Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
2000-01-01
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
Generalized Net Model of the Cognitive and Neural Algorithm for Adaptive Resonance Theory 1
Directory of Open Access Journals (Sweden)
Todor Petkov
2013-12-01
Full Text Available The artificial neural networks are inspired by biological properties of human and animal brains. One of the neural networks type is called ART [4]. The abbreviation of ART stands for Adaptive Resonance Theory that has been invented by Stephen Grossberg in 1976 [5]. ART represents a family of Neural Networks. It is a cognitive and neural theory that describes how the brain autonomously learns to categorize, recognize and predict objects and events in the changing world. In this paper we introduce a GN model that represent ART1 Neural Network learning algorithm [1]. The purpose of this model is to explain when the input vector will be clustered or rejected among all nodes by the network. It can also be used for explanation and optimization of ART1 learning algorithm.
Ho, Ching S.; Liou, Juin J.; Georgiopoulos, Michael; Christodoulou, Christos G.
1994-03-01
This paper presents an analog circuit design and implementation for an adaptive resonance theory neural network architecture called the augmented ART1 neural network (AART1-NN). Practical monolithic operational amplifiers (Op-Amps) LM741 and LM318 are selected to implement the circuit, and a simple compensation scheme is developed to adjust the Op-Amp electrical characteristics to meet the design requirement. A 7-node prototype circuit has been designed and verified using the Pspice circuit simulator run on a Sun workstation. Results simulated from the AART1-NN circuit using the LM741, LM318, and ideal Op-Amps are presented and compared.
International Nuclear Information System (INIS)
Cai, Yuan; Wang, Jian-zhou; Tang, Yun; Yang, Yu-chen
2011-01-01
This paper presents a neural network based on adaptive resonance theory, named distributed ART (adaptive resonance theory) and HS-ARTMAP (Hyper-spherical ARTMAP network), applied to the electric load forecasting problem. The distributed ART combines the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multi-layer perceptions. The HS-ARTMAP, a hybrid of an RBF (Radial Basis Function)-network-like module which uses hyper-sphere basis function substitute the Gaussian basis function and an ART-like module, performs incremental learning capabilities in function approximation problem. The HS-ARTMAP only receives the compressed distributed coding processed by distributed ART to deal with the proliferation problem which ARTMAP (adaptive resonance theory map) architecture often encounters and still performs well in electric load forecasting. To demonstrate the performance of the methodology, data from New South Wales and Victoria in Australia are illustrated. Results show that the developed method is much better than the traditional BP and single HS-ARTMAP neural network. -- Research highlights: → The processing of the presented network is based on compressed distributed data. It's an innovation among the adaptive resonance theory architecture. → The presented network decreases the proliferation the Fuzzy ARTMAP architectures usually encounter. → The network on-line forecasts electrical load accurately, stably. → Both one-period and multi-period load forecasting are executed using data of different cities.
Detection of network attacks based on adaptive resonance theory
Bukhanov, D. G.; Polyakov, V. M.
2018-05-01
The paper considers an approach to intrusion detection systems using a neural network of adaptive resonant theory. It suggests the structure of an intrusion detection system consisting of two types of program modules. The first module manages connections of user applications by preventing the undesirable ones. The second analyzes the incoming network traffic parameters to check potential network attacks. After attack detection, it notifies the required stations using a secure transmission channel. The paper describes the experiment on the detection and recognition of network attacks using the test selection. It also compares the obtained results with similar experiments carried out by other authors. It gives findings and conclusions on the sufficiency of the proposed approach. The obtained information confirms the sufficiency of applying the neural networks of adaptive resonant theory to analyze network traffic within the intrusion detection system.
Optical resonators and neural networks
Anderson, Dana Z.
1986-08-01
It may be possible to implement neural network models using continuous field optical architectures. These devices offer the inherent parallelism of propagating waves and an information density in principle dictated by the wavelength of light and the quality of the bulk optical elements. Few components are needed to construct a relatively large equivalent network. Various associative memories based on optical resonators have been demonstrated in the literature, a ring resonator design is discussed in detail here. Information is stored in a holographic medium and recalled through a competitive processes in the gain medium supplying energy to the ring rsonator. The resonator memory is the first realized example of a neural network function implemented with this kind of architecture.
Neural principles of memory and a neural theory of analogical insight
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).
Theories of Person Perception Predict Patterns of Neural Activity During Mentalizing.
Thornton, Mark A; Mitchell, Jason P
2017-08-22
Social life requires making inferences about other people. What information do perceivers spontaneously draw upon to make such inferences? Here, we test 4 major theories of person perception, and 1 synthetic theory that combines their features, to determine whether the dimensions of such theories can serve as bases for describing patterns of neural activity during mentalizing. While undergoing functional magnetic resonance imaging, participants made social judgments about well-known public figures. Patterns of brain activity were then predicted using feature encoding models that represented target people's positions on theoretical dimensions such as warmth and competence. All 5 theories of person perception proved highly accurate at reconstructing activity patterns, indicating that each could describe the informational basis of mentalizing. Cross-validation indicated that the theories robustly generalized across both targets and participants. The synthetic theory consistently attained the best performance-approximately two-thirds of noise ceiling accuracy--indicating that, in combination, the theories considered here can account for much of the neural representation of other people. Moreover, encoding models trained on the present data could reconstruct patterns of activity associated with mental state representations in independent data, suggesting the use of a common neural code to represent others' traits and states. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Frequency-difference-dependent stochastic resonance in neural systems
Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong
2017-08-01
Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.
Neural fields theory and applications
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 ...
Neural computation and the computational theory of cognition.
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.
Applied neutron resonance theory
International Nuclear Information System (INIS)
Froehner, F.H.
1980-01-01
Utilisation of resonance theory in basic and applications-oriented neutron cross section work is reviewed. The technically important resonance formalisms, principal concepts and methods as well as representative computer programs for resonance parameter extraction from measured data, evaluation of resonance data, calculation of Doppler-broadened cross sections and estimation of level-statistical quantities from resonance parameters are described. (author)
Applied neutron resonance theory
International Nuclear Information System (INIS)
Froehner, F.H.
1978-07-01
Utilisation of resonance theory in basic and applications-oriented neutron cross section work is reviewed. The technically important resonance formalisms, principal concepts and methods as well as representative computer programs for resonance parameter extraction from measured data, evaluation of resonance data, calculation of Doppler-broadened cross sections and estimation of level-statistical quantities from resonance parameters are described. (orig.) [de
Fuzzy neural network theory and application
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
One-loop renormalization of Resonance Chiral Theory: scalar and pseudoscalar resonances
International Nuclear Information System (INIS)
Rosell, Ignasi; Ruiz-FemenIa, Pedro; Portoles, Jorge
2005-01-01
We consider the Resonance Chiral Theory with one multiplet of scalar and pseudoscalar resonances, up to bilinear couplings in the resonance fields, and evaluate its β-function at one-loop with the use of the background field method. Thus we also provide the full set of operators that renormalize the theory at one loop and render it finite
A neural theory of visual attention
DEFF Research Database (Denmark)
Bundesen, Claus; Habekost, Thomas; Kyllingsbæk, Søren
2005-01-01
A neural theory of visual attention (NTVA) is presented. NTVA is a neural interpretation of C. Bundesen's (1990) theory of visual attention (TVA). In NTVA, visual processing capacity is distributed across stimuli by dynamic remapping of receptive fields of cortical cells such that more processing...... resources (cells) are devoted to behaviorally important objects than to less important ones. By use of the same basic equations used in TVA, NTVA accounts for a wide range of known attentional effects in human performance (reaction times and error rates) and a wide range of effects observed in firing rates...
Theory of mind in schizophrenia: exploring neural mechanisms of belief attribution.
Lee, Junghee; Quintana, Javier; Nori, Poorang; Green, Michael F
2011-01-01
Although previous behavioral studies have shown that schizophrenia patients have impaired theory of mind (ToM), the neural mechanisms associated with this impairment are poorly understood. This study aimed to identify the neural mechanisms of ToM in schizophrenia, using functional magnetic resonance imaging (fMRI) with a belief attribution task. In the scanner, 12 schizophrenia patients and 13 healthy control subjects performed the belief attribution task with three conditions: a false belief condition, a false photograph condition, and a simple reading condition. For the false belief versus simple reading conditions, schizophrenia patients showed reduced neural activation in areas including the temporoparietal junction (TPJ) and medial prefrontal cortex (MPFC) compared with controls. Further, during the false belief versus false photograph conditions, we observed increased activations in the TPJ and the MPFC in healthy controls, but not in schizophrenia patients. For the false photograph versus simple reading condition, both groups showed comparable neural activations. Schizophrenia patients showed reduced task-related activation in the TPJ and the MPFC during the false belief condition compared with controls, but not for the false photograph condition. This pattern suggests that reduced activation in these regions is associated with, and specific to, impaired ToM in schizophrenia.
Nonequilibrium landscape theory of neural networks
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.
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.
One-loop Renormalization of Resonance Chiral Theory with Scalar and Pseudoscalar Resonances
International Nuclear Information System (INIS)
Rosell, I.
2007-01-01
The divergent part of the generating functional of the Resonance Chiral Theory is evaluated up to one loop when one multiplet of scalar and pseudoscalar resonances are included and interaction terms which couple up to two resonances are considered. Hence we obtain the renormalization of the couplings of the initial Lagrangian and, moreover, the complete list of operators that make this theory finite, at this order
Resonator memories and optical novelty filters
Anderson, Dana Z.; Erle, Marie C.
Optical resonators having holographic elements are potential candidates for storing information that can be accessed through content addressable or associative recall. Closely related to the resonator memory is the optical novelty filter, which can detect the differences between a test object and a set of reference objects. We discuss implementations of these devices using continuous optical media such as photorefractive materials. The discussion is framed in the context of neural network models. There are both formal and qualitative similarities between the resonator memory and optical novelty filter and network models. Mode competition arises in the theory of the resonator memory, much as it does in some network models. We show that the role of the phenomena of "daydreaming" in the real-time programmable optical resonator is very much akin to the role of "unlearning" in neural network memories. The theory of programming the real-time memory for a single mode is given in detail. This leads to a discussion of the optical novelty filter. Experimental results for the resonator memory, the real-time programmable memory, and the optical tracking novelty filter are reviewed. We also point to several issues that need to be addressed in order to implement more formal models of neural networks.
Theory of coherent resonance energy transfer
International Nuclear Information System (INIS)
Jang, Seogjoo; Cheng, Y.-C.; Reichman, David R.; Eaves, Joel D.
2008-01-01
A theory of coherent resonance energy transfer is developed combining the polaron transformation and a time-local quantum master equation formulation, which is valid for arbitrary spectral densities including common modes. The theory contains inhomogeneous terms accounting for nonequilibrium initial preparation effects and elucidates how quantum coherence and nonequilibrium effects manifest themselves in the coherent energy transfer dynamics beyond the weak resonance coupling limit of the Foerster and Dexter (FD) theory. Numerical tests show that quantum coherence can cause significant changes in steady state donor/acceptor populations from those predicted by the FD theory and illustrate delicate cooperation of nonequilibrium and quantum coherence effects on the transient population dynamics.
A Neural Theory of Visual Attention: Bridging Cognition and Neurophysiology
Bundesen, Claus; Habekost, Thomas; Kyllingsbaek, Soren
2005-01-01
A neural theory of visual attention (NTVA) is presented. NTVA is a neural interpretation of C. Bundesen's (1990) theory of visual attention (TVA). In NTVA, visual processing capacity is distributed across stimuli by dynamic remapping of receptive fields of cortical cells such that more processing resources (cells) are devoted to behaviorally…
Neural network segmentation of magnetic resonance images
International Nuclear Information System (INIS)
Frederick, B.
1990-01-01
Neural networks are well adapted to the task of grouping input patterns into subsets which share some similarity. Moreover, once trained, they can generalize their classification rules to classify new data sets. Sets of pixel intensities from magnetic resonance (MR) images provide a natural input to a neural network; by varying imaging parameters, MR images can reflect various independent physical parameters of tissues in their pixel intensities. A neural net can then be trained to classify physically similar tissue types based on sets of pixel intensities resulting from different imaging studies on the same subject. This paper reports that a neural network classifier for image segmentation was implanted on a Sun 4/60, and was tested on the task of classifying tissues of canine head MR images. Four images of a transaxial slice with different imaging sequences were taken as input to the network (three spin-echo images and an inversion recovery image). The training set consisted of 691 representative samples of gray matter, white matter, cerebrospinal fluid, bone, and muscle preclassified by a neuroscientist. The network was trained using a fast backpropagation algorithm to derive the decision criteria to classify any location in the image by its pixel intensities, and the image was subsequently segmented by the classifier
Artificial neural networks for stiffness estimation in magnetic resonance elastography.
Murphy, Matthew C; Manduca, Armando; Trzasko, Joshua D; Glaser, Kevin J; Huston, John; Ehman, Richard L
2018-07-01
To investigate the feasibility of using artificial neural networks to estimate stiffness from MR elastography (MRE) data. Artificial neural networks were fit using model-based training patterns to estimate stiffness from images of displacement using a patch size of ∼1 cm in each dimension. These neural network inversions (NNIs) were then evaluated in a set of simulation experiments designed to investigate the effects of wave interference and noise on NNI accuracy. NNI was also tested in vivo, comparing NNI results against currently used methods. In 4 simulation experiments, NNI performed as well or better than direct inversion (DI) for predicting the known stiffness of the data. Summary NNI results were also shown to be significantly correlated with DI results in the liver (R 2 = 0.974) and in the brain (R 2 = 0.915), and also correlated with established biological effects including fibrosis stage in the liver and age in the brain. Finally, repeatability error was lower in the brain using NNI compared to DI, and voxel-wise modeling using NNI stiffness maps detected larger effects than using DI maps with similar levels of smoothing. Artificial neural networks represent a new approach to inversion of MRE data. Summary results from NNI and DI are highly correlated and both are capable of detecting biologically relevant signals. Preliminary evidence suggests that NNI stiffness estimates may be more resistant to noise than an algebraic DI approach. Taken together, these results merit future investigation into NNIs to improve the estimation of stiffness in small regions. Magn Reson Med 80:351-360, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
Energy Technology Data Exchange (ETDEWEB)
Jones, Christian Birk [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Photovoltaic and Grid Integration Group; Robinson, Matt [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Mechanical Engineering; Yasaei, Yasser [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Caudell, Thomas [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Martinez-Ramon, Manel [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Mammoli, Andrea [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Mechanical Engineering
2016-07-01
Optimal integration of thermal energy storage within commercial building applications requires accurate load predictions. Several methods exist that provide an estimate of a buildings future needs. Methods include component-based models and data-driven algorithms. This work implemented a previously untested algorithm for this application that is called a Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network (ANN). The LAPART algorithm provided accurate results over a two month period where minimal historical data and a small amount of input types were available. These results are significant, because common practice has often overlooked the implementation of an ANN. ANN have often been perceived to be too complex and require large amounts of data to provide accurate results. The LAPART neural network was implemented in an on-line learning manner. On-line learning refers to the continuous updating of training data as time occurs. For this experiment, training began with a singe day and grew to two months of data. This approach provides a platform for immediate implementation that requires minimal time and effort. The results from the LAPART algorithm were compared with statistical regression and a component-based model. The comparison was based on the predictions linear relationship with the measured data, mean squared error, mean bias error, and cost savings achieved by the respective prediction techniques. The results show that the LAPART algorithm provided a reliable and cost effective means to predict the building load for the next day.
Vanishing chiral couplings in the large-NC resonance theory
International Nuclear Information System (INIS)
Portoles, Jorge; Rosell, Ignasi; Ruiz-Femenia, Pedro
2007-01-01
The construction of a resonance theory involving hadrons requires implementing the information from higher scales into the couplings of the effective Lagrangian. We consider the large-N C chiral resonance theory incorporating scalars and pseudoscalars, and we find that, by imposing LO short-distance constraints on form factors of QCD currents constructed within this theory, the chiral low-energy constants satisfy resonance saturation at NLO in the 1/N C expansion
Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network
DEFF Research Database (Denmark)
Míguez González, M; López Peña, F.; Díaz Casás, V.
2011-01-01
Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been acknowle......Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been...... acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network...
Using Neural Data to Test A Theory of Investor Behavior: An Application to Realization Utility.
Frydman, Cary; Barberis, Nicholas; Camerer, Colin; Bossaerts, Peter; Rangel, Antonio
2014-04-01
We use measures of neural activity provided by functional magnetic resonance imaging (fMRI) to test the "realization utility" theory of investor behavior, which posits that people derive utility directly from the act of realizing gains and losses. Subjects traded stocks in an experimental market while we measured their brain activity. We find that all subjects exhibit a strong disposition effect in their trading, even though it is suboptimal. Consistent with the realization utility explanation for this behavior, we find that activity in the ventromedial prefrontal cortex, an area known to encode the value of options during choices, correlates with the capital gains of potential trades; that the neural measures of realization utility correlate across subjects with their individual tendency to exhibit a disposition effect; and that activity in the ventral striatum, an area known to encode information about changes in the present value of experienced utility, exhibits a positive response when subjects realize capital gains. These results provide support for the realization utility model and, more generally, demonstrate how neural data can be helpful in testing models of investor behavior.
Continuous neutron slowing down theory applied to resonances
International Nuclear Information System (INIS)
Segev, M.
1977-01-01
Neutronic formalisms that discretize the neutron slowing down equations in large numerical intervals currently account for the bulk effect of resonances in a given interval by the narrow resonance approximation (NRA). The NRA reduces the original problem to an efficient numerical formalism through two assumptions: resonance narrowness with respect to the scattering bands in the slowing down equations and resonance narrowness with respect to the numerical intervals. Resonances at low energies are narrow neither with respect to the slowing down ranges nor with respect to the numerical intervals, which are usually of a fixed lethargy width. Thus, there are resonances to which the NRA is not applicable. To stay away from the NRA, the continuous slowing down (CSD) theory of Stacey was invoked. The theory is based on a linear expansion in lethargy of the collision density in integrals of the slowing down equations and had notable success in various problems. Applying CSD theory to the assessment of bulk resonance effects raises the problem of obtaining efficient quadratures for integrals involved in the definition of the so-called ''moderating parameter.'' The problem was solved by two approximations: (a) the integrals were simplified through a rationale, such that the correct integrals were reproduced for very narrow or very wide resonances, and (b) the temperature-broadened resonant line shapes were replaced by nonbroadened line shapes to enable analytical integration. The replacement was made in such a way that the integrated capture and scattering probabilities in each resonance were preserved. The resulting formalism is more accurate than the narrow-resonance formalisms and is equally as efficient
Chemical shift of neutron resonances and some ideas on neutron resonances and scattering theory
International Nuclear Information System (INIS)
Ignatovich, V.K.; )
2002-01-01
The dependence of positions of neutron resonances in nuclei in condensed matter on chemical environment is considered. A possibility of theoretical description of neutron resonances, different from R-matrix theory is investigated. Some contradictions of standard scattering theory are discussed and a new approach without these contradictions is formulated [ru
Direct imaging of neural currents using ultra-low field magnetic resonance techniques
Volegov, Petr L [Los Alamos, NM; Matlashov, Andrei N [Los Alamos, NM; Mosher, John C [Los Alamos, NM; Espy, Michelle A [Los Alamos, NM; Kraus, Jr., Robert H.
2009-08-11
Using resonant interactions to directly and tomographically image neural activity in the human brain using magnetic resonance imaging (MRI) techniques at ultra-low field (ULF), the present inventors have established an approach that is sensitive to magnetic field distributions local to the spin population in cortex at the Larmor frequency of the measurement field. Because the Larmor frequency can be readily manipulated (through varying B.sub.m), one can also envision using ULF-DNI to image the frequency distribution of the local fields in cortex. Such information, taken together with simultaneous acquisition of MEG and ULF-NMR signals, enables non-invasive exploration of the correlation between local fields induced by neural activity in cortex and more `distant` measures of brain activity such as MEG and EEG.
Combinatorial neural codes from a mathematical coding theory perspective.
Curto, Carina; Itskov, Vladimir; Morrison, Katherine; Roth, Zachary; Walker, Judy L
2013-07-01
Shannon's seminal 1948 work gave rise to two distinct areas of research: information theory and mathematical coding theory. While information theory has had a strong influence on theoretical neuroscience, ideas from mathematical coding theory have received considerably less attention. Here we take a new look at combinatorial neural codes from a mathematical coding theory perspective, examining the error correction capabilities of familiar receptive field codes (RF codes). We find, perhaps surprisingly, that the high levels of redundancy present in these codes do not support accurate error correction, although the error-correcting performance of receptive field codes catches up to that of random comparison codes when a small tolerance to error is introduced. However, receptive field codes are good at reflecting distances between represented stimuli, while the random comparison codes are not. We suggest that a compromise in error-correcting capability may be a necessary price to pay for a neural code whose structure serves not only error correction, but must also reflect relationships between stimuli.
Ongoing neural development of affective theory of mind in adolescence.
Vetter, Nora C; Weigelt, Sarah; Döhnel, Katrin; Smolka, Michael N; Kliegel, Matthias
2014-07-01
Affective Theory of Mind (ToM), an important aspect of ToM, involves the understanding of affective mental states. This ability is critical in the developmental phase of adolescence, which is often related with socio-emotional problems. Using a developmentally sensitive behavioral task in combination with functional magnetic resonance imaging, the present study investigated the neural development of affective ToM throughout adolescence. Eighteen adolescent (ages 12-14 years) and 18 young adult women (aged 19-25 years) were scanned while evaluating complex affective mental states depicted by actors in video clips. The ventromedial prefrontal cortex (vmPFC) showed significantly stronger activation in adolescents in comparison to adults in the affective ToM condition. Current results indicate that the vmPFC might be involved in the development of affective ToM processing in adolescence. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
Classification of behavior using unsupervised temporal neural networks
International Nuclear Information System (INIS)
Adair, K.L.
1998-03-01
Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem
Neutron resonance absorption theory
International Nuclear Information System (INIS)
Reuss, P.
1991-11-01
After some recalls on the physics of neutron resonance absorption during their slowing down, this paper presents the main features of the theoretical developments performed by the french school of reactor physics: the effective reaction rate method so called Livolant-Jeanpierre theory, the generalizations carried out by the author, and the probability table method [fr
Nano-resonator frequency response based on strain gradient theory
International Nuclear Information System (INIS)
Miandoab, Ehsan Maani; Yousefi-Koma, Aghil; Pishkenari, Hossein Nejat; Fathi, Mohammad
2014-01-01
This paper aims to explore the dynamic behaviour of a nano-resonator under ac and dc excitation using strain gradient theory. To achieve this goal, the partial differential equation of nano-beam vibration is first converted to an ordinary differential equation by the Galerkin projection method and the lumped model is derived. Lumped parameters of the nano-resonator, such as linear and nonlinear springs and damper coefficients, are compared with those of classical theory and it is demonstrated that beams with smaller thickness display greater deviation from classical parameters. Stable and unstable equilibrium points based on classic and non-classical theories are also compared. The results show that, regarding the applied dc voltage, the dynamic behaviours expected by classical and non-classical theories are significantly different, such that one theory predicts the un-deformed shape as the stable condition, while the other theory predicts that the beam will experience bi-stability. To obtain the frequency response of the nano-resonator, a general equation including cubic and quadratic nonlinearities in addition to parametric electrostatic excitation terms is derived, and the analytical solution is determined using a second-order multiple scales method. Based on frequency response analysis, the softening and hardening effects given by two theories are investigated and compared, and it is observed that neglecting the size effect can lead to two completely different predictions in the dynamic behaviour of the resonators. The findings of this article can be helpful in the design and characterization of the size-dependent dynamic behaviour of resonators on small scales. (paper)
Directory of Open Access Journals (Sweden)
Christopher L Buckley
2018-01-01
Full Text Available During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results
Buckley, Christopher L; Toyoizumi, Taro
2018-01-01
During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence
Signal Processing and Neural Network Simulator
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.
Principles and theory of resonance power supplies
International Nuclear Information System (INIS)
Sreenivas, A.; Karady, G.G.
1991-01-01
The resonance power supply is widely used and proved to be an efficient method to supply accelerator magnets. The literature describes several power supply circuits but no comprehensive theory of operation is presented. This paper presents a mathematical method which describes the operation of the resonance power supply and it can be used for accurate design of components
Giant resonances: reaction theory approach
International Nuclear Information System (INIS)
Toledo Piza, A.F.R. de; Foglia, G.A.
1989-09-01
The study of giant resonances through the use of reaction theory approach is presented and discussed. Measurements of cross-sections to the many available decay channels following excitation of giant multipole resonances (GMR) led one to view these phenomena as complicated dynamical syndromes so that theoretical requirements for their study must be extended beyond the traditional bounds of nuclear structure models. The spectra of decay products following GMR excitation in heavy nuclei are well described by statistical model (Hauser-Feshback, HF) predictions indicated that spreading of the collective modes plays a major role in shaping exclusive cross-sections. (A.C.A.S.) [pt
Powell, Joanne L; Grossi, Davide; Corcoran, Rhiannon; Gobet, Fernand; García-Fiñana, Marta
2017-07-04
Chess involves the capacity to reason iteratively about potential intentional choices of an opponent and therefore involves high levels of explicit theory of mind [ToM] (i.e. ability to infer mental states of others) alongside clear, strategic rule-based decision-making. Functional magnetic resonance imaging was used on 12 healthy male novice chess players to identify cortical regions associated with chess, ToM and empathizing. The blood-oxygenation-level-dependent (BOLD) response for chess and empathizing tasks was extracted from each ToM region. Results showed neural overlap between ToM, chess and empathizing tasks in right-hemisphere temporo-parietal junction (TPJ) [BA40], left-hemisphere superior temporal gyrus [BA22] and posterior cingulate gyrus [BA23/31]. TPJ is suggested to underlie the capacity to reason iteratively about another's internal state in a range of tasks. Areas activated by ToM and empathy included right-hemisphere orbitofrontal cortex and bilateral middle temporal gyrus: areas that become active when there is need to inhibit one's own experience when considering the internal state of another and for visual evaluation of action rationality. Results support previous findings, that ToM recruits a neural network with each region sub-serving a supporting role depending on the nature of the task itself. In contrast, a network of cortical regions primarily located within right- and left-hemisphere medial-frontal and parietal cortex, outside the internal representational network, was selectively recruited during the chess task. We hypothesize that in our cohort of novice chess players the strategy was to employ an iterative thinking pattern which in part involved mentalizing processes and recruited core ToM-related regions. Copyright © 2017. Published by Elsevier Ltd.
Stieltjes-moment-theory technique for calculating resonance width's
International Nuclear Information System (INIS)
Hazi, A.U.
1978-12-01
A recently developed method for calculating the widths of atomic and molecular resonances is reviewed. The method is based on the golden-rule definition of the resonance width, GAMMA(E). The method uses only square-integrable, L 2 , basis functions to describe both the resonant and the non-resonant parts of the scattering wave function. It employs Stieltjes-moment-theory techniques to extract a continuous approximation for the width discrete representation of the background continuum. Its implementation requires only existing atomic and molecular structure codes. Many-electron effects, such as correlation and polarization, are easily incorporated into the calculation of the width via configuration interaction techniques. Once the width, GAMMA(E), has been determined, the energy shift can be computed by a straightforward evaluation of the required principal-value integral. The main disadvantage of the method is that it provides only the total width of a resonance which decays into more than one channel in a multichannel problem. A review of the various aspects of the theory is given first, and then representative results that have been obtained with this method for several atomic and molecular resonances are discussed. 28 references, 3 figures, 4 tables
Workshop on Thermal Field Theory to Neural Networks
Veneziano, Gabriele; Aurenche, Patrick
1996-01-01
Tanguy Altherr was a Fellow in the Theory Division at CERN, on leave from LAPP (CNRS) Annecy. At the time of his accidental death in July 1994, he was only 31.A meeting was organized at CERN, covering the various aspects of his scientific interests: thermal field theory and its applications to hot or dense media, neural networks and its applications to high energy data analysis. Speakers were among his closest collaborators and friends.
Neural entrainment to the rhythmic structure of music.
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.
Issues of effective field theories with resonances
International Nuclear Information System (INIS)
Gegelia, J.; Japaridze, G.
2014-01-01
We address some issues of renormalization and symmetries of effective field theories with unstable particles - resonances. We also calculate anomalous contributions in the divergence of the singlet axial current in an effective field theory of massive SU(N) Yang-Mills fields interacting with fermions and discuss their possible relevance to the strong CP problem. (author)
Theory of the cancellation of 4-photon resonances by an off-resonance 3-photon cancellation
DEFF Research Database (Denmark)
Elk, M.; Lambropoulos, P.; Tang, X.
1992-01-01
We present a complete account of our recent work [Phys. Rev. A 44, 31 (1991)] in which we investigate the theory of cancellation by interference between the absorption of three fundamental laser photons and one third-harmonic photon. The theory is formulated in terms of the density matrix so...... as to take detunings, dephasing, and laser bandwidth into account. The result is a theory of cancellation for finite detuning that explains how four-photon resonances can be canceled by a three-photon mechanism if there is an atomic level at near-three-photon resonance. The treatment is extended to focused...
Extending unified-theory-of-reinforcement neural networks to steady-state operant behavior.
Calvin, Olivia L; McDowell, J J
2016-06-01
The unified theory of reinforcement has been used to develop models of behavior over the last 20 years (Donahoe et al., 1993). Previous research has focused on the theory's concordance with the respondent behavior of humans and animals. In this experiment, neural networks were developed from the theory to extend the unified theory of reinforcement to operant behavior on single-alternative variable-interval schedules. This area of operant research was selected because previously developed neural networks could be applied to it without significant alteration. Previous research with humans and animals indicates that the pattern of their steady-state behavior is hyperbolic when plotted against the obtained rate of reinforcement (Herrnstein, 1970). A genetic algorithm was used in the first part of the experiment to determine parameter values for the neural networks, because values that were used in previous research did not result in a hyperbolic pattern of behavior. After finding these parameters, hyperbolic and other similar functions were fitted to the behavior produced by the neural networks. The form of the neural network's behavior was best described by an exponentiated hyperbola (McDowell, 1986; McLean and White, 1983; Wearden, 1981), which was derived from the generalized matching law (Baum, 1974). In post-hoc analyses the addition of a baseline rate of behavior significantly improved the fit of the exponentiated hyperbola and removed systematic residuals. The form of this function was consistent with human and animal behavior, but the estimated parameter values were not. Copyright © 2016 Elsevier B.V. All rights reserved.
Time dependent resonating Hartree-Bogoliubov theory
International Nuclear Information System (INIS)
Nishiyama, Seiya; Fukutome, Hideo.
1989-01-01
Very recently, we have developed a theory of excitations in superconducting Fermion systems with large quantum fluctuations that can be described by resonance of time dependent non-orthogonal Hartree-Bogoliubov (HB) wave functions with different correlation structures. We have derived a new kind of variation equation called the time dependent Resonating HB equation, in order to determine both the time dependent Resonating HB wave functions and coefficients of a superposition of the HB wave functions. Further we have got a new approximation for excitations from time dependent small fluctuations of the Resonating HB ground state, i.e., the Resonating HB RPA. The Res HB RPA equation is represented in a given single particle basis. It, however, has drawbacks that the constraints for the Res HB RPA amplitudes are not taken into account and the equation contains equations which are not independent. We shall derive another form of the Res HB RPA equation eliminating these drawbacks. The Res HB RPA gives a unified description of the vibrons and resonons and their interactions. (author)
Atomic many-body theory of giant resonances
International Nuclear Information System (INIS)
Kelly, H.P.; Altun, Z.
1987-01-01
In this paper the use of many-body perturbation theory (MBPT) to include effects of electron correlations is discussed. The various physical processes contributing to the broad photoionization cross sections of the rare gases are studied in terms of the relevant many-body diagrams. Use of the random phase approximation with exchange (RPAE) is discussed by Amusia and Cherepkov. Calculations using the relativistic RPAE are reviewed by Johnson. In addition, many-body perturbation theory (MBPT) is used to study resonances which are due to excitation of bound states degenerate with the continuum. Very interesting giant resonance structure can occur when an inner shell electron is excited into a vacant open-shell orbital of the same principal quantum number. A particular example which is studied is the neutral manganese atom 3p 6 3d 5 4s 2 ( 6 S), in which the spins of the five 3d electrons are aligned. A very large resonance occurs in the 3d and 4s cross sections due to 3p → 3d excitation near 51 eV, and calculations of this resonance by MBPT and RPAE are discussed. A second example of this type of resonance occurs in open-shell rare-earth atoms with configurations 4d 10 4f/sup n/5s 2 5p 6 s 2 . Calculations and experimental results will be discussed for the case of europium with a half-filled sub-shell 4f 7 . 71 references, 15 figures
Theory of inelastic effects in resonant atom-surface scattering
International Nuclear Information System (INIS)
Evans, D.K.
1983-01-01
The progress of theoretical and experimental developments in atom-surface scattering is briefly reviewed. The formal theory of atom-surface resonant scattering is reviewed and expanded, with both S and T matrix approaches being explained. The two-potential formalism is shown to be useful for dealing with the problem in question. A detailed theory based on the S-matrix and the two-potential formalism is presented. This theory takes account of interactions between the incident atoms and the surface phonons, with resonant effects being displayed explicitly. The Debye-Waller attenuation is also studied. The case in which the atom-surface potential is divided into an attractive part V/sub a/ and a repulsive part V/sub r/ is considered at length. Several techniques are presented for handling the scattering due to V/sub r/, for the case in which V/sub r/ is taken to be the hard corrugated surface potential. The theory is used to calculate the scattered intensities for the system 4 He/LiF(001). A detailed comparison with experiment is made, with polar scans, azimuthal scans, and time-of-flight measurements being considered. The theory is seen to explain the location and signature of resonant features, and to provide reasonable overall agreement with the experimental results
Neural field theory of perceptual echo and implications for estimating brain connectivity
Robinson, P. A.; Pagès, J. C.; Gabay, N. C.; Babaie, T.; Mukta, K. N.
2018-04-01
Neural field theory is used to predict and analyze the phenomenon of perceptual echo in which random input stimuli at one location are correlated with electroencephalographic responses at other locations. It is shown that this echo correlation (EC) yields an estimate of the transfer function from the stimulated point to other locations. Modal analysis then explains the observed spatiotemporal structure of visually driven EC and the dominance of the alpha frequency; two eigenmodes of similar amplitude dominate the response, leading to temporal beating and a line of low correlation that runs from the crown of the head toward the ears. These effects result from mode splitting and symmetry breaking caused by interhemispheric coupling and cortical folding. It is shown how eigenmodes obtained from functional magnetic resonance imaging experiments can be combined with temporal dynamics from EC or other evoked responses to estimate the spatiotemporal transfer function between any two points and hence their effective connectivity.
Theory of neutron resonance cross sections for safety applications
International Nuclear Information System (INIS)
Froehner, F.H.
1992-09-01
Neutron resonances exert a strong influence on the behaviour of nuclear reactors, especially on their response to the temperature changes accompanying power excursions, and also on the efficiency of shielding materials. The relevant theory of neutron resonance cross sections including the practically important approximations is reviewed, both for the resolved and the unresolved resonance region. Numerical techniques for Doppler broadening of resonances are presented, and the construction of group constants and especially of self-shielding factors for neutronics calculations is outlined. (orig.) [de
Directory of Open Access Journals (Sweden)
Xin Wang
2016-01-01
Full Text Available In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.
Neutral Theory and Scale-Free Neural Dynamics
Martinello, Matteo; Hidalgo, Jorge; Maritan, Amos; di Santo, Serena; Plenz, Dietmar; Muñoz, Miguel A.
2017-10-01
Neural tissues have been consistently observed to be spontaneously active and to generate highly variable (scale-free distributed) outbursts of activity in vivo and in vitro. Understanding whether these heterogeneous patterns of activity stem from the underlying neural dynamics operating at the edge of a phase transition is a fascinating possibility, as criticality has been argued to entail many possible important functional advantages in biological computing systems. Here, we employ a well-accepted model for neural dynamics to elucidate an alternative scenario in which diverse neuronal avalanches, obeying scaling, can coexist simultaneously, even if the network operates in a regime far from the edge of any phase transition. We show that perturbations to the system state unfold dynamically according to a "neutral drift" (i.e., guided only by stochasticity) with respect to the background of endogenous spontaneous activity, and that such a neutral dynamics—akin to neutral theories of population genetics and of biogeography—implies marginal propagation of perturbations and scale-free distributed causal avalanches. We argue that causal information, not easily accessible to experiments, is essential to elucidate the nature and statistics of neural avalanches, and that neutral dynamics is likely to play an important role in the cortex functioning. We discuss the implications of these findings to design new empirical approaches to shed further light on how the brain processes and stores information.
Theory of Neural Information Processing Systems
International Nuclear Information System (INIS)
Galla, Tobias
2006-01-01
It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 10 11 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kuehn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the
Modern Theory of Gratings Resonant Scattering: Analysis Techniques and Phenomena
Sirenko, Yuriy K
2010-01-01
Diffraction gratings are one of the most popular objects of analysis in electromagnetic theory. The requirements of applied optics and microwave engineering lead to many new problems and challenges for the theory of diffraction gratings, which force us to search for new methods and tools for their resolution. In Modern Theory of Gratings, the authors present results of the electromagnetic theory of diffraction gratings that will constitute the base of further development of this theory, which meet the challenges provided by modern requirements of fundamental and applied science. This volume covers: spectral theory of gratings (Chapter 1) giving reliable grounds for physical analysis of space-frequency and space-time transformations of the electromagnetic field in open periodic resonators and waveguides; authentic analytic regularization procedures (Chapter 2) that, in contradistinction to the traditional frequency-domain approaches, fit perfectly for the analysis of resonant wave scattering processes; paramet...
The role of resonances in chiral perturbation theory
International Nuclear Information System (INIS)
Ecker, G.; Rafael, E. de
1988-09-01
The strong interactions of low-lying meson resonances (spin ≤ 1) with the octet of pseudoscalar mesons (π,Κ,η) are considered to lowest order in the derivative expansion of chiral SU(3). The resonance contributions to the coupling constants of the O(p 4 ) effective chiral lagrangian involving pseudoscalar fields only are determined. These low-energy coupling constants are found to be dominated by the resonance contributions. Although we do not treat the vector and axial-vector mesons as gauge bosons of local chiral symmetry, vector meson dominance emerges as a prominent result of our analysis. As a further application of chiral resonance couplings, we calculate the electromagnetic pion mass difference to lowest order in chiral perturbation theory with explicit resonance fields. 29 refs., 2 figs., 5 tabs. (Author)
Directory of Open Access Journals (Sweden)
Billy W. Day
2010-11-01
Full Text Available Biosensors have been used extensively in the scientific community for several purposes, most notably to determine association and dissociation kinetics, protein-ligand, protein-protein, or nucleic acid hybridization interactions. A number of different types of biosensors are available in the field, each with real or perceived benefits over the others. This review discusses the basic theory and operational arrangements of four commercially available types of optical biosensors: surface plasmon resonance, resonant mirror, resonance waveguide grating, and dual polarization interferometry. The different applications these techniques offer are discussed from experiments and results reported in recently published literature. Additionally, recent advancements or modifications to the current techniques are also discussed.
Control of autonomous robot using neural networks
Barton, Adam; Volna, Eva
2017-07-01
The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.
International Nuclear Information System (INIS)
Oya, Takahide; Asai, Tetsuya; Amemiya, Yoshihito
2007-01-01
Neuromorphic computing based on single-electron circuit technology is gaining prominence because of its massively increased computational efficiency and the increasing relevance of computer technology and nanotechnology [Likharev K, Mayr A, Muckra I, Tuerel O. CrossNets: High-performance neuromorphic architectures for CMOL circuits. Molec Electron III: Ann NY Acad Sci 1006;2003:146-63; Oya T, Schmid A, Asai T, Leblebici Y, Amemiya Y. On the fault tolerance of a clustered single-electron neural network for differential enhancement. IEICE Electron Expr 2;2005:76-80]. The maximum impact of these technologies will be strongly felt when single-electron circuits based on fault- and noise-tolerant neural structures can operate at room temperature. In this paper, inspired by stochastic resonance (SR) in an ensemble of spiking neurons [Collins JJ, Chow CC, Imhoff TT. Stochastic resonance without tuning. Nature 1995;376:236-8], we propose our design of a basic single-electron neural component and report how we examined its statistical results on a network
Bykov, Dmitry A; Doskolovich, Leonid L; Soifer, Victor A
2017-01-23
We study resonances of guided-mode resonant gratings in conical mounting. By developing 2D time-dependent coupled-mode theory we obtain simple approximations of the transmission and reflection coefficients. Being functions of the incident light's frequency and in-plane wave vector components, the obtained approximations can be considered as multi-variable generalizations of the Fano line shape. We show that the approximations are in good agreement with the rigorously calculated transmission and reflection spectra. We use the developed theory to investigate angular tolerances of the considered structures and to obtain mode excitation conditions. In particular, we obtain the cross-polarization mode excitation conditions in the case of conical mounting.
Schultz, Wolfram
2004-04-01
Neurons in a small number of brain structures detect rewards and reward-predicting stimuli and are active during the expectation of predictable food and liquid rewards. These neurons code the reward information according to basic terms of various behavioural theories that seek to explain reward-directed learning, approach behaviour and decision-making. The involved brain structures include groups of dopamine neurons, the striatum including the nucleus accumbens, the orbitofrontal cortex and the amygdala. The reward information is fed to brain structures involved in decision-making and organisation of behaviour, such as the dorsolateral prefrontal cortex and possibly the parietal cortex. The neural coding of basic reward terms derived from formal theories puts the neurophysiological investigation of reward mechanisms on firm conceptual grounds and provides neural correlates for the function of rewards in learning, approach behaviour and decision-making.
Heavy baryon chiral perturbation theory and the spin 3/2 delta resonances
Energy Technology Data Exchange (ETDEWEB)
Kambor, J.
1996-12-31
Heavy baryon chiral perturbation theory is briefly reviewed, paying particular attention to the role of the spin 3/2 delta resonances. The concept of resonance saturation for the baryonic sector is critically discussed. Starting from a relativistic formulation of the pion-nucleon-delta system, the heavy baryon chiral Lagrangian including spin 3/2 resonances is constructed by means of a 1/m-expansion. The effective theory obtained admits a systematic expansion in terms of soft momenta, the pion mass M{sub {pi}} and the delta-nucleon mass difference {Delta}. (author). 22 refs.
Psychosis-proneness and neural correlates of self-inhibition in theory of mind.
Directory of Open Access Journals (Sweden)
Lisette van der Meer
Full Text Available Impaired Theory of Mind (ToM has been repeatedly reported as a feature of psychotic disorders. ToM is crucial in social interactions and for the development of social behavior. It has been suggested that reasoning about the belief of others, requires inhibition of the self-perspective. We investigated the neural correlates of self-inhibition in nineteen low psychosis prone (PP and eighteen high PP subjects presenting with subclinical features. High PP subjects have a more than tenfold increased risk of developing a schizophrenia-spectrum disorder. Brain activation was measured with functional Magnetic Resonance Imaging during a ToM task differentiating between self-perspective inhibition and belief reasoning. Furthermore, to test underlying inhibitory mechanisms, we included a stop-signal task. We predicted worse behavioral performance for high compared to low PP subjects on both tasks. Moreover, based on previous neuroimaging results, different activation patterns were expected in the inferior frontal gyrus (IFG in high versus low PP subjects in self-perspective inhibition and simple response inhibition. Results showed increased activation in left IFG during self-perspective inhibition, but not during simple response inhibition, for high PP subjects as compared to low PP subjects. High and low PP subjects showed equal behavioral performance. The results suggest that at a neural level, high PP subjects need more resources for inhibiting the self-perspective, but not for simple motor response inhibition, to equal the performance of low PP subjects. This may reflect a compensatory mechanism, which may no longer be available for patients with schizophrenia-spectrum disorders resulting in ToM impairments.
Psychosis-proneness and neural correlates of self-inhibition in theory of mind.
van der Meer, Lisette; Groenewold, Nynke A; Pijnenborg, Marieke; Aleman, André
2013-01-01
Impaired Theory of Mind (ToM) has been repeatedly reported as a feature of psychotic disorders. ToM is crucial in social interactions and for the development of social behavior. It has been suggested that reasoning about the belief of others, requires inhibition of the self-perspective. We investigated the neural correlates of self-inhibition in nineteen low psychosis prone (PP) and eighteen high PP subjects presenting with subclinical features. High PP subjects have a more than tenfold increased risk of developing a schizophrenia-spectrum disorder. Brain activation was measured with functional Magnetic Resonance Imaging during a ToM task differentiating between self-perspective inhibition and belief reasoning. Furthermore, to test underlying inhibitory mechanisms, we included a stop-signal task. We predicted worse behavioral performance for high compared to low PP subjects on both tasks. Moreover, based on previous neuroimaging results, different activation patterns were expected in the inferior frontal gyrus (IFG) in high versus low PP subjects in self-perspective inhibition and simple response inhibition. Results showed increased activation in left IFG during self-perspective inhibition, but not during simple response inhibition, for high PP subjects as compared to low PP subjects. High and low PP subjects showed equal behavioral performance. The results suggest that at a neural level, high PP subjects need more resources for inhibiting the self-perspective, but not for simple motor response inhibition, to equal the performance of low PP subjects. This may reflect a compensatory mechanism, which may no longer be available for patients with schizophrenia-spectrum disorders resulting in ToM impairments.
A comparative study of two neural networks for document retrieval
International Nuclear Information System (INIS)
Hui, S.C.; Goh, A.
1997-01-01
In recent years there has been specific interest in adopting advanced computer techniques in the field of document retrieval. This interest is generated by the fact that classical methods such as the Boolean search, the vector space model or even probabilistic retrieval cannot handle the increasing demands of end-users in satisfying their needs. The most recent attempt is the application of the neural network paradigm as a means of providing end-users with a more powerful retrieval mechanism. Neural networks are not only good pattern matchers but also highly versatile and adaptable. In this paper, we demonstrate how to apply two neural networks, namely Adaptive Resonance Theory and Fuzzy Kohonen Neural Network, for document retrieval. In addition, a comparison of these two neural networks based on performance is also given
Gilbert, Andrew R.; Akkal, Dalila; Almeida, Jorge R. C.; Mataix-Cols, David; Kalas, Catherine; Devlin, Bernie; Birmaher, Boris; Phillips, Mary L.
2009-01-01
The use of functional magnetic resonance imaging on a group of pediatric subjects with obsessive compulsive disorder reveals that this group has reduced activity in neural regions underlying emotional processing, cognitive processing, and motor performance as compared to control subjects.
International Nuclear Information System (INIS)
Wei Duqu; Luo Xiaoshu
2007-01-01
In this paper, we investigate coherence resonance (CR) and noise-induced synchronization in Hindmarsh-Rose (HR) neural network with three different types of topologies: regular, random, and small-world. It is found that the additive noise can induce CR in HR neural network with different topologies and its coherence is optimized by a proper noise level. It is also found that as coupling strength increases the plateau in the measure of coherence curve becomes broadened and the effects of network topology is more pronounced simultaneously. Moreover, we find that increasing the probability p of the network topology leads to an enhancement of noise-induced synchronization in HR neurons network.
Fundamentals and approximations of multilevel resonance theory for reactor physics applications
International Nuclear Information System (INIS)
Moore, M.S.
1980-01-01
The formal theory of nuclear reactions leads to any of a number of alternative representations for describing resonance behavior. None of these is satisfactory for applications, and, depending on the problem to be addressed, approximate expressions are used. The specializations and approximations found to be most useful by evaluators are derived from R-matrix theory and are discussed from the viewpoint of convenience in numerical calculations. Finally, we illustrate the application of the theory by reviewing a particular example: the spin-separated neutron-induced cross sections of 235 U in the resolved and unresolved resonance regions and the use of these results in the U.S. evaluated nuclear data file ENDF/B. (author)
Resonances, scattering theory and rigged Hilbert spaces
International Nuclear Information System (INIS)
Parravicini, G.; Gorini, V.; Sudarshan, E.C.G.
1979-01-01
The problem of decaying states and resonances is examined within the framework of scattering theory in a rigged Hilbert space formalism. The stationary free, in, and out eigenvectors of formal scattering theory, which have a rigorous setting in rigged Hilbert space, are considered to be analytic functions of the energy eigenvalue. The value of these analytic functions at any point of regularity, real or complex, is an eigenvector with eigenvalue equal to the position of the point. The poles of the eigenvector families give origin to other eigenvectors of the Hamiltonian; the singularities of the out eigenvector family are the same as those of the continued S matrix, so that resonances are seen as eigenvectors of the Hamiltonian with eigenvalue equal to their location in the complex energy plane. Cauchy theorem then provides for expansions in terms of complete sets of eigenvectors with complex eigenvalues of the Hamiltonian. Applying such expansions to the survival amplitude of a decaying state, one finds that resonances give discrete contributions with purely exponential time behavior; the background is of course present, but explicitly separated. The resolvent of the Hamiltonian, restricted to the nuclear space appearing in the rigged Hilbert space, can be continued across the absolutely continuous spectrum; the singularities of the continuation are the same as those of the out eigenvectors. The free, in and out eigenvectors with complex eigenvalues and those corresponding to resonances can be approximated by physical vectors in the Hilbert space, as plane waves can. The need for having some further physical information in addition to the specification of the total Hamiltonian is apparent in the proposed framework. The formalism is applied to the Lee-Friedrichs model. 48 references
Neural network diagnosis of avascular necrosis from magnetic resonance images
Manduca, Armando; Christy, Paul S.; Ehman, Richard L.
1993-09-01
We have explored the use of artificial neural networks to diagnose avascular necrosis (AVN) of the femoral head from magnetic resonance images. We have developed multi-layer perceptron networks, trained with conjugate gradient optimization, which diagnose AVN from single sagittal images of the femoral head with 100% accuracy on the training data and 97% accuracy on test data. These networks use only the raw image as input (with minimal preprocessing to average the images down to 32 X 32 size and to scale the input data values) and learn to extract their own features for the diagnosis decision. Various experiments with these networks are described.
Double giant resonances in time-dependent relativistic mean-field theory
International Nuclear Information System (INIS)
Ring, P.; Podobnik, B.
1996-01-01
Collective vibrations in spherical nuclei are described in the framework of time-dependent relativistic mean-field theory (RMFT). Isoscalar quadrupole and isovector dipole oscillations that correspond to giant resonances are studied, and possible excitations of higher modes are investigated. We find evidence for modes which can be interpreted as double resonances. In a quantized RMFT they correspond to two-phonon states. (orig.)
International Nuclear Information System (INIS)
Vijayakumar, C.; Bhargava, Sunil; Gharpure, Damayanti Chandrashekhar
2008-01-01
A novel Neuro - level set shape detection algorithm is proposed and evaluated for segmentation and grading of brain tumours. The algorithm evaluates vascular and cellular information provided by dynamic contrast susceptibility magnetic resonance images and apparent diffusion coefficient maps. The proposed neural shape detection algorithm is based on the levels at algorithm (shape detection algorithm) and utilizes a neural block to provide the speed image for the level set methods. In this study, two different architectures of level set method have been implemented and their results are compared. The results show that the proposed Neuro-shape detection performs better in differentiating the tumor, edema, necrosis in reconstructed images of perfusion and diffusion weighted magnetic resonance images. (author)
Giant dipole resonance by many levels theory
International Nuclear Information System (INIS)
Mondaini, R.P.
1977-01-01
The many levels theory is applied to photonuclear effect, in particular, in giant dipole resonance. A review about photonuclear dipole absorption, comparing with atomic case is done. The derivation of sum rules; their modifications by introduction of the concepts of effective charges and mass and the Siegert theorem. The experimental distributions are compared with results obtained by curve adjustment. (M.C.K.) [pt
BOOK REVIEW: Theory of Neural Information Processing Systems
Galla, Tobias
2006-04-01
It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the
Wang, Jing; Cherkassky, Vladimir L; Yang, Ying; Chang, Kai-Min Kevin; Vargas, Robert; Diana, Nicholas; Just, Marcel Adam
2016-01-01
The generativity and complexity of human thought stem in large part from the ability to represent relations among concepts and form propositions. The current study reveals how a given object such as rabbit is neurally encoded differently and identifiably depending on whether it is an agent ("the rabbit punches the monkey") or a patient ("the monkey punches the rabbit"). Machine-learning classifiers were trained on functional magnetic resonance imaging (fMRI) data evoked by a set of short videos that conveyed agent-verb-patient propositions. When tested on a held-out video, the classifiers were able to reliably identify the thematic role of an object from its associated fMRI activation pattern. Moreover, when trained on one subset of the study participants, classifiers reliably identified the thematic roles in the data of a left-out participant (mean accuracy = .66), indicating that the neural representations of thematic roles were common across individuals.
Gyrokinetic theory of perpendicular cyclotron resonance in a nonuniformly magnetized plasma
International Nuclear Information System (INIS)
Lashmore-Davies, C.N.; Dendy, R.O.
1989-01-01
The extension of gyrokinetic theory to arbitrary frequencies by Chen and Tsai [Phys. Fluids 26, 141 (1983); Plasma Phys. 25, 349 (1983)] is used to study cyclotron absorption in a straight magnetic field with a perpendicular, linear gradient in strength. The analysis includes the effects of magnetic field variation across the Larmor orbit and is restricted to propagation perpendicular to the field. It yields the following results for propagation into the field gradient. The standard optical depths for the fundamental O-mode and second harmonic X-mode resonances are obtained from the absorption profiles given in this paper, without invoking relativistic mass variation [see also Antonsen and Manheimer, Phys. Fluids 21, 2295 (1978)]. The compressional Alfven wave is shown to undergo perpendicular cyclotron damping at the fundamental minority resonance in a two-ion species plasma and at second harmonic resonance in a single-ion species plasma. Ion Bernstein waves propagating into the second harmonic resonance are no longer unattenuated, but are increasingly damped as they approach the resonance. It is shown how the kinetic power flow affects absorption profiles, yielding information previously obtainable only from full-wave theory. In all cases, the perpendicular cyclotron damping arises from the inclusion of magnetic field variation across the Larmor orbit
Semiclassical theory of resonance inelastic electron-molecule collisions
International Nuclear Information System (INIS)
Kazanskij, A.K.
1986-01-01
Semiclassical approach to the theory of resonance electron-molecule collisions, unlocal with respect to interatomic distance was developed. Two problems were considered: modified adiabatic approach for sigle-pole approximation of R-matrix and Fano-Feshbach-Bardsley theory. It is shown that these problems are similar in semiclassical approximation. A simple equation system with coefficients expressed in quadratures was obtained. It enables to determine amplitudes of all processes (including dissociation adhesion, association ejection, free-free and free-bound transitions) in energetic representation with respect to nucleus vibrations in molecule with allowance for both descrete and continuous spectra of nucleus motion in molecule. Quantitative investigation of the system results to the notion of dynamic energy curve of intermediate state, generalizing the motion of such curve in boomerang theory
Multiple Literacies Theory: Discourse, Sensation, Resonance and Becoming
Masny, Diana
2012-01-01
This thematic issue on education and the politics of becoming focuses on how a Multiple Literacies Theory (MLT) plugs into practice in education. MLT does this by creating an assemblage between discourse, text, resonance and sensations. What does this produce? Becoming AND how one might live are the product of an assemblage (May, 2005; Semetsky,…
Nuclear reactor pump diagnostics via noise analysis/artificial neural networks
International Nuclear Information System (INIS)
Keyvan, S.; Rabelo, L.C.
1991-01-01
A feasibility study is performed on the utilization of artificial neural networks as a tool for reactor diagnostics. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of degradation of pump shaft are analyzed as a semi-benchmark test to study the feasibility of neural networks for pattern recognition. The Adaptive Resonance Theory (ART 2) paradigm of artificial neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques, and is capable of distinguishing between these signals and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data via the ART 2 paradigm
Reaction theory for analysis of nuclear giant resonances production and decay processes
International Nuclear Information System (INIS)
Foglia, G.A.
1991-01-01
The existence of mixing parameters connected to the different decay forms of the giant resonances was theoretically justified, and their energy dependence determined as well using a reaction theory which treats in a consistent manner the giant multipolar resonances formation and their different decay modes. (L.C.J.A.)
NL(q) Theory: A Neural Control Framework with Global Asymptotic Stability Criteria.
Vandewalle, Joos; De Moor, Bart L.R.; Suykens, Johan A.K.
1997-06-01
In this paper a framework for model-based neural control design is presented, consisting of nonlinear state space models and controllers, parametrized by multilayer feedforward neural networks. The models and closed-loop systems are transformed into so-called NL(q) system form. NL(q) systems represent a large class of nonlinear dynamical systems consisting of q layers with alternating linear and static nonlinear operators that satisfy a sector condition. For such NL(q)s sufficient conditions for global asymptotic stability, input/output stability (dissipativity with finite L(2)-gain) and robust stability and performance are presented. The stability criteria are expressed as linear matrix inequalities. In the analysis problem it is shown how stability of a given controller can be checked. In the synthesis problem two methods for neural control design are discussed. In the first method Narendra's dynamic backpropagation for tracking on a set of specific reference inputs is modified with an NL(q) stability constraint in order to ensure, e.g., closed-loop stability. In a second method control design is done without tracking on specific reference inputs, but based on the input/output stability criteria itself, within a standard plant framework as this is done, for example, in H( infinity ) control theory and &mgr; theory. Copyright 1997 Elsevier Science Ltd.
Directory of Open Access Journals (Sweden)
R. Vatankhah
Full Text Available Abstract This paper investigates the vibration behavior of micro-resonators based on the strain gradient theory, a non-classical continuum theory capable of capturing the size effect appearing in micro-scale structures. The micro-resonator is modeled as a clamped-clamped micro-beam with an attached mass subjected to an axial force. The governing equations of motion and both classical and non-classical sets of boundary conditions are developed based on the strain gradient theory. The normalized natural frequency of the micro-resonator is evaluated and the influences of various parameters are assessed. In addition, the current results are compared to those of the classical and modified couple stress continuum theories.
The early years of string theory: The dual resonance model
International Nuclear Information System (INIS)
Ramond, P.
1987-10-01
This paper reviews the past quantum mechanical history of the dual resonance model which is an early string theory. The content of this paper is listed as follows: historical review, the Veneziano amplitude, the operator formalism, the ghost story, and the string story
Study of squeeze film damping in a micro-beam resonator based on micro-polar theory
Directory of Open Access Journals (Sweden)
Mina Ghanbari
Full Text Available In this paper, squeeze film damping in a micro-beam resonator based on micro-polar theory has been investigated. The proposed model for this study consists of a clamped-clamped micro-beam bounded between two fixed layers. The gap between the micro-beam and layers is filled with air. As fluid behaves differently in micro scale than macro, the micro-scale fluid field in the gap has been modeled based on micro-polar theory. Equation of motion governing transverse deflection of the micro- beam based on modified couple stress theory and also non-linear Reynolds equation of the fluid field based on micropolar theory have been non-dimensionalized, linearized and solved simultaneously in order to calculate the quality factor of the resonator. The effect of micropolar parameters of air on the quality factor has been investigated. The quality factor of the of the micro-beam resonator for different values of non-dimensionalized length scale of the beam, squeeze number and also non-dimensionalized pressure has been calculated and compared to the obtained values of quality factor based on classical theory.
International Nuclear Information System (INIS)
Slipicevic, K.
1968-12-01
Following a review of the existing theories od resonance absorption this thesis includes a new approach for calculating the effective resonance integral of absorbed neutrons, new approximate formula for the penetration factor, an analysis of the effective resonance integral and the correction of the resonance integral taking into account the interference of potential and resonance dissipation. A separate chapter is devoted to calculation of the effective resonance integral for the regular reactor lattice with cylindrical fuel elements
Miyazaki, Hideki T; Miyazaki, Hiroshi; Miyano, Kenjiro
2003-09-01
We have recently identified the resonant scattering from dielectric bispheres in the specular direction, which has long been known as the specular resonance, to be a type of rainbow (a caustic) and a general phenomenon for bispheres. We discuss the details of the specular resonance on the basis of systematic calculations. In addition to the rigorous theory, which precisely describes the scattering even in the resonance regime, the ray-tracing method, which gives the scattering in the geometrical-optics limit, is used. Specular resonance is explicitly defined as strong scattering in the direction of the specular reflection from the symmetrical axis of the bisphere whose intensity exceeds that of the scattering from noninteracting bispheres. Then the range of parameters for computing a particular specular resonance is specified. This resonance becomes prominent in a wide range of refractive indices (from 1.2 to 2.2) in a wide range of size parameters (from five to infinity) and for an arbitrarily polarized light incident within an angle of 40 degrees to the symmetrical axis. This particular scattering can stay evident even when the spheres are not in contact or the sizes of the spheres are different. Thus specular resonance is a common and robust phenomenon in dielectric bispheres. Furthermore, we demonstrate that various characteristic features in the scattering from bispheres can be explained successfully by using intuitive and simple representations. Most of the significant scatterings other than the specular resonance are also understandable as caustics in geometrical-optics theory. The specular resonance becomes striking at the smallest size parameter among these caustics because its optical trajectory is composed of only the refractions at the surfaces and has an exceptionally large intensity. However, some characteristics are not accounted for by geometrical optics. In particular, the oscillatory behaviors of their scattering intensity are well described by
An integrative neural model of social perception, action observation, and theory of mind
Yang, Daniel Y.-J.; Rosenblau, Gabriela; Keifer, Cara; Pelphrey, Kevin A.
2016-01-01
In the field of social neuroscience, major branches of research have been instrumental in describing independent components of typical and aberrant social information processing, but the field as a whole lacks a comprehensive model that integrates different branches. We review existing research related to the neural basis of three key neural systems underlying social information processing: social perception, action observation, and theory of mind. We propose an integrative model that unites these three processes and highlights the posterior superior temporal sulcus (pSTS), which plays a central role in all three systems. Furthermore, we integrate these neural systems with the dual system account of implicit and explicit social information processing. Large-scale meta-analyses based on Neurosynth confirmed that the pSTS is at the intersection of the three neural systems. Resting-state functional connectivity analysis with 1000 subjects confirmed that the pSTS is connected to all other regions in these systems. The findings presented in this review are specifically relevant for psychiatric research especially disorders characterized by social deficits such as autism spectrum disorder. PMID:25660957
Multispectral Image classification using the theories of neural networks
International Nuclear Information System (INIS)
Ardisasmita, M.S.; Subki, M.I.R.
1997-01-01
Image classification is the one of the important part of digital image analysis. the objective of image classification is to identify and regroup the features occurring in an image into one or several classes in terms of the object. basic to the understanding of multispectral classification is the concept of the spectral response of an object as a function of the electromagnetic radiation and the wavelength of the spectrum. new approaches to classification has been developed to improve the result of analysis, these state-of-the-art classifiers are based upon the theories of neural networks. Neural network classifiers are algorithmes which mimic the computational abilities of the human brain. Artificial neurons are simple emulation's of biological neurons; they take in information from sensors or other artificial neurons, perform very simple operations on this data, and pass the result to other recognize the spectral signature of each image pixel. Neural network image classification has been divided into supervised and unsupervised training procedures. In the supervised approach, examples of each cover type can be located and the computer can compute spectral signatures to categorize all pixels in a digital image into several land cover classes. In supervised classification, spectral signatures are generated by mathematically grouping and it does not require analyst-specified training data. Thus, in the supervised approach we define useful information categories and then examine their spectral reparability; in the unsupervised approach the computer determines spectrally sapable classes and then we define thei information value
Opponent appetitive-aversive neural processes underlie predictive learning of pain relief.
Seymour, Ben; O'Doherty, John P; Koltzenburg, Martin; Wiech, Katja; Frackowiak, Richard; Friston, Karl; Dolan, Raymond
2005-09-01
Termination of a painful or unpleasant event can be rewarding. However, whether the brain treats relief in a similar way as it treats natural reward is unclear, and the neural processes that underlie its representation as a motivational goal remain poorly understood. We used fMRI (functional magnetic resonance imaging) to investigate how humans learn to generate expectations of pain relief. Using a pavlovian conditioning procedure, we show that subjects experiencing prolonged experimentally induced pain can be conditioned to predict pain relief. This proceeds in a manner consistent with contemporary reward-learning theory (average reward/loss reinforcement learning), reflected by neural activity in the amygdala and midbrain. Furthermore, these reward-like learning signals are mirrored by opposite aversion-like signals in lateral orbitofrontal cortex and anterior cingulate cortex. This dual coding has parallels to 'opponent process' theories in psychology and promotes a formal account of prediction and expectation during pain.
International Nuclear Information System (INIS)
SivaRanjan, Uppala; Ramachandran, Ramesh
2014-01-01
A quantum-mechanical model integrating the concepts of reduced density matrix and effective Hamiltonians is proposed to explain the multi-spin effects observed in rotational resonance (R 2 ) nuclear magnetic resonance (NMR) experiments. Employing this approach, the spin system of interest is described in a reduced subspace inclusive of its coupling to the surroundings. Through suitable model systems, the utility of our theory is demonstrated and verified with simulations emerging from both analytic and numerical methods. The analytic results presented in this article provide an accurate description/interpretation of R 2 experimental results and could serve as a test-bed for distinguishing coherent/incoherent effects in solid-state NMR
Energy Technology Data Exchange (ETDEWEB)
SivaRanjan, Uppala; Ramachandran, Ramesh, E-mail: rramesh@iisermohali.ac.in [Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER) Mohali, Sector 81, Manauli, P.O. Box-140306, Mohali, Punjab (India)
2014-02-07
A quantum-mechanical model integrating the concepts of reduced density matrix and effective Hamiltonians is proposed to explain the multi-spin effects observed in rotational resonance (R{sup 2}) nuclear magnetic resonance (NMR) experiments. Employing this approach, the spin system of interest is described in a reduced subspace inclusive of its coupling to the surroundings. Through suitable model systems, the utility of our theory is demonstrated and verified with simulations emerging from both analytic and numerical methods. The analytic results presented in this article provide an accurate description/interpretation of R{sup 2} experimental results and could serve as a test-bed for distinguishing coherent/incoherent effects in solid-state NMR.
International Nuclear Information System (INIS)
Pal, Sourav; Sajeev, Y.; Vaval, Nayana
2006-01-01
The Fock space multi-reference coupled-cluster (FSMRCC) method is used for the study of the shape resonance energy and width in an electron-atom/molecule collision. The procedure is based upon combining a complex absorbing potential (CAP) with FSMRCC theory. Accurate resonance parameters are obtained by solving a small non-Hermitian eigen-value problem. We study the shape resonances in e - -C 2 H 4 and e - -Mg
Directory of Open Access Journals (Sweden)
Adam M. Goodman
2017-09-01
Full Text Available Consumer buying motivations can be distinguished into three categories: functional, experiential, or symbolic motivations (Keller, 1993. Although prior neuroimaging studies have examined the neural substrates which enable these motivations, direct comparisons between these three types of consumer motivations have yet to be made. In the current study, we used 7 Tesla (7T functional magnetic resonance imaging (fMRI to assess the neural correlates of each motivation by instructing participants to view common consumer goods while emphasizing either functional, experiential, or symbolic values of these products. The results demonstrated mostly consistent activations between symbolic and experiential motivations. Although, these motivations differed in that symbolic motivation was associated with medial frontal gyrus (MFG activation, whereas experiential motivation was associated with posterior cingulate cortex (PCC activation. Functional motivation was associated with dorsolateral prefrontal cortex (DLPFC activation, as compared to other motivations. These findings provide a neural basis for how symbolic and experiential motivations may be similar, yet different in subtle ways. Furthermore, the dissociation of functional motivation within the DLPFC supports the notion that this motivation relies on executive function processes relatively more than hedonic motivation. These findings provide a better understanding of the underlying neural functioning which may contribute to poor self-control choices.
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.
Ryan, Nicholas P; Catroppa, Cathy; Beare, Richard; Silk, Timothy J; Hearps, Stephen J; Beauchamp, Miriam H; Yeates, Keith O; Anderson, Vicki A
2017-09-01
Deficits in theory of mind (ToM) are common after neurological insult acquired in the first and second decade of life, however the contribution of large-scale neural networks to ToM deficits in children with brain injury is unclear. Using paediatric traumatic brain injury (TBI) as a model, this study investigated the sub-acute effect of paediatric traumatic brain injury on grey-matter volume of three large-scale, domain-general brain networks (the Default Mode Network, DMN; the Central Executive Network, CEN; and the Salience Network, SN), as well as two domain-specific neural networks implicated in social-affective processes (the Cerebro-Cerebellar Mentalizing Network, CCMN and the Mirror Neuron/Empathy Network, MNEN). We also evaluated prospective structure-function relationships between these large-scale neural networks and cognitive, affective and conative ToM. 3D T1- weighted magnetic resonance imaging sequences were acquired sub-acutely in 137 children [TBI: n = 103; typically developing (TD) children: n = 34]. All children were assessed on measures of ToM at 24-months post-injury. Children with severe TBI showed sub-acute volumetric reductions in the CCMN, SN, MNEN, CEN and DMN, as well as reduced grey-matter volumes of several hub regions of these neural networks. Volumetric reductions in the CCMN and several of its hub regions, including the cerebellum, predicted poorer cognitive ToM. In contrast, poorer affective and conative ToM were predicted by volumetric reductions in the SN and MNEN, respectively. Overall, results suggest that cognitive, affective and conative ToM may be prospectively predicted by individual differences in structure of different neural systems-the CCMN, SN and MNEN, respectively. The prospective relationship between cerebellar volume and cognitive ToM outcomes is a novel finding in our paediatric brain injury sample and suggests that the cerebellum may play a role in the neural networks important for ToM. These findings are
An integrative neural model of social perception, action observation, and theory of mind.
Yang, Daniel Y-J; Rosenblau, Gabriela; Keifer, Cara; Pelphrey, Kevin A
2015-04-01
In the field of social neuroscience, major branches of research have been instrumental in describing independent components of typical and aberrant social information processing, but the field as a whole lacks a comprehensive model that integrates different branches. We review existing research related to the neural basis of three key neural systems underlying social information processing: social perception, action observation, and theory of mind. We propose an integrative model that unites these three processes and highlights the posterior superior temporal sulcus (pSTS), which plays a central role in all three systems. Furthermore, we integrate these neural systems with the dual system account of implicit and explicit social information processing. Large-scale meta-analyses based on Neurosynth confirmed that the pSTS is at the intersection of the three neural systems. Resting-state functional connectivity analysis with 1000 subjects confirmed that the pSTS is connected to all other regions in these systems. The findings presented in this review are specifically relevant for psychiatric research especially disorders characterized by social deficits such as autism spectrum disorder. Copyright © 2015 Elsevier Ltd. All rights reserved.
Empirical tests of a theory of language, mathematics, and matter.
Abler, William L
2008-01-01
In an earlier paper (Abler, 2006), I proposed a theory of language, especially sentences, based on the symmetrical structure of the equation. Here, I use the structure of equations to deduce neural structures (e.g., mirror neurons or intra-cellular macromolecules, or crystals, or resonations) that might generate them. Ultimately, the properties described are a consequence of dimensional properties of matter
Energy Technology Data Exchange (ETDEWEB)
Yao, De-Liang [Institute for Advanced Simulation, Institut für Kernphysik and Jülich Center for Hadron Physics,Forschungszentrum Jülich, D-52425 Jülich (Germany); Siemens, D. [Institut für Theoretische Physik II, Ruhr-Universität Bochum, D-44780 Bochum (Germany); Bernard, V. [Groupe de Physique Théorique, Institut de Physique Nucléaire, UMR 8606,CNRS, University Paris-Sud, Université Paris-Saclay, 91405 Orsay Cedex (France); Epelbaum, E. [Institut für Theoretische Physik II, Ruhr-Universität Bochum, D-44780 Bochum (Germany); Gasparyan, A.M. [Institut für Theoretische Physik II, Ruhr-Universität Bochum, D-44780 Bochum (Germany); SSC RF ITEP, Bolshaya Cheremushkinskaya 25, 117218 Moscow (Russian Federation); Gegelia, J. [Institute for Advanced Simulation, Institut für Kernphysik and Jülich Center for Hadron Physics,Forschungszentrum Jülich, D-52425 Jülich (Germany); Tbilisi State University, 0186 Tbilisi (Georgia); Krebs, H. [Institut für Theoretische Physik II, Ruhr-Universität Bochum, D-44780 Bochum (Germany); Meißner, Ulf-G. [Helmholtz Institut für Strahlen- und Kernphysik andBethe Center for Theoretical Physics, Universität Bonn, D-53115 Bonn (Germany); Institute for Advanced Simulation, Institut für Kernphysik and Jülich Center for Hadron Physics,Forschungszentrum Jülich, D-52425 Jülich (Germany)
2016-05-05
We present the results of a third order calculation of the pion-nucleon scattering amplitude in a chiral effective field theory with pions, nucleons and delta resonances as explicit degrees of freedom. We work in a manifestly Lorentz invariant formulation of baryon chiral perturbation theory using dimensional regularization and the extended on-mass-shell renormalization scheme. In the delta resonance sector, the on mass-shell renormalization is realized as a complex-mass scheme. By fitting the low-energy constants of the effective Lagrangian to the S- and P-partial waves a satisfactory description of the phase shifts from the analysis of the Roy-Steiner equations is obtained. We predict the phase shifts for the D and F waves and compare them with the results of the analysis of the George Washington University group. The threshold parameters are calculated both in the delta-less and delta-full cases. Based on the determined low-energy constants, we discuss the pion-nucleon sigma term. Additionally, in order to determine the strangeness content of the nucleon, we calculate the octet baryon masses in the presence of decuplet resonances up to next-to-next-to-leading order in SU(3) baryon chiral perturbation theory. The octet baryon sigma terms are predicted as a byproduct of this calculation.
Localized Plasmon resonance in metal nanoparticles using Mie theory
Duque, J. S.; Blandón, J. S.; Riascos, H.
2017-06-01
In this work, scattering light by colloidal metal nanoparticles with spherical shape was studied. Optical properties such as diffusion efficiencies of extinction and absorption Q ext and Q abs were calculated using Mie theory. We employed a MATLAB program to calculate the Mie efficiencies and the radial dependence of electric field intensities emitted for colloidal metal nanoparticles (MNPs). By UV-Vis spectroscopy we have determined the LSPR for Cu nanoparticles (CuNPs), Ni nanoparticles (NiNPs) and Co nanoparticles (CoNPs) grown by laser ablation technique. The peaks of resonances appear in 590nm, 384nm and 350nm for CuNPs, NiNPs and CoNPs respectively suspended in water. Changing the medium to acetone and ethanol we observed a shift of the resonance peaks, these values agreed with our simulations results.
Neural Model for Left-Handed CPW Bandpass Filter Loaded Split Ring Resonator
Liu, Haiwen; Wang, Shuxin; Tan, Mingtao; Zhang, Qijun
2010-02-01
Compact left-handed coplanar waveguide (CPW) bandpass filter loaded split ring resonator (SRR) is presented in this paper. The proposed filter exhibits a quasi-elliptic function response and its circuit size occupies only 12 × 11.8 mm2 (≈0.21 λg × 0.20 λg). Also, a simple circuit model is given and the parametric study of this filter is discussed. Then, with the aid of NeuroModeler software, a five-layer feed-forward perceptron neural networks model is built up to optimize the proposed filter design fast and accurately. Finally, this newly left-handed CPW bandpass filter was fabricated and measured. A good agreement between simulations and measurement verifies the proposed left-handed filter and the validity of design methodology.
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.
Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram
2017-04-01
Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.
Energy Technology Data Exchange (ETDEWEB)
Yoo, Jaeg Won; Cho, Sunh Oh; Jeong, Young Uk; Lee, Byung Cheol; Lee, Jong Min
2000-10-01
In this report we present a theoretical study of bare optical resonators having in mind to extend it to active resonators. To compute diffractional losses, phase shifts, intensity distributions and phases of radiation fields on mirrors, we coded a package of numerical procedures on bases of a pair of integral equations. Two numerical schemes, a matrix formalism and an iterative method, are programmed for finding numeric solutions to the pair of integral equations. The iterative method had been tried by Fox and Li, but it was not applicable to cases for high Fresnel numbers since the numerical errors involved propagate and accumulate uncontrollably. In this report, we implemented the matrix method to extend the computational limit further. A great deal of case studies are carried out with various configurations of stable and unstable resonators. Our results presented in this report show not only a good agreement with the results previously obtained by Fox and Li, but also a legitimacy of our numerical procedures in high Fresnel numbers.
Ma, Fuyin; Wu, Jiu Hui; Huang, Meng
2015-09-01
In order to overcome the influence of the structural resonance on the continuous structures and obtain a lightweight thin-layer structure which can effectively isolate the low-frequency noises, an elastic membrane structure was proposed. In the low-frequency range below 500 Hz, the sound transmission loss (STL) of this membrane type structure is greatly higher than that of the current sound insulation material EVA (ethylene-vinyl acetate copo) of vehicle, so it is possible to replace the EVA by the membrane-type metamaterial structure in practice engineering. Based on the band structure, modal shapes, as well as the sound transmission simulation, the sound insulation mechanism of the designed membrane-type acoustic metamaterials was analyzed from a new perspective, which had been validated experimentally. It is suggested that in the frequency range above 200 Hz for this membrane-mass type structure, the sound insulation effect was principally not due to the low-level locally resonant mode of the mass block, but the continuous vertical resonant modes of the localized membrane. So based on such a physical property, a resonant modal group theory is initially proposed in this paper. In addition, the sound insulation mechanism of the membrane-type structure and thin plate structure were combined by the membrane/plate resonant theory.
Directory of Open Access Journals (Sweden)
Budhitama Subagdja
2016-06-01
Full Text Available One of the fundamental challenges in reinforcement learning is to setup a proper balance between exploration and exploitation to obtain the maximum cummulative reward in the long run. Most protocols for exploration bound the overall values to a convergent level of performance. If new knowledge is inserted or the environment is suddenly changed, the issue becomes more intricate as the exploration must compromise the pre-existing knowledge. This paper presents a type of multi-channel adaptive resonance theory (ART neural network model called fusion ART which serves as a fuzzy approximator for reinforcement learning with inherent features that can regulate the exploration strategy. This intrinsic regulation is driven by the condition of the knowledge learnt so far by the agent. The model offers a stable but incremental reinforcement learning that can involve prior rules as bootstrap knowledge for guiding the agent to select the right action. Experiments in obstacle avoidance and navigation tasks demonstrate that in the configuration of learning wherein the agent learns from scratch, the inherent exploration model in fusion ART model is comparable to the basic E-greedy policy. On the other hand, the model is demonstrated to deal with prior knowledge and strike a balance between exploration and exploitation.
From behavior to neural dynamics: An integrated theory of attention
Buschman, Timothy J.; Kastner, Sabine
2015-01-01
The brain has a limited capacity and therefore needs mechanisms to selectively enhance the information most relevant to one’s current behavior. We refer to these mechanisms as ‘attention’. Attention acts by increasing the strength of selected neural representations and preferentially routing them through the brain’s large-scale network. This is a critical component of cognition and therefore has been a central topic in cognitive neuroscience. Here we review a diverse literature that has studied attention at the level of behavior, networks, circuits and neurons. We then integrate these disparate results into a unified theory of attention. PMID:26447577
Schiffer, Boris; Pawliczek, Christina; Müller, Bernhard W; Wiltfang, Jens; Brüne, Martin; Forsting, Michael; Gizewski, Elke R; Leygraf, Norbert; Hodgins, Sheilagh
2017-10-21
Among violent offenders with schizophrenia, there are 2 sub-groups, one with and one without, conduct disorder (CD) and antisocial personality disorder (ASPD), who differ as to treatment response and alterations of brain structure. The present study aimed to determine whether the 2 groups also differ in Theory of Mind and neural activations subsuming this task. Five groups of men were compared: 3 groups of violent offenders-schizophrenia plus CD/ASPD, schizophrenia with no history of antisocial behavior prior to illness onset, and CD/ASPD with no severe mental illness-and 2 groups of non-offenders, one with schizophrenia and one without (H). Participants completed diagnostic interviews, the Psychopathy Checklist Screening Version Interview, the Interpersonal Reactivity Index, authorized access to clinical and criminal files, and underwent functional magnetic resonance imaging while completing an adapted version of the Reading-the-Mind-in-the-Eyes Task (RMET). Relative to H, nonviolent and violent men with schizophrenia and not CD/ASPD performed more poorly on the RMET, while violent offenders with CD/ASPD, both those with and without schizophrenia, performed similarly. The 2 groups of violent offenders with CD/ASPD, both those with and without schizophrenia, relative to the other groups, displayed higher levels of activation in a network of prefrontal and temporal-parietal regions and reduced activation in the amygdala. Relative to men without CD/ASPD, both groups of violent offenders with CD/ASPD displayed a distinct pattern of neural responses during emotional/mental state attribution pointing to distinct and comparatively successful processing of social information. © The Author 2017. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Gerstner, Wulfram
2017-01-01
Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. PMID:28422957
Application of neural networks to group technology
Caudell, Thomas P.; Smith, Scott D. G.; Johnson, G. C.; Wunsch, Donald C., II
1991-08-01
Adaptive resonance theory (ART) neural networks are being developed for application to the industrial engineering problem of group technology--the reuse of engineering designs. Two- and three-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or families of similar parts. These representations, in their basic form, amount to bit maps of the part, and can become very large when the part is represented in high resolution. This paper describes an enhancement to an algorithmic form of ART-1 that allows it to operate directly on compressed input representations and to generate compressed memory templates. The performance of this compressed algorithm is compared to that of the regular algorithm on real engineering designs and a significant savings in memory storage as well as a speed up in execution is observed. In additions, a `neural database'' system under development is described. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall the family when presented a similar design. This application is of large practical value to industry, making it possible to avoid duplication of design efforts.
Yu, Yali; Wang, Mengxia; Lima, Dimas
2018-04-01
In order to develop a novel alcoholism detection method, we proposed a magnetic resonance imaging (MRI)-based computer vision approach. We first use contrast equalization to increase the contrast of brain slices. Then, we perform Haar wavelet transform and principal component analysis. Finally, we use back propagation neural network (BPNN) as the classification tool. Our method yields a sensitivity of 81.71±4.51%, a specificity of 81.43±4.52%, and an accuracy of 81.57±2.18%. The Haar wavelet gives better performance than db4 wavelet and sym3 wavelet.
A generalization of the Livolant-Jeanpierre theory for resonance absorption calculation
International Nuclear Information System (INIS)
Reuss, P.
1985-04-01
Because of the large number of heavy nuclide resonances a detailed neutron flux calculation in the epithermal range cannot be made by standard nuclear reactor codes: it would need several tens of thousand of energy points. However, by using pre-calculated effective reaction rates only a few tens of groups are sufficient for accurate spectrum and reaction rate calculations, if a consistent formalism is used. Such a formalism was elaborated in the 1970s by M. Livolant and F. Jeanpierre (L.-J.) for the ''one resonant nuclide - one resonant zone'' problem, and was implemented in the APOLLO code. In practical cases there are several resonant nuclides and often resonant zones of different characteristics, e.g. a lattice constituted with different kinds of pins, a lattice with irregular ''water-holes'', a fuel element with temperature (therefore Doppler effect) gradients,... Since these problem cannot be correctly treated by APOLLO, a generalization of the formalism was derived. The basic principles were retained, and our aim was to construct an algorithm which would not require too expensive calculations. After a brief recall of the L.-J. theory, equations for the most general case are presented, some approximations for practical calculations proposed, and numerical tests on significant examples commented
Prototype-Incorporated Emotional Neural Network.
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.
Tetraquark resonances computed with static lattice QCD potentials and scattering theory
Directory of Open Access Journals (Sweden)
Bicudo Pedro
2018-01-01
Full Text Available We study tetraquark resonances with lattice QCD potentials computed for two static quarks and two dynamical quarks, the Born-Oppenheimer approximation and the emergent wave method of scattering theory. As a proof of concept we focus on systems with isospin I = 0, but consider different relative angular momenta l of the heavy b quarks. We compute the phase shifts and search for S and T matrix poles in the second Riemann sheet. We predict a new tetraquark resonance for l = 1, decaying into two B mesons, with quantum numbers I(JP = 0(1−, mass m=10576−4+4 MeV and decay width Γ=112−103+90 MeV.
Neural Based Orthogonal Data Fitting The EXIN Neural Networks
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
Design of a universal two-layered neural network derived from the PLI theory
Hu, Chia-Lun J.
2004-05-01
The if-and-only-if (IFF) condition that a set of M analog-to-digital vector-mapping relations can be learned by a one-layered-feed-forward neural network (OLNN) is that all the input analog vectors dichotomized by the i-th output bit must be positively, linearly independent, or PLI. If they are not PLI, then the OLNN just cannot learn no matter what learning rules is employed because the solution of the connection matrix does not exist mathematically. However, in this case, one can still design a parallel-cascaded, two-layered, perceptron (PCTLP) to acheive this general mapping goal. The design principle of this "universal" neural network is derived from the major mathematical properties of the PLI theory - changing the output bits of the dependent relations existing among the dichotomized input vectors to make the PLD relations PLI. Then with a vector concatenation technique, the required mapping can still be learned by this PCTLP system with very high efficiency. This paper will report in detail the mathematical derivation of the general design principle and the design procedures of the PCTLP neural network system. It then will be verified in general by a practical numerical example.
Differential theory of learning for efficient neural network pattern recognition
Hampshire, John B., II; Vijaya Kumar, Bhagavatula
1993-09-01
We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.
Modeling the Process of Color Image Recognition Using ART2 Neural Network
Directory of Open Access Journals (Sweden)
Todor Petkov
2015-09-01
Full Text Available This paper thoroughly describes the use of unsupervised adaptive resonance theory ART2 neural network for the purposes of image color recognition of x-ray images and images taken by nuclear magnetic resonance. In order to train the network, the pixel values of RGB colors are regarded as learning vectors with three values, one for red, one for green and one for blue were used. At the end the trained network was tested by the values of pictures and determines the design, or how to visualize the converted picture. As a result we had the same pictures with colors according to the network. Here we use the generalized net to prepare a model that describes the process of the color image recognition.
Neural-network classifiers for automatic real-world aerial image recognition
Greenberg, Shlomo; Guterman, Hugo
1996-08-01
We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.
International Nuclear Information System (INIS)
Biglari, H.; Chen, L.
1991-10-01
A complete theory of wave-particle interactions is presented whereby both circulating and trapped energetic ions can destabilize kinetic ballooning modes in tokamaks. Four qualitatively different types of resonances, involving wave-precessional drift, wave-transit, wave-bounce, and precessional drift-bounce interactions, are identified, and the destabilization potential of each is assessed. For a characteristic slowing-down distribution function, the dominant interaction is that which taps those resonant ions with the highest energy. Implications of the theory for present and future generation fusion experiments are discussed. 16 refs
Social discounting involves modulation of neural value signals by temporoparietal junction
Strombach, Tina; Weber, Bernd; Hangebrauk, Zsofia; Kenning, Peter; Karipidis, Iliana I.; Tobler, Philippe N.; Kalenscher, Tobias
2015-01-01
Most people are generous, but not toward everyone alike: generosity usually declines with social distance between individuals, a phenomenon called social discounting. Despite the pervasiveness of social discounting, social distance between actors has been surprisingly neglected in economic theory and neuroscientific research. We used functional magnetic resonance imaging (fMRI) to study the neural basis of this process to understand the neural underpinnings of social decision making. Participants chose between selfish and generous alternatives, yielding either a large reward for the participant alone, or smaller rewards for the participant and another individual at a particular social distance. We found that generous choices engaged the temporoparietal junction (TPJ). In particular, the TPJ activity was scaled to the social-distance–dependent conflict between selfish and generous motives during prosocial choice, consistent with ideas that the TPJ promotes generosity by facilitating overcoming egoism bias. Based on functional coupling data, we propose and provide evidence for a biologically plausible neural model according to which the TPJ supports social discounting by modulating basic neural value signals in the ventromedial prefrontal cortex to incorporate social-distance–dependent other-regarding preferences into an otherwise exclusively own-reward value representation. PMID:25605887
Directory of Open Access Journals (Sweden)
Qingbai Zhao
Full Text Available The key components of insight include breaking mental sets and forming the novel, task-related associations. The majority of researchers have agreed that the anterior cingulate cortex may mediate processes of breaking one's mental set, while the exact neural correlates of forming novel associations are still debatable. In the present study, we used a paradigm of answer selection to explore brain activations of insight by using event-related functional magnetic resonance imaging during solving Chinese 'chengyu' (in Chinese pinyin riddles. Based on the participant's choice, the trials were classified into the insight and non-insight conditions. Both stimulus-locked and response-locked analyses are conducted to detect the neural activity corresponding to the early and late periods of insight solution, respectively. Our data indicate that the early period of insight solution shows more activation in the middle temporal gyrus, the middle frontal gyrus and the anterior cingulate cortex. These activities might be associated to the extensive semantic processing, as well as detecting and resolving cognitive conflicts. In contrast, the late period of insight solution produced increased activities in the hippocampus and the amygdala, possibly reflecting the forming of novel association and the concomitant "Aha" feeling. Our study supports the key role of hippocampus in forming novel associations, and indicates a dynamic neural network during insight solution.
Single-site neural tube closure in human embryos revisited.
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.
Kohli, Akshay; Blitzer, David N; Lefco, Ray W; Barter, Joseph W; Haynes, M Ryan; Colalillo, Sam A; Ly, Martina; Zink, Caroline F
2018-05-08
Researchers have yet to apply a formal operationalized theory of motivation to neurobiology that would more accurately and precisely define neural activity underlying motivation. We overcome this challenge with the novel application of the Expectancy Theory of Motivation to human fMRI to identify brain activity that explicitly reflects motivation. Expectancy Theory quantitatively describes how individual constructs determine motivation by defining motivation force as the product of three variables: expectancy - belief that effort will better performance; instrumentality - belief that successful performance leads to particular outcome, and valence - outcome desirability. Here, we manipulated information conveyed by reward-predicting cues such that relative cue-evoked activity patterns could be statistically mapped to individual Expectancy Theory variables. The variable associated with activity in any voxel is only reported if it replicated between two groups of healthy participants. We found signals in midbrain, ventral striatum, sensorimotor cortex, and visual cortex that specifically map to motivation itself, rather than other factors. This is important because, for the first time, it empirically clarifies approach motivation neural signals during reward anticipation. It also highlights the effectiveness of the application of Expectancy Theory to neurobiology to more precisely and accurately probe motivation neural correlates than has been achievable previously. Copyright © 2018 Elsevier Inc. All rights reserved.
Diphoton resonance in F-theory inspired flipped SO(10)
Energy Technology Data Exchange (ETDEWEB)
Leontaris, George K. [Ioannina University, Physics Department, Theory Division, Ioannina (Greece); Shafi, Qaisar [University of Delaware, Department of Physics and Astronomy, Bartol Research Institute, Newark, DE (United States)
2016-10-15
Motivated by the di-photon excess at 750 GeV reported by the ATLAS and CMS experiments, we present an F-theory inspired flipped SO(10) model embedded in E{sub 6}. The low energy spectrum includes the three MSSM chiral families, vector-like colour triplets, several pairs of charged SU(2){sub L} singlet fields (E{sup c}, anti E{sup c}), as well as MSSM singlets, one or more of which could contribute to the di-photon resonance. A total decay width in the multi-GeV range can arise from couplings involving the singlet and MSSM fields. (orig.)
Neural processing associated with cognitive and affective Theory of Mind in adolescents and adults.
Sebastian, Catherine L; Fontaine, Nathalie M G; Bird, Geoffrey; Blakemore, Sarah-Jayne; Brito, Stephane A De; McCrory, Eamon J P; Viding, Essi
2012-01-01
Theory of Mind (ToM) is the ability to attribute thoughts, intentions and beliefs to others. This involves component processes, including cognitive perspective taking (cognitive ToM) and understanding emotions (affective ToM). This study assessed the distinction and overlap of neural processes involved in these respective components, and also investigated their development between adolescence and adulthood. While data suggest that ToM develops between adolescence and adulthood, these populations have not been compared on cognitive and affective ToM domains. Using fMRI with 15 adolescent (aged 11-16 years) and 15 adult (aged 24-40 years) males, we assessed neural responses during cartoon vignettes requiring cognitive ToM, affective ToM or physical causality comprehension (control). An additional aim was to explore relationships between fMRI data and self-reported empathy. Both cognitive and affective ToM conditions were associated with neural responses in the classic ToM network across both groups, although only affective ToM recruited medial/ventromedial PFC (mPFC/vmPFC). Adolescents additionally activated vmPFC more than did adults during affective ToM. The specificity of the mPFC/vmPFC response during affective ToM supports evidence from lesion studies suggesting that vmPFC may integrate affective information during ToM. Furthermore, the differential neural response in vmPFC between adult and adolescent groups indicates developmental changes in affective ToM processing.
Bhatia, Anand K.
2008-01-01
Applications of the hybrid theory to the scattering of electrons from Ile+ and Li++ and resonances in these systems, A. K. Bhatia, NASA/Goddard Space Flight Center- The Hybrid theory of electron-hydrogen elastic scattering [I] is applied to the S-wave scattering of electrons from He+ and Li++. In this method, both short-range and long-range correlations are included in the Schrodinger equation at the same time. Phase shifts obtained in this calculation have rigorous lower bounds to the exact phase shifts and they are compared with those obtained using the Feshbach projection operator formalism [2], the close-coupling approach [3], and Harris-Nesbet method [4]. The agreement among all the calculations is very good. These systems have doubly-excited or Feshbach resonances embedded in the continuum. The resonance parameters for the lowest ' S resonances in He and Li+ are calculated and they are compared with the results obtained using the Feshbach projection operator formalism [5,6]. It is concluded that accurate resonance parameters can be obtained by the present method, which has the advantage of including corrections due to neighboring resonances and the continuum in which these resonances are embedded.
Sensor signal analysis by neural networks for surveillance in nuclear reactors
International Nuclear Information System (INIS)
Keyvan, S.; Rabelo, L.C.
1992-01-01
The application of neural networks as a tool for reactor diagnostics is examined here. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of the degradation of a pump shaft are analyzed as a semi-benchmark test to study the feasibility of neural networks for monitoring and surveillance in nuclear reactors. The Adaptive Resonance Theory (ART 2 and ART 2-A) paradigm of neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques, and is capable of distinguishing these signals apart and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data, and provides an evaluation on the performance of ART 2-A and ART 2 for reactor signal analysis. The selection of ART 2 is due to its desired design principles such as unsupervised learning, stability-plasticity, search-direct access, and the match-reset tradeoffs
Classifying magnetic resonance image modalities with convolutional neural networks
Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis
2018-02-01
Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.
International Nuclear Information System (INIS)
Cisneros S, A.; McIntosh, H.V.
1982-01-01
A discussion of the nature of quantum mechanical resonances is presented from the point of view of the spectral theory of operators. In the case of Bohr-Feshbach resonances, graphs are presented to illustrate the theory showing the decay of a doubly excited metastable state and the excitation of the resonance by an incident particle with proper energy. A characterization of resonances is given as well as a procedure to determine widths using the spectral density function. A sufficient condition is given for the validity of the Breit-Wigner formula for Bohr-Feshbach resonances. (author)
Large-scale inverse and forward modeling of adaptive resonance in the tinnitus decompensation.
Low, Yin Fen; Trenado, Carlos; Delb, Wolfgang; D'Amelio, Roberto; Falkai, Peter; Strauss, Daniel J
2006-01-01
Neural correlates of psychophysiological tinnitus models in humans may be used for their neurophysiological validation as well as for their refinement and improvement to better understand the pathogenesis of the tinnitus decompensation and to develop new therapeutic approaches. In this paper we make use of neural correlates of top-down projections, particularly, a recently introduced synchronization stability measure, together with a multiscale evoked response potential (ERP) model in order to study and evaluate the tinnitus decompensation by using a hybrid inverse-forward mathematical methodology. The neural synchronization stability, which according to the underlying model is linked to the focus of attention on the tinnitus signal, follows the experimental and inverse way and allows to discriminate between a group of compensated and decompensated tinnitus patients. The multiscale ERP model, which works in the forward direction, is used to consolidate hypotheses which are derived from the experiments for a known neural source dynamics related to attention. It is concluded that both methodologies agree and support each other in the description of the discriminatory character of the neural correlate proposed, but also help to fill the gap between the top-down adaptive resonance theory and the Jastreboff model of tinnitus.
Theory and computation of triply excited resonances: Application to states of He-
International Nuclear Information System (INIS)
Nicolaides, C.A.; Piangos, N.A.; Komninos, Y.
1993-01-01
Autoionizing multiply excited states offer unusual challenges to the theory of electronic structure and spectra because of the presence of strong electron correlations, of their occasional weak binding, of their proximity to more than one threshold, and of their degeneracy with many continua. Here we discuss a theory that addresses these difficulties in conjunction with the computation of their wave functions and intrinsic properties. Emphasis is given on the justification of the possible presence of self-consistently obtained open-channel-like (OCL) correlating configurations in the square-integrable representation of such states and on their effect on the energy E and the width Γ. Application of the theory has allowed the prediction of two hitherto unknown He - triply excited resonances, the 2s2p 2 2 P (E=59.71 eV, above the He ground state, Γ=79 meV) and the 2p 3 2 Do (E=59.46 eV, Γ=282 meV) (1 a.u.=27.2116 eV). These resonances are above the singly excited states of He and are embedded in its doubly excited spectrum. The relatively broad 2p 3 2 Do state interacts strongly with the He 2s2p 3 Po εd continuum. The effect of this interaction has been studied in terms of the coupling with fixed core scattering states as well as with a self-consistently computed OCL bound configuration
Neural correlates of sad feelings in healthy girls.
Lévesque, J; Joanette, Y; Mensour, B; Beaudoin, G; Leroux, J-M; Bourgouin, P; Beauregard, M
2003-01-01
Emotional development is indisputably one of the cornerstones of personality development during infancy. According to the differential emotions theory (DET), primary emotions are constituted of three distinct components: the neural-evaluative, the expressive, and the experiential. The DET further assumes that these three components are biologically based and functional nearly from birth. Such a view entails that the neural substrate of primary emotions must be similar in children and adults. Guided by this assumption of the DET, the present functional magnetic resonance imaging study was conducted to identify the neural correlates of sad feelings in healthy children. Fourteen healthy girls (aged 8-10) were scanned while they watched sad film excerpts aimed at externally inducing a transient state of sadness (activation task). Emotionally neutral film excerpts were also presented to the subjects (reference task). The subtraction of the brain activity measured during the viewing of the emotionally neutral film excerpts from that noted during the viewing of the sad film excerpts revealed that sad feelings were associated with significant bilateral activations of the midbrain, the medial prefrontal cortex (Brodmann area [BA] 10), and the anterior temporal pole (BA 21). A significant locus of activation was also noted in the right ventrolateral prefrontal cortex (BA 47). These results are compatible with those of previous functional neuroimaging studies of sadness in adults. They suggest that the neural substrate underlying the subjective experience of sadness is comparable in children and adults. Such a similitude provides empirical support to the DET assumption that the neural substrate of primary emotions is biologically based.
International Conference on Artificial Neural Networks (ICANN)
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...
Hou, Saing Paul; Haddad, Wassim M; Meskin, Nader; Bailey, James M
2015-12-01
With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state, that is, lack of responsiveness to noxious stimuli. In this paper, we use dynamical system theory to develop a mechanistic mean field model for neural activity to study the abrupt transition from consciousness to unconsciousness as the concentration of the anesthetic agent increases. The proposed synaptic drive firing-rate model predicts the conscious-unconscious transition as the applied anesthetic concentration increases, where excitatory neural activity is characterized by a Poincaré-Andronov-Hopf bifurcation with the awake state transitioning to a stable limit cycle and then subsequently to an asymptotically stable unconscious equilibrium state. Furthermore, we address the more general question of synchronization and partial state equipartitioning of neural activity without mean field assumptions. This is done by focusing on a postulated subset of inhibitory neurons that are not themselves connected to other inhibitory neurons. Finally, several numerical experiments are presented to illustrate the different aspects of the proposed theory.
International Nuclear Information System (INIS)
Krasnoshchekov, Sergey V.; Isayeva, Elena V.; Stepanov, Nikolay F.
2014-01-01
The second-order vibrational Hamiltonian of a semi-rigid polyatomic molecule when resonances are present can be reduced to a quasi-diagonal form using second-order vibrational perturbation theory. Obtaining exact vibrational energy levels requires subsequent numerical diagonalization of the Hamiltonian matrix including the first- and second-order resonance coupling coefficients. While the first-order Fermi resonance constants can be easily calculated, the evaluation of the second-order Darling-Dennison constants requires more complicated algebra for seven individual cases with different numbers of creation-annihilation vibrational quanta. The difficulty in precise evaluation of the Darling-Dennison coefficients is associated with the previously unrecognized interference with simultaneously present Fermi resonances that affect the form of the canonically transformed Hamiltonian. For the first time, we have presented the correct form of the general expression for the evaluation of the Darling-Dennison constants that accounts for the underlying effect of Fermi resonances. The physically meaningful criteria for selecting both Fermi and Darling-Dennison resonances are discussed and illustrated using numerical examples
Laterally Primed Adaptive Resonance Theory
Energy Technology Data Exchange (ETDEWEB)
2017-07-19
LAPART is an artificial neural network algorithm written in the Python programming language. The algorithm can learn patterns using multi-dimensional hyper boxes. It can also perfrom regression and classification calculations based on learned associations.
Patterns of work attitudes: A neural network approach
Mengov, George D.; Zinovieva, Irina L.; Sotirov, George R.
2000-05-01
In this paper we introduce a neural networks based approach to analyzing empirical data and models from work and organizational psychology (WOP), and suggest possible implications for the practice of managers and business consultants. With this method it becomes possible to have quantitative answers to a bunch of questions like: What are the characteristics of an organization in terms of its employees' motivation? What distinct attitudes towards the work exist? Which pattern is most desirable from the standpoint of productivity and professional achievement? What will be the dynamics of behavior as quantified by our method, during an ongoing organizational change or consultancy intervention? Etc. Our investigation is founded on the theoretical achievements of Maslow (1954, 1970) in human motivation, and of Hackman & Oldham (1975, 1980) in job diagnostics, and applies the mathematical algorithm of the dARTMAP variation (Carpenter et al., 1998) of the Adaptive Resonance Theory (ART) neural networks introduced by Grossberg (1976). We exploit the ART capabilities to visualize the knowledge accumulated in the network's long-term memory in order to interpret the findings in organizational research.
Barratt Impulsivity and Neural Regulation of Physiological Arousal.
Directory of Open Access Journals (Sweden)
Sheng Zhang
Full Text Available Theories of personality have posited an increased arousal response to external stimulation in impulsive individuals. However, there is a dearth of studies addressing the neural basis of this association.We recorded skin conductance in 26 individuals who were assessed with Barratt Impulsivity Scale (BIS-11 and performed a stop signal task during functional magnetic resonance imaging. Imaging data were processed and modeled with Statistical Parametric Mapping. We used linear regressions to examine correlations between impulsivity and skin conductance response (SCR to salient events, identify the neural substrates of arousal regulation, and examine the relationship between the regulatory mechanism and impulsivity.Across subjects, higher impulsivity is associated with greater SCR to stop trials. Activity of the ventromedial prefrontal cortex (vmPFC negatively correlated to and Granger caused skin conductance time course. Furthermore, higher impulsivity is associated with a lesser strength of Granger causality of vmPFC activity on skin conductance, consistent with diminished control of physiological arousal to external stimulation. When men (n = 14 and women (n = 12 were examined separately, however, there was evidence suggesting association between impulsivity and vmPFC regulation of arousal only in women.Together, these findings confirmed the link between Barratt impulsivity and heightened arousal to salient stimuli in both genders and suggested the neural bases of altered regulation of arousal in impulsive women. More research is needed to explore the neural processes of arousal regulation in impulsive individuals and in clinical conditions that implicate poor impulse control.
Barratt Impulsivity and Neural Regulation of Physiological Arousal.
Zhang, Sheng; Hu, Sien; Hu, Jianping; Wu, Po-Lun; Chao, Herta H; Li, Chiang-shan R
2015-01-01
Theories of personality have posited an increased arousal response to external stimulation in impulsive individuals. However, there is a dearth of studies addressing the neural basis of this association. We recorded skin conductance in 26 individuals who were assessed with Barratt Impulsivity Scale (BIS-11) and performed a stop signal task during functional magnetic resonance imaging. Imaging data were processed and modeled with Statistical Parametric Mapping. We used linear regressions to examine correlations between impulsivity and skin conductance response (SCR) to salient events, identify the neural substrates of arousal regulation, and examine the relationship between the regulatory mechanism and impulsivity. Across subjects, higher impulsivity is associated with greater SCR to stop trials. Activity of the ventromedial prefrontal cortex (vmPFC) negatively correlated to and Granger caused skin conductance time course. Furthermore, higher impulsivity is associated with a lesser strength of Granger causality of vmPFC activity on skin conductance, consistent with diminished control of physiological arousal to external stimulation. When men (n = 14) and women (n = 12) were examined separately, however, there was evidence suggesting association between impulsivity and vmPFC regulation of arousal only in women. Together, these findings confirmed the link between Barratt impulsivity and heightened arousal to salient stimuli in both genders and suggested the neural bases of altered regulation of arousal in impulsive women. More research is needed to explore the neural processes of arousal regulation in impulsive individuals and in clinical conditions that implicate poor impulse control.
Hu, Zhongwei; Autschbach, Jochen; Jensen, Lasse
2014-09-28
Resonance hyper-Rayleigh scattering (HRS) of molecules and metal clusters have been simulated based on a time-dependent density functional theory approach. The resonance first-order hyperpolarizability (β) is obtained by implementing damped quadratic response theory using the (2n + 1) rule. To test this implementation, the prototypical dipolar molecule para-nitroaniline (p-NA) and the octupolar molecule crystal violet are used as benchmark systems. Moreover, small silver clusters Ag 8 and Ag 20 are tested with a focus on determining the two-photon resonant enhancement arising from the strong metal transition. Our results show that, on a per atom basis, the small silver clusters possess two-photon enhanced HRS comparable to that of larger nanoparticles. This finding indicates the potential interest of using small metal clusters for designing new nonlinear optical materials.
The neural basis of intuitive and counterintuitive moral judgment
Wiech, Katja; Shackel, Nicholas; Farias, Miguel; Savulescu, Julian; Tracey, Irene
2012-01-01
Neuroimaging studies on moral decision-making have thus far largely focused on differences between moral judgments with opposing utilitarian (well-being maximizing) and deontological (duty-based) content. However, these studies have investigated moral dilemmas involving extreme situations, and did not control for two distinct dimensions of moral judgment: whether or not it is intuitive (immediately compelling to most people) and whether it is utilitarian or deontological in content. By contrasting dilemmas where utilitarian judgments are counterintuitive with dilemmas in which they are intuitive, we were able to use functional magnetic resonance imaging to identify the neural correlates of intuitive and counterintuitive judgments across a range of moral situations. Irrespective of content (utilitarian/deontological), counterintuitive moral judgments were associated with greater difficulty and with activation in the rostral anterior cingulate cortex, suggesting that such judgments may involve emotional conflict; intuitive judgments were linked to activation in the visual and premotor cortex. In addition, we obtained evidence that neural differences in moral judgment in such dilemmas are largely due to whether they are intuitive and not, as previously assumed, to differences between utilitarian and deontological judgments. Our findings therefore do not support theories that have generally associated utilitarian and deontological judgments with distinct neural systems. PMID:21421730
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...
Spectral approach to optical resonator theory
International Nuclear Information System (INIS)
Feit, M.D.; Fleck, J.A. Jr.
1981-01-01
A new computational method for unloaded optical resonators is developed based on the discrete Fourier analysis of informaton generated by repated iterations of the optical field corresponding to transits between reflectors. The method is a straightforward extension of the propagating beam method developed earlier for optical fibers for extracting modal properties from numerical solutions to the paraxial scalar wave equation. The method requires computation of a field correlation function, whose Fourier transform reveals the eigenmodes as resonant peaks. Analysis of the location and breadth of these peaks determines the resonator eigenvalues When the eigenvalues are known, additional discrete Fourier transforms of the field are used to generate the mode eigenfunctions. This new method makes possible the unambiguous identification and accurate characterization of the entire spectrum of transverse resonator modes
The width of the Δ-resonance at two loop order in baryon chiral perturbation theory
Energy Technology Data Exchange (ETDEWEB)
Gegelia, Jambul, E-mail: j.gegelia@fz-juelich.de [Institute for Advanced Simulation, Institut für Kernphysik and Jülich Center for Hadron Physics, Forschungszentrum Jülich, D-52425 Jülich (Germany); Tbilisi State University, 0186 Tbilisi, Georgia (United States); Meißner, Ulf-G., E-mail: meissner@hiskp.uni-bonn.de [Helmholtz Institut für Strahlen- und Kernphysik and Bethe Center for Theoretical Physics, Universität Bonn, D-53115 Bonn (Germany); Institute for Advanced Simulation, Institut für Kernphysik and Jülich Center for Hadron Physics, Forschungszentrum Jülich, D-52425 Jülich (Germany); Siemens, Dmitrij, E-mail: dmitrij.siemens@rub.de [Institut für Theoretische Physik II, Ruhr-Universität Bochum, D-44780 Bochum (Germany); Yao, De-Liang, E-mail: d.yao@fz-juelich.de [Institute for Advanced Simulation, Institut für Kernphysik and Jülich Center for Hadron Physics, Forschungszentrum Jülich, D-52425 Jülich (Germany)
2016-12-10
We calculate the width of the delta resonance at leading two-loop order in baryon chiral perturbation theory. This gives a correlation between the leading pion–nucleon–delta and pion–delta couplings, which is relevant for the analysis of pion–nucleon scattering and other processes.
Neural pathway in the right hemisphere underlies verbal insight problem solving.
Zhao, Q; Zhou, Z; Xu, H; Fan, W; Han, L
2014-01-03
Verbal insight problem solving means to break mental sets, to select the novel semantic information and to form novel, task-related associations. Although previous studies have identified the brain regions associated with these key processes, the interaction among these regions during insight is still unclear. In the present study, we explored the functional connectivity between the key regions during solving Chinese 'chengyu' riddles by using event-related functional magnetic resonance imaging. Results showed that both insight and noninsight solutions activated the bilateral inferior frontal gyri, middle temporal gyri and hippocampi, and these regions constituted a frontal to temporal to hippocampal neural pathway. Compared with noninsight solution, insight solution had a stronger functional connectivity between the inferior frontal gyrus and middle temporal gyrus in the right hemisphere. Our study reveals the neural pathway of information processing during verbal insight problem solving, and supports the right-hemisphere advantage theory of insight. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.
Mitri, Farid
2014-11-01
The generalized theory of resonance scattering (GTRS) by an elastic spherical target in acoustics is extended to describe the arbitrary scattering of a finite beam using the addition theorem for the spherical wave functions of the first kind under a translation of the coordinate origin. The advantage of the proposed method over the standard discrete spherical harmonics transform previously used in the GTRS formalism is the computation of the off-axial beam-shape coefficients (BSCs) stemming from a closed-form partial-wave series expansion representing the axial BSCs in spherical coordinates. With this general method, the arbitrary acoustical scattering can be evaluated for any particle shape and size, whether the particle is partially or completely illuminated by the incident beam. Numerical examples for the axial and off-axial resonance scattering from an elastic sphere placed arbitrarily in the field of a finite circular piston transducer with uniform vibration are provided. Moreover, the 3-D resonance directivity patterns illustrate the theory and reveal some properties of the scattering. Numerous applications involving the scattering phenomenon in imaging, particle manipulation, and the characterization of multiphase flows can benefit from the present analysis because all physically realizable beams radiate acoustical waves from finite transducers as opposed to waves of infinite extent.
Classification of brain compartments and head injury lesions by neural networks applied to MRI
International Nuclear Information System (INIS)
Kischell, E.R.; Kehtarnavaz, N.; Hillman, G.R.; Levin, H.; Lilly, M.; Kent, T.A.
1995-01-01
An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and 'unknown'. A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician's report used to train the neural network. (orig.)
Classification of brain compartments and head injury lesions by neural networks applied to MRI
Energy Technology Data Exchange (ETDEWEB)
Kischell, E R [Dept. of Electrical Engineering, Texas A and M Univ., College Station, TX (United States); Kehtarnavaz, N [Dept. of Electrical Engineering, Texas A and M Univ., College Station, TX (United States); Hillman, G R [Dept. of Pharmacology, Univ. of Texas Medical Branch, Galveston, TX (United States); Levin, H [Dept. of Neurosurgery, Univ. of Texas Medical Branch, Galveston, TX (United States); Lilly, M [Dept. of Neurosurgery, Univ. of Texas Medical Branch, Galveston, TX (United States); Kent, T A [Dept. of Neurology and Psychiatry, Univ. of Texas Medical Branch, Galveston, TX (United States)
1995-10-01
An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and `unknown`. A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician`s report used to train the neural network. (orig.)
Neutron Resonance Theory for Nuclear Reactor Applications: Modern Theory and Practices.
Energy Technology Data Exchange (ETDEWEB)
Hwang, Richard N. [Argonne National Lab. (ANL), Argonne, IL (United States); Blomquist, Roger N. [Argonne National Lab. (ANL), Argonne, IL (United States); Leal, Luiz C. [Inst. de Radioprotection et de SÃ»rete Nucleaire (ISRN), Fontenay-aux-Roses (France); Yang, Won Sik [Purdue Univ., West Lafayette, IN (United States)
2016-09-24
The neutron resonance phenomena constitute one of the most fundamental subjects in nuclear physics as well as in reactor physics. It is the area where the concepts of nuclear interaction and the treatment of the neutronic balance in reactor fuel lattices become intertwined. The latter requires the detailed knowledge of resonance structures of many nuclides of practical interest to the development of nuclear energy. The most essential element in reactor physics is to provide an accurate account of the intricate balance between the neutrons produced by the fission process and neutrons lost due to the absorption process as well as those leaking out of the reactor system. The presence of resonance structures in many major nuclides obviously plays an important role in such processes. There has been a great deal of theoretical and practical interest in resonance reactions since Fermi’s discovery of resonance absorption of neutrons as they were slowed down in water. The resonance absorption became the center of attention when the question was raised as to the feasibility of the self-sustaining chain reaction in a natural uranium-fueled system. The threshold of the nuclear era was crossed almost eighty years ago when Fermi and Szilard observed that a substantial reduction in resonance absorption is possible if the uranium was made into the form of lumps instead of a homogeneous mixture with water. In the West, the first practical method for estimating the resonance escape probability in a reactor cell was pioneered by Wigner et al in early forties.
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
Theory of Electric-Field Effects on Electron-Spin-Resonance Hyperfine Couplings
International Nuclear Information System (INIS)
Karna, S.P.
1997-01-01
A quantum mechanical theory of the effects of a uniform electric field on electron-spin-resonance hyperfine couplings is presented. The electric-field effects are described in terms of perturbation coefficients which can be used to probe the local symmetry as well as the strength of the electric field at paramagnetic sites in a solid. Results are presented for the first-order perturbation coefficients describing the Bloembergen effect (linear electric-field effect on hyperfine coupling tensor) for the O atom and the OH radical. copyright 1997 The American Physical Society
Rotstein, Horacio G
2014-01-01
We investigate the dynamic mechanisms of generation of subthreshold and phase resonance in two-dimensional linear and linearized biophysical (conductance-based) models, and we extend our analysis to account for the effect of simple, but not necessarily weak, types of nonlinearities. Subthreshold resonance refers to the ability of neurons to exhibit a peak in their voltage amplitude response to oscillatory input currents at a preferred non-zero (resonant) frequency. Phase-resonance refers to the ability of neurons to exhibit a zero-phase (or zero-phase-shift) response to oscillatory input currents at a non-zero (phase-resonant) frequency. We adapt the classical phase-plane analysis approach to account for the dynamic effects of oscillatory inputs and develop a tool, the envelope-plane diagrams, that captures the role that conductances and time scales play in amplifying the voltage response at the resonant frequency band as compared to smaller and larger frequencies. We use envelope-plane diagrams in our analysis. We explain why the resonance phenomena do not necessarily arise from the presence of imaginary eigenvalues at rest, but rather they emerge from the interplay of the intrinsic and input time scales. We further explain why an increase in the time-scale separation causes an amplification of the voltage response in addition to shifting the resonant and phase-resonant frequencies. This is of fundamental importance for neural models since neurons typically exhibit a strong separation of time scales. We extend this approach to explain the effects of nonlinearities on both resonance and phase-resonance. We demonstrate that nonlinearities in the voltage equation cause amplifications of the voltage response and shifts in the resonant and phase-resonant frequencies that are not predicted by the corresponding linearized model. The differences between the nonlinear response and the linear prediction increase with increasing levels of the time scale separation between
Neural Networks for Modeling and Control of Particle Accelerators
Edelen, A.L.; Chase, B.E.; Edstrom, D.; Milton, S.V.; Stabile, P.
2016-01-01
We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.
Resonance probe; La sonde a resonance
Energy Technology Data Exchange (ETDEWEB)
Lepechinsky, D; Messiaen, A; Rolland, P [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires
1966-07-01
After a brief review of papers recently published on the resonance probe as a tool for plasma diagnostics, the main features of the theory proposed by one of us are recalled. In this theory the geometry of the resonator formed by the probe, the ion sheath and the plasma is explicitly taken into account with the quasi-static and cold plasma approximations. Some new results emerging from this theory are indicated and a comparison with experimental data obtained with a spherical probe placed in a quiescent mercury-vapour plasma is made. A good quantitative agreement has been observed, indicating that the theory is satisfactory and justifying the assumptions involved. Nevertheless it appears that in some cases experimental results can only be interpreted when non collisional damping phenomena are taken into consideration. (author) [French] Apres un apercu des etudes recemment publiees sur la sonde a resonance pour le diagnostic des plasmas, on rappelle l'essentiel de la theorie proposee par l'un de nous ou il est tenu compte explicitement de la geometrie du resonateur forme par le systeme sonde-gaine ionique-plasma dans l'approximation quasi-statique et du plasma froid. On indique quelques resultats nouveaux pouvant etre tires de cette theorie et on la confronte avec les donnees experimentales obtenues pour une sonde spherique placee dans un plasma de mercure en equilibre. Un tres bon accord quantitatif a ete constate, indiquant que la theorie est satisfaisante et justifiant les approximations faites dans celle-ci. Il apparait toutefois que certains resultats experimentaux ne peuvent etre interpretes qu'en tenant compte des phenomenes d'amortissement non collisionnels. (auteur)
International Nuclear Information System (INIS)
Shore, B.W.
1977-01-01
The long-time average of level populations in a coherently-excited anharmonic sequence of energy levels (e.g., an anharmonic oscillator) exhibits sharp resonances as a function of laser frequency. For simple linearly-increasing anharmonicity, each resonance is a superposition of various multiphoton resonances (e.g., a superposition of 3, 5, 7, . . . photon resonances), each having its own characteristic width predictable from perturbation theory
Neural constructivism or self-organization?
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
Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons
Rigatos, Gerasimos G
2015-01-01
This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.
Theory of RF superconductivity for resonant cavities
Gurevich, Alex
2017-03-01
An overview of a theory of electromagnetic response of superconductors in strong radio-frequency (RF) electromagnetic fields is given with the emphasis on applications to superconducting resonant cavities for particle accelerators. The paper addresses fundamentals of the BCS surface resistance, the effect of subgap states and trapped vortices on the residual surface resistance at low RF fields, and a nonlinear surface resistance at strong fields, particularly the effect of the RF field suppression of the surface resistance. These issues are essential for the understanding of the field dependence of high quality factors Q({B}a)˜ {10}10{--}{10}11 achieved on the Nb cavities at 1.3-2 K in strong RF fields B a close to the depairing limit, and the extended Q({B}a) rise which has been observed on Ti and N-treated Nb cavities. Possible ways of further increase of Q({B}a) and the breakdown field by optimizing impurity concentration at the surface and by multilayer nanostructuring with materials other than Nb are discussed.
Directory of Open Access Journals (Sweden)
Tang L
2018-04-01
Full Text Available Li-Yuan Tang,1,* Hai-Jun Li,2,* Xin Huang,1 Jing Bao,1 Zubin Sethi,3 Lei Ye,1 Qing Yuan,1 Pei-Wen Zhu,1 Nan Jiang,1 Gui-Ping Gao,1 Yi Shao1 1Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China; 2Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China; 3The Department of Medicine, University of Miami, Coral Gables, FL, USA *These authors contributed equally to this work Objective: Previous neuroimaging studies have demonstrated that pain-related diseases are associated with brain function and anatomical abnormalities, whereas altered synchronous neural activity in acute eye pain (EP patients has not been investigated. The purpose of this study was to explore whether or not synchronous neural activity changes were measured with the regional homogeneity (ReHo method in acute EP patients.Methods: A total of 20 patients (15 males and 5 females with EP and 20 healthy controls (HCs consisting of 15 and 5 age-, sex-, and education-matched males and females, respectively, underwent resting-state functional magnetic resonance imaging. The ReHo method was applied to assess synchronous neural activity changes.Results: Compared with HCs, acute EP patients had significantly lower ReHo values in the left precentral/postcentral gyrus (Brodmann area [BA]3/4, right precentral/postcentral gyrus (BA3/4, and left middle frontal gyrus (BA6. In contrast, higher ReHo values in acute EP patients were observed in the left superior frontal gyrus (BA11, right inferior parietal lobule (BA39/40, and left precuneus (BA7. However, no relationship was found between the mean ReHo signal values of the different areas and clinical manifestations, which included both the duration and degree of pain in EP patients.Conclusion: Our study highlighted that acute EP patients showed altered synchronous neural activities in many brain regions, including somatosensory regions. These
Regeta, Khrystyna; Allan, Michael; Winstead, Carl; McKoy, Vincent; Mašín, Zdeněk; Gorfinkiel, Jimena D
2016-01-14
We measured differential cross sections for elastic (rotationally integrated) electron scattering on pyrimidine, both as a function of angle up to 180(∘) at electron energies of 1, 5, 10, and 20 eV and as a function of electron energy in the range 0.1-14 eV. The experimental results are compared to the results of the fixed-nuclei Schwinger variational and R-matrix theoretical methods, which reproduce satisfactorily the magnitudes and shapes of the experimental cross sections. The emphasis of the present work is on recording detailed excitation functions revealing resonances in the excitation process. Resonant structures are observed at 0.2, 0.7, and 4.35 eV and calculations for different symmetries confirm their assignment as the X̃(2)A2, Ã(2)B1, and B̃(2)B1 shape resonances. As a consequence of superposition of coherent resonant amplitudes with background scattering the B̃(2)B1 shape resonance appears as a peak, a dip, or a step function in the cross sections recorded as a function of energy at different scattering angles and this effect is satisfactorily reproduced by theory. The dip and peak contributions at different scattering angles partially compensate, making the resonance nearly invisible in the integral cross section. Vibrationally integrated cross sections were also measured at 1, 5, 10 and 20 eV and the question of whether the fixed-nuclei cross sections should be compared to vibrationally elastic or vibrationally integrated cross section is discussed.
Neural dichotomy of word concreteness: a view from functional neuroimaging.
Kumar, Uttam
2016-02-01
Our perception about the representation and processing of concrete and abstract concepts is based on the fact that concrete words are highly imagined and remembered faster than abstract words. In order to explain the processing differences between abstract and concrete concepts, various theories have been proposed, yet there is no unanimous consensus about its neural implication. The present study investigated the processing of concrete and abstract words during an orthography judgment task (implicit semantic processing) using functional magnetic resonance imaging to validate the involvement of the neural regions. Relative to non-words, both abstract and concrete words show activation in the regions of bilateral hemisphere previously associated with semantic processing. The common areas (conjunction analyses) observed for abstract and concrete words are bilateral inferior frontal gyrus (BA 44/45), left superior parietal (BA 7), left fusiform gyrus and bilateral middle occipital. The additional areas for abstract words were noticed in bilateral superior temporal and bilateral middle temporal region, whereas no distinct region was noticed for concrete words. This suggests that words with abstract concepts recruit additional language regions in the brain.
Lehar, Steven
2003-01-01
Visual illusions and perceptual grouping phenomena offer an invaluable tool for probing the computational mechanism of low-level visual processing. Some illusions, like the Kanizsa figure, reveal illusory contours that form edges collinear with the inducing stimulus. This kind of illusory contour has been modeled by neural network models by way of cells equipped with elongated spatial receptive fields designed to detect and complete the collinear alignment. There are, however, other illusory groupings which are not so easy to account for in neural network terms. The Ehrenstein illusion exhibits an illusory contour that forms a contour orthogonal to the stimulus instead of collinear with it. Other perceptual grouping effects reveal illusory contours that exhibit a sharp corner or vertex, and still others take the form of vertices defined by the intersection of three, four, or more illusory contours that meet at a point. A direct extension of the collinear completion models to account for these phenomena tends towards a combinatorial explosion, because it would suggest cells with specialized receptive fields configured to perform each of those completion types, each of which would have to be replicated at every location and every orientation across the visual field. These phenomena therefore challenge the adequacy of the neural network approach to account for these diverse perceptual phenomena. I have proposed elsewhere an alternative paradigm of neurocomputation in the harmonic resonance theory (Lehar 1999, see website), whereby pattern recognition and completion are performed by spatial standing waves across the neural substrate. The standing waves perform a computational function analogous to that of the spatial receptive fields of the neural network approach, except that, unlike that paradigm, a single resonance mechanism performs a function equivalent to a whole array of spatial receptive fields of different spatial configurations and of different orientations
Coupled-resonator optical waveguides
DEFF Research Database (Denmark)
Raza, Søren; Grgic, Jure; Pedersen, Jesper Goor
2010-01-01
Coupled-resonator optical waveguides hold potential for slow-light propagation of optical pulses. The dispersion properties may adequately be analyzed within the framework of coupled-mode theory. We extend the standard coupled-mode theory for such structures to also include complex-valued paramet......Coupled-resonator optical waveguides hold potential for slow-light propagation of optical pulses. The dispersion properties may adequately be analyzed within the framework of coupled-mode theory. We extend the standard coupled-mode theory for such structures to also include complex...
International Nuclear Information System (INIS)
Hou Zhijian; Lian Zhiwei; Yao Ye; Yuan Xinjian
2006-01-01
A novel method integrating rough sets (RS) theory and an artificial neural network (ANN) based on data-fusion technique is presented to forecast an air-conditioning load. Data-fusion technique is the process of combining multiple sensors data or related information to estimate or predict entity states. In this paper, RS theory is applied to find relevant factors to the load, which are used as inputs of an artificial neural-network to predict the cooling load. To improve the accuracy and enhance the robustness of load forecasting results, a general load-prediction model, by synthesizing multi-RSAN (MRAN), is presented so as to make full use of redundant information. The optimum principle is employed to deduce the weights of each RSAN model. Actual prediction results from a real air-conditioning system show that, the MRAN forecasting model is better than the individual RSAN and moving average (AMIMA) ones, whose relative error is within 4%. In addition, individual RSAN forecasting results are better than that of ARIMA
Neural correlates of viewing paintings
DEFF Research Database (Denmark)
Vartanian, Oshin; Skov, Martin
2014-01-01
Many studies involving functional magnetic resonance imaging (fMRI) have exposed participants to paintings under varying task demands. To isolate neural systems that are activated reliably across fMRI studies in response to viewing paintings regardless of variation in task demands, a quantitative...
Neural Correlates of Impaired Reward-Effort Integration in Remitted Bulimia Nervosa.
Mueller, Stefanie Verena; Morishima, Yosuke; Schwab, Simon; Wiest, Roland; Federspiel, Andrea; Hasler, Gregor
2018-03-01
The integration of reward magnitudes and effort costs is required for an effective behavioral guidance. This reward-effort integration was reported to be dependent on dopaminergic neurotransmission. As bulimia nervosa has been associated with a dysregulated dopamine system and catecholamine depletion led to reward-processing deficits in remitted bulimia nervosa, the purpose of this study was to identify the role of catecholamine dysfunction and its relation to behavioral and neural reward-effort integration in bulimia nervosa. To investigate the interaction between catecholamine functioning and behavioral, and neural responses directly, 17 remitted bulimic (rBN) and 21 healthy individuals (HC) received alpha-methyl-paratyrosine (AMPT) over 24 h to achieve catecholamine depletion in a randomized, crossover study design. We used functional magnetic resonance imaging (fMRI) and the monetary incentive delay (MID) task to assess reward-effort integration in relation to catecholaminergic neurotransmission at the behavioral and neural level. AMPT reduced the ability to integrate rewards and efforts effectively in HC participants. In contrast, in rBN participants, the reduced reward-effort integration was associated with illness duration in the sham condition and unrelated to catecholamine depletion. Regarding neural activation, AMPT decreased the reward anticipation-related neural activation in the anteroventral striatum. This decrease was associated with the AMPT-induced reduction of monetary earning in HC in contrast to rBN participants. Our findings contributed to the theory of a desensitized dopaminergic system in bulimia nervosa. A disrupted processing of reward magnitudes and effort costs might increase the probability of maintenance of bulimic symptoms.
Lang, Gernot; Vicari, Marco; Siller, Alexander; Kubosch, Eva J; Hennig, Juergen; Südkamp, Norbert P; Izadpanah, Kaywan; Kubosch, David
2018-04-06
Introduction Lumbar spinal stenosis (LSS) is a kinetic-dependent disease typically aggravating during spinal loading. To date, assessment of LSS is usually performed with magnetic resonance imaging (MRI). However, conventional supine MRI is associated with significant drawbacks as it does not truly reflect physiological loads, experienced by discoligamentous structures during erect posture. Consequently, supine MRI often fails to reveal the source of pain and/or disability caused by LSS. The present study sought to assess neural dimensions via MRI in supine, upright, and upright-hyperlordotic position in order to evaluate the impact of patient positioning on neural narrowing. Therefore, radiological measures such as neuroforaminal dimensions, central canal volume, sagittal listhesis, and lumbar lordosis at spinal level L4/5 were extracted and stratified according to patient posture. Materials and methods Overall, 10 subjects were enclosed in this experimental study. MRI was performed in three different positions: (1) 0° supine (SP), (2) 80° upright (UP), and (3) 80° upright + hyperlordotic (HY) posture. Upright MRI was conducted utilizing a 0.25T open-configuration scanner equipped with a rotatable examination bed allowing for true standing MRI. Radiographic outcome of upright MRI imaging was extracted and evaluated according to patient positioning. Results Upright MRI-based assessment of neural dimensions was successfully accomplished in all subjects. Overall, radiographic parameters revealed a significant decrease of neural dimensions from supine to upright position: Specifically, mean foraminal area decreased from SP to UP by 13.3% (P ≤ 0.05) as well as from SP to HY position by 21% (P ≤ 0.05). Supplementation of hyperlordosis did not result in additional narrowing of neural elements (P ≥ 0.05). Furthermore, central canal volume revealed a decrease of 7% at HY and 8% at UP compared to SP position (P ≥ 0.05). Assessment of lumbar lordosis yielded in a
Have we met before? Neural correlates of emotional learning in women with social phobia.
Laeger, Inga; Keuper, Kati; Heitmann, Carina; Kugel, Harald; Dobel, Christian; Eden, Annuschka; Arolt, Volker; Zwitserlood, Pienie; Dannlowski, Udo; Zwanzger, Peter
2014-05-01
Altered memory processes are thought to be a key mechanism in the etiology of anxiety disorders, but little is known about the neural correlates of fear learning and memory biases in patients with social phobia. The present study therefore examined whether patients with social phobia exhibit different patterns of neural activation when confronted with recently acquired emotional stimuli. Patients with social phobia and a group of healthy controls learned to associate pseudonames with pictures of persons displaying either a fearful or a neutral expression. The next day, participants read the pseudonames in the magnetic resonance imaging scanner. Afterwards, 2 memory tests were carried out. We enrolled 21 patients and 21 controls in our study. There were no group differences for learning performance, and results of the memory tests were mixed. On a neural level, patients showed weaker amygdala activation than controls for the contrast of names previously associated with fearful versus neutral faces. Social phobia severity was negatively related to amygdala activation. Moreover, a detailed psychophysiological interaction analysis revealed an inverse correlation between disorder severity and frontolimbic connectivity for the emotional > neutral pseudonames contrast. Our sample included only women. Our results support the theory of a disturbed cortico limbic interplay, even for recently learned emotional stimuli. We discuss the findings with regard to the vigilance-avoidance theory and contrast them to results indicating an oversensitive limbic system in patients with social phobia.
Theory of resonant multiphoton ionization of krypton by intense ultraviolet laser radiation
International Nuclear Information System (INIS)
Tang, X.; Lambropoulos, P.; L'Huillier, A.; Dixit, S.N.
1989-01-01
We present a theoretical interpretation of the experimental results on three-photon-resonant four-photon ionization of Kr reported by Landen, Perry, and Campbell [Phys. Rev. Lett. 59, 2558 (1987)] and Perry and Landen [Phys. Rev. A 38, 2815 (1988)]. Our calculations are based on multichannel quantum-defect theory combined with a density-matrix formalism describing the spatiotemporal development of the process. We obtain good agreement with the data, which even at intensities as high as 10 14 W/cm 2 show the imprint of the underlying atomic structure
Medical Imaging with Neural Networks
International Nuclear Information System (INIS)
Pattichis, C.; Cnstantinides, A.
1994-01-01
The objective of this paper is to provide an overview of the recent developments in the use of artificial neural networks in medical imaging. The areas of medical imaging that are covered include : ultrasound, magnetic resonance, nuclear medicine and radiological (including computerized tomography). (authors)
Properties of resonance wave functions.
More, R. M.; Gerjuoy, E.
1973-01-01
Construction and study of resonance wave functions corresponding to poles of the Green's function for several illustrative models of theoretical interest. Resonance wave functions obtained from the Siegert and Kapur-Peierls definitions of the resonance energies are compared. The comparison especially clarifies the meaning of the normalization constant of the resonance wave functions. It is shown that the wave functions may be considered renormalized in a sense analogous to that of quantum field theory. However, this renormalization is entirely automatic, and the theory has neither ad hoc procedures nor infinite quantities.
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...
Ahmadi, Bahman; Nariman-zadeh, Nader; Jamali, Ali
2017-06-01
In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms.
A new wind power prediction method based on chaotic theory and Bernstein Neural Network
International Nuclear Information System (INIS)
Wang, Cong; Zhang, Hongli; Fan, Wenhui; Fan, Xiaochao
2016-01-01
The accuracy of wind power prediction is important for assessing the security and economy of the system operation when wind power connects to the grids. However, multiple factors cause a long delay and large errors in wind power prediction. Hence, efficient wind power forecasting approaches are still required for practical applications. In this paper, a new wind power forecasting method based on Chaos Theory and Bernstein Neural Network (BNN) is proposed. Firstly, the largest Lyapunov exponent as a judgment for wind power system's chaotic behavior is made. Secondly, Phase Space Reconstruction (PSR) is used to reconstruct the wind power series' phase space. Thirdly, the prediction model is constructed using the Bernstein polynomial and neural network. Finally, the weights and thresholds of the model are optimized by Primal Dual State Transition Algorithm (PDSTA). The practical hourly data of wind power generation in Xinjiang is used to test this forecaster. The proposed forecaster is compared with several current prominent research findings. Analytical results indicate that the forecasting error of PDSTA + BNN is 3.893% for 24 look-ahead hours, and has lower errors obtained compared with the other forecast methods discussed in this paper. The results of all cases studying confirm the validity of the new forecast method. - Highlights: • Lyapunov exponent is used to verify chaotic behavior of wind power series. • Phase Space Reconstruction is used to reconstruct chaotic wind power series. • A new Bernstein Neural Network to predict wind power series is proposed. • Primal dual state transition algorithm is chosen as the training strategy of BNN.
International Nuclear Information System (INIS)
Hategan, Cornel
2002-01-01
Theory of Threshold Phenomena in Quantum Scattering is developed in terms of Reduced Scattering Matrix. Relationships of different types of threshold anomalies both to nuclear reaction mechanisms and to nuclear reaction models are established. Magnitude of threshold effect is related to spectroscopic factor of zero-energy neutron state. The Theory of Threshold Phenomena, based on Reduced Scattering Matrix, does establish relationships between different types of threshold effects and nuclear reaction mechanisms: the cusp and non-resonant potential scattering, s-wave threshold anomaly and compound nucleus resonant scattering, p-wave anomaly and quasi-resonant scattering. A threshold anomaly related to resonant or quasi resonant scattering is enhanced provided the neutron threshold state has large spectroscopic amplitude. The Theory contains, as limit cases, Cusp Theories and also results of different nuclear reactions models as Charge Exchange, Weak Coupling, Bohr and Hauser-Feshbach models. (author)
Heiden, Uwe
1980-01-01
The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica ted throughout the text. However, they are not explored in de tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be havior of neurons or neuron pools. In this respect the essay is writt...
Medical Imaging with Neural Networks
Energy Technology Data Exchange (ETDEWEB)
Pattichis, C [Department of Computer Science, University of Cyprus, Kallipoleos 75, P.O.Box 537, Nicosia (Cyprus); Cnstantinides, A [Department of Electrical Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BT (United Kingdom)
1994-12-31
The objective of this paper is to provide an overview of the recent developments in the use of artificial neural networks in medical imaging. The areas of medical imaging that are covered include : ultrasound, magnetic resonance, nuclear medicine and radiological (including computerized tomography). (authors). 61 refs, 4 tabs.
O'Nions, Elizabeth; Sebastian, Catherine L.; McCrory, Eamon; Chantiluke, Kaylita; Happé, Francesca; Viding, Essi
2014-01-01
Individuals with autism spectrum disorders (ASD) have difficulty understanding other minds (Theory of Mind; ToM), with atypical processing evident at both behavioural and neural levels. Individuals with conduct problems and high levels of callous-unemotional (CU) traits (CP/HCU) exhibit reduced responsiveness to others' emotions and difficulties…
Glover, Paul M; Watkins, Roger H; O'Neill, George C; Ackerley, Rochelle; Sanchez-Panchuelo, Rosa; McGlone, Francis; Brookes, Matthew J; Wessberg, Johan; Francis, Susan T
2017-10-01
Intra-neural microstimulation (INMS) is a technique that allows the precise delivery of low-current electrical pulses into human peripheral nerves. Single unit INMS can be used to stimulate individual afferent nerve fibres during microneurography. Combining this with neuroimaging allows the unique monitoring of central nervous system activation in response to unitary, controlled tactile input, with functional magnetic resonance imaging (fMRI) providing exquisite spatial localisation of brain activity and magnetoencephalography (MEG) high temporal resolution. INMS systems suitable for use within electrophysiology laboratories have been available for many years. We describe an INMS system specifically designed to provide compatibility with both ultra-high field (7T) fMRI and MEG. Numerous technical and safety issues are addressed. The system is fully analogue, allowing for arbitrary frequency and amplitude INMS stimulation. Unitary recordings obtained within both the MRI and MEG screened-room environments are comparable with those obtained in 'clean' electrophysiology recording environments. Single unit INMS (current met. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.
Feedforward Nonlinear Control Using Neural Gas Network
Machón-González, Iván; López-García, Hilario
2017-01-01
Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the ...
On the elementary neural forms of interaction rituals
DEFF Research Database (Denmark)
Heinskou, Marie Bruvik; Liebst, Lasse Suonperä
Randall Collins’ interaction ritual (IR) theory suggests solidarity as neurologically hardwired in the capacity for rhythmic entrainment. Yet, this article suggests that IR theory may benefit from being tied more firmly to recent neurological research, specifically Stephen W. Porges......’ neurophysiological polyvagal theory. IR theory does not sufficiently acknowledge the autonomic nervous system as a system involving a phylogenetically ordered response hierarchy, of which only one subsystem supports prosocial behavior. The ritual ingredients of shared attention and mood may be clarified as part...... of a social engagement system, neurally regulating attention and arousal via brain-face-heart circuits. This allows rhythmic entrainment to be specified as a neural epiphenomenon of the social engagement system. The polyvagal perspective, moreover, challenges IR theory to reconsider the importance...
Local Dynamics in Trained Recurrent Neural Networks.
Rivkind, Alexander; Barak, Omri
2017-06-23
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.
Local Dynamics in Trained Recurrent Neural Networks
Rivkind, Alexander; Barak, Omri
2017-06-01
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.
Dissociated neural processing for decisions in managers and non-managers.
Caspers, Svenja; Heim, Stefan; Lucas, Marc G; Stephan, Egon; Fischer, Lorenz; Amunts, Katrin; Zilles, Karl
2012-01-01
Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing.
International Nuclear Information System (INIS)
Malinowski, E.R.
1978-01-01
Based on the theory of error for abstract factor analysis described earlier, a theory of error for target factor analysis is developed. The theory shows how the error in the data matrix mixes with the error in the target test vector. The apparent error in a target test is found to be a vector sum of the real error in the target vector and the real error in the predicted vector. The theory predicts the magnitudes of these errors without requiring any a priori knowledge of the error in the data matrix or the target vector. A reliability function and a spoil function are developed for the purpose of assessing the validity and the worthiness of a target vector. Examples from model data, mass spectrometry and nuclear magnetic resonance spectrometry are presented. (Auth.)
Zero-range effective field theory for resonant wino dark matter. Part III. Annihilation effects
Braaten, Eric; Johnson, Evan; Zhang, Hong
2018-01-01
Near a critical value of the wino mass where there is a zero-energy S-wave resonance at the neutral-wino-pair threshold, low-energy winos can be described by a zero-range effective field theory (ZREFT) in which the winos interact nonperturbatively through a contact interaction and through Coulomb interactions. The effects of wino-pair annihilation into electroweak gauge bosons are taken into account through the analytic continuation of the real parameters for the contact interaction to comple...
Pursey, Kirrilly M; Stanwell, Peter; Callister, Robert J; Brain, Katherine; Collins, Clare E; Burrows, Tracy L
2014-01-01
Emerging evidence from recent neuroimaging studies suggests that specific food-related behaviors contribute to the development of obesity. The aim of this review was to report the neural responses to visual food cues, as assessed by functional magnetic resonance imaging (fMRI), in humans of differing weight status. Published studies to 2014 were retrieved and included if they used visual food cues, studied humans >18 years old, reported weight status, and included fMRI outcomes. Sixty studies were identified that investigated the neural responses of healthy weight participants (n = 26), healthy weight compared to obese participants (n = 17), and weight-loss interventions (n = 12). High-calorie food images were used in the majority of studies (n = 36), however, image selection justification was only provided in 19 studies. Obese individuals had increased activation of reward-related brain areas including the insula and orbitofrontal cortex in response to visual food cues compared to healthy weight individuals, and this was particularly evident in response to energy dense cues. Additionally, obese individuals were more responsive to food images when satiated. Meta-analysis of changes in neural activation post-weight loss revealed small areas of convergence across studies in brain areas related to emotion, memory, and learning, including the cingulate gyrus, lentiform nucleus, and precuneus. Differential activation patterns to visual food cues were observed between obese, healthy weight, and weight-loss populations. Future studies require standardization of nutrition variables and fMRI outcomes to enable more direct comparisons between studies.
Neural correlates of "Theory of Mind" in very preterm born children.
Mossad, Sarah I; Smith, Mary Lou; Pang, Elizabeth W; Taylor, Margot J
2017-11-01
Very preterm (VPT) birth (Theory of Mind (ToM); the ability to attribute mental states to others and understand that those beliefs can differ from one's own or reality. The neural bases for ToM deficits in VPT born children have not been examined. We used magnetoencephalography (MEG) for its excellent spatial and temporal resolution to determine the neural underpinnings of ToM in 24 VPT and 24 full-term born (FT) children (7-13 years). VPT children performed more poorly on neuropsychological measures of ToM but not inhibition. In the MEG task, both FT children and VPT children recruited regions involved in false belief processing such as the rIFG (VPT: 275-350 ms, FT: 250-375 ms) and left inferior temporal gyrus (VPT: 375-450 ms, FT: 325-375 ms) and right fusiform gyrus (VPT: 150-200 ms, FT: 175-250 ms). The rIPL (included in the temporal-parietal junction) was recruited in FT children (475-575 ms) and the lTPJ in VPT children (500-575 ms). However, activations in all regions were reduced in the VPT compared to the FT group. We suggest that with increasing social-cognitive demands such as varying the type of scenarios in the standardized measure of ToM, reduced activations in the rIFG and TPJ in the VPT group may reflect the decreased performance. With access to both spatial and temporal information, we discuss the role of domain general and specific regions of the ToM network in both groups. Hum Brain Mapp 38:5577-5589, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Theory-restricted resonant x-ray reflectometry of quantum materials
Fürsich, Katrin; Zabolotnyy, Volodymyr B.; Schierle, Enrico; Dudy, Lenart; Kirilmaz, Ozan; Sing, Michael; Claessen, Ralph; Green, Robert J.; Haverkort, Maurits W.; Hinkov, Vladimir
2018-04-01
The delicate interplay of competing phases in quantum materials is dominated by parameters such as the crystal field potential, the spin-orbit coupling, and, in particular, the electronic correlation strength. Whereas small quantitative variations of the parameter values can thus qualitatively change the material, these values can hitherto hardly be obtained with reasonable precision, be it theoretically or experimentally. Here we propose a solution combining resonant x-ray reflectivity (RXR) with multiplet ligand field theory (MLFT). We first perform ab initio DFT calculations within the MLFT framework to get initial parameter values, which we then use in a fit of the theoretical model to RXR. To validate our method, we apply it to NiO and SrTiO3 and obtain parameter values, which are amended by as much as 20 % compared to the ab initio results. Our approach is particularly useful to investigate topologically trivial and nontrivial correlated insulators, staggered moments in magnetically or orbitally ordered materials, and reconstructed interfaces.
Liu, Fang; Zhou, Zhaoye; Jang, Hyungseok; Samsonov, Alexey; Zhao, Gengyan; Kijowski, Richard
2018-04-01
To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint. A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts. The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions. The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med 79:2379-2391, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
Energy Technology Data Exchange (ETDEWEB)
Punjabi, A; Vahala, G [College of William and Mary, Williamsburg, VA (USA). Dept. of Physics
1983-12-01
The point model for the toroidal core plasma in the ELMO Bumpy Torus (with neoclassical non-resonant electrons) is examined in the light of catastrophe theory. Even though the point model equations do not constitute a gradient dynamic system, the equilibrium surfaces are similar to those of the canonical cusp catastrophe. The point model is then extended to incorporate ion cyclotron resonance heating. A detailed parametric study of the equilibria is presented. Further, the nonlinear time evolution of these equilibria is studied, and it is observed that the point model obeys the delay convention (and hence hysteresis) and shows catastrophes at the fold edges of the equilibrium surfaces. Tentative applications are made to experimental results.
Theory of mind network activity is altered in subjects with familial liability for schizophrenia
Mohnke, Sebastian; Erk, Susanne; Schnell, Knut; Romanczuk-Seiferth, Nina; Schmierer, Phöbe; Romund, Lydia; Garbusow, Maria; Wackerhagen, Carolin; Ripke, Stephan; Grimm, Oliver; Haller, Leila; Witt, Stephanie H.; Degenhardt, Franziska; Tost, Heike; Heinz, Andreas; Meyer-Lindenberg, Andreas; Walter, Henrik
2016-01-01
As evidenced by a multitude of studies, abnormalities in Theory of Mind (ToM) and its neural processing might constitute an intermediate phenotype of schizophrenia. If so, neural alterations during ToM should be observable in unaffected relatives of patients as well, since they share a considerable amount of genetic risk. While behaviorally, impaired ToM function is confirmed meta-analytically in relatives, evidence on aberrant function of the neural ToM network is sparse and inconclusive. The present study therefore aimed to further explore the neural correlates of ToM in relatives of schizophrenia. About 297 controls and 63 unaffected first-degree relatives of patients with schizophrenia performed a ToM task during functional magnetic resonance imaging. Consistent with the literature relatives exhibited decreased activity of the medial prefrontal cortex. Additionally, increased recruitment of the right middle temporal gyrus and posterior cingulate cortex was found, which was related to subclinical paranoid symptoms in relatives. These results further support decreased medial prefrontal activation during ToM as an intermediate phenotype of genetic risk for schizophrenia. Enhanced recruitment of posterior ToM areas in relatives might indicate inefficiency mechanisms in the presence of genetic risk. PMID:26341902
Neural Network to Solve Concave Games
Liu, Zixin; Wang, Nengfa
2014-01-01
The issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concave games are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Finally, two classic games’ simulation results are given to illustrate the theoretical results.
Advances in magnetic resonance 10
Waugh, John S
2013-01-01
Advances in Magnetic Resonance, Volume 10, presents a variety of contributions to the theory and practice of magnetic resonance. The book contains three chapters that examine superoperators in magnetic resonance; ultrasonically modulated paramagnetic resonance; and the utility of electron paramagnetic resonance (EPR) and electron-nuclear double-resonance (ENDOR) techniques for studying low-frequency modes of atomic fluctuations and their significance for understanding the mechanism of structural phase transitions in solids.
Optimal system size for complex dynamics in random neural networks near criticality
Energy Technology Data Exchange (ETDEWEB)
Wainrib, Gilles, E-mail: wainrib@math.univ-paris13.fr [Laboratoire Analyse Géométrie et Applications, Université Paris XIII, Villetaneuse (France); García del Molino, Luis Carlos, E-mail: garciadelmolino@ijm.univ-paris-diderot.fr [Institute Jacques Monod, Université Paris VII, Paris (France)
2013-12-15
In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.
Optimal system size for complex dynamics in random neural networks near criticality
International Nuclear Information System (INIS)
Wainrib, Gilles; García del Molino, Luis Carlos
2013-01-01
In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices
International Nuclear Information System (INIS)
Liu Dan-Dan; Zhang Hong
2011-01-01
We report theoretical studies on the plasmon resonances in linear Au atomic chains by using ab initio time-dependent density functional theory. The dipole responses are investigated each as a function of chain length. They converge into a single resonance in the longitudinal mode but split into two transverse modes. As the chain length increases, the longitudinal plasmon mode is redshifted in energy while the transverse modes shift in the opposite direction (blueshifts). In addition, the energy gap between the two transverse modes reduces with chain length increasing. We find that there are unique characteristics, different from those of other metallic chains. These characteristics are crucial to atomic-scale engineering of single-molecule sensing, optical spectroscopy, and so on. (condensed matter: electronic structure, electrical, magnetic, and optical properties)
O'Sullivan, James A; Shamma, Shihab A; Lalor, Edmund C
2015-05-06
The human brain has evolved to operate effectively in highly complex acoustic environments, segregating multiple sound sources into perceptually distinct auditory objects. A recent theory seeks to explain this ability by arguing that stream segregation occurs primarily due to the temporal coherence of the neural populations that encode the various features of an individual acoustic source. This theory has received support from both psychoacoustic and functional magnetic resonance imaging (fMRI) studies that use stimuli which model complex acoustic environments. Termed stochastic figure-ground (SFG) stimuli, they are composed of a "figure" and background that overlap in spectrotemporal space, such that the only way to segregate the figure is by computing the coherence of its frequency components over time. Here, we extend these psychoacoustic and fMRI findings by using the greater temporal resolution of electroencephalography to investigate the neural computation of temporal coherence. We present subjects with modified SFG stimuli wherein the temporal coherence of the figure is modulated stochastically over time, which allows us to use linear regression methods to extract a signature of the neural processing of this temporal coherence. We do this under both active and passive listening conditions. Our findings show an early effect of coherence during passive listening, lasting from ∼115 to 185 ms post-stimulus. When subjects are actively listening to the stimuli, these responses are larger and last longer, up to ∼265 ms. These findings provide evidence for early and preattentive neural computations of temporal coherence that are enhanced by active analysis of an auditory scene. Copyright © 2015 the authors 0270-6474/15/357256-08$15.00/0.
Experiments on shear Alfven resonance in a tokamak
International Nuclear Information System (INIS)
Prager, S.C.; Witherspoon, F.D.; Kieras, C.E.; Kortbawi, D.; Sprott, J.C.; Tataronis, J.A.
1983-02-01
Detailed observations have been made of the spatial structure of the wave magnetic field. Measurements of the resonance properties such as radial location, wave polarization, resonance width and risetime are all consistent with shear Alfven resonance theory, although several measurements require improvement in resolution. The resonance location agrees with prediction of a fully two-dimensional ideal MHD theory for the Tokapole II device. To complete the identification a frequency scan and careful comparison of the observed resonance with antenna loading will be undertaken
The effect of the neural activity on topological properties of growing neural networks.
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.
Modeling fMRI signals can provide insights into neural processing in the cerebral cortex.
Vanni, Simo; Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo
2015-08-01
Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. Copyright © 2015 the American Physiological Society.
Why we stay with our social partners: Neural mechanisms of stay/leave decision-making.
Heijne, Amber; Rossi, Filippo; Sanfey, Alan G
2017-09-03
How do we decide to keep interacting (e.g., stay) with a social partner or to switch (e.g., leave) to another? This paper investigated the neural mechanisms of stay/leave decision-making. We hypothesized that these decisions fit within a framework of value-based decision-making, and explored four potential mechanisms underlying a hypothesized bias to stay. Twenty-six participants underwent functional Magnetic Resonance Imaging (fMRI) while completing social and nonsocial versions of a stay/leave decision-making task. On each trial, participants chose between four alternative options, after which they received a monetary reward. Crucially, in the social condition, reward magnitude was ostensibly determined by the generosity of social partners, whereas in the nonsocial condition, reward amounts were ostensibly determined in a pre-programmed manner. Results demonstrated that participants were more likely to stay with options of relatively high expected value, with these values updated through Reinforcement Learning mechanisms and represented neurally within ventromedial prefrontal cortex. Moreover, we demonstrated that greater brain activity in ventromedial prefrontal cortex, caudate nucleus, and septo-hypothalamic regions for social versus nonsocial decisions to stay may underlie a bias towards staying with social partners in particular. These findings complement existing social psychological theories by investigating the neural mechanisms of actual stay/leave decisions.
Ortigue, S; Bianchi-Demicheli, F; Hamilton, A F de C; Grafton, S T
2007-07-01
Throughout the ages, love has been defined as a motivated and goal-directed mechanism with explicit and implicit mechanisms. Recent evidence demonstrated that the explicit representation of love recruits subcorticocortical pathways mediating reward, emotion, and motivation systems. However, the neural basis of the implicit (unconscious) representation of love remains unknown. To assess this question, we combined event-related functional magnetic resonance imaging (fMRI) with a behavioral subliminal priming paradigm embedded in a lexical decision task. In this task, the name of either a beloved partner, a neutral friend, or a passionate hobby was subliminally presented before a target stimulus (word, nonword, or blank), and participants were required to decide if the target was a word or not. Behavioral results showed that subliminal presentation of either a beloved's name (love prime) or a passion descriptor (passion prime) enhanced reaction times in a similar fashion. Subliminal presentation of a friend's name (friend prime) did not show any beneficial effects. Functional results showed that subliminal priming with a beloved's name (as opposed to either a friend's name or a passion descriptor) specifically recruited brain areas involved in abstract representations of others and the self, in addition to motivation circuits shared with other sources of passion. More precisely, love primes recruited the fusiform and angular gyri. Our findings suggest that love, as a subliminal prime, involves a specific neural network that surpasses a dopaminergic-motivation system.
Controlling Parametric Resonance
DEFF Research Database (Denmark)
Galeazzi, Roberto; Pettersen, Kristin Ytterstad
2012-01-01
the authors review the conditions for the onset of parametric resonance, and propose a nonlinear control strategy in order to both induce the resonant oscillations and to stabilize the unstable motion. Lagrange’s theory is used to derive the dynamics of the system and input–output feedback linearization...
Real-time flood forecasts & risk assessment using a possibility-theory based fuzzy neural network
Khan, U. T.
2016-12-01
Globally floods are one of the most devastating natural disasters and improved flood forecasting methods are essential for better flood protection in urban areas. Given the availability of high resolution real-time datasets for flood variables (e.g. streamflow and precipitation) in many urban areas, data-driven models have been effectively used to predict peak flow rates in river; however, the selection of input parameters for these types of models is often subjective. Additionally, the inherit uncertainty associated with data models along with errors in extreme event observations means that uncertainty quantification is essential. Addressing these concerns will enable improved flood forecasting methods and provide more accurate flood risk assessments. In this research, a new type of data-driven model, a quasi-real-time updating fuzzy neural network is developed to predict peak flow rates in urban riverine watersheds. A possibility-to-probability transformation is first used to convert observed data into fuzzy numbers. A possibility theory based training regime is them used to construct the fuzzy parameters and the outputs. A new entropy-based optimisation criterion is used to train the network. Two existing methods to select the optimum input parameters are modified to account for fuzzy number inputs, and compared. These methods are: Entropy-Wavelet-based Artificial Neural Network (EWANN) and Combined Neural Pathway Strength Analysis (CNPSA). Finally, an automated algorithm design to select the optimum structure of the neural network is implemented. The overall impact of each component of training this network is to replace the traditional ad hoc network configuration methods, with one based on objective criteria. Ten years of data from the Bow River in Calgary, Canada (including two major floods in 2005 and 2013) are used to calibrate and test the network. The EWANN method selected lagged peak flow as a candidate input, whereas the CNPSA method selected lagged
Representation of neutron noise data using neural networks
International Nuclear Information System (INIS)
Korsah, K.; Damiano, B.; Wood, R.T.
1992-01-01
This paper describes a neural network-based method of representing neutron noise spectra using a model developed at the Oak Ridge National Laboratory (ORNL). The backpropagation neural network learned to represent neutron noise data in terms of four descriptors, and the network response matched calculated values to within 3.5 percent. These preliminary results are encouraging, and further research is directed towards the application of neural networks in a diagnostics system for the identification of the causes of changes in structural spectral resonances. This work is part of our current investigation of advanced technologies such as expert systems and neural networks for neutron noise data reduction, analysis, and interpretation. The objective is to improve the state-of-the-art of noise analysis as a diagnostic tool for nuclear power plants and other mechanical systems
International Nuclear Information System (INIS)
Geng, L. S.; Camalich, J. Martin; Vacas, M. J. Vicente
2009-01-01
We present a calculation of the leading SU(3)-breaking O(p 3 ) corrections to the electromagnetic moments and charge radius of the lowest-lying decuplet resonances in covariant chiral perturbation theory. In particular, the magnetic dipole moment of the members of the decuplet is predicted fixing the only low-energy constant (LEC) present up to this order with the well-measured magnetic dipole moment of the Ω - . We predict μ Δ ++ =6.04(13) and μ Δ + =2.84(2), which agree well with the current experimental information. For the electric quadrupole moment and the charge radius, we use state-of-the-art lattice QCD results to determine the corresponding LECs, whereas for the magnetic octupole moment there is no unknown LEC up to the order considered here, and we obtain a pure prediction. We compare our results with those reported in large N c , lattice QCD, heavy-baryon chiral perturbation theory, and other models.
International Nuclear Information System (INIS)
Son, Minho; Urbano, Alfredo
2016-01-01
We interpret the recently observed excess in the diphoton invariant mass as a new spin-0 resonant particle. On theoretical grounds, an interesting question is whether this new scalar resonance belongs to a strongly coupled sector or a well-defined weakly coupled theory. A possible UV-completion that has been widely considered in literature is based on the existence of new vector-like fermions whose loop contributions — Yukawa-coupled to the new resonance — explain the observed signal rate. The large total width preliminarily suggested by data seems to favor a large Yukawa coupling, at the border of a healthy perturbative definition. This potential problem can be fixed by introducing multiple vector-like fermions or large electric charges, bringing back the theory to a weakly coupled regime. However, this solution risks to be only a low-energy mirage: large multiplicity or electric charge can dangerously reintroduce the strong regime by modifying the renormalization group running of the dimensionless couplings. This issue is also tightly related to the (in)stability of the scalar potential. First, we study — in the theoretical setup described above — the parametric behavior of the diphoton signal rate, total width, and one-loop β functions. Then, we numerically solve the renormalization group equations, taking into account the observed diphoton signal rate and total width, to investigate the fate of the weakly coupled theory. We find that — with the only exception of few fine-tuned directions — weakly coupled interpretations of the excess are brought back to a strongly coupled regime if the running is taken into account.
Electron paramagnetic resonance
Al'tshuler, S A
2013-01-01
Electron Paramagnetic Resonance is a comprehensive text on the field of electron paramagnetic resonance, covering both the theoretical background and the results of experiment. This book is composed of eight chapters that cover theoretical materials and experimental data on ionic crystals, since these are the materials that have been most extensively studied by the methods of paramagnetic resonance. The opening chapters provide an introduction to the basic principles of electron paramagnetic resonance and the methods of its measurement. The next chapters are devoted to the theory of spectra an
Theory of triplet-triplet annihilation in optically detected magnetic resonance
Keevers, T. L.; McCamey, D. R.
2016-01-01
Triplet-triplet annihilation allows two low-energy photons to be upconverted into a single high-energy photon. By essentially engineering the solar spectrum, this allows solar cells to be made more efficient and even exceed the Shockley-Quiesser limit. Unfortunately, optimizing the reaction pathway is difficult, especially with limited access to the microscopic time scales and states involved in the process. Optical measurements can provide detailed information: triplet-triplet annihilation is intrinsically spin dependent and exhibits substantial magnetoluminescence in the presence of a static magnetic field. Pulsed optically detected magnetic resonance is especially suitable, since it combines high spin sensitivity with coherent manipulation. In this paper, we develop a time-domain theory of triplet-triplet annihilation for complexes with arbitrary spin-spin coupling. We identify unique "Rabi fingerprints" for each coupling regime and show that this can be used to characterize the microscopic Hamiltonian.
Special resonances in two- and three-cluster systems
International Nuclear Information System (INIS)
Orlowski, M.
1979-01-01
In the framework of Schmid's N-cluster theory the resonance theory of Wildermuth-Benoehr is extended to three clusters. This three-cluster resonance model is solved in a mathematically exact formalism. The main topic of this formalism is the asymptotic behaviour of the full three-body-resolvent in the differential directions of the six-dimensional position space of the Jacobi coordinates. The scattering amplitudes and cross sections in all two-body channels and breakup are explicitly presented. Furthermore a very illustrative kinematical three-body model, the so called 'three-body-neb', is developed. Special regards in this connection are devoted to the analysis of possible interference possibilities of the main three-body-resonance with other resonance types of the three-body model. In a further section the Pauli-resonances are studied i) in the Wildermuth resonating group theory, ii) in Schmid's simulation models. It is shown under which circumstances Pauli-resonances may be positive energy bound states. (orig./HSI) [de
Chu, Larry F; Lin, Joanne C; Clemenson, Anna; Encisco, Ellen; Sun, John; Hoang, Dan; Alva, Heather; Erlendson, Matthew; Clark, J David; Younger, Jarred W
2015-08-01
Opioid analgesics are frequently prescribed for chronic pain. One expected consequence of long-term opioid use is the development of physical dependence. Although previous resting state functional magnetic resonance imaging (fMRI) studies have demonstrated signal changes in reward-associated areas following morphine administration, the effects of acute withdrawal on the human brain have been less well-investigated. In an earlier study by our laboratory, ondansetron was shown to be effective in preventing symptoms associated with opioid withdrawal. The purpose of this current study was to characterize neural activity associated with acute opioid withdrawal and examine whether these changes are modified by ondansetron. Ten participants were enrolled in this placebo-controlled, randomized, double-blind, crossover study and attended three acute opioid withdrawal sessions. Participants received either placebo or ondansetron (8Ymg IV) before morphine administration (10Ymg/70Ykg IV). Participants then underwent acute naloxone-precipitated withdrawal during a resting state fMRI scan. Objective and subjective opioid withdrawal symptoms were assessed. Imaging results showed that naloxone-precipitated opioid withdrawal was associated with increased neural activity in several reward processing regions, including the right pregenual cingulate, putamen, and bilateral caudate, and decreased neural activity in networks involved in sensorimotor integration. Ondansetron pretreatment did not have a significant effect on the imaging correlates of opioid withdrawal. This study presents a preliminary investigation of the regional changes in neural activity during acute opioid withdrawal. The fMRI acute opioid withdrawal model may serve as a tool for studying opioid dependence and withdrawal in human participants. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Slipicevic, K [Institute of Nuclear Sciences Boris Kidric, Vinca, Beograd (Yugoslavia)
1968-07-01
Following a review of the existing theories od resonance absorption this thesis includes a new approach for calculating the effective resonance integral of absorbed neutrons, new approximate formula for the penetration factor, an analysis of the effective resonance integral and the correction of the resonance integral taking into account the interference of potential and resonance dissipation. A separate chapter is devoted to calculation of the effective resonance integral for the regular reactor lattice with cylindrical fuel elements.
Parallel consensual neural networks.
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.
Neural control and transient analysis of the LCL-type resonant converter
Zouggar, S.; Nait Charif, H.; Azizi, M.
2000-07-01
This paper proposes a generalised inverse learning structure to control the LCL converter. A feedforward neural network is trained to act as an inverse model of the LCL converter then both are cascaded such that the composed system results in an identity mapping between desired response and the LCL output voltage. Using the large signal model, we analyse the transient output response of the controlled LCL converter in the case of large variation of the load. The simulation results show the efficiency of using neural networks to regulate the LCL converter.
Neural theory for the perception of causal actions.
Fleischer, Falk; Christensen, Andrea; Caggiano, Vittorio; Thier, Peter; Giese, Martin A
2012-07-01
The efficient prediction of the behavior of others requires the recognition of their actions and an understanding of their action goals. In humans, this process is fast and extremely robust, as demonstrated by classical experiments showing that human observers reliably judge causal relationships and attribute interactive social behavior to strongly simplified stimuli consisting of simple moving geometrical shapes. While psychophysical experiments have identified critical visual features that determine the perception of causality and agency from such stimuli, the underlying detailed neural mechanisms remain largely unclear, and it is an open question why humans developed this advanced visual capability at all. We created pairs of naturalistic and abstract stimuli of hand actions that were exactly matched in terms of their motion parameters. We show that varying critical stimulus parameters for both stimulus types leads to very similar modulations of the perception of causality. However, the additional form information about the hand shape and its relationship with the object supports more fine-grained distinctions for the naturalistic stimuli. Moreover, we show that a physiologically plausible model for the recognition of goal-directed hand actions reproduces the observed dependencies of causality perception on critical stimulus parameters. These results support the hypothesis that selectivity for abstract action stimuli might emerge from the same neural mechanisms that underlie the visual processing of natural goal-directed action stimuli. Furthermore, the model proposes specific detailed neural circuits underlying this visual function, which can be evaluated in future experiments.
The Geometric Theory of Roof Reflector Resonators
1976-12-01
reflector, if properly oriented, (The terms "roof-top prism ," "right-angle prism ," and - incorrectly - " Porro prism " are encountered in .the literature...Q-switch prisms ) in laser resonators have been infrequent compared to the attention given spherical mirrors. This chapter summarizes the relevant...designator (Refs 42 and 43). In one experiment, a 900 roof prism was tested in a resonator with a 70% reflecting filat mirror. Thus, in Fig. 2, the right roof
Theory of electrically controlled resonant tunneling spin devices
Ting, David Z. -Y.; Cartoixa, Xavier
2004-01-01
We report device concepts that exploit spin-orbit coupling for creating spin polarized current sources using nonmagnetic semiconductor resonant tunneling heterostructures, without external magnetic fields. The resonant interband tunneling psin filter exploits large valence band spin-orbit interaction to provide strong spin selectivity.
Explicit versus implicit neural processing of musical emotions
Bogert, Brigitte; Numminen-Kontti, Taru; Gold, Benjamin; Sams, Mikko; Numminen, Jussi; Burunat, Iballa; Lampinen, Jouko; Brattico, Elvira
2016-01-01
Music is often used to regulate emotions and mood. Typically, music conveys and induces emotions even when one does not attend to them. Studies on the neural substrates of musical emotions have, however, only examined brain activity when subjects have focused on the emotional content of the music. Here we address with functional magnetic resonance imaging (fMRI) the neural processing of happy, sad, and fearful music with a paradigm in which 56 subjects were instructed to either classify the e...
The theory of coherent resonance tunneling of interacting electrons
International Nuclear Information System (INIS)
Elesin, V. F.
2001-01-01
Analytical solutions of the Schrödinger equation for a two-barrier structure (resonance-tunnel diode) with open boundary conditions are found within the model of coherent tunneling of interacting electrons. Simple expressions for resonance current are derived which enable one to analyze the current-voltage characteristics, the conditions of emergence of hysteresis, and singularities of the latter depending on the parameters of resonance-tunnel diode. It is demonstrated that the hysteresis is realized if the current exceeds some critical value proportional to the square of resonance level width.
International Nuclear Information System (INIS)
Koike, Hiroki; Kirimura, Kazuki; Yamaji, Kazuya; Kosaka, Shinya; Yamamoto, Akio
2018-01-01
A unified resonance self-shielding method, which can treat general sub-divided fuel regions, is developed for lattice physics calculations in reactor physics field. In a past study, a hybrid resonance treatment has been developed by theoretically integrating equivalence theory and ultra-fine-group slowing-down calculation. It can be applied to a wide range of neutron spectrum conditions including low moderator density ranges in severe accident states, as long as each fuel region is not sub-divided. In order to extend the method for radially and azimuthally sub-divided multi-region geometry, a new resonance treatment is established by incorporating the essence of sub-group method. The present method is composed of two-step flux calculation, i.e. 'coarse geometry + fine energy' (first step) and 'fine geometry + coarse energy' (second step) calculations. The first step corresponds to a hybrid model of the equivalence theory and the ultra-fine-group calculation, and the second step corresponds to the sub-group method. From the verification results, effective cross-sections by the new method show good agreement with the continuous energy Monte-Carlo results for various multi-region geometries including non-uniform fuel compositions and temperature distributions. The present method can accurately generate effective cross-sections with short computation time in general lattice physics calculations. (author)
Feedforward Nonlinear Control Using Neural Gas Network
Directory of Open Access Journals (Sweden)
Iván Machón-González
2017-01-01
Full Text Available Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the plant constitutes a piece-wise linear approximation of the nonlinear system and each neuron represents a local linear model for which a linear controller is designed. The neural gas model works as an observer and a controller at the same time. A state feedback control is implemented by estimation of the state variables based on the local transfer function that was provided by the local linear model. The gradient vectors obtained by the supervised neural gas algorithm provide a robust procedure for feedforward nonlinear control, that is, supposing the inexistence of disturbances.
Grover, D.; Seth, R. K.
2018-05-01
Analysis and numerical results are presented for the thermoelastic dissipation of a homogeneous isotropic, thermally conducting, Kelvin-Voigt type circular micro-plate based on Kirchhoff's Love plate theory utilizing generalized viscothermoelasticity theory of dual-phase-lagging model. The analytical expressions for thermoelastic damping of vibration and frequency shift are obtained for generalized dual-phase-lagging model and coupled viscothermoelastic plates. The scaled thermoelastic damping has been illustrated in case of circular plate and axisymmetric circular plate for fixed aspect ratio for clamped and simply supported boundary conditions. It is observed that the damping of vibrations significantly depend on time delay and mechanical relaxation times in addition to thermo-mechanical coupling in circular plate under resonance conditions and plate dimensions.
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.
Theoretical foundations of electron spin resonance
Harriman, John E
2013-01-01
Theoretical Foundations of Electron Spin Resonance deals with the theoretical approach to electron paramagnetic resonance. The book discusses electron spin resonance in applications related to polyatomic, probably organic, free radicals in condensed phases. The book also focuses on essentially static phenomena, that is, the description and determination of stationary-state energy levels. The author reviews the Dirac theory of the electron in which a four-component wave function is responsible for the behavior of the electron. The author then connects this theory with the nonrelativistic wave f
International Nuclear Information System (INIS)
Payne, M.G.; Deng, L.; Garrett, W.R.
1998-01-01
We present a theory for two- and three-photon excitation, optical shifting, and four-wave mixing when a first laser is tuned onto, or near, a two-photon resonance and a second much more intense laser is tuned near or on resonance between the two-photon resonance and a second excited state. When the second excited state has a dipole-allowed transition back to the ground state and the concentration is sufficiently high, a destructive interference is produced between three-photon coupling of the ground state and the second excited state and one-photon coupling between the same states by the internally generated four-wave mixing field. This interference leads to several striking effects. For instance, as the onset of the interference occurs, the optical shifts in the two-photon resonance excitation line shape become smaller in copropagating geometry so that the line shapes for multiphoton ionization enhanced by the two-photon resonance eventually become unaffected by the second laser. In the same range of concentrations the four-wave mixing field evolves to a concentration-independent intensity. With counterpropagating laser beams the line shape exhibits normal optical shifts like those observed for both copropagating and counterpropagating laser beams at very low concentrations. The theoretical work presented here extends our earlier works by including the effect of laser bandwidth and by removing the restriction of having the second laser be tuned far from three-photon resonance. In this way we have now included, as a special case, the effect of both laser bandwidth and interference on laser-induced transparency. Unlike other effects related to odd-photon destructive interference, the effect of a broad bandwidth is to bring about the predicted effects at much lower concentrations. Studies in rubidium show good agreement between theory and experiment for both ionization line shapes and four-wave mixing intensity as a function of concentration. copyright 1998 The
Dissociated neural processing for decisions in managers and non-managers.
Directory of Open Access Journals (Sweden)
Svenja Caspers
Full Text Available Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing.
Lattices of dielectric resonators
Trubin, Alexander
2016-01-01
This book provides the analytical theory of complex systems composed of a large number of high-Q dielectric resonators. Spherical and cylindrical dielectric resonators with inferior and also whispering gallery oscillations allocated in various lattices are considered. A new approach to S-matrix parameter calculations based on perturbation theory of Maxwell equations, developed for a number of high-Q dielectric bodies, is introduced. All physical relationships are obtained in analytical form and are suitable for further computations. Essential attention is given to a new unified formalism of the description of scattering processes. The general scattering task for coupled eigen oscillations of the whole system of dielectric resonators is described. The equations for the expansion coefficients are explained in an applicable way. The temporal Green functions for the dielectric resonator are presented. The scattering process of short pulses in dielectric filter structures, dielectric antennas and lattices of d...
Field-theoretic approach to fluctuation effects in neural networks
International Nuclear Information System (INIS)
Buice, Michael A.; Cowan, Jack D.
2007-01-01
A well-defined stochastic theory for neural activity, which permits the calculation of arbitrary statistical moments and equations governing them, is a potentially valuable tool for theoretical neuroscience. We produce such a theory by analyzing the dynamics of neural activity using field theoretic methods for nonequilibrium statistical processes. Assuming that neural network activity is Markovian, we construct the effective spike model, which describes both neural fluctuations and response. This analysis leads to a systematic expansion of corrections to mean field theory, which for the effective spike model is a simple version of the Wilson-Cowan equation. We argue that neural activity governed by this model exhibits a dynamical phase transition which is in the universality class of directed percolation. More general models (which may incorporate refractoriness) can exhibit other universality classes, such as dynamic isotropic percolation. Because of the extremely high connectivity in typical networks, it is expected that higher-order terms in the systematic expansion are small for experimentally accessible measurements, and thus, consistent with measurements in neocortical slice preparations, we expect mean field exponents for the transition. We provide a quantitative criterion for the relative magnitude of each term in the systematic expansion, analogous to the Ginsburg criterion. Experimental identification of dynamic universality classes in vivo is an outstanding and important question for neuroscience
Neural networks with discontinuous/impact activations
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...
Complex-valued neural networks advances and applications
Hirose, Akira
2013-01-01
Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and
Backward wave oscillators with rippled wall resonators: Analytic theory and numerical simulation
International Nuclear Information System (INIS)
Swegle, J.A.; Poukey, J.W.
1985-01-01
The 3-D analytic theory is based on the approximation that the device is infinitely long. In the absence of an electron beam, the theory is exact and allows us to compute the dispersion characteristics of the cold structure. With the inclusion of a thin electron beam, we can compute the growth rates resulting from the interaction between a waveguide mode of the structure and the slower space charge wave on the beam. In the limit of low beam currents, the full dispersion relation based on an electromagnetic analysis can be placed in correspondence with the circuit theory of Pierce. Numerical simulations permit us to explore the saturated, large amplitude operating regime for TM axisymmetric modes. The scaling of operating frequency, peak power, and operating efficiency with beam and resonator parameters is examined. The analytic theory indicates that growth rates are largest for the TM 01 modes and decrease with both the radial and azimuthal mode numbers. Another interesting trend is that for a fixed cathode voltage and slow wave structure, growth rates peak for a beam current below the space charge limiting value and decrease for both larger and smaller currents. The simulations show waves that grow from noise without any input signal, so that the system functions as an oscillator. The TM 01 mode predominates in all simulations. While a minimum device length is required for the start of oscillations, it appears that if the slow wave structure is too long, output power is decreased by a transfer of wave energy back to the electrons. Comparisons have been made between the analytical and numerical results, as well as with experimental data obtained at Sandia National Laboratories
International Nuclear Information System (INIS)
Kaslik, E.; Balint, St.
2009-01-01
In this paper, a bifurcation analysis is undertaken for a discrete-time Hopfield neural network of two neurons with two different delays and self-connections. Conditions ensuring the asymptotic stability of the null solution are found, with respect to two characteristic parameters of the system. It is shown that for certain values of these parameters, Fold or Neimark-Sacker bifurcations occur, but Flip and codimension 2 (Fold-Neimark-Sacker, double Neimark-Sacker, resonance 1:1 and Flip-Neimark-Sacker) bifurcations may also be present. The direction and the stability of the Neimark-Sacker bifurcations are investigated by applying the center manifold theorem and the normal form theory
Pattern activation/recognition theory of mind.
du Castel, Bertrand
2015-01-01
In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call "Pattern Activation/Recognition Theory of Mind." While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.
The calculation of Feshbach resonances using coupled propagator equations
International Nuclear Information System (INIS)
Zhan, Hongbin; Zhang, Yinchun; Winkler, P.
1994-01-01
A coupled channel theory of resonances has been formulated within the propagator approach of man-body theory and applied to the 1s3s 2 resonance of e-helium scattering. This system has previously been studied both experimentally and theoretically. These results for the width of the resonance agree well with these earlier findings
Confinement-induced resonances in anharmonic waveguides
Energy Technology Data Exchange (ETDEWEB)
Peng Shiguo [Department of Physics, Tsinghua University, Beijing 100084 (China); Centre for Atom Optics and Ultrafast Spectroscopy, Swinburne University of Technology, Melbourne 3122 (Australia); Hu Hui; Liu Xiaji; Drummond, Peter D. [Centre for Atom Optics and Ultrafast Spectroscopy, Swinburne University of Technology, Melbourne 3122 (Australia)
2011-10-15
We develop the theory of anharmonic confinement-induced resonances (ACIRs). These are caused by anharmonic excitation of the transverse motion of the center of mass (c.m.) of two bound atoms in a waveguide. As the transverse confinement becomes anisotropic, we find that the c.m. resonant solutions split for a quasi-one-dimensional (1D) system, in agreement with recent experiments. This is not found in harmonic confinement theories. A new resonance appears for repulsive couplings (a{sub 3D}>0) for a quasi-two-dimensional (2D) system, which is also not seen with harmonic confinement. After inclusion of anharmonic energy corrections within perturbation theory, we find that these ACIRs agree extremely well with anomalous 1D and 2D confinement-induced resonance positions observed in recent experiments. Multiple even- and odd-order transverse ACIRs are identified in experimental data, including up to N=4 transverse c.m. quantum numbers.
Self-other resonance, its control and prosocial inclinations: Brain-behavior relationships.
Christov-Moore, Leonardo; Iacoboni, Marco
2016-04-01
Humans seem to place a positive reward value on prosocial behavior. Evidence suggests that this prosocial inclination is driven by our reflexive tendency to share in the observed sensations, emotions and behavior of others, or "self-other resonance". In this study, we examine how neural correlates of self-other resonance relate to prosocial decision-making. Subjects performed two tasks while undergoing fMRI: observation of a human hand pierced by a needle, and observation and imitation of emotional facial expressions. Outside the scanner, subjects played the Dictator Game with players of low or high income (represented by neutral-expression headshots). Subjects' offers in the Dictator Game were correlated with activity in neural systems associated with self-other resonance and anticorrelated with activity in systems implicated in the control of pain, affect, and imitation. Functional connectivity between areas involved in self-other resonance and top-down control was negatively correlated with subjects' offers. This study suggests that the interaction between self-other resonance and top-down control processes are an important component of prosocial inclinations towards others, even when biological stimuli associated with self-other resonance are limited. These findings support a view of prosocial decision-making grounded in embodied cognition. © 2016 Wiley Periodicals, Inc.
Towards a unifying neural theory of social cognition
Keysers, Christian; Gazzola, Valeria; Anders, S; Ende, G; Junghoffer, M; Kissler, J; Wildgruber, D
2006-01-01
Humans can effortlessly understand a lot of what is going on in other peoples' minds. Understanding the neural basis of this capacity has proven quite difficult. Since the discovery of mirror neurons, a number of successful experiments have approached the question of how we understand the actions of
Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory
Xue, Gui; Love, Bradley C.; Preston, Alison R.; Poldrack, Russell A.
2014-01-01
Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. PMID:24872552
Consecutive Acupuncture Stimulations Lead to Significantly Decreased Neural Responses
Yeo, S.; Choe, I.H.; Noort, M.W.M.L. van den; Bosch, M.P.C.; Lim, S.
2010-01-01
Objective: Functional magnetic resonance imaging (fMRI), in combination with block design paradigms with consecutive acupuncture stimulations, has often been used to investigate the neural responses to acupuncture. In this study, we investigated whether previous acupuncture stimulations can affect
Using brain stimulation to disentangle neural correlates of conscious vision.
de Graaf, Tom A; Sack, Alexander T
2014-01-01
Research into the neural correlates of consciousness (NCCs) has blossomed, due to the advent of new and increasingly sophisticated brain research tools. Neuroimaging has uncovered a variety of brain processes that relate to conscious perception, obtained in a range of experimental paradigms. But methods such as functional magnetic resonance imaging or electroencephalography do not always afford inference on the functional role these brain processes play in conscious vision. Such empirical NCCs could reflect neural prerequisites, neural consequences, or neural substrates of a conscious experience. Here, we take a closer look at the use of non-invasive brain stimulation (NIBS) techniques in this context. We discuss and review how NIBS methodology can enlighten our understanding of brain mechanisms underlying conscious vision by disentangling the empirical NCCs.
Onuki, Y.; Hibiya, T.
2016-02-01
The baroclinic tides are thought to be the dominant energy source for turbulent mixing in the ocean interior. In contrast to the geography of the energy conversion rates from the barotropic to baroclinic tides, which has been clarified in recent numerical studies, the global distribution of the energy sink for the resulting low-mode baroclinic tides remains obscure. A key to resolve this issue is the resonant wave-wave interactions, which transfer part of the baroclinic tidal energy to the background internal wave field enhancing the local energy dissipation rates. Recent field observations and numerical studies have pointed out that parametric subharmonic instability (PSI), one of the resonant interactions, causes significant energy sink of baroclinic tidal energy at mid-latitudes. The purpose of this study is to analyze the quantitative aspect of PSI to demonstrate the global distribution of the intensity of resonant wave interactions, namely, the attenuation rate of low-mode baroclinic tidal energy. Our approach is basically following the weak turbulence theory, which is the standard theory for resonant wave-wave interactions, where techniques of singular perturbation and statistical physics are employed. This study is, however, different from the classical theory in some points; we have reformulated the weak turbulence theory to be applicable to low-mode internal waves and also developed its numerical calculation method so that the effects of stratification profile and oceanic total depth can be taken into account. We have calculated the attenuation rate of low-mode baroclinic tidal waves interacting with the background Garrett-Munk internal wave field. The calculated results clearly show the rapid attenuation of baroclinic tidal energy at mid-latitudes, in agreement with the results from field observations and also show the zonal inhomogeneity of the attenuation rate caused by the density structures associated with the subtropical gyre. This study is expected
Neural Computation and the Computational Theory of Cognition
Piccinini, Gualtiero; Bahar, Sonya
2013-01-01
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…
Bi-Frequency Modulated Quasi-Resonant Converters: Theory and Applications
Zhang, Yuefeng
1995-01-01
To avoid the variable frequency operation of quasi -resonant converters, many soft-switching PWM converters have been proposed, all of them require an auxiliary switch, which will increase the cost and complexity of the power supply system. In this thesis, a new kind of technique for quasi -resonant converters has been proposed, which is called the bi-frequency modulation technique. By operating the quasi-resonant converters at two switching frequencies, this technique enables quasi-resonant converters to achieve the soft-switching, at fixed switching frequencies, without an auxiliary switch. The steady-state analysis of four commonly used quasi-resonant converters, namely, ZVS buck, ZCS buck, ZVS boost, and ZCS boost converter has been presented. Using the concepts of equivalent sources, equivalent sinks, and resonant tank, the large signal models of these four quasi -resonant converters were developed. Based on these models, the steady-state control characteristics of BFM ZVS buck, BFM ZCS buck, BFM ZVS boost, and BFM ZCS boost converter have been derived. The functional block and design consideration of the bi-frequency controller were presented, and one of the implementations of the bi-frequency controller was given. A complete design example has been presented. Both computer simulations and experimental results have verified that the bi-frequency modulated quasi-resonant converters can achieve soft-switching, at fixed switching frequencies, without an auxiliary switch. One of the application of bi-frequency modulation technique is for EMI reduction. The basic principle of using BFM technique for EMI reduction was introduced. Based on the spectral analysis, the EMI performances of the PWM, variable-frequency, and bi-frequency modulated control signals was evaluated, and the BFM control signals show the lowest EMI emission. The bi-frequency modulated technique has also been applied to the power factor correction. A BFM zero -current switching boost converter has
Advances in magnetic and optical resonance
Warren, Warren S
1997-01-01
Since 1965, Advances in Magnetic and Optical Resonance has provided researchers with timely expositions of fundamental new developments in the theory of, experimentation with, and application of magnetic and optical resonance.
Stochastic resonance in small-world neuronal networks with hybrid electrical–chemical synapses
International Nuclear Information System (INIS)
Wang, Jiang; Guo, Xinmeng; Yu, Haitao; Liu, Chen; Deng, Bin; Wei, Xile; Chen, Yingyuan
2014-01-01
Highlights: •We study stochastic resonance in small-world neural networks with hybrid synapses. •The resonance effect depends largely on the probability of chemical synapse. •An optimal chemical synapse probability exists to evoke network resonance. •Network topology affects the stochastic resonance in hybrid neuronal networks. - Abstract: The dependence of stochastic resonance in small-world neuronal networks with hybrid electrical–chemical synapses on the probability of chemical synapse and the rewiring probability is investigated. A subthreshold periodic signal is imposed on one single neuron within the neuronal network as a pacemaker. It is shown that, irrespective of the probability of chemical synapse, there exists a moderate intensity of external noise optimizing the response of neuronal networks to the pacemaker. Moreover, the effect of pacemaker driven stochastic resonance of the system depends largely on the probability of chemical synapse. A high probability of chemical synapse will need lower noise intensity to evoke the phenomenon of stochastic resonance in the networked neuronal systems. In addition, for fixed noise intensity, there is an optimal chemical synapse probability, which can promote the propagation of the localized subthreshold pacemaker across neural networks. And the optimal chemical synapses probability turns even larger as the coupling strength decreases. Furthermore, the small-world topology has a significant impact on the stochastic resonance in hybrid neuronal networks. It is found that increasing the rewiring probability can always enhance the stochastic resonance until it approaches the random network limit
DEFF Research Database (Denmark)
Vuust, Peter; Gebauer, Line K; Witek, Maria A G
2014-01-01
. According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Fourth, empirical studies of neural and behavioral effects of syncopation, polyrhythm and groove will be reported, and we...
Learning in neural networks based on a generalized fluctuation theorem
Hayakawa, Takashi; Aoyagi, Toshio
2015-11-01
Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.
SPR imaging combined with cyclic voltammetry for the detection of neural activity
Directory of Open Access Journals (Sweden)
Hui Li
2014-03-01
Full Text Available Surface plasmon resonance (SPR detects changes in refractive index at a metal-dielectric interface. In this study, SPR imaging (SPRi combined with cyclic voltammetry (CV was applied to detect neural activity in isolated bullfrog sciatic nerves. The neural activities induced by chemical and electrical stimulation led to an SPR response, and the activities were recorded in real time. The activities of different parts of the sciatic nerve were recorded and compared. The results demonstrated that SPR imaging combined with CV is a powerful tool for the investigation of neural activity.
Neural correlates of taste perception in congenital olfactory impairment
DEFF Research Database (Denmark)
Gagnon, Léa; Vestergaard, Martin; Madsen, Kristoffer
2014-01-01
taste identification accuracy and its neural correlates using functional magnetic resonance imaging (fMRI) in 12 congenitally olfactory impaired individuals and 8 normosmic controls. Results showed that taste identification was worse in congenitally olfactory impaired compared to control subjects. The fMRI...
Artificial Neural Networks For Hadron Hadron Cross-sections
International Nuclear Information System (INIS)
ELMashad, M.; ELBakry, M.Y.; Tantawy, M.; Habashy, D.M.
2011-01-01
In recent years artificial neural networks (ANN ) have emerged as a mature and viable framework with many applications in various areas. Artificial neural networks theory is sometimes used to refer to a branch of computational science that uses neural networks as models to either simulate or analyze complex phenomena and/or study the principles of operation of neural networks analytically. In this work a model of hadron- hadron collision using the ANN technique is present, the hadron- hadron based ANN model calculates the cross sections of hadron- hadron collision. The results amply demonstrate the feasibility of such new technique in extracting the collision features and prove its effectiveness
Kuisma, Mikael; Sakko, Arto; Rossi, Tuomas P.; Larsen, Ask H.; Enkovaara, Jussi; Lehtovaara, Lauri; Rantala, Tapio T.
2015-01-01
We observe using ab initio methods that localized surface plasmon resonances in icosahedral silver nanoparticles enter the asymptotic region already between diameters of 1 and 2 nm, converging close to the classical quasistatic limit around 3.4 eV. We base the observation on time-dependent density-functional theory simulations of the icosahedral silver clusters Ag$_{55}$ (1.06 nm), Ag$_{147}$ (1.60 nm), Ag$_{309}$ (2.14 nm), and Ag$_{561}$ (2.68 nm). The simulation method combines the adiabat...
Kaiplavil, Sreekumar; Rivens, Ian; ter Haar, Gail
2013-07-01
Ultrasound imparted air-recoil resonance (UIAR), a new method for acoustic power estimation, is introduced with emphasis on therapeutic high-intensity focused ultrasound (HIFU) monitoring applications. Advantages of this approach over existing practices include fast response; electrical and magnetic inertness, and hence MRI compatibility; portability; high damage threshold and immunity to vibration and interference; low cost; etc. The angle of incidence should be fixed for accurate measurement. However, the transducer-detector pair can be aligned in any direction with respect to the force of gravity. In this sense, the operation of the device is orientation independent. The acoustic response of a pneumatically coupled pair of Helmholtz resonators, with one of them acting as the sensor head, is used for the estimation of acoustic power. The principle is valid in the case of pulsed/ burst as well as continuous ultrasound exposure, the former being more sensitive and accurate. An electro-acoustic theory has been developed for describing the dynamics of pressure flow and resonance in the system considering various thermo- viscous loss mechanisms. Experimental observations are found to be in agreement with theoretical results. Assuming the window damage threshold (~10 J·mm(-2)) and accuracy of RF power estimation are the upper and lower scale-limiting factors, the performance of the device was examined for an RF power range of 5 mW to 100 W with a HIFU transducer operating at 1.70 MHz, and an average nonlinearity of ~1.5% was observed. The device is also sensitive to sub-milliwatt powers. The frequency response was analyzed at 0.85, 1.70, 2.55, and 3.40 MHz and the results are presented with respective theoretical estimates. Typical response time is in the millisecond regime. Output drift is about 3% for resonant and 5% for nonresonant modes. The principle has been optimized to demonstrate a general-purpose acoustic power meter.
Energy Technology Data Exchange (ETDEWEB)
Long Jiang Zhang; Guifen Yang; Jianzhong Yin; Yawu Liu; Ji Qi [Dept. of Radiology, Tianjin First Central Hospital of Tianjin Medical Univ, Tianjin (China)
2007-07-15
Background: Many studies have claimed the existence of attention alterations in cirrhotic patients without overt hepatic encephalopathy (HE). No functional magnetic resonance imaging (fMRI) study in this respect has been published. Purpose: To investigate the neural basis of cognitive control deficiency in cirrhotic patients using fMRI. Material and Methods: 14 patients with hepatic cirrhosis and 14 healthy volunteers were included in the study. A modified Stroop task with Chinese characters was used as the target stimulus, and block-design fMRI was used to acquire resource data, including four stimulus blocks and five control blocks, each presented alternatively. Image analysis was performed using statistical parametric mapping 99. After fMRI examinations were complete, behavior tests of Stroop interference were performed for all subjects. Overall reaction time and error numbers were recorded. Results: Both healthy volunteers and patients with hepatic cirrhosis had Stroop interference effects. Patients with hepatic cirrhosis had more errors and longer reaction time in performing an incongruous color-naming task than healthy volunteers (P<0.001); there was no significant difference in performing an incongruous word-reading task (P 0.066). Compared with controls, patients with hepatic cirrhosis had greater activation of the bilateral prefrontal cortex and parietal cortex when performing the incongruous word-reading task. With increased conflict, activation of the anterior cingulate cortex (ACC), bilateral prefrontal cortex (PFC), parietal lobe, and temporal fusiform gyrus (TFG) was decreased when patients with hepatic cirrhosis performed the incongruous color-naming task. Conclusion: This study demonstrates that patients with hepatic cirrhostic have cognitive control deficiency. The abnormal brain network of the ACC-PFC-parietal lobe-TFG is the neural basis of cognitive control impairment in cirrhotic patients.
International Nuclear Information System (INIS)
Long Jiang Zhang; Guifen Yang; Jianzhong Yin; Yawu Liu; Ji Qi
2007-01-01
Background: Many studies have claimed the existence of attention alterations in cirrhotic patients without overt hepatic encephalopathy (HE). No functional magnetic resonance imaging (fMRI) study in this respect has been published. Purpose: To investigate the neural basis of cognitive control deficiency in cirrhotic patients using fMRI. Material and Methods: 14 patients with hepatic cirrhosis and 14 healthy volunteers were included in the study. A modified Stroop task with Chinese characters was used as the target stimulus, and block-design fMRI was used to acquire resource data, including four stimulus blocks and five control blocks, each presented alternatively. Image analysis was performed using statistical parametric mapping 99. After fMRI examinations were complete, behavior tests of Stroop interference were performed for all subjects. Overall reaction time and error numbers were recorded. Results: Both healthy volunteers and patients with hepatic cirrhosis had Stroop interference effects. Patients with hepatic cirrhosis had more errors and longer reaction time in performing an incongruous color-naming task than healthy volunteers (P<0.001); there was no significant difference in performing an incongruous word-reading task (P 0.066). Compared with controls, patients with hepatic cirrhosis had greater activation of the bilateral prefrontal cortex and parietal cortex when performing the incongruous word-reading task. With increased conflict, activation of the anterior cingulate cortex (ACC), bilateral prefrontal cortex (PFC), parietal lobe, and temporal fusiform gyrus (TFG) was decreased when patients with hepatic cirrhosis performed the incongruous color-naming task. Conclusion: This study demonstrates that patients with hepatic cirrhostic have cognitive control deficiency. The abnormal brain network of the ACC-PFC-parietal lobe-TFG is the neural basis of cognitive control impairment in cirrhotic patients
Neural reactivation links unconscious thought to decision-making performance.
Creswell, John David; Bursley, James K; Satpute, Ajay B
2013-12-01
Brief periods of unconscious thought (UT) have been shown to improve decision making compared with making an immediate decision (ID). We reveal a neural mechanism for UT in decision making using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Participants (N = 33) encoded information on a set of consumer products (e.g. 48 attributes describing four different cars), and we manipulated whether participants (i) consciously thought about this information (conscious thought), (ii) completed a difficult 2-back working memory task (UT) or (iii) made an immediate decision about the consumer products (ID) in a within-subjects blocked design. To differentiate UT neural activity from 2-back working memory neural activity, participants completed an independent 2-back task and this neural activity was subtracted from neural activity occurring during the UT 2-back task. Consistent with a neural reactivation account, we found that the same regions activated during the encoding of complex decision information (right dorsolateral prefrontal cortex and left intermediate visual cortex) continued to be activated during a subsequent 2-min UT period. Moreover, neural reactivation in these regions was predictive of subsequent behavioral decision-making performance after the UT period. These results provide initial evidence for post-encoding unconscious neural reactivation in facilitating decision making.
Estimation of Conditional Quantile using Neural Networks
DEFF Research Database (Denmark)
Kulczycki, P.; Schiøler, Henrik
1999-01-01
The problem of estimating conditional quantiles using neural networks is investigated here. A basic structure is developed using the methodology of kernel estimation, and a theory guaranteeing con-sistency on a mild set of assumptions is provided. The constructed structure constitutes a basis...... for the design of a variety of different neural networks, some of which are considered in detail. The task of estimating conditional quantiles is related to Bayes point estimation whereby a broad range of applications within engineering, economics and management can be suggested. Numerical results illustrating...... the capabilities of the elaborated neural network are also given....
Green’s function theory of ferromagnetic resonance in magnetic superlattices with damping
International Nuclear Information System (INIS)
Qiu, R.K.; Guo, F.F.; Zhang, Z.D.
2016-01-01
We explore a quantum Green’s-function method to study the resonance absorption of magnetic materials. The relationship between the resonance magnon (spin wave) density and the resonance frequency of a superlattice consisting of two magnetic layers with damping and antiferromagnetic interlayer exchange coupling is studied. The effects of temperature, interlayer coupling, anisotropy, external magnetic field and damping on the the resonance frequency and resonance magnon density are investigated. The resonance excitation probability for a magnon is proportional to the resonance magnon density. In the classic methods, the imaginary part of magnetic permeability represents the resonance absorption in magnetic materials. In the quantum approach, the resonance magnon density can be used to estimate the strength of the resonance absorption. In the present work, a quantum approach is developed to study resonance absorption of magnetic materials and the results show the method to obtain a magnetic multilayered materials with both high resonance frequency and high resonance absorption.
Green’s function theory of ferromagnetic resonance in magnetic superlattices with damping
Energy Technology Data Exchange (ETDEWEB)
Qiu, R.K., E-mail: rkqiu@163.com [Shenyang University of Technology, Shenyang 110870 (China); Guo, F.F. [Shenyang University of Technology, Shenyang 110870 (China); Zhang, Z.D. [Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016 (China)
2016-02-01
We explore a quantum Green’s-function method to study the resonance absorption of magnetic materials. The relationship between the resonance magnon (spin wave) density and the resonance frequency of a superlattice consisting of two magnetic layers with damping and antiferromagnetic interlayer exchange coupling is studied. The effects of temperature, interlayer coupling, anisotropy, external magnetic field and damping on the the resonance frequency and resonance magnon density are investigated. The resonance excitation probability for a magnon is proportional to the resonance magnon density. In the classic methods, the imaginary part of magnetic permeability represents the resonance absorption in magnetic materials. In the quantum approach, the resonance magnon density can be used to estimate the strength of the resonance absorption. In the present work, a quantum approach is developed to study resonance absorption of magnetic materials and the results show the method to obtain a magnetic multilayered materials with both high resonance frequency and high resonance absorption.
A convolutional neural network to filter artifacts in spectroscopic MRI.
Gurbani, Saumya S; Schreibmann, Eduard; Maudsley, Andrew A; Cordova, James Scott; Soher, Brian J; Poptani, Harish; Verma, Gaurav; Barker, Peter B; Shim, Hyunsuk; Cooper, Lee A D
2018-03-09
Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning. © 2018 International Society for Magnetic Resonance in Medicine.
Compensatory recruitment of neural resources in chronic alcoholism.
Chanraud, Sandra; Sullivan, Edith V
2014-01-01
Functional recovery occurs with sustained sobriety, but the neural mechanisms enabling recovery are only now emerging. Theories about promising mechanisms involve concepts of neuroadaptation, where excessive alcohol consumption results in untoward structural and functional brain changes which are subsequently candidates for reversal with sobriety. Views on functional adaptation in chronic alcoholism have expanded with results from neuroimaging studies. Here, we first describe and define the concept of neuroadaptation according to emerging theories based on the growing literature in aging-related cognitive functioning. Then we describe findings as they apply to chronic alcoholism and factors that could influence compensation, such as functional brain reserve and the integrity of brain structure. Finally, we review brain plasticity based on physiologic mechanisms that could underlie mechanisms of neural compensation. Where possible, we provide operational criteria to define functional and neural compensation. © 2014 Elsevier B.V. All rights reserved.
Hiremath, Chaitra; Dey, Avyarthana
2017-01-01
Abstract Background: Self-reflection is the process of conscious evaluation of one’s traits, abilities, and attitudes. Deficient self-reflective processes might underlie lack of insight into schizophrenia. The limited research literature on the neural correlates of self-reflection in schizophrenia is inconclusive. In this study, we investigated the neural correlates of self-reflection in schizophrenia patients attending a tertiary care hospital in India. Methods: Nineteen male schizophrenia patients (mean age = 32.68 ± 7.11, mean years of education =15.21 ± 1.93) and 19 male healthy controls (mean age = 26.96 ± 4.67, mean years of education = 18.11 ± 3.13) participated in the study. Participants performed a previously validated self-reflection task while undergoing functional magnetic resonance imaging (fMRI; 3-Tesla). The task comprised of 144 words subdivided into 4 domains: Self-reflection, Other-reflection, Affect labeling, and Perceptual. The task was presented as 3 runs of 8 blocks each. The images were preprocessed and analyzed using SPM-12. After preprocessing, contrasts comparing Self-reflection with the other domains were modeled at the individual subject level. In second-level analysis, the first-level contrasts were entered into a 2-sample t test to compare patient and healthy control groups. The results were thresholded at P Self-reflection > Other-reflection contrast, schizophrenia patients demonstrated greater activation of right and left superior parietal lobules (BA 5 and 7), right inferior parietal lobule (BA 39), left parahippocampal gyrus (BA 36), and left premotor cortex (BA 6). For the Self-reflection > Affect labeling contrast, patients showed greater activation of precuneus (BA 7) and right inferior occipital gyrus (BA 19), and lesser activation of left inferior frontal gyrus (BA 45 and 47). And for the Self-reflection > Perceptual contrast, patients showed greater activation of left middle frontal gyrus (BA 10
Complex basis functions for molecular resonances: Methodology and applications
White, Alec; McCurdy, C. William; Head-Gordon, Martin
The computation of positions and widths of metastable electronic states is a challenge for molecular electronic structure theory because, in addition to the difficulty of the many-body problem, such states obey scattering boundary conditions. These resonances cannot be addressed with naïve application of traditional bound state electronic structure theory. Non-Hermitian electronic structure methods employing complex basis functions is one way that we may rigorously treat resonances within the framework of traditional electronic structure theory. In this talk, I will discuss our recent work in this area including the methodological extension from single determinant SCF-based approaches to highly correlated levels of wavefunction-based theory such as equation of motion coupled cluster and many-body perturbation theory. These approaches provide a hierarchy of theoretical methods for the computation of positions and widths of molecular resonances. Within this framework, we may also examine properties of resonances including the dependence of these parameters on molecular geometry. Some applications of these methods to temporary anions and dianions will also be discussed.
Action Potential Modulation of Neural Spin Networks Suggests Possible Role of Spin
Hu, H P
2004-01-01
In this paper we show that nuclear spin networks in neural membranes are modulated by action potentials through J-coupling, dipolar coupling and chemical shielding tensors and perturbed by microscopically strong and fluctuating internal magnetic fields produced largely by paramagnetic oxygen. We suggest that these spin networks could be involved in brain functions since said modulation inputs information carried by the neural spike trains into them, said perturbation activates various dynamics within them and the combination of the two likely produce stochastic resonance thus synchronizing said dynamics to the neural firings. Although quantum coherence is desirable and may indeed exist, it is not required for these spin networks to serve as the subatomic components for the conventional neural networks.
Neural network models of categorical perception.
Damper, R I; Harnad, S R
2000-05-01
Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.
Resonant non-Gaussianity with equilateral properties
International Nuclear Information System (INIS)
Gwyn, Rhiannon; Rummel, Markus
2012-11-01
We discuss the effect of superimposing multiple sources of resonant non-Gaussianity, which arise for instance in models of axion inflation. The resulting sum of oscillating shape contributions can be used to ''Fourier synthesize'' different non-oscillating shapes in the bispectrum. As an example we reproduce an approximately equilateral shape from the superposition of O(10) oscillatory contributions with resonant shape. This implies a possible degeneracy between the equilateral-type non-Gaussianity typical of models with non-canonical kinetic terms, such as DBI inflation, and an equilateral-type shape arising from a superposition of resonant-type contributions in theories with canonical kinetic terms. The absence of oscillations in the 2-point function together with the structure of the resonant N-point functions, imply that detection of equilateral non-Gaussianity at a level greater than the PLANCK sensitivity of f NL ∝O(5) will rule out a resonant origin. We comment on the questions arising from possible embeddings of this idea in a string theory setting.
O'Nions, Elizabeth; Sebastian, Catherine L; McCrory, Eamon; Chantiluke, Kaylita; Happé, Francesca; Viding, Essi
2014-09-01
Individuals with autism spectrum disorders (ASD) have difficulty understanding other minds (Theory of Mind; ToM), with atypical processing evident at both behavioural and neural levels. Individuals with conduct problems and high levels of callous-unemotional (CU) traits (CP/HCU) exhibit reduced responsiveness to others' emotions and difficulties interacting with others, but nonetheless perform normally in experimental tests of ToM. The present study aimed to examine the neural underpinnings of ToM in children (aged 10-16) with ASD (N = 16), CP/HCU (N = 16) and typically developing (TD) controls (N = 16) using a non-verbal cartoon vignette task. Whilst individuals with ASD were predicted to show reduced fMRI responses across regions involved in ToM processing, CP/HCU individuals were predicted to show no differences compared with TD controls. The analyses indicated that neural responses did not differ between TD and CP/HCU groups during ToM. TD and CP/HCU children exhibited significantly greater medial prefrontal cortex responses during ToM than did the ASD group. Within the ASD group, responses in medial prefrontal cortex and right temporoparietal junction (TPJ) correlated with symptom severity as measured by the Autism Diagnostic Observation Schedule (ADOS). Findings suggest that although both ASD and CP/HCU are characterized by social difficulties, only children with ASD display atypical neural processing associated with ToM. © 2014 The Authors. Developmental Science Published by John Wiley & Sons Ltd.
Load reduction test method of similarity theory and BP neural networks of large cranes
Yang, Ruigang; Duan, Zhibin; Lu, Yi; Wang, Lei; Xu, Gening
2016-01-01
Static load tests are an important means of supervising and detecting a crane's lift capacity. Due to space restrictions, however, there are difficulties and potential danger when testing large bridge cranes. To solve the loading problems of large-tonnage cranes during testing, an equivalency test is proposed based on the similarity theory and BP neural networks. The maximum stress and displacement of a large bridge crane is tested in small loads, combined with the training neural network of a similar structure crane through stress and displacement data which is collected by a physics simulation progressively loaded to a static load test load within the material scope of work. The maximum stress and displacement of a crane under a static load test load can be predicted through the relationship of stress, displacement, and load. By measuring the stress and displacement of small tonnage weights, the stress and displacement of large loads can be predicted, such as the maximum load capacity, which is 1.25 times the rated capacity. Experimental study shows that the load reduction test method can reflect the lift capacity of large bridge cranes. The load shedding predictive analysis for Sanxia 1200 t bridge crane test data indicates that when the load is 1.25 times the rated lifting capacity, the predicted displacement and actual displacement error is zero. The method solves the problem that lifting capacities are difficult to obtain and testing accidents are easily possible when 1.25 times related weight loads are tested for large tonnage cranes.
Multilayer Integrated Film Bulk Acoustic Resonators
Zhang, Yafei
2013-01-01
Multilayer Integrated Film Bulk Acoustic Resonators mainly introduces the theory, design, fabrication technology and application of a recently developed new type of device, multilayer integrated film bulk acoustic resonators, at the micro and nano scale involving microelectronic devices, integrated circuits, optical devices, sensors and actuators, acoustic resonators, micro-nano manufacturing, multilayer integration, device theory and design principles, etc. These devices can work at very high frequencies by using the newly developed theory, design, and fabrication technology of nano and micro devices. Readers in fields of IC, electronic devices, sensors, materials, and films etc. will benefit from this book by learning the detailed fundamentals and potential applications of these advanced devices. Prof. Yafei Zhang is the director of the Ministry of Education’s Key Laboratory for Thin Films and Microfabrication Technology, PRC; Dr. Da Chen was a PhD student in Prof. Yafei Zhang’s research group.
Yong, Yook-Kong; Patel, Mihir S; Tanaka, Masako
2010-08-01
A novel analytical/numerical method for calculating the resonator Q and its equivalent electrical parameters due to viscoelastic, conductivity, and mounting supports losses is presented. The method presented will be quite useful for designing new resonators and reducing the time and costs of prototyping. There was also a necessity for better and more realistic modeling of the resonators because of miniaturization and the rapid advances in the frequency ranges of telecommunication. We present new 3-D finite elements models of quartz resonators with viscoelasticity, conductivity, and mounting support losses. The losses at the mounting supports were modeled by perfectly matched layers (PMLs). A previously published theory for dissipative anisotropic piezoelectric solids was formulated in a weak form for finite element (FE) applications. PMLs were placed at the base of the mounting supports to simulate the energy losses to a semi-infinite base substrate. FE simulations were carried out for free vibrations and forced vibrations of quartz tuning fork and AT-cut resonators. Results for quartz tuning fork and thickness shear AT-cut resonators were presented and compared with experimental data. Results for the resonator Q and the equivalent electrical parameters were compared with their measured values. Good equivalences were found. Results for both low- and high-Q AT-cut quartz resonators compared well with their experimental values. A method for estimating the Q directly from the frequency spectrum obtained for free vibrations was also presented. An important determinant of the quality factor Q of a quartz resonator is the loss of energy from the electrode area to the base via the mountings. The acoustical characteristics of the plate resonator are changed when the plate is mounted onto a base substrate. The base affects the frequency spectra of the plate resonator. A resonator with a high Q may not have a similarly high Q when mounted on a base. Hence, the base is an
Gebre, Melat; Woodbury, Anna; Napadow, Vitaly; Krishnamurthy, Venkatagiri; Krishnamurthy, Lisa C; Sniecinski, Roman; Crosson, Bruce
2018-02-06
patient comorbidities and preferences, prescribed by a pain management practitioner. The PENFS group will include the above therapies in addition to the PENFS treatments. The PENFS subject group will have the Neuro-Stim System placed on the ear for 5 days then removed and replaced once per week for 4 weeks. The primary outcome will be resting functional magnetic resonance imaging connectivity between DMN and insula, which will also be correlated with pain relief and functional improvements. This connectivity will be analyzed utilizing functional connectivity magnetic resonance imaging (fcMRI) and will be compared with patient-reported analgesic improvements as indicated by the DVPRS and patient-reported analgesic medication consumption. Pain and function will be further evaluated using Patient-Reported Outcomes Measurement Information System measures and measures describing a person's functional status from Activity and Participation section of the International Classification of Functioning Disability and Health. This trial has been funded by the Veterans Health Administration Program Office. This study attained approval by the Emory University/Veterans Affairs (VA) institutional review board and VA Research & Development committee. Institutional review board expedited approval was granted on 2/7/17 (IRB00092224). The study start date is 6/1/17 and estimated completion date is 5/31/20. The recruitment started in June 2017. This is a feasibility study that is meant to demonstrate the practicality of using fcMRI to study the neural correlates of PENFS outcomes and provide information regarding power calculations in order to design and execute a larger randomized controlled clinical trial to determine the efficacy of PENFS for improving pain and function. ClinicalTrials.gov NCT03008837; https://clinicaltrials.gov/ct2/show/NCT03008837 (Archived by WebCite at http://www.webcitation.org/6wrY3NmaQ). ©Melat Gebre, Anna Woodbury, Vitaly Napadow, Venkatagiri Krishnamurthy, Lisa C
Self-reported empathy and neural activity during action imitation and observation in schizophrenia
Horan, William P.; Iacoboni, Marco; Cross, Katy A.; Korb, Alex; Lee, Junghee; Nori, Poorang; Quintana, Javier; Wynn, Jonathan K.; Green, Michael F.
2014-01-01
Introduction: Although social cognitive impairments are key determinants of functional outcome in schizophrenia their neural bases are poorly understood. This study investigated neural activity during imitation and observation of finger movements and facial expressions in schizophrenia, and their correlates with self-reported empathy. Methods: 23 schizophrenia outpatients and 23 healthy controls were studied with functional magnetic resonance imaging (fMRI) while they imitated, executed, o...
Directory of Open Access Journals (Sweden)
Agustin Ibanez
Full Text Available BACKGROUND: Adults with bipolar disorder (BD have cognitive impairments that affect face processing and social cognition. However, it remains unknown whether these deficits in euthymic BD have impaired brain markers of emotional processing. METHODOLOGY/PRINCIPAL FINDINGS: We recruited twenty six participants, 13 controls subjects with an equal number of euthymic BD participants. We used an event-related potential (ERP assessment of a dual valence task (DVT, in which faces (angry and happy, words (pleasant and unpleasant, and face-word simultaneous combinations are presented to test the effects of the stimulus type (face vs word and valence (positive vs. negative. All participants received clinical, neuropsychological and social cognition evaluations. ERP analysis revealed that both groups showed N170 modulation of stimulus type effects (face > word. BD patients exhibited reduced and enhanced N170 to facial and semantic valence, respectively. The neural source estimation of N170 was a posterior section of the fusiform gyrus (FG, including the face fusiform area (FFA. Neural generators of N170 for faces (FG and FFA were reduced in BD. In these patients, N170 modulation was associated with social cognition (theory of mind. CONCLUSIONS/SIGNIFICANCE: This is the first report of euthymic BD exhibiting abnormal N170 emotional discrimination associated with theory of mind impairments.
Ibanez, Agustin; Urquina, Hugo; Petroni, Agustín; Baez, Sandra; Lopez, Vladimir; do Nascimento, Micaela; Herrera, Eduar; Guex, Raphael; Hurtado, Esteban; Blenkmann, Alejandro; Beltrachini, Leandro; Gelormini, Carlos; Sigman, Mariano; Lischinsky, Alicia; Torralva, Teresa; Torrente, Fernando; Cetkovich, Marcelo; Manes, Facundo
2012-01-01
Adults with bipolar disorder (BD) have cognitive impairments that affect face processing and social cognition. However, it remains unknown whether these deficits in euthymic BD have impaired brain markers of emotional processing. We recruited twenty six participants, 13 controls subjects with an equal number of euthymic BD participants. We used an event-related potential (ERP) assessment of a dual valence task (DVT), in which faces (angry and happy), words (pleasant and unpleasant), and face-word simultaneous combinations are presented to test the effects of the stimulus type (face vs word) and valence (positive vs. negative). All participants received clinical, neuropsychological and social cognition evaluations. ERP analysis revealed that both groups showed N170 modulation of stimulus type effects (face > word). BD patients exhibited reduced and enhanced N170 to facial and semantic valence, respectively. The neural source estimation of N170 was a posterior section of the fusiform gyrus (FG), including the face fusiform area (FFA). Neural generators of N170 for faces (FG and FFA) were reduced in BD. In these patients, N170 modulation was associated with social cognition (theory of mind). This is the first report of euthymic BD exhibiting abnormal N170 emotional discrimination associated with theory of mind impairments.
The neural system of metacognition accompanying decision-making in the prefrontal cortex
Qiu, Lirong; Su, Jie; Ni, Yinmei; Bai, Yang; Zhang, Xuesong; Li, Xiaoli
2018-01-01
Decision-making is usually accompanied by metacognition, through which a decision maker monitors uncertainty regarding a decision and may then consequently revise the decision. These metacognitive processes can occur prior to or in the absence of feedback. However, the neural mechanisms of metacognition remain controversial. One theory proposes an independent neural system for metacognition in the prefrontal cortex (PFC); the other, that metacognitive processes coincide and overlap with the systems used for the decision-making process per se. In this study, we devised a novel “decision–redecision” paradigm to investigate the neural metacognitive processes involved in redecision as compared to the initial decision-making process. The participants underwent a perceptual decision-making task and a rule-based decision-making task during functional magnetic resonance imaging (fMRI). We found that the anterior PFC, including the dorsal anterior cingulate cortex (dACC) and lateral frontopolar cortex (lFPC), were more extensively activated after the initial decision. The dACC activity in redecision positively scaled with decision uncertainty and correlated with individual metacognitive uncertainty monitoring abilities—commonly occurring in both tasks—indicating that the dACC was specifically involved in decision uncertainty monitoring. In contrast, the lFPC activity seen in redecision processing was scaled with decision uncertainty reduction and correlated with individual accuracy changes—positively in the rule-based decision-making task and negatively in the perceptual decision-making task. Our results show that the lFPC was specifically involved in metacognitive control of decision adjustment and was subject to different control demands of the tasks. Therefore, our findings support that a separate neural system in the PFC is essentially involved in metacognition and further, that functions of the PFC in metacognition are dissociable. PMID:29684004
Keller, James M; Fogel, David B
2016-01-01
This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...
The Laplacian spectrum of neural networks
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
Interactions among resonances in the unresolved region
International Nuclear Information System (INIS)
Queiroz Bogado Leite, S. de.
1982-11-01
The theory on resonance absorption in the unresolved region is reviewed and a subroutine is presented, optional to UNRES in MC 2 code. Comparisons with the isolated resonance model suggest the necessity, in some cases, of considering interference and overlapping effects among resonances of the system. (Author) [pt
National Research Council Canada - National Science Library
Omidvar, Omid; Elliott, David L
1997-01-01
... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...
Neural basis of individualistic and collectivistic views of self.
Chiao, Joan Y; Harada, Tokiko; Komeda, Hidetsugu; Li, Zhang; Mano, Yoko; Saito, Daisuke; Parrish, Todd B; Sadato, Norihiro; Iidaka, Tetsuya
2009-09-01
Individualism and collectivism refer to cultural values that influence how people construe themselves and their relation to the world. Individualists perceive themselves as stable entities, autonomous from other people and their environment, while collectivists view themselves as dynamic entities, continually defined by their social context and relationships. Despite rich understanding of how individualism and collectivism influence social cognition at a behavioral level, little is known about how these cultural values modulate neural representations underlying social cognition. Using cross-cultural functional magnetic resonance imaging (fMRI), we examined whether the cultural values of individualism and collectivism modulate neural activity within medial prefrontal cortex (MPFC) during processing of general and contextual self judgments. Here, we show that neural activity within the anterior rostral portion of the MPFC during processing of general and contextual self judgments positively predicts how individualistic or collectivistic a person is across cultures. These results reveal two kinds of neural representations of self (eg, a general self and a contextual self) within MPFC and demonstrate how cultural values of individualism and collectivism shape these neural representations. 2008 Wiley-Liss, Inc.
Development of modularity in the neural activity of children's brains
International Nuclear Information System (INIS)
Chen, Man; Deem, Michael W
2015-01-01
We study how modularity of the human brain changes as children develop into adults. Theory suggests that modularity can enhance the response function of a networked system subject to changing external stimuli. Thus, greater cognitive performance might be achieved for more modular neural activity, and modularity might likely increase as children develop. The value of modularity calculated from functional magnetic resonance imaging (fMRI) data is observed to increase during childhood development and peak in young adulthood. Head motion is deconvolved from the fMRI data, and it is shown that the dependence of modularity on age is independent of the magnitude of head motion. A model is presented to illustrate how modularity can provide greater cognitive performance at short times, i.e. task switching. A fitness function is extracted from the model. Quasispecies theory is used to predict how the average modularity evolves with age, illustrating the increase of modularity during development from children to adults that arises from selection for rapid cognitive function in young adults. Experiments exploring the effect of modularity on cognitive performance are suggested. Modularity may be a potential biomarker for injury, rehabilitation, or disease. (paper)
Wang, Weiping; Yuan, Manman; Luo, Xiong; Liu, Linlin; Zhang, Yao
2018-01-01
Proportional delay is a class of unbounded time-varying delay. A class of bidirectional associative memory (BAM) memristive neural networks with multiple proportional delays is concerned in this paper. First, we propose the model of BAM memristive neural networks with multiple proportional delays and stochastic perturbations. Furthermore, by choosing suitable nonlinear variable transformations, the BAM memristive neural networks with multiple proportional delays can be transformed into the BAM memristive neural networks with constant delays. Based on the drive-response system concept, differential inclusions theory and Lyapunov stability theory, some anti-synchronization criteria are obtained. Finally, the effectiveness of proposed criteria are demonstrated through numerical examples.
Doubly resonant multiphoton ionization
International Nuclear Information System (INIS)
Crance, M.
1978-01-01
A particular case of doubly resonant multiphoton ionization is theoretically investigated. More precisely, two levels quasi-resonant with two successive harmonics of the field frequency are considered. The method used is based on the effective operator formalism first introduced for this problem by Armstrong, Beers and Feneuille. The main result is to show the possibility of observing large interference effects on the width of the resonances. Moreover this treatment allows us to make more precise the connection between effective operator formalism and standard perturbation theory
International Nuclear Information System (INIS)
Moon, Sang Ki
1995-02-01
This thesis applies new information techniques, artificial neural networks, (ANNs) and fuzzy theory, to the investigation of the critical heat flux (CHF) phenomenon for water flow in vertical round tubes. The work performed are (a) classification and prediction of CHF based on fuzzy clustering and ANN, (b) prediction and parametric trends analysis of CHF using ANN with the introduction of dimensionless parameters, and (c) detection of CHF occurrence using fuzzy rule and spatiotemporal neural network (STN). Fuzzy clustering and ANN are used for classification and prediction of the CHF using primary system parameters. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulted clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanisms. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. Parametric trends of the CHF are analyzed by applying artificial neural networks to a CHF data base for water flow in uniformly heated vertical round tubes. The analyses are performed from three viewpoints, i.e., for fixed inlet conditions, for fixed exit conditions, and based on local conditions hypothesis. In order to remove the necessity of data classification, Katto and Groeneveld et al.'s dimensionless parameters are introduced in training the ANNs with the experimental CHF data. The trained ANNs predict the CHF better than any other conventional correlations, showing RMS error of 8.9%, 13.1%, and 19.3% for fixed inlet conditions, for fixed exit conditions, and for local
Advances in magnetic resonance 12
Waugh, John S
2013-01-01
Advances in Magnetic Resonance, Volume 12, presents a variety of contributions to the theory and practice of magnetic resonance. The book contains six chapters and begins with a discussion of diffusion and self-diffusion measurements by nuclear magnetic resonance. This is followed by separate chapters on spin-lattice relaxation time in hydrogen isotope mixtures; the principles of optical detection of nuclear spin alignment and nuclear quadropole resonance; and the spin-1 behavior, including the relaxation of the quasi-invariants of the motion of a system of pairs of dipolar coupled spin-1/2 nu
Advances in magnetic resonance 11
Waugh, John S
2013-01-01
Advances in Magnetic Resonance, Volume 11, presents a variety of contributions to the theory and practice of magnetic resonance. The book contains three chapters and begins with a discussion of the principles and applications of dynamic nuclear polarization, with emphasis on molecular motions and collisions, intermolecular couplings, and chemical interactions. Subsequent chapters focus on the assessment of a proposed broadband decoupling method and studies of time-domain (or Fourier transform) multiple-quantum nuclear magnetic resonance.
Identifying Emotions on the Basis of Neural Activation.
Kassam, Karim S; Markey, Amanda R; Cherkassky, Vladimir L; Loewenstein, George; Just, Marcel Adam
2013-01-01
We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.
Neural network-based model reference adaptive control system.
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.
Understanding overbidding: using the neural circuitry of reward to design economic auctions.
Delgado, Mauricio R; Schotter, Andrew; Ozbay, Erkut Y; Phelps, Elizabeth A
2008-09-26
We take advantage of our knowledge of the neural circuitry of reward to investigate a puzzling economic phenomenon: Why do people overbid in auctions? Using functional magnetic resonance imaging (fMRI), we observed that the social competition inherent in an auction results in a more pronounced blood oxygen level-dependent (BOLD) response to loss in the striatum, with greater overbidding correlated with the magnitude of this response. Leveraging these neuroimaging results, we design a behavioral experiment that demonstrates that framing an experimental auction to emphasize loss increases overbidding. These results highlight a role for the contemplation of loss in understanding the tendency to bid "too high." Current economic theories suggest overbidding may result from either "joy of winning" or risk aversion. By combining neuroeconomic and behavioral economic techniques, we find that another factor, namely loss contemplation in a social context, may mediate overbidding in auctions.
Baryons and baryon resonances in nuclear matter
Lenske, Horst; Dhar, Madhumita; Gaitanos, Theodoros; Cao, Xu
2018-01-01
Theoretical approaches to the production of hyperons and baryon resonances in elementary hadronic reactions and heavy ion collisions are reviewed. The focus is on the production and interactions of baryons in the lowest SU(3) flavor octet and states from the next higher SU(3) flavor decuplet. Approaches using the SU(3) formalism for interactions of mesons and baryons and effective field theory for hyperons are discussed. An overview of application to free space and in-medium baryon-baryon interactions is given and the relation to a density functional theory is indicated. The intimate connection between baryon resonances and strangeness production is shown first for reactions on the nucleon. Pion-induced hypernuclear reactions are shown to proceed essentially through the excitation of intermediate nucleon resonances. Transport theory in conjunction with a statistical fragmentation model is an appropriate description of hypernuclear production in antiproton and heavy ion induced fragmentation reactions. The excitation of subnuclear degrees of freedom in peripheral heavy ion collisions at relativistic energies is reviewed. The status of in-medium resonance physics is discussed.
Resonance – Journal of Science Education | Indian Academy of ...
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education. B Sury. Articles written in Resonance – Journal of Science Education. Volume 1 Issue 11 November 1996 pp 42-50 General Article. Combinatorial Group Theory Group Theory via Generators and Relations · B Sury · More Details Fulltext PDF. Volume 3 Issue 2 ...
Introduction to spiking neural networks: Information processing, learning and applications.
Ponulak, Filip; Kasinski, Andrzej
2011-01-01
The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.
Numerical Solution of Fuzzy Differential Equations with Z-numbers Using Bernstein Neural Networks
Directory of Open Access Journals (Sweden)
Raheleh Jafari
2017-01-01
Full Text Available The uncertain nonlinear systems can be modeled with fuzzy equations or fuzzy differential equations (FDEs by incorporating the fuzzy set theory. The solutions of them are applied to analyze many engineering problems. However, it is very difficult to obtain solutions of FDEs. In this paper, the solutions of FDEs are approximated by two types of Bernstein neural networks. Here, the uncertainties are in the sense of Z-numbers. Initially the FDE is transformed into four ordinary differential equations (ODEs with Hukuhara differentiability. Then neural models are constructed with the structure of ODEs. With modified back propagation method for Z- number variables, the neural networks are trained. The theory analysis and simulation results show that these new models, Bernstein neural networks, are effective to estimate the solutions of FDEs based on Z-numbers.
Spatiotemporal Stochastic Resonance:Theory and Experiment
Peter, Jung
1996-03-01
The amplification of weak periodic signals in bistable or excitable systems via stochastic resonance has been studied intensively over the last years. We are going one step further and ask: Can noise enhance spatiotemporal patterns in excitable media and can this effect be observed in nature? To this end, we are looking at large, two dimensional arrays of coupled excitable elements. Due to the coupling, excitation can propagate through the array in form of nonlinear waves. We observe target waves, rotating spiral waves and other wave forms. If the coupling between the elements is below a critical threshold, any excitational pattern will die out in the absence of noise. Below this threshold, large scale rotating spiral waves - as they are observed above threshold - can be maintained by a proper level of the noise[1]. Furthermore, their geometric features, such as the curvature can be controlled by the homogeneous noise level[2]. If the noise level is too large, break up of spiral waves and collisions with spontaneously nucleated waves yields spiral turbulence. Driving our array with a spatiotemporal pattern, e.g. a rotating spiral wave, we show that for weak coupling the excitational response of the array shows stochastic resonance - an effect we have termed spatiotemporal stochastic resonance. In the last part of the talk I'll make contact with calcium waves, observed in astrocyte cultures and hippocampus slices[3]. A. Cornell-Bell and collaborators[3] have pointed out the role of calcium waves for long-range glial signaling. We demonstrate the similarity of calcium waves with nonlinear waves in noisy excitable media. The noise level in the tissue is characterized by spontaneous activity and can be controlled by applying neuro-transmitter substances[3]. Noise effects in our model are compared with the effect of neuro-transmitters on calcium waves. [1]P. Jung and G. Mayer-Kress, CHAOS 5, 458 (1995). [2]P. Jung and G. Mayer-Kress, Phys. Rev. Lett.62, 2682 (1995). [3
The neural signature of emotional memories in serial crimes.
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.
Competition and Cooperation in Neural Nets : U.S.-Japan Joint Seminar
Arbib, Michael
1982-01-01
The human brain, wi th its hundred billion or more neurons, is both one of the most complex systems known to man and one of the most important. The last decade has seen an explosion of experimental research on the brain, but little theory of neural networks beyond the study of electrical properties of membranes and small neural circuits. Nonetheless, a number of workers in Japan, the United States and elsewhere have begun to contribute to a theory which provides techniques of mathematical analysis and computer simulation to explore properties of neural systems containing immense numbers of neurons. Recently, it has been gradually recognized that rather independent studies of the dynamics of pattern recognition, pattern format::ion, motor control, self-organization, etc. , in neural systems do in fact make use of common methods. We find that a "competition and cooperation" type of interaction plays a fundamental role in parallel information processing in the brain. The present volume brings together 23 papers ...
Neural mechanisms of the mind, Aristotle, Zadeh, and fMRI.
Perlovsky, Leonid I
2010-05-01
Processes in the mind: perception, cognition, concepts, instincts, emotions, and higher cognitive abilities for abstract thinking, beautiful music are considered here within a neural modeling fields (NMFs) paradigm. Its fundamental mathematical mechanism is a process "from vague-fuzzy to crisp," called dynamic logic (DL). This paper discusses why this paradigm is necessary mathematically, and relates it to a psychological description of the mind. Surprisingly, the process from "vague to crisp" corresponds to Aristotelian understanding of mental functioning. Recent functional magnetic resonance imaging (fMRI) measurements confirmed this process in neural mechanisms of perception.
Heeger, David J.
2017-01-01
Most models of sensory processing in the brain have a feedforward architecture in which each stage comprises simple linear filtering operations and nonlinearities. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence (with deep convolutional neural nets) are based on this architecture. However, neocortex is not a feedforward architecture. This paper proposes a first step toward an alternative computational framework in which neural activity in each brain area depends on a combination of feedforward drive (bottom-up from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation). The relative contributions of feedforward drive, feedback drive, and prior drive are controlled by a handful of state parameters, which I hypothesize correspond to neuromodulators and oscillatory activity. In some states, neural responses are dominated by the feedforward drive and the theory is identical to a conventional feedforward model, thereby preserving all of the desirable features of those models. In other states, the theory is a generative model that constructs a sensory representation from an abstract representation, like memory recall. In still other states, the theory combines prior expectation with sensory input, explores different possible perceptual interpretations of ambiguous sensory inputs, and predicts forward in time. The theory, therefore, offers an empirically testable framework for understanding how the cortex accomplishes inference, exploration, and prediction. PMID:28167793
Heeger, David J
2017-02-21
Most models of sensory processing in the brain have a feedforward architecture in which each stage comprises simple linear filtering operations and nonlinearities. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence (with deep convolutional neural nets) are based on this architecture. However, neocortex is not a feedforward architecture. This paper proposes a first step toward an alternative computational framework in which neural activity in each brain area depends on a combination of feedforward drive (bottom-up from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation). The relative contributions of feedforward drive, feedback drive, and prior drive are controlled by a handful of state parameters, which I hypothesize correspond to neuromodulators and oscillatory activity. In some states, neural responses are dominated by the feedforward drive and the theory is identical to a conventional feedforward model, thereby preserving all of the desirable features of those models. In other states, the theory is a generative model that constructs a sensory representation from an abstract representation, like memory recall. In still other states, the theory combines prior expectation with sensory input, explores different possible perceptual interpretations of ambiguous sensory inputs, and predicts forward in time. The theory, therefore, offers an empirically testable framework for understanding how the cortex accomplishes inference, exploration, and prediction.
Resonant non-Gaussianity with equilateral properties
Energy Technology Data Exchange (ETDEWEB)
Gwyn, Rhiannon [Max-Planck-Institut fuer Gravitationsphysik (Albert-Einstein-Institut), Potsdam (Germany); Rummel, Markus [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Westphal, Alexander [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
2012-11-15
We discuss the effect of superimposing multiple sources of resonant non-Gaussianity, which arise for instance in models of axion inflation. The resulting sum of oscillating shape contributions can be used to ''Fourier synthesize'' different non-oscillating shapes in the bispectrum. As an example we reproduce an approximately equilateral shape from the superposition of O(10) oscillatory contributions with resonant shape. This implies a possible degeneracy between the equilateral-type non-Gaussianity typical of models with non-canonical kinetic terms, such as DBI inflation, and an equilateral-type shape arising from a superposition of resonant-type contributions in theories with canonical kinetic terms. The absence of oscillations in the 2-point function together with the structure of the resonant N-point functions, imply that detection of equilateral non-Gaussianity at a level greater than the PLANCK sensitivity of f{sub NL} {proportional_to}O(5) will rule out a resonant origin. We comment on the questions arising from possible embeddings of this idea in a string theory setting.
Spectra of resonance surface photoionization
Energy Technology Data Exchange (ETDEWEB)
Antsiferov, V.V.; Smirnov, G.I.; Telegin, G.G. [Budker Nuclear Physics Institute, Novosibirsk (Russian Federation)
1995-09-01
The theory of nonactivated electron transfer between atoms interacting reasonantly with coherent radiation and a metal surface is developed. The spectral resonances in photoabsorption and surface photoionization are found to be related to nonlinear interference effects in the interaction between discrete atomic levels and the continuum formed by the quasi-continuous electron spectrum of a normal metal. The asymmetry in the resonance surface photoionization spectrum is shown to have a shape typical of the Fano autoionization resonances. 18 refs.
Neural Alterations in Acquired Age-Related Hearing Loss
Directory of Open Access Journals (Sweden)
Raksha Anand Mudar
2016-06-01
Full Text Available Hearing loss is one of the most prevalent chronic health conditions in older adults. Growing evidence suggests that hearing loss is associated with reduced cognitive functioning and incident dementia. In this mini-review, we briefly examine literature on anatomical and functional alterations in the brains of adults with acquired age-associated hearing loss, which may underlie the cognitive consequences observed in this population, focusing on studies that have used structural and functional magnetic resonance imaging, diffusion tensor imaging, and event-related electroencephalography. We discuss structural and functional alterations observed in the temporal and frontal cortices and the limbic system. These neural alterations are discussed in the context of common cause, information-degradation, and sensory-deprivation hypotheses, and we suggest possible rehabilitation strategies. Although we are beginning to learn more about changes in neural architecture and functionality related to age-associated hearing loss, much work remains to be done. Understanding the neural alterations will provide objective markers for early identification of neural consequences of age-associated hearing loss and for evaluating benefits of intervention approaches.
Beghin, Christian; Randriamboarison, Orelien; Hamelin, Michel; Karkoschka, Erich; Sotin, Christophe; Whitten, Robert C.; Berthelier, Jean-Jacques; Grard, Rejean; Simoes, Fernando
2013-01-01
This study presents an approximate model for the atypical Schumann resonance in Titan's atmosphere that accounts for the observations of electromagnetic waves and the measurements of atmospheric conductivity performed with the Huygens Atmospheric Structure and Permittivity, Wave and Altimetry (HASI-PWA) instrumentation during the descent of the Huygens Probe through Titan's atmosphere in January 2005. After many years of thorough analyses of the collected data, several arguments enable us to claim that the Extremely Low Frequency (ELF) wave observed at around 36 Hz displays all the characteristics of the second harmonic of a Schumann resonance. On Earth, this phenomenon is well known to be triggered by lightning activity. Given the lack of evidence of any thunderstorm activity on Titan, we proposed in early works a model based on an alternative powering mechanism involving the electric current sheets induced in Titan's ionosphere by the Saturn's magnetospheric plasma flow. The present study is a further step in improving the initial model and corroborating our preliminary assessments. We first develop an analytic theory of the guided modes that appear to be the most suitable for sustaining Schumann resonances in Titan's atmosphere. We then introduce the characteristics of the Huygens electric field measurements in the equations, in order to constrain the physical parameters of the resonating cavity. The latter is assumed to be made of different structures distributed between an upper boundary, presumably made of a succession of thin ionized layers of stratospheric aerosols spread up to 150 km and a lower quasi-perfect conductive surface hidden beneath the non-conductive ground. The inner reflecting boundary is proposed to be a buried water-ammonia ocean lying at a likely depth of 55-80 km below a dielectric icy crust. Such estimate is found to comply with models suggesting that the internal heat could be transferred upwards by thermal conduction of the crust, while
Marsden, Karen E; Ma, Wei Ji; Deci, Edward L; Ryan, Richard M; Chiu, Pearl H
2015-06-01
The duration and quality of human performance depend on both intrinsic motivation and external incentives. However, little is known about the neuroscientific basis of this interplay between internal and external motivators. Here, we used functional magnetic resonance imaging to examine the neural substrates of intrinsic motivation, operationalized as the free-choice time spent on a task when this was not required, and tested the neural and behavioral effects of external reward on intrinsic motivation. We found that increased duration of free-choice time was predicted by generally diminished neural responses in regions associated with cognitive and affective regulation. By comparison, the possibility of additional reward improved task accuracy, and specifically increased neural and behavioral responses following errors. Those individuals with the smallest neural responses associated with intrinsic motivation exhibited the greatest error-related neural enhancement under the external contingency of possible reward. Together, these data suggest that human performance is guided by a "tonic" and "phasic" relationship between the neural substrates of intrinsic motivation (tonic) and the impact of external incentives (phasic).
Estimating Conditional Distributions by Neural Networks
DEFF Research Database (Denmark)
Kulczycki, P.; Schiøler, Henrik
1998-01-01
Neural Networks for estimating conditionaldistributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency property is considered from a mild set of assumptions. A number of applications...
Discrete Neural Signatures of Basic Emotions.
Saarimäki, Heini; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P; Lampinen, Jouko; Vuilleumier, Patrik; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri
2016-06-01
Categorical models of emotions posit neurally and physiologically distinct human basic emotions. We tested this assumption by using multivariate pattern analysis (MVPA) to classify brain activity patterns of 6 basic emotions (disgust, fear, happiness, sadness, anger, and surprise) in 3 experiments. Emotions were induced with short movies or mental imagery during functional magnetic resonance imaging. MVPA accurately classified emotions induced by both methods, and the classification generalized from one induction condition to another and across individuals. Brain regions contributing most to the classification accuracy included medial and inferior lateral prefrontal cortices, frontal pole, precentral and postcentral gyri, precuneus, and posterior cingulate cortex. Thus, specific neural signatures across these regions hold representations of different emotional states in multimodal fashion, independently of how the emotions are induced. Similarity of subjective experiences between emotions was associated with similarity of neural patterns for the same emotions, suggesting a direct link between activity in these brain regions and the subjective emotional experience. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Resonance – Journal of Science Education | Indian Academy of ...
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 3; Issue 9. Optoelectronic Implementation of Neural Networks - Use of Optics in Computing. R Ramachandran. General Article Volume 3 Issue 9 September 1998 pp 45-55. Fulltext. Click here to view fulltext PDF. Permanent link:
An adaptive orienting theory of error processing.
Wessel, Jan R
2018-03-01
The ability to detect and correct action errors is paramount to safe and efficient goal-directed behaviors. Existing work on the neural underpinnings of error processing and post-error behavioral adaptations has led to the development of several mechanistic theories of error processing. These theories can be roughly grouped into adaptive and maladaptive theories. While adaptive theories propose that errors trigger a cascade of processes that will result in improved behavior after error commission, maladaptive theories hold that error commission momentarily impairs behavior. Neither group of theories can account for all available data, as different empirical studies find both impaired and improved post-error behavior. This article attempts a synthesis between the predictions made by prominent adaptive and maladaptive theories. Specifically, it is proposed that errors invoke a nonspecific cascade of processing that will rapidly interrupt and inhibit ongoing behavior and cognition, as well as orient attention toward the source of the error. It is proposed that this cascade follows all unexpected action outcomes, not just errors. In the case of errors, this cascade is followed by error-specific, controlled processing, which is specifically aimed at (re)tuning the existing task set. This theory combines existing predictions from maladaptive orienting and bottleneck theories with specific neural mechanisms from the wider field of cognitive control, including from error-specific theories of adaptive post-error processing. The article aims to describe the proposed framework and its implications for post-error slowing and post-error accuracy, propose mechanistic neural circuitry for post-error processing, and derive specific hypotheses for future empirical investigations. © 2017 Society for Psychophysiological Research.
Neural Substrates for Processing Task-Irrelevant Sad Images in Adolescents
Wang, Lihong; Huettel, Scott; De Bellis, Michael D.
2008-01-01
Neural systems related to cognitive and emotional processing were examined in adolescents using event-related functional magnetic resonance imaging (fMRI). Ten healthy adolescents performed an emotional oddball task. Subjects detected infrequent circles (targets) within a continual stream of phase-scrambled images (standards). Sad and neutral…
Abdul Jameel, Abdul Gani; Oudenhoven, Vincent Van; Emwas, Abdul-Hamid M.; Sarathy, Mani
2018-01-01
Machine learning algorithms are attracting significant interest for predicting complex chemical phenomenon. In this work, a model to predict research octane number (RON) and motor octane number (MON) of pure hydrocarbons, hydrocarbon-ethanol blends and gasoline-ethanol blends has been developed using artificial neural networks (ANN) and molecular parameters from 1H nuclear Magnetic Resonance (NMR) spectroscopy. RON and MON of 128 pure hydrocarbons, 123 hydrocarbon-ethanol blends of known composition and 30 FACE (fuels for advanced combustion engines) gasoline-ethanol blends were utilized as a dataset to develop the ANN model. The effect of weight % of seven functional groups including paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups and ethanolic OH groups on RON and MON was studied. The effect of branching (i.e., methyl substitution), denoted by a parameter termed as branching index (BI), and molecular weight (MW) were included as inputs along with the seven functional groups to predict RON and MON. The topology of the developed ANN models for RON (9-540-314-1) and MON (9-340-603-1) have two hidden layers and a large number of nodes, and was validated against experimentally measured RON and MON of pure hydrocarbons, hydrocarbon-ethanol and gasoline-ethanol blends; a good correlation (R2=0.99) between the predicted and the experimental data was obtained. The average error of prediction for both RON and MON was found to be 1.2 which is close to the range of experimental uncertainty. This shows that the functional groups in a molecule or fuel can be used to predict its ON, and the complex relationship between them can be captured by tools like ANN.
Abdul Jameel, Abdul Gani
2018-04-17
Machine learning algorithms are attracting significant interest for predicting complex chemical phenomenon. In this work, a model to predict research octane number (RON) and motor octane number (MON) of pure hydrocarbons, hydrocarbon-ethanol blends and gasoline-ethanol blends has been developed using artificial neural networks (ANN) and molecular parameters from 1H nuclear Magnetic Resonance (NMR) spectroscopy. RON and MON of 128 pure hydrocarbons, 123 hydrocarbon-ethanol blends of known composition and 30 FACE (fuels for advanced combustion engines) gasoline-ethanol blends were utilized as a dataset to develop the ANN model. The effect of weight % of seven functional groups including paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups and ethanolic OH groups on RON and MON was studied. The effect of branching (i.e., methyl substitution), denoted by a parameter termed as branching index (BI), and molecular weight (MW) were included as inputs along with the seven functional groups to predict RON and MON. The topology of the developed ANN models for RON (9-540-314-1) and MON (9-340-603-1) have two hidden layers and a large number of nodes, and was validated against experimentally measured RON and MON of pure hydrocarbons, hydrocarbon-ethanol and gasoline-ethanol blends; a good correlation (R2=0.99) between the predicted and the experimental data was obtained. The average error of prediction for both RON and MON was found to be 1.2 which is close to the range of experimental uncertainty. This shows that the functional groups in a molecule or fuel can be used to predict its ON, and the complex relationship between them can be captured by tools like ANN.
Wang, Shinn-Fwu
2009-01-01
A small-displacement sensor based on total-internal reflection theory and surface plasmon resonance technology is proposed for use in heterodyne interferometry. A small displacement can be obtained simply by measuring the variation in phase difference between s- and p-polarization states with the small-displacement sensor. The theoretical displacement resolution of the small-displacement sensor can reach 0.45 nm. The sensor has some additional advantages, e.g., a simple optical setup, high resolution, high sensitivity and rapid measurement. Its feasibility is also demonstrated.
Identifying Emotions on the Basis of Neural Activation.
Directory of Open Access Journals (Sweden)
Karim S Kassam
Full Text Available We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1 neural activation of the same individual in other trials, 2 neural activation of other individuals who experienced similar trials, and 3 neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.
Neural network for solving convex quadratic bilevel programming problems.
He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie
2014-03-01
In this paper, using the idea of successive approximation, we propose a neural network to solve convex quadratic bilevel programming problems (CQBPPs), which is modeled by a nonautonomous differential inclusion. Different from the existing neural network for CQBPP, the model has the least number of state variables and simple structure. Based on the theory of nonsmooth analysis, differential inclusions and Lyapunov-like method, the limit equilibrium points sequence of the proposed neural networks can approximately converge to an optimal solution of CQBPP under certain conditions. Finally, simulation results on two numerical examples and the portfolio selection problem show the effectiveness and performance of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.
The neural basis of economic decision-making in the ultimatum game
Sanfey, A.G.; Rilling, J.K.; Aronson, J.A.; Nystrom, L.E.; Cohen, J.D.
2003-01-01
The nascent field of neuroeconomics seeks to ground economic decision-making in the biological substrate of the brain. We used functional magnetic resonance imaging of Ultimatum Game players to investigate neural substrates of cognitive and emotional processes involved in economic decision-making.
International Nuclear Information System (INIS)
Lind, P.
1993-02-01
The completeness properties of the discrete set of bound state, virtual states and resonances characterizing the system of a single nonrelativistic particle moving in a central cutoff potential is investigated. From a completeness relation in terms of these discrete states and complex scattering states one can derive several Resonant State Expansions (RSE). It is interesting to obtain purely discrete expansion which, if valid, would significantly simplify the treatment of the continuum. Such expansions can be derived using Mittag-Leffler (ML) theory for a cutoff potential and it would be nice to see if one can obtain the same expansions starting from an eigenfunction theory that is not restricted to a finite sphere. The RSE of Greens functions is especially important, e.g. in the continuum RPA (CRPA) method of treating giant resonances in nuclear physics. The convergence of RSE is studied in simple cases using square well wavefunctions in order to achieve high numerical accuracy. Several expansions can be derived from each other by using the theory of analytic functions and one can the see how to obtain a natural discretization of the continuum. Since the resonance wavefunctions are oscillating with an exponentially increasing amplitude, and therefore have to be interpreted through some regularization procedure, every statement made about quantities involving such states is checked by numerical calculations.Realistic nuclear wavefunctions, generated by a Wood-Saxon potential, are used to test also the usefulness of RSE in a realistic nuclear calculation. There are some fundamental differences between different symmetries of the integral contour that defines the continuum in RSE. One kind of symmetry is necessary to have an expansion of the unity operator that is idempotent. Another symmetry must be used if we want purely discrete expansions. These are found to be of the same form as given by ML. (29 refs.)
Observation of the M1 giant resonance by resonance averaging in 106Pd
International Nuclear Information System (INIS)
Kopecky, J.
1987-01-01
An investigation of capture of 2 keV and 24 keV neutrons in a 105 Pd target resulted in resonance-averaged intensities of primary gamma rays with energies between 5.2 and 9.5 MeV. From these intensities the gamma ray strength functions have been evaluated for E1, M1 and E2 radiation and compared with predictions of the giant resonance theory. The inclusion of an energy dependent spreading width for the E1 giant resonance is necessary. The energy distribution of M1 reduced strength is consistent with an interpretation of a broad resonance around 8.8 MeV. E2 data agrees satisfactorily with the giant extrapolation. (orig.)
The neural bases underlying social risk perception in purchase decisions.
Yokoyama, Ryoichi; Nozawa, Takayuki; Sugiura, Motoaki; Yomogida, Yukihito; Takeuchi, Hikaru; Akimoto, Yoritaka; Shibuya, Satoru; Kawashima, Ryuta
2014-05-01
Social considerations significantly influence daily purchase decisions, and the perception of social risk (i.e., the anticipated disapproval of others) is crucial in dissuading consumers from making purchases. However, the neural basis for consumers' perception of social risk remains undiscovered, and this novel study clarifies the relevant neural processes. A total of 26 volunteers were scanned while they evaluated purchase intention of products (purchase intention task) and their anticipation of others' disapproval for possessing a product (social risk task), using functional magnetic resonance imaging (fMRI). The fMRI data from the purchase intention task was used to identify the brain region associated with perception of social risk during purchase decision making by using subjective social risk ratings for a parametric modulation analysis. Furthermore, we aimed to explore if there was a difference between participants' purchase decisions and their explicit evaluations of social risk, with reference to the neural activity associated with social risk perception. For this, subjective social risk ratings were used for a parametric modulation analysis on fMRI data from the social risk task. Analysis of the purchase intention task revealed a significant positive correlation between ratings of social risk and activity in the anterior insula, an area of the brain that is known as part of the emotion-related network. Analysis of the social risk task revealed a significant positive correlation between ratings of social risk and activity in the temporal parietal junction and the medial prefrontal cortex, which are known as theory-of-mind regions. Our results suggest that the anterior insula processes consumers' social risk implicitly to prompt consumers not to buy socially unacceptable products, whereas ToM-related regions process such risk explicitly in considering the anticipated disapproval of others. These findings may prove helpful in understanding the mental
International Nuclear Information System (INIS)
Elliott, C.J.; Feldman, B.J.
1979-02-01
A detailed theoretical analysis is presented of the interaction of intense near-resonant monochromatic radiation with an N-level anharmonic oscillator. In particular, the phenomenon of multiple photon resonance, the process by which an N-level system resonantly absorbs two or more photons simultaneously, is investigated. Starting from the Schroedinger equation, diagrammatic techniques are developed that allow the resonant process to be analyzed quantitatively, in analogy with well-known two-level coherent phenomena. In addition, multiple photon Stark shifts of the resonances, shifts absent in two-level theory, are obtained from the diagrams. Insights into the nature of multiple photon resonances are gained by comparing the quantum mechanical system with classical coupled pendulums whose equations of motion possess identical eigenvalues and eigenvectors. In certain limiting cases, including that of the resonantly excited N-level harmonic oscillator and that of the equally spaced N-level system with equal matrix elements, analytic results are derived. The influence of population relaxation and phase-disrupting collisions on the multiple photon process are also analyzed, the latter by extension of the diagrammatic technique to the density matrix equations of motion. 11 figures
Parametric Resonance in Dynamical Systems
Nijmeijer, Henk
2012-01-01
Parametric Resonance in Dynamical Systems discusses the phenomenon of parametric resonance and its occurrence in mechanical systems,vehicles, motorcycles, aircraft and marine craft, and micro-electro-mechanical systems. The contributors provide an introduction to the root causes of this phenomenon and its mathematical equivalent, the Mathieu-Hill equation. Also included is a discussion of how parametric resonance occurs on ships and offshore systems and its frequency in mechanical and electrical systems. This book also: Presents the theory and principles behind parametric resonance Provides a unique collection of the different fields where parametric resonance appears including ships and offshore structures, automotive vehicles and mechanical systems Discusses ways to combat, cope with and prevent parametric resonance including passive design measures and active control methods Parametric Resonance in Dynamical Systems is ideal for researchers and mechanical engineers working in application fields such as MEM...
Banzato, T; Cherubini, G B; Atzori, M; Zotti, A
2018-05-01
An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and post-contrast T1-weighted (T1W), and T2-weighted (T2W) MR images. The images were randomly assigned to a training set, a validation set and a test set, comprising 60%, 10% and 30% of images, respectively. The combination of DNN and MR sequence displaying the highest discriminating accuracy was used to develop an image classifier to predict the grading of new cases. The algorithm based on transfer learning using the established DNN did not provide satisfactory results, whereas the newly designed DNN had high classification accuracy. On the basis of classification accuracy, an image classifier built on the newly designed DNN using post-contrast T1W images was developed. This image classifier correctly predicted the grading of 8 out of 10 images not included in the data set. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
Longitudinal links between childhood peer acceptance and the neural correlates of sharing
Will, G.-J. (Geert-Jan); E.A. Crone (Eveline); P.A.C. van Lier (Pol); Güroğlu, B. (Berna)
2016-01-01
textabstractChildhood peer acceptance is associated with high levels of prosocial behavior and advanced perspective taking skills. Yet, the neurobiological mechanisms underlying these associations have not been studied. This functional magnetic resonance imaging study examined the neural correlates
Resonance – Journal of Science Education | Indian Academy of ...
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 7; Issue 4. Glial Cells: The Other Cells of the Nervous System - Astrocytes – Star Performers in the Neural Tissue. Medha S Rajadhyaksha Daya Manghani. Series Article Volume 7 Issue 4 April 2002 pp 20-26 ...
Experimental test of resonant absorption theory. Final report, January 1, 1978-December 31, 1979
International Nuclear Information System (INIS)
Yablonovitch, E.
1979-05-01
This experimental research has probed the nature of resonant absorption (RA) of laser light by laser-produced plasmas. The plasmas were created by optical breakdown of a shockfront produced in an electrothermal shock tube. This procedure allows the density structure of the plasma, and in particular, the orientation of the plasma critical-density surface, to be reproducibly formed from one shot to the next. Thus, for the first time, RA has been controllably and reproducibly studied in isolation from other plasma physics. The angular distribution of fast electrons emitted by RA and wavebreaking has been studied, and it is observed that the emission is directed in a narrow cone centered on the shockfront density-gradient vector, in agreement with the theory of wavebreaking
Neural decoding of visual imagery during sleep.
Horikawa, T; Tamaki, M; Miyawaki, Y; Kamitani, Y
2013-05-03
Visual imagery during sleep has long been a topic of persistent speculation, but its private nature has hampered objective analysis. Here we present a neural decoding approach in which machine-learning models predict the contents of visual imagery during the sleep-onset period, given measured brain activity, by discovering links between human functional magnetic resonance imaging patterns and verbal reports with the assistance of lexical and image databases. Decoding models trained on stimulus-induced brain activity in visual cortical areas showed accurate classification, detection, and identification of contents. Our findings demonstrate that specific visual experience during sleep is represented by brain activity patterns shared by stimulus perception, providing a means to uncover subjective contents of dreaming using objective neural measurement.
Qualitative analysis and control of complex neural networks with delays
Wang, Zhanshan; Zheng, Chengde
2016-01-01
This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.
Directory of Open Access Journals (Sweden)
Yongkun Li
2009-01-01
Full Text Available Based on the theory of calculus on time scales, the homeomorphism theory, Lyapunov functional method, and some analysis techniques, sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of the equilibrium point of Cohen-Grossberg bidirectional associative memory (BAM neural networks with distributed delays and impulses on time scales. This is the first time applying the time-scale calculus theory to unify the discrete-time and continuous-time Cohen-Grossberg BAM neural network with impulses under the same framework.
Macroscopic Neural Theories of Cognition
2014-03-01
studies of working memory in schizophrenia . Human Brain Mapping, 25(1), 60-69. Goertzel, B., Lian, R. T., Arel, I., de Garis, H., & Chen, S...organization of behavior; a neuropsychological theory. New York: Wiley. Heil, J. (2003). Mental causation. In S. P. Stich & T. A. Warfield (Eds.), The...531-536. Plaut, D. C. (1995). Double dissociation without modularity: Evidence from connectionist neuropsychology . Journal of Clinical and
Neural networks of human nature and nurture
Directory of Open Access Journals (Sweden)
Daniel S. Levine
2009-11-01
Full Text Available Neural network methods have facilitated the unification of several unfortunate splits in psychology, including nature versus nurture. We review the contributions of this methodology and then discuss tentative network theories of caring behavior, of uncaring behavior, and of how the frontal lobes are involved in the choices between them. The implications of our theory are optimistic about the prospects of society to encourage the human potential for caring.
Stimulus-dependent suppression of chaos in recurrent neural networks
International Nuclear Information System (INIS)
Rajan, Kanaka; Abbott, L. F.; Sompolinsky, Haim
2010-01-01
Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural circuits and responses driven by external stimuli. Using mean-field analysis, we ask how a neural network that intrinsically generates chaotic patterns of activity can remain sensitive to extrinsic input. We find that inputs not only drive network responses, but they also actively suppress ongoing activity, ultimately leading to a phase transition in which chaos is completely eliminated. The critical input intensity at the phase transition is a nonmonotonic function of stimulus frequency, revealing a 'resonant' frequency at which the input is most effective at suppressing chaos even though the power spectrum of the spontaneous activity peaks at zero and falls exponentially. A prediction of our analysis is that the variance of neural responses should be most strongly suppressed at frequencies matching the range over which many sensory systems operate.
A new delay-independent condition for global robust stability of neural networks with time delays.
Samli, Ruya
2015-06-01
This paper studies the problem of robust stability of dynamical neural networks with discrete time delays under the assumptions that the network parameters of the neural system are uncertain and norm-bounded, and the activation functions are slope-bounded. By employing the results of Lyapunov stability theory and matrix theory, new sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point for delayed neural networks are presented. The results reported in this paper can be easily tested by checking some special properties of symmetric matrices associated with the parameter uncertainties of neural networks. We also present a numerical example to show the effectiveness of the proposed theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Wei, Ruoyu; Cao, Jinde; Alsaedi, Ahmed
2018-02-01
This paper investigates the finite-time synchronization and fixed-time synchronization problems of inertial memristive neural networks with time-varying delays. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, several sufficient conditions are derived to ensure finite-time synchronization of inertial memristive neural networks. Then, for the purpose of making the setting time independent of initial condition, we consider the fixed-time synchronization. A novel criterion guaranteeing the fixed-time synchronization of inertial memristive neural networks is derived. Finally, three examples are provided to demonstrate the effectiveness of our main results.
Bauermeister, Christoph; Schwalger, Tilo; Russell, David F; Neiman, Alexander B; Lindner, Benjamin
2013-01-01
Stochastic signals with pronounced oscillatory components are frequently encountered in neural systems. Input currents to a neuron in the form of stochastic oscillations could be of exogenous origin, e.g. sensory input or synaptic input from a network rhythm. They shape spike firing statistics in a characteristic way, which we explore theoretically in this report. We consider a perfect integrate-and-fire neuron that is stimulated by a constant base current (to drive regular spontaneous firing), along with Gaussian narrow-band noise (a simple example of stochastic oscillations), and a broadband noise. We derive expressions for the nth-order interval distribution, its variance, and the serial correlation coefficients of the interspike intervals (ISIs) and confirm these analytical results by computer simulations. The theory is then applied to experimental data from electroreceptors of paddlefish, which have two distinct types of internal noisy oscillators, one forcing the other. The theory provides an analytical description of their afferent spiking statistics during spontaneous firing, and replicates a pronounced dependence of ISI serial correlation coefficients on the relative frequency of the driving oscillations, and furthermore allows extraction of certain parameters of the intrinsic oscillators embedded in these electroreceptors.
Identifying the neural substrates of intrinsic motivation during task performance.
Lee, Woogul; Reeve, Johnmarshall
2017-10-01
Intrinsic motivation is the inherent tendency to seek out novelty and challenge, to explore and investigate, and to stretch and extend one's capacities. When people imagine performing intrinsically motivating tasks, they show heightened anterior insular cortex (AIC) activity. To fully explain the neural system of intrinsic motivation, however, requires assessing neural activity while people actually perform intrinsically motivating tasks (i.e., while answering curiosity-inducing questions or solving competence-enabling anagrams). Using event-related functional magnetic resonance imaging, we found that the neural system of intrinsic motivation involves not only AIC activity, but also striatum activity and, further, AIC-striatum functional interactions. These findings suggest that subjective feelings of intrinsic satisfaction (associated with AIC activations), reward processing (associated with striatum activations), and their interactions underlie the actual experience of intrinsic motivation. These neural findings are consistent with the conceptualization of intrinsic motivation as the pursuit and satisfaction of subjective feelings (interest and enjoyment) as intrinsic rewards.
Research on Fault Diagnosis Method Based on Rule Base Neural Network
Directory of Open Access Journals (Sweden)
Zheng Ni
2017-01-01
Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.
Spin glasses and neural networks
International Nuclear Information System (INIS)
Parga, N.; Universidad Nacional de Cuyo, San Carlos de Bariloche
1989-01-01
The mean-field theory of spin glass models has been used as a prototype of systems with frustration and disorder. One of the most interesting related systems are models of associative memories. In these lectures we review the main concepts developed to solve the Sherrington-Kirkpatrick model and its application to neural networks. (orig.)
water demand prediction using artificial neural network
African Journals Online (AJOL)
user
2017-01-01
Jan 1, 2017 ... Interface for activation and deactivation of valves. •. Interface demand ... process could be done and monitored at the computer terminal as expected of a .... [15] Arbib, M. A.The Handbook of Brain Theory and Neural. Networks.
Theory of peak coalescence in Fourier transform ion cyclotron resonance mass spectrometry.
Boldin, Ivan A; Nikolaev, Eugene N
2009-10-01
Peak coalescence, i.e. the merging of two close peaks in a Fourier transform ion cyclotron resonance (FTICR) mass spectrum at a high number of ions, plays an important role in various FTICR experiments. In order to describe the coalescence phenomenon we would like to propose a new theory of motion for ion clouds with close mass-to-charge ratios, driven by a uniform magnetic field and Coulomb interactions between the clouds. We describe the motion of the ion clouds in terms of their averaged drift motion in crossed magnetic and electric fields. The ion clouds are considered to be of constant size and their motion is studied in two dimensions. The theory deals with the first-order approximation of the equations of motion in relation to dm/m, where dm is the mass difference and m is the mass of a single ion. The analysis was done for an arbitrary inter-cloud interaction potential, which makes it possible to analyze finite-size ion clouds of any shape. The final analytical expression for the condition of the onset of coalescence is found for the case of uniformly charged spheres. An algorithm for finding this condition for an arbitrary interaction potential is proposed. The critical number of ions for the peak coalescence to take place is shown to depend quadratically on the magnetic field strength and to be proportional to the cyclotron radius and inversely proportional to the ion masses. Copyright (c) 2009 John Wiley & Sons, Ltd.
Advances in magnetic resonance 1
Waugh, John S
2013-01-01
Advances in Magnetic Resonance, Volume 1, discusses developments in various areas of magnetic resonance. The subject matter ranges from original theoretical contributions through syntheses of points of view toward series of phenomena to critical and painstaking tabulations of experimental data. The book contains six chapters and begins with a discussion of the theory of relaxation processes. This is followed by separate chapters on the development of magnetic resonance techniques for studying rate processes in chemistry and the application of these techniques to various problems; the geometri
Introductory photoemission theory
International Nuclear Information System (INIS)
Arai, Hiroko; Fujikawa, Takashi
2010-01-01
An introductory review is presented on the basis of many-body scattering theory. Some fundamental aspects of photoemission theory are discussed in detail. A few applications are also discussed; photoelectron diffraction, depth distribution function and multi-atom resonant photoemission are also discussed briefly. (author)
An assessment of the Crossed Porro Prism Resonator
See, B. A.; Fueloep, K.; Seymour, R.
1980-08-01
Lasers with crossed porro prism resonators for military laser rangefinder and designator applications are studied. Properties of these devices are reviewed and advantages over normal mirror resonators are examined. The theory of operating is treated and the mechanical stability and other features of the laser are examined and compared to standard mirror resonators.
Würz, W.; Sartorius, D.; Kloker, M.; Borodulin, V. I.; Kachanov, Y. S.; Smorodsky, B. V.
2012-09-01
Transition prediction in two-dimensional laminar boundary layers developing on airfoil sections at subsonic speeds and very low turbulence levels is still a challenge. The commonly used semi-empirical prediction tools are mainly based on linear stability theory and do not account for nonlinear effects present unavoidably starting with certain stages of transition. One reason is the lack of systematic investigations of the weakly nonlinear stages of transition, especially of the strongest interactions of the instability modes predominant in non-self-similar boundary layers. The present paper is devoted to the detailed experimental, numerical, and theoretical study of weakly nonlinear subharmonic resonances of Tollmien-Schlichting waves in an airfoil boundary layer, representing main candidates for the strongest mechanism of these initial nonlinear stages. The experimental approach is based on phase-locked hot-wire measurements under controlled disturbance conditions using a new disturbance source being capable to produce well-defined, complex wave compositions in a wide range of streamwise and spanwise wave numbers. The tests were performed in a low-turbulence wind tunnel at a chord Reynolds number of Re = 0.7 × 106. Direct numerical simulations (DNS) were utilized to provide a detailed comparison for the test cases. The results of weakly nonlinear theory (WNT) enabled a profound understanding of the underlying physical mechanisms observed in the experiments and DNS. The data obtained in experiment, DNS and WNT agree basically and provide a high degree of reliability of the results. Interactions occurring between components of various initial frequency-wavenumber spectra of instability waves are investigated by systematic variation of parameters. It is shown that frequency-detuned and spanwise-wavenumber-detuned subharmonic-type resonant interactions have an extremely large spectral width. Similar to results obtained for self-similar base flows it is found that the
Theory of inclusive pionic reactions
International Nuclear Information System (INIS)
Oset, E.; Salcedo, L.L.; Strottman, D.
1985-01-01
A theory is developed for all the inclusive pion nuclear reactions, quasielastic, single charge exchange, double charge exchange and absorption, around the resonance region. The theory is based on the isobar hole model and makes an expansion in the number of particle-hole excitations. Up to 3p3h for pion absorption and 2p2h for quasielastic or charge exchange, where good convergence is found, are considered. The results obtained with this theory agree remarkably well with experiment for the different reactions and different nuclei in a wide region of energies around resonance
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
Fabry-Perot confocal resonator optical associative memory
Burns, Thomas J.; Rogers, Steven K.; Vogel, George A.
1993-03-01
A unique optical associative memory architecture is presented that combines the optical processing environment of a Fabry-Perot confocal resonator with the dynamic storage and recall properties of volume holograms. The confocal resonator reduces the size and complexity of previous associative memory architectures by folding a large number of discrete optical components into an integrated, compact optical processing environment. Experimental results demonstrate the system is capable of recalling a complete object from memory when presented with partial information about the object. A Fourier optics model of the system's operation shows it implements a spatially continuous version of a discrete, binary Hopfield neural network associative memory.
Quasi-projective synchronization of fractional-order complex-valued recurrent neural networks.
Yang, Shuai; Yu, Juan; Hu, Cheng; Jiang, Haijun
2018-08-01
In this paper, without separating the complex-valued neural networks into two real-valued systems, the quasi-projective synchronization of fractional-order complex-valued neural networks is investigated. First, two new fractional-order inequalities are established by using the theory of complex functions, Laplace transform and Mittag-Leffler functions, which generalize traditional inequalities with the first-order derivative in the real domain. Additionally, different from hybrid control schemes given in the previous work concerning the projective synchronization, a simple and linear control strategy is designed in this paper and several criteria are derived to ensure quasi-projective synchronization of the complex-valued neural networks with fractional-order based on the established fractional-order inequalities and the theory of complex functions. Moreover, the error bounds of quasi-projective synchronization are estimated. Especially, some conditions are also presented for the Mittag-Leffler synchronization of the addressed neural networks. Finally, some numerical examples with simulations are provided to show the effectiveness of the derived theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.
Study of inelastic proton scattering at isobaric analog resonances
International Nuclear Information System (INIS)
Davis, S.L.
1974-01-01
Inelastic proton scattering at isobaric analog resonances (IAR's) was studied using the targets 138 Ba and 92 Mo. Differential cross sections and analyzing powers were measured at the 10.00, 10.63, 11.09, 11.45, and 11.70 MeV resonances in 138 Ba + p and at the 5.89, 6.09, and 6.55 MeV resonances in 92 Mo + p. In addition, a new measurement, the spin flip asymmetry, was developed. The experiment was performed by using a polarized beam to make spin flip measurements. Angular distributions for the spin flip probability and spin flip asymmetry were measured at all of the above energies except for the lowest three resonances in 138 Ba, where only the spin flip probability was measured. A DWBA code modified to include the coherent addition of resonance amplitudes was used to analyze the 138 Ba data. The partial widths extracted from this analysis were converted to expansion coefficients for parent states in 139 Ba. The coefficients were found to be in good agreement with unified model calculations. For 92 Mo, inelastic polarizations, deduced from the spin flip and spin flip asymmetry, were found to be large. Attempts using Hauser Feshbach theory to describe both the cross section and polarization data repeatedly failed for both the 6.55 and 5.87 MeV IAR's. This failure represents strong evidence that Hauser Feshbach theory is not valid when extended to describe scattering at an IAR. The 92 Mo data were analyzed using a reaction theory modified to include channel-channel correlations. This theory predicts that the enhanced compound scattering is identical to the resonance scattering. Good fits have been obtained with the use of this modified Hauser Feshbach theory. (U.S.)
Neural networks in signal processing
International Nuclear Information System (INIS)
Govil, R.
2000-01-01
Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)
Principles of neural information processing
Seelen, Werner v
2016-01-01
In this fundamental book the authors devise a framework that describes the working of the brain as a whole. It presents a comprehensive introduction to the principles of Neural Information Processing as well as recent and authoritative research. The books´ guiding principles are the main purpose of neural activity, namely, to organize behavior to ensure survival, as well as the understanding of the evolutionary genesis of the brain. Among the developed principles and strategies belong self-organization of neural systems, flexibility, the active interpretation of the world by means of construction and prediction as well as their embedding into the world, all of which form the framework of the presented description. Since, in brains, their partial self-organization, the lifelong adaptation and their use of various methods of processing incoming information are all interconnected, the authors have chosen not only neurobiology and evolution theory as a basis for the elaboration of such a framework, but also syst...
23rd Workshop of the Italian Neural Networks Society (SIREN)
Esposito, Anna; Morabito, Francesco
2014-01-01
This volume collects a selection of contributions which has been presented at the 23rd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Vietri sul Mare, Salerno, Italy during May 23-24, 2013. 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 two main components, a special session and a group of regular sessions featuring different aspects and point of views of artificial neural networks, artificial and natural intelligence, as well as psychological and cognitive theories for modeling human behaviors and human machine interactions, including Information Communication applications of compelling interest. .
A model of interval timing by neural integration.
Simen, Patrick; Balci, Fuat; de Souza, Laura; Cohen, Jonathan D; Holmes, Philip
2011-06-22
We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes, that correlations among them can be largely cancelled by balancing excitation and inhibition, that neural populations can act as integrators, and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys, and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule's predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior.
The image recognition based on neural network and Bayesian decision
Wang, Chugege
2018-04-01
The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.
Qu, Yang; Telzer, Eva H
2017-01-01
The current research examined whether culture shapes the beliefs, practices, and neural basis of emotion regulation. Twenty-nine American and Chinese participants reported their implicit theory of emotion and frequency of reappraisal use. They also underwent an fMRI scan while completing an emotion regulation task. Chinese (vs. American) participants reported more frequent use of reappraisal, which was mediated by their higher incremental theory of emotion (i.e., believing that emotion is changeable through effort). Although there were some cultural similarities in neural activation during emotion regulation, Chinese participants showed less ventrolateral prefrontal cortex (VLPFC) activation than American participants when regulating negative emotions. Lower VLPFC activation was associated with higher incremental theory of emotion and more frequent use of cognitive reappraisal. Findings suggest that culture may shape how individuals perceive and engage in emotion regulation, and ultimately, the neural mechanisms underlying emotion regulation. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Neural Network for Sparse Reconstruction
Directory of Open Access Journals (Sweden)
Qingfa Li
2014-01-01
Full Text Available We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems. Neural network can be implemented by circuits and can be seen as an important method for solving optimization problems, especially large scale problems. Smoothing approximation is an efficient technique for solving nonsmooth optimization problems. We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion. In theory, the proposed network can converge to the optimal solution set of the given problem. Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper.
Neural controller for adaptive movements with unforeseen payloads.
Kuperstein, M; Wang, J
1990-01-01
A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3% of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.
Latosińska, J N; Latosińska, M; Seliger, J; Žagar, V; Maurin, J K; Kazimierczuk, Z
2012-02-09
Isothioureas, inhibitors of nitric oxide synthases, have been studied experimentally in solid state by nuclear quadrupole double resonance (NQDR) and X-ray methods and theoretically by the quantum theory of atoms in molecules/density functional theory. Resonance frequencies on (14)N have been detected and assigned to particular nitrogen sites in each molecule. The crystal packings of (S)-3,4-dichlorobenzyl-N-methylisothiouronium chloride with the disordered chlorine positions in benzene ring and (S)-butyloisothiouronium bromide have been resolved in X-ray diffraction studies. (14)N NQDR spectra have been found good indicators of isomer type and strength of intra- or intermolecular N-H···X (X = Cl, Br) interactions. From among all salts studied, only for (S)-2,3,4,5,6-pentabromobenzylisothiouronium chloride are both nitrogen sites equivalent, which has been explained by the slow exchange. This unique structural feature can be a key factor in the high biological activity of (S)-2,3,4,5,6-pentabromobenzylisothiouronium salts.
Neural Entrainment to Speech Modulates Speech Intelligibility
Riecke, Lars; Formisano, Elia; Sorger, Bettina; Baskent, Deniz; Gaudrain, Etienne
2018-01-01
Speech is crucial for communication in everyday life. Speech-brain entrainment, the alignment of neural activity to the slow temporal fluctuations (envelope) of acoustic speech input, is a ubiquitous element of current theories of speech processing. Associations between speech-brain entrainment and
Advances in magnetic resonance 2
Waugh, John S
2013-01-01
Advances in Magnetic Resonance, Volume 2, features a mixture of experimental and theoretical contributions. The book contains four chapters and begins with an ambitious and general treatment of the problem of signal-to-noise ratio in magnetic resonance. This is followed by separate chapters on the interpretation of nuclear relaxation in fluids, with special reference to hydrogen; and various aspects of molecular theory of importance in NMR.
EEG-fMRI Bayesian framework for neural activity estimation: a simulation study
Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Del Gratta, Cosimo
2016-12-01
Objective. Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results. First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.
Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.
Carpenter, Gail A.
1997-11-01
A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.
Suprathreshold stochastic resonance in neural processing tuned by correlation.
Durrant, Simon; Kang, Yanmei; Stocks, Nigel; Feng, Jianfeng
2011-07-01
Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.
Magnetic resonance studies of solid polymers; Etude des polymeres solides par resonance magnetique
Energy Technology Data Exchange (ETDEWEB)
Lenk, R [Commissariat a l' Energie Atomique, Grenoble (France). Centre d' Etudes Nucleaires
1969-07-01
This paper is a review of the application of nuclear magnetic resonance (NMR) to solid polymers. In the first, theoretical part, the elements of the theory of NMR, which are necessary for the study of the properties of solid polymers are discussed: the moments method, nuclear relaxation and the distribution of correlation times. In the second part the experimental results are presented. (author) [French] Cette etude est une recherche bibliographique sur l'application de la resonance magnetique nucleaire (RMN) aux polymeres solides. Dans la premiere partie theorique on discute les elements de la theorie de RMN, necessaires pour l'etude des proprietes des polymeres solides: la methode des moments, la relaxation nucleaire et la distribution des temps de correlation. La deuxieme partie presente les resultats des experiences. (auteur)
Di-photon resonance and Dark Matter as heavy pions
Redi, Michele; Tesi, Andrea; Vigiani, Elena
2016-05-13
We analyse confining gauge theories where the 750 GeV di-photon resonance is a composite techni-pion that undergoes anomalous decays into SM vectors. These scenarios naturally contain accidentally stable techni-pions Dark Matter candidates. The di-photon resonance can acquire a larger width by decaying into Dark Matter through the CP-violating $\\theta$-term of the new gauge theory reproducing the cosmological Dark Matter density as thermal relic.
Enhanced energy storage in chaotic optical resonators
Liu, Changxu; Di Falco, Andrea; Molinari, Diego P.; Khan, Yasser; Ooi, Boon S.; Krauss, Thomas F.; Fratalocchi, Andrea
2013-01-01
Chaos is a phenomenon that occurs in many aspects of contemporary science. In classical dynamics, chaos is defined as a hypersensitivity to initial conditions. The presence of chaos is often unwanted, as it introduces unpredictability, which makes it difficult to predict or explain experimental results. Conversely, we demonstrate here how chaos can be used to enhance the ability of an optical resonator to store energy. We combine analytic theory with ab initio simulations and experiments in photonic-crystal resonators to show that a chaotic resonator can store six times more energy than its classical counterpart of the same volume. We explain the observed increase by considering the equipartition of energy among all degrees of freedom of the chaotic resonator (that is, the cavity modes) and discover a convergence of their lifetimes towards a single value. A compelling illustration of the theory is provided by enhanced absorption in deformed polystyrene microspheres. © 2013 Macmillan Publishers Limited. All rights reserved.
Enhanced energy storage in chaotic optical resonators
Liu, Changxu
2013-05-05
Chaos is a phenomenon that occurs in many aspects of contemporary science. In classical dynamics, chaos is defined as a hypersensitivity to initial conditions. The presence of chaos is often unwanted, as it introduces unpredictability, which makes it difficult to predict or explain experimental results. Conversely, we demonstrate here how chaos can be used to enhance the ability of an optical resonator to store energy. We combine analytic theory with ab initio simulations and experiments in photonic-crystal resonators to show that a chaotic resonator can store six times more energy than its classical counterpart of the same volume. We explain the observed increase by considering the equipartition of energy among all degrees of freedom of the chaotic resonator (that is, the cavity modes) and discover a convergence of their lifetimes towards a single value. A compelling illustration of the theory is provided by enhanced absorption in deformed polystyrene microspheres. © 2013 Macmillan Publishers Limited. All rights reserved.
Resonance charge exchange between excited states in slow proton-hydrogen collisions
International Nuclear Information System (INIS)
Tolstikhina, Inga Yu.; Kato, Daiji
2010-01-01
The theory of resonance charge exchange in slow collisions of a proton with a hydrogen atom in the excited state is developed. It extends the Firsov-Demkov theory of resonance charge exchange to the case of degenerate initial and final states. The theory is illustrated by semiclassical and quantum calculations of charge exchange cross sections between states with n=2 in parabolic and spherical coordinates. The results are compared with existing close-coupling calculations.
Nuclear theory. 1998 progress report
International Nuclear Information System (INIS)
1998-01-01
Summaries of progress made on the following topics are given: (1) nonresonant contributions to inelastic N→Δ(1232) parity violation; (2) neutron distribution effects in elastic nuclear parity violation; (3) Wilson RG for scalar-plus-fermion field theories at finite density; (4) Perturbation theory for spin ladders using angular momentum coupled bases; (5) mean-field theory for spin ladders using angular momentum density; (6) finite temperature renormalization group effective potentials for the linear Sigma model; (7) negative-parity baryon resonances from lattice QCD; (8) the N→Δ electromagnetic transition amplitudes from QCD sum rules; and (9) higher nucleon resonances in exclusive reactions (γ, πN) on nuclei
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
The Neural-fuzzy Thermal Error Compensation Controller on CNC Machining Center
Tseng, Pai-Chung; Chen, Shen-Len
The geometric errors and structural thermal deformation are factors that influence the machining accuracy of Computer Numerical Control (CNC) machining center. Therefore, researchers pay attention to thermal error compensation technologies on CNC machine tools. Some real-time error compensation techniques have been successfully demonstrated in both laboratories and industrial sites. The compensation results still need to be enhanced. In this research, the neural-fuzzy theory has been conducted to derive a thermal prediction model. An IC-type thermometer has been used to detect the heat sources temperature variation. The thermal drifts are online measured by a touch-triggered probe with a standard bar. A thermal prediction model is then derived by neural-fuzzy theory based on the temperature variation and the thermal drifts. A Graphic User Interface (GUI) system is also built to conduct the user friendly operation interface with Insprise C++ Builder. The experimental results show that the thermal prediction model developed by neural-fuzzy theory methodology can improve machining accuracy from 80µm to 3µm. Comparison with the multi-variable linear regression analysis the compensation accuracy is increased from ±10µm to ±3µm.
Laser Resonators and Beam Propagation Fundamentals, Advanced Concepts and Applications
Hodgson, Norman
2005-01-01
Optical Resonators provides a detailed discussion of the properties of optical resonators for lasers from basic theory to recent research. In addition to describing the fundamental theories of resonators such as geometrical optics, diffraction, and polarisation the characteristics of all important resonator schemes and their calculation are presented. Experimental examples, practical problems and a collection of measurement techniques support the comprehensive treatment of the subject. Optical Resonators is the only book currently available that provides a comprehensive overview of the the subject. Combined with the structure of the text and the autonomous nature of the chapters this work will be as suitable for those new to the field as it will be invaluable to specialists conducting research. This second edition has been enlarged by new sections on Q-switching and resonators with internal phase/amplitude control. In addition, the whole book has been brought up-to-date.
Application of neural network in τ→ρυτ polarization analysis
International Nuclear Information System (INIS)
Zhang Ziping; Wang Yifang; Innocente, V.
1994-01-01
An artificial neutral network was built to select events in the τ→ρυ τ polarization analysis at LEP/L3, much better selection efficiency has been achieved. Detailed studies show that no systematic errors or bias have been introduced by the application of neural network. A polarization of P τ = -0.129 +- 0.050 +- 0.050 for this channel was obtained by using a sample of 8977 τ + τ - pairs collected near the peak of Z 0 resonance. The neural network training method and some details are described
Statistical mechanics of attractor neural network models with synaptic depression
International Nuclear Information System (INIS)
Igarashi, Yasuhiko; Oizumi, Masafumi; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato
2009-01-01
Synaptic depression is known to control gain for presynaptic inputs. Since cortical neurons receive thousands of presynaptic inputs, and their outputs are fed into thousands of other neurons, the synaptic depression should influence macroscopic properties of neural networks. We employ simple neural network models to explore the macroscopic effects of synaptic depression. Systems with the synaptic depression cannot be analyzed due to asymmetry of connections with the conventional equilibrium statistical-mechanical approach. Thus, we first propose a microscopic dynamical mean field theory. Next, we derive macroscopic steady state equations and discuss the stabilities of steady states for various types of neural network models.
Introduction to neural networks in high energy physics
International Nuclear Information System (INIS)
Therhaag, J.
2013-01-01
Artificial neural networks are a well established tool in high energy physics, playing an important role in both online and offline data analysis. Nevertheless they are often perceived as black boxes which perform obscure operations beyond the control of the user, resulting in a skepticism against any results that may be obtained using them. The situation is not helped by common explanations which try to draw analogies between artificial neural networks and the human brain, for the brain is an even more complex black box itself. In this introductory text, I will take a problem-oriented approach to neural network techniques, showing how the fundamental concepts arise naturally from the demand to solve classification tasks which are frequently encountered in high energy physics. Particular attention is devoted to the question how probability theory can be used to control the complexity of neural networks. (authors)
Neural activity in the hippocampus during conflict resolution.
Sakimoto, Yuya; Okada, Kana; Hattori, Minoru; Takeda, Kozue; Sakata, Shogo
2013-01-15
This study examined configural association theory and conflict resolution models in relation to hippocampal neural activity during positive patterning tasks. According to configural association theory, the hippocampus is important for responses to compound stimuli in positive patterning tasks. In contrast, according to the conflict resolution model, the hippocampus is important for responses to single stimuli in positive patterning tasks. We hypothesized that if configural association theory is applicable, and not the conflict resolution model, the hippocampal theta power should be increased when compound stimuli are presented. If, on the other hand, the conflict resolution model is applicable, but not configural association theory, then the hippocampal theta power should be increased when single stimuli are presented. If both models are valid and applicable in the positive patterning task, we predict that the hippocampal theta power should be increased by presentation of both compound and single stimuli during the positive patterning task. To examine our hypotheses, we measured hippocampal theta power in rats during a positive patterning task. The results showed that hippocampal theta power increased during the presentation of a single stimulus, but did not increase during the presentation of a compound stimulus. This finding suggests that the conflict resolution model is more applicable than the configural association theory for describing neural activity during positive patterning tasks. Copyright © 2012 Elsevier B.V. All rights reserved.
Theory and Experimental and Chemical Instabilities
1989-01-31
Thresholds, Hysteresis, and Neuromodulation of Signal-to-Noise; and Statistical-Mechanical Theory of Many-body Effects in Reaction Rates. T Ic 2 UL3...submitted to the Journal of Physical Chemistry. 6. Noise in Neural Networks: Thresholds, Hysteresis, and Neuromodulation of Signal-to-Noise. We study a...neural-network model including Gaussian noise, higher-order neuronal interactions, and neuromodulation . For a first-order network, there is a
Perceptual load-dependent neural correlates of distractor interference inhibition.
Directory of Open Access Journals (Sweden)
Jiansong Xu
2011-01-01
Full Text Available The load theory of selective attention hypothesizes that distractor interference is suppressed after perceptual processing (i.e., in the later stage of central processing at low perceptual load of the central task, but in the early stage of perceptual processing at high perceptual load. Consistently, studies on the neural correlates of attention have found a smaller distractor-related activation in the sensory cortex at high relative to low perceptual load. However, it is not clear whether the distractor-related activation in brain regions linked to later stages of central processing (e.g., in the frontostriatal circuits is also smaller at high rather than low perceptual load, as might be predicted based on the load theory.We studied 24 healthy participants using functional magnetic resonance imaging (fMRI during a visual target identification task with two perceptual loads (low vs. high. Participants showed distractor-related increases in activation in the midbrain, striatum, occipital and medial and lateral prefrontal cortices at low load, but distractor-related decreases in activation in the midbrain ventral tegmental area and substantia nigra (VTA/SN, striatum, thalamus, and extensive sensory cortices at high load.Multiple levels of central processing involving midbrain and frontostriatal circuits participate in suppressing distractor interference at either low or high perceptual load. For suppressing distractor interference, the processing of sensory inputs in both early and late stages of central processing are enhanced at low load but inhibited at high load.
Perceptual load-dependent neural correlates of distractor interference inhibition.
Xu, Jiansong; Monterosso, John; Kober, Hedy; Balodis, Iris M; Potenza, Marc N
2011-01-18
The load theory of selective attention hypothesizes that distractor interference is suppressed after perceptual processing (i.e., in the later stage of central processing) at low perceptual load of the central task, but in the early stage of perceptual processing at high perceptual load. Consistently, studies on the neural correlates of attention have found a smaller distractor-related activation in the sensory cortex at high relative to low perceptual load. However, it is not clear whether the distractor-related activation in brain regions linked to later stages of central processing (e.g., in the frontostriatal circuits) is also smaller at high rather than low perceptual load, as might be predicted based on the load theory. We studied 24 healthy participants using functional magnetic resonance imaging (fMRI) during a visual target identification task with two perceptual loads (low vs. high). Participants showed distractor-related increases in activation in the midbrain, striatum, occipital and medial and lateral prefrontal cortices at low load, but distractor-related decreases in activation in the midbrain ventral tegmental area and substantia nigra (VTA/SN), striatum, thalamus, and extensive sensory cortices at high load. Multiple levels of central processing involving midbrain and frontostriatal circuits participate in suppressing distractor interference at either low or high perceptual load. For suppressing distractor interference, the processing of sensory inputs in both early and late stages of central processing are enhanced at low load but inhibited at high load.
Trenga, Anthony P; Singla, Anuj; Feger, Mark A; Abel, Mark F
2016-08-01
Congenital malformations of the bony vertebral column are often accompanied by spinal cord anomalies; these observations have been reinforced with the use of magnetic resonance imaging (MRI). We hypothesized that the incidence of cord anomalies will increase as the number and complexity of bony vertebral abnormalities increases. All patients aged ≤13 years (n = 75) presenting to the pediatric spine clinic from 2003-2013 with congenital bony spinal deformity and both radiographs and MRI were analyzed retrospectively for bone and neural pathology. Chi-squared analysis was used to compare groups for categorical dependent variables. Independent t tests were used for continuous dependent variables. Significance was set at p formation had a higher incidence of cord anomalies (73 %) than failures of formation (50 %) or segmentation (45 %) alone (p = 0.065). Deformities in the sacrococcygeal area had the highest rate of spinal cord anomalies (13 of 15 patients, 87 %). In 35 cases (47 %), MRI revealed additional bony anomalies that were not seen on the radiographs. As the number of bony malformations increased, we found a higher incidence of cord anomalies. Clinicians should have increased suspicion of spinal cord pathology in the presence of mixed failures of segmentation and formation.
Neural Generalized Predictive Control of a non-linear Process
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1998-01-01
The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability qu...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem.......The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...
Adaptive exponential synchronization of delayed neural networks with reaction-diffusion terms
International Nuclear Information System (INIS)
Sheng Li; Yang Huizhong; Lou Xuyang
2009-01-01
This paper presents an exponential synchronization scheme for a class of neural networks with time-varying and distributed delays and reaction-diffusion terms. An adaptive synchronization controller is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory. At the same time, the update laws of parameters are proposed to guarantee the synchronization of delayed neural networks with all parameters unknown. It is shown that the approaches developed here extend and improve the ideas presented in recent literatures.
Spectral theory of infinite-area hyperbolic surfaces
Borthwick, David
2016-01-01
This text introduces geometric spectral theory in the context of infinite-area Riemann surfaces, providing a comprehensive account of the most recent developments in the field. For the second edition the context has been extended to general surfaces with hyperbolic ends, which provides a natural setting for development of the spectral theory while still keeping technical difficulties to a minimum. All of the material from the first edition is included and updated, and new sections have been added. Topics covered include an introduction to the geometry of hyperbolic surfaces, analysis of the resolvent of the Laplacian, scattering theory, resonances and scattering poles, the Selberg zeta function, the Poisson formula, distribution of resonances, the inverse scattering problem, Patterson-Sullivan theory, and the dynamical approach to the zeta function. The new sections cover the latest developments in the field, including the spectral gap, resonance asymptotics near the critical line, and sharp geometric constan...
Improving the accuracy of Møller-Plesset perturbation theory with neural networks
McGibbon, Robert T.; Taube, Andrew G.; Donchev, Alexander G.; Siva, Karthik; Hernández, Felipe; Hargus, Cory; Law, Ka-Hei; Klepeis, John L.; Shaw, David E.
2017-10-01
Noncovalent interactions are of fundamental importance across the disciplines of chemistry, materials science, and biology. Quantum chemical calculations on noncovalently bound complexes, which allow for the quantification of properties such as binding energies and geometries, play an essential role in advancing our understanding of, and building models for, a vast array of complex processes involving molecular association or self-assembly. Because of its relatively modest computational cost, second-order Møller-Plesset perturbation (MP2) theory is one of the most widely used methods in quantum chemistry for studying noncovalent interactions. MP2 is, however, plagued by serious errors due to its incomplete treatment of electron correlation, especially when modeling van der Waals interactions and π-stacked complexes. Here we present spin-network-scaled MP2 (SNS-MP2), a new semi-empirical MP2-based method for dimer interaction-energy calculations. To correct for errors in MP2, SNS-MP2 uses quantum chemical features of the complex under study in conjunction with a neural network to reweight terms appearing in the total MP2 interaction energy. The method has been trained on a new data set consisting of over 200 000 complete basis set (CBS)-extrapolated coupled-cluster interaction energies, which are considered the gold standard for chemical accuracy. SNS-MP2 predicts gold-standard binding energies of unseen test compounds with a mean absolute error of 0.04 kcal mol-1 (root-mean-square error 0.09 kcal mol-1), a 6- to 7-fold improvement over MP2. To the best of our knowledge, its accuracy exceeds that of all extant density functional theory- and wavefunction-based methods of similar computational cost, and is very close to the intrinsic accuracy of our benchmark coupled-cluster methodology itself. Furthermore, SNS-MP2 provides reliable per-conformation confidence intervals on the predicted interaction energies, a feature not available from any alternative method.
Mondt, K.; Struys, E.; Noort, M.W.M.L. van den; Balériaux, D.; Metens, T.; Paquier, P.; Craen, P. van de; Bosch, M.P.C.; Denolin, V.
2011-01-01
Many children in bilingual regions follow lessons in a language at school (school-language) that they hardly ever speak at home or in other informal settings. What are the neural effects of this phenomenon? This functional magnetic resonance imaging (fMRI) study investigates the effects of using
Neural Networks for Modeling and Control of Particle Accelerators
Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.
2016-04-01
Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.
Beyond the language given: the neural correlates of inferring speaker meaning.
Bašnáková, Jana; Weber, Kirsten; Petersson, Karl Magnus; van Berkum, Jos; Hagoort, Peter
2014-10-01
Even though language allows us to say exactly what we mean, we often use language to say things indirectly, in a way that depends on the specific communicative context. For example, we can use an apparently straightforward sentence like "It is hard to give a good presentation" to convey deeper meanings, like "Your talk was a mess!" One of the big puzzles in language science is how listeners work out what speakers really mean, which is a skill absolutely central to communication. However, most neuroimaging studies of language comprehension have focused on the arguably much simpler, context-independent process of understanding direct utterances. To examine the neural systems involved in getting at contextually constrained indirect meaning, we used functional magnetic resonance imaging as people listened to indirect replies in spoken dialog. Relative to direct control utterances, indirect replies engaged dorsomedial prefrontal cortex, right temporo-parietal junction and insula, as well as bilateral inferior frontal gyrus and right medial temporal gyrus. This suggests that listeners take the speaker's perspective on both cognitive (theory of mind) and affective (empathy-like) levels. In line with classic pragmatic theories, our results also indicate that currently popular "simulationist" accounts of language comprehension fail to explain how listeners understand the speaker's intended message. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Noise-enhanced categorization in a recurrently reconnected neural network
International Nuclear Information System (INIS)
Monterola, Christopher; Zapotocky, Martin
2005-01-01
We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails
Noise-enhanced categorization in a recurrently reconnected neural network
Monterola, Christopher; Zapotocky, Martin
2005-03-01
We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails.
A Study of ρ-ω Mixing in Resonance Chiral Theory
Chen, Yun-Hua; Yao, De-Liang; Zheng, Han-Qing
2018-01-01
The strong and electromagnetic corrections to ρ-ω mixing are calculated using an SU(2) version of resonance chiral theory up to next-to-leading orders in 1/{N}C expansion, respectively. Up to our accuracy, the effect of the momentum dependence of ρ-ω mixing is incorporated due to the inclusion of loop contributions. We analyze the impact of ρ-ω mixing on the pion vector form factor by performing numerical fit to the data extracted from {e}+{e}-\\to {π }+{π }- and τ \\to {ν }τ 2π , while the decay width of ω \\to {π }+{π }- is taken into account as a constraint. It is found that the momentum dependence is significant in a good description of the experimental data. In addition, based on the fitted values of the involved parameters, we analyze the decay width of ω \\to {π }+{π }-, which turns out to be highly dominated by the ρ-ω mixing effect. Supported in part by the Fundamental Research Funds for the Central Universities under Grant No. 06500077, the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund, under Grant Nos. FIS2014-51948-C2-1-P, FIS2014-51948-C2-2-P, SEV-2014-0398, and Generalitat Valenciana under Grant No. PROMETEOII/2014/0068
The dynamic brain: from spiking neurons to neural masses and cortical fields.
Directory of Open Access Journals (Sweden)
Gustavo Deco
2008-08-01
Full Text Available The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space-time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI, electroencephalogram (EEG, and magnetoencephalogram (MEG. Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the
Type-2 fuzzy neural networks and their applications
Aliev, Rafik Aziz
2014-01-01
This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing. It provides a self-contained exposition of the foundation of type-2 fuzzy neural networks and presents a vast compendium of its applications to control, forecasting, decision making, system identification and other real problems. Type-2 Fuzzy Neural Networks and Their Applications is helpful for teachers and students of universities and colleges, for scientis
DWI-based neural fingerprinting technology: a preliminary study on stroke analysis.
Ye, Chenfei; Ma, Heather Ting; Wu, Jun; Yang, Pengfei; Chen, Xuhui; Yang, Zhengyi; Ma, Jingbo
2014-01-01
Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI) has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI) on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.
Directory of Open Access Journals (Sweden)
A. Posadas
2009-02-01
Full Text Available The study of water movement in soils is of fundamental importance in hydrologic science. It is generally accepted that in most soils, water and solutes flow through unsaturated zones via preferential paths or fingers. This paper combines magnetic resonance imaging (MRI with both fractal and multifractal theory to characterize preferential flow in three dimensions. A cubic double-layer column filled with fine and coarse textured sand was placed into a 500 gauss MRI system. Water infiltration through the column (0.15×0.15×0.15 m^{3} was recorded in steady state conditions. Twelve sections with a voxel volume of 0.1×0.1×10 mm^{3} each were obtained and characterized using fractal and multifractal theory. The MRI system provided a detailed description of the preferential flow under steady state conditions and was also useful in understanding the dynamics of the formation of the fingers. The f(α multifractal spectrum was very sensitive to the variation encountered at each horizontally-oriented slice of the column and provided a suitable characterization of the dynamics of the process identifying four spatial domains. In conclusion, MRI and fractal and multifractal analysis were able to characterize and describe the preferential flow process in soils. Used together, the two methods provide a good alternative to study flow transport phenomena in soils and in porous media.
Neural multigrid for gauge theories and other disordered systems
International Nuclear Information System (INIS)
Baeker, M.; Kalkreuter, T.; Mack, G.; Speh, M.
1992-09-01
We present evidence that multigrid works for wave equations in disordered systems, e.g. in the presence of gauge fields, no matter how strong the disorder, but one needs to introduce a 'neural computations' point of view into large scale simulations: First, the system must learn how to do the simulations efficiently, then do the simulation (fast). The method can also be used to provide smooth interpolation kernels which are needed in multigrid Monte Carlo updates. (orig.)
Neural activity when people solve verbal problems with insight.
Directory of Open Access Journals (Sweden)
Mark Jung-Beeman
2004-04-01
Full Text Available People sometimes solve problems with a unique process called insight, accompanied by an "Aha!" experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1 revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2 revealed a sudden burst of high-frequency (gamma-band neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.
Context-Dependent Neural Modulations in the Perception of Duration.
Murai, Yuki; Yotsumoto, Yuko
2016-01-01
Recent neuroimaging studies have revealed that distinct brain networks are recruited in the perception of sub- and supra-second timescales, whereas psychophysical studies have suggested that there are common or continuous mechanisms for perceiving these two durations. The present study aimed to elucidate the neural implementation of such continuity by examining the neural correlates of peri-second timing. We measured neural activity during a duration reproduction task using functional magnetic resonance imaging. Our results replicate the findings of previous studies in showing that separate neural networks are recruited for sub-versus supra-second time perception: motor systems including the motor cortex and the supplementary motor area for sub-second perception, and the frontal, parietal, and auditory cortical areas for supra-second perception. We further found that the peri-second perception activated both the sub- and supra-second networks, and that the timing system that processed duration perception in previous trials was more involved in subsequent peri-second processing. These results indicate that the sub- and supra-second timing systems overlap at around 1 s, and cooperate to optimally encode duration based on the hysteresis of previous trials.
2014-01-01
Background There appears to be an inconsistency in experimental paradigms used in fMRI research on moral judgments. As stimuli, moral dilemmas or moral statements/ pictures that induce emotional reactions are usually employed; a main difference between these stimuli is the perspective of the participants reflecting first-person (moral dilemmas) or third-person perspective (moral reactions). The present study employed functional magnetic resonance imaging (fMRI) in order to investigate the neural correlates of moral judgments in either first- or third-person perspective. Results Our results indicate that different neural mechanisms appear to be involved in these perspectives. Although conjunction analysis revealed common activation in the anterior medial prefrontal cortex, third person-perspective elicited unique activations in hippocampus and visual cortex. The common activation can be explained by the role the anterior medial prefrontal cortex may play in integrating different information types and also by its involvement in theory of mind. Our results also indicate that the so-called "actor-observer bias" affects moral evaluation in the third-person perspective, possibly due to the involvement of the hippocampus. We suggest two possible ways in which the hippocampus may support the process of moral judgment: by the engagement of episodic memory and its role in understanding the behaviors and emotions of others. Conclusion We posit that these findings demonstrate that first or third person perspectives in moral cognition involve distinct neural processes, that are important to different aspects of moral judgments. These results are important to a deepened understanding of neural correlates of moral cognition—the so-called “first tradition” of neuroethics, with the caveat that any results must be interpreted and employed with prudence, so as to heed neuroethics “second tradition” that sustains the pragmatic evaluation of outcomes, capabilities and
Resonance saturation of the chiral couplings at next-to-leading order in 1/NC
International Nuclear Information System (INIS)
Rosell, Ignasi; Ruiz-Femenia, Pedro; Sanz-Cillero, Juan Jose
2009-01-01
The precision obtainable in phenomenological applications of chiral perturbation theory is currently limited by our lack of knowledge on the low-energy constants (LECs). The assumption that the most important contributions to the LECs come from the dynamics of the low-lying resonances, often referred to as the resonance saturation hypothesis, has stimulated the use of large-N C resonance Lagrangians in order to obtain explicit values for the LECs. We study the validity of the resonance saturation assumption at the next-to-leading order in the 1/N C expansion within the framework of resonance chiral theory. We find that, by imposing QCD short-distance constraints, the chiral couplings can be written in terms of the resonance masses and couplings and do not depend explicitly on the coefficients of the chiral operators in the Goldstone boson sector of resonance chiral theory. As we argue, this is the counterpart formulation of the resonance saturation statement in the context of the resonance Lagrangian. Going beyond leading order in the 1/N C counting allows us to keep full control of the renormalization scale dependence of the LEC estimates.
O'Donnell, Cian; Gonçalves, J Tiago; Portera-Cailliau, Carlos; Sejnowski, Terrence J
2017-10-11
A leading theory holds that neurodevelopmental brain disorders arise from imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this one-dimensional model is rich enough to capture the multiple neural circuit alterations underlying brain disorders. Here, we combined computational simulations with analysis of in vivo two-photon Ca 2+ imaging data from somatosensory cortex of Fmr1 knock-out (KO) mice, a model of Fragile-X Syndrome, to test the E/I imbalance theory. We found that: (1) The E/I imbalance model cannot account for joint alterations in the observed neural firing rates and correlations; (2) Neural circuit function is vastly more sensitive to changes in some cellular components over others; (3) The direction of circuit alterations in Fmr1 KO mice changes across development. These findings suggest that the basic E/I imbalance model should be updated to higher dimensional models that can better capture the multidimensional computational functions of neural circuits.
The neural components of empathy: Predicting daily prosocial behavior
Morelli, Sylvia A.; Rameson, Lian T.; Lieberman, Matthew D.
2012-01-01
Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic response...
General theory of spontaneous emission near exceptional points.
Pick, Adi; Zhen, Bo; Miller, Owen D; Hsu, Chia W; Hernandez, Felipe; Rodriguez, Alejandro W; Soljačić, Marin; Johnson, Steven G
2017-05-29
We present a general theory of spontaneous emission at exceptional points (EPs)-exotic degeneracies in non-Hermitian systems. Our theory extends beyond spontaneous emission to any light-matter interaction described by the local density of states (e.g., absorption, thermal emission, and nonlinear frequency conversion). Whereas traditional spontaneous-emission theories imply infinite enhancement factors at EPs, we derive finite bounds on the enhancement, proving maximum enhancement of 4 in passive systems with second-order EPs and significantly larger enhancements (exceeding 400×) in gain-aided and higher-order EP systems. In contrast to non-degenerate resonances, which are typically associated with Lorentzian emission curves in systems with low losses, EPs are associated with non-Lorentzian lineshapes, leading to enhancements that scale nonlinearly with the resonance quality factor. Our theory can be applied to dispersive media, with proper normalization of the resonant modes.
Nano-topography Enhances Communication in Neural Cells Networks
Onesto, V.
2017-08-23
Neural cells are the smallest building blocks of the central and peripheral nervous systems. Information in neural networks and cell-substrate interactions have been heretofore studied separately. Understanding whether surface nano-topography can direct nerve cells assembly into computational efficient networks may provide new tools and criteria for tissue engineering and regenerative medicine. In this work, we used information theory approaches and functional multi calcium imaging (fMCI) techniques to examine how information flows in neural networks cultured on surfaces with controlled topography. We found that substrate roughness Sa affects networks topology. In the low nano-meter range, S-a = 0-30 nm, information increases with Sa. Moreover, we found that energy density of a network of cells correlates to the topology of that network. This reinforces the view that information, energy and surface nano-topography are tightly inter-connected and should not be neglected when studying cell-cell interaction in neural tissue repair and regeneration.
Acoustic resonances of fluid-immersed elastic cylinders and spheroids: Theory and experiment
Niemiec, Jan; Überall, Herbert; Bao, X. L.
2002-05-01
Frequency resonances in the scattering of acoustic waves from a target object are caused by the phase matching of surface waves repeatedly encircling the object. This is exemplified here by considering elastic finite cylinders and spheroids, and the phase-matching condition provides a means of calculating the complex resonance frequencies of such objects. Tank experiments carried out at Catholic University, or at the University of Le Havre, France by G. Maze and J. Ripoche, have been interpreted using this approach. The experiments employed sound pulses to measure arrival times, which allowed identification of the surface paths taken by the surface waves, thus giving rise to resonances in the scattering amplitude. A calculation of the resonance frequencies using the T-matrix approach showed satisfactory agreement with the experimental resonance frequencies that were either measured directly (as at Le Havre), or that were obtained by the interpretation of measured arrival times (at Catholic University) using calculated surface wave paths, and the extraction of resonance frequencies therefrom, on the basis of the phase-matching condition. