The what and where of adding channel noise to the Hodgkin-Huxley equations.

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Joshua H Goldwyn

2011-11-01

Full Text Available Conductance-based equations for electrically active cells form one of the most widely studied mathematical frameworks in computational biology. This framework, as expressed through a set of differential equations by Hodgkin and Huxley, synthesizes the impact of ionic currents on a cell's voltage--and the highly nonlinear impact of that voltage back on the currents themselves--into the rapid push and pull of the action potential. Later studies confirmed that these cellular dynamics are orchestrated by individual ion channels, whose conformational changes regulate the conductance of each ionic current. Thus, kinetic equations familiar from physical chemistry are the natural setting for describing conductances; for small-to-moderate numbers of channels, these will predict fluctuations in conductances and stochasticity in the resulting action potentials. At first glance, the kinetic equations provide a far more complex (and higher-dimensional description than the original Hodgkin-Huxley equations or their counterparts. This has prompted more than a decade of efforts to capture channel fluctuations with noise terms added to the equations of Hodgkin-Huxley type. Many of these approaches, while intuitively appealing, produce quantitative errors when compared to kinetic equations; others, as only very recently demonstrated, are both accurate and relatively simple. We review what works, what doesn't, and why, seeking to build a bridge to well-established results for the deterministic equations of Hodgkin-Huxley type as well as to more modern models of ion channel dynamics. As such, we hope that this review will speed emerging studies of how channel noise modulates electrophysiological dynamics and function. We supply user-friendly MATLAB simulation code of these stochastic versions of the Hodgkin-Huxley equations on the ModelDB website (accession number 138950 and http://www.amath.washington.edu/~etsb/tutorials.html.

Biophysical synaptic dynamics in an analog VLSI network of Hodgkin-Huxley neurons.

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Yu, Theodore; Cauwenberghs, Gert

2009-01-01

We study synaptic dynamics in a biophysical network of four coupled spiking neurons implemented in an analog VLSI silicon microchip. The four neurons implement a generalized Hodgkin-Huxley model with individually configurable rate-based kinetics of opening and closing of Na+ and K+ ion channels. The twelve synapses implement a rate-based first-order kinetic model of neurotransmitter and receptor dynamics, accounting for NMDA and non-NMDA type chemical synapses. The implemented models on the chip are fully configurable by 384 parameters accounting for conductances, reversal potentials, and pre/post-synaptic voltage-dependence of the channel kinetics. We describe the models and present experimental results from the chip characterizing single neuron dynamics, single synapse dynamics, and multi-neuron network dynamics showing phase-locking behavior as a function of synaptic coupling strength. The 3mm x 3mm microchip consumes 1.29 mW power making it promising for applications including neuromorphic modeling and neural prostheses.

Phase-locking and chaos in a silent Hodgkin-Huxley neuron exposed to sinusoidal electric field

International Nuclear Information System (INIS)

Che Yanqiu; Wang Jiang; Si Wenjie; Fei Xiangyang

2009-01-01

Neuronal firing patterns are related to the information processing in neural system. This paper investigates the response characteristics of a silent Hodgkin-Huxley neuron to the stimulation of externally-applied sinusoidal electric field. The neuron exhibits both p:q phase-locked (i.e. a periodic oscillation defined as p action potentials generated by q cycle stimulations) and chaotic behaviors, depending on the values of stimulus frequencies and amplitudes. In one-parameter space, a rich bifurcation structure including period-adding without chaos and phase-locking alternated with chaos suggests frequency discrimination of the neuronal firing patterns. Furthermore, by mapping out Arnold tongues, we partition the amplitude-frequency parameter space in terms of the qualitative behaviors of the neuron. Thus the neuron's information (firing patterns) encodes the stimulus information (amplitude and frequency), and vice versa

Past as Prediction: Newcomb, Huxley, The Eclipse of Thales, and The Power of Science

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Stanley, Matthew

2009-12-01

The ancient eclipse of Thales was an important, if peculiar, focus of scientific attention in the 19th century. Victorian-era astronomers first used it as data with which to calibrate their lunar theories, but its status became strangely malleable as the century progressed. The American astronomer Simon Newcomb re-examined the eclipse and rejected it as the basis for lunar theory. But strangely, it was the unprecedented accuracy of Newcomb's calculations that led the British biologist T.H. Huxley to declare the eclipse to be the quintessential example of the power of science. Huxley argued that astronomy's ability to create "retrospective prophecy” showed how scientific reasoning was superior to religion (and incidentally, helped support Darwin's theories). Both Newcomb and Huxley declared that prediction (of past and future) was what gave science its persuasive power. The eclipse of Thales's strange journey through Victorian astronomy reveals how these two influential scientists made the case for the social and cultural authority of science.

Memristor, Hodgkin–Huxley, and Edge of Chaos

International Nuclear Information System (INIS)

Chua, Leon

2013-01-01

From a pedagogical point of view, the memristor is defined in this tutorial as any 2-terminal device obeying a state-dependent Ohm’s law. This tutorial also shows that from an experimental point of view, the memristor can be defined as any 2-terminal device that exhibits the fingerprints of ‘pinched’ hysteresis loops in the v–i plane. It also shows that memristors endowed with a continuum of equilibrium states can be used as non-volatile analog memories. This tutorial shows that memristors span a much broader vista of complex phenomena and potential applications in many fields, including neurobiology. In particular, this tutorial presents toy memristors that can mimic the classic habituation and LTP learning phenomena. It also shows that sodium and potassium ion-channel memristors are the key to generating the action potential in the Hodgkin–Huxley equations, and that they are the key to resolving several unresolved anomalies associated with the Hodgkin–Huxley equations. This tutorial ends with an amazing new result derived from the new principle of local activity, which uncovers a minuscule life-enabling ‘Goldilocks zone’, dubbed the edge of chaos, where complex phenomena, including creativity and intelligence, may emerge. From an information processing perspective, this tutorial shows that synapses are locally-passive memristors, and that neurons are made of locally-active memristors. (tutorial)

Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals

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Yu, Lianchun; Liu, Liwei

2014-03-01

The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

Stochastic resonance on Newman-Watts networks of Hodgkin-Huxley neurons with local periodic driving

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Ozer, Mahmut [Zonguldak Karaelmas University, Engineering Faculty, Department of Electrical and Electronics Engineering, 67100 Zonguldak (Turkey)], E-mail: mahmutozer2002@yahoo.com; Perc, Matjaz [University of Maribor, Faculty of Natural Sciences and Mathematics, Department of Physics, Koroska cesta 160, SI-2000 Maribor (Slovenia); Uzuntarla, Muhammet [Zonguldak Karaelmas University, Engineering Faculty, Department of Electrical and Electronics Engineering, 67100 Zonguldak (Turkey)

2009-03-02

We study the phenomenon of stochastic resonance on Newman-Watts small-world networks consisting of biophysically realistic Hodgkin-Huxley neurons with a tunable intensity of intrinsic noise via voltage-gated ion channels embedded in neuronal membranes. Importantly thereby, the subthreshold periodic driving is introduced to a single neuron of the network, thus acting as a pacemaker trying to impose its rhythm on the whole ensemble. We show that there exists an optimal intensity of intrinsic ion channel noise by which the outreach of the pacemaker extends optimally across the whole network. This stochastic resonance phenomenon can be further amplified via fine-tuning of the small-world network structure, and depends significantly also on the coupling strength among neurons and the driving frequency of the pacemaker. In particular, we demonstrate that the noise-induced transmission of weak localized rhythmic activity peaks when the pacemaker frequency matches the intrinsic frequency of subthreshold oscillations. The implications of our findings for weak signal detection and information propagation across neural networks are discussed.

Spiking sychronization regulated by noise in three types of Hodgkin—Huxley neuronal networks

International Nuclear Information System (INIS)

Zhang Zheng-Zhen; Zeng Shang-You; Tang Wen-Yan; Hu Jin-Lin; Zeng Shao-Wen; Ning Wei-Lian; Qiu Yi; Wu Hui-Si

2012-01-01

In this paper, we study spiking synchronization in three different types of Hodgkin—Huxley neuronal networks, which are the small-world, regular, and random neuronal networks. All the neurons are subjected to subthreshold stimulus and external noise. It is found that in each of all the neuronal networks there is an optimal strength of noise to induce the maximal spiking synchronization. We further demonstrate that in each of the neuronal networks there is a range of synaptic conductance to induce the effect that an optimal strength of noise maximizes the spiking synchronization. Only when the magnitude of the synaptic conductance is moderate, will the effect be considerable. However, if the synaptic conductance is small or large, the effect vanishes. As the connections between neurons increase, the synaptic conductance to maximize the effect decreases. Therefore, we show quantitatively that the noise-induced maximal synchronization in the Hodgkin—Huxley neuronal network is a general effect, regardless of the specific type of neuronal network

Spike Neural Models Part II: Abstract Neural Models

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Johnson, Melissa G.

2018-02-01

Full Text Available Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF model which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

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Han, Bing; Taha, Tarek M

2010-04-01

There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

Toward Petascale Biologically Plausible Neural Networks

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Long, Lyle

This talk will describe an approach to achieving petascale neural networks. Artificial intelligence has been oversold for many decades. Computers in the beginning could only do about 16,000 operations per second. Computer processing power, however, has been doubling every two years thanks to Moore's law, and growing even faster due to massively parallel architectures. Finally, 60 years after the first AI conference we have computers on the order of the performance of the human brain (1016 operations per second). The main issues now are algorithms, software, and learning. We have excellent models of neurons, such as the Hodgkin-Huxley model, but we do not know how the human neurons are wired together. With careful attention to efficient parallel computing, event-driven programming, table lookups, and memory minimization massive scale simulations can be performed. The code that will be described was written in C + + and uses the Message Passing Interface (MPI). It uses the full Hodgkin-Huxley neuron model, not a simplified model. It also allows arbitrary network structures (deep, recurrent, convolutional, all-to-all, etc.). The code is scalable, and has, so far, been tested on up to 2,048 processor cores using 107 neurons and 109 synapses.

Crisis of interspike intervals in Hodgkin-Huxley model

International Nuclear Information System (INIS)

Jin Wuyin; Xu Jianxue; Wu Ying; Hong Ling; Wei Yaobing

2006-01-01

The bifurcations of the chaotic attractor in a Hodgkin-Huxley (H-H) model under stimulation of periodic signal is presented in this work, where the frequency of signal is taken as the controlling parameter. The chaotic behavior is realized over a wide range of frequency and is visualized by using interspike intervals (ISIs). Many kinds of abrupt undergoing changes of the ISIs are observed in different frequency regions, such as boundary crisis, interior crisis and merging crisis displaying alternately along with the changes of external signal frequency. And there are logistic-like bifurcation behaviors, e.g., periodic windows and fractal structures in ISIs dynamics. The saddle-node bifurcations resulting in collapses of chaos to period-6 orbit in dynamics of ISIs are identified

Study Under AC Stimulation on Excitement Properties of Weighted Small-World Biological Neural Networks with Side-Restrain Mechanism

International Nuclear Information System (INIS)

Yuan Wujie; Luo Xiaoshu; Jiang Pinqun

2007-01-01

In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current (AC) stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.

Chimera States in Neural Oscillators

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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.

Single-site neural tube closure in human embryos revisited.

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de Bakker, Bernadette S; Driessen, Stan; Boukens, Bastiaan J D; van den Hoff, Maurice J B; Oostra, Roelof-Jan

2017-10-01

Since the multi-site closure theory was first proposed in 1991 as explanation for the preferential localizations of neural tube defects, the closure of the neural tube has been debated. Although the multi-site closure theory is much cited in clinical literature, single-site closure is most apparent in literature concerning embryology. Inspired by Victor Hamburgers (1900-2001) statement that "our real teacher has been and still is the embryo, who is, incidentally, the only teacher who is always right", we decided to critically review both theories of neural tube closure. To verify the theories of closure, we studied serial histological sections of 10 mouse embryos between 8.5 and 9.5 days of gestation and 18 human embryos of the Carnegie collection between Carnegie stage 9 (19-21 days) and 13 (28-32 days). Neural tube closure was histologically defined by the neuroepithelial remodeling of the two adjoining neural fold tips in the midline. We did not observe multiple fusion sites in neither mouse nor human embryos. A meta-analysis of case reports on neural tube defects showed that defects can occur at any level of the neural axis. Our data indicate that the human neural tube fuses at a single site and, therefore, we propose to reinstate the single-site closure theory for neural tube closure. We showed that neural tube defects are not restricted to a specific location, thereby refuting the reasoning underlying the multi-site closure theory. Clin. Anat. 30:988-999, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A numerical scheme for the generalized Burgers–Huxley equation

Directory of Open Access Journals (Sweden)

Brajesh K. Singh

2016-10-01

Full Text Available In this article, a numerical solution of generalized Burgers–Huxley (gBH equation is approximated by using a new scheme: modified cubic B-spline differential quadrature method (MCB-DQM. The scheme is based on differential quadrature method in which the weighting coefficients are obtained by using modified cubic B-splines as a set of basis functions. This scheme reduces the equation into a system of first-order ordinary differential equation (ODE which is solved by adopting SSP-RK43 scheme. Further, it is shown that the proposed scheme is stable. The efficiency of the proposed method is illustrated by four numerical experiments, which confirm that obtained results are in good agreement with earlier studies. This scheme is an easy, economical and efficient technique for finding numerical solutions for various kinds of (nonlinear physical models as compared to the earlier schemes.

Detection of nonstationary transition to synchronized states of a neural network using recurrence analyses

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Budzinski, R. C.; Boaretto, B. R. R.; Prado, T. L.; Lopes, S. R.

2017-07-01

We study the stability of asymptotic states displayed by a complex neural network. We focus on the loss of stability of a stationary state of networks using recurrence quantifiers as tools to diagnose local and global stabilities as well as the multistability of a coupled neural network. Numerical simulations of a neural network composed of 1024 neurons in a small-world connection scheme are performed using the model of Braun et al. [Int. J. Bifurcation Chaos 08, 881 (1998), 10.1142/S0218127498000681], which is a modified model from the Hodgkin-Huxley model [J. Phys. 117, 500 (1952)]. To validate the analyses, the results are compared with those produced by Kuramoto's order parameter [Chemical Oscillations, Waves, and Turbulence (Springer-Verlag, Berlin Heidelberg, 1984)]. We show that recurrence tools making use of just integrated signals provided by the networks, such as local field potential (LFP) (LFP signals) or mean field values bring new results on the understanding of neural behavior occurring before the synchronization states. In particular we show the occurrence of different stationary and nonstationarity asymptotic states.

Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

Science.gov (United States)

Yu, T; Sejnowski, T J; Cauwenberghs, G

2011-10-01

We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

Spike trains in Hodgkin-Huxley model and ISIs of acupuncture manipulations

International Nuclear Information System (INIS)

Wang Jiang; Si Wenjie; Che Yanqiu; Fei Xiangyang

2008-01-01

The Hodgkin-Huxley equations (HH) are parameterized by a number of parameters and shows a variety of qualitatively different behaviors depending on the parameter values. Under stimulation of an external periodic voltage, the ISIs (interspike intervals) of a HH model are investigated in this work, while the frequency of the voltage is taken as the controlling parameter. As well-known, the science of acupuncture and moxibustion is an important component of Traditional Chinese Medicine with a long history. Although there are a number of different acupuncture manipulations, the method for distinguishing them is rarely investigated. With the idea of ISI, we study the electrical signal time series at the spinal dorsal horn produced by three different acupuncture manipulations in Zusanli point and present an effective way to distinguish them

Spike trains in Hodgkin-Huxley model and ISIs of acupuncture manipulations

Energy Technology Data Exchange (ETDEWEB)

Wang Jiang [School of Electrical and Automation Engineering, Tianjin University, Tianjin 300072 (China)], E-mail: jiangwang@tju.edu.cn; Si Wenjie; Che Yanqiu; Fei Xiangyang [School of Electrical and Automation Engineering, Tianjin University, Tianjin 300072 (China)

2008-05-15

The Hodgkin-Huxley equations (HH) are parameterized by a number of parameters and shows a variety of qualitatively different behaviors depending on the parameter values. Under stimulation of an external periodic voltage, the ISIs (interspike intervals) of a HH model are investigated in this work, while the frequency of the voltage is taken as the controlling parameter. As well-known, the science of acupuncture and moxibustion is an important component of Traditional Chinese Medicine with a long history. Although there are a number of different acupuncture manipulations, the method for distinguishing them is rarely investigated. With the idea of ISI, we study the electrical signal time series at the spinal dorsal horn produced by three different acupuncture manipulations in Zusanli point and present an effective way to distinguish them.

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

Science.gov (United States)

Jimenez-Romero, Cristian; Johnson, Jeffrey

2017-01-01

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

Synchronization of a coupled Hodgkin-Huxley neurons via high order sliding-mode feedback

Energy Technology Data Exchange (ETDEWEB)

Aguilar-Lopez, R. [Division de Ciencias Basicas e Ingenieria, Universidad Autonoma Metropolitana, Av. San Pablo No. 180, Reynosa-Tamaulipas, 02200 Azcapotzalco, Mexico, D.F. (Mexico)], E-mail: raguilar@correo.azc.uam.mx; Martinez-Guerra, R. [Departamento de Control Automatico, CINVESTAV-IPN, Apartado Postal 14-740, Mexico, D.F. C.P. 07360 (Mexico)], E-mail: rguerra@ctrl.cinvestav.mx

2008-07-15

This work deals with the synchronizations of two both coupled Hodgkin-Huxley (H-H) neurons, where the master neuron posses inner noise and the slave neuron is considered in a resting state, (without inner noise) and an exciting state (with inner noise). The synchronization procedure is done via a feedback control, considering a class of high order sliding-mode controller which provides chattering reduction and finite time synchronization convergence, with a satisfactory performance. Theoretical analysis is done in order to show the closed-loop stability of the proposed controller and the calculated finite time for convergence. The main results are illustrated via numerical experiments.

Synchronization of a coupled Hodgkin-Huxley neurons via high order sliding-mode feedback

International Nuclear Information System (INIS)

Aguilar-Lopez, R.; Martinez-Guerra, R.

2008-01-01

This work deals with the synchronizations of two both coupled Hodgkin-Huxley (H-H) neurons, where the master neuron posses inner noise and the slave neuron is considered in a resting state, (without inner noise) and an exciting state (with inner noise). The synchronization procedure is done via a feedback control, considering a class of high order sliding-mode controller which provides chattering reduction and finite time synchronization convergence, with a satisfactory performance. Theoretical analysis is done in order to show the closed-loop stability of the proposed controller and the calculated finite time for convergence. The main results are illustrated via numerical experiments

Spiking Regularity and Coherence in Complex Hodgkin–Huxley Neuron Networks

International Nuclear Information System (INIS)

Zhi-Qiang, Sun; Ping, Xie; Wei, Li; Peng-Ye, Wang

2010-01-01

We study the effects of the strength of coupling between neurons on the spiking regularity and coherence in a complex network with randomly connected Hodgkin–Huxley neurons driven by colored noise. It is found that for the given topology realization and colored noise correlation time, there exists an optimal strength of coupling, at which the spiking regularity of the network reaches the best level. Moreover, when the temporal regularity reaches the best level, the spatial coherence of the system has already increased to a relatively high level. In addition, for the given number of neurons and noise correlation time, the values of average regularity and spatial coherence at the optimal strength of coupling are nearly independent of the topology realization. Furthermore, there exists an optimal value of colored noise correlation time at which the spiking regularity can reach its best level. These results may be helpful for understanding of the real neuron world. (cross-disciplinary physics and related areas of science and technology)

On non-linear dynamics and an optimal control synthesis of the action potential of membranes (ideal and non-ideal cases) of the Hodgkin-Huxley (HH) mathematical model

International Nuclear Information System (INIS)

Chavarette, Fabio Roberto; Balthazar, Jose Manoel; Rafikov, Marat; Hermini, Helder Anibal

2009-01-01

In this paper, we have studied the plasmatic membrane behavior using an electric circuit developed by Hodgkin and Huxley in 1952 and have dealt with the variation of the amount of time related to the potassium and sodium conductances in the squid axon. They developed differential equations for the propagation of electric signals; the dynamics of the Hodgkin-Huxley model have been extensively studied both from the view point of its their biological implications and as a test bed for numerical methods, which can be applied to more complex models. Recently, an irregular chaotic movement of the action potential of the membrane was observed for a number of techniques of control with the objective to stabilize the variation of this potential. This paper analyzes the non-linear dynamics of the Hodgkin-Huxley mathematical model, and we present some modifications in the governing equations of the system in order to make it a non-ideal one (taking into account that the energy source has a limited power supply). We also developed an optimal linear control design for the action potential of membranes. Here, we discuss the conditions that allow the use of control linear feedback for this kind of non-linear system.

Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.

Science.gov (United States)

Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

2017-01-01

In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

Deciphering Neural Codes of Memory during Sleep

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Chen, Zhe; Wilson, Matthew A.

2017-01-01

Memories of experiences are stored in the cerebral cortex. Sleep is critical for consolidating hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms on memory consolidation, and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. Here, we revisit important experimental findings on sleep-associated memory (i.e., neural activity patterns in sleep that reflect memory processing) and review computational approaches for analyzing sleep-associated neural codes (SANC). We focus on two analysis paradigms for sleep-associated memory, and propose a new unsupervised learning framework (“memory first, meaning later”) for unbiased assessment of SANC. PMID:28390699

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

Science.gov (United States)

Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R

2017-01-01

Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

John Dewey em visita ao jardim de Huxley: uma discussão sobre a teoria da evolução e as concepções éticas

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José Cláudio Morelli Matos

2010-05-01

Full Text Available http://dx.doi.org/10.5007/1677-2954.2010v9n2p199 John Dewey escreve um de seus primeiros trabalhos sobre ética em resposta às idéias de Thomas Huxley. Huxley, em seu artigo “Evolution and Ethics” (1894, defendia uma oposição entre os processos naturais e os processos éticos. A ética, segundo ele, é contrária aos princípios que se observa na natureza. Para Dewey, por outro lado, não há dualismo entre as regularidades naturais e as regularidades sociais. Dewey pretende, em seu artigo homônimo ao de Huxley (1898, reconstruir o sentido de noções fundamentais ao pensamento evolutivo como “adaptado”, “luta pela vida” e “seleção natural”. Dando início a uma versão evolucionista da reflexão ética, Dewey supõe a continuidade entre o mundo natural e o mundo dos valores. Suas conclusões indicam não só uma abordagem evolutiva das questões éticas, mas a proposição de valores e condutas que estejam em conformidade com o crescimento e a mudança constante nos ambientes e nas funções adaptativas, tais como se observa no mundo natural.

Canards Existence in FitzHugh-Nagumo and Hodgkin-Huxley Neuronal Models

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Jean-Marc Ginoux

2015-01-01

Full Text Available In a previous paper we have proposed a new method for proving the existence of “canard solutions” for three- and four-dimensional singularly perturbed systems with only one fast variable which improves the methods used until now. The aim of this work is to extend this method to the case of four-dimensional singularly perturbed systems with two slow and two fast variables. This method enables stating a unique generic condition for the existence of “canard solutions” for such four-dimensional singularly perturbed systems which is based on the stability of folded singularities (pseudo singular points in this case of the normalized slow dynamics deduced from a well-known property of linear algebra. This unique generic condition is identical to that provided in previous works. Application of this method to the famous coupled FitzHugh-Nagumo equations and to the Hodgkin-Huxley model enables showing the existence of “canard solutions” in such systems.

Hybrid B-Spline Collocation Method for Solving the Generalized Burgers-Fisher and Burgers-Huxley Equations

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Imtiaz Wasim

2018-01-01

Full Text Available In this study, we introduce a new numerical technique for solving nonlinear generalized Burgers-Fisher and Burgers-Huxley equations using hybrid B-spline collocation method. This technique is based on usual finite difference scheme and Crank-Nicolson method which are used to discretize the time derivative and spatial derivatives, respectively. Furthermore, hybrid B-spline function is utilized as interpolating functions in spatial dimension. The scheme is verified unconditionally stable using the Von Neumann (Fourier method. Several test problems are considered to check the accuracy of the proposed scheme. The numerical results are in good agreement with known exact solutions and the existing schemes in literature.

Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints.

Science.gov (United States)

Kostal, Lubomir; Kobayashi, Ryota

2015-10-01

Information theory quantifies the ultimate limits on reliable information transfer by means of the channel capacity. However, the channel capacity is known to be an asymptotic quantity, assuming unlimited metabolic cost and computational power. We investigate a single-compartment Hodgkin-Huxley type neuronal model under the spike-rate coding scheme and address how the metabolic cost and the decoding complexity affects the optimal information transmission. We find that the sub-threshold stimulation regime, although attaining the smallest capacity, allows for the most efficient balance between the information transmission and the metabolic cost. Furthermore, we determine post-synaptic firing rate histograms that are optimal from the information-theoretic point of view, which enables the comparison of our results with experimental data. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Delay-enhanced coherence of spiral waves in noisy Hodgkin-Huxley neuronal networks

International Nuclear Information System (INIS)

Wang Qingyun; Perc, Matjaz; Duan Zhisheng; Chen Guanrong

2008-01-01

We study the spatial dynamics of spiral waves in noisy Hodgkin-Huxley neuronal ensembles evoked by different information transmission delays and network topologies. In classical settings of coherence resonance the intensity of noise is fine-tuned so as to optimize the system's response. Here, we keep the noise intensity constant, and instead, vary the length of information transmission delay amongst coupled neurons. We show that there exists an intermediate transmission delay by which the spiral waves are optimally ordered, hence indicating the existence of delay-enhanced coherence of spatial dynamics in the examined system. Additionally, we examine the robustness of this phenomenon as the diffusive interaction topology changes towards the small-world type, and discover that shortcut links amongst distant neurons hinder the emergence of coherent spiral waves irrespective of transmission delay length. Presented results thus provide insights that could facilitate the understanding of information transmission delay on realistic neuronal networks

Control of bursting synchronization in networks of Hodgkin-Huxley-type neurons with chemical synapses.

Science.gov (United States)

Batista, C A S; Viana, R L; Ferrari, F A S; Lopes, S R; Batista, A M; Coninck, J C P

2013-04-01

Thermally sensitive neurons present bursting activity for certain temperature ranges, characterized by fast repetitive spiking of action potential followed by a short quiescent period. Synchronization of bursting activity is possible in networks of coupled neurons, and it is sometimes an undesirable feature. Control procedures can suppress totally or partially this collective behavior, with potential applications in deep-brain stimulation techniques. We investigate the control of bursting synchronization in small-world networks of Hodgkin-Huxley-type thermally sensitive neurons with chemical synapses through two different strategies. One is the application of an external time-periodic electrical signal and another consists of a time-delayed feedback signal. We consider the effectiveness of both strategies in terms of protocols of applications suitable to be applied by pacemakers.

A quantitative overview of biophysical forces impinging on neural function

International Nuclear Information System (INIS)

Mueller, Jerel K; Tyler, William J

2014-01-01

The fundamentals of neuronal membrane excitability are globally described using the Hodgkin-Huxley (HH) model. The HH model, however, does not account for a number of biophysical phenomena associated with action potentials or propagating nerve impulses. Physical mechanisms underlying these processes, such as reversible heat transfer and axonal swelling, have been compartmentalized and separately investigated to reveal neuronal activity is not solely influenced by electrical or biochemical factors. Instead, mechanical forces and thermodynamics also govern neuronal excitability and signaling. To advance our understanding of neuronal function and dysfunction, compartmentalized analyses of electrical, chemical, and mechanical processes need to be revaluated and integrated into more comprehensive theories. The present perspective is intended to provide a broad overview of biophysical forces that can influence neural function, but which have been traditionally underappreciated in neuroscience. Further, several examples where mechanical forces have been shown to exert their actions on nervous system development, signaling, and plasticity are highlighted to underscore their importance in sculpting neural function. By considering the collective actions of biophysical forces influencing neuronal activity, our working models can be expanded and new paradigms can be applied to the investigation and characterization of brain function and dysfunction. (topical review)

The ISI distribution of the stochastic Hodgkin-Huxley neuron.

Science.gov (United States)

Rowat, Peter F; Greenwood, Priscilla E

2014-01-01

The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all giving slightly different results. The obvious, and essential, question is: which method is the most accurate and which is most computationally efficient? Here we make a contribution to the answer. We compare interspike interval histograms from simulated data using four different approximate stochastic differential equation (SDE) models of the stochastic Hodgkin-Huxley neuron, as well as the exact Markov chain model simulated by the Gillespie algorithm. One of the recent SDE models is the same as the Kurtz approximation first published in 1978. All the models considered give similar ISI histograms over a wide range of deterministic and stochastic input. Three features of these histograms are an initial peak, followed by one or more bumps, and then an exponential tail. We explore how these features depend on deterministic input and on level of channel noise, and explain the results using the stochastic dynamics of the model. We conclude with a rough ranking of the four SDE models with respect to the similarity of their ISI histograms to the histogram of the exact Markov chain model.

Resonant Activation in a Stochastic Hodgkin-Huxley Model: Interplay between noise and suprathreshold driving effect

DEFF Research Database (Denmark)

Pankratova, Evgeniya; Polovinkin, A.V.; Mosekilde, Erik

2005-01-01

The paper considers an excitable Hodgkin-Huxley system subjected to a strong periodic forcing in the presence of random noise. The influence of the forcing frequency on the response of the system is examined in the realm of suprathreshold amplitudes. Our results confirm that the presence of noise...... a minimum as functions of the forcing frequency. The destructive influence of noise on the interspike interval can also be reduced. With driving signals in a certain frequency range, the system can show stable periodic spiking even for relatively large noise intensities. Outside this frequency range, noise...... of similar intensity destroys the regularity of the spike trains by suppressing the generation of some of the spikes....

Spiral Wave in Small-World Networks of Hodgkin-Huxley Neurons

International Nuclear Information System (INIS)

Ma Jun; Zhang Cairong; Yang Lijian; Wu Ying

2010-01-01

The effect of small-world connection and noise on the formation and transition of spiral wave in the networks of Hodgkin-Huxley neurons are investigated in detail. Some interesting results are found in our numerical studies. i) The quiescent neurons are activated to propagate electric signal to others by generating and developing spiral wave from spiral seed in small area. ii) A statistical factor is defined to describe the collective properties and phase transition induced by the topology of networks and noise. iii) Stable rotating spiral wave can be generated and keeps robust when the rewiring probability is below certain threshold, otherwise, spiral wave can not be developed from the spiral seed and spiral wave breakup occurs for a stable rotating spiral wave. iv) Gaussian white noise is introduced on the membrane of neurons to study the noise-induced phase transition on spiral wave in small-world networks of neurons. It is confirmed that Gaussian white noise plays active role in supporting and developing spiral wave in the networks of neurons, and appearance of smaller factor of synchronization indicates high possibility to induce spiral wave. (interdisciplinary physics and related areas of science and technology)

Deriving Behaviour of Hodgkin Huxley model with fever dynamics: A computational study

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Hasan ESKALEN

2017-10-01

Full Text Available A single neuron can be modeled by the set of differential equations. Hodgkin-Huxley (HH model, the one of the most famous neuron model, can be considered as a dynamical system with four independent variables. Here we studied to reduce the number of differential equation required for conductance based HH model under strong inhibitory noise. Exponential Integrate and Fire (EIF model, one independent variable, is used as a reduced model of HH model by using current-voltage (I-V curve of the original model. The required reduction parameters are determined from this curve. The behaviour of HH model and its reduced EIF (rEIF model are in good agreement in sub-threshold level. Above-threshold behaviour of reduced EIF model and original model compared in terms of threshold voltage under strong inhibitory noise. Our numerical simulations clearly show that sub-threshold behaviour of HH model perfectly reduced to rEIF model.

Suppression of anomalous synchronization and nonstationary behavior of neural network under small-world topology

Science.gov (United States)

Boaretto, B. R. R.; Budzinski, R. C.; Prado, T. L.; Kurths, J.; Lopes, S. R.

2018-05-01

It is known that neural networks under small-world topology can present anomalous synchronization and nonstationary behavior for weak coupling regimes. Here, we propose methods to suppress the anomalous synchronization and also to diminish the nonstationary behavior occurring in weakly coupled neural network under small-world topology. We consider a network of 2000 thermally sensitive identical neurons, based on the model of Hodgkin-Huxley in a small-world topology, with the probability of adding non local connection equal to p = 0 . 001. Based on experimental protocols to suppress anomalous synchronization, as well as nonstationary behavior of the neural network dynamics, we make use of (i) external stimulus (pulsed current); (ii) biologic parameters changing (neuron membrane conductance changes); and (iii) body temperature changes. Quantification analysis to evaluate phase synchronization makes use of the Kuramoto's order parameter, while recurrence quantification analysis, particularly the determinism, computed over the easily accessible mean field of network, the local field potential (LFP), is used to evaluate nonstationary states. We show that the methods proposed can control the anomalous synchronization and nonstationarity occurring for weak coupling parameter without any effect on the individual neuron dynamics, neither in the expected asymptotic synchronized states occurring for large values of the coupling parameter.

Identifying generalized Fitzhugh-Nagumo equation from a numerical solution of Hodgkin-Huxley model

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Nikola V. Georgiev

2003-01-01

Full Text Available An analytic time series in the form of numerical solution (in an appropriate finite time interval of the Hodgkin-Huxley current clamped (HHCC system of four differential equations, well known in the neurophysiology as an exact empirical model of excitation of a giant axon of Loligo, is presented. Then we search for a second-order differential equation of generalized Fitzhugh-Nagumo (GFN type, having as a solution the given single component (action potential of the numerical solution. The given time series is used as a basis for reconstructing orders, powers, and coefficients of the polynomial right-hand sides of GFN equation approximately governing the process of action potential. For this purpose, a new geometrical method for determining phase space dimension of the unknown dynamical system (GFN equation and a specific modification of least squares method for identifying unknown coefficients are developed and applied.

The Neural Foundations of Reaction and Action in Aversive Motivation.

Science.gov (United States)

Campese, Vincent D; Sears, Robert M; Moscarello, Justin M; Diaz-Mataix, Lorenzo; Cain, Christopher K; LeDoux, Joseph E

2016-01-01

Much of the early research in aversive learning concerned motivation and reinforcement in avoidance conditioning and related paradigms. When the field transitioned toward the focus on Pavlovian threat conditioning in isolation, this paved the way for the clear understanding of the psychological principles and neural and molecular mechanisms responsible for this type of learning and memory that has unfolded over recent decades. Currently, avoidance conditioning is being revisited, and with what has been learned about associative aversive learning, rapid progress is being made. We review, below, the literature on the neural substrates critical for learning in instrumental active avoidance tasks and conditioned aversive motivation.

Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model

NARCIS (Netherlands)

S.M. Bohte (Sander)

2012-01-01

htmlabstractNeural adaptation underlies the ability of neurons to maximize encoded informa- tion over a wide dynamic range of input stimuli. While adaptation is an intrinsic feature of neuronal models like the Hodgkin-Huxley model, the challenge is to in- tegrate adaptation in models of neural

A self-organizing state-space-model approach for parameter estimation in hodgkin-huxley-type models of single neurons.

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Dimitrios V Vavoulis

Full Text Available Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm, often in combination with a local search method (such as gradient descent in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a

Cutaneous vascular anomalies associated with neural tube defects: nomenclature and pathology revisited.

Science.gov (United States)

Maugans, Todd; Sheridan, Rachel M; Adams, Denise; Gupta, Anita

2011-07-01

Lumbosacral cutaneous vascular anomalies associated with neural tube defects are frequently described in the literature as "hemangiomas." The classification system for pediatric vascular anomalies developed by the International Society for the Study of Vascular Anomalies provides a framework to accurately diagnose these lesions. To apply this classification to vascular cutaneous anomalies overlying myelodysplasias. A retrospective analysis of patients with neural tube defects and lumbosacral cutaneous vascular lesions was performed. All eligible patients had detailed histopathologic analysis of skin and spinal cord/placode lesions. Clinical and radiologic features were analyzed. Conventional histology and GLUT-1 immunostaining were performed to differentiate infantile capillary hemangiomas from capillary vascular malformations. Ten cases with cutaneous lesions associated with neural tube defects were reviewed. Five lesions were diagnosed as infantile capillary hemangiomas based upon histology and positive GLUT-1 endothelial reactivity. These lesions had a strong association with dermal sinus tracts. No reoperations were required for residual intraspinal vascular lesions, and overlying cutaneous vascular anomalies involuted with time. The remaining 5 lesions were diagnosed as capillary malformations. These occurred with both open and closed neural tube defects, did not involute, and demonstrated enlargement and darkening due to vascular congestion. The International Society for the Study of Vascular Anomalies scheme should be used to describe the cutaneous vascular lesions associated with neural tube defects: infantile capillary hemangiomas and capillary malformations. We advocate that these lesions be described as "vascular anomalies" or "stains" pending accurate diagnosis by clinical, histological, and immunohistochemical evaluations.

Energy consumption in Hodgkin–Huxley type fast spiking neuron model exposed to an external electric field

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K. Usha

2016-09-01

Full Text Available This paper evaluates the change in metabolic energy required to maintain the signalling activity of neurons in the presence of an external electric field. We have analysed the Hodgkin–Huxley type conductance based fast spiking neuron model as electrical circuit by changing the frequency and amplitude of the applied electric field. The study has shown that, the presence of electric field increases the membrane potential, electrical energy supply and metabolic energy consumption. As the amplitude of applied electric field increases by keeping a constant frequency, the membrane potential increases and consequently the electrical energy supply and metabolic energy consumption increases. On increasing the frequency of the applied field, the peak value of membrane potential after depolarization gradually decreases as a result electrical energy supply decreases which results in a lower rate of hydrolysis of ATP molecules.

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

Science.gov (United States)

Zenke, Friedemann; Ganguli, Surya

2018-04-13

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Studies of phase return map and symbolic dynamics in a periodically driven Hodgkin—Huxley neuron

International Nuclear Information System (INIS)

Ding Jiong; Zhang Hong; Tong Qin-Ye; Chen Zhuo

2014-01-01

How neuronal spike trains encode external information is a hot topic in neurodynamics studies. In this paper, we investigate the dynamical states of the Hodgkin—Huxley neuron under periodic forcing. Depending on the parameters of the stimulus, the neuron exhibits periodic, quasiperiodic and chaotic spike trains. In order to analyze these spike trains quantitatively, we use the phase return map to describe the dynamical behavior on a one-dimensional (1D) map. According to the monotonicity or discontinuous point of the 1D map, the spike trains are transformed into symbolic sequences by implementing a coarse-grained algorithm — symbolic dynamics. Based on the ordering rules of symbolic dynamics, the parameters of the external stimulus can be measured in high resolution with finite length symbolic sequences. A reasonable explanation for why the nervous system can discriminate or cognize the small change of the external signals in a short time is also presented. (general)

Predicting the past: ancient eclipses and Airy, Newcomb, and Huxley on the authority of science.

Science.gov (United States)

Stanley, Matthew

2012-06-01

Greek historical accounts of ancient eclipses were an important, if peculiar, focus of scientific attention in the nineteenth century. Victorian-era astronomers tried to correct the classical histories using scientific methods, then used those histories as data with which to calibrate their lunar theories, then rejected the histories as having any relevance at all. The specific dating of these eclipses--apparently a simple exercise in celestial mechanics--became bound up with tensions between scientific and humanistic approaches to the past as well as with wider social debates over the power and authority of science in general. The major figures discussed here, including G. B. Airy, Simon Newcomb, and T. H. Huxley, argued that the critical question was whether science could speak authoritatively about the past. To them, the ability of science to talk about the past indicated its power to talk about the future; it was also the fulcrum of fierce boundary disputes among science, history, and religion.

Anti-electromagnetic interference analysis of equivalent circuit of ion channel based on the Hodgkin-Huxley model

International Nuclear Information System (INIS)

Chu, J; Chang, X L; Zhao, M; Man, M H; Wei, M; Yuan, L

2013-01-01

With the continuous improvement of circuit integration and working clock frequency in the electronic system, it is increasingly easy for the system to be affected by electromagnetic waves, and electromagnetic susceptibility and vulnerability become more severe. However, living beings in nature have shown extraordinary compatibility, immunity and adaptability to the electromagnetism at the same time. In addition, the ion channel on the neuron cytomembrane is a typical representation of b ioelectrical immunity . So the Hodgkin-Huxley circuit model with one capacitor in parallel with some power supplies and resistors was adopted to simulate the ion channel on the neuron cytomembrane. Through analysis, the circuit model can be used to simulate some electrical characteristics of biological neuron cells, and then acquire a certain level of anti-electromagnetic interference ability. This method will be useful for improving the reliability, compatibility and anti-interference capability of the electronic system in the complicated electromagnetic environment.

Challenging normative orthodoxies in depression: Huxley's Utopia or Dante's Inferno?

Science.gov (United States)

Cutcliffe, John R; Lakeman, Richard

2010-04-01

Although there appears to be a widespread consensus that depression is a ubiquitous human experience, definitions of depression, its prevalence, and how mental health services respond to it have changed significantly over time, particularly during recent decades. Epistemological limitations notwithstanding, it is now estimated that approximately 121 million people experience depression. At the same time, it should be acknowledged that the last two decades have seen the widespread acceptance of depression as a chemical imbalance and a massive corresponding increase in the prescription of antidepressants, most notably of selective serotonin reuptake inhibitors (SSRIs). However, questions have been raised about the effectiveness and iatrogenic side effects of antidepressants; related questions have also been asked about whose interests are served by the marketing and sales of these drugs. Accordingly, this article attempts to problematize the normative orthodoxy concerning depression and creates a "space" in which an alternative can be articulated and enacted. In so doing, the article finds that the search for a world where the automatic response to depression is a pharmacological intervention not only ignores the use of alternative efficacious treatment options but may also inhibit the persons' chance to explore the meaning of their experience and thus prevent people from individual growth and personal development. Interestingly, in worlds analogous to this pharmacologically induced depression-free state, such as utopias like that in Huxley's Brave New World, no "properly conditioned citizen" is depressed or suicidal. Yet, in the same Brave New World, no one is free to suffer, to be different, or crucially, to be independent. Copyright 2010 Elsevier Inc. All rights reserved.

Saddle Slow Manifolds and Canard Orbits in [Formula: see text] and Application to the Full Hodgkin-Huxley Model.

Science.gov (United States)

Hasan, Cris R; Krauskopf, Bernd; Osinga, Hinke M

2018-04-19

Many physiological phenomena have the property that some variables evolve much faster than others. For example, neuron models typically involve observable differences in time scales. The Hodgkin-Huxley model is well known for explaining the ionic mechanism that generates the action potential in the squid giant axon. Rubin and Wechselberger (Biol. Cybern. 97:5-32, 2007) nondimensionalized this model and obtained a singularly perturbed system with two fast, two slow variables, and an explicit time-scale ratio ε. The dynamics of this system are complex and feature periodic orbits with a series of action potentials separated by small-amplitude oscillations (SAOs); also referred to as mixed-mode oscillations (MMOs). The slow dynamics of this system are organized by two-dimensional locally invariant manifolds called slow manifolds which can be either attracting or of saddle type.In this paper, we introduce a general approach for computing two-dimensional saddle slow manifolds and their stable and unstable fast manifolds. We also develop a technique for detecting and continuing associated canard orbits, which arise from the interaction between attracting and saddle slow manifolds, and provide a mechanism for the organization of SAOs in [Formula: see text]. We first test our approach with an extended four-dimensional normal form of a folded node. Our results demonstrate that our computations give reliable approximations of slow manifolds and canard orbits of this model. Our computational approach is then utilized to investigate the role of saddle slow manifolds and associated canard orbits of the full Hodgkin-Huxley model in organizing MMOs and determining the firing rates of action potentials. For ε sufficiently large, canard orbits are arranged in pairs of twin canard orbits with the same number of SAOs. We illustrate how twin canard orbits partition the attracting slow manifold into a number of ribbons that play the role of sectors of rotations. The upshot is that we

eWOM, Revisit Intention, Destination Trust and Gender

OpenAIRE

Abubakar, Abubakar Mohammed; Ilkan, Mustafa; Al-Tal, Raad Meshall; Eluwole, Kayode

2017-01-01

This article investigates the impact of eWOM on intention to revisit and destination trust, and the moderating role of gender in medical tourism industry. Result from structural equation modeling (n=240) suggests the following: (1) that eWOM influences intention to revisit and destination trust; (2) that destination trust influences intention to revisit; (3) that the impact of eWOM on intention to revisit is about 1.3 times higher in men; (4) that the impact of eWOM on destination trust is ab...

Dynamical mean-field theory of noisy spiking neuron ensembles: Application to the Hodgkin-Huxley model

International Nuclear Information System (INIS)

Hasegawa, Hideo

2003-01-01

A dynamical mean-field approximation (DMA) previously proposed by the present author [H. Hasegawa, Phys. Rev E 67, 041903 (2003)] has been extended to ensembles described by a general noisy spiking neuron model. Ensembles of N-unit neurons, each of which is expressed by coupled K-dimensional differential equations (DEs), are assumed to be subject to spatially correlated white noises. The original KN-dimensional stochastic DEs have been replaced by K(K+2)-dimensional deterministic DEs expressed in terms of means and the second-order moments of local and global variables: the fourth-order contributions are taken into account by the Gaussian decoupling approximation. Our DMA has been applied to an ensemble of Hodgkin-Huxley (HH) neurons (K=4), for which effects of the noise, the coupling strength, and the ensemble size on the response to a single-spike input have been investigated. Numerical results calculated by the DMA theory are in good agreement with those obtained by direct simulations, although the former computation is about a thousand times faster than the latter for a typical HH neuron ensemble with N=100

Special issue on Computational Neuroscience - PREFACE

DEFF Research Database (Denmark)

Ditlevsen, Susanne; Lansky, Petr

2014-01-01

This Special Issue of Mathematical Biosciences and Engineering contains ten selected papers presented at the Neural Coding 2012 workshop. Neuroscience is traditionally very close to mathematics which stems from the famous theoretical work of McCulloch--Pitts and Hodgkin--Huxley in the middle...

Diprosopia revisited in light of the recognized role of neural crest cells in facial development.

Science.gov (United States)

Carles, D; Weichhold, W; Alberti, E M; Léger, F; Pigeau, F; Horovitz, J

1995-01-01

The aim of this study is to compare the theory of embryogenesis of the face with human diprosopia. This peculiar form of conjoined twinning is of great interest because 1) only the facial structures are duplicated and 2) almost all cases have a rather monomorphic pattern. The hypothesis is that an initial duplication of the notochord leads to two neural plates and subsequently duplicated neural crests. In those conditions, derivatives of the neural crests will be partially or totally duplicated; therefore, in diprosopia, the duplicated facial structures would be considered to be neural crest derivatives. If these structures are identical to those that are experimentally demonstrated to be neural crest derivatives in animals, these findings are an argument to apply this theory of facial embryogenesis in man. Serial horizontal sections of the face of two diprosopic fetuses (11 and 21 weeks gestation) were studied macro- and microscopically to determine the external and internal structures that are duplicated. Complete postmortem examination was performed in search for additional malformations. The face of both fetuses showed a very similar morphologic pattern with duplication of ocular, nasal, and buccal structures. The nasal fossae and the anterior part of the tongue were also duplicated, albeit the posterior part and the pharyngolaryngeal structures were unique. Additional facial clefts were present in both fetuses. Extrafacial anomalies were represented by a craniorachischisis, two fused vertebral columns and, in the older fetus, by a complex cardiac malformation morphologically identical to malformations induced by removal or grafting of additional cardiac neural crest cells in animals. These pathological findings could identify the facial structures that are neural crest derivatives in man. They are similar to those experimentally demonstrated to be neural crest derivatives in animals. In this respect, diprosopia could be considered as the end of a spectrum

Clique-Based Neural Associative Memories with Local Coding and Precoding.

Science.gov (United States)

Mofrad, Asieh Abolpour; Parker, Matthew G; Ferdosi, Zahra; Tadayon, Mohammad H

2016-08-01

Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons

International Nuclear Information System (INIS)

Yoshioka, Masahiko

2002-01-01

We study associative memory neural networks of the Hodgkin-Huxley type of spiking neurons in which multiple periodic spatiotemporal patterns of spike timing are memorized as limit-cycle-type attractors. In encoding the spatiotemporal patterns, we assume the spike-timing-dependent synaptic plasticity with the asymmetric time window. Analysis for periodic solution of retrieval state reveals that if the area of the negative part of the time window is equivalent to the positive part, then crosstalk among encoded patterns vanishes. Phase transition due to the loss of the stability of periodic solution is observed when we assume fast α function for direct interaction among neurons. In order to evaluate the critical point of this phase transition, we employ Floquet theory in which the stability problem of the infinite number of spiking neurons interacting with α function is reduced to the eigenvalue problem with the finite size of matrix. Numerical integration of the single-body dynamics yields the explicit value of the matrix, which enables us to determine the critical point of the phase transition with a high degree of precision

Lakatos Revisited.

Science.gov (United States)

Court, Deborah

1999-01-01

Revisits and reviews Imre Lakatos' ideas on "Falsification and the Methodology of Scientific Research Programmes." Suggests that Lakatos' framework offers an insightful way of looking at the relationship between theory and research that is relevant not only for evaluating research programs in theoretical physics, but in the social…

A study of quantum mechanical probabilities in the classical Hodgkin-Huxley model.

Science.gov (United States)

Moradi, N; Scholkmann, F; Salari, V

2015-03-01

The Hodgkin-Huxley (HH) model is a powerful model to explain different aspects of spike generation in excitable cells. However, the HH model was proposed in 1952 when the real structure of the ion channel was unknown. It is now common knowledge that in many ion-channel proteins the flow of ions through the pore is governed by a gate, comprising a so-called "selectivity filter" inside the ion channel, which can be controlled by electrical interactions. The selectivity filter (SF) is believed to be responsible for the selection and fast conduction of particular ions across the membrane of an excitable cell. Other (generally larger) parts of the molecule such as the pore-domain gate control the access of ions to the channel protein. In fact, two types of gates are considered here for ion channels: the "external gate", which is the voltage sensitive gate, and the "internal gate" which is the selectivity filter gate (SFG). Some quantum effects are expected in the SFG due to its small dimensions, which may play an important role in the operation of an ion channel. Here, we examine parameters in a generalized model of HH to see whether any parameter affects the spike generation. Our results indicate that the previously suggested semi-quantum-classical equation proposed by Bernroider and Summhammer (BS) agrees strongly with the HH equation under different conditions and may even provide a better explanation in some cases. We conclude that the BS model can refine the classical HH model substantially.

Oxidative phosphorylation revisited

DEFF Research Database (Denmark)

Nath, Sunil; Villadsen, John

2015-01-01

The fundamentals of oxidative phosphorylation and photophosphorylation are revisited. New experimental data on the involvement of succinate and malate anions respectively in oxidative phosphorylation and photophosphorylation are presented. These new data offer a novel molecular mechanistic...

Circular revisit orbits design for responsive mission over a single target

Science.gov (United States)

Li, Taibo; Xiang, Junhua; Wang, Zhaokui; Zhang, Yulin

2016-10-01

The responsive orbits play a key role in addressing the mission of Operationally Responsive Space (ORS) because of their capabilities. These capabilities are usually focused on supporting specific targets as opposed to providing global coverage. One subtype of responsive orbits is repeat coverage orbit which is nearly circular in most remote sensing applications. This paper deals with a special kind of repeating ground track orbit, referred to as circular revisit orbit. Different from traditional repeat coverage orbits, a satellite on circular revisit orbit can visit a target site at both the ascending and descending stages in one revisit cycle. This typology of trajectory allows a halving of the traditional revisit time and does a favor to get useful information for responsive applications. However the previous reported numerical methods in some references often cost lots of computation or fail to obtain such orbits. To overcome this difficulty, an analytical method to determine the existence conditions of the solutions to revisit orbits is presented in this paper. To this end, the mathematical model of circular revisit orbit is established under the central gravity model and the J2 perturbation. A constraint function of the circular revisit orbit is introduced, and the monotonicity of that function has been studied. The existent conditions and the number of such orbits are naturally worked out. Taking the launch cost into consideration, optimal design model of circular revisit orbit is established to achieve a best orbit which visits a target twice a day in the morning and in the afternoon respectively for several days. The result shows that it is effective to apply circular revisit orbits in responsive application such as reconnoiter of natural disaster.

Revisiting Okun's Relationship

NARCIS (Netherlands)

Dixon, R.; Lim, G.C.; van Ours, Jan

2016-01-01

Our paper revisits Okun's relationship between observed unemployment rates and output gaps. We include in the relationship the effect of labour market institutions as well as age and gender effects. Our empirical analysis is based on 20 OECD countries over the period 1985-2013. We find that the

Numerical simulations of piecewise deterministic Markov processes with an application to the stochastic Hodgkin-Huxley model

Science.gov (United States)

Ding, Shaojie; Qian, Min; Qian, Hong; Zhang, Xuejuan

2016-12-01

The stochastic Hodgkin-Huxley model is one of the best-known examples of piecewise deterministic Markov processes (PDMPs), in which the electrical potential across a cell membrane, V(t), is coupled with a mesoscopic Markov jump process representing the stochastic opening and closing of ion channels embedded in the membrane. The rates of the channel kinetics, in turn, are voltage-dependent. Due to this interdependence, an accurate and efficient sampling of the time evolution of the hybrid stochastic systems has been challenging. The current exact simulation methods require solving a voltage-dependent hitting time problem for multiple path-dependent intensity functions with random thresholds. This paper proposes a simulation algorithm that approximates an alternative representation of the exact solution by fitting the log-survival function of the inter-jump dwell time, H(t), with a piecewise linear one. The latter uses interpolation points that are chosen according to the time evolution of the H(t), as the numerical solution to the coupled ordinary differential equations of V(t) and H(t). This computational method can be applied to all PDMPs. Pathwise convergence of the approximated sample trajectories to the exact solution is proven, and error estimates are provided. Comparison with a previous algorithm that is based on piecewise constant approximation is also presented.

Teaching and learning the Hodgkin-Huxley model based on software developed in NEURON's programming language hoc.

Science.gov (United States)

Hernández, Oscar E; Zurek, Eduardo E

2013-05-15

We present a software tool called SENB, which allows the geometric and biophysical neuronal properties in a simple computational model of a Hodgkin-Huxley (HH) axon to be changed. The aim of this work is to develop a didactic and easy-to-use computational tool in the NEURON simulation environment, which allows graphical visualization of both the passive and active conduction parameters and the geometric characteristics of a cylindrical axon with HH properties. The SENB software offers several advantages for teaching and learning electrophysiology. First, SENB offers ease and flexibility in determining the number of stimuli. Second, SENB allows immediate and simultaneous visualization, in the same window and time frame, of the evolution of the electrophysiological variables. Third, SENB calculates parameters such as time and space constants, stimuli frequency, cellular area and volume, sodium and potassium equilibrium potentials, and propagation velocity of the action potentials. Furthermore, it allows the user to see all this information immediately in the main window. Finally, with just one click SENB can save an image of the main window as evidence. The SENB software is didactic and versatile, and can be used to improve and facilitate the teaching and learning of the underlying mechanisms in the electrical activity of an axon using the biophysical properties of the squid giant axon.

Standing and travelling waves in a spherical brain model: The Nunez model revisited

Science.gov (United States)

Visser, S.; Nicks, R.; Faugeras, O.; Coombes, S.

2017-06-01

The Nunez model for the generation of electroencephalogram (EEG) signals is naturally described as a neural field model on a sphere with space-dependent delays. For simplicity, dynamical realisations of this model either as a damped wave equation or an integro-differential equation, have typically been studied in idealised one dimensional or planar settings. Here we revisit the original Nunez model to specifically address the role of spherical topology on spatio-temporal pattern generation. We do this using a mixture of Turing instability analysis, symmetric bifurcation theory, centre manifold reduction and direct simulations with a bespoke numerical scheme. In particular we examine standing and travelling wave solutions using normal form computation of primary and secondary bifurcations from a steady state. Interestingly, we observe spatio-temporal patterns which have counterparts seen in the EEG patterns of both epileptic and schizophrenic brain conditions.

Tourists' perceptions and intention to revisit Norway

OpenAIRE

Lazar, Ana Florina; Komolikova-Blindheim, Galyna

2016-01-01

Purpose - The overall purpose of this study is to explore tourists' perceptions and their intention to revisit Norway. The aim is to find out what are the factors that drive the overall satisfaction, the willingness to recommend and the revisit intention of international tourists that spend their holiday in Norway. Design-Method-Approach - the Theory of Planned Behavior (Ajzen 1991), is used as a framework to investigate tourists' intention and behavior towards Norway as destination. The o...

Predictors and Outcomes of Revisits in Older Adults Discharged from the Emergency Department.

Science.gov (United States)

de Gelder, Jelle; Lucke, Jacinta A; de Groot, Bas; Fogteloo, Anne J; Anten, Sander; Heringhaus, Christian; Dekkers, Olaf M; Blauw, Gerard J; Mooijaart, Simon P

2018-04-01

To study predictors of emergency department (ED) revisits and the association between ED revisits and 90-day functional decline or mortality. Multicenter cohort study. One academic and two regional Dutch hospitals. Older adults discharged from the ED (N=1,093). At baseline, data on demographic characteristics, illness severity, and geriatric parameters (cognition, functional capacity) were collected. All participants were prospectively followed for an unplanned revisit within 30 days and for functional decline and mortality 90 days after the initial visit. The median age was 79 (interquartile range 74-84), and 114 participants (10.4%) had an ED revisit within 30 days of discharge. Age (hazard ratio (HR)=0.96, 95% confidence interval (CI)=0.92-0.99), male sex (HR=1.61, 95% CI=1.05-2.45), polypharmacy (HR=2.06, 95% CI=1.34-3.16), and cognitive impairment (HR=1.71, 95% CI=1.02-2.88) were independent predictors of a 30-day ED revisit. The area under the receiver operating characteristic curve to predict an ED revisit was 0.65 (95% CI=0.60-0.70). In a propensity score-matched analysis, individuals with an ED revisit were at higher risk (odds ratio=1.99 95% CI=1.06-3.71) of functional decline or mortality. Age, male sex, polypharmacy, and cognitive impairment were independent predictors of a 30-day ED revisit, but no useful clinical prediction model could be developed. However, an early ED revisit is a strong new predictor of adverse outcomes in older adults. © 2018 The Authors. The Journal of the American Geriatrics Society published by Wiley Periodicals, Inc. on behalf of The American Geriatrics Society.

Sensemaking Revisited

DEFF Research Database (Denmark)

Holt, Robin; Cornelissen, Joep

2014-01-01

We critique and extend theory on organizational sensemaking around three themes. First, we investigate sense arising non-productively and so beyond any instrumental relationship with things; second, we consider how sense is experienced through mood as well as our cognitive skills of manipulation ...... research by revisiting Weick’s seminal reading of Norman Maclean’s book surrounding the tragic events of a 1949 forest fire at Mann Gulch, USA....

Global robust stability of delayed neural networks: Estimating upper limit of norm of delayed connection weight matrix

International Nuclear Information System (INIS)

Singh, Vimal

2007-01-01

The question of estimating the upper limit of -parallel B -parallel 2 , which is a key step in some recently reported global robust stability criteria for delayed neural networks, is revisited ( B denotes the delayed connection weight matrix). Recently, Cao, Huang, and Qu have given an estimate of the upper limit of -parallel B -parallel 2 . In the present paper, an alternative estimate of the upper limit of -parallel B -parallel 2 is highlighted. It is shown that the alternative estimate may yield some new global robust stability results

EDITORIAL: Special issue on applied neurodynamics: from neural dynamics to neural engineering Special issue on applied neurodynamics: from neural dynamics to neural engineering

Science.gov (United States)

Chiel, Hillel J.; Thomas, Peter J.

2011-12-01

, the sun, earth and moon) proved to be far more difficult. In the late nineteenth century, Poincaré made significant progress on this problem, introducing a geometric method of reasoning about solutions to differential equations (Diacu and Holmes 1996). This work had a powerful impact on mathematicians and physicists, and also began to influence biology. In his 1925 book, based on his work starting in 1907, and that of others, Lotka used nonlinear differential equations and concepts from dynamical systems theory to analyze a wide variety of biological problems, including oscillations in the numbers of predators and prey (Lotka 1925). Although little was known in detail about the function of the nervous system, Lotka concluded his book with speculations about consciousness and the implications this might have for creating a mathematical formulation of biological systems. Much experimental work in the 1930s and 1940s focused on the biophysical mechanisms of excitability in neural tissue, and Rashevsky and others continued to apply tools and concepts from nonlinear dynamical systems theory as a means of providing a more general framework for understanding these results (Rashevsky 1960, Landahl and Podolsky 1949). The publication of Hodgkin and Huxley's classic quantitative model of the action potential in 1952 created a new impetus for these studies (Hodgkin and Huxley 1952). In 1955, FitzHugh published an important paper that summarized much of the earlier literature, and used concepts from phase plane analysis such as asymptotic stability, saddle points, separatrices and the role of noise to provide a deeper theoretical and conceptual understanding of threshold phenomena (Fitzhugh 1955, Izhikevich and FitzHugh 2006). The Fitzhugh-Nagumo equations constituted an important two-dimensional simplification of the four-dimensional Hodgkin and Huxley equations, and gave rise to an extensive literature of analysis. Many of the papers in this special issue build on tools

A Structural Equation Model of Risk Perception of Rockfall for Revisit Intention

OpenAIRE

Ya-Fen Lee; Yun-Yao Chi

2014-01-01

The study aims to explore the relationship between risk perception of rockfall and revisit intention using a Structural Equation Modeling (SEM) analysis. A total of 573 valid questionnaires are collected from travelers to Taroko National Park, Taiwan. The findings show the majority of travelers have the medium perception of rockfall risk, and are willing to revisit the Taroko National Park. The revisit intention to Taroko National Park is influenced by hazardous preferences, willingness-to-pa...

Pregnancy as protest in interwar British women's writing: an antecedent alternative to Aldous Huxley's Brave New World.

Science.gov (United States)

Bigman, Fran

2016-12-01

Accounts that take Aldous Huxley's Brave New World (1932) as representative of interwar reproductive dystopia fail to recognise that the novel expresses both an interest and an anxiety about the possibility of new reproductive technologies to transform sex, gender, and the family that were widely shared by writers in different genres and perhaps expressed best by those likely to be most affected: women. This article explores three earlier works-Charlotte Haldane's Man's World (1926), Vera Brittain's Halcyon, or the Future of Monogamy (1929), and Naomi Mitchison's Comments on Birth Control (1930)-in which pregnancy, instead of figuring as illness or debility, becomes a form of resistance to the status quo. These works engage with biomedicine, however, rather than abjuring it. Through a reading of these works, this article argues that the intersection of medical humanities and science fiction (SF) can enrich both: medical humanities can push SF to go beyond the canon, and SF can challenge any characterisation of literature in the medical humanities as purely fantastical by demonstrating how it responds to the hopes and anxieties of a particular time. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

The Faraday effect revisited

DEFF Research Database (Denmark)

Cornean, Horia; Nenciu, Gheorghe

2009-01-01

This paper is the second in a series revisiting the (effect of) Faraday rotation. We formulate and prove the thermodynamic limit for the transverse electric conductivity of Bloch electrons, as well as for the Verdet constant. The main mathematical tool is a regularized magnetic and geometric...

Remembered Experiences and Revisit Intentions

DEFF Research Database (Denmark)

Barnes, Stuart; Mattsson, Jan; Sørensen, Flemming

2016-01-01

Tourism is an experience-intensive sector in which customers seek and pay for experiences above everything else. Remembering past tourism experiences is also crucial for an understanding of the present, including the predicted behaviours of visitors to tourist destinations. We adopt a longitudinal...... approach to memory data collection from psychological science, which has the potential to contribute to our understanding of tourist behaviour. In this study, we examine the impact of remembered tourist experiences in a safari park. In particular, using matched survey data collected longitudinally and PLS...... path modelling, we examine the impact of positive affect tourist experiences on the development of revisit intentions. We find that longer-term remembered experiences have the strongest impact on revisit intentions, more so than predicted or immediate memory after an event. We also find that remembered...

Revisiting the Okun relationship

NARCIS (Netherlands)

Dixon, R. (Robert); Lim, G.C.; J.C. van Ours (Jan)

2017-01-01

textabstractOur article revisits the Okun relationship between observed unemployment rates and output gaps. We include in the relationship the effect of labour market institutions as well as age and gender effects. Our empirical analysis is based on 20 OECD countries over the period 1985–2013. We

Bounded Intention Planning Revisited

OpenAIRE

Sievers Silvan; Wehrle Martin; Helmert Malte

2014-01-01

Bounded intention planning provides a pruning technique for optimal planning that has been proposed several years ago. In addition partial order reduction techniques based on stubborn sets have recently been investigated for this purpose. In this paper we revisit bounded intention planning in the view of stubborn sets.

Revisiting Nursing Research in Nigeria

African Journals Online (AJOL)

2016-08-18

Aug 18, 2016 ... health care research, it is therefore pertinent to revisit the state of nursing research in the country. .... platforms, updated libraries with electronic resource ... benchmarks for developing countries of 26%, [17] the amount is still ...

Time functions revisited

Science.gov (United States)

Fathi, Albert

2015-07-01

In this paper we revisit our joint work with Antonio Siconolfi on time functions. We will give a brief introduction to the subject. We will then show how to construct a Lipschitz time function in a simplified setting. We will end with a new result showing that the Aubry set is not an artifact of our proof of existence of time functions for stably causal manifolds.

Revisiting tourist behavior via destination brand worldness

Directory of Open Access Journals (Sweden)

Murat Kayak

2016-11-01

Full Text Available Taking tourists’ perspective rather than destination offerings as its core concept, this study introduces “perceived destination brand worldness” as a variable. Perceived destination brand worldness is defined as the positive perception that a tourist has of a country that is visited by tourists from all over the world. Then, the relationship between perceived destination brand worldness and intention to revisit is analyzed using partial least squares regression. This empirical study selects Taiwanese tourists as its sample, and the results show that perceived destination brand worldness is a direct predictor of intention to revisit. In light of these empirical findings and observations, practical and theoretical implications are discussed.

Teaching and learning the Hodgkin-Huxley model based on software developed in NEURON’s programming language hoc

Science.gov (United States)

2013-01-01

Background We present a software tool called SENB, which allows the geometric and biophysical neuronal properties in a simple computational model of a Hodgkin-Huxley (HH) axon to be changed. The aim of this work is to develop a didactic and easy-to-use computational tool in the NEURON simulation environment, which allows graphical visualization of both the passive and active conduction parameters and the geometric characteristics of a cylindrical axon with HH properties. Results The SENB software offers several advantages for teaching and learning electrophysiology. First, SENB offers ease and flexibility in determining the number of stimuli. Second, SENB allows immediate and simultaneous visualization, in the same window and time frame, of the evolution of the electrophysiological variables. Third, SENB calculates parameters such as time and space constants, stimuli frequency, cellular area and volume, sodium and potassium equilibrium potentials, and propagation velocity of the action potentials. Furthermore, it allows the user to see all this information immediately in the main window. Finally, with just one click SENB can save an image of the main window as evidence. Conclusions The SENB software is didactic and versatile, and can be used to improve and facilitate the teaching and learning of the underlying mechanisms in the electrical activity of an axon using the biophysical properties of the squid giant axon. PMID:23675833

Dynamics from seconds to hours in Hodgkin-Huxley model with time-dependent ion concentrations and buffer reservoirs.

Directory of Open Access Journals (Sweden)

Niklas Hübel

2014-12-01

Full Text Available The classical Hodgkin-Huxley (HH model neglects the time-dependence of ion concentrations in spiking dynamics. The dynamics is therefore limited to a time scale of milliseconds, which is determined by the membrane capacitance multiplied by the resistance of the ion channels, and by the gating time constants. We study slow dynamics in an extended HH framework that includes time-dependent ion concentrations, pumps, and buffers. Fluxes across the neuronal membrane change intra- and extracellular ion concentrations, whereby the latter can also change through contact to reservoirs in the surroundings. Ion gain and loss of the system is identified as a bifurcation parameter whose essential importance was not realized in earlier studies. Our systematic study of the bifurcation structure and thus the phase space structure helps to understand activation and inhibition of a new excitability in ion homeostasis which emerges in such extended models. Also modulatory mechanisms that regulate the spiking rate can be explained by bifurcations. The dynamics on three distinct slow times scales is determined by the cell volume-to-surface-area ratio and the membrane permeability (seconds, the buffer time constants (tens of seconds, and the slower backward buffering (minutes to hours. The modulatory dynamics and the newly emerging excitable dynamics corresponds to pathological conditions observed in epileptiform burst activity, and spreading depression in migraine aura and stroke, respectively.

Dynamics from seconds to hours in Hodgkin-Huxley model with time-dependent ion concentrations and buffer reservoirs.

Science.gov (United States)

Hübel, Niklas; Dahlem, Markus A

2014-12-01

The classical Hodgkin-Huxley (HH) model neglects the time-dependence of ion concentrations in spiking dynamics. The dynamics is therefore limited to a time scale of milliseconds, which is determined by the membrane capacitance multiplied by the resistance of the ion channels, and by the gating time constants. We study slow dynamics in an extended HH framework that includes time-dependent ion concentrations, pumps, and buffers. Fluxes across the neuronal membrane change intra- and extracellular ion concentrations, whereby the latter can also change through contact to reservoirs in the surroundings. Ion gain and loss of the system is identified as a bifurcation parameter whose essential importance was not realized in earlier studies. Our systematic study of the bifurcation structure and thus the phase space structure helps to understand activation and inhibition of a new excitability in ion homeostasis which emerges in such extended models. Also modulatory mechanisms that regulate the spiking rate can be explained by bifurcations. The dynamics on three distinct slow times scales is determined by the cell volume-to-surface-area ratio and the membrane permeability (seconds), the buffer time constants (tens of seconds), and the slower backward buffering (minutes to hours). The modulatory dynamics and the newly emerging excitable dynamics corresponds to pathological conditions observed in epileptiform burst activity, and spreading depression in migraine aura and stroke, respectively.

Revisiting Inter-Genre Similarity

DEFF Research Database (Denmark)

Sturm, Bob L.; Gouyon, Fabien

2013-01-01

We revisit the idea of ``inter-genre similarity'' (IGS) for machine learning in general, and music genre recognition in particular. We show analytically that the probability of error for IGS is higher than naive Bayes classification with zero-one loss (NB). We show empirically that IGS does...... not perform well, even for data that satisfies all its assumptions....

A Hydrostatic Paradox Revisited

Science.gov (United States)

Ganci, Salvatore

2012-01-01

This paper revisits a well-known hydrostatic paradox, observed when turning upside down a glass partially filled with water and covered with a sheet of light material. The phenomenon is studied in its most general form by including the mass of the cover. A historical survey of this experiment shows that a common misunderstanding of the phenomenon…

THE INFLUENCE OF DESTINATION IMAGE AND TOURIST SATISFACTION TOWARD REVISIT INTENTION OF SETU BABAKAN BETAWI CULTURAL VILLAGE

OpenAIRE

Wibowo, Setyo Ferry; Sazali, Adnan; Kresnamurti R. P., Agung

2016-01-01

The purpose of this research are: 1) To find out the description of destination image, tourist satisfaction, and revisit intention of Betawi cultural village Setu Babakan, 2) test empirically the influence of destination image toward revisit intention of Betawi cultural village Setu Babakan, 3) test empirically the influence of tourist satisfaction toward revisit intention of Betawi cultural village Setu Babakan, 4) test empirically the influence of destination image toward revisit intention ...

Seven Issues, Revisited

OpenAIRE

Whitehead, Jim; De Bra, Paul; Grønbæk, Kaj; Larsen, Deena; Legget, John; schraefel, monica m.c.

2002-01-01

It has been 15 years since the original presentation by Frank Halasz at Hypertext'87 on seven issues for the next generation of hypertext systems. These issues are: Search and Query Composites Virtual Structures Computation in/over hypertext network Versioning Collaborative Work Extensibility and Tailorability Since that time, these issues have formed the nucleus of multiple research agendas within the Hypertext community. Befitting this direction-setting role, the issues have been revisited ...

'Felson Signs' revisited

International Nuclear Information System (INIS)

George, Phiji P.; Irodi, Aparna; Keshava, Shyamkumar N.; Lamont, Anthony C.

2014-01-01

In this article we revisit, with the help of images, those classic signs in chest radiography described by Dr Benjamin Felson himself, or other illustrious radiologists of his time, cited and discussed in 'Chest Roentgenology'. We briefly describe the causes of the signs, their utility and the differential diagnosis to be considered when each sign is seen. Wherever possible, we use CT images to illustrate the basis of some of these classic radiographic signs.

THE INFLUENCE OF DESTINATION IMAGE AND TOURIST SATISFACTION TOWARD REVISIT INTENTION OF SETU BABAKAN BETAWI CULTURAL VILLAGE

Directory of Open Access Journals (Sweden)

Setyo Ferry Wibowo

2016-04-01

Full Text Available The purpose of this research are: 1 To find out the description of destination image, tourist satisfaction, and revisit intention of Betawi cultural village Setu Babakan, 2 test empirically the influence of destination image toward revisit intention of Betawi cultural village Setu Babakan, 3 test empirically the influence of tourist satisfaction toward revisit intention of Betawi cultural village Setu Babakan, 4 test empirically the influence of destination image toward revisit intention of Betawi cultural village Setu Babakan. The object of this research was 200 respondents who have ever visit to Betawi cultural village Setu Babakan at one time. The results of descriptive test explained that the destination image provided is good according to the tourist, so that the tourist is satisfied and want to revisit intention of Betawi cultural village Setu Babakan. The hypothesis test shows: 1 the influence of destination image toward revisit intention by -30%, 2 the influence of tourist satisfaction toward revisit intention by 118%, 3 the influence of destination image toward tourist satisfaction by 92%.

Leadership and Management Theories Revisited

DEFF Research Database (Denmark)

Madsen, Mona Toft

2001-01-01

The goal of the paper is to revisit and analyze key contributions to the understanding of leadership and management. As a part of the discussion a role perspective that allows for additional and/or integrated leader dimensions, including a change-centered, will be outlined. Seemingly, a major...

Schroedinger's variational method of quantization revisited

International Nuclear Information System (INIS)

Yasue, K.

1980-01-01

Schroedinger's original quantization procedure is revisited in the light of Nelson's stochastic framework of quantum mechanics. It is clarified why Schroedinger's proposal of a variational problem led us to a true description of quantum mechanics. (orig.)

A practical method of predicting client revisit intention in a hospital setting.

Science.gov (United States)

Lee, Kyun Jick

2005-01-01

Data mining (DM) models are an alternative to traditional statistical methods for examining whether higher customer satisfaction leads to higher revisit intention. This study used a total of 906 outpatients' satisfaction data collected from a nationwide survey interviews conducted by professional interviewers on a face-to-face basis in South Korea, 1998. Analyses showed that the relationship between overall satisfaction with hospital services and outpatients' revisit intention, along with word-of-mouth recommendation as intermediate variables, developed into a nonlinear relationship. The five strongest predictors of revisit intention were overall satisfaction, intention to recommend to others, awareness of hospital promotion, satisfaction with physician's kindness, and satisfaction with treatment level.

The phenomenology of the psychotic break and Huxley's trip: substance use and the onset of psychosis.

Science.gov (United States)

Nelson, Barnaby; Sass, Louis A

2008-01-01

While considerable research attention has been devoted to the causal relationship between substance use and psychosis, the phenomenology of the association between the two has largely been ignored. This is a significant shortcoming, because it blinds researchers to the possibility that there may be elements of the subjective experience of substance use and psychosis that contribute to their apparent relationship in empirical studies. The current paper examines the phenomenology of the onset of psychosis and the phenomenology of substance intoxication through consideration of two texts: Sass's account of the phenomenology of psychosis onset and Huxley's account of the experience of hallucinogenic intoxication. Sass's account of psychosis onset includes four components: Unreality, Fragmentation, Mere Being, and Apophany. The analysis reveals significant parallels - and also some differences - between this account and the phenomenology of substance intoxication. We discuss the implications of this for the causal relationship between psychosis and substance use and suggest several ways of understanding the overlapping phenomenologies. This includes the suggestion of a shared factor, perhaps best described as psychotic-like experience, which seems to involve a breakdown of the sign-referent relationship and relationship with the common-sense, practical world. However, in the onset of psychosis, this breakdown is primarily experienced as a sense of alienation from self and world, whereas in the hallucinogenic state a sense of mystical union and revelation seems predominant. Further research may extend this analysis by looking at experiences with other drugs, particularly cannabis, and by examining the phenomenology of psychotic disorder beyond the first episode. (c) 2008 S. Karger AG, Basel.

The Faraday effect revisited: General theory

DEFF Research Database (Denmark)

Cornean, Horia Decebal; Nenciu, Gheorghe; Pedersen, Thomas Garm

This paper is the first in a series revisiting the Faraday effect, or more generally, the theory of electronic quantum transport/optical response in bulk media in the presence of a constant magnetic field. The independent electron approximation is assumed. For free electrons, the transverse...

Revisited

DEFF Research Database (Denmark)

Tegtmeier, Silke; Meyer, Verena; Pakura, Stefanie

2017-01-01

were captured when they described entrepreneurs. Therefore, this paper aims to revisit gender role stereotypes among young adults. Design/methodology/approach: To measure stereotyping, participants were asked to describe entrepreneurs in general and either women or men in general. The Schein......Purpose: Entrepreneurship is shaped by a male norm, which has been widely demonstrated in qualitative studies. The authors strive to complement these methods by a quantitative approach. First, gender role stereotypes were measured in entrepreneurship. Second, the explicit notions of participants......: The images of men and entrepreneurs show a high and significant congruence (r = 0.803), mostly in those adjectives that are untypical for men and entrepreneurs. The congruence of women and entrepreneurs was low (r = 0.152) and insignificant. Contrary to the participants’ beliefs, their explicit notions did...

Who should do the dishes now? Revisiting gender and housework in contemporary urban South Wales

OpenAIRE

Mannay, Dawn

2016-01-01

This chapter revisits Jane Pilcher’s (1994) seminal work ‘Who should do the dishes? Three generations of Welsh women talking about men and housework’, which was originally published in Our Sister’s Land: the changing identities of women in Wales. As discussed in the introductory chapter, I began revisiting classic Welsh studies as part of my doctoral study Mothers and daughters on the margins: gender, generation and education (Mannay, 2012); this lead to the later publication of a revisiting ...

The Faraday effect revisited: General theory

DEFF Research Database (Denmark)

Cornean, Horia Decebal; Nenciu, Gheorghe; Pedersen, Thomas Garm

2006-01-01

This paper is the first in a series revisiting the Faraday effect, or more generally, the theory of electronic quantum transport/optical response in bulk media in the presence of a constant magnetic field. The independent electron approximation is assumed. At zero temperature and zero frequency...

Examining Relationships of Destination Image, Service Quality, e-WOM, and Revisit Intention to Sabang Island, Indonesia

Directory of Open Access Journals (Sweden)

Rangga Restu Prayogo

2016-12-01

Full Text Available The purpose of this research is to study the relationship among destination image, service quality, e-WOM, and revisit intentions in the tourism industry. A questionnaire given to tourists who visit one of the farrest island in western part of Indonesia, Sabang Island and using sampling through the convenience sampling. A structural equation model (SEM test with WarpPLS 3.0 was used to test the relationship between research variables. This research gathered from 150 respondents. The empirical results from PLS-SEM showed that; the destination image positive affect e-WOM and revisit intention; service quality affect e-WOM and revisit intention; e-WOM positive affect to revisit intention tourists. The implications and future research issues were discussed.

Can adult neural stem cells create new brains? Plasticity in the adult mammalian neurogenic niches: realities and expectations in the era of regenerative biology.

Science.gov (United States)

Kazanis, Ilias

2012-02-01

Since the first experimental reports showing the persistence of neurogenic activity in the adult mammalian brain, this field of neurosciences has expanded significantly. It is now widely accepted that neural stem and precursor cells survive during adulthood and are able to respond to various endogenous and exogenous cues by altering their proliferation and differentiation activity. Nevertheless, the pathway to therapeutic applications still seems to be long. This review attempts to summarize and revisit the available data regarding the plasticity potential of adult neural stem cells and of their normal microenvironment, the neurogenic niche. Recent data have demonstrated that adult neural stem cells retain a high level of pluripotency and that adult neurogenic systems can switch the balance between neurogenesis and gliogenesis and can generate a range of cell types with an efficiency that was not initially expected. Moreover, adult neural stem and precursor cells seem to be able to self-regulate their interaction with the microenvironment and even to contribute to its synthesis, altogether revealing a high level of plasticity potential. The next important step will be to elucidate the factors that limit this plasticity in vivo, and such a restrictive role for the microenvironment is discussed in more details.

Deterministic Graphical Games Revisited

DEFF Research Database (Denmark)

Andersson, Daniel; Hansen, Kristoffer Arnsfelt; Miltersen, Peter Bro

2008-01-01

We revisit the deterministic graphical games of Washburn. A deterministic graphical game can be described as a simple stochastic game (a notion due to Anne Condon), except that we allow arbitrary real payoffs but disallow moves of chance. We study the complexity of solving deterministic graphical...... games and obtain an almost-linear time comparison-based algorithm for computing an equilibrium of such a game. The existence of a linear time comparison-based algorithm remains an open problem....

Studies of stimulus parameters for seizure disruption using neural network simulations.

Science.gov (United States)

Anderson, William S; Kudela, Pawel; Cho, Jounhong; Bergey, Gregory K; Franaszczuk, Piotr J

2007-08-01

A large scale neural network simulation with realistic cortical architecture has been undertaken to investigate the effects of external electrical stimulation on the propagation and evolution of ongoing seizure activity. This is an effort to explore the parameter space of stimulation variables to uncover promising avenues of research for this therapeutic modality. The model consists of an approximately 800 mum x 800 mum region of simulated cortex, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. The cell dynamics are governed by a modified version of the Hodgkin-Huxley equations in single compartment format. Axonal connections are patterned after histological data and published models of local cortical wiring. Stimulation induced action potentials take place at the axon initial segments, according to threshold requirements on the applied electric field distribution. Stimulation induced action potentials in horizontal axonal branches are also separately simulated. The calculations are performed on a 16 node distributed 32-bit processor system. Clear differences in seizure evolution are presented for stimulated versus the undisturbed rhythmic activity. Data is provided for frequency dependent stimulation effects demonstrating a plateau effect of stimulation efficacy as the applied frequency is increased from 60 to 200 Hz. Timing of the stimulation with respect to the underlying rhythmic activity demonstrates a phase dependent sensitivity. Electrode height and position effects are also presented. Using a dipole stimulation electrode arrangement, clear orientation effects of the dipole with respect to the model connectivity is also demonstrated. A sensitivity analysis of these results as a function of the stimulation threshold is also provided.

Revisiting Hansen Solubility Parameters by Including Thermodynamics

NARCIS (Netherlands)

Louwerse, Manuel J; Fernández-Maldonado, Ana María; Rousseau, Simon; Moreau-Masselon, Chloe; Roux, Bernard; Rothenberg, Gadi

2017-01-01

The Hansen solubility parameter approach is revisited by implementing the thermodynamics of dissolution and mixing. Hansen's pragmatic approach has earned its spurs in predicting solvents for polymer solutions, but for molecular solutes improvements are needed. By going into the details of entropy

Bottomonium spectrum revisited

CERN Document Server

Segovia, Jorge; Entem, David R.; Fernández, Francisco

2016-01-01

We revisit the bottomonium spectrum motivated by the recently exciting experimental progress in the observation of new bottomonium states, both conventional and unconventional. Our framework is a nonrelativistic constituent quark model which has been applied to a wide range of hadronic observables from the light to the heavy quark sector and thus the model parameters are completely constrained. Beyond the spectrum, we provide a large number of electromagnetic, strong and hadronic decays in order to discuss the quark content of the bottomonium states and give more insights about the better way to determine their properties experimentally.

Hospital revisit rate after a diagnosis of conversion disorder.

Science.gov (United States)

Merkler, Alexander E; Parikh, Neal S; Chaudhry, Simriti; Chait, Alanna; Allen, Nicole C; Navi, Babak B; Kamel, Hooman

2016-04-01

To estimate the hospital revisit rate of patients diagnosed with conversion disorder (CD). Using administrative data, we identified all patients discharged from California, Florida and New York emergency departments (EDs) and acute care hospitals between 2005 and 2011 with a primary discharge diagnosis of CD. Patients discharged with a primary diagnosis of seizure or transient global amnesia (TGA) served as control groups. Our primary outcome was the rate of repeat ED visits and hospital admissions after initial presentation. Poisson regression was used to compare rates between diagnosis groups while adjusting for demographic characteristics. We identified 7946 patients discharged with a primary diagnosis of CD. During a mean follow-up of 3.0 (±1.6) years, patients with CD had a median of three (IQR, 1-9) ED or inpatient revisits, compared with 0 (IQR, 0-2) in patients with TGA and 3 (IQR, 1-7) in those with seizures. Revisit rates were 18.25 (95% CI, 18.10 to 18.40) visits per 100 patients per month in those with CD, 3.90 (95% CI, 3.84 to 3.95) in those with TGA and 17.78 (95% CI, 17.75 to 17.81) in those with seizures. As compared to CD, the incidence rate ratio for repeat ED visits or hospitalisations was 0.89 (95% CI, 0.86 to 0.93) for seizure disorder and 0.32 (95% CI 0.31 to 0.34) for TGA. CD is associated with a substantial hospital revisit rate. Our findings suggest that CD is not an acute, time-limited response to stress, but rather that CD is a manifestation of a broader pattern of chronic neuropsychiatric disease. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

The Future of Engineering Education--Revisited

Science.gov (United States)

Wankat, Phillip C.; Bullard, Lisa G.

2016-01-01

This paper revisits the landmark CEE series, "The Future of Engineering Education," published in 2000 (available free in the CEE archives on the internet) to examine the predictions made in the original paper as well as the tools and approaches documented. Most of the advice offered in the original series remains current. Despite new…

Spontaneous revisitation during visual exploration as a link among strategic behavior, learning, and the hippocampus.

Science.gov (United States)

Voss, Joel L; Warren, David E; Gonsalves, Brian D; Federmeier, Kara D; Tranel, Dan; Cohen, Neal J

2011-08-02

Effective exploratory behaviors involve continuous updating of sensory sampling to optimize the efficacy of information gathering. Despite some work on this issue in animals, little information exists regarding the cognitive or neural mechanisms for this sort of behavioral optimization in humans. Here we examined a visual exploration phenomenon that occurred when human subjects studying an array of objects spontaneously looked "backward" in their scanning paths to view recently seen objects again. This "spontaneous revisitation" of recently viewed objects was associated with enhanced hippocampal activity and superior subsequent memory performance in healthy participants, but occurred only rarely in amnesic patients with severe damage to the hippocampus. These findings demonstrate the necessity of the hippocampus not just in the aspects of long-term memory with which it has been associated previously, but also in the short-term adaptive control of behavior. Functional neuroimaging showed hippocampal engagement occurring in conjunction with frontocerebellar circuits, thereby revealing some of the larger brain circuitry essential for the strategic deployment of information-seeking behaviors that optimize learning.

Revisiting Weak Simulation for Substochastic Markov Chains

DEFF Research Database (Denmark)

Jansen, David N.; Song, Lei; Zhang, Lijun

2013-01-01

of the logic PCTL\\x, and its completeness was conjectured. We revisit this result and show that soundness does not hold in general, but only for Markov chains without divergence. It is refuted for some systems with substochastic distributions. Moreover, we provide a counterexample to completeness...

Revisiting the formal foundation of Probabilistic Databases

NARCIS (Netherlands)

Wanders, B.; van Keulen, Maurice

2015-01-01

One of the core problems in soft computing is dealing with uncertainty in data. In this paper, we revisit the formal foundation of a class of probabilistic databases with the purpose to (1) obtain data model independence, (2) separate metadata on uncertainty and probabilities from the raw data, (3)

Revisiting Mutual Fund Performance Evaluation

OpenAIRE

Angelidis, Timotheos; Giamouridis, Daniel; Tessaromatis, Nikolaos

2012-01-01

Mutual fund manager excess performance should be measured relative to their self-reported benchmark rather than the return of a passive portfolio with the same risk characteristics. Ignoring the self-reported benchmark introduces biases in the measurement of stock selection and timing components of excess performance. We revisit baseline empirical evidence in mutual fund performance evaluation utilizing stock selection and timing measures that address these biases. We introduce a new factor e...

Deterministic Graphical Games Revisited

DEFF Research Database (Denmark)

Andersson, Klas Olof Daniel; Hansen, Kristoffer Arnsfelt; Miltersen, Peter Bro

2012-01-01

Starting from Zermelo’s classical formal treatment of chess, we trace through history the analysis of two-player win/lose/draw games with perfect information and potentially infinite play. Such chess-like games have appeared in many different research communities, and methods for solving them......, such as retrograde analysis, have been rediscovered independently. We then revisit Washburn’s deterministic graphical games (DGGs), a natural generalization of chess-like games to arbitrary zero-sum payoffs. We study the complexity of solving DGGs and obtain an almost-linear time comparison-based algorithm...

Metamorphosis in Craniiformea revisited

DEFF Research Database (Denmark)

Altenburger, Andreas; Wanninger, Andreas; Holmer, Lars E.

2013-01-01

We revisited the brachiopod fold hypothesis and investigated metamorphosis in the craniiform brachiopod Novocrania anomala. Larval development is lecithotrophic and the dorsal (brachial) valve is secreted by dorsal epithelia. We found that the juvenile ventral valve, which consists only of a thin...... brachiopods during metamorphosis to cement their pedicle to the substrate. N. anomala is therefore not initially attached by a valve but by material corresponding to pedicle cuticle. This is different to previous descriptions, which had led to speculations about a folding event in the evolution of Brachiopoda...

The hard-core model on random graphs revisited

International Nuclear Information System (INIS)

Barbier, Jean; Krzakala, Florent; Zhang, Pan; Zdeborová, Lenka

2013-01-01

We revisit the classical hard-core model, also known as independent set and dual to vertex cover problem, where one puts particles with a first-neighbor hard-core repulsion on the vertices of a random graph. Although the case of random graphs with small and very large average degrees respectively are quite well understood, they yield qualitatively different results and our aim here is to reconciliate these two cases. We revisit results that can be obtained using the (heuristic) cavity method and show that it provides a closed-form conjecture for the exact density of the densest packing on random regular graphs with degree K ≥ 20, and that for K > 16 the nature of the phase transition is the same as for large K. This also shows that the hard-code model is the simplest mean-field lattice model for structural glasses and jamming

The Effects of Korean Medical Service Quality and Satisfaction on Revisit Intention of the United Arab Emirates Government Sponsored Patients.

Science.gov (United States)

Lee, Seoyoung; Kim, Eun-Kyung

2017-06-01

The purpose of this study was to investigate medical service quality, satisfaction and to examine factors influencing hospital revisit intention of the United Arab Emirates government sponsored patients in Korea. A total of 152 UAE government sponsored patients who visited Korean hospitals participated in the questionnaire survey from August to November 2016. Stepwise multiple regression was used to identify the factors that affected the revisit intention of the participants. The mean scores of medical service quality, satisfaction, and revisit intention were 5.72 out of 7, 88.88 out of 100, 4.59 out of 5, respectively. Medical service quality and satisfaction, Medical service quality and revisit intention, satisfaction and revisit intention were positively correlated. Medical service of physician, visiting routes and responsiveness of medical service quality explained about 23.8% of revisit intention. There are needs for physicians to communicate with patients while ensuring sufficient consultation time based on excellent medical skills and nurses to respond immediately for the patients' needs through an empathic encounter in order to improve medical service quality and patient satisfaction so that to increase the revisit intention of the United Arab Emirates government sponsored patients. Further, it is necessary for the hospitals to have support plans for providing country specialized services in consideration of the UAE culture to ensure that physicians' and nurses' competencies are not undervalued by non-medical service elements such as interpreters and meals. Copyright © 2017. Published by Elsevier B.V.

Coccolithophorids in polar waters: Wigwamma spp. revisited

DEFF Research Database (Denmark)

Thomsen, Helge Abildhauge; Østergaard, Jette B.; Heldal, Mikal

2013-01-01

A contingent of weakly calcified coccolithophorid genera and species were described from polar regions almost 40 years ago. In the interim period a few additional findings have been reported enlarging the realm of some of the species. The genus Wigwamma is revisited here with the purpose of provi...... appearance of the coccolith armour of the cell...

Moods as ups and downs of the motivation pendulum: Revisiting Reinforcement Sensitivity Theory (RST in Bipolar Disorder

Directory of Open Access Journals (Sweden)

Tal eGonen

2014-11-01

Full Text Available Motivation is a key neurobehavioral concept underlying adaptive responses to environmental incentives and threats. As such, dysregulation of motivational processes may be critical in the formation of abnormal behavioral patterns/tendencies. According to the long standing model of the Reinforcement Sensitivity Theory (RST, motivation behaviors are driven by three neurobehavioral systems mediating the sensitivity to punishment, reward or goal-conflict. Corresponding to current neurobehavioral theories in psychiatry, this theory links abnormal motivational drives to abnormal behavior; viewing depression and mania as two abnormal extremes of reward driven processes leading to either under or over approach tendencies, respectively. We revisit the RST framework in the context of bipolar disorder (BD and challenge this concept by suggesting that dysregulated interactions of both punishment and reward related processes better account for the psychological and neural abnormalities observed in BD. We further present an integrative model positing that the three parallel motivation systems currently proposed by the RST model, can be viewed as subsystems in a large-scale neurobehavioral network of motivational decision making.

The significance test controversy revisited the fiducial Bayesian alternative

CERN Document Server

Lecoutre, Bruno

2014-01-01

The purpose of this book is not only to revisit the “significance test controversy,”but also to provide a conceptually sounder alternative. As such, it presents a Bayesian framework for a new approach to analyzing and interpreting experimental data. It also prepares students and researchers for reporting on experimental results. Normative aspects: The main views of statistical tests are revisited and the philosophies of Fisher, Neyman-Pearson and Jeffrey are discussed in detail. Descriptive aspects: The misuses of Null Hypothesis Significance Tests are reconsidered in light of Jeffreys’ Bayesian conceptions concerning the role of statistical inference in experimental investigations. Prescriptive aspects: The current effect size and confidence interval reporting practices are presented and seriously questioned. Methodological aspects are carefully discussed and fiducial Bayesian methods are proposed as a more suitable alternative for reporting on experimental results. In closing, basic routine procedures...

Satellite failures revisited

Science.gov (United States)

Balcerak, Ernie

2012-12-01

In January 1994, the two geostationary satellites known as Anik-E1 and Anik-E2, operated by Telesat Canada, failed one after the other within 9 hours, leaving many northern Canadian communities without television and data services. The outage, which shut down much of the country's broadcast television for hours and cost Telesat Canada more than $15 million, generated significant media attention. Lam et al. used publicly available records to revisit the event; they looked at failure details, media coverage, recovery effort, and cost. They also used satellite and ground data to determine the precise causes of those satellite failures. The researchers traced the entire space weather event from conditions on the Sun through the interplanetary medium to the particle environment in geostationary orbit.

Pockets of Participation: Revisiting Child-Centred Participation Research

Science.gov (United States)

Franks, Myfanwy

2011-01-01

This article revisits the theme of the clash of interests and power relations at work in participatory research which is prescribed from above. It offers a possible route toward solving conflict between adult-led research carried out by young researchers, funding requirements and organisational constraints. The article explores issues of…

Faraday effect revisited: sum rules and convergence issues

DEFF Research Database (Denmark)

Cornean, Horia; Nenciu, Gheorghe

2010-01-01

This is the third paper of a series revisiting the Faraday effect. The question of the absolute convergence of the sums over the band indices entering the Verdet constant is considered. In general, sum rules and traces per unit volume play an important role in solid-state physics, and they give...

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.

Short or Long End of the Lever? Associations between Provider Communication of the "Asthma-Action Plan" and Outpatient Revisits for Pediatric Asthma.

Science.gov (United States)

Rangachari, Pavani; Mehta, Renuka; Rethemeyer, R Karl; Ferrang, Carole; Dennis, Clifton; Redd, Vickie

2015-10-01

At the Children's Hospital of Georgia (CHOG), we found that outpatient revisits for pediatric asthma were significantly above national norms. According to the NIH, costly hospital revisits for asthma can be prevented through guidelines-based self-management of asthma, central to which, is the use of a written Asthma-Action Plan (AAP). The asthma services literature has emphasized the role of the healthcare provider in promoting asthma self-management using the AAP, to prevent hospital revisits. On the other hand, the asthma policy literature has emphasized the need for community-based interventions to promote asthma self-management. A gap remains in understanding the extent of leverage that healthcare providers may have in preventing hospital revisits for asthma, through effective communication of AAP in the outpatient setting. Our study sought to address this gap. We conducted a 6-month intervention to implement "patient-and-family-centered communication of the AAP" in CHOG outpatient clinics, based on the "change-management" theoretical framework. Provider communication of AAP was assessed through a survey of "Parent Understanding of the Child's AAP." A quasi-experimental approach was used to measure outpatient revisits for pediatric asthma, pre- and post-intervention. Survey results showed that provider communication of the AAP was unanimously perceived highly positively by parents of pediatric asthma patients, across various metrics of patient-centered care. However, there were no statistically significant differences in outpatient "revisit behavior" for pediatric asthma between pre- and post-intervention periods after controlling for several demographic variables. Additionally, revisits remained significantly above national norms. Results suggest limited potential of "effective provider communication of AAP," in reducing outpatient revisits for pediatric asthma; and indicate need for broader community-based interventions to address patient life variables

Additively homomorphic encryption with a double decryption mechanism, revisited

NARCIS (Netherlands)

Peter, Andreas; Kronberg, M.; Trei, W.; Katzenbeisser, S.

We revisit the notion of additively homomorphic encryption with a double decryption mechanism (DD-PKE), which allows for additions in the encrypted domain while having a master decryption procedure that can decrypt all properly formed ciphertexts by using a special master secret. This type of

Moral Judgment Development across Cultures: Revisiting Kohlberg's Universality Claims

Science.gov (United States)

Gibbs, John C.; Basinger, Karen S.; Grime, Rebecca L.; Snarey, John R.

2007-01-01

This article revisits Kohlberg's cognitive developmental claims that stages of moral judgment, facilitative processes of social perspective-taking, and moral values are commonly identifiable across cultures. Snarey [Snarey, J. (1985). "The cross-cultural universality of social-moral development: A critical review of Kohlbergian research."…

Radiative corrections to double-Dalitz decays revisited

Science.gov (United States)

Kampf, Karol; Novotný, Jiři; Sanchez-Puertas, Pablo

2018-03-01

In this study, we revisit and complete the full next-to-leading order corrections to pseudoscalar double-Dalitz decays within the soft-photon approximation. Comparing to the previous study, we find small differences, which are nevertheless relevant for extracting information about the pseudoscalar transition form factors. Concerning the latter, these processes could offer the opportunity to test them—for the first time—in their double-virtual regime.

A Feminist Revisit to the First-Year Curriculum.

Science.gov (United States)

Bernstein, Anita

1996-01-01

A seminar at Chicago-Kent College of Law (Illinois) that reviews six first-year law school courses by focusing on feminist issues in course content and structure is described. The seminar functions as both a review and a shift in perspective. Courses revisited include civil procedure, contracts, criminal law, justice and the legal system,…

Quantum duel revisited

International Nuclear Information System (INIS)

Schmidt, Alexandre G M; Paiva, Milena M

2012-01-01

We revisit the quantum two-person duel. In this problem, both Alice and Bob each possess a spin-1/2 particle which models dead and alive states for each player. We review the Abbott and Flitney result—now considering non-zero α 1 and α 2 in order to decide if it is better for Alice to shoot or not the second time—and we also consider a duel where players do not necessarily start alive. This simple assumption allows us to explore several interesting special cases, namely how a dead player can win the duel shooting just once, or how can Bob revive Alice after one shot, and the better strategy for Alice—being either alive or in a superposition of alive and dead states—fighting a dead opponent. (paper)

The Importance of Being a Complement: CED Effects Revisited

Science.gov (United States)

Jurka, Johannes

2010-01-01

This dissertation revisits subject island effects (Ross 1967, Chomsky 1973) cross-linguistically. Controlled acceptability judgment studies in German, English, Japanese and Serbian show that extraction out of specifiers is consistently degraded compared to extraction out of complements, indicating that the Condition on Extraction domains (CED,…

The Effect of Brand Equity and Perceived Value on Customer Revisit Intention: A Study in Quick-Service Restaurants in Vietnam

Directory of Open Access Journals (Sweden)

Ly Thi Minh Pham

2016-10-01

Full Text Available The purpose of this study is to examine how brand equity, from a customer point of view, influences quick-service restaurant revisit intention. The authors propose a conceptual framework in which three dimensions of brand equity including brand associations combined with brand awareness, perceived quality, brand loyalty and perceived value are related to revisit intention. Data from 570 customers who had visited four quick-service restaurants in Ho Chi Minh City were used for the structural equation modelling (SEM analysis. The results show that strong brand equity is significantly correlated with revisit intention. Additionally, the effect of brand equity on revisit intention was mediated by perceived value, among others. Overall, this study emphasizes the importance of perceived value in lodging in the customer’s mind. Finally, managerial implications are presented based on the study results.

Literary Origins of the Term "School Psychologist" Revisited

Science.gov (United States)

Fagan, Thomas K.

2005-01-01

Previous research on the literary origins of the term "school psychologist" is revisited, and conclusions are revised in light of new evidence. It appears that the origin of the term in the American literature occurred as early as 1898 in an article by Hugo Munsterberg, predating the usage by Wilhelm Stern in 1911. The early references to the…

A Multi-Level Model of Moral Functioning Revisited

Science.gov (United States)

Reed, Don Collins

2009-01-01

The model of moral functioning scaffolded in the 2008 "JME" Special Issue is here revisited in response to three papers criticising that volume. As guest editor of that Special Issue I have formulated the main body of this response, concerning the dynamic systems approach to moral development, the problem of moral relativism and the role of…

Short or Long End of the Lever? Associations between Provider Communication of the “Asthma-Action Plan” and Outpatient Revisits for Pediatric Asthma

Science.gov (United States)

Rangachari, Pavani; Mehta, Renuka; Rethemeyer, R. Karl; Ferrang, Carole; Dennis, Clifton; Redd, Vickie

2017-01-01

Background At the Children’s Hospital of Georgia (CHOG), we found that outpatient revisits for pediatric asthma were significantly above national norms. According to the NIH, costly hospital revisits for asthma can be prevented through guidelines-based self-management of asthma, central to which, is the use of a written Asthma-Action Plan (AAP). Purpose The asthma services literature has emphasized the role of the healthcare provider in promoting asthma self-management using the AAP, to prevent hospital revisits. On the other hand, the asthma policy literature has emphasized the need for community-based interventions to promote asthma self-management. A gap remains in understanding the extent of leverage that healthcare providers may have in preventing hospital revisits for asthma, through effective communication of AAP in the outpatient setting. Our study sought to address this gap. Methods We conducted a 6-month intervention to implement “patient-and-family-centered communication of the AAP” in CHOG outpatient clinics, based on the “change-management” theoretical framework. Provider communication of AAP was assessed through a survey of “Parent Understanding of the Child’s AAP.” A quasi-experimental approach was used to measure outpatient revisits for pediatric asthma, pre- and post-intervention. Results Survey results showed that provider communication of the AAP was unanimously perceived highly positively by parents of pediatric asthma patients, across various metrics of patient-centered care. However, there were no statistically significant differences in outpatient “revisit behavior” for pediatric asthma between pre- and post-intervention periods after controlling for several demographic variables. Additionally, revisits remained significantly above national norms. Conclusions Results suggest limited potential of “effective provider communication of AAP,” in reducing outpatient revisits for pediatric asthma; and indicate need for

Reframing in dentistry: Revisited

Directory of Open Access Journals (Sweden)

Sivakumar Nuvvula

2013-01-01

Full Text Available The successful practice of dentistry involves a good combination of technical skills and soft skills. Soft skills or communication skills are not taught extensively in dental schools and it can be challenging to learn and at times in treating dental patients. Guiding the child′s behavior in the dental operatory is one of the preliminary steps to be taken by the pediatric dentist and one who can successfully modify the behavior can definitely pave the way for a life time comprehensive oral care. This article is an attempt to revisit a simple behavior guidance technique, reframing and explain the possible psychological perspectives behind it for better use in the clinical practice.

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.

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.

Revisiting Jack Goody to Rethink Determinisms in Literacy Studies

Science.gov (United States)

Collin, Ross

2013-01-01

This article revisits Goody's arguments about literacy's influence on social arrangements, culture, cognition, economics, and other domains of existence. Whereas some of his arguments tend toward technological determinism (i.e., literacy causes change in the world), other of his arguments construe literacy as a force that shapes and is shaped by…

A control center design revisited: learning from users’ appropriation

DEFF Research Database (Denmark)

Souza da Conceição, Carolina; Cordeiro, Cláudia

2014-01-01

This paper aims to present the lessons learned during a control center design project by revisiting another control center from the same company designed two and a half years before by the same project team. In light of the experience with the first project and its analysis, the designers and res...

Neural activity in the medial temporal lobe reveals the fidelity of mental time travel.

Science.gov (United States)

Kragel, James E; Morton, Neal W; Polyn, Sean M

2015-02-18

Neural circuitry in the medial temporal lobe (MTL) is critically involved in mental time travel, which involves the vivid retrieval of the details of past experience. Neuroscientific theories propose that the MTL supports memory of the past by retrieving previously encoded episodic information, as well as by reactivating a temporal code specifying the position of a particular event within an episode. However, the neural computations supporting these abilities are underspecified. To test hypotheses regarding the computational mechanisms supported by different MTL subregions during mental time travel, we developed a computational model that linked a blood oxygenation level-dependent signal to cognitive operations, allowing us to predict human performance in a memory search task. Activity in the posterior MTL, including parahippocampal cortex, reflected how strongly one reactivates the temporal context of a retrieved memory, allowing the model to predict whether the next memory will correspond to a nearby moment in the study episode. A signal in the anterior MTL, including perirhinal cortex, indicated the successful retrieval of list items, without providing information regarding temporal organization. A hippocampal signal reflected both processes, consistent with theories that this region binds item and context information together to form episodic memories. These findings provide evidence for modern theories that describe complementary roles of the hippocampus and surrounding parahippocampal and perirhinal cortices during the retrieval of episodic memories, shaping how humans revisit the past. Copyright © 2015 the authors 0270-6474/15/352914-13$15.00/0.

PENGARUH GUIDE PERFORMANCE DAN QUALITY TOURISM SERVICE TERHADAP REVISIT INTENTION DI KEBUN RAYA BOGOR

Directory of Open Access Journals (Sweden)

Rian Andriani

2016-11-01

ABSTRACT Bogor Botanical Gardens is one of tourist destination in Bogor City, West Java, which the geographical in a strategic location between two big cities, Jakarta and Bandung. This location factor is can become an opportunity for Bogor Botanical Gardens to become a favorite tourist destination in Bogor City. Guide Performance and Quality Tourism Service is one of part of the tourism, so the researcher focus to Guide Performance and Quality Tourism Service To Revisit Intention in Bogor Botanical Gardens. In this research, the researcher used quantitative methode with descriptive verificative. The researcher used 100 respondent for a sample. In this research, the researcher used multiple linier regresion analysis with hypotesis test used determination coefficient test and F test. The classical assumption of this research is used normality test, multicollinearity test, heteroskedastisitas test and auto correlation test. In this research, the Guide Performance and Quality Tourism Service factors has an influence 33,2% of Revisit Intention in Bogor Botanical Garden. Keywords: Guide Performance, Quality Tourism Service and Revisit Intention

Revisiting the Political Economy of Communication

Directory of Open Access Journals (Sweden)

Nicholas Garnham

2014-02-01

The task of the paper and the seminar was to revisit some of Nicholas Garnham’s ideas, writings and contributions to the study of the Political Economy of Communication and to reflect on the concepts, history, current status and perspectives of this field and the broader study of political economy today. The topics covered include Raymond Williams’ cultural materialism, Pierre Bourdieu’s sociology of culture, the debate between Political Economy and Cultural Studies, information society theory, Karl Marx’s theory and the critique of capitalism.

The vacuum structure, special relativity theory and quantum mechanics revisited: a field theory-no-geometry approach

International Nuclear Information System (INIS)

Bogolubov, N.N. Jr.; Prykarpatsky, A.K.; Ufuk Taneri

2008-07-01

The main fundamental principles characterizing the vacuum field structure are formulated and the modeling of the related vacuum medium and charged point particle dynamics by means of de- vised field theoretic tools are analyzed. The Maxwell electrodynamic theory is revisited and newly derived from the suggested vacuum field structure principles and the classical special relativity theory relationship between the energy and the corresponding point particle mass is revisited and newly obtained. The Lorentz force expression with respect to arbitrary non-inertial reference frames is revisited and discussed in detail, and some new interpretations of relations between the special relativity theory and quantum mechanics are presented. The famous quantum-mechanical Schroedinger type equations for a relativistic point particle in the external potential and magnetic fields within the quasiclassical approximation as the Planck constant (h/2π) → 0 and the light velocity c → ∞ are obtained. (author)

K Mbugua The Problem of Hell Revisited pp93-103

African Journals Online (AJOL)

K Mbugua

The Problem of Hell Revisited: Towards a Gentler Theology of Hell 93 ... One who, after He has killed, has authority to cast into hell; yes, I tell you, fear. Him! .... Suppose your spouse or parent or child goes to hell and you go to heaven.

What matters to infrequent customers: a pragmatic approach to understanding perceived value and intention to revisit trendy coffee café.

Science.gov (United States)

Ting, Hiram; Thurasamy, Ramayah

2016-01-01

Notwithstanding the rise of trendy coffee café, little is done to investigate revisit intention towards the café in the context of developing markets. In particular, there is a lack of study which provides theoretical and practical explanation to the perceptions and behaviours of infrequent customers. Hence, the study aims to look into the subject matter by using the theory of reasoned action and social exchange theory as the underpinning basis. The framework proposed by Pine and Gilmore (Strat Leadersh 28:18-23, 2000), which asserts the importance of product quality, service quality and experience quality in a progressive manner, is used to decompose perceived value in the model so as to determine their effects on intention to revisit the café. Given the importance to gain practical insights into revisit intention of infrequent customers, pragmatism stance is assumed. Explanatory sequential mixed-method design is thus adopted whereby qualitative approach is used to confirm and complement quantitative findings. Self-administered questionnaire-based survey is first administered before personal interview is carried out at various cafés. Partial least squares structural equation modelling and content analysis are appropriated successively. In the quantitative findings, although product quality, service quality and experience quality are found to have positive effect on perceived value and revisit intention towards trendy coffee café, experience quality is found to have the greater effect than the others among the infrequent customers. The qualitative findings not only confirm their importance, but most importantly explain the favourable impressions they have at trendy coffee café based on their last in-store experience. While product and service quality might not necessary stimulate them to revisit trendy coffee café, experience quality driven by purposes of visit would likely affect their intention to revisit. As retaining customers is of utmost importance to

Optical imaging of neuronal activity and visualization of fine neural structures in non-desheathed nervous systems.

Directory of Open Access Journals (Sweden)

Christopher John Goldsmith

Full Text Available Locating circuit neurons and recording from them with single-cell resolution is a prerequisite for studying neural circuits. Determining neuron location can be challenging even in small nervous systems because neurons are densely packed, found in different layers, and are often covered by ganglion and nerve sheaths that impede access for recording electrodes and neuronal markers. We revisited the voltage-sensitive dye RH795 for its ability to stain and record neurons through the ganglion sheath. Bath-application of RH795 stained neuronal membranes in cricket, earthworm and crab ganglia without removing the ganglion sheath, revealing neuron cell body locations in different ganglion layers. Using the pyloric and gastric mill central pattern generating neurons in the stomatogastric ganglion (STG of the crab, Cancer borealis, we found that RH795 permeated the ganglion without major residue in the sheath and brightly stained somatic, axonal and dendritic membranes. Visibility improved significantly in comparison to unstained ganglia, allowing the identification of somata location and number of most STG neurons. RH795 also stained axons and varicosities in non-desheathed nerves, and it revealed the location of sensory cell bodies in peripheral nerves. Importantly, the spike activity of the sensory neuron AGR, which influences the STG motor patterns, remained unaffected by RH795, while desheathing caused significant changes in AGR activity. With respect to recording neural activity, RH795 allowed us to optically record membrane potential changes of sub-sheath neuronal membranes without impairing sensory activity. The signal-to-noise ratio was comparable with that previously observed in desheathed preparations and sufficiently high to identify neurons in single-sweep recordings and synaptic events after spike-triggered averaging. In conclusion, RH795 enabled staining and optical recording of neurons through the ganglion sheath and is therefore both a

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.

Radiative corrections to neutrino deep inelastic scattering revisited

International Nuclear Information System (INIS)

Arbuzov, Andrej B.; Bardin, Dmitry Yu.; Kalinovskaya, Lidia V.

2005-01-01

Radiative corrections to neutrino deep inelastic scattering are revisited. One-loop electroweak corrections are re-calculated within the automatic SANC system. Terms with mass singularities are treated including higher order leading logarithmic corrections. Scheme dependence of corrections due to weak interactions is investigated. The results are implemented into the data analysis of the NOMAD experiment. The present theoretical accuracy in description of the process is discussed

Doppler Processing with Ultra-Wideband (UWB) Radar Revisited

Science.gov (United States)

2018-01-01

REPORT TYPE Technical Note 3. DATES COVERED (From - To) December 2017 4. TITLE AND SUBTITLE Doppler Processing with Ultra-Wideband (UWB) Radar...unlimited. 13. SUPPLEMENTARY NOTES 14. ABSTRACT This technical note revisits previous work performed at the US Army Research Laboratory related to...target considered previously is proportional to a delayed version of the transmitted signal, up to a complex constant factor. We write the received

Dynamics of Shape Fluctuations of Quasi-spherical Vesicles Revisited

DEFF Research Database (Denmark)

Miao, L.; Lomholt, Michael Andersen; Kleis, J.

2002-01-01

In this paper, the dynamics of spontaneous shape fluctuations of a single, giant quasi-spherical vesicle formed from a single lipid species is revisited theoretically. A coherent physical theory for the dynamics is developed based on a number of fundamental principles and considerations, and a sy......In this paper, the dynamics of spontaneous shape fluctuations of a single, giant quasi-spherical vesicle formed from a single lipid species is revisited theoretically. A coherent physical theory for the dynamics is developed based on a number of fundamental principles and considerations...... of the phenomenological constants in a canonical continuum description of fluid lipid-bilayer membranes and shown the consequences of this new interpretation in terms of the characteristics of the dynamics of vesicle shape fluctuations. Moreover, we have used the systematic formulation of our theory as a framework...... against which we have discussed the previously existing theories and their discrepancies. Finally, we have made a systematic prediction about the system-dependent characteristics of the relaxation dynamics of shape fluctuations of quasi-spherical vesicles with a view of experimental studies...

Revisiting Constructivist Teaching Methods in Ontario Colleges Preparing for Accreditation

Science.gov (United States)

Schultz, Rachel A.

2015-01-01

At the time of writing, the first community colleges in Ontario were preparing for transition to an accreditation model from an audit system. This paper revisits constructivist literature, arguing that a more pragmatic definition of constructivism effectively blends positivist and interactionist philosophies to achieve both student centred…

Thorbecke Revisited : The Role of Doctrinaire Liberalism in Dutch Politics

NARCIS (Netherlands)

Drentje, Jan

2011-01-01

Thorbecke Revisited: The Role of Doctrinaire Liberalism in Dutch Politics In the political history of the nineteenth century Thorbecke played a crucial role. As the architect of the 1848 liberal constitutional reform he led three cabinets. In many ways he dominated the political discourse during the

Revisiting Cementoblastoma with a Rare Case Presentation

Directory of Open Access Journals (Sweden)

Vijayanirmala Subramani

2017-01-01

Full Text Available Cementoblastoma is a rare benign odontogenic neoplasm which is characterized by the proliferation of cellular cementum. Diagnosis of cementoblastoma is challenging because of its protracted clinical, radiographic features, and bland histological appearance; most often cementoblastoma is often confused with other cementum and bone originated lesions. The aim of this article is to overview/revisit, approach the diagnosis of cementoblastoma, and also present a unique radiographic appearance of a cementoblastoma lesion associated with an impacted tooth.

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

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

Increased 30-Day Emergency Department Revisits Among Homeless Patients with Mental Health Conditions

Directory of Open Access Journals (Sweden)

Chun Nok Lam

2016-09-01

Full Text Available Introduction: Patients with mental health conditions frequently use emergency medical services. Many suffer from substance use and homelessness. If they use the emergency department (ED as their primary source of care, potentially preventable frequent ED revisits and hospital readmissions can worsen an already crowded healthcare system. However, the magnitude to which homelessness affects health service utilization among patients with mental health conditions remains unclear in the medical community. This study assessed the impact of homelessness on 30-day ED revisits and hospital readmissions among patients presenting with mental health conditions in an urban, safety-net hospital. Methods: We conducted a secondary analysis of administrative data on all adult ED visits in 2012 in an urban safety-net hospital. Patient demographics, mental health status, homelessness, insurance coverage, level of acuity, and ED disposition per ED visit were analyzed using multilevel modeling to control for multiple visits nested within patients. We performed multivariate logistic regressions to evaluate if homelessness moderated the likelihood of mental health patients’ 30-day ED revisits and hospital readmissions. Results: Study included 139,414 adult ED visits from 92,307 unique patients (43.5±15.1 years, 51.3% male, 68.2% Hispanic/Latino. Nearly 8% of patients presented with mental health conditions, while 4.6% were homeless at any time during the study period. Among patients with mental health conditions, being homeless contributed to an additional 28.0% increase in likelihood (4.28 to 5.48 odds of 30-day ED revisits and 38.2% increase in likelihood (2.04 to 2.82 odds of hospital readmission, compared to non-homeless, non-mental health (NHNM patients as the base category. Adjusted predicted probabilities showed that homeless patients presenting with mental health conditions have a 31.1% chance of returning to the ED within 30-day post discharge and a 3

Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

Science.gov (United States)

Fang, Kuai; Shen, Chaopeng; Kifer, Daniel; Yang, Xiao

2017-11-01

The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedules. Utilizing a state-of-the-art time series deep learning neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 moisture product with atmospheric forcings, model-simulated moisture, and static physiographic attributes as inputs. The system removes most of the bias with model simulations and improves predicted moisture climatology, achieving small test root-mean-square errors (0.87 for over 75% of Continental United States, including the forested southeast. As the first application of LSTM in hydrology, we show the proposed network avoids overfitting and is robust for both temporal and spatial extrapolation tests. LSTM generalizes well across regions with distinct climates and environmental settings. With high fidelity to SMAP, LSTM shows great potential for hindcasting, data assimilation, and weather forecasting.

Surface tension in soap films: revisiting a classic demonstration

International Nuclear Information System (INIS)

Behroozi, F

2010-01-01

We revisit a classic demonstration for surface tension in soap films and introduce a more striking variation of it. The demonstration shows how the film, pulling uniformly and normally on a loose string, transforms it into a circular arc under tension. The relationship between the surface tension and the string tension is analysed and presented in a useful graphical form. (letters and comments)

The Neutrosophic Logic View to Schrodinger's Cat Paradox, Revisited

Directory of Open Access Journals (Sweden)

Florentin Smarandache

2008-07-01

Full Text Available The present article discusses Neutrosophic logic view to Schrodinger's cat paradox. We argue that this paradox involves some degree of indeterminacy (unknown which Neutrosophic logic can take into consideration, whereas other methods including Fuzzy logic cannot. To make this proposition clear, we revisit our previous paper by offering an illustration using modified coin tossing problem, known as Parrondo's game.

Surface tension in soap films: revisiting a classic demonstration

Energy Technology Data Exchange (ETDEWEB)

Behroozi, F [Department of Physics, University of Northern Iowa, Cedar Falls, IA 50614 (United States)], E-mail: behroozi@uni.edu

2010-01-15

We revisit a classic demonstration for surface tension in soap films and introduce a more striking variation of it. The demonstration shows how the film, pulling uniformly and normally on a loose string, transforms it into a circular arc under tension. The relationship between the surface tension and the string tension is analysed and presented in a useful graphical form. (letters and comments)

Revisiting the quantum harmonic oscillator via unilateral Fourier transforms

International Nuclear Information System (INIS)

Nogueira, Pedro H F; Castro, Antonio S de

2016-01-01

The literature on the exponential Fourier approach to the one-dimensional quantum harmonic oscillator problem is revised and criticized. It is shown that the solution of this problem has been built on faulty premises. The problem is revisited via the Fourier sine and cosine transform method and the stationary states are properly determined by requiring definite parity and square-integrable eigenfunctions. (paper)

Transport benchmarks for one-dimensional binary Markovian mixtures revisited

International Nuclear Information System (INIS)

Malvagi, F.

2013-01-01

The classic benchmarks for transport through a binary Markovian mixture are revisited to look at the probability distribution function of the chosen 'results': reflection, transmission and scalar flux. We argue that the knowledge of the ensemble averaged results is not sufficient for reliable predictions: a measure of the dispersion must also be obtained. An algorithm to estimate this dispersion is tested. (author)

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

Resolution of Reflection Seismic Data Revisited

DEFF Research Database (Denmark)

Hansen, Thomas Mejer; Mosegaard, Klaus; Zunino, Andrea

The Rayleigh Principle states that the minimum separation between two reflectors that allows them to be visually separated is the separation where the wavelet maxima from the two superimposed reflections combine into one maximum. This happens around Δtres = λb/8, where λb is the predominant...... lower vertical resolution of reflection seismic data. In the following we will revisit think layer model and demonstrate that there is in practice no limit to the vertical resolution using the parameterization of Widess (1973), and that the vertical resolution is limited by the noise in the data...

Sloan Digital Sky Survey Photometric Calibration Revisited

International Nuclear Information System (INIS)

Marriner, John

2012-01-01

The Sloan Digital Sky Survey calibration is revisited to obtain the most accurate photometric calibration. A small but significant error is found in the flat-fielding of the Photometric telescope used for calibration. Two SDSS star catalogs are compared and the average difference in magnitude as a function of right ascension and declination exhibits small systematic errors in relative calibration. The photometric transformation from the SDSS Photometric Telescope to the 2.5 m telescope is recomputed and compared to synthetic magnitudes computed from measured filter bandpasses.

REVISITING A CLASSIC: THE PARKER–MOFFATT PROBLEM

International Nuclear Information System (INIS)

Pezzi, O.; Servidio, S.; Valentini, F.; Malara, F.; Veltri, P.; Parashar, T. N.; Yang, Y.; Matthaeus, W. H.; Vásconez, C. L.

2017-01-01

The interaction of two colliding Alfvén wave packets is described here by means of magnetohydrodynamics (MHD) and hybrid kinetic numerical simulations. The MHD evolution revisits the theoretical insights described by Moffatt, Parker, Kraichnan, Chandrasekhar, and Elsässer in which the oppositely propagating large-amplitude wave packets interact for a finite time, initiating turbulence. However, the extension to include compressive and kinetic effects, while maintaining the gross characteristics of the simpler classic formulation, also reveals intriguing features that go beyond the pure MHD treatment.

REVISITING A CLASSIC: THE PARKER–MOFFATT PROBLEM

Energy Technology Data Exchange (ETDEWEB)

Pezzi, O.; Servidio, S.; Valentini, F.; Malara, F.; Veltri, P. [Dipartimento di Fisica, Università della Calabria, 87036 Rende (CS) (Italy); Parashar, T. N.; Yang, Y.; Matthaeus, W. H. [Department of Physics and Astronomy, University of Delaware, DE 19716 (United States); Vásconez, C. L. [Departamento de Física, Escuela Politécnica Nacional, Quito (Ecuador)

2017-01-10

The interaction of two colliding Alfvén wave packets is described here by means of magnetohydrodynamics (MHD) and hybrid kinetic numerical simulations. The MHD evolution revisits the theoretical insights described by Moffatt, Parker, Kraichnan, Chandrasekhar, and Elsässer in which the oppositely propagating large-amplitude wave packets interact for a finite time, initiating turbulence. However, the extension to include compressive and kinetic effects, while maintaining the gross characteristics of the simpler classic formulation, also reveals intriguing features that go beyond the pure MHD treatment.

Sloan Digital Sky Survey Photometric Calibration Revisited

Energy Technology Data Exchange (ETDEWEB)

Marriner, John; /Fermilab

2012-06-29

The Sloan Digital Sky Survey calibration is revisited to obtain the most accurate photometric calibration. A small but significant error is found in the flat-fielding of the Photometric telescope used for calibration. Two SDSS star catalogs are compared and the average difference in magnitude as a function of right ascension and declination exhibits small systematic errors in relative calibration. The photometric transformation from the SDSS Photometric Telescope to the 2.5 m telescope is recomputed and compared to synthetic magnitudes computed from measured filter bandpasses.

Revisiting deforestation in Africa (1990–2010): One more lost ...

African Journals Online (AJOL)

This spotlight revisits the dynamics and prognosis outlined in the late 1980's published in Déforestation en Afrique. This book on deforestation in Africa utilized available statistical data from the 1980's and was a pioneering self - styled attempt to provide a holistic viewpoint of the ongoing trends pertaining to deforestation in ...

Revisiting Individual Creativity Assessment: Triangulation in Subjective and Objective Assessment Methods

Science.gov (United States)

Park, Namgyoo K.; Chun, Monica Youngshin; Lee, Jinju

2016-01-01

Compared to the significant development of creativity studies, individual creativity research has not reached a meaningful consensus regarding the most valid and reliable method for assessing individual creativity. This study revisited 2 of the most popular methods for assessing individual creativity: subjective and objective methods. This study…

Logistics Innovation Process Revisited

DEFF Research Database (Denmark)

Gammelgaard, Britta; Su, Shong-Iee Ivan; Yang, Su-Lan

2011-01-01

Purpose – The purpose of this paper is to learn more about logistics innovation processes and their implications for the focal organization as well as the supply chain, especially suppliers. Design/methodology/approach – The empirical basis of the study is a longitudinal action research project...... that was triggered by the practical needs of new ways of handling material flows of a hospital. This approach made it possible to revisit theory on logistics innovation process. Findings – Apart from the tangible benefits reported to the case hospital, five findings can be extracted from this study: the logistics...... innovation process model may include not just customers but also suppliers; logistics innovation in buyer-supplier relations may serve as an alternative to outsourcing; logistics innovation processes are dynamic and may improve supplier partnerships; logistics innovations in the supply chain are as dependent...

Life quality index revisited

DEFF Research Database (Denmark)

Ditlevsen, Ove Dalager

2004-01-01

The derivation of the life quality index (LQI) is revisited for a revision. This revision takes into account the unpaid but necessary work time needed to stay alive in clean and healthy conditions to be fit for effective wealth producing work and to enjoyable free time. Dimension analysis...... at birth should not vary between countries. Finally the distributional assumptions are relaxed as compared to the assumptions made in an earlier work by the author. These assumptions concern the calculation of the life expectancy change due to the removal of an accident source. Moreover a simple public...... consistency problems with the standard power function expression of the LQI are pointed out. It is emphasized that the combination coefficient in the convex differential combination between the relative differential of the gross domestic product per capita and the relative differential of the expected life...

Klein's double discontinuity revisited

DEFF Research Database (Denmark)

Winsløw, Carl; Grønbæk, Niels

2014-01-01

Much effort and research has been invested into understanding and bridging the ‘gaps’ which many students experience in terms of contents and expectations as they begin university studies with a heavy component of mathematics, typically in the form of calculus courses. We have several studies...... of bridging measures, success rates and many other aspects of these “entrance transition” problems. In this paper, we consider the inverse transition, experienced by university students as they revisit core parts of high school mathematics (in particular, calculus) after completing the undergraduate...... mathematics courses which are mandatory to become a high school teacher of mathematics. To what extent does the “advanced” experience enable them to approach the high school calculus in a deeper and more autonomous way ? To what extent can “capstone” courses support such an approach ? How could it be hindered...

Rereading Albert B. Lord's The Singer of Tales . Revisiting the ...

African Journals Online (AJOL)

Access to a fresh set of video-recordings of Sesotho praise-poetry made in the year 2000 enabled the author to revisit his adaptation of Albert Lord's definition of the formula as a dynamic compositional device that the oral poet utilizes during delivery. The basic adaptation made in 1983 pertains to heroic praises (dithoko tsa ...

Re-Visit to the School Nurse and Adolescents' Medicine Use

Science.gov (United States)

Borup, Ina K.; Andersen, Anette; Holstein, Bjorn E.

2011-01-01

Objective: To examine if students who re-visit the school nurse use medicines differently than other students when exposed to aches and psychological problems. Methods: The study includes all 11-, 13- and 15-year-old students from a random sample of schools in Denmark, response rate 87 per cent, n = 5,205. The data collection followed the…

A virtual water maze revisited: Two-year changes in navigation performance and their neural correlates in healthy adults.

Science.gov (United States)

Daugherty, Ana M; Raz, Naftali

2017-02-01

Age-related declines in spatial navigation are associated with deficits in procedural and episodic memory and deterioration of their neural substrates. For the lack of longitudinal evidence, the pace and magnitude of these declines and their neural mediators remain unclear. Here we examined virtual navigation in healthy adults (N=213, age 18-77 years) tested twice, two years apart, with complementary indices of navigation performance (path length and complexity) measured over six learning trials at each occasion. Slopes of skill acquisition curves and longitudinal change therein were estimated in structural equation modeling, together with change in regional brain volumes and iron content (R2* relaxometry). Although performance on the first trial did not differ between occasions separated by two years, the slope of path length improvement over trials was shallower and end-of-session performance worse at follow-up. Advanced age, higher pulse pressure, smaller cerebellar and caudate volumes, and greater caudate iron content were associated with longer search paths, i.e. poorer navigation performance. In contrast, path complexity diminished faster over trials at follow-up, albeit less so in older adults. Improvement in path complexity after two years was predicted by lower baseline hippocampal iron content and larger parahippocampal volume. Thus, navigation path length behaves as an index of perceptual-motor skill that is vulnerable to age-related decline, whereas path complexity may reflect cognitive mapping in episodic memory that improves with repeated testing, although not enough to overcome age-related deficits. Copyright © 2016 Elsevier Inc. All rights reserved.

An analytic algorithm for global coverage of the revisiting orbit and its application to the CFOSAT satellite

Science.gov (United States)

Xu, Ming; Huang, Li

2014-08-01

This paper addresses a new analytic algorithm for global coverage of the revisiting orbit and its application to the mission revisiting the Earth within long periods of time, such as Chinese-French Oceanic Satellite (abbr., CFOSAT). In the first, it is presented that the traditional design methodology of the revisiting orbit for some imaging satellites only on the single (ascending or descending) pass, and the repeating orbit is employed to perform the global coverage within short periods of time. However, the selection of the repeating orbit is essentially to yield the suboptimum from the rare measure of rational numbers of passes per day, which will lose lots of available revisiting orbits. Thus, an innovative design scheme is proposed to check both rational and irrational passes per day to acquire the relationship between the coverage percentage and the altitude. To improve the traditional imaging only on the single pass, the proposed algorithm is mapping every pass into its ascending and descending nodes on the specified latitude circle, and then is accumulating the projected width on the circle by the field of view of the satellite. The ergodic geometry of coverage percentage produced from the algorithm is affecting the final scheme, such as the optimal one owning the largest percentage, and the balance one possessing the less gradient in its vicinity, and is guiding to heuristic design for the station-keeping control strategies. The application of CFOSAT validates the feasibility of the algorithm.

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.

Impact of Bounded Noise and Rewiring on the Formation and Instability of Spiral Waves in a Small-World Network of Hodgkin-Huxley Neurons.

Science.gov (United States)

Yao, Yuangen; Deng, Haiyou; Ma, Chengzhang; Yi, Ming; Ma, Jun

2017-01-01

Spiral waves are observed in the chemical, physical and biological systems, and the emergence of spiral waves in cardiac tissue is linked to some diseases such as heart ventricular fibrillation and epilepsy; thus it has importance in theoretical studies and potential medical applications. Noise is inevitable in neuronal systems and can change the electrical activities of neuron in different ways. Many previous theoretical studies about the impacts of noise on spiral waves focus an unbounded Gaussian noise and even colored noise. In this paper, the impacts of bounded noise and rewiring of network on the formation and instability of spiral waves are discussed in small-world (SW) network of Hodgkin-Huxley (HH) neurons through numerical simulations, and possible statistical analysis will be carried out. Firstly, we present SW network of HH neurons subjected to bounded noise. Then, it is numerically demonstrated that bounded noise with proper intensity σ, amplitude A, or frequency f can facilitate the formation of spiral waves when rewiring probability p is below certain thresholds. In other words, bounded noise-induced resonant behavior can occur in the SW network of neurons. In addition, rewiring probability p always impairs spiral waves, while spiral waves are confirmed to be robust for small p, thus shortcut-induced phase transition of spiral wave with the increase of p is induced. Furthermore, statistical factors of synchronization are calculated to discern the phase transition of spatial pattern, and it is confirmed that larger factor of synchronization is approached with increasing of rewiring probability p, and the stability of spiral wave is destroyed.

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)

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

Science.gov (United States)

Chrol-Cannon, Joseph; Jin, Yaochu

2014-11-01

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

The Peter Effect Revisited: Reading Habits and Attitudes of College Students

Science.gov (United States)

Applegate, Anthony J.; Applegate, Mary DeKonty; Mercantini, Martha A.; McGeehan, Catherine M.; Cobb, Jeanne B.; DeBoy, Joanne R.; Modla, Virginia B.; Lewinski, Kimberly E.

2014-01-01

Certainly a primary goal of literacy education is the creation of avid, enthusiastic, and highly motivated readers. However, in this article revisiting the Peter Effect (Applegate & Applegate, 2004), researchers surveyed more than 1,000 college sophomores and found strikingly low levels of enthusiasm for reading. Only 46.6% of surveyed…

Ambulatory thyroidectomy: A multistate study of revisits and complications

OpenAIRE

Orosco, RK; Lin, HW; Bhattacharyya, N

2015-01-01

© 2015 American Academy of Otolaryngology - Head and Neck Surgery Foundation. Objective. Determine rates and reasons for revisits after ambulatory adult thyroidectomy. Study Design. Cross-sectional analysis of multistate ambulatory surgery and hospital databases. Setting. Ambulatory surgery data from the State Ambulatory Surgery Databases of California, Florida, Iowa, and New York for calendar years 2010 and 2011. Subjects and Methods. Ambulatory thyroidectomy cases were linked to state ambul...

Taï chimpanzees anticipate revisiting high-valued fruit trees from further distances.

Science.gov (United States)

Ban, Simone D; Boesch, Christophe; Janmaat, Karline R L

2014-11-01

The use of spatio-temporal memory has been argued to increase food-finding efficiency in rainforest primates. However, the exact content of this memory is poorly known to date. This study investigated what specific information from previous feeding visits chimpanzees (Pan troglodytes verus), in Taï National Park, Côte d'Ivoire, take into account when they revisit the same feeding trees. By following five adult females for many consecutive days, we tested from what distance the females directed their travels towards previously visited feeding trees and how previous feeding experiences and fruit tree properties influenced this distance. To exclude the influence of sensory cues, the females' approach distance was measured from their last significant change in travel direction until the moment they entered the tree's maximum detection field. We found that chimpanzees travelled longer distances to trees at which they had previously made food grunts and had rejected fewer fruits compared to other trees. In addition, the results suggest that the chimpanzees were able to anticipate the amount of fruit that they would find in the trees. Overall, our findings are consistent with the hypothesis that chimpanzees act upon a retrieved memory of their last feeding experiences long before they revisit feeding trees, which would indicate a daily use of long-term prospective memory. Further, the results are consistent with the possibility that positive emotional experiences help to trigger prospective memory retrieval in forest areas that are further away and have fewer cues associated with revisited feeding trees.

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

Science.gov (United States)

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

High precision mass measurements in Ψ and Υ families revisited

International Nuclear Information System (INIS)

Artamonov, A.S.; Baru, S.E.; Blinov, A.E.

2000-01-01

High precision mass measurements in Ψ and Υ families performed in 1980-1984 at the VEPP-4 collider with OLYA and MD-1 detectors are revisited. The corrections for the new value of the electron mass are presented. The effect of the updated radiative corrections has been calculated for the J/Ψ(1S) and Ψ(2S) mass measurements [ru

Neural chips, neural computers and application in high and superhigh energy physics experiments

International Nuclear Information System (INIS)

Nikityuk, N.M.; )

2001-01-01

Architecture peculiarity and characteristics of series of neural chips and neural computes used in scientific instruments are considered. Tendency of development and use of them in high energy and superhigh energy physics experiments are described. Comparative data which characterize the efficient use of neural chips for useful event selection, classification elementary particles, reconstruction of tracks of charged particles and for search of hypothesis Higgs particles are given. The characteristics of native neural chips and accelerated neural boards are considered [ru

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...

Commentary on "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning"

Science.gov (United States)

Hewitt, Jim

2015-01-01

The article, "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning" is an interesting study that operates at the intersection of learning theory and learning analytics. The authors observe that the relationship between learning theory and research in the learning analytics field is constrained by several…

Double digest revisited : Complexity and Approximability in the Presence of Noisy Data

NARCIS (Netherlands)

Cieliebak, Mark; Eidenbenz, Stephan; Woeginger, Gerhard; Warnow, Tandy; Zhu, Binhai

2003-01-01

We revisit the Double Digest problem, which occurs in sequencing of large DNA strings and consists of reconstructing the relative positions of cut sites from two different enzymes: we first show that Double Digest is strongly NP-complete, improving upon previous results that only showed weak

First-principles lattice-gas Hamiltonian revisited: O-Pd(100)

OpenAIRE

Kappus, Wolfgang

2016-01-01

The methodology of deriving an adatom lattice-gas Hamiltonian (LGH) from first principles (FP) calculations is revisited. Such LGH cluster expansions compute a large set of lateral pair-, trio-, quarto interactions by solving a set of linear equations modelling regular adatom configurations and their FP energies. The basic assumption of truncating interaction terms beyond fifth nearest neighbors does not hold when adatoms show longer range interactions, e.g. substrate mediated elastic interac...

Advanced Change Theory Revisited: An Article Critique

Directory of Open Access Journals (Sweden)

R. Scott Pochron

2008-12-01

Full Text Available The complexity of life in 21st century society requires new models for leading and managing change. With that in mind, this paper revisits the model for Advanced Change Theory (ACT as presented by Quinn, Spreitzer, and Brown in their article, “Changing Others Through Changing Ourselves: The Transformation of Human Systems” (2000. The authors present ACT as a potential model for facilitating change in complex organizations. This paper presents a critique of the article and summarizes opportunities for further exploring the model in the light of current trends in developmental and integral theory.

Revisiting fifth forces in the Galileon model

Energy Technology Data Exchange (ETDEWEB)

Burrage, Clare [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany). Gruppe Theorie; Seery, David [Sussex Univ., Brighton (United Kingdom). Dept. of Physics and Astronomy

2010-05-15

A Galileon field is one which obeys a spacetime generalization of the non- relativistic Galilean invariance. Such a field may possess non-canonical kinetic terms, but ghost-free theories with a well-defined Cauchy problem exist, constructed using a finite number of relevant operators. The interactions of this scalar with matter are hidden by the Vainshtein effect, causing the Galileon to become weakly coupled near heavy sources. We revisit estimates of the fifth force mediated by a Galileon field, and show that the parameters of the model are less constrained by experiment than previously supposed. (orig.)

Large J expansion in ABJM theory revisited.

Science.gov (United States)

Dimov, H; Mladenov, S; Rashkov, R C

Recently there has been progress in the computation of the anomalous dimensions of gauge theory operators at strong coupling by making use of the AdS/CFT correspondence. On the string theory side they are given by dispersion relations in the semiclassical regime. We revisit the problem of a large-charge expansion of the dispersion relations for simple semiclassical strings in an [Formula: see text] background. We present the calculation of the corresponding anomalous dimensions of the gauge theory operators to an arbitrary order using three different methods. Although the results of the three methods look different, power series expansions show their consistency.

The power reinforcement framework revisited

DEFF Research Database (Denmark)

Nielsen, Jeppe; Andersen, Kim Normann; Danziger, James N.

2016-01-01

Whereas digital technologies are often depicted as being capable of disrupting long-standing power structures and facilitating new governance mechanisms, the power reinforcement framework suggests that information and communications technologies tend to strengthen existing power arrangements within...... public organizations. This article revisits the 30-yearold power reinforcement framework by means of an empirical analysis on the use of mobile technology in a large-scale programme in Danish public sector home care. It explores whether and to what extent administrative management has controlled decision......-making and gained most benefits from mobile technology use, relative to the effects of the technology on the street-level workers who deliver services. Current mobile technology-in-use might be less likely to be power reinforcing because it is far more decentralized and individualized than the mainly expert...

Location coding by opponent neural populations in the auditory cortex.

Directory of Open Access Journals (Sweden)

G Christopher Stecker

2005-03-01

Full Text Available Although the auditory cortex plays a necessary role in sound localization, physiological investigations in the cortex reveal inhomogeneous sampling of auditory space that is difficult to reconcile with localization behavior under the assumption of local spatial coding. Most neurons respond maximally to sounds located far to the left or right side, with few neurons tuned to the frontal midline. Paradoxically, psychophysical studies show optimal spatial acuity across the frontal midline. In this paper, we revisit the problem of inhomogeneous spatial sampling in three fields of cat auditory cortex. In each field, we confirm that neural responses tend to be greatest for lateral positions, but show the greatest modulation for near-midline source locations. Moreover, identification of source locations based on cortical responses shows sharp discrimination of left from right but relatively inaccurate discrimination of locations within each half of space. Motivated by these findings, we explore an opponent-process theory in which sound-source locations are represented by differences in the activity of two broadly tuned channels formed by contra- and ipsilaterally preferring neurons. Finally, we demonstrate a simple model, based on spike-count differences across cortical populations, that provides bias-free, level-invariant localization-and thus also a solution to the "binding problem" of associating spatial information with other nonspatial attributes of sounds.

Revisit boundary conditions for the self-adjoint angular flux formulation

Energy Technology Data Exchange (ETDEWEB)

Wang, Yaqi [Idaho National Lab. (INL), Idaho Falls, ID (United States); Gleicher, Frederick N. [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-03-01

We revisit the boundary conditions for SAAF. We derived the equivalent parity variational form ready for coding up. The more rigorous approach of evaluating odd parity should be solving the odd parity equation coupled with the even parity. We proposed a symmetric reflecting boundary condition although neither positive definiteness nor even-odd decoupling is achieved. A simple numerical test verifies the validity of these boundary conditions.

Place attachment and social legitimacy: Revisiting the sustainable entrepreneurship journey

OpenAIRE

Kibler, E; Fink, M; Lang, R; Munoz, PA

2015-01-01

This paper revisits the sustainable entrepreneurship journey by introducing a ‘place- based’ sustainable venture path model. We suggest that distinguishing between emo- tional (‘caring about the place’) and instrumental (‘using the place’) place attachment of sustainable entrepreneurs deepens our understanding of how place-based challenges of sustainable venture legitimacy are managed over time. We conclude with avenues for future sustainable entrepreneurship research.

Teacher Communication Concerns Revisited: Calling into Question the Gnawing Pull towards Equilibrium

Science.gov (United States)

Dannels, Deanna P.

2015-01-01

This study revisits the long-standing teacher communication concerns framework originating over three decades ago. Analysis of 10 years of contemporary GTA teacher communication concerns reveals a typology of 10 concerns, which taken together construct teaching as a process of negotiating relationships, managing identities, and focusing attention.…

Revisiting Learning in Higher Education--Framing Notions Redefined through an Ecological Perspective

Science.gov (United States)

Damsa, Crina; Jornet, Alfredo

2016-01-01

This article employs an ecological perspective as a means of revisiting the notion of learning, with a particular focus on learning in higher education. Learning is reconceptualised as a process entailing mutually constitutive, epistemic, social and affective relations in which knowledge, identity and agency become collective achievements of whole…

Lorentz violation naturalness revisited

Energy Technology Data Exchange (ETDEWEB)

Belenchia, Alessio; Gambassi, Andrea; Liberati, Stefano [SISSA - International School for Advanced Studies, via Bonomea 265, 34136 Trieste (Italy); INFN, Sezione di Trieste, via Valerio 2, 34127 Trieste (Italy)

2016-06-08

We revisit here the naturalness problem of Lorentz invariance violations on a simple toy model of a scalar field coupled to a fermion field via a Yukawa interaction. We first review some well-known results concerning the low-energy percolation of Lorentz violation from high energies, presenting some details of the analysis not explicitly discussed in the literature and discussing some previously unnoticed subtleties. We then show how a separation between the scale of validity of the effective field theory and that one of Lorentz invariance violations can hinder this low-energy percolation. While such protection mechanism was previously considered in the literature, we provide here a simple illustration of how it works and of its general features. Finally, we consider a case in which dissipation is present, showing that the dissipative behaviour does not percolate generically to lower mass dimension operators albeit dispersion does. Moreover, we show that a scale separation can protect from unsuppressed low-energy percolation also in this case.

Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network

Science.gov (United States)

Fang, K.; Shen, C.; Kifer, D.; Yang, X.

2017-12-01

The Soil Moisture Active Passive (SMAP) mission has delivered high-quality and valuable sensing of surface soil moisture since 2015. However, its short time span, coarse resolution, and irregular revisit schedule have limited its use. Utilizing a state-of-the-art deep-in-time neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 soil moisture data using climate forcing, model-simulated moisture, and static physical attributes as inputs. The system removes most of the bias with model simulations and also improves predicted moisture climatology, achieving a testing accuracy of 0.025 to 0.03 in most parts of Continental United States (CONUS). As the first application of LSTM in hydrology, we show that it is more robust than simpler methods in either temporal or spatial extrapolation tests. We also discuss roles of different predictors, the effectiveness of regularization algorithms and impacts of training strategies. With high fidelity to SMAP products, our data can aid various applications including data assimilation, weather forecasting, and soil moisture hindcasting.

The Malady of UNESCO's Archive

Directory of Open Access Journals (Sweden)

Peter Jackson

2014-10-01

Full Text Available This paper offers a critical examination of UNESCO's cultural heritage conven-tions with special regard to the declared transhumanism of the organization's first director-general, Sir Julian Huxley. While Huxley's advocation of eugenics is a well-established fact, this part of his intellectual heritage is usually not considered overtly aligned to his ideas about cultural preservation. On closer consideration, however, improvement and preservation (both cultural and biological turn out to be closely associated concerns in the field of Huxley's intellectual vision.

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.

Quark matter revisited with non-extensive MIT bag model

Energy Technology Data Exchange (ETDEWEB)

Cardoso, Pedro H.G.; Nunes da Silva, Tiago; Menezes, Debora P. [Universidade Federal de Santa Catarina, Departamento de Fisica, CFM, Florianopolis (Brazil); Deppman, Airton [Instituto de Fisica da Universidade de Sao Paulo, Sao Paulo (Brazil)

2017-10-15

In this work we revisit the MIT bag model to describe quark matter within both the usual Fermi-Dirac and the Tsallis statistics. We verify the effects of the non-additivity of the latter by analysing two different pictures: the first order phase transition of the QCD phase diagram and stellar matter properties. While the QCD phase diagram is visually affected by the Tsallis statistics, the resulting effects on quark star macroscopic properties are barely noticed. (orig.)

The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

Science.gov (United States)

Pla, Patrick; Monsoro-Burq, Anne H

2018-05-28

The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

My First CMC Article Revisited: A Window on Spanish L2 Interlanguage

Science.gov (United States)

Blake, Robert

2016-01-01

The computer-assisted language learning (CALL) field seems to change overnight with new technological affordances. Blake revisits his 2000 "LLT" article on computer-mediation communication (CMC) in order to reflect on how the field has examined this topic over the past decade or so. While the Interaction Hypothesis continues to guide…

Risk prediction of emergency department revisit 30 days post discharge: a prospective study.

Directory of Open Access Journals (Sweden)

Shiying Hao

Full Text Available Among patients who are discharged from the Emergency Department (ED, about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization.A decision tree based model with discriminant Electronic Medical Record (EMR features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN, Maine's Health Information Exchange (HIE, between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns.Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.

Neural network regulation driven by autonomous neural firings

Science.gov (United States)

Cho, Myoung Won

2016-07-01

Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

The Best and the Rest: Revisiting the Norm of Normality of Individual Performance

Science.gov (United States)

O'Boyle, Ernest, Jr.; Aguinis, Herman

2012-01-01

We revisit a long-held assumption in human resource management, organizational behavior, and industrial and organizational psychology that individual performance follows a Gaussian (normal) distribution. We conducted 5 studies involving 198 samples including 633,263 researchers, entertainers, politicians, and amateur and professional athletes.…

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.

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

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.

The Levy sections theorem revisited

International Nuclear Information System (INIS)

Figueiredo, Annibal; Gleria, Iram; Matsushita, Raul; Silva, Sergio Da

2007-01-01

This paper revisits the Levy sections theorem. We extend the scope of the theorem to time series and apply it to historical daily returns of selected dollar exchange rates. The elevated kurtosis usually observed in such series is then explained by their volatility patterns. And the duration of exchange rate pegs explains the extra elevated kurtosis in the exchange rates of emerging markets. In the end, our extension of the theorem provides an approach that is simpler than the more common explicit modelling of fat tails and dependence. Our main purpose is to build up a technique based on the sections that allows one to artificially remove the fat tails and dependence present in a data set. By analysing data through the lenses of the Levy sections theorem one can find common patterns in otherwise very different data sets

The Levy sections theorem revisited

Science.gov (United States)

Figueiredo, Annibal; Gleria, Iram; Matsushita, Raul; Da Silva, Sergio

2007-06-01

This paper revisits the Levy sections theorem. We extend the scope of the theorem to time series and apply it to historical daily returns of selected dollar exchange rates. The elevated kurtosis usually observed in such series is then explained by their volatility patterns. And the duration of exchange rate pegs explains the extra elevated kurtosis in the exchange rates of emerging markets. In the end, our extension of the theorem provides an approach that is simpler than the more common explicit modelling of fat tails and dependence. Our main purpose is to build up a technique based on the sections that allows one to artificially remove the fat tails and dependence present in a data set. By analysing data through the lenses of the Levy sections theorem one can find common patterns in otherwise very different data sets.

"Rapid Revisit" Measurements of Sea Surface Winds Using CYGNSS

Science.gov (United States)

Park, J.; Johnson, J. T.

2017-12-01

The Cyclone Global Navigation Satellite System (CYGNSS) is a space-borne GNSS-R (GNSS-Reflectometry) mission that launched December 15, 2016 for ocean surface wind speed measurements. CYGNSS includes 8 small satellites in the same LEO orbit, so that the mission provides wind speed products having unprecedented coverage both in time and space to study multi-temporal behaviors of oceanic winds. The nature of CYGNSS coverage results in some locations on Earth experiencing multiple wind speed measurements within a short period of time (a "clump" of observations in time resulting in a "rapid revisit" series of measurements). Such observations could seemingly provide indications of regions experiencing rapid changes in wind speeds, and therefore be of scientific utility. Temporally "clumped" properties of CYGNSS measurements are investigated using early CYGNSS L1/L2 measurements, and the results show that clump durations and spacing vary with latitude. For example, the duration of a clump can extend as long as a few hours at higher latitudes, with gaps between clumps ranging from 6 to as high as 12 hours depending on latitude. Examples are provided to indicate the potential of changes within a clump to produce a "rapid revisit" product for detecting convective activity. Also, we investigate detector design for identifying convective activities. Results from analyses using recent CYGNSS L2 winds will be provided in the presentation.

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.

Re-visiting RHIC snakes: OPERA fields, n₀ dance

Energy Technology Data Exchange (ETDEWEB)

Meot, F. [Brookhaven National Lab. (BNL), Upton, NY (United States); Gupta, R. [Brookhaven National Lab. (BNL), Upton, NY (United States); Huang, H. [Brookhaven National Lab. (BNL), Upton, NY (United States); Ranjbar, V. [Brookhaven National Lab. (BNL), Upton, NY (United States); Robert-Demolaize, G. [Brookhaven National Lab. (BNL), Upton, NY (United States)

2017-09-22

In this Tech. Note RHIC snakes and stable spin direction $\\vector{n}$₀(s) are re-visited, based on OPERA-computed field maps of the former. The numerical simulations so undertaken provide various outcomes regarding RHIC optics and spin dynamics, in relation with orbital and focusing effects resulting from the use of this realistic 3-D representation of the snakes.

Serotype-specific mortality from invasive Streptococcus pneumoniae disease revisited

DEFF Research Database (Denmark)

Martens, Pernille; Worm, Signe Westring; Lundgren, Bettina

2004-01-01

Serotype-specific mortality from invasive Streptococcus pneumoniae disease revisited.Martens P, Worm SW, Lundgren B, Konradsen HB, Benfield T. Department of Infectious Diseases 144, Hvidovre University Hospital, DK-2650 Hvidovre, Denmark. pernillemartens@yahoo.com BACKGROUND: Invasive infection...... with Streptococcus pneumoniae (pneumococci) causes significant morbidity and mortality. Case series and experimental data have shown that the capsular serotype is involved in the pathogenesis and a determinant of disease outcome. METHODS: Retrospective review of 464 cases of invasive disease among adults diagnosed...

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

Directory of Open Access Journals (Sweden)

Alexander eHuk

2012-10-01

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

Biodiversity Meets Neuroscience: From the Sequencing Ship (Ship-Seq) to Deciphering Parallel Evolution of Neural Systems in Omic’s Era

Science.gov (United States)

Moroz, Leonid L.

2015-01-01

The origins of neural systems and centralized brains are one of the major transitions in evolution. These events might occur more than once over 570–600 million years. The convergent evolution of neural circuits is evident from a diversity of unique adaptive strategies implemented by ctenophores, cnidarians, acoels, molluscs, and basal deuterostomes. But, further integration of biodiversity research and neuroscience is required to decipher critical events leading to development of complex integrative and cognitive functions. Here, we outline reference species and interdisciplinary approaches in reconstructing the evolution of nervous systems. In the “omic” era, it is now possible to establish fully functional genomics laboratories aboard of oceanic ships and perform sequencing and real-time analyses of data at any oceanic location (named here as Ship-Seq). In doing so, fragile, rare, cryptic, and planktonic organisms, or even entire marine ecosystems, are becoming accessible directly to experimental and physiological analyses by modern analytical tools. Thus, we are now in a position to take full advantages from countless “experiments” Nature performed for us in the course of 3.5 billion years of biological evolution. Together with progress in computational and comparative genomics, evolutionary neuroscience, proteomic and developmental biology, a new surprising picture is emerging that reveals many ways of how nervous systems evolved. As a result, this symposium provides a unique opportunity to revisit old questions about the origins of biological complexity. PMID:26163680

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

Directory of Open Access Journals (Sweden)

Chernoded Andrey

2017-01-01

Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

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 ...

The Assassination of John F. Kennedy: Revisiting the Medical Data

OpenAIRE

Rohrich, Rod J.; Nagarkar, Purushottam; Stokes, Mike; Weinstein, Aaron; Mantik, David W.; Jensen, J. Arthur

2014-01-01

Thank you for publishing "The Assassination of John F. Kennedy: Revisiting the Medical Data."1 The central conclusion of this study is that the assassination remains controversial and that some of the controversy must be attributable to the "reporting and handling of the medical evidence." With the greatest respect for you and Dr. Robert McClelland, let me argue that your text and on-line interviews perpetuate the central misunderstanding of the assassination and there...

Dispute Resolution and Technology: Revisiting the Justification of Conflict Management

OpenAIRE

Koulu, Riikka

2016-01-01

This study, Dispute Resolution and Technology: Revisiting the Justification of Conflict Management, belongs to the fields of procedural law, legal theory and law and technology studies. In this study the changes in dispute resolution caused by technology are evaluated. The overarching research question of this study is how does implementing technology to dispute resolution challenge the justification of law as a legitimised mode of violence? Before answering such an abstract research question...

Deja vu: The Unified Command Plan of the Future Revisited

Science.gov (United States)

2011-05-19

Approved for Public Release; Distribution is Unlimited Déjà vu : The Unified Command Plan of the Future Revisited A Monograph by Lieutenant...DD-MM-YYYY) 19-05-2011 2. REPORT TYPE Monograph 3. DATES COVERED (From - To) JUL 2010 – MAY 2011 4. TITLE AND SUBTITLE Déjà vu : The Unified...i SCHOOL OF ADVANCED MILITARY STUDIES MONOGRAPH APPROVAL Lieutenant Colonel Edward Francis Martignetti Title of Monograph: Déjà vu : The Unified

Interpretation of transport barriers and of subneoclassical transport in the framework of the revisited neoclassical theory

International Nuclear Information System (INIS)

Rogister, A.L.

1999-01-01

'Subneoclassical' heat fluxes are predicted in the high collisionality regime by the revisited neoclassical theory, which includes the roles of Finite Larmor Radius effects and Inertia, that we published earlier. Unlike conventional neoclassical theory, the revisited theory further provides a non degenerate ambipolarity constraint which defines unambiguously the radial electric field. Together with the parallel momentum equation, the ambipolarity constraint leads, under some conditions, to radial electric field profiles with high negative shear akin to those observed in spontaneous edge transport barriers. The predictions of the theory are outlined, with emphasis laid on the interpretation of experimental results such as magnitude of the jumps, width of the shear layer, local scaling laws. Extension of the theory to triggered transitions and cold pulse propagation studies is suggested. (author)

How does the Ambience of Cafe Affect the Revisit Intention Among its Patrons? A S on the Cafes in Ipoh, Perak

Directory of Open Access Journals (Sweden)

AbuThahir Sharmeela-Banu Syed

2018-01-01

Full Text Available Food service industry is growing rapidly as a result of the changing consumer lifestyle. The food service industry is highly competitive due to the increasing number of new entrants offering inventive food products and services. In order to be outstanding in such competitive industry, retailers nowadays opt to emphasize on their store environment. Past studies discovered that store environment stimulates emotions that significantly boost customer revisit intention. As a result, retailers attempt to differentiate their store by combining various environmental stimuli to create an attractive ambience that will in turn draw in the customers. Hence, this study attempts to investigate the impact of various café ambience factors on the patrons’ revisit intention. The patrons of cafes in Ipoh, Perak were selected using purposive sampling technique to be the respondents of this study. 250 questionnaires were collected and Partial Least Square technique was used to analyse the data collected. Findings show that all the five factors of café ambience namely lighting, music, decoration, cleanliness and layouts were significantly influencing the patrons’ revisit intention. Of these five factors, lighting was most influential while music was the least influential in affecting the patrons’ revisit intention. Accordingly, this study lists several recommendations for practitioners and academics with regards to the store environment and its impact on the repurchase intention.

Revisiting reflexology: Concept, evidence, current practice, and practitioner training

OpenAIRE

Embong, Nurul Haswani; Soh, Yee Chang; Ming, Long Chiau; Wong, Tin Wui

2015-01-01

Reflexology is basically a study of how one part of the human body relates to another part of the body. Reflexology practitioners rely on the reflexes map of the feet and hands to all the internal organs and other human body parts. They believe that by applying the appropriate pressure and massage certain spots on the feet and hands, all other body parts could be energized and rejuvenated. This review aimed to revisit the concept of reflexology and examine its effectiveness, practices, and th...

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...

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...

NeuroMEMS: Neural Probe Microtechnologies

Directory of Open Access Journals (Sweden)

Sam Musallam

2008-10-01

Full Text Available Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer’s, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

Revisiting the Metaphor of the Island: Challenging "World Culture" from an Island Misunderstood

Science.gov (United States)

Rappleye, Jeremy

2015-01-01

This article revisits the newly "discovered" island that world culture theorists have repeatedly utilised to explain their theoretical stance, conceptual preferences and methodological approach. Yet, it seeks to (re)connect world culture with the real world by replacing their imagined atoll with a real one--the island-nation of Japan. In…

Revisiting R-invariant direct gauge mediation

Energy Technology Data Exchange (ETDEWEB)

Chiang, Cheng-Wei [Center for Mathematics and Theoretical Physics andDepartment of Physics, National Central University,Taoyuan, Taiwan 32001, R.O.C. (China); Institute of Physics, Academia Sinica,Taipei, Taiwan 11529, R.O.C. (China); Physics Division, National Center for Theoretical Sciences,Hsinchu, Taiwan 30013, R.O.C. (China); Kavli IPMU (WPI), UTIAS, University of Tokyo,Kashiwa, Chiba 277-8583 (Japan); Harigaya, Keisuke [Department of Physics, University of California,Berkeley, California 94720 (United States); Theoretical Physics Group, Lawrence Berkeley National Laboratory,Berkeley, California 94720 (United States); ICRR, University of Tokyo,Kashiwa, Chiba 277-8582 (Japan); Ibe, Masahiro [Kavli IPMU (WPI), UTIAS, University of Tokyo,Kashiwa, Chiba 277-8583 (Japan); ICRR, University of Tokyo,Kashiwa, Chiba 277-8582 (Japan); Yanagida, Tsutomu T. [Kavli IPMU (WPI), UTIAS, University of Tokyo,Kashiwa, Chiba 277-8583 (Japan)

2016-03-21

We revisit a special model of gauge mediated supersymmetry breaking, the “R-invariant direct gauge mediation.” We pay particular attention to whether the model is consistent with the minimal model of the μ-term, i.e., a simple mass term of the Higgs doublets in the superpotential. Although the incompatibility is highlighted in view of the current experimental constraints on the superparticle masses and the observed Higgs boson mass, the minimal μ-term can be consistent with the R-invariant gauge mediation model via a careful choice of model parameters. We derive an upper limit on the gluino mass from the observed Higgs boson mass. We also discuss whether the model can explain the 3σ excess of the Z+jets+E{sub T}{sup miss} events reported by the ATLAS collaboration.

Revisiting kaon physics in general Z scenario

Directory of Open Access Journals (Sweden)

Motoi Endo

2017-08-01

Full Text Available New physics contributions to the Z penguin are revisited in the light of the recently-reported discrepancy of the direct CP violation in K→ππ. Interference effects between the standard model and new physics contributions to ΔS=2 observables are taken into account. Although the effects are overlooked in the literature, they make experimental bounds significantly severer. It is shown that the new physics contributions must be tuned to enhance B(KL→π0νν¯, if the discrepancy of the direct CP violation is explained with satisfying the experimental constraints. The branching ratio can be as large as 6×10−10 when the contributions are tuned at the 10% level.

Revisiting the Decision of Death in Hurst v. Florida.

Science.gov (United States)

Cooke, Brian K; Ginory, Almari; Zedalis, Jennifer

2016-12-01

The United States Supreme Court has considered the question of whether a judge or a jury must make the findings necessary to support imposition of the death penalty in several notable cases, including Spaziano v. Florida (1984), Hildwin v. Florida (1989), and Ring v. Arizona (2002). In 2016, the U.S. Supreme Court revisited the subject in Hurst v. Florida Florida Statute § 921.141 allows the judge, after weighing aggravating and mitigating circumstances, to enter a sentence of life imprisonment or death. Before Hurst, Florida's bifurcated sentencing proceedings included an advisory sentence from jurors and a separate judicial hearing without juror involvement. In Hurst, the Court revisited the question of whether Florida's capital sentencing scheme violates the Sixth Amendment, which requires a jury, not a judge, to find each fact necessary to impose a sentence of death in light of Ring In an eight-to-one decision, the Court reversed the judgment of the Florida Supreme Court, holding that the Sixth Amendment requires a jury to find the aggravating factors necessary for imposing the death penalty. The role of Florida juries in capital sentencing proceedings was thereby elevated from advisory to determinative. We examine the Court's decision and offer commentary regarding this shift from judge to jury in the final imposition of the death penalty and the overall effect of this landmark case. © 2016 American Academy of Psychiatry and the Law.

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

Magnetic moments revisited

International Nuclear Information System (INIS)

Towner, I.S.; Khanna, F.C.

1984-01-01

Consideration of core polarization, isobar currents and meson-exchange processes gives a satisfactory understanding of the ground-state magnetic moments in closed-shell-plus (or minus)-one nuclei, A = 3, 15, 17, 39 and 41. Ever since the earliest days of the nuclear shell model the understanding of magnetic moments of nuclear states of supposedly simple configurations, such as doubly closed LS shells +-1 nucleon, has been a challenge for theorists. The experimental moments, which in most cases are known with extraordinary precision, show a small yet significant departure from the single-particle Schmidt values. The departure, however, is difficult to evaluate precisely since, as will be seen, it results from a sensitive cancellation between several competing corrections each of which can be as large as the observed discrepancy. This, then, is the continuing fascination of magnetic moments. In this contribution, we revisit the subjet principally to identify the role played by isobar currents, which are of much concern at this conference. But in so doing we warn quite strongly of the dangers of considering just isobar currents in isolation; equal consideration must be given to competing processes which in this context are the mundane nuclear structure effects, such as core polarization, and the more popular meson-exchange currents

Growth of the firm and foreign trade: Adrian Wood's theory revisited

OpenAIRE

Catermol, Fabrício

2006-01-01

This paper analyzes the growth of the firm by foreign trade. The theory of Adrian Wood is revisited for the analysis of growth and profit trade-off and improved to cope with growth by exports. The main outcome of this paper is that low domestic demand can be a very important factor to firm choices growth by foreign market. However, the growth of domestic demand does not necessarily reduce exports.

Revisiting the description of Protein-Protein interfaces. Part II: Experimental study

OpenAIRE

Cazals , Frédéric; Proust , Flavien

2006-01-01

This paper provides a detailed experimental study of an interface model developed in the companion article F. Cazals and F. Proust, Revisiting the description of Protein-Protein interfaces. Part I: algorithms. Our experimental study is concerned with the usual database of protein-protein complexes, split into five families (Proteases, Immune system, Enzyme Complexes, Signal transduction, Misc.) Our findings, which bear some contradictions with usual statements are the following: (i)Connectivi...

Small-angle scattering theory revisited: Photocurrent and spatial localization

DEFF Research Database (Denmark)

Basse, N.P.; Zoletnik, S.; Michelsen, Poul

2005-01-01

In this paper theory on collective scattering measurements of electron density fluctuations in fusion plasmas is revisited. We present the first full derivation of the expression for the photocurrent beginning at the basic scattering concepts. Thereafter we derive detailed expressions for the auto......- and crosspower spectra obtained from measurements. These are discussed and simple simulations made to elucidate the physical meaning of the findings. In this context, the known methods of obtaining spatial localization are discussed and appraised. Where actual numbers are applied, we utilize quantities from two...

Article Review: Advanced Change Theory Revisited: An Article Critique

Directory of Open Access Journals (Sweden)

R. Scott Pochron

2008-12-01

Full Text Available The complexity of life in 21st century society requires new models for leadingand managing change. With that in mind, this paper revisits the model for AdvancedChange Theory (ACT as presented by Quinn, Spreitzer, and Brown in their article,“Changing Others Through Changing Ourselves: The Transformation of HumanSystems” (2000. The authors present ACT as a potential model for facilitating change incomplex organizations. This paper presents a critique of the article and summarizesopportunities for further exploring the model in the light of current trends indevelopmental and integral theory.

Creative Experience and Revisit Intention of Handmade Oriental Parasol Umbrella in Kaohsiung

OpenAIRE

Yi-Ju Lee

2015-01-01

This study identified the hypothesised relationship between creative experience, and revisit intention of handmade oriental parasol umbrella in Kaohsiung, Taiwan. A face-to-face questionnaire survey was administered in Meinong town, Kaohsiung. The components of creative experience were found as "sense of achievement", "unique learning" and "interaction with instructors" in creative tourism. The result also revealed significant positive relationships between creative exper...

Characteristics of revisits of children at risk for serious infections in pediatric emergency care

NARCIS (Netherlands)

E. De Vos-Kerkhof (Evelien); D.H.F. Geurts (Dorien); E.W. Steyerberg (Ewout); M. Lakhanpaul (Monica); H.A. Moll (Henriëtte); R. Oostenbrink (Rianne)

2018-01-01

textabstractIn this study, we aimed to identify characteristics of (unscheduled) revisits and its optimal time frame after Emergency Department (ED) discharge. Children with fever, dyspnea, or vomiting/diarrhea (1 month–16 years) who attended the ED of Erasmus MC-Sophia, Rotterdam (2010–2013), the

Biodiversity Meets Neuroscience: From the Sequencing Ship (Ship-Seq) to Deciphering Parallel Evolution of Neural Systems in Omic's Era.

Science.gov (United States)

Moroz, Leonid L

2015-12-01

The origins of neural systems and centralized brains are one of the major transitions in evolution. These events might occur more than once over 570-600 million years. The convergent evolution of neural circuits is evident from a diversity of unique adaptive strategies implemented by ctenophores, cnidarians, acoels, molluscs, and basal deuterostomes. But, further integration of biodiversity research and neuroscience is required to decipher critical events leading to development of complex integrative and cognitive functions. Here, we outline reference species and interdisciplinary approaches in reconstructing the evolution of nervous systems. In the "omic" era, it is now possible to establish fully functional genomics laboratories aboard of oceanic ships and perform sequencing and real-time analyses of data at any oceanic location (named here as Ship-Seq). In doing so, fragile, rare, cryptic, and planktonic organisms, or even entire marine ecosystems, are becoming accessible directly to experimental and physiological analyses by modern analytical tools. Thus, we are now in a position to take full advantages from countless "experiments" Nature performed for us in the course of 3.5 billion years of biological evolution. Together with progress in computational and comparative genomics, evolutionary neuroscience, proteomic and developmental biology, a new surprising picture is emerging that reveals many ways of how nervous systems evolved. As a result, this symposium provides a unique opportunity to revisit old questions about the origins of biological complexity. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

Neurophysiology and neural engineering: a review.

Science.gov (United States)

Prochazka, Arthur

2017-08-01

Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight. Copyright © 2017 the American Physiological Society.

Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

NARCIS (Netherlands)

D. Zambrano (Davide); S.M. Bohte (Sander)

2016-01-01

textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

Post-Inflationary Gravitino Production Revisited

CERN Document Server

Ellis, John; Nanopoulos, Dimitri V.; Olive, Keith A.; Peloso, Marco

2016-01-01

We revisit gravitino production following inflation. As a first step, we review the standard calculation of gravitino production in the thermal plasma formed at the end of post-inflationary reheating when the inflaton has completely decayed. Next we consider gravitino production prior to the completion of reheating, assuming that the inflaton decay products thermalize instantaneously while they are still dilute. We then argue that instantaneous thermalization is in general a good approximation, and also show that the contribution of non-thermal gravitino production via the collisions of inflaton decay products prior to thermalization is relatively small. Our final estimate of the gravitino-to-entropy ratio is approximated well by a standard calculation of gravitino production in the post-inflationary thermal plasma assuming total instantaneous decay and thermalization at a time $t \\simeq 1.2/\\Gamma_\\phi$. Finally, in light of our calculations, we consider potential implications of upper limits on the gravitin...

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)

Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

Science.gov (United States)

Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

2017-10-01

This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

Revisiting the Gun Ownership and Violence Link; a multi- level analysis of victimisation survey data.

NARCIS (Netherlands)

van Kesteren, J.N.

2014-01-01

The link between gun ownership victimisation by violent crime remains one of the most contested issues in criminology. Some authors claim that high gun availability facilitates serious violence. Others claim that gun ownership prevents crime. This article revisits these issues using individual and

Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

Science.gov (United States)

Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

2015-01-01

Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

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

Directory of Open Access Journals (Sweden)

S. Lee Hong

2012-09-01

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

NP-hardness of the cluster minimization problem revisited

Science.gov (United States)

Adib, Artur B.

2005-10-01

The computational complexity of the 'cluster minimization problem' is revisited (Wille and Vennik 1985 J. Phys. A: Math. Gen. 18 L419). It is argued that the original NP-hardness proof does not apply to pairwise potentials of physical interest, such as those that depend on the geometric distance between the particles. A geometric analogue of the original problem is formulated, and a new proof for such potentials is provided by polynomial time transformation from the independent set problem for unit disk graphs. Limitations of this formulation are pointed out, and new subproblems that bear more direct consequences to the numerical study of clusters are suggested.

NP-hardness of the cluster minimization problem revisited

International Nuclear Information System (INIS)

Adib, Artur B

2005-01-01

The computational complexity of the 'cluster minimization problem' is revisited (Wille and Vennik 1985 J. Phys. A: Math. Gen. 18 L419). It is argued that the original NP-hardness proof does not apply to pairwise potentials of physical interest, such as those that depend on the geometric distance between the particles. A geometric analogue of the original problem is formulated, and a new proof for such potentials is provided by polynomial time transformation from the independent set problem for unit disk graphs. Limitations of this formulation are pointed out, and new subproblems that bear more direct consequences to the numerical study of clusters are suggested

NP-hardness of the cluster minimization problem revisited

Energy Technology Data Exchange (ETDEWEB)

Adib, Artur B [Physics Department, Brown University, Providence, RI 02912 (United States)

2005-10-07

The computational complexity of the 'cluster minimization problem' is revisited (Wille and Vennik 1985 J. Phys. A: Math. Gen. 18 L419). It is argued that the original NP-hardness proof does not apply to pairwise potentials of physical interest, such as those that depend on the geometric distance between the particles. A geometric analogue of the original problem is formulated, and a new proof for such potentials is provided by polynomial time transformation from the independent set problem for unit disk graphs. Limitations of this formulation are pointed out, and new subproblems that bear more direct consequences to the numerical study of clusters are suggested.

Rural-Nonrural Disparities in Postsecondary Educational Attainment Revisited

Science.gov (United States)

Byun, Soo-yong; Meece, Judith L.; Irvin, Matthew J.

2013-01-01

Using data from the National Educational Longitudinal Study, this study revisited rural-nonrural disparities in educational attainment by considering a comprehensive set of factors that constrain and support youth's college enrollment and degree completion. Results showed that rural students were more advantaged in community social resources compared to nonrural students, and these resources were associated with a significant increase in the likelihood of bachelor's degree attainment. Yet results confirmed that rural students lagged behind nonrural students in attaining a bachelor's degree largely due to their lower socioeconomic background. The findings present a more comprehensive picture of the complexity of geographic residence in shaping college enrollment and degree attainment. PMID:24285873

Sequential neural models with stochastic layers

DEFF Research Database (Denmark)

Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

2016-01-01

How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

An Improvement on LSB Matching and LSB Matching Revisited Steganography Methods

OpenAIRE

Qazanfari, Kazem; Safabakhsh, Reza

2017-01-01

The aim of the steganography methods is to communicate securely in a completely undetectable manner. LSB Matching and LSB Matching Revisited steganography methods are two general and esiest methods to achieve this aim. Being secured against first order steganalysis methods is the most important feature of these methods. On the other hand, these methods don't consider inter pixel dependency. Therefore, recently, several steganalysis methods are proposed that by using co-occurrence matrix detec...

Forecasting of Satisfaction and Revisit Intention of Indonesia Shoppers in Shopping Malls

OpenAIRE

Ayuni, Risca Fitri

2017-01-01

. Due to tight competition, changing value proposition of customer and shiftingof mall functions become center for leisure purposes, shopping mall developer mustfocus on create shopper satisfaction to influence their post-transaction behaviors. Thisstudy investigates the relationship between mall personality, self-congruity, perceivedquality, shopping value, shopper satisfaction and revisit intention. Two hundredrespondents participated in this study. In order to achieve the aim of this study...

Sparse random matrices: The eigenvalue spectrum revisited

International Nuclear Information System (INIS)

Semerjian, Guilhem; Cugliandolo, Leticia F.

2003-08-01

We revisit the derivation of the density of states of sparse random matrices. We derive a recursion relation that allows one to compute the spectrum of the matrix of incidence for finite trees that determines completely the low concentration limit. Using the iterative scheme introduced by Biroli and Monasson [J. Phys. A 32, L255 (1999)] we find an approximate expression for the density of states expected to hold exactly in the opposite limit of large but finite concentration. The combination of the two methods yields a very simple geometric interpretation of the tails of the spectrum. We test the analytic results with numerical simulations and we suggest an indirect numerical method to explore the tails of the spectrum. (author)

Highway 61 Revisited: Bob Dilan i francuski poststrukturalizam / Highway 61 Revisited: Bob Dilan and French Poststructuralism

Directory of Open Access Journals (Sweden)

Nikola Dedić

2013-06-01

Full Text Available The main aim of this text is to show parallels between rock music and poststructuralist philosophy. As a case study one of the most celebrated rock albums of all times – Bob Dylan’s Highway 61 Revisited from 1965 is taken. It is one of the crucial albums in the history of popular culture which influenced further development of rock music within American counter culture of the 60s. Dylan’s turn from the politics of American New Left and folk movement, his relation towards the notions of the author and intertextuality, and his connection with experimental usage of language in the manner of avant-garde and neoavant-garde poetry, are juxtaposed with the main philosophical standpoints of Jean-François Lyotard, Jean Baudrillard, Roland Barthes and Julia Kristeva which historically and chronologically coincide with the appearance of Dylan’s album.

Structuralism's Relevance in a Post-Structural Era: Re-Visiting Research on Multicultural Curricular Studies

Science.gov (United States)

Shim, Jenna Min

2011-01-01

At the current historical juncture in which differences and inequalities are surfacing greater than ever in the world, societies, and schools, the main goal of this essay is to revisit the aspects of structuralism that can potentially contribute productively to understanding the invisible structures and forces that everyone carries (mostly…

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.

Creative-Dynamics Approach To Neural Intelligence

Science.gov (United States)

Zak, Michail A.

1992-01-01

Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

The skeptical green consumer revisited: testing the relationship between green consumerism and skepticism toward advertising

NARCIS (Netherlands)

Matthes, J.; Wonneberger, A.

2014-01-01

This article revisits the widely believed notion of the skeptical green consumer, in other words, that green consumers tend to distrust green advertising. Study 1, a survey of U.S. consumers, found no positive relationship between green consumerism and general ad skepticism. However, green

A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles

Directory of Open Access Journals (Sweden)

Zhong Liu

2018-05-01

Full Text Available In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM, the uncertain map (UM, and the digital pheromone map (DPM are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius

The role of brand destination experience in determining revisit intention

DEFF Research Database (Denmark)

Mattsson, Jan; Barnes, Stuart; Sørensen, Flemming

Destination branding has developed considerably as a topic area in the last decade with numerous conceptualizations focusing on different aspects of the brand. However, a unified view has not yet emerged. This paper examines destination branding via a new conceptualization, brand destination...... experience, which provides a more holistic and unified view of the brand destination. The research uses a logistic regression model to determine the role of satisfaction and brand experience in determining revisit intentions. The study also examines differences among subgroups and four brand experience sub...

NMDA Receptor Signaling Is Important for Neural Tube Formation and for Preventing Antiepileptic Drug-Induced Neural Tube Defects.

Science.gov (United States)

Sequerra, Eduardo B; Goyal, Raman; Castro, Patricio A; Levin, Jacqueline B; Borodinsky, Laura N

2018-05-16

Failure of neural tube closure leads to neural tube defects (NTDs), which can have serious neurological consequences or be lethal. Use of antiepileptic drugs (AEDs) during pregnancy increases the incidence of NTDs in offspring by unknown mechanisms. Here we show that during Xenopus laevis neural tube formation, neural plate cells exhibit spontaneous calcium dynamics that are partially mediated by glutamate signaling. We demonstrate that NMDA receptors are important for the formation of the neural tube and that the loss of their function induces an increase in neural plate cell proliferation and impairs neural cell migration, which result in NTDs. We present evidence that the AED valproic acid perturbs glutamate signaling, leading to NTDs that are rescued with varied efficacy by preventing DNA synthesis, activating NMDA receptors, or recruiting the NMDA receptor target ERK1/2. These findings may prompt mechanistic identification of AEDs that do not interfere with neural tube formation. SIGNIFICANCE STATEMENT Neural tube defects are one of the most common birth defects. Clinical investigations have determined that the use of antiepileptic drugs during pregnancy increases the incidence of these defects in the offspring by unknown mechanisms. This study discovers that glutamate signaling regulates neural plate cell proliferation and oriented migration and is necessary for neural tube formation. We demonstrate that the widely used antiepileptic drug valproic acid interferes with glutamate signaling and consequently induces neural tube defects, challenging the current hypotheses arguing that they are side effects of this antiepileptic drug that cause the increased incidence of these defects. Understanding the mechanisms of neurotransmitter signaling during neural tube formation may contribute to the identification and development of antiepileptic drugs that are safer during pregnancy. Copyright © 2018 the authors 0270-6474/18/384762-12$15.00/0.

Explaining tourists satisfaction and intention to revisit Nha Trang, Viet Nam

OpenAIRE

Tran, Thi Ai Cam

2011-01-01

The first purpose of the thesis is to find how visitors evaluate the quality of different facets or attributes of a destination image of Nha Trang, how satisfied they are with Nha Trang, loyalty intention to revisit and willingness to recommend Nha Trang to others. The second is to investigate what “image” is most important to explain global satisfaction with visiting Nha Trang. The third is to investigate how perceived quality, satisfaction and other motivational or dismotivational factors ...

Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

Directory of Open Access Journals (Sweden)

Christopher Bergmeir

2012-01-01

Full Text Available Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b accessibility of all of the SNNSalgorithmic functionality from R using a low-level interface, and (c a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNSfile formats.

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 ...

Revisiting Organizational Credibility and Organizational Reputation – A Situational Crisis Communication Approach

OpenAIRE

Jamal Jamilah; Abu Bakar Hassan

2017-01-01

Organizational credibility, the extent of which an organization as the source of messages is perceived as trustworthy and reliable, is one important aspect to determine organization’s survival. The perceived credibility of the messages will either strengthen or worsen an organization reputation. The primary objective of this paper is to revisit the concept of organizational credibility and its interaction with organizational outcomes such as organizational reputation. Based on the situational...

Chondroitin sulfate effects on neural stem cell differentiation.

Science.gov (United States)

Canning, David R; Brelsford, Natalie R; Lovett, Neil W

2016-01-01

We have investigated the role chondroitin sulfate has on cell interactions during neural plate formation in the early chick embryo. Using tissue culture isolates from the prospective neural plate, we have measured neural gene expression profiles associated with neural stem cell differentiation. Removal of chondroitin sulfate from stage 4 neural plate tissue leads to altered associations of N-cadherin-positive neural progenitors and causes changes in the normal sequence of neural marker gene expression. Absence of chondroitin sulfate in the neural plate leads to reduced Sox2 expression and is accompanied by an increase in the expression of anterior markers of neural regionalization. Results obtained in this study suggest that the presence of chondroitin sulfate in the anterior chick embryo is instrumental in maintaining cells in the neural precursor state.

Service-Learning in Crisis Communication Education: Revisiting Coombs' Objectives for the Crisis Communication Course

Science.gov (United States)

Maresh-Fuehrer, Michelle M.

2015-01-01

The purpose of this study was to revisit Coombs' suggestions for teaching the crisis communication course using service-learning as a framework. The author sought to assess the effectiveness of using this method in terms of the benefits to both students and the partnering organization and students' perceptions of whether they met the learning…

Revisiting coupled Shukla-Varma and convective cell mode in classical and quantum dusty magnetoplasmas

Science.gov (United States)

Masood, W.; Mirza, Arshad M.; Nargis, Shahida

2010-08-01

The coupled Shukla-Varma (SV) and convective cell mode is revisited in classical and quantum dusty magnetoplasmas. It is shown that the inclusion of electron thermal effects modifies the original coupled SV and convective cell mode. It is also discussed how the quantum effects can be incorporated in the coupled SV and convective cell mode.

The Faraday effect revisited General theory

CERN Document Server

Cornean, H D; Pedersen, T G

2005-01-01

This paper is the first in a series revisiting the Faraday effect, or more generally, the theory of electronic quantum transport/optical response in bulk media in the presence of a constant magnetic field. The independent electron approximation is assumed. For free electrons, the transverse conductivity can be explicitly computed and coincides with the classical result. In the general case, using magnetic perturbation theory, the conductivity tensor is expanded in powers of the strength of the magnetic field $B$. Then the linear term in $B$ of this expansion is written down in terms of the zero magnetic field Green function and the zero field current operator. In the periodic case, the linear term in $B$ of the conductivity tensor is expressed in terms of zero magnetic field Bloch functions and energies. No derivatives with respect to the quasimomentum appear and thereby all ambiguities are removed, in contrast to earlier work.

Re-visiting the electrophysiology of language.

Science.gov (United States)

Obleser, Jonas

2015-09-01

This editorial accompanies a special issue of Brain and Language re-visiting old themes and new leads in the electrophysiology of language. The event-related potential (ERP) as a series of characteristic deflections ("components") over time and their distribution on the scalp has been exploited by speech and language researchers over decades to find support for diverse psycholinguistic models. Fortunately, methodological and statistical advances have allowed human neuroscience to move beyond some of the limitations imposed when looking at the ERP only. Most importantly, we currently witness a refined and refreshed look at "event-related" (in the literal sense) brain activity that relates itself more closely to the actual neurobiology of speech and language processes. It is this imminent change in handling and interpreting electrophysiological data of speech and language experiments that this special issue intends to capture. Copyright © 2015 Elsevier Inc. All rights reserved.

Neural processing of auditory signals and modular neural control for sound tropism of walking machines

DEFF Research Database (Denmark)

Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

2005-01-01

and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right....... The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....

Revisiting the Performance of MACD and RSI Oscillators

Directory of Open Access Journals (Sweden)

Terence Tai-Leung Chong

2014-02-01

Full Text Available Chong and Ng (2008 find that the Moving Average Convergence–Divergence (MACD and Relative Strength Index (RSI rules can generate excess return in the London Stock Exchange. This paper revisits the performance of the two trading rules in the stock markets of five other OECD countries. It is found that the MACD(12,26,0 and RSI(21,50 rules consistently generate significant abnormal returns in the Milan Comit General and the S&P/TSX Composite Index. In addition, the RSI(14,30/70 rule is also profitable in the Dow Jones Industrials Index. The results shed some light on investors’ belief in these two technical indicators in different developed markets.

Revisiting the level scheme of the proton emitter 151Lu

International Nuclear Information System (INIS)

Wang, F.; Sun, B.H.; Liu, Z.; Scholey, C.; Eeckhaudt, S.; Grahn, T.; Greenlees, P.T.; Jones, P.; Julin, R.; Juutinen, S.; Kettelhut, S.; Leino, M.; Nyman, M.; Rahkila, P.; Saren, J.; Sorri, J.; Uusitalo, J.; Ashley, S.F.; Cullen, I.J.; Garnsworthy, A.B.; Gelletly, W.; Jones, G.A.; Pietri, S.; Podolyak, Z.; Steer, S.; Thompson, N.J.; Walker, P.M.; Williams, S.; Bianco, L.; Darby, I.G.; Joss, D.T.; Page, R.D.; Pakarinen, J.; Rigby, S.; Cullen, D.M.; Khan, S.; Kishada, A.; Gomez-Hornillos, M.B.; Simpson, J.; Jenkins, D.G.; Niikura, M.; Seweryniak, D.; Shizuma, Toshiyuki

2015-01-01

An experiment aiming to search for new isomers in the region of proton emitter 151 Lu was performed at the Accelerator Laboratory of the University of Jyväskylä (JYFL), by combining the high resolution γ-ray array JUROGAM, gas-filled RITU separator and GREAT detectors with the triggerless total data readout acquisition (TDR) system. In this proceeding, we revisit the level scheme of 151 Lu by using the proton-tagging technique. A level scheme consistent with the latest experimental results is obtained, and 3 additional levels are identified at high excitation energies. (author)

The importance of jet bending in gamma-ray AGNs—revisited

Energy Technology Data Exchange (ETDEWEB)

Graham, P. J. [School of Physics, University of New South Wales, Sydney, NSW 2052 (Australia); Tingay, S. J. [International Centre for Radio Astronomy Research, Curtin University, Bentley, WA (Australia)

2014-04-01

We investigate the hypothesis that γ-ray-quiet active galactic nuclei (AGNs) have a greater tendency for jet bending than γ-ray-loud AGNs, revisiting the analysis of Tingay et al. We perform a statistical analysis using a large sample of 351 radio-loud AGNs along with γ-ray identifications from the Fermi Large Area Telescope (LAT). Our results show no statistically significant differences in jet-bending properties between γ-ray-loud and γ-ray-quiet populations, indicating that jet bending is not a significant factor for γ-ray detection in AGNs.

Silent cries, dancing tears: the metapsychology of art revisited/revised.

Science.gov (United States)

Aragno, Anna

2011-04-01

Against the backdrop of a broad survey of the literature on applied psychoanalysis, a number of concepts underpinning the metapsychology of art are revisited and revised: sublimation; interrelationships between primary and secondary processes; symbolization; "fantasy"; and "cathexis." Concepts embedded in dichotomous or drive/energic contexts are examined and reformulated in terms of a continuum of semiotic processes. Freudian dream structure is viewed as a biological/natural template for nonrepressive artistic forms of sublimation. The synthesis presented proposes a model of continuous rather than discontinuous processes, in a nonenergic, biosemiotic metatheoretical framework.

Neural overlap in processing music and speech

Science.gov (United States)

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

2015-01-01

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

Simulation of Two-Way Pushdown Automata Revisited

Directory of Open Access Journals (Sweden)

Robert Glück

2013-09-01

Full Text Available The linear-time simulation of 2-way deterministic pushdown automata (2DPDA by the Cook and Jones constructions is revisited. Following the semantics-based approach by Jones, an interpreter is given which, when extended with random-access memory, performs a linear-time simulation of 2DPDA. The recursive interpreter works without the dump list of the original constructions, which makes Cook's insight into linear-time simulation of exponential-time automata more intuitive and the complexity argument clearer. The simulation is then extended to 2-way nondeterministic pushdown automata (2NPDA to provide for a cubic-time recognition of context-free languages. The time required to run the final construction depends on the degree of nondeterminism. The key mechanism that enables the polynomial-time simulations is the sharing of computations by memoization.

Neural network to diagnose lining condition

Science.gov (United States)

Yemelyanov, V. A.; Yemelyanova, N. Y.; Nedelkin, A. A.; Zarudnaya, M. V.

2018-03-01

The paper presents data on the problem of diagnosing the lining condition at the iron and steel works. The authors describe the neural network structure and software that are designed and developed to determine the lining burnout zones. The simulation results of the proposed neural networks are presented. The authors note the low learning and classification errors of the proposed neural networks. To realize the proposed neural network, the specialized software has been developed.

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

Science.gov (United States)

Gafarov, F M; Gafarova, V R

2016-09-01

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

Distractor dwelling, skipping, and revisiting determine target absent performance in difficult visual search

Directory of Open Access Journals (Sweden)

Gernot Horstmann

2016-08-01

Full Text Available Some targets in visual search are more difficult to find than others. In particular, a target that is similar to the distractors is more difficult to find than a target that is dissimilar to the distractors. Efficiency differences between easy and difficult searches are manifest not only in target-present trials but also in target-absent trials. In fact, even physically identical displays are searched through with different efficiency depending on the searched-for target. Here, we monitored eye movements in search for a target similar to the distractors (difficult search versus a target dissimilar to the distractors (easy search. We aimed to examine three hypotheses concerning the causes of differential search efficiencies in target-absent trials: (a distractor dwelling (b distractor skipping, and (c distractor revisiting. Reaction times increased with target similarity which is consistent with existing theories and replicates earlier results. Eye movement data indicated guidance in target trials, even though search was very slow. Dwelling, skipping, and revisiting contributed to low search efficiency in difficult search, with dwelling being the strongest factor. It is argued that differences in dwell time account for a large amount of total search time differences.

Distractor Dwelling, Skipping, and Revisiting Determine Target Absent Performance in Difficult Visual Search

Science.gov (United States)

Horstmann, Gernot; Herwig, Arvid; Becker, Stefanie I.

2016-01-01

Some targets in visual search are more difficult to find than others. In particular, a target that is similar to the distractors is more difficult to find than a target that is dissimilar to the distractors. Efficiency differences between easy and difficult searches are manifest not only in target-present trials but also in target-absent trials. In fact, even physically identical displays are searched through with different efficiency depending on the searched-for target. Here, we monitored eye movements in search for a target similar to the distractors (difficult search) versus a target dissimilar to the distractors (easy search). We aimed to examine three hypotheses concerning the causes of differential search efficiencies in target-absent trials: (a) distractor dwelling (b) distractor skipping, and (c) distractor revisiting. Reaction times increased with target similarity which is consistent with existing theories and replicates earlier results. Eye movement data indicated guidance in target trials, even though search was very slow. Dwelling, skipping, and revisiting contributed to low search efficiency in difficult search, with dwelling being the strongest factor. It is argued that differences in dwell time account for a large amount of total search time differences. PMID:27574510

Neural overlap in processing music and speech.

Science.gov (United States)

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

2015-03-19

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

The Super-GUT CMSSM Revisited

CERN Document Server

Ellis, John

2016-01-01

We revisit minimal supersymmetric SU(5) grand unification (GUT) models in which the soft supersymmetry-breaking parameters of the minimal supersymmetric Standard Model (MSSM) are universal at some input scale, $M_{in}$, above the supersymmetric gauge coupling unification scale, $M_{GUT}$. As in the constrained MSSM (CMSSM), we assume that the scalar masses and gaugino masses have common values, $m_0$ and $m_{1/2}$ respectively, at $M_{in}$, as do the trilinear soft supersymmetry-breaking parameters $A_0$. Going beyond previous studies of such a super-GUT CMSSM scenario, we explore the constraints imposed by the lower limit on the proton lifetime and the LHC measurement of the Higgs mass, $m_h$. We find regions of $m_0$, $m_{1/2}$, $A_0$ and the parameters of the SU(5) superpotential that are compatible with these and other phenomenological constraints such as the density of cold dark matter, which we assume to be provided by the lightest neutralino. Typically, these allowed regions appear for $m_0$ and $m_{1/...

Critical boundary sine-Gordon revisited

International Nuclear Information System (INIS)

Hasselfield, M.; Lee, Taejin; Semenoff, G.W.; Stamp, P.C.E.

2006-01-01

We revisit the exact solution of the two space-time dimensional quantum field theory of a free massless boson with a periodic boundary interaction and self-dual period. We analyze the model by using a mapping to free fermions with a boundary mass term originally suggested in Ref. [J. Polchinski, L. Thorlacius, Phys. Rev. D 50 (1994) 622]. We find that the entire SL (2, C) family of boundary states of a single boson are boundary sine-Gordon states and we derive a simple explicit expression for the boundary state in fermion variables and as a function of sine-Gordon coupling constants. We use this expression to compute the partition function. We observe that the solution of the model has a strong-weak coupling generalization of T-duality. We then examine a class of recently discovered conformal boundary states for compact bosons with radii which are rational numbers times the self-dual radius. These have simple expression in fermion variables. We postulate sine-Gordon-like field theories with discrete gauge symmetries for which they are the appropriate boundary states

Post-inflationary gravitino production revisited

Energy Technology Data Exchange (ETDEWEB)

Ellis, John [Theoretical Particle Physics and Cosmology Group, Department of Physics, King' s College London, London WC2R 2LS (United Kingdom); Garcia, Marcos A.G.; Olive, Keith A. [William I. Fine Theoretical Physics Institute, School of Physics and Astronomy, University of Minnesota, 116 Church Street SE, Minneapolis, MN 55455 (United States); Nanopoulos, Dimitri V. [George P. and Cynthia W. Mitchell Institute for Fundamental Physics and Astronomy, Texas A and M University, College Station, TX 77843 (United States); Peloso, Marco, E-mail: john.ellis@cern.ch, E-mail: garciagarcia@physics.umn.edu, E-mail: dimitri@physics.tamu.edu, E-mail: olive@physics.umn.edu, E-mail: peloso@physics.umn.edu [School of Physics and Astronomy and Minnesota Institute for Astrophysics, University of Minnesota, 116 Church Street SE, Minneapolis, MN 55455 (United States)

2016-03-01

We revisit gravitino production following inflation. As a first step, we review the standard calculation of gravitino production in the thermal plasma formed at the end of post-inflationary reheating when the inflaton has completely decayed. Next we consider gravitino production prior to the completion of reheating, assuming that the inflaton decay products thermalize instantaneously while they are still dilute. We then argue that instantaneous thermalization is in general a good approximation, and also show that the contribution of non-thermal gravitino production via the collisions of inflaton decay products prior to thermalization is relatively small. Our final estimate of the gravitino-to-entropy ratio is approximated well by a standard calculation of gravitino production in the post-inflationary thermal plasma assuming total instantaneous decay and thermalization at a time t ≅ 1.2/Γ{sub φ}. Finally, in light of our calculations, we consider potential implications of upper limits on the gravitino abundance for models of inflation, with particular attention to scenarios for inflaton decays in supersymmetric Starobinsky-like models.

Complex-Valued Neural Networks

CERN Document Server

Hirose, Akira

2012-01-01

This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

Memristor-based neural networks

International Nuclear Information System (INIS)

Thomas, Andy

2013-01-01

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

Investigating predictors of visiting, using, and revisiting an online health-communication program: a longitudinal study.

Science.gov (United States)

Van 't Riet, Jonathan; Crutzen, Rik; De Vries, Hein

2010-09-02

Online health communication has the potential to reach large audiences, with the additional advantages that it can be operational at all times and that the costs per visitor are low. Furthermore, research shows that Internet-delivered interventions can be effective in changing health behaviors. However, exposure to Internet-delivered health-communication programs is generally low. Research investigating predictors of exposure is needed to be able to effectively disseminate online interventions. In the present study, the authors used a longitudinal design with the aim of identifying demographic, psychological, and behavioral predictors of visiting, using, and revisiting an online program promoting physical activity in the general population. A webpage was created providing the public with information about health and healthy behavior. The website included a "physical activity check," which consisted of a physical activity computer-tailoring expert system where visitors could check whether their physical activity levels were in line with recommendations. Visitors who consented to participate in the present study (n = 489) filled in a questionnaire that assessed demographics, mode of recruitment, current physical activity levels, and health motivation. Immediately after, participants received tailored feedback concerning their current physical activity levels and completed a questionnaire assessing affective and cognitive user experience, attitude toward being sufficiently physically active, and intention to be sufficiently physically active. Three months later, participants received an email inviting them once more to check whether their physical activity level had changed. Analyses of visiting showed that more women (67.5%) than men (32.5%) visited the program. With regard to continued use, native Dutch participants (odds ratio [OR] = 2.81, 95% confidence interval [CI] = 1.16-6.81, P = .02) and participants with a strong motivation to be healthy (OR = 1.46, CI = 1

Neural crest contributions to the lamprey head

Science.gov (United States)

McCauley, David W.; Bronner-Fraser, Marianne

2003-01-01

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

Operator entanglement of two-qubit joint unitary operations revisited: Schmidt number approach

Energy Technology Data Exchange (ETDEWEB)

Xia, Hui-Zhi; Li, Chao; Yang, Qing; Yang, Ming, E-mail: mingyang@ahu.edu.cn [Key Laboratory of Opto-electronic Information Acquisition and Manipulation, Ministry of Education, School of Physics and Material Science, Anhui University Hefei (China); Cao, Zhuo-Liang [School of Electronic Information Engineering, Hefei Normal University (China)

2012-08-15

The operator entanglement of two-qubit joint unitary operations is revisited. The Schmidt number, an important attribute of a two-qubit unitary operation, may have connection with the entanglement measure of the unitary operator. We find that the entanglement measure of a two-qubit unitary operators is classified by the Schmidt number of the unitary operators. We also discuss the exact relation between the operator entanglement and the parameters of the unitary operator. (author)

Revisit the spin-FET: Multiple reflection, inelastic scattering, and lateral size effects

OpenAIRE

Xu, Luting; Li, Xin-Qi; Sun, Qing-feng

2014-01-01

We revisit the spin-injected field effect transistor (spin-FET) by simulating a lattice model based on recursive lattice Green's function approach. In the one-dimensional case and coherent regime, the simulated results reveal noticeable differences from the celebrated Datta-Das model, which motivate thus an improved treatment and lead to analytic and generalized result. The simulation also allows us to address inelastic scattering (using B\\"uttiker's fictitious reservoir approach) and lateral...

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.

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

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

Science.gov (United States)

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

2010-01-01

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

Entropy of measurement and erasure: Szilard's membrane model revisited

Science.gov (United States)

Leff, Harvey S.; Rex, Andrew F.

1994-11-01

It is widely believed that measurement is accompanied by irreversible entropy increase. This conventional wisdom is based in part on Szilard's 1929 study of entropy decrease in a thermodynamic system by intelligent intervention (i.e., a Maxwell's demon) and Brillouin's association of entropy with information. Bennett subsequently argued that information acquisition is not necessarily irreversible, but information erasure must be dissipative (Landauer's principle). Inspired by the ensuing debate, we revisit the membrane model introduced by Szilard and find that it can illustrate and clarify (1) reversible measurement, (2) information storage, (3) decoupling of the memory from the system being measured, and (4) entropy increase associated with memory erasure and resetting.

Neural plasticity of development and learning.

Science.gov (United States)

Galván, Adriana

2010-06-01

Development and learning are powerful agents of change across the lifespan that induce robust structural and functional plasticity in neural systems. An unresolved question in developmental cognitive neuroscience is whether development and learning share the same neural mechanisms associated with experience-related neural plasticity. In this article, I outline the conceptual and practical challenges of this question, review insights gleaned from adult studies, and describe recent strides toward examining this topic across development using neuroimaging methods. I suggest that development and learning are not two completely separate constructs and instead, that they exist on a continuum. While progressive and regressive changes are central to both, the behavioral consequences associated with these changes are closely tied to the existing neural architecture of maturity of the system. Eventually, a deeper, more mechanistic understanding of neural plasticity will shed light on behavioral changes across development and, more broadly, about the underlying neural basis of cognition. (c) 2010 Wiley-Liss, Inc.

N2A: a computational tool for modeling from neurons to algorithms

Directory of Open Access Journals (Sweden)

Fredrick eRothganger

2014-01-01

Full Text Available The exponential increase in available neural data has combined with the exponential growth in computing (Moore’s law to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique challenges to neuroscientists. Computational models in neuroscience are increasingly broad efforts, often involving the collaboration of experts in different domains. Furthermore, the size and detail of models have grown to levels for which understanding the implications of variability and assumptions is no longer trivial. Here, we introduce the model design platform N2A which aims to facilitate the design and validation of biologically realistic models. N2A uses a hierarchical representation of neural information to enable the integration of models from different users. N2A streamlines computational validation of a model by natively implementing standard tools in sensitivity analysis and uncertainty quantification. The part-relationship representation allows both network-level analysis and dynamical simulations. We will demonstrate how N2A can be used in a range of examples, including a simple Hodgkin-Huxley cable model, basic parameter sensitivity of an 80/20 network, and the expression of the structural plasticity of a growing dendrite and stem cell proliferation and differentiation.

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

Revisit to diffraction anomalous fine structure

International Nuclear Information System (INIS)

Kawaguchi, T.; Fukuda, K.; Tokuda, K.; Shimada, K.; Ichitsubo, T.; Oishi, M.; Mizuki, J.; Matsubara, E.

2014-01-01

The diffraction anomalous fine structure method has been revisited by applying this measurement technique to polycrystalline samples and using an analytical method with the logarithmic dispersion relation. The diffraction anomalous fine structure (DAFS) method that is a spectroscopic analysis combined with resonant X-ray diffraction enables the determination of the valence state and local structure of a selected element at a specific crystalline site and/or phase. This method has been improved by using a polycrystalline sample, channel-cut monochromator optics with an undulator synchrotron radiation source, an area detector and direct determination of resonant terms with a logarithmic dispersion relation. This study makes the DAFS method more convenient and saves a large amount of measurement time in comparison with the conventional DAFS method with a single crystal. The improved DAFS method has been applied to some model samples, Ni foil and Fe 3 O 4 powder, to demonstrate the validity of the measurement and the analysis of the present DAFS method

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...

The critical catastrophe revisited

International Nuclear Information System (INIS)

De Mulatier, Clélia; Rosso, Alberto; Dumonteil, Eric; Zoia, Andrea

2015-01-01

The neutron population in a prototype model of nuclear reactor can be described in terms of a collection of particles confined in a box and undergoing three key random mechanisms: diffusion, reproduction due to fissions, and death due to absorption events. When the reactor is operated at the critical point, and fissions are exactly compensated by absorptions, the whole neutron population might in principle go to extinction because of the wild fluctuations induced by births and deaths. This phenomenon, which has been named critical catastrophe, is nonetheless never observed in practice: feedback mechanisms acting on the total population, such as human intervention, have a stabilizing effect. In this work, we revisit the critical catastrophe by investigating the spatial behaviour of the fluctuations in a confined geometry. When the system is free to evolve, the neutrons may display a wild patchiness (clustering). On the contrary, imposing a population control on the total population acts also against the local fluctuations, and may thus inhibit the spatial clustering. The effectiveness of population control in quenching spatial fluctuations will be shown to depend on the competition between the mixing time of the neutrons (i.e. the average time taken for a particle to explore the finite viable space) and the extinction time

Simplified LQG Control with Neural Networks

DEFF Research Database (Denmark)

Sørensen, O.

1997-01-01

A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...

SUSY S4×SU(5) revisited

International Nuclear Information System (INIS)

Hagedorn, Claudia; King, Stephen F.; Luhn, Christoph

2012-01-01

Following the recent results from Daya Bay and RENO, which measure the lepton mixing angle θ 13 l ≈0.15, we revisit a supersymmetric (SUSY) S 4 ×SU(5) model, which predicts tri-bimaximal (TB) mixing in the neutrino sector with θ 13 l being too small in its original version. We show that introducing one additional S 4 singlet flavon into the model gives rise to a sizable θ 13 l via an operator which leads to the breaking of one of the two Z 2 symmetries preserved in the neutrino sector at leading order (LO). The results of the original model for fermion masses, quark mixing and the solar mixing angle are maintained to good precision. The atmospheric and solar mixing angle deviations from TB mixing are subject to simple sum rule bounds.

Neutrino dark energy. Revisiting the stability issue

Energy Technology Data Exchange (ETDEWEB)

Eggers Bjaelde, O.; Hannestad, S. [Aarhus Univ. (Denmark). Dept. of Physics and Astronomy; Brookfield, A.W. [Sheffield Univ. (United Kingdom). Dept. of Applied Mathematics and Dept. of Physics, Astro-Particle Theory and Cosmology Group; Van de Bruck, C. [Sheffield Univ. (United Kingdom). Dept. of Applied Mathematics, Astro-Particle Theory and Cosmology Group; Mota, D.F. [Heidelberg Univ. (Germany). Inst. fuer Theoretische Physik]|[Institute of Theoretical Astrophysics, Oslo (Norway); Schrempp, L. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Tocchini-Valentini, D. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Physics and Astronomy

2007-05-15

A coupling between a light scalar field and neutrinos has been widely discussed as a mechanism for linking (time varying) neutrino masses and the present energy density and equation of state of dark energy. However, it has been pointed out that the viability of this scenario in the non-relativistic neutrino regime is threatened by the strong growth of hydrodynamic perturbations associated with a negative adiabatic sound speed squared. In this paper we revisit the stability issue in the framework of linear perturbation theory in a model independent way. The criterion for the stability of a model is translated into a constraint on the scalar-neutrino coupling, which depends on the ratio of the energy densities in neutrinos and cold dark matter. We illustrate our results by providing meaningful examples both for stable and unstable models. (orig.)

Fuzzy neural network theory and application

CERN Document Server

Liu, Puyin

2004-01-01

This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he

Practical neural network recipies in C++

CERN Document Server

Masters

2014-01-01

This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assum

Experiments on the Divergence between Willingness to Pay and Willingness to Accept: The Issue Revisited

OpenAIRE

Rodolfo M. Nayga, Jr.; Wipon Aiew; Richard Woodward

2005-01-01

Many empirical studies have discovered large discrepancies between willingness to pay (WTP) and willingness to accept (WTA) measures. This paper revisits the WTP and WTA divergence issue using a non-hypothetical market experiment, actual products, cash, and exchange in a market setting. We find WTA/WTP ratios that are significantly lower than most such studies.

Using destination image to predict visitors' intention to revisit three Hudson River Valley, New York, communities

Science.gov (United States)

Rudy M. Schuster; Laura Sullivan; Duarte Morais; Diane Kuehn

2009-01-01

This analysis explores the differences in Affective and Cognitive Destination Image among three Hudson River Valley (New York) tourism communities. Multiple regressions were used with six dimensions of visitors' images to predict future intention to revisit. Two of the three regression models were significant. The only significantly contributing independent...

Rhesus monkey neural stem cell transplantation promotes neural regeneration in rats with hippocampal lesions

Directory of Open Access Journals (Sweden)

Li-juan Ye

2016-01-01

Full Text Available Rhesus monkey neural stem cells are capable of differentiating into neurons and glial cells. Therefore, neural stem cell transplantation can be used to promote functional recovery of the nervous system. Rhesus monkey neural stem cells (1 × 105 cells/μL were injected into bilateral hippocampi of rats with hippocampal lesions. Confocal laser scanning microscopy demonstrated that green fluorescent protein-labeled transplanted cells survived and grew well. Transplanted cells were detected at the lesion site, but also in the nerve fiber-rich region of the cerebral cortex and corpus callosum. Some transplanted cells differentiated into neurons and glial cells clustering along the ventricular wall, and integrated into the recipient brain. Behavioral tests revealed that spatial learning and memory ability improved, indicating that rhesus monkey neural stem cells noticeably improve spatial learning and memory abilities in rats with hippocampal lesions.

Global Robust Stability of Switched Interval Neural Networks with Discrete and Distributed Time-Varying Delays of Neural Type

Directory of Open Access Journals (Sweden)

Huaiqin Wu

2012-01-01

Full Text Available By combing the theories of the switched systems and the interval neural networks, the mathematics model of the switched interval neural networks with discrete and distributed time-varying delays of neural type is presented. A set of the interval parameter uncertainty neural networks with discrete and distributed time-varying delays of neural type are used as the individual subsystem, and an arbitrary switching rule is assumed to coordinate the switching between these networks. By applying the augmented Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI techniques, a delay-dependent criterion is achieved to ensure to such switched interval neural networks to be globally asymptotically robustly stable in terms of LMIs. The unknown gain matrix is determined by solving this delay-dependent LMIs. Finally, an illustrative example is given to demonstrate the validity of the theoretical results.

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....

Noradrenergic modulation of neural erotic stimulus perception.

Science.gov (United States)

Graf, Heiko; Wiegers, Maike; Metzger, Coraline Danielle; Walter, Martin; Grön, Georg; Abler, Birgit

2017-09-01

We recently investigated neuromodulatory effects of the noradrenergic agent reboxetine and the dopamine receptor affine amisulpride in healthy subjects on dynamic erotic stimulus processing. Whereas amisulpride left sexual functions and neural activations unimpaired, we observed detrimental activations under reboxetine within the caudate nucleus corresponding to motivational components of sexual behavior. However, broadly impaired subjective sexual functioning under reboxetine suggested effects on further neural components. We now investigated the same sample under these two agents with static erotic picture stimulation as alternative stimulus presentation mode to potentially observe further neural treatment effects of reboxetine. 19 healthy males were investigated under reboxetine, amisulpride and placebo for 7 days each within a double-blind cross-over design. During fMRI static erotic picture were presented with preceding anticipation periods. Subjective sexual functions were assessed by a self-reported questionnaire. Neural activations were attenuated within the caudate nucleus, putamen, ventral striatum, the pregenual and anterior midcingulate cortex and in the orbitofrontal cortex under reboxetine. Subjective diminished sexual arousal under reboxetine was correlated with attenuated neural reactivity within the posterior insula. Again, amisulpride left neural activations along with subjective sexual functioning unimpaired. Neither reboxetine nor amisulpride altered differential neural activations during anticipation of erotic stimuli. Our results verified detrimental effects of noradrenergic agents on neural motivational but also emotional and autonomic components of sexual behavior. Considering the overlap of neural network alterations with those evoked by serotonergic agents, our results suggest similar neuromodulatory effects of serotonergic and noradrenergic agents on common neural pathways relevant for sexual behavior. Copyright © 2017 Elsevier B.V. and

Emerging trends in neuro engineering and neural computation

CERN Document Server

Lee, Kendall; Garmestani, Hamid; Lim, Chee

2017-01-01

This book focuses on neuro-engineering and neural computing, a multi-disciplinary field of research attracting considerable attention from engineers, neuroscientists, microbiologists and material scientists. It explores a range of topics concerning the design and development of innovative neural and brain interfacing technologies, as well as novel information acquisition and processing algorithms to make sense of the acquired data. The book also highlights emerging trends and advances regarding the applications of neuro-engineering in real-world scenarios, such as neural prostheses, diagnosis of neural degenerative diseases, deep brain stimulation, biosensors, real neural network-inspired artificial neural networks (ANNs) and the predictive modeling of information flows in neuronal networks. The book is broadly divided into three main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologie...

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...

Artificial Neural Network Analysis System

Science.gov (United States)

2001-02-27

Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

The equilibrium of neural firing: A mathematical theory

Energy Technology Data Exchange (ETDEWEB)

Lan, Sizhong, E-mail: lsz@fuyunresearch.org [Fuyun Research, Beijing, 100055 (China)

2014-12-15

Inspired by statistical thermodynamics, we presume that neuron system has equilibrium condition with respect to neural firing. We show that, even with dynamically changeable neural connections, it is inevitable for neural firing to evolve to equilibrium. To study the dynamics between neural firing and neural connections, we propose an extended communication system where noisy channel has the tendency towards fixed point, implying that neural connections are always attracted into fixed points such that equilibrium can be reached. The extended communication system and its mathematics could be useful back in thermodynamics.

Cellular Analysis of Adult Neural Stem Cells for Investigating Prion Biology.

Science.gov (United States)

Haigh, Cathryn L

2017-01-01

Traditional primary and secondary cell cultures have been used for the investigation of prion biology and disease for many years. While both types of cultures produce highly valid and immensely valuable results, they also have their limitations; traditional cell lines are often derived from cancers, therefore subject to numerous DNA changes, and primary cultures are labor-intensive and expensive to produce requiring sacrifice of many animals. Neural stem cell (NSC) cultures are a relatively new technology to be used for the study of prion biology and disease. While NSCs are subject to their own limitations-they are generally cultured ex vivo in environments that artificially force their growth-they also have their own unique advantages. NSCs retain the ability for self-renewal and can therefore be propagated in culture similarly to secondary cultures without genetic manipulation. In addition, NSCs are multipotent; they can be induced to differentiate into mature cells of central nervous system (CNS) linage. The combination of self-renewal and multipotency allows NSCs to be used as a primary cell line over multiple generations saving time, costs, and animal harvests, thus providing a valuable addition to the existing cell culture repertoire used for investigation of prion biology and disease. Furthermore, NSC cultures can be generated from mice of any genotype, either by embryonic harvest or harvest from adult brain, allowing gene expression to be studied without further genetic manipulation. This chapter describes a standard method of culturing adult NSCs and assays for monitoring NSC growth, migration, and differentiation and revisits basic reactive oxygen species detection in the context of NSC cultures.

Parameterization Of Solar Radiation Using Neural Network

International Nuclear Information System (INIS)

Jiya, J. D.; Alfa, B.

2002-01-01

This paper presents a neural network technique for parameterization of global solar radiation. The available data from twenty-one stations is used for training the neural network and the data from other ten stations is used to validate the neural model. The neural network utilizes latitude, longitude, altitude, sunshine duration and period number to parameterize solar radiation values. The testing data was not used in the training to demonstrate the performance of the neural network in unknown stations to parameterize solar radiation. The results indicate a good agreement between the parameterized solar radiation values and actual measured values

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.

Recycling signals in the neural crest

OpenAIRE

Taneyhill, Lisa A.; Bronner-Fraser, Marianne E.

2006-01-01

Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

Recycling signals in the neural crest.

Science.gov (United States)

Taneyhill, Lisa A; Bronner-Fraser, Marianne

2005-01-01

Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

Neural control of magnetic suspension systems

Science.gov (United States)

Gray, W. Steven

1993-01-01

The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

Neural components of altruistic punishment

Directory of Open Access Journals (Sweden)

Emily eDu

2015-02-01

Full Text Available Altruistic punishment, which occurs when an individual incurs a cost to punish in response to unfairness or a norm violation, may play a role in perpetuating cooperation. The neural correlates underlying costly punishment have only recently begun to be explored. Here we review the current state of research on the neural basis of altruism from the perspectives of costly punishment, emphasizing the importance of characterizing elementary neural processes underlying a decision to punish. In particular, we emphasize three cognitive processes that contribute to the decision to altruistically punish in most scenarios: inequity aversion, cost-benefit calculation, and social reference frame to distinguish self from others. Overall, we argue for the importance of understanding the neural correlates of altruistic punishment with respect to the core computations necessary to achieve a decision to punish.

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…

Asian Lifelong Learning in the Context of a Global Knowledge Economy: A Task Re-Visited

Science.gov (United States)

Han, Soonghee

2007-01-01

This article revisits and reinterprets my previous paper. It is a snapshot of the lifelong learning system building in selected Asian countries, reflected in the mirror of the Asian Financial Crisis in the 1997s and the aftermath of that event. I reconsidered the arguments (1) the economic recession had delivered a global dimension of lifelong…

Neural computation and the computational theory of cognition.

Science.gov (United States)

Piccinini, Gualtiero; Bahar, Sonya

2013-04-01

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism-neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. Copyright © 2012 Cognitive Science Society, Inc.

Revisiting "Kindergarten as Academic Boot Camp": A Nationwide Study of Ability Grouping and Psycho-Social Development

Science.gov (United States)

Catsambis, Sophia; Buttaro, Anthony, Jr.

2012-01-01

We revisit Harry L. Gracey's perspective of kindergarten as academic boot camp where, at school entry, children acquire the student role through a structured program of activities. We provide further insights into the crucial mechanisms of socialization that occur in U.S. kindergartens by examining the relationship between within-class ability…

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.

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

Vestindien Revisited: I kølvandet på kuldampere og cruiseturister

DEFF Research Database (Denmark)

Brichet, Nathalia Sofie; Hastrup, Frida; Ørstedholm, Maroe

2017-01-01

What traces did Denmark officially leave behind when the West Indies were sold to the United States in 1917? And how are the islands still affected today by Denmark’s presence on the islands? The M/S Maritime Museum tries to answer these questions in its special new exhibition, ‘U.S. VIRGIN ISLAN...... special exhibition “U.S. VIRGIN ISLANDS REVISITED – in the wake of colliers and cruise tourists,” to commemorate the centennial anniversary of selling the West Indies to the United States of America....

The Tension between the Decision and Control Perspectives of Accounting Revisited

DEFF Research Database (Denmark)

Christensen, John

2009-01-01

to use different accounting systems for different purposes. That is not the norm. The tensions are managed within a single accounting system and that leads to trade-offs in the accounting system. I will revisit these conflicts of uses of accounting systems using product costing, transfer pricing and fair......The accounting system is a carefully managed information system which is used for multiple purposes. Traditionally, the uses are categorized using the main headings of decision and control. Numerous conflicts are the consequence of the multi-purpose accounting system. The easy way out is apparently...

Dynamic decomposition of spatiotemporal neural signals.

Directory of Open Access Journals (Sweden)

Luca Ambrogioni

2017-05-01

Full Text Available Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

Neural networks with discontinuous/impact activations

CERN Document Server

Akhmet, Marat

2014-01-01

This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

Science.gov (United States)

Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

2016-01-01

Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

A comparative study of patients' and nurses' perceptions of the quality of nursing services, satisfaction and intent to revisit the hospital: a questionnaire survey.

Science.gov (United States)

Lee, Mi Aie; Yom, Young-Hee

2007-05-01

Although it is very important to clarify the factors influencing the patients' and nurses' satisfaction with nursing services, very little research has been performed in this area. The purpose of this study was to compare the nursing service quality, satisfaction and intent to revisit the hospital perceived by hospitalized patients and nurses in Korea. SERVQUAL scale, an overall satisfaction and intent to revisit the hospital questionnaires were used. The sample consisted of 272 patients and 282 nurses. The data were collected using paper and pencil self-rating questionnaires and analyzed using frequency, %, mean, standard deviation, t-test and Pearson correlation coefficient. Overall, nurses' expectations and performance were higher than those of patients, while patients' overall satisfaction with nursing and medical care was higher than that of nurses. There was a strong positive relationship between satisfaction with nursing and medical care and intent to revisit the hospital for both groups. The performance was relatively lower than expectations, resulting in poor nursing care quality. Differences between expectations and performance for both patients and nurses need to be further reduced.

Stability of a neural predictive controller scheme on a neural model

DEFF Research Database (Denmark)

Luther, Jim Benjamin; Sørensen, Paul Haase

2009-01-01

In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system.......In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...

Differentiation between non-neural and neural contributors to ankle joint stiffness in cerebral palsy

NARCIS (Netherlands)

De Gooijer-van de Groep, K.L.; De Vlugt, E.; De Groot, J.H.; Van der Heijden-Maessen, H.C.M.; Wielheesen, D.H.M.; Van Wijlen-Hempel, R.M.S.; Arendzen, J.H.; Meskers, C.G.M.

2013-01-01

Background Spastic paresis in cerebral palsy (CP) is characterized by increased joint stiffness that may be of neural origin, i.e. improper muscle activation caused by e.g. hyperreflexia or non-neural origin, i.e. altered tissue viscoelastic properties (clinically: “spasticity” vs. “contracture”).

Infrared neural stimulation (INS) inhibits electrically evoked neural responses in the deaf white cat

Science.gov (United States)

Richter, Claus-Peter; Rajguru, Suhrud M.; Robinson, Alan; Young, Hunter K.

2014-03-01

Infrared neural stimulation (INS) has been used in the past to evoke neural activity from hearing and partially deaf animals. All the responses were excitatory. In Aplysia californica, Duke and coworkers demonstrated that INS also inhibits neural responses [1], which similar observations were made in the vestibular system [2, 3]. In deaf white cats that have cochleae with largely reduced spiral ganglion neuron counts and a significant degeneration of the organ of Corti, no cochlear compound action potentials could be observed during INS alone. However, the combined electrical and optical stimulation demonstrated inhibitory responses during irradiation with infrared light.

Neurosecurity: security and privacy for neural devices.

Science.gov (United States)

Denning, Tamara; Matsuoka, Yoky; Kohno, Tadayoshi

2009-07-01

An increasing number of neural implantable devices will become available in the near future due to advances in neural engineering. This discipline holds the potential to improve many patients' lives dramatically by offering improved-and in some cases entirely new-forms of rehabilitation for conditions ranging from missing limbs to degenerative cognitive diseases. The use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals. The authors define "neurosecurity"-a version of computer science security principles and methods applied to neural engineering-and discuss why neurosecurity should be a critical consideration in the design of future neural devices.

Neuromorphic Silicon Neuron Circuits

Science.gov (United States)

Indiveri, Giacomo; Linares-Barranco, Bernabé; Hamilton, Tara Julia; van Schaik, André; Etienne-Cummings, Ralph; Delbruck, Tobi; Liu, Shih-Chii; Dudek, Piotr; Häfliger, Philipp; Renaud, Sylvie; Schemmel, Johannes; Cauwenberghs, Gert; Arthur, John; Hynna, Kai; Folowosele, Fopefolu; Saighi, Sylvain; Serrano-Gotarredona, Teresa; Wijekoon, Jayawan; Wang, Yingxue; Boahen, Kwabena

2011-01-01

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. PMID:21747754

Neuromorphic silicon neuron circuits

Directory of Open Access Journals (Sweden)

Giacomo eIndiveri

2011-05-01

Full Text Available Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance based Hodgkin-Huxley models to bi-dimensional generalized adaptive Integrate and Fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

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.

Leukemia and ionizing radiation revisited

Energy Technology Data Exchange (ETDEWEB)

Cuttler, J.M. [Cuttler & Associates Inc., Vaughan, Ontario (Canada); Welsh, J.S. [Loyola University-Chicago, Dept. or Radiation Oncology, Stritch School of Medicine, Maywood, Illinois (United States)

2016-03-15

A world-wide radiation health scare was created in the late 19508 to stop the testing of atomic bombs and block the development of nuclear energy. In spite of the large amount of evidence that contradicts the cancer predictions, this fear continues. It impairs the use of low radiation doses in medical diagnostic imaging and radiation therapy. This brief article revisits the second of two key studies, which revolutionized radiation protection, and identifies a serious error that was missed. This error in analyzing the leukemia incidence among the 195,000 survivors, in the combined exposed populations of Hiroshima and Nagasaki, invalidates use of the LNT model for assessing the risk of cancer from ionizing radiation. The threshold acute dose for radiation-induced leukemia, based on about 96,800 humans, is identified to be about 50 rem, or 0.5 Sv. It is reasonable to expect that the thresholds for other cancer types are higher than this level. No predictions or hints of excess cancer risk (or any other health risk) should be made for an acute exposure below this value until there is scientific evidence to support the LNT hypothesis. (author)

Neural Networks in Control Applications

DEFF Research Database (Denmark)

Sørensen, O.

The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

Cooperating attackers in neural cryptography.

Science.gov (United States)

Shacham, Lanir N; Klein, Einat; Mislovaty, Rachel; Kanter, Ido; Kinzel, Wolfgang

2004-06-01

A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.

An Examination of the Relationship between Rural Tourists’ Satisfaction, Revisitation and Information Preferences: A Korean Case Study

Directory of Open Access Journals (Sweden)

Hee-Sun Cho

2014-09-01

Full Text Available To encourage the sustainability of rural tourism and to achieve success in the tourist industry, an understanding of the factors by which tourists are motivated to visit rural areas is required. This study aims to measure factors affecting rural tourists’ satisfaction in relation to different aspects of a destination and to increase the likelihood of revisitation and recommendation. This study also attempts to examine differences in relation to satisfaction depending on the information source preference. Overall satisfaction was influenced by physical infrastructure, service quality and satisfaction level with tour programs. However, the quality of services was more related to tourists’ intentions to revisit and recommend, suggesting that its qualitative improvement can contribute to vitalizing stagnant domestic tourism. The findings revealed that tourists’ satisfaction was high when people mainly gained tourist information through formal government sources, word-of-mouth and Internet advertising, suggesting that the positive correlation between tourists’ satisfaction and information sources reflects the reliability and credibility of those sources.

The Laplacian spectrum of neural networks

Science.gov (United States)

de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

2014-01-01

The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

Decentralized neural control application to robotics

CERN Document Server

Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

2017-01-01

This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

Role of the extracellular matrix during neural crest cell migration.

Science.gov (United States)

Perris, R; Perissinotto, D

2000-07-01

Once specified to become neural crest (NC), cells occupying the dorsal portion of the neural tube disrupt their cadherin-mediated cell-cell contacts, acquire motile properties, and embark upon an extensive migration through the embryo to reach their ultimate phenotype-specific sites. The understanding of how this movement is regulated is still rather fragmentary due to the complexity of the cellular and molecular interactions involved. An additional intricate aspect of the regulation of NC cell movement is that the timings, modes and patterns of NC cell migration are intimately associated with the concomitant phenotypic diversification that cells undergo during their migratory phase and the fact that these changes modulate the way that moving cells interact with their microenvironment. To date, two interplaying mechanisms appear central for the guidance of the migrating NC cells through the embryo: one involves secreted signalling molecules acting through their cognate protein kinase/phosphatase-type receptors and the other is contributed by the multivalent interactions of the cells with their surrounding extracellular matrix (ECM). The latter ones seem fundamental in light of the central morphogenetic role played by the intracellular signals transduced through the cytoskeleton upon integrin ligation, and the convergence of these signalling cascades with those triggered by cadherins, survival/growth factor receptors, gap junctional communications, and stretch-activated calcium channels. The elucidation of the importance of the ECM during NC cell movement is presently favoured by the augmenting knowledge about the macromolecular structure of the specific ECM assembled during NC development and the functional assaying of its individual constituents via molecular and genetic manipulations. Collectively, these data propose that NC cell migration may be governed by time- and space-dependent alterations in the expression of inhibitory ECM components; the relative ratio

The random field Blume-Capel model revisited

Science.gov (United States)

Santos, P. V.; da Costa, F. A.; de Araújo, J. M.

2018-04-01

We have revisited the mean-field treatment for the Blume-Capel model under the presence of a discrete random magnetic field as introduced by Kaufman and Kanner (1990). The magnetic field (H) versus temperature (T) phase diagrams for given values of the crystal field D were recovered in accordance to Kaufman and Kanner original work. However, our main goal in the present work was to investigate the distinct structures of the crystal field versus temperature phase diagrams as the random magnetic field is varied because similar models have presented reentrant phenomenon due to randomness. Following previous works we have classified the distinct phase diagrams according to five different topologies. The topological structure of the phase diagrams is maintained for both H - T and D - T cases. Although the phase diagrams exhibit a richness of multicritical phenomena we did not found any reentrant effect as have been seen in similar models.

PERANAN SERVICE QUALITY TERHADAP PATIENT REVISIT INTENTION MELALUI PATIENT SATISFACTION PADA RUMAH SAKIT UMUM DAN RUMAH SAKIT SWASTA TIPE B

Directory of Open Access Journals (Sweden)

Febry Adhiana

2008-03-01

Findin s: There are no differences between private and public hospitals in service quality, patient satisf Um and patient revisit intention. Finally the implications of the results are highlighted for health :are managers.

22nd Italian Workshop on Neural Nets

CERN Document Server

Bassis, Simone; Esposito, Anna; Morabito, Francesco

2013-01-01

This volume collects a selection of contributions which has been presented at the 22nd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Italy, Vietri sul Mare (Salerno), during May 17-19, 2012. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop-  is organized in three main components, two special sessions and a group of regular sessions featuring different aspects and point of views of artificial neural networks and natural intelligence, also including applications of present compelling interest.

Analysis of neural networks through base functions

NARCIS (Netherlands)

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

Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more

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

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.

MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

Directory of Open Access Journals (Sweden)

Artur Popko

2013-06-01

Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

Boolean Factor Analysis by Attractor Neural Network

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.

2007-01-01

Roč. 18, č. 3 (2007), s. 698-707 ISSN 1045-9227 R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Institutional research plan: CEZ:AV0Z10300504 Keywords : recurrent neural network * Hopfield-like neural network * associative memory * unsupervised learning * neural network architecture * neural network application * statistics * Boolean factor analysis * dimensionality reduction * features clustering * concepts search * information retrieval Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.769, year: 2007

Revisited neoclassical transport theory for steep, collisional plasma edge profiles

International Nuclear Information System (INIS)

Rogister, A.L.

1994-01-01

Published neoclassical results are misleading as concerns the plasma edge for they do not adequately take the peculiar local conditions into account, in particular the fact that the density and temperature variation length-scales are quite small. Coupled novel neoclassical equations obtain, not only for the evolution of the density and temperatures, but also for the radial electric field and the evolution of the parallel ion momentum: gyro-stresses and inertia indeed upset the otherwise de facto ambipolarity of particle transport and a radial electric field necessarily builds up. The increased nonlinear character of these revisited neoclassical equations widens the realm of possible plasma behaviors. (author)

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

Science.gov (United States)

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

2017-01-01

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

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

Science.gov (United States)

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

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

Aleksi J. Sihvonen

2017-07-01

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

Non-invasive neural stimulation

Science.gov (United States)

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

2017-05-01

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

Neural activation in stress-related exhaustion

DEFF Research Database (Denmark)

Gavelin, Hanna Malmberg; Neely, Anna Stigsdotter; Andersson, Micael

2017-01-01

The primary purpose of this study was to investigate the association between burnout and neural activation during working memory processing in patients with stress-related exhaustion. Additionally, we investigated the neural effects of cognitive training as part of stress rehabilitation. Fifty...... association between burnout level and working memory performance was found, however, our findings indicate that frontostriatal neural responses related to working memory were modulated by burnout severity. We suggest that patients with high levels of burnout need to recruit additional cognitive resources...... to uphold task performance. Following cognitive training, increased neural activation was observed during 3-back in working memory-related regions, including the striatum, however, low sample size limits any firm conclusions....

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....

Neural constructivism or self-organization?

NARCIS (Netherlands)

van der Maas, H.L.J.; Molenaar, P.C.M.

2000-01-01

Comments on the article by S. R. Quartz et al (see record 1998-00749-001) which discussed the constructivist perspective of interaction between cognition and neural processes during development and consequences for theories of learning. Three arguments are given to show that neural constructivism

Program Helps Simulate Neural Networks

Science.gov (United States)

Villarreal, James; Mcintire, Gary

1993-01-01

Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

Grammatical Relations and Grammatical Categories in Malay; the Indonesian Prefix MeN- Revisited

OpenAIRE

Tjia, Johnny

2015-01-01

The lexical roots of Malay are flexible with regard to their grammatical categories, which presents a problem in providing grammatical evidence for their category determination. This paper attempts to propose the use of affixes as one way to deal with the issue. Data from Indonesian and Ambon (Malay) language are among others given for clarification. The grammatical evidence from Indonesian active meN-, together with other affixes, are revisited as they can contribute to our understanding of ...

Prototype-Incorporated Emotional Neural Network.

Science.gov (United States)

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

Modular representation of layered neural networks.

Science.gov (United States)

Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

2018-01-01

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

38 CFR 17.149 - Sensori-neural aids.

Science.gov (United States)

2010-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 1 2010-07-01 2010-07-01 false Sensori-neural aids. 17... Prosthetic, Sensory, and Rehabilitative Aids § 17.149 Sensori-neural aids. (a) Notwithstanding any other provision of this part, VA will furnish needed sensori-neural aids (i.e., eyeglasses, contact lenses...

Genetic Algorithm Optimized Neural Networks Ensemble as ...

African Journals Online (AJOL)

NJD

Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous ... process by training a number of neural networks. .... Matlab® version 6.1 was employed for building principal component ... provide a fair simulation of calibration data set with some degree.

Fuzzy logic and neural networks basic concepts & application

CERN Document Server

Alavala, Chennakesava R

2008-01-01

About the Book: The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required. Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank

Revisit complexation between DNA and polyethylenimine — Effect of length of free polycationic chains on gene transfection

DEFF Research Database (Denmark)

Yue, Yanan; Jin, Fan; Deng, Rui

2011-01-01

Our revisit of the complexation between DNA and polyethylenimine (PEI) by using a combination of laser light scattering and gel electrophoresis confirms that nearly all the DNA chains are complexed with PEI to form polyplexes when the molar ratio of nitrogen from PEI to phosphate from DNA (N:P) r...

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

Multi-national knowledge strategies, policy and the upgrading process of regions: Revisiting the automotive industry in Ostrava and Shanghai

NARCIS (Netherlands)

Tuijl, E. van; Carvalho, L.; Winden, W. van; Jacobs, W.A.A.

2012-01-01

This paper revisits how and why new multinational knowledge-based strategies and multi-level governmental policies influence the upgrading process of regions in developing economies. Automotive multinationals traditionally exploited local asset conditions, but it is shown that they have also been

Nonequilibrium landscape theory of neural networks

Science.gov (United States)

Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

2013-01-01

The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

Nonequilibrium landscape theory of neural networks.

Science.gov (United States)

Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

2013-11-05

The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

Application of neural network to CT

International Nuclear Information System (INIS)

Ma, Xiao-Feng; Takeda, Tatsuoki

1999-01-01

This paper presents a new method for two-dimensional image reconstruction by using a multilayer neural network. Multilayer neural networks are extensively investigated and practically applied to solution of various problems such as inverse problems or time series prediction problems. From learning an input-output mapping from a set of examples, neural networks can be regarded as synthesizing an approximation of multidimensional function (that is, solving the problem of hypersurface reconstruction, including smoothing and interpolation). From this viewpoint, neural networks are well suited to the solution of CT image reconstruction. Though a conventionally used object function of a neural network is composed of a sum of squared errors of the output data, we can define an object function composed of a sum of residue of an integral equation. By employing an appropriate line integral for this integral equation, we can construct a neural network that can be used for CT. We applied this method to some model problems and obtained satisfactory results. As it is not necessary to discretized the integral equation using this reconstruction method, therefore it is application to the problem of complicated geometrical shapes is also feasible. Moreover, in neural networks, interpolation is performed quite smoothly, as a result, inverse mapping can be achieved smoothly even in case of including experimental and numerical errors, However, use of conventional back propagation technique for optimization leads to an expensive computation cost. To overcome this drawback, 2nd order optimization methods or parallel computing will be applied in future. (J.P.N.)

Methodology of Neural Design: Applications in Microwave Engineering

Directory of Open Access Journals (Sweden)

Z. Raida

2006-06-01

Full Text Available In the paper, an original methodology for the automatic creation of neural models of microwave structures is proposed and verified. Following the methodology, neural models of the prescribed accuracy are built within the minimum CPU time. Validity of the proposed methodology is verified by developing neural models of selected microwave structures. Functionality of neural models is verified in a design - a neural model is joined with a genetic algorithm to find a global minimum of a formulated objective function. The objective function is minimized using different versions of genetic algorithms, and their mutual combinations. The verified methodology of the automated creation of accurate neural models of microwave structures, and their association with global optimization routines are the most important original features of the paper.

Differentiation between non-neural and neural contributors to ankle joint stiffness in cerebral palsy.

Science.gov (United States)

de Gooijer-van de Groep, Karin L; de Vlugt, Erwin; de Groot, Jurriaan H; van der Heijden-Maessen, Hélène C M; Wielheesen, Dennis H M; van Wijlen-Hempel, Rietje M S; Arendzen, J Hans; Meskers, Carel G M

2013-07-23

Spastic paresis in cerebral palsy (CP) is characterized by increased joint stiffness that may be of neural origin, i.e. improper muscle activation caused by e.g. hyperreflexia or non-neural origin, i.e. altered tissue viscoelastic properties (clinically: "spasticity" vs. "contracture"). Differentiation between these components is hard to achieve by common manual tests. We applied an assessment instrument to obtain quantitative measures of neural and non-neural contributions to ankle joint stiffness in CP. Twenty-three adolescents with CP and eleven healthy subjects were seated with their foot fixated to an electrically powered single axis footplate. Passive ramp-and-hold rotations were applied over full ankle range of motion (RoM) at low and high velocities. Subject specific tissue stiffness, viscosity and reflexive torque were estimated from ankle angle, torque and triceps surae EMG activity using a neuromuscular model. In CP, triceps surae reflexive torque was on average 5.7 times larger (p = .002) and tissue stiffness 2.1 times larger (p = .018) compared to controls. High tissue stiffness was associated with reduced RoM (p therapy.

Differentiation state determines neural effects on microvascular endothelial cells

International Nuclear Information System (INIS)

Muffley, Lara A.; Pan, Shin-Chen; Smith, Andria N.; Ga, Maricar; Hocking, Anne M.; Gibran, Nicole S.

2012-01-01

Growing evidence indicates that nerves and capillaries interact paracrinely in uninjured skin and cutaneous wounds. Although mature neurons are the predominant neural cell in the skin, neural progenitor cells have also been detected in uninjured adult skin. The aim of this study was to characterize differential paracrine effects of neural progenitor cells and mature sensory neurons on dermal microvascular endothelial cells. Our results suggest that neural progenitor cells and mature sensory neurons have unique secretory profiles and distinct effects on dermal microvascular endothelial cell proliferation, migration, and nitric oxide production. Neural progenitor cells and dorsal root ganglion neurons secrete different proteins related to angiogenesis. Specific to neural progenitor cells were dipeptidyl peptidase-4, IGFBP-2, pentraxin-3, serpin f1, TIMP-1, TIMP-4 and VEGF. In contrast, endostatin, FGF-1, MCP-1 and thrombospondin-2 were specific to dorsal root ganglion neurons. Microvascular endothelial cell proliferation was inhibited by dorsal root ganglion neurons but unaffected by neural progenitor cells. In contrast, microvascular endothelial cell migration in a scratch wound assay was inhibited by neural progenitor cells and unaffected by dorsal root ganglion neurons. In addition, nitric oxide production by microvascular endothelial cells was increased by dorsal root ganglion neurons but unaffected by neural progenitor cells. -- Highlights: ► Dorsal root ganglion neurons, not neural progenitor cells, regulate microvascular endothelial cell proliferation. ► Neural progenitor cells, not dorsal root ganglion neurons, regulate microvascular endothelial cell migration. ► Neural progenitor cells and dorsal root ganglion neurons do not effect microvascular endothelial tube formation. ► Dorsal root ganglion neurons, not neural progenitor cells, regulate microvascular endothelial cell production of nitric oxide. ► Neural progenitor cells and dorsal root

Revisiting Johnson and Jackson boundary conditions for granular flows

Energy Technology Data Exchange (ETDEWEB)

Li, Tingwen; Benyahia, Sofiane

2012-07-01

In this article, we revisit Johnson and Jackson boundary conditions for granular flows. The oblique collision between a particle and a flat wall is analyzed by adopting the classic rigid-body theory and a more realistic semianalytical model. Based on the kinetic granular theory, the input parameter for the partial-slip boundary conditions, specularity coefficient, which is not measurable in experiments, is then interpreted as a function of the particle-wall restitution coefficient, the frictional coefficient, and the normalized slip velocity at the wall. An analytical expression for the specularity coefficient is suggested for a flat, frictional surface with a low frictional coefficient. The procedure for determining the specularity coefficient for a more general problem is outlined, and a working approximation is provided.

Proposal of a model of mammalian neural induction

Science.gov (United States)

Levine, Ariel J.; Brivanlou, Ali H.

2009-01-01

How does the vertebrate embryo make a nervous system? This complex question has been at the center of developmental biology for many years. The earliest step in this process – the induction of neural tissue – is intimately linked to patterning of the entire early embryo, and the molecular and embryological basis these processes are beginning to emerge. Here, we analyze classic and cutting-edge findings on neural induction in the mouse. We find that data from genetics, tissue explants, tissue grafting, and molecular marker expression support a coherent framework for mammalian neural induction. In this model, the gastrula organizer of the mouse embryo inhibits BMP signaling to allow neural tissue to form as a default fate – in the absence of instructive signals. The first neural tissue induced is anterior and subsequent neural tissue is posteriorized to form the midbrain, hindbrain, and spinal cord. The anterior visceral endoderm protects the pre-specified anterior neural fate from similar posteriorization, allowing formation of forebrain. This model is very similar to the default model of neural induction in the frog, thus bridging the evolutionary gap between amphibians and mammals. PMID:17585896

Revisiting the melting temperature of NpO2 and the challenges associated with high temperature actinide compound measurements

NARCIS (Netherlands)

Böhler, R.; Welland, M.J.; De Bruycker, F.; Boboridis, K.; Janssen, A.; Eloirdi, R.; Konings, R.J.M.; Manara, D.

2012-01-01

This work revisits the melting behaviour of neptunium dioxide, an actinide compound which can be produced in the nuclear fuel during operation, and which has an important impact on the nuclear fuel and waste radioactivity especially on the very long term. The present experimental approach employs

Revisiting instanton corrections to the Konishi multiplet

Energy Technology Data Exchange (ETDEWEB)

Alday, Luis F. [Mathematical Institute, University of Oxford,Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG (United Kingdom); Korchemsky, Gregory P. [Institut de Physique Théorique, Université Paris Saclay, CNRS, CEA,F-91191 Gif-sur-Yvette (France)

2016-12-01

We revisit the calculation of instanton effects in correlation functions in N=4 SYM involving the Konishi operator and operators of twist two. Previous studies revealed that the scaling dimensions and the OPE coefficients of these operators do not receive instanton corrections in the semiclassical approximation. We go beyond this approximation and demonstrate that, while operators belonging to the same N=4 supermultiplet ought to have the same conformal data, the evaluation of quantum instanton corrections for one operator can be mapped into a semiclassical computation for another operator in the same supermultiplet. This observation allows us to compute explicitly the leading instanton correction to the scaling dimension of operators in the Konishi supermultiplet as well as to their structure constants in the OPE of two half-BPS scalar operators. We then use these results, together with crossing symmetry, to determine instanton corrections to scaling dimensions of twist-four operators with large spin.

PERCEPTION OF JUSTICE, POST SERVICE RECOVERY SATISFACTION, INTENTION TO REVISIT AND WOM RECOMMENDATIONS OF FOREIGN TOURISTS VISITING BALI

Directory of Open Access Journals (Sweden)

I Nyoman Sudiarta

2014-03-01

Full Text Available This study aimed to determine the effect of perceptions of distributive, procedural and interactional justice on post-service recovery satisfaction and post-service recovery satisfaction effect on the intention to revisit and WOM recommendations of foreign tourists to Bali. The respondents of this study were foreign tourists who visited Bali and ever experienced complaint. The number of eligible samples was 100 respondents. The questionnaire was given to tourists visiting tourist attractions of Tanah Lot, Kintamani and Besakih. Data were analyzed using multivariate statistical analysis, namely structural equation modeling (SEM. The results of this study indicated that the perception of distributive justice, procedural and interactional had a positive and significant effect on the post-service recovery satisfaction of foreign tourists who visited Bali. The study also found a positive and significant effect of post-service recovery satisfaction on the intention to revisit and the intention of recommending positive WOM of foreign tourists who visited Bali.

Tuning Neural Phase Entrainment to Speech.

Science.gov (United States)

Falk, Simone; Lanzilotti, Cosima; Schön, Daniele

2017-08-01

Musical rhythm positively impacts on subsequent speech processing. However, the neural mechanisms underlying this phenomenon are so far unclear. We investigated whether carryover effects from a preceding musical cue to a speech stimulus result from a continuation of neural phase entrainment to periodicities that are present in both music and speech. Participants listened and memorized French metrical sentences that contained (quasi-)periodic recurrences of accents and syllables. Speech stimuli were preceded by a rhythmically regular or irregular musical cue. Our results show that the presence of a regular cue modulates neural response as estimated by EEG power spectral density, intertrial coherence, and source analyses at critical frequencies during speech processing compared with the irregular condition. Importantly, intertrial coherences for regular cues were indicative of the participants' success in memorizing the subsequent speech stimuli. These findings underscore the highly adaptive nature of neural phase entrainment across fundamentally different auditory stimuli. They also support current models of neural phase entrainment as a tool of predictive timing and attentional selection across cognitive domains.

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Neural synchronization during face-to-face communication.

Science.gov (United States)

Jiang, Jing; Dai, Bohan; Peng, Danling; Zhu, Chaozhe; Liu, Li; Lu, Chunming

2012-11-07

Although the human brain may have evolutionarily adapted to face-to-face communication, other modes of communication, e.g., telephone and e-mail, increasingly dominate our modern daily life. This study examined the neural difference between face-to-face communication and other types of communication by simultaneously measuring two brains using a hyperscanning approach. The results showed a significant increase in the neural synchronization in the left inferior frontal cortex during a face-to-face dialog between partners but none during a back-to-back dialog, a face-to-face monologue, or a back-to-back monologue. Moreover, the neural synchronization between partners during the face-to-face dialog resulted primarily from the direct interactions between the partners, including multimodal sensory information integration and turn-taking behavior. The communicating behavior during the face-to-face dialog could be predicted accurately based on the neural synchronization level. These results suggest that face-to-face communication, particularly dialog, has special neural features that other types of communication do not have and that the neural synchronization between partners may underlie successful face-to-face communication.

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).

Neural Network Based Load Frequency Control for Restructuring ...

African Journals Online (AJOL)

Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

Neural substrates of decision-making.

Science.gov (United States)

Broche-Pérez, Y; Herrera Jiménez, L F; Omar-Martínez, E

2016-06-01

Decision-making is the process of selecting a course of action from among 2 or more alternatives by considering the potential outcomes of selecting each option and estimating its consequences in the short, medium and long term. The prefrontal cortex (PFC) has traditionally been considered the key neural structure in decision-making process. However, new studies support the hypothesis that describes a complex neural network including both cortical and subcortical structures. The aim of this review is to summarise evidence on the anatomical structures underlying the decision-making process, considering new findings that support the existence of a complex neural network that gives rise to this complex neuropsychological process. Current evidence shows that the cortical structures involved in decision-making include the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). This process is assisted by subcortical structures including the amygdala, thalamus, and cerebellum. Findings to date show that both cortical and subcortical brain regions contribute to the decision-making process. The neural basis of decision-making is a complex neural network of cortico-cortical and cortico-subcortical connections which includes subareas of the PFC, limbic structures, and the cerebellum. Copyright © 2014 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.

Grammatical relations and grammatical categories in Malay; The Indonesian prefix meN- revisited

Directory of Open Access Journals (Sweden)

Johnny Tjia

2015-04-01

Full Text Available The lexical roots of Malay are flexible with regard to their grammatical categories, which presents a problem in providing grammatical evidence for their category determination. This paper attempts to propose the use of affixes as one way to deal with the issue. Data from Indonesian and Ambon (Malay language are among others given for clarification. The grammatical evidence from Indonesian active meN-, together with other affixes, are revisited as they can contribute to our understanding of the matter.

Genetic algorithm for neural networks optimization

Science.gov (United States)

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

Neural network recognition of mammographic lesions

International Nuclear Information System (INIS)

Oldham, W.J.B.; Downes, P.T.; Hunter, V.

1987-01-01

A method for recognition of mammographic lesions through the use of neural networks is presented. Neural networks have exhibited the ability to learn the shape andinternal structure of patterns. Digitized mammograms containing circumscribed and stelate lesions were used to train a feedfoward synchronous neural network that self-organizes to stable attractor states. Encoding of data for submission to the network was accomplished by performing a fractal analysis of the digitized image. This results in scale invariant representation of the lesions. Results are discussed

Optical-Correlator Neural Network Based On Neocognitron

Science.gov (United States)

Chao, Tien-Hsin; Stoner, William W.

1994-01-01

Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

2006-07-01

An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

International Nuclear Information System (INIS)

Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R.

2006-01-01

An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK

Directory of Open Access Journals (Sweden)

R. Hadapiningradja Kusumodestoni

2015-11-01

Full Text Available ABSTRAK Prediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/- 0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network.

Artificial neural networks a practical course

CERN Document Server

da Silva, Ivan Nunes; Andrade Flauzino, Rogerio; Liboni, Luisa Helena Bartocci; dos Reis Alves, Silas Franco

2017-01-01

This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

Early-Transition Output Decline Revisited

Directory of Open Access Journals (Sweden)

Crt Kostevc

2016-05-01

Full Text Available In this paper we revisit the issue of aggregate output decline that took place in the early transition period. We propose an alternative explanation of output decline that is applicable to Central- and Eastern-European countries. In the first part of the paper we develop a simple dynamic general equilibrium model that builds on work by Gomulka and Lane (2001. In particular, we consider price liberalization, interpreted as elimination of distortionary taxation, as a trigger of the output decline. We show that price liberalization in interaction with heterogeneous adjustment costs and non-employment benefits lead to aggregate output decline and surge in wage inequality. While these patterns are consistent with actual dynamics in CEE countries, this model cannot generate output decline in all sectors. Instead sectors that were initially taxed even exhibit output growth. Thus, in the second part we consider an alternative general equilibrium model with only one production sector and two types of labor and distortion in a form of wage compression during the socialist era. The trigger for labor mobility and consequently output decline is wage liberalization. Assuming heterogeneity of workers in terms of adjustment costs and non-employment benefits can explain output decline in all industries.

Neutrino assisted GUT baryogenesis revisited

Science.gov (United States)

Huang, Wei-Chih; Päs, Heinrich; Zeißner, Sinan

2018-03-01

Many grand unified theory (GUT) models conserve the difference between the baryon and lepton number, B -L . These models can create baryon and lepton asymmetries from heavy Higgs or gauge boson decays with B +L ≠0 but with B -L =0 . Since the sphaleron processes violate B +L , such GUT-generated asymmetries will finally be washed out completely, making GUT baryogenesis scenarios incapable of reproducing the observed baryon asymmetry of the Universe. In this work, we revisit the idea to revive GUT baryogenesis, proposed by Fukugita and Yanagida, where right-handed neutrinos erase the lepton asymmetry before the sphaleron processes can significantly wash out the original B +L asymmetry, and in this way one can prevent a total washout of the initial baryon asymmetry. By solving the Boltzmann equations numerically for baryon and lepton asymmetries in a simplified 1 +1 flavor scenario, we can confirm the results of the original work. We further generalize the analysis to a more realistic scenario of three active and two right-handed neutrinos to highlight flavor effects of the right-handed neutrinos. Large regions in the parameter space of the Yukawa coupling and the right-handed neutrino mass featuring successful baryogenesis are identified.

Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

DEFF Research Database (Denmark)

Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

2015-01-01

here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated......Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...

In-vitro differentiation induction of neural stem cells

NARCIS (Netherlands)

Balasubramaniyan, Veerakumar

2006-01-01

Neurale stamcellen maken de drie belangrijkste celtypes van ons zenuwstelsel aan. Veerakumar Balasubramaniyan onderzocht hoe neurale stamcellen kunnen worden aangezet tot het aanmaken van specifieke neurale celtypes. Met behulp van genetische technieken lukte het hem oligodendrocyten te verkrijgen:

Neural entrainment to the rhythmic structure of music.

Science.gov (United States)

Tierney, Adam; Kraus, Nina

2015-02-01

The neural resonance theory of musical meter explains musical beat tracking as the result of entrainment of neural oscillations to the beat frequency and its higher harmonics. This theory has gained empirical support from experiments using simple, abstract stimuli. However, to date there has been no empirical evidence for a role of neural entrainment in the perception of the beat of ecologically valid music. Here we presented participants with a single pop song with a superimposed bassoon sound. This stimulus was either lined up with the beat of the music or shifted away from the beat by 25% of the average interbeat interval. Both conditions elicited a neural response at the beat frequency. However, although the on-the-beat condition elicited a clear response at the first harmonic of the beat, this frequency was absent in the neural response to the off-the-beat condition. These results support a role for neural entrainment in tracking the metrical structure of real music and show that neural meter tracking can be disrupted by the presentation of contradictory rhythmic cues.

Adaptive optimization and control using neural networks

Energy Technology Data Exchange (ETDEWEB)

Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

1993-10-22

Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

Intelligent neural network diagnostic system

International Nuclear Information System (INIS)

Mohamed, A.H.

2010-01-01

Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI.

Science.gov (United States)

Wang, Jun; Breen, Daniel; Akinin, Abraham; Broccard, Frederic; Abarbanel, Henry D I; Cauwenberghs, Gert

2017-12-01

Representing the biophysics of neuronal dynamics and behavior offers a principled analysis-by-synthesis approach toward understanding mechanisms of nervous system functions. We report on a set of procedures assimilating and emulating neurobiological data on a neuromorphic very large scale integrated (VLSI) circuit. The analog VLSI chip, NeuroDyn, features 384 digitally programmable parameters specifying for 4 generalized Hodgkin-Huxley neurons coupled through 12 conductance-based chemical synapses. The parameters also describe reversal potentials, maximal conductances, and spline regressed kinetic functions for ion channel gating variables. In one set of experiments, we assimilated membrane potential recorded from one of the neurons on the chip to the model structure upon which NeuroDyn was designed using the known current input sequence. We arrived at the programmed parameters except for model errors due to analog imperfections in the chip fabrication. In a related set of experiments, we replicated songbird individual neuron dynamics on NeuroDyn by estimating and configuring parameters extracted using data assimilation from intracellular neural recordings. Faithful emulation of detailed biophysical neural dynamics will enable the use of NeuroDyn as a tool to probe electrical and molecular properties of functional neural circuits. Neuroscience applications include studying the relationship between molecular properties of neurons and the emergence of different spike patterns or different brain behaviors. Clinical applications include studying and predicting effects of neuromodulators or neurodegenerative diseases on ion channel kinetics.

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…

Recent Advances in Neural Recording Microsystems

Directory of Open Access Journals (Sweden)

Benoit Gosselin

2011-04-01

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

International Conference on Artificial Neural Networks (ICANN)

CERN Document Server

Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

2015-01-01

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

Neural neworks in a management information systems

Directory of Open Access Journals (Sweden)

Jana Weinlichová

2009-01-01

Full Text Available For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of manager issues. Those products are given as primary support for manager issues solving. We were tried to find reciprocally between products using Neural Networks and between Management Information Systems for finding a real possibility of applying Neural Networks as a direct part of Management Information Systems (MIS. In the article are presented possibilities to apply Neural Networks on different types of tasks in MIS.

Neural Networks in Mobile Robot Motion

Directory of Open Access Journals (Sweden)

Danica Janglová

2004-03-01

Full Text Available This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.

Optimal neural computations require analog processors

Energy Technology Data Exchange (ETDEWEB)

Beiu, V.

1998-12-31

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

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.

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....

On the neural mechanisms subserving consciousness and attention

Directory of Open Access Journals (Sweden)

Catherine eTallon-Baudry

2012-01-01

Full Text Available Consciousness, as described in the experimental literature, is a multi-faceted phenomenon, that impinges on other well-studied concepts such as attention and control. Do consciousness and attention refer to different aspects of the same core phenomenon, or do they correspond to distinct functions? One possibility to address this question is to examine the neural mechanisms underlying consciousness and attention. If consciousness and attention pertain to the same concept, they should rely on shared neural mechanisms. Conversely, if their underlying mechanisms are distinct, then consciousness and attention should be considered as distinct entities. This paper therefore reviews neurophysiological facts arguing in favor or against a tight relationship between consciousness and attention. Three neural mechanisms that have been associated with both attention and consciousness are examined (neural amplification, involvement of the fronto-parietal network, and oscillatory synchrony, to conclude that the commonalities between attention and consciousness at the neural level may have been overestimated. Last but not least, experiments in which both attention and consciousness were probed at the neural level point toward a dissociation between the two concepts. It therefore appears from this review that consciousness and attention rely on distinct neural properties, although they can interact at the behavioral level. It is proposed that a "cumulative influence model", in which attention and consciousness correspond to distinct neural mechanisms feeding a single decisional process leading to behavior, fits best with available neural and behavioral data. In this view, consciousness should not be considered as a top-level executive function but should rather be defined by its experiential properties.

Adaptive nonlinear control using input normalized neural networks

International Nuclear Information System (INIS)

Leeghim, Henzeh; Seo, In Ho; Bang, Hyo Choong

2008-01-01

An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small

Antenna analysis using neural networks

Science.gov (United States)

Smith, William T.

1992-01-01

Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

Neural networks in economic modelling : An empirical study

NARCIS (Netherlands)

Verkooijen, W.J.H.

1996-01-01

This dissertation addresses the statistical aspects of neural networks and their usability for solving problems in economics and finance. Neural networks are discussed in a framework of modelling which is generally accepted in econometrics. Within this framework a neural network is regarded as a

Efficient computation in adaptive artificial spiking neural networks

NARCIS (Netherlands)

D. Zambrano (Davide); R.B.P. Nusselder (Roeland); H.S. Scholte; S.M. Bohte (Sander)

2017-01-01

textabstractArtificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of

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.

Interpretable neural networks with BP-SOM

NARCIS (Netherlands)

Weijters, A.J.M.M.; Bosch, van den A.P.J.; Pobil, del A.P.; Mira, J.; Ali, M.

1998-01-01

Artificial Neural Networks (ANNS) are used successfully in industry and commerce. This is not surprising since neural networks are especially competitive for complex tasks for which insufficient domain-specific knowledge is available. However, interpretation of models induced by ANNS is often

A high-speed analog neural processor

NARCIS (Netherlands)

Masa, P.; Masa, Peter; Hoen, Klaas; Hoen, Klaas; Wallinga, Hans

1994-01-01

Targeted at high-energy physics research applications, our special-purpose analog neural processor can classify up to 70 dimensional vectors within 50 nanoseconds. The decision-making process of the implemented feedforward neural network enables this type of computation to tolerate weight

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.

Requirement of mouse BCCIP for neural development and progenitor proliferation.

Directory of Open Access Journals (Sweden)

Yi-Yuan Huang

Full Text Available Multiple DNA repair pathways are involved in the orderly development of neural systems at distinct stages. The homologous recombination (HR pathway is required to resolve stalled replication forks and critical for the proliferation of progenitor cells during neural development. BCCIP is a BRCA2 and CDKN1A interacting protein implicated in HR and inhibition of DNA replication stress. In this study, we determined the role of BCCIP in neural development using a conditional BCCIP knock-down mouse model. BCCIP deficiency impaired embryonic and postnatal neural development, causing severe ataxia, cerebral and cerebellar defects, and microcephaly. These development defects are associated with spontaneous DNA damage and subsequent cell death in the proliferative cell populations of the neural system during embryogenesis. With in vitro neural spheroid cultures, BCCIP deficiency impaired neural progenitor's self-renewal capability, and spontaneously activated p53. These data suggest that BCCIP and its anti-replication stress functions are essential for normal neural development by maintaining an orderly proliferation of neural progenitors.

Neural crest cells: from developmental biology to clinical interventions.

Science.gov (United States)

Noisa, Parinya; Raivio, Taneli

2014-09-01

Neural crest cells are multipotent cells, which are specified in embryonic ectoderm in the border of neural plate and epiderm during early development by interconnection of extrinsic stimuli and intrinsic factors. Neural crest cells are capable of differentiating into various somatic cell types, including melanocytes, craniofacial cartilage and bone, smooth muscle, and peripheral nervous cells, which supports their promise for cell therapy. In this work, we provide a comprehensive review of wide aspects of neural crest cells from their developmental biology to applicability in medical research. We provide a simplified model of neural crest cell development and highlight the key external stimuli and intrinsic regulators that determine the neural crest cell fate. Defects of neural crest cell development leading to several human disorders are also mentioned, with the emphasis of using human induced pluripotent stem cells to model neurocristopathic syndromes. © 2014 Wiley Periodicals, Inc.

Neural network-based model reference adaptive control system.

Science.gov (United States)

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

A fuzzy neural network for sensor signal estimation

International Nuclear Information System (INIS)

Na, Man Gyun

2000-01-01

In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique. Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors

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.

Implantable Neural Interfaces for Sharks

Science.gov (United States)

2007-05-01

technology for recording and stimulating from the auditory and olfactory sensory nervous systems of the awake, swimming nurse shark , G. cirratum (Figures...overlay of the central nervous system of the nurse shark on a horizontal MR image. Implantable Neural Interfaces for Sharks ...Neural Interfaces for Characterizing Population Responses to Odorants and Electrical Stimuli in the Nurse Shark , Ginglymostoma cirratum.” AChemS Abs

Introduction to Concepts in Artificial Neural Networks

Science.gov (United States)

Niebur, Dagmar

1995-01-01

This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

Direct adaptive control using feedforward neural networks

OpenAIRE

Cajueiro, Daniel Oliveira; Hemerly, Elder Moreira

2003-01-01

ABSTRACT: This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the conver...

Meta-analysis in clinical trials revisited.

Science.gov (United States)

DerSimonian, Rebecca; Laird, Nan

2015-11-01

In this paper, we revisit a 1986 article we published in this Journal, Meta-Analysis in Clinical Trials, where we introduced a random-effects model to summarize the evidence about treatment efficacy from a number of related clinical trials. Because of its simplicity and ease of implementation, our approach has been widely used (with more than 12,000 citations to date) and the "DerSimonian and Laird method" is now often referred to as the 'standard approach' or a 'popular' method for meta-analysis in medical and clinical research. The method is especially useful for providing an overall effect estimate and for characterizing the heterogeneity of effects across a series of studies. Here, we review the background that led to the original 1986 article, briefly describe the random-effects approach for meta-analysis, explore its use in various settings and trends over time and recommend a refinement to the method using a robust variance estimator for testing overall effect. We conclude with a discussion of repurposing the method for Big Data meta-analysis and Genome Wide Association Studies for studying the importance of genetic variants in complex diseases. Published by Elsevier Inc.

Time series prediction with simple recurrent neural networks ...

African Journals Online (AJOL)

A hybrid of the two called Elman-Jordan (or Multi-recurrent) neural network is also being used. In this study, we evaluated the performance of these neural networks on three established bench mark time series prediction problems. Results from the experiments showed that Jordan neural network performed significantly ...

The Challenges of Neural Mind-reading Paradigms

Directory of Open Access Journals (Sweden)

Oscar eVilarroya

2013-06-01

Full Text Available Neural mind-reading studies, based on multivariate pattern analysis (MVPA methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: a the BOLD signal is a marker of neural activity; b the BOLD pattern identified by a MVPA is a neurally sound pattern; c the MVPA’s feature space is a good mapping of the neural representation of a stimulus, and d the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

The challenges of neural mind-reading paradigms.

Science.gov (United States)

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neural representation of a stimulus, and (d) the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

Inversion of a lateral log using neural networks

International Nuclear Information System (INIS)

Garcia, G.; Whitman, W.W.

1992-01-01

In this paper a technique using neural networks is demonstrated for the inversion of a lateral log. The lateral log is simulated by a finite difference method which in turn is used as an input to a backpropagation neural network. An initial guess earth model is generated from the neural network, which is then input to a Marquardt inversion. The neural network reacts to gross and subtle data features in actual logs and produces a response inferred from the knowledge stored in the network during a training process. The neural network inversion of lateral logs is tested on synthetic and field data. Tests using field data resulted in a final earth model whose simulated lateral is in good agreement with the actual log data

Neural network modeling for near wall turbulent flow

International Nuclear Information System (INIS)

Milano, Michele; Koumoutsakos, Petros

2002-01-01

A neural network methodology is developed in order to reconstruct the near wall field in a turbulent flow by exploiting flow fields provided by direct numerical simulations. The results obtained from the neural network methodology are compared with the results obtained from prediction and reconstruction using proper orthogonal decomposition (POD). Using the property that the POD is equivalent to a specific linear neural network, a nonlinear neural network extension is presented. It is shown that for a relatively small additional computational cost nonlinear neural networks provide us with improved reconstruction and prediction capabilities for the near wall velocity fields. Based on these results advantages and drawbacks of both approaches are discussed with an outlook toward the development of near wall models for turbulence modeling and control

Convolutional over Recurrent Encoder for Neural Machine Translation

Directory of Open Access Journals (Sweden)

Dakwale Praveen

2017-06-01

Full Text Available Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN. In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.

Comparison of 2D and 3D neural induction methods for the generation of neural progenitor cells from human induced pluripotent stem cells

DEFF Research Database (Denmark)

Chandrasekaran, Abinaya; Avci, Hasan; Ochalek, Anna

2017-01-01

Neural progenitor cells (NPCs) from human induced pluripotent stem cells (hiPSCs) are frequently induced using 3D culture methodologies however, it is unknown whether spheroid-based (3D) neural induction is actually superior to monolayer (2D) neural induction. Our aim was to compare the efficiency......), cortical layer (TBR1, CUX1) and glial markers (SOX9, GFAP, AQP4). Electron microscopy demonstrated that both methods resulted in morphologically similar neural rosettes. However, quantification of NPCs derived from 3D neural induction exhibited an increase in the number of PAX6/NESTIN double positive cells...... the electrophysiological properties between the two induction methods. In conclusion, 3D neural induction increases the yield of PAX6+/NESTIN+ cells and gives rise to neurons with longer neurites, which might be an advantage for the production of forebrain cortical neurons, highlighting the potential of 3D neural...

Runoff Modelling in Urban Storm Drainage by Neural Networks

DEFF Research Database (Denmark)

Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld

1995-01-01

A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....

Conservation laws for steady flow and solitons in a multifluid plasma revisited

International Nuclear Information System (INIS)

Mace, R. L.; McKenzie, J. F.; Webb, G. M.

2007-01-01

The conservation laws used in constructing the governing equations for planar solitons in multifluid plasmas are revisited. In particular, the concept of generalized vorticity facilitates the derivation of some general ''Bernoulli theorems,'' which reduce, in specific instances, to conservation laws previously deduced by other means. These theorems clarify the underlying physical principles that give rise to the conserved quantities. As an example of the usefulness of the techniques, even for relatively simple flows and progressive waves, the equations governing stationary nonlinear whistler waves propagating parallel to an ambient magnetic field are derived using generalized vorticity concepts

Application of neural networks in CRM systems

Directory of Open Access Journals (Sweden)

Bojanowska Agnieszka

2017-01-01

Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.

Gaussian entanglement revisited

Science.gov (United States)

Lami, Ludovico; Serafini, Alessio; Adesso, Gerardo

2018-02-01

We present a novel approach to the separability problem for Gaussian quantum states of bosonic continuous variable systems. We derive a simplified necessary and sufficient separability criterion for arbitrary Gaussian states of m versus n modes, which relies on convex optimisation over marginal covariance matrices on one subsystem only. We further revisit the currently known results stating the equivalence between separability and positive partial transposition (PPT) for specific classes of Gaussian states. Using techniques based on matrix analysis, such as Schur complements and matrix means, we then provide a unified treatment and compact proofs of all these results. In particular, we recover the PPT-separability equivalence for: (i) Gaussian states of 1 versus n modes; and (ii) isotropic Gaussian states. In passing, we also retrieve (iii) the recently established equivalence between separability of a Gaussian state and and its complete Gaussian extendability. Our techniques are then applied to progress beyond the state of the art. We prove that: (iv) Gaussian states that are invariant under partial transposition are necessarily separable; (v) the PPT criterion is necessary and sufficient for separability for Gaussian states of m versus n modes that are symmetric under the exchange of any two modes belonging to one of the parties; and (vi) Gaussian states which remain PPT under passive optical operations can not be entangled by them either. This is not a foregone conclusion per se (since Gaussian bound entangled states do exist) and settles a question that had been left unanswered in the existing literature on the subject. This paper, enjoyable by both the quantum optics and the matrix analysis communities, overall delivers technical and conceptual advances which are likely to be useful for further applications in continuous variable quantum information theory, beyond the separability problem.

'Counterfeit deviance' revisited.

Science.gov (United States)

Griffiths, Dorothy; Hingsburger, Dave; Hoath, Jordan; Ioannou, Stephanie

2013-09-01

The field has seen a renewed interest in exploring the theory of 'counterfeit deviance' for persons with intellectual disability who sexually offend. The term was first presented in 1991 by Hingsburger, Griffiths and Quinsey as a means to differentiate in clinical assessment a subgroup of persons with intellectual disability whose behaviours appeared like paraphilia but served a function that was not related to paraphilia sexual urges or fantasies. Case observations were put forward to provide differential diagnosis of paraphilia in persons with intellectual disabilities compared to those with counterfeit deviance. The brief paper was published in a journal that is no longer available and as such much of what is currently written on the topic is based on secondary sources. The current paper presents a theoretical piece to revisit the original counterfeit deviance theory to clarify the myths and misconceptions that have arisen and evaluate the theory based on additional research and clinical findings. The authors also propose areas where there may be a basis for expansion of the theory. The theory of counterfeit deviance still has relevance as a consideration for clinicians when assessing the nature of a sexual offence committed by a person with an intellectual disability. Clinical differentiation of paraphilia from counterfeit deviance provides a foundation for intervention that is designed to specifically treat the underlying factors that contributed to the offence for a given individual. Counterfeit deviance is a concept that continues to provide areas for consideration for clinicians regarding the assessment and treatment of an individual with an intellectual disability who has sexually offended. It is not and never was an explanation for all sexually offending behavior among persons with intellectual disabilities. © 2013 John Wiley & Sons Ltd.

Serotonin, neural markers and memory

Directory of Open Access Journals (Sweden)

Alfredo eMeneses

2015-07-01

Full Text Available Diverse neuropsychiatric disorders present dysfunctional memory and no effective treatment exits for them; likely as result of the absence of neural markers associated to memory. Neurotransmitter systems and signaling pathways have been implicated in memory and dysfunctional memory; however, their role is poorly understood. Hence, neural markers and cerebral functions and dysfunctions are revised. To our knowledge no previous systematic works have been published addressing these issues. The interactions among behavioral tasks, control groups and molecular changes and/or pharmacological effects are mentioned. Neurotransmitter receptors and signaling pathways, during normal and abnormally functioning memory with an emphasis on the behavioral aspects of memory are revised. With focus on serotonin, since as it is a well characterized neurotransmitter, with multiple pharmacological tools, and well characterized downstream signaling in mammals’ species. 5-HT1A, 5-HT4, 5-HT5, 5-HT6 and 5-HT7 receptors as well as SERT (serotonin transporter seem to be useful neural markers and/or therapeutic targets. Certainly, if the mentioned evidence is replicated, then the translatability from preclinical and clinical studies to neural changes might be confirmed. Hypothesis and theories might provide appropriate limits and perspectives of evidence

Vestibular hearing and neural synchronization.

Science.gov (United States)

Emami, Seyede Faranak; Daneshi, Ahmad

2012-01-01

Objectives. Vestibular hearing as an auditory sensitivity of the saccule in the human ear is revealed by cervical vestibular evoked myogenic potentials (cVEMPs). The range of the vestibular hearing lies in the low frequency. Also, the amplitude of an auditory brainstem response component depends on the amount of synchronized neural activity, and the auditory nerve fibers' responses have the best synchronization with the low frequency. Thus, the aim of this study was to investigate correlation between vestibular hearing using cVEMPs and neural synchronization via slow wave Auditory Brainstem Responses (sABR). Study Design. This case-control survey was consisted of twenty-two dizzy patients, compared to twenty healthy controls. Methods. Intervention comprised of Pure Tone Audiometry (PTA), Impedance acoustic metry (IA), Videonystagmography (VNG), fast wave ABR (fABR), sABR, and cVEMPs. Results. The affected ears of the dizzy patients had the abnormal findings of cVEMPs (insecure vestibular hearing) and the abnormal findings of sABR (decreased neural synchronization). Comparison of the cVEMPs at affected ears versus unaffected ears and the normal persons revealed significant differences (P < 0.05). Conclusion. Safe vestibular hearing was effective in the improvement of the neural synchronization.

Unjoined primary and secondary neural tubes: junctional neural tube defect, a new form of spinal dysraphism caused by disturbance of junctional neurulation.

Science.gov (United States)

Eibach, Sebastian; Moes, Greg; Hou, Yong Jin; Zovickian, John; Pang, Dachling

2017-10-01

Primary and secondary neurulation are the two known processes that form the central neuraxis of vertebrates. Human phenotypes of neural tube defects (NTDs) mostly fall into two corresponding categories consistent with the two types of developmental sequence: primary NTD features an open skin defect, an exposed, unclosed neural plate (hence an open neural tube defect, or ONTD), and an unformed or poorly formed secondary neural tube, and secondary NTD with no skin abnormality (hence a closed NTD) and a malformed conus caudal to a well-developed primary neural tube. We encountered three cases of a previously unrecorded form of spinal dysraphism in which the primary and secondary neural tubes are individually formed but are physically separated far apart and functionally disconnected from each other. One patient was operated on, in whom both the lumbosacral spinal cord from primary neurulation and the conus from secondary neurulation are each anatomically complete and endowed with functioning segmental motor roots tested by intraoperative triggered electromyography and direct spinal cord stimulation. The remarkable feature is that the two neural tubes are unjoined except by a functionally inert, probably non-neural band. The developmental error of this peculiar malformation probably occurs during the critical transition between the end of primary and the beginning of secondary neurulation, in a stage aptly called junctional neurulation. We describe the current knowledge concerning junctional neurulation and speculate on the embryogenesis of this new class of spinal dysraphism, which we call junctional neural tube defect.

The neural signature of emotional memories in serial crimes.

Science.gov (United States)

Chassy, Philippe

2017-10-01

Neural plasticity is the process whereby semantic information and emotional responses are stored in neural networks. It is hypothesized that the neural networks built over time to encode the sexual fantasies that motivate serial killers to act should display a unique, detectable activation pattern. The pathological neural watermark hypothesis posits that such networks comprise activation of brain sites that reflect four cognitive components: autobiographical memory, sexual arousal, aggression, and control over aggression. The neural sites performing these cognitive functions have been successfully identified by previous research. The key findings are reviewed to hypothesise the typical pattern of activity that serial killers should display. Through the integration of biological findings into one framework, the neural approach proposed in this paper is in stark contrast with the many theories accounting for serial killers that offer non-medical taxonomies. The pathological neural watermark hypothesis offers a new framework to understand and detect deviant individuals. The technical and legal issues are briefly discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Enhancing neural-network performance via assortativity

International Nuclear Information System (INIS)

Franciscis, Sebastiano de; Johnson, Samuel; Torres, Joaquin J.

2011-01-01

The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations - assortativity - on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computationally, which is independent of how the topology may have evolved. We show how the robustness to noise is greatly enhanced in assortative (positively correlated) neural networks, especially if it is the hub neurons that store the information.

Permutation parity machines for neural synchronization

International Nuclear Information System (INIS)

Reyes, O M; Kopitzke, I; Zimmermann, K-H

2009-01-01

Synchronization of neural networks has been studied in recent years as an alternative to cryptographic applications such as the realization of symmetric key exchange protocols. This paper presents a first view of the so-called permutation parity machine, an artificial neural network proposed as a binary variant of the tree parity machine. The dynamics of the synchronization process by mutual learning between permutation parity machines is analytically studied and the results are compared with those of tree parity machines. It will turn out that for neural synchronization, permutation parity machines form a viable alternative to tree parity machines

Neural codes of seeing architectural styles.

Science.gov (United States)

Choo, Heeyoung; Nasar, Jack L; Nikrahei, Bardia; Walther, Dirk B

2017-01-10

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people's visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

Mode Choice Modeling Using Artificial Neural Networks

OpenAIRE

Edara, Praveen Kumar

2003-01-01

Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

Destination Brand Equity, Satisfaction And Revisit Intention: An Application In TRNC As a Tourism Destination

OpenAIRE

Sarvari, Neda Gholizadeh

2012-01-01

ABSTRACT: This study revisits the previous studies carried out by several researchers on Customer – Based Brand Equity with an intension to further investigate the applications and testing of the Customer-Based Brand Equity (CBBE) model in relation to destination branding. The study specifically examines the effects of Brand Equity Dimensions (Brand Awareness, Brand Loyalty, Brand Value, Brand Quality and Brand Image) on Tourists Satisfaction and ultimately on Future Behaviours that result i...

Neural network and its application to CT imaging

Energy Technology Data Exchange (ETDEWEB)

Nikravesh, M.; Kovscek, A.R.; Patzek, T.W. [Lawrence Berkeley National Lab., CA (United States)] [and others

1997-02-01

We present an integrated approach to imaging the progress of air displacement by spontaneous imbibition of oil into sandstone. We combine Computerized Tomography (CT) scanning and neural network image processing. The main aspects of our approach are (I) visualization of the distribution of oil and air saturation by CT, (II) interpretation of CT scans using neural networks, and (III) reconstruction of 3-D images of oil saturation from the CT scans with a neural network model. Excellent agreement between the actual images and the neural network predictions is found.

Neural networks and applications tutorial

Science.gov (United States)

Guyon, I.

1991-09-01

The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

The Allolobophora sturanyi species group revisited: Integrated taxonomy and new taxa (Clitellata: Megadrili

Directory of Open Access Journals (Sweden)

Szederjesi, T.

2016-04-01

Full Text Available The Allolobophora sturanyi Rosa, 1895 species group is revisited using DNA barcoding and morphology. Barcoding results corroborated the previous treatment of the Allolobophora sturanyi subspecies and furthermore proved that the morphologically similar Allolobophora gestroides Zicsi, 1970 species belong to this species group. Elaboration of new samples from the Apuseni Mts resulted in discovery of a new subspecies A. sturanyi biharica ssp. nov. from the summit of the Bihor range, and a new species A. zicsica from the Vladeasa range similar to A. gestroides described from Northern Hungary.

Thermal ignition revisited with two-dimensional molecular dynamics: role of fluctuations in activated collisions

OpenAIRE

Sirmas, Nick; Radulescu, Matei I.

2016-01-01

The problem of thermal ignition in a homogeneous gas is revisited from a molecular dynamics perspective. A two-dimensional model is adopted, which assumes reactive disks of type A and B in a fixed area that react to form type C products if an activation threshold for impact is surpassed. Such a reaction liberates kinetic energy to the product particles, representative of the heat release. The results for the ignition delay are compared with those obtained from the continuum description assumi...

Neural decoding of collective wisdom with multi-brain computing.

Science.gov (United States)

Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry

2012-01-02

Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally

Revisiting the dilatation operator of the Wilson-Fisher fixed point

Energy Technology Data Exchange (ETDEWEB)

Liendo, Pedro [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany). Theory Group

2017-01-15

We revisit the order ε dilatation operator of the Wilson-Fisher fixed point obtained by Kehrein, Pismak, and Wegner in light of recent results in conformal field theory. Our approach is algebraic and based only on symmetry principles. The starting point of our analysis is that the first correction to the dilatation operator is a conformal invariant, which implies that its form is fixed up to an infinite set of coefficients associated with the scaling dimensions of higher-spin currents. These coefficients can be fixed using well-known perturbative results, however, they were recently re-obtained using CFT arguments without relying on perturbation theory. Our analysis then implies that all order-ε scaling dimensions of the Wilson-Fisher fixed point can be fixed by symmetry.

Neural network signal understanding for instrumentation

DEFF Research Database (Denmark)

Pau, L. F.; Johansen, F. S.

1990-01-01

understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given...

Neural Control of the Immune System

Science.gov (United States)

Sundman, Eva; Olofsson, Peder S.

2014-01-01

Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

Artificial neural networks in neutron dosimetry

Energy Technology Data Exchange (ETDEWEB)

Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A. [Unidades Academicas de Estudios Nucleares, UAZ, A.P. 336, 98000 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Depto. de Ingenieria Nuclear, Universidad Politecnica de Madrid, (Spain)

2005-07-01

An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the {chi}{sup 2}- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

Artificial neural networks in neutron dosimetry

International Nuclear Information System (INIS)

Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A.; Gallego, E.; Lorente, A.

2005-01-01

An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the χ 2 - test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

Hardware Acceleration of Adaptive Neural Algorithms.

Energy Technology Data Exchange (ETDEWEB)

James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-11-01

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

Neural correlates of HIV risk feelings.

Science.gov (United States)

Häcker, Frank E K; Schmälzle, Ralf; Renner, Britta; Schupp, Harald T

2015-04-01

Field studies on HIV risk perception suggest that people rely on impressions they have about the safety of their partner. The present fMRI study investigated the neural correlates of the intuitive perception of risk. First, during an implicit condition, participants viewed a series of unacquainted persons and performed a task unrelated to HIV risk. In the following explicit condition, participants evaluated the HIV risk for each presented person. Contrasting responses for high and low HIV risk revealed that risky stimuli evoked enhanced activity in the anterior insula and medial prefrontal regions, which are involved in salience processing and frequently activated by threatening and negative affect-related stimuli. Importantly, neural regions responding to explicit HIV risk judgments were also enhanced in the implicit condition, suggesting a neural mechanism for intuitive impressions of riskiness. Overall, these findings suggest the saliency network as neural correlate for the intuitive sensing of risk. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Carbon emission from global hydroelectric reservoirs revisited.

Science.gov (United States)

Li, Siyue; Zhang, Quanfa

2014-12-01

Substantial greenhouse gas (GHG) emissions from hydropower reservoirs have been of great concerns recently, yet the significant carbon emitters of drawdown area and reservoir downstream (including spillways and turbines as well as river reaches below dams) have not been included in global carbon budget. Here, we revisit GHG emission from hydropower reservoirs by considering reservoir surface area, drawdown zone and reservoir downstream. Our estimates demonstrate around 301.3 Tg carbon dioxide (CO2)/year and 18.7 Tg methane (CH4)/year from global hydroelectric reservoirs, which are much higher than recent observations. The sum of drawdown and downstream emission, which is generally overlooked, represents 42 % CO2 and 67 % CH4 of the total emissions from hydropower reservoirs. Accordingly, the global average emissions from hydropower are estimated to be 92 g CO2/kWh and 5.7 g CH4/kWh. Nonetheless, global hydroelectricity could currently reduce approximate 2,351 Tg CO2eq/year with respect to fuel fossil plant alternative. The new findings show a substantial revision of carbon emission from the global hydropower reservoirs.

Artificial neural networks for plasma spectroscopy analysis

International Nuclear Information System (INIS)

Morgan, W.L.; Larsen, J.T.; Goldstein, W.H.

1992-01-01

Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics

Hardware implementation of stochastic spiking neural networks.

Science.gov (United States)

Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

2012-08-01

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

Chitosan derived co-spheroids of neural stem cells and mesenchymal stem cells for neural regeneration.

Science.gov (United States)

Han, Hao-Wei; Hsu, Shan-Hui

2017-10-01

Chitosan has been considered as candidate biomaterials for neural applications. The effective treatment of neurodegeneration or injury to the central nervous system (CNS) is still in lack nowadays. Adult neural stem cells (NSCs) represents a promising cell source to treat the CNS diseases but they are limited in number. Here, we developed the core-shell spheroids of NSCs (shell) and mesenchymal stem cells (MSCs, core) by co-culturing cells on the chitosan surface. The NSCs in chitosan derived co-spheroids displayed a higher survival rate than those in NSC homo-spheroids. The direct interaction of NSCs with MSCs in the co-spheroids increased the Notch activity and differentiation tendency of NSCs. Meanwhile, the differentiation potential of MSCs in chitosan derived co-spheroids was significantly enhanced toward neural lineages. Furthermore, NSC homo-spheroids and NSC/MSC co-spheroids derived on chitosan were evaluated for their in vivo efficacy by the embryonic and adult zebrafish brain injury models. The locomotion activity of zebrafish receiving chitosan derived NSC homo-spheroids or NSC/MSC co-spheroids was partially rescued in both models. Meanwhile, the higher survival rate was observed in the group of adult zebrafish implanted with chitosan derived NSC/MSC co-spheroids as compared to NSC homo-spheroids. These evidences indicate that chitosan may provide an extracellular matrix-like environment to drive the interaction and the morphological assembly between NSCs and MSCs and promote their neural differentiation capacities, which can be used for neural regeneration. Copyright © 2017 Elsevier B.V. All rights reserved.

Dynamic training algorithm for dynamic neural networks

International Nuclear Information System (INIS)

Tan, Y.; Van Cauwenberghe, A.; Liu, Z.

1996-01-01

The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper

The quest for a Quantum Neural Network

OpenAIRE

Schuld, M.; Sinayskiy, I.; Petruccione, F.

2014-01-01

With the overwhelming success in the field of quantum information in the last decades, the "quest" for a Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking properties of neural computing. This article presents a systematic approach to QNN research, which so far consists of a conglomeration of ideas and proposals. It outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quant...

Searle's"Dualism Revisited"

Energy Technology Data Exchange (ETDEWEB)

P., Henry

2008-11-20

A recent article in which John Searle claims to refute dualism is examined from a scientific perspective. John Searle begins his recent article 'Dualism Revisited' by stating his belief that the philosophical problem of consciousness has a scientific solution. He then claims to refute dualism. It is therefore appropriate to examine his arguments against dualism from a scientific perspective. Scientific physical theories contain two kinds of descriptions: (1) Descriptions of our empirical findings, expressed in an every-day language that allows us communicate to each other our sensory experiences pertaining to what we have done and what we have learned; and (2) Descriptions of a theoretical model, expressed in a mathematical language that allows us to communicate to each other certain ideas that exist in our mathematical imaginations, and that are believed to represent, within our streams of consciousness, certain aspects of reality that we deem to exist independently of their being perceived by any human observer. These two parts of our scientific description correspond to the two aspects of our general contemporary dualistic understanding of the total reality in which we are imbedded, namely the empirical-mental aspect and the theoretical-physical aspect. The duality question is whether this general dualistic understanding of ourselves should be regarded as false in some important philosophical or scientific sense.

Individualist Biocentrism vs. Holism Revisited

Directory of Open Access Journals (Sweden)

Katie McShane

2014-06-01

Full Text Available While holist views such as ecocentrism have considerable intuitive appeal, arguing for the moral considerability of ecological wholes such as ecosystems has turned out to be a very difficult task. In the environmental ethics literature, individualist biocentrists have persuasively argued that individual organisms—but not ecological wholes—are properly regarded as having a good of their own . In this paper, I revisit those arguments and contend that they are fatally flawed. The paper proceeds in five parts. First, I consider some problems brought about by climate change for environmental conservation strategies and argue that these problems give us good pragmatic reasons to want a better account of the welfare of ecological wholes. Second, I describe the theoretical assumptions from normative ethics that form the background of the arguments against holism. Third, I review the arguments given by individualist biocentrists in favour of individualism over holism. Fourth, I review recent work in the philosophy of biology on the units of selection problem, work in medicine on the human biome, and work in evolutionary biology on epigenetics and endogenous viral elements. I show how these developments undermine both the individualist arguments described above as well as the distinction between individuals and wholes as it has been understood by individualists. Finally, I consider five possible theoretical responses to these problems.