is able to find good combinations. Cycle length is reached and safety parameters are fulfilled.

  6. Switchable coupling for superconducting qubits using double resonance in the presence of crosstalk

    International Nuclear Information System (INIS)

    Ashhab, S.; Nori, Franco

    2007-01-01

    Several methods have been proposed recently to achieve switchable coupling between superconducting qubits. We discuss some of the main considerations regarding the feasibility of implementing one of those proposals: The double-resonance method. We analyze mainly issues related to the achievable effective coupling strength and the effects of crosstalk on this coupling mechanism. We also find a crosstalk-assisted coupling channel that can be an attractive alternative when implementing the double-resonance coupling proposal

  7. 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

  8. Incorporating an optical waveguide into a neural interface

    Energy Technology Data Exchange (ETDEWEB)

    Tolosa, Vanessa; Delima, Terri L.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tooker, Angela C.

    2016-11-08

    An optical waveguide integrated into a multielectrode array (MEA) neural interface includes a device body, at least one electrode in the device body, at least one electrically conducting lead coupled to the at least one electrode, at least one optical channel in the device body, and waveguide material in the at least one optical channel. The fabrication of a neural interface device includes the steps of providing a device body, providing at least one electrode in the device body, providing at least one electrically conducting lead coupled to the at least one electrode, providing at least one optical channel in the device body, and providing a waveguide material in the at least one optical channel.

  9. Path coupling and aggregate path coupling

    CERN Document Server

    Kovchegov, Yevgeniy

    2018-01-01

    This book describes and characterizes an extension to the classical path coupling method applied to statistical mechanical models, referred to as aggregate path coupling. In conjunction with large deviations estimates, the aggregate path coupling method is used to prove rapid mixing of Glauber dynamics for a large class of statistical mechanical models, including models that exhibit discontinuous phase transitions which have traditionally been more difficult to analyze rigorously. The book shows how the parameter regions for rapid mixing for several classes of statistical mechanical models are derived using the aggregate path coupling method.

  10. Assessing Landslide Hazard Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, Farzad; Choobbasti, Asskar Janalizadeh; Barari, Amin

    2011-01-01

    failure" which is main concentration of the current research and "liquefaction failure". Shear failures along shear planes occur when the shear stress along the sliding surfaces exceed the effective shear strength. These slides have been referred to as landslide. An expert system based on artificial...... and factor of safety. It can be stated that the trained neural networks are capable of predicting the stability of slopes and safety factor of landslide hazard in study area with an acceptable level of confidence. Landslide hazard analysis and mapping can provide useful information for catastrophic loss...... reduction, and assist in the development of guidelines for sustainable land use planning. The analysis is used to identify the factors that are related to landslides and to predict the landslide hazard in the future based on such a relationship....

  11. Dynamics of Time Delay-Induced Multiple Synchronous Behaviors in Inhibitory Coupled Neurons

    Science.gov (United States)

    Gu, Huaguang; Zhao, Zhiguo

    2015-01-01

    The inhibitory synapse can induce synchronous behaviors different from the anti-phase synchronous behaviors, which have been reported in recent studies. In the present paper, synchronous behaviors are investigated in the motif model composed of reciprocal inhibitory coupled neurons with endogenous bursting and time delay. When coupling strength is weak, synchronous behavior appears at a single interval of time delay within a bursting period. When coupling strength is strong, multiple synchronous behaviors appear at different intervals of time delay within a bursting period. The different bursting patterns of synchronous behaviors, and time delays and coupling strengths that can induce the synchronous bursting patterns can be well interpreted by the dynamics of the endogenous bursting pattern of isolated neuron, which is acquired by the fast-slow dissection method, combined with the inhibitory coupling current. For an isolated neuron, when a negative impulsive current with suitable strength is applied at different phases of the bursting, multiple different bursting patterns can be induced. For a neuron in the motif, the inhibitory coupling current, of which the application time and strength is modulated by time delay and coupling strength, can cause single or multiple synchronous firing patterns like the negative impulsive current when time delay and coupling strength is suitable. The difference compared to the previously reported multiple synchronous behaviors that appear at time delays wider than a period of the endogenous firing is discussed. The results present novel examples of synchronous behaviors in the neuronal network with inhibitory synapses and provide a reasonable explanation. PMID:26394224

  12. Multilayer Neural Networks with Extensively Many Hidden Units

    International Nuclear Information System (INIS)

    Rosen-Zvi, Michal; Engel, Andreas; Kanter, Ido

    2001-01-01

    The information processing abilities of a multilayer neural network with a number of hidden units scaling as the input dimension are studied using statistical mechanics methods. The mapping from the input layer to the hidden units is performed by general symmetric Boolean functions, whereas the hidden layer is connected to the output by either discrete or continuous couplings. Introducing an overlap in the space of Boolean functions as order parameter, the storage capacity is found to scale with the logarithm of the number of implementable Boolean functions. The generalization behavior is smooth for continuous couplings and shows a discontinuous transition to perfect generalization for discrete ones

  13. Stimulus-dependent maximum entropy models of neural population codes.

    Directory of Open Access Journals (Sweden)

    Einat Granot-Atedgi

    Full Text Available Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME model-a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.

  14. Microfluidic engineered high cell density three-dimensional neural cultures

    Science.gov (United States)

    Cullen, D. Kacy; Vukasinovic, Jelena; Glezer, Ari; La Placa, Michelle C.

    2007-06-01

    Three-dimensional (3D) neural cultures with cells distributed throughout a thick, bioactive protein scaffold may better represent neurobiological phenomena than planar correlates lacking matrix support. Neural cells in vivo interact within a complex, multicellular environment with tightly coupled 3D cell-cell/cell-matrix interactions; however, thick 3D neural cultures at cell densities approaching that of brain rapidly decay, presumably due to diffusion limited interstitial mass transport. To address this issue, we have developed a novel perfusion platform that utilizes forced intercellular convection to enhance mass transport. First, we demonstrated that in thick (>500 µm) 3D neural cultures supported by passive diffusion, cell densities =104 cells mm-3), continuous medium perfusion at 2.0-11.0 µL min-1 improved viability compared to non-perfused cultures (p death and matrix degradation. In perfused cultures, survival was dependent on proximity to the perfusion source at 2.00-6.25 µL min-1 (p 90% viability in both neuronal cultures and neuronal-astrocytic co-cultures. This work demonstrates the utility of forced interstitial convection in improving the survival of high cell density 3D engineered neural constructs and may aid in the development of novel tissue-engineered systems reconstituting 3D cell-cell/cell-matrix interactions.

  15. Neural networks within multi-core optic fibers.

    Science.gov (United States)

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

    2016-07-07

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

  16. An artifical neural network for detection of simulated dental caries

    Energy Technology Data Exchange (ETDEWEB)

    Kositbowornchai, S. [Khon Kaen Univ. (Thailand). Dept. of Oral Diagnosis; Siriteptawee, S.; Plermkamon, S.; Bureerat, S. [Khon Kaen Univ. (Thailand). Dept. of Mechanical Engineering; Chetchotsak, D. [Khon Kaen Univ. (Thailand). Dept. of Industrial Engineering

    2006-08-15

    Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

  17. An artifical neural network for detection of simulated dental caries

    International Nuclear Information System (INIS)

    Kositbowornchai, S.; Siriteptawee, S.; Plermkamon, S.; Bureerat, S.; Chetchotsak, D.

    2006-01-01

    Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

  18. Rock strength under explosive loading

    International Nuclear Information System (INIS)

    Rimer, N.; Proffer, W.

    1993-01-01

    This presentation emphasizes the importance of a detailed description of the nonlinear deviatoric (strength) response of the surrounding rock in the numerical simulation of underground nuclear explosion phenomenology to the late times needed for test ban monitoring applications. We will show how numerical simulations which match ground motion measurements in volcanic tuffs and in granite use the strength values obtained from laboratory measurements on small core samples of these rocks but also require much lower strength values after the ground motion has interacted with the rock. The underlying physical mechanisms for the implied strength reduction are not yet well understood, and in fact may depend on the particular rock type. However, constitutive models for shock damage and/or effective stress have been used successfully at S-Cubed in both the Geophysics Program (primarily for DARPA) and the Containment Support Program (for DNA) to simulate late time ground motions measured at NTS in many different rock types

  19. Strength Training and Your Child

    Science.gov (United States)

    ... in organized sports or activities such as baseball, soccer, or gymnastics usually can safely to start strength ... as biking and running, adequate hydration, and healthy nutrition. Reviewed by: Mary L. Gavin, MD Date reviewed: ...

  20. Semiconductor ring lasers coupled by a single waveguide

    Science.gov (United States)

    Coomans, W.; Gelens, L.; Van der Sande, G.; Mezosi, G.; Sorel, M.; Danckaert, J.; Verschaffelt, G.

    2012-06-01

    We experimentally and theoretically study the characteristics of semiconductor ring lasers bidirectionally coupled by a single bus waveguide. This configuration has, e.g., been suggested for use as an optical memory and as an optical neural network motif. The main results are that the coupling can destabilize the state in which both rings lase in the same direction, and it brings to life a state with equal powers at both outputs. These are both undesirable for optical memory operation. Although the coupling between the rings is bidirectional, the destabilization occurs due to behavior similar to an optically injected laser system.

  1. 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

  2. The neural basis of the imitation drive.

    Science.gov (United States)

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

    2016-01-01

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

  3. Computational modeling of seizure dynamics using coupled neuronal networks: factors shaping epileptiform activity.

    Directory of Open Access Journals (Sweden)

    Sebastien Naze

    2015-05-01

    Full Text Available Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion

  4. Characteristics of structural loess strength and preliminary framework for joint strength formula

    OpenAIRE

    Rong-jian Li; Jun-ding Liu; Rui Yan; Wen Zheng; Sheng-jun Shao

    2014-01-01

    The strength of structural loess consists of the shear strength and tensile strength. In this study, the stress path, the failure envelope of principal stress (Kf line), and the strength failure envelope of structurally intact loess and remolded loess were analyzed through three kinds of tests: the tensile strength test, the uniaxial compressive strength test, and the conventional triaxial shear strength test. Then, in order to describe the tensile strength and shear strength of structural lo...

  5. Generalized Projective Synchronization between Two Different Neural Networks with Mixed Time Delays

    Directory of Open Access Journals (Sweden)

    Xuefei Wu

    2012-01-01

    Full Text Available The generalized projective synchronization (GPS between two different neural networks with nonlinear coupling and mixed time delays is considered. Several kinds of nonlinear feedback controllers are designed to achieve GPS between two different such neural networks. Some results for GPS of these neural networks are proved theoretically by using the Lyapunov stability theory and the LaSalle invariance principle. Moreover, by comparison, we determine an optimal nonlinear controller from several ones and provide an adaptive update law for it. Computer simulations are provided to show the effectiveness and feasibility of the proposed methods.

  6. Application of artificial neural network for heat transfer in porous cone

    Science.gov (United States)

    Athani, Abdulgaphur; Ahamad, N. Ameer; Badruddin, Irfan Anjum

    2018-05-01

    Heat transfer in porous medium is one of the classical areas of research that has been active for many decades. The heat transfer in porous medium is generally studied by using numerical methods such as finite element method; finite difference method etc. that solves coupled partial differential equations by converting them into simpler forms. The current work utilizes an alternate method known as artificial neural network that mimics the learning characteristics of neurons. The heat transfer in porous medium fixed in a cone is predicted using backpropagation neural network. The artificial neural network is able to predict this behavior quite accurately.

  7. Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

    Science.gov (United States)

    Lähivaara, Timo; Kärkkäinen, Leo; Huttunen, Janne M. J.; Hesthaven, Jan S.

    2018-02-01

    We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, we consider a high-order discontinuous Galerkin method while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, we estimate the material porosity and tortuosity while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirms the feasibility and accuracy of this approach.

  8. Dynamics of a neural system with a multiscale architecture

    Science.gov (United States)

    Breakspear, Michael; Stam, Cornelis J

    2005-01-01

    The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested. PMID:16087448

  9. Oscillatory neural representations in the sensory thalamus predict neuropathic pain relief by deep brain stimulation.

    Science.gov (United States)

    Huang, Yongzhi; Green, Alexander L; Hyam, Jonathan; Fitzgerald, James; Aziz, Tipu Z; Wang, Shouyan

    2018-01-01

    Understanding the function of sensory thalamic neural activity is essential for developing and improving interventions for neuropathic pain. However, there is a lack of investigation of the relationship between sensory thalamic oscillations and pain relief in patients with neuropathic pain. This study aims to identify the oscillatory neural characteristics correlated with pain relief induced by deep brain stimulation (DBS), and develop a quantitative model to predict pain relief by integrating characteristic measures of the neural oscillations. Measures of sensory thalamic local field potentials (LFPs) in thirteen patients with neuropathic pain were screened in three dimensional feature space according to the rhythm, balancing, and coupling neural behaviours, and correlated with pain relief. An integrated approach based on principal component analysis (PCA) and multiple regression analysis is proposed to integrate the multiple measures and provide a predictive model. This study reveals distinct thalamic rhythms of theta, alpha, high beta and high gamma oscillations correlating with pain relief. The balancing and coupling measures between these neural oscillations were also significantly correlated with pain relief. The study enriches the series research on the function of thalamic neural oscillations in neuropathic pain and relief, and provides a quantitative approach for predicting pain relief by DBS using thalamic neural oscillations. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Strategies for waveguide coupling for SRF cavities

    International Nuclear Information System (INIS)

    Doolittle, L.R.

    1998-01-01

    Despite widespread use of coaxial couplers in SRF cavities, a single, simple waveguide coupling can be used both to transmit generator power to a cavity, and to remove a large class of Higher Order Modes (HOMs, produced by the beam). There are balances and tradeoffs to be made, such as the coupling strength of the various frequencies, the transverse component of the coupler fields on the beam axis, and the magnitude of the surface fields and currents. This paper describes those design constraints, categories of solutions, and examples from the CEBAF Energy Upgrade studies

  11. Training-induced changes in muscle CSA,muscle strength, EMG and rate of force development in elderly subjects after long-term unilateral disuse

    DEFF Research Database (Denmark)

    Suetta, Charlotte; Aagaard, Per; Rosted, Anne

    2004-01-01

    , maximal isometric strength, RFD, and muscle activation in elderly men and women recovering from long-term muscle disuse and subsequent hip surgery. The improvement in both muscle mass and neural function is likely to have important functional implications for elderly individuals........ Thirty subjects completed the trial. In the strength-training group, significant increases were observed in maximal isometric muscle strength (24%, P impulse (27-32%, P

  12. Cosmic growth signatures of modified gravitational strength

    Energy Technology Data Exchange (ETDEWEB)

    Denissenya, Mikhail; Linder, Eric V., E-mail: mikhail.denissenya@nu.edu.kz, E-mail: evlinder@lbl.gov [Energetic Cosmos Laboratory, Nazarbayev University, Astana, 010000 Kazakhstan (Kazakhstan)

    2017-06-01

    Cosmic growth of large scale structure probes the entire history of cosmic expansion and gravitational coupling. To get a clear picture of the effects of modification of gravity we consider a deviation in the coupling strength (effective Newton's constant) at different redshifts, with different durations and amplitudes. We derive, analytically and numerically, the impact on the growth rate and growth amplitude. Galaxy redshift surveys can measure a product of these through redshift space distortions and we connect the modified gravity to the observable in a way that may provide a useful parametrization of the ability of future surveys to test gravity. In particular, modifications during the matter dominated era can be treated by a single parameter, the ''area'' of the modification, to an accuracy of ∼0.3% in the observables. We project constraints on both early and late time gravity for the Dark Energy Spectroscopic Instrument and discuss what is needed for tightening tests of gravity to better than 5% uncertainty.

  13. A comparison between wavelet based static and dynamic neural network approaches for runoff prediction

    Science.gov (United States)

    Shoaib, Muhammad; Shamseldin, Asaad Y.; Melville, Bruce W.; Khan, Mudasser Muneer

    2016-04-01

    In order to predict runoff accurately from a rainfall event, the multilayer perceptron type of neural network models are commonly used in hydrology. Furthermore, the wavelet coupled multilayer perceptron neural network (MLPNN) models has also been found superior relative to the simple neural network models which are not coupled with wavelet. However, the MLPNN models are considered as static and memory less networks and lack the ability to examine the temporal dimension of data. Recurrent neural network models, on the other hand, have the ability to learn from the preceding conditions of the system and hence considered as dynamic models. This study for the first time explores the potential of wavelet coupled time lagged recurrent neural network (TLRNN) models for runoff prediction using rainfall data. The Discrete Wavelet Transformation (DWT) is employed in this study to decompose the input rainfall data using six of the most commonly used wavelet functions. The performance of the simple and the wavelet coupled static MLPNN models is compared with their counterpart dynamic TLRNN models. The study found that the dynamic wavelet coupled TLRNN models can be considered as alternative to the static wavelet MLPNN models. The study also investigated the effect of memory depth on the performance of static and dynamic neural network models. The memory depth refers to how much past information (lagged data) is required as it is not known a priori. The db8 wavelet function is found to yield the best results with the static MLPNN models and with the TLRNN models having small memory depths. The performance of the wavelet coupled TLRNN models with large memory depths is found insensitive to the selection of the wavelet function as all wavelet functions have similar performance.

  14. 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...

  15. 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.

  16. Controlled perturbation-induced switching in pulse-coupled oscillator networks

    International Nuclear Information System (INIS)

    Schittler Neves, Fabio; Timme, Marc

    2009-01-01

    Pulse-coupled systems such as spiking neural networks exhibit nontrivial invariant sets in the form of attracting yet unstable saddle periodic orbits where units are synchronized into groups. Heteroclinic connections between such orbits may in principle support switching processes in these networks and enable novel kinds of neural computations. For small networks of coupled oscillators, we here investigate under which conditions and how system symmetry enforces or forbids certain switching transitions that may be induced by perturbations. For networks of five oscillators, we derive explicit transition rules that for two cluster symmetries deviate from those known from oscillators coupled continuously in time. A third symmetry yields heteroclinic networks that consist of sets of all unstable attractors with that symmetry and the connections between them. Our results indicate that pulse-coupled systems can reliably generate well-defined sets of complex spatiotemporal patterns that conform to specific transition rules. We briefly discuss possible implications for computation with spiking neural systems.

  17. Controlled perturbation-induced switching in pulse-coupled oscillator networks

    Energy Technology Data Exchange (ETDEWEB)

    Schittler Neves, Fabio; Timme, Marc [Network Dynamics Group, Max Planck Institute for Dynamics and Self-Organization, Goettingen, D-37073 (Germany); Bernstein Center for Computational Neuroscience (BCCN), Goettingen (Germany)], E-mail: neves@nld.ds.mpg.de, E-mail: timme@nld.ds.mpg.de

    2009-08-28

    Pulse-coupled systems such as spiking neural networks exhibit nontrivial invariant sets in the form of attracting yet unstable saddle periodic orbits where units are synchronized into groups. Heteroclinic connections between such orbits may in principle support switching processes in these networks and enable novel kinds of neural computations. For small networks of coupled oscillators, we here investigate under which conditions and how system symmetry enforces or forbids certain switching transitions that may be induced by perturbations. For networks of five oscillators, we derive explicit transition rules that for two cluster symmetries deviate from those known from oscillators coupled continuously in time. A third symmetry yields heteroclinic networks that consist of sets of all unstable attractors with that symmetry and the connections between them. Our results indicate that pulse-coupled systems can reliably generate well-defined sets of complex spatiotemporal patterns that conform to specific transition rules. We briefly discuss possible implications for computation with spiking neural systems.

  18. An improved superconducting neural circuit and its application for a neural network solving a combinatorial optimization problem

    International Nuclear Information System (INIS)

    Onomi, T; Nakajima, K

    2014-01-01

    We have proposed a superconducting Hopfield-type neural network for solving the N-Queens problem which is one of combinatorial optimization problems. The sigmoid-shape function of a neuron output is represented by the output of coupled SQUIDs gate consisting of a single-junction and a double-junction SQUIDs. One of the important factors for an improvement of the network performance is an improvement of a threshold characteristic of a neuron circuit. In this paper, we report an improved design of coupled SQUID gates for a superconducting neural network. A step-like function with a steep threshold at a rising edge is desirable for a neuron circuit to solve a combinatorial optimization problem. A neuron circuit is composed of two coupled SQUIDs gates with a cascade connection in order to obtain such characteristics. The designed neuron circuit is fabricated by a 2.5 kA/cm 2 Nb/AlOx/Nb process. The operation of a fabricated neuron circuit is experimentally demonstrated. Moreover, we discuss about the performance of the neural network using the improved neuron circuits and delayed negative self-connections.

  19. Stability regions for synchronized τ-periodic orbits of coupled maps with coupling delay τ

    Energy Technology Data Exchange (ETDEWEB)

    Karabacak, Özkan, E-mail: ozkan2917@gmail.com [Department of Electronics and Communication Engineering, Istanbul Technical University, 34469 Istanbul (Turkey); Department of Electronic Systems, Aalborg University, 9220 Aalborg East (Denmark); Alikoç, Baran, E-mail: alikoc@itu.edu.tr [Department of Control and Automation Engineering, Istanbul Technical University, 34469 Istanbul (Turkey); Atay, Fatihcan M., E-mail: atay@member.ams.org [Department of Mathematics, Bilkent University, 06800 Ankara (Turkey)

    2016-09-15

    Motivated by the chaos suppression methods based on stabilizing an unstable periodic orbit, we study the stability of synchronized periodic orbits of coupled map systems when the period of the orbit is the same as the delay in the information transmission between coupled units. We show that the stability region of a synchronized periodic orbit is determined by the Floquet multiplier of the periodic orbit for the uncoupled map, the coupling constant, the smallest and the largest Laplacian eigenvalue of the adjacency matrix. We prove that the stabilization of an unstable τ-periodic orbit via coupling with delay τ is possible only when the Floquet multiplier of the orbit is negative and the connection structure is not bipartite. For a given coupling structure, it is possible to find the values of the coupling strength that stabilizes unstable periodic orbits. The most suitable connection topology for stabilization is found to be the all-to-all coupling. On the other hand, a negative coupling constant may lead to destabilization of τ-periodic orbits that are stable for the uncoupled map. We provide examples of coupled logistic maps demonstrating the stabilization and destabilization of synchronized τ-periodic orbits as well as chaos suppression via stabilization of a synchronized τ-periodic orbit.

  20. 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....

  1. 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

  2. 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…

  3. Homogenization of heterogeneously coupled bistable ODE's - applied to excitation waves in pancreatic islets of Langerhans

    DEFF Research Database (Denmark)

    Pedersen, Morten Gram

    2004-01-01

    We consider a lattice of coupled identical differential equations. The coupling is between nearest neighbors and of resistance type, but the strength of coupling varies from site to site. Such a lattice can, for example, model an islet of Langerhans, where the sites in the lattice model individua...

  4. Imaging of neural oscillations with embedded inferential and group prevalence statistics

    Science.gov (United States)

    2018-01-01

    Magnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. For that reason, imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be

  5. Artificial neural networks in NDT

    International Nuclear Information System (INIS)

    Abdul Aziz Mohamed

    2001-01-01

    Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

  6. Projective synchronization of time-varying delayed neural network with adaptive scaling factors

    International Nuclear Information System (INIS)

    Ghosh, Dibakar; Banerjee, Santo

    2013-01-01

    Highlights: • Projective synchronization in coupled delayed neural chaotic systems with modulated delay time is introduced. • An adaptive rule for the scaling factors is introduced. • This scheme is highly applicable in secure communication. -- Abstract: In this work, the projective synchronization between two continuous time delayed neural systems with time varying delay is investigated. A sufficient condition for synchronization for the coupled systems with modulated delay is presented analytically with the help of the Krasovskii–Lyapunov approach. The effect of adaptive scaling factors on synchronization are also studied in details. Numerical simulations verify the effectiveness of the analytic results

  7. Reactive Strength Index: A Poor Indicator of Reactive Strength?

    Science.gov (United States)

    Healy, Robin; Kenny, Ian; Harrison, Drew

    2017-11-28

    The primary aim was to assess the relationships between reactive strength measures and associated kinematic and kinetic performance variables achieved during drop jumps. A secondary aim was to highlight issues with the use of reactive strength measures as performance indicators. Twenty eight national and international level sprinters, consisting of fourteen men and women, participated in this cross-sectional analysis. Athletes performed drop jumps from a 0.3 m box onto a force platform with dependent variables contact time (CT), landing time (TLand), push-off time (TPush), flight time (FT), jump height (JH), reactive strength index (RSI, calculated as JH / CT), reactive strength ratio (RSR, calculated as FT / CT) and vertical leg spring stiffness (Kvert) recorded. Pearson's correlation test found very high to near perfect relationships between RSI and RSR (r = 0.91 to 0.97), with mixed relationships found between RSI, RSR and the key performance variables, (Men: r = -0.86 to -0.71 between RSI/RSR and CT, r = 0.80 to 0.92 between RSI/RSR and JH; Women: r = -0.85 to -0.56 between RSR and CT, r = 0.71 between RSI and JH). This study demonstrates that the method of assessing reactive strength (RSI versus RSR) may be influenced by the performance strategies adopted i.e. whether an athlete achieves their best reactive strength scores via low CTs, high JHs or a combination. Coaches are advised to limit the variability in performance strategies by implementing upper and / or lower CT thresholds to accurately compare performances between individuals.

  8. Predicting Magnetoelectric Coupling in Layered and Graded Composites

    Directory of Open Access Journals (Sweden)

    Mirza Bichurin

    2017-07-01

    Full Text Available Magnetoelectric (ME interaction in magnetostrictive-piezoelectric multiferroic structures consists in inducing the electric field across the structure in an applied magnetic field and is a product property of magnetostriction and piezoelectricity in components. ME voltage coefficient that is the ratio of induced electric field to applied magnetic field is the key parameter of ME coupling strength. It has been known that the ME coupling strength is dictated by the product of the piezoelectric and piezomagnetic coefficients of initial phases. As a result, using the laminates with graded piezoelectric and piezomagnetic parameters are a new pathway to the increase in the ME coupling strength. Recently developed models predict stronger ME interactions in composites based on graded components compared to homogeneous ones. We discuss predicting the ME coupling strength for layered structures of homogeneous and compositionally graded magnetostrictive and piezoelectric components based on the graphs of ME voltage coefficients against composite parameters. For obtaining the graphs, we developed equations for ME output in applied magnetic field for possible modes of operation and layered structure configurations. In particular, our studies have been performed on low-frequency ME coupling, enhanced ME effect in electromechanical resonance (EMR region for longitudinal and bending modes. Additionally, ME coupling at magnetic resonance in magnetostrictive component and at overlapping the EMR and magnetic resonance is investigated. We considered symmetric trilayers and asymmetric bilayers of magnetostrictive and piezoelectric components and multilayered structures based on compositionally stepped initial components.

  9. Neural precursors of future liking and affective reciprocity.

    Science.gov (United States)

    Zerubavel, Noam; Hoffman, Mark Anthony; Reich, Adam; Ochsner, Kevin N; Bearman, Peter

    2018-04-24

    Why do certain group members end up liking each other more than others? How does affective reciprocity arise in human groups? The prediction of interpersonal sentiment has been a long-standing pursuit in the social sciences. We combined fMRI and longitudinal social network data to test whether newly acquainted group members' reward-related neural responses to images of one another's faces predict their future interpersonal sentiment, even many months later. Specifically, we analyze associations between relationship-specific valuation activity and relationship-specific future liking. We found that one's own future (T2) liking of a particular group member is predicted jointly by actor's initial (T1) neural valuation of partner and by that partner's initial (T1) neural valuation of actor. These actor and partner effects exhibited equivalent predictive strength and were robust when statistically controlling for each other, both individuals' initial liking, and other potential drivers of liking. Behavioral findings indicated that liking was initially unreciprocated at T1 yet became strongly reciprocated by T2. The emergence of affective reciprocity was partly explained by the reciprocal pathways linking dyad members' T1 neural data both to their own and to each other's T2 liking outcomes. These findings elucidate interpersonal brain mechanisms that define how we ultimately end up liking particular interaction partners, how group members' initially idiosyncratic sentiments become reciprocated, and more broadly, how dyads evolve. This study advances a flexible framework for researching the neural foundations of interpersonal sentiments and social relations that-conceptually, methodologically, and statistically-emphasizes group members' neural interdependence. Copyright © 2018 the Author(s). Published by PNAS.

  10. Travelling waves in models of neural tissue: from localised structures to periodic waves

    NARCIS (Netherlands)

    Meijer, Hil Gaétan Ellart; Coombes, Stephen

    2014-01-01

    We consider travelling waves (fronts, pulses and periodics) in spatially extended one dimensional neural field models. We demonstrate for an excitatory field with linear adaptation that, in addition to an expected stable pulse solution, a stable anti-pulse can exist. Varying the adaptation strength

  11. Mixed Analog/Digital Matrix-Vector Multiplier for Neural Network Synapses

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Bruun, Erik; Dietrich, Casper

    1996-01-01

    In this work we present a hardware efficient matrix-vector multiplier architecture for artificial neural networks with digitally stored synapse strengths. We present a novel technique for manipulating bipolar inputs based on an analog two's complements method and an accurate current rectifier...

  12. Precipitation Nowcast using Deep Recurrent Neural Network

    Science.gov (United States)

    Akbari Asanjan, A.; Yang, T.; Gao, X.; Hsu, K. L.; Sorooshian, S.

    2016-12-01

    An accurate precipitation nowcast (0-6 hours) with a fine temporal and spatial resolution has always been an important prerequisite for flood warning, streamflow prediction and risk management. Most of the popular approaches used for forecasting precipitation can be categorized into two groups. One type of precipitation forecast relies on numerical modeling of the physical dynamics of atmosphere and another is based on empirical and statistical regression models derived by local hydrologists or meteorologists. Given the recent advances in artificial intelligence, in this study a powerful Deep Recurrent Neural Network, termed as Long Short-Term Memory (LSTM) model, is creatively used to extract the patterns and forecast the spatial and temporal variability of Cloud Top Brightness Temperature (CTBT) observed from GOES satellite. Then, a 0-6 hours precipitation nowcast is produced using a Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) algorithm, in which the CTBT nowcast is used as the PERSIANN algorithm's raw inputs. Two case studies over the continental U.S. have been conducted that demonstrate the improvement of proposed approach as compared to a classical Feed Forward Neural Network and a couple simple regression models. The advantages and disadvantages of the proposed method are summarized with regard to its capability of pattern recognition through time, handling of vanishing gradient during model learning, and working with sparse data. The studies show that the LSTM model performs better than other methods, and it is able to learn the temporal evolution of the precipitation events through over 1000 time lags. The uniqueness of PERSIANN's algorithm enables an alternative precipitation nowcast approach as demonstrated in this study, in which the CTBT prediction is produced and used as the inputs for generating precipitation nowcast.

  13. Strong coupling analogue of the Born series

    International Nuclear Information System (INIS)

    Dolinszky, T.

    1989-10-01

    In a given partial wave, the strength of the centrifugal term to be incorporated into the WKBA solutions in different spatial regions can be adjusted so as to make the first order wave functions everywhere smooth and, in strong coupling, exactly reproduce Quantum Mechanics throughout the space. The relevant higher order approximations supply an absolute convergent series expansion of the exact scattering state. (author) 4 refs.; 2 figs.; 2 tabs

  14. Lifting strength in two-person teamwork.

    Science.gov (United States)

    Lee, Tzu-Hsien

    2016-01-01

    This study examined the effects of lifting range, hand-to-toe distance, and lifting direction on single-person lifting strengths and two-person teamwork lifting strengths. Six healthy males and seven healthy females participated in this study. Two-person teamwork lifting strengths were examined in both strength-matched and strength-unmatched groups. Our results showed that lifting strength significantly decreased with increasing lifting range or hand-to-toe distance. However, lifting strengths were not affected by lifting direction. Teamwork lifting strength did not conform to the law of additivity for both strength-matched and strength-unmatched groups. In general, teamwork lifting strength was dictated by the weaker of the two members, implying that weaker members might be exposed to a higher potential danger in teamwork exertions. To avoid such overexertion in teamwork, members with significantly different strength ability should not be assigned to the same team.

  15. Mobile Position Estimation using Artificial Neural Network in CDMA Cellular Systems

    Directory of Open Access Journals (Sweden)

    Omar Waleed Abdulwahhab

    2017-01-01

    Full Text Available Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA and received signal strength (RSS are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through that region. The results show that the neural network has good performance compared with two other analytical methods which are average positioning method and optimal positioning method.

  16. A laboratory experiment on coupled non-identical pendulums

    International Nuclear Information System (INIS)

    Li Ang; Zeng Jingyi; Yang Hujiang; Xiao Jinghua

    2011-01-01

    In this paper, coupled pendulums with different lengths are studied. Through steel magnets, each pendulum is coupled with others, and a stepping motor is used to drive the whole system. To record the data automatically, we designed a data acquisition system with a CCD camera connected to a computer. The coupled system shows in-phase, locked-phase and anti-phase synchronizations when the driving frequency and the coupling strength are changed. With background knowledge from general physics and the simplicity of the equipment, this experiment is easy to implement and would be of interest to undergraduate students.

  17. Dopamine replacement modulates oscillatory coupling between premotor and motor cortical areas in Parkinson's disease

    DEFF Research Database (Denmark)

    Herz, Damian Marc; Florin, Esther; Christensen, Mark Schram

    2014-01-01

    PM to SMA and significantly strengthened coupling in the feedback connection from M1 to lPM expressed as β-β as well as θ-β coupling. Enhancement in cross-frequency θ-β coupling from M1 to lPM was correlated with levodopa-induced improvement in motor function. The results show that PD is associated...... with an altered neural communication between premotor and motor cortical areas, which can be modulated by dopamine replacement....

  18. Oscillatory phase dynamics in neural entrainment underpin illusory percepts of time.

    Science.gov (United States)

    Herrmann, Björn; Henry, Molly J; Grigutsch, Maren; Obleser, Jonas

    2013-10-02

    Neural oscillatory dynamics are a candidate mechanism to steer perception of time and temporal rate change. While oscillator models of time perception are strongly supported by behavioral evidence, a direct link to neural oscillations and oscillatory entrainment has not yet been provided. In addition, it has thus far remained unaddressed how context-induced illusory percepts of time are coded for in oscillator models of time perception. To investigate these questions, we used magnetoencephalography and examined the neural oscillatory dynamics that underpin pitch-induced illusory percepts of temporal rate change. Human participants listened to frequency-modulated sounds that varied over time in both modulation rate and pitch, and judged the direction of rate change (decrease vs increase). Our results demonstrate distinct neural mechanisms of rate perception: Modulation rate changes directly affected listeners' rate percept as well as the exact frequency of the neural oscillation. However, pitch-induced illusory rate changes were unrelated to the exact frequency of the neural responses. The rate change illusion was instead linked to changes in neural phase patterns, which allowed for single-trial decoding of percepts. That is, illusory underestimations or overestimations of perceived rate change were tightly coupled to increased intertrial phase coherence and changes in cerebro-acoustic phase lag. The results provide insight on how illusory percepts of time are coded for by neural oscillatory dynamics.

  19. Neural processes underlying cultural differences in cognitive persistence.

    Science.gov (United States)

    Telzer, Eva H; Qu, Yang; Lin, Lynda C

    2017-08-01

    Self-improvement motivation, which occurs when individuals seek to improve upon their competence by gaining new knowledge and improving upon their skills, is critical for cognitive, social, and educational adjustment. While many studies have delineated the neural mechanisms supporting extrinsic motivation induced by monetary rewards, less work has examined the neural processes that support intrinsically motivated behaviors, such as self-improvement motivation. Because cultural groups traditionally vary in terms of their self-improvement motivation, we examined cultural differences in the behavioral and neural processes underlying motivated behaviors during cognitive persistence in the absence of extrinsic rewards. In Study 1, 71 American (47 females, M=19.68 years) and 68 Chinese (38 females, M=19.37 years) students completed a behavioral cognitive control task that required cognitive persistence across time. In Study 2, 14 American and 15 Chinese students completed the same cognitive persistence task during an fMRI scan. Across both studies, American students showed significant declines in cognitive performance across time, whereas Chinese participants demonstrated effective cognitive persistence. These behavioral effects were explained by cultural differences in self-improvement motivation and paralleled by increasing activation and functional coupling between the inferior frontal gyrus (IFG) and ventral striatum (VS) across the task among Chinese participants, neural activation and coupling that remained low in American participants. These findings suggest a potential neural mechanism by which the VS and IFG work in concert to promote cognitive persistence in the absence of extrinsic rewards. Thus, frontostriatal circuitry may be a neurobiological signal representing intrinsic motivation for self-improvement that serves an adaptive function, increasing Chinese students' motivation to engage in cognitive persistence. Copyright © 2017 Elsevier Inc. All rights

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

    Directory of Open Access Journals (Sweden)

    Callihan Phillip

    2008-12-01

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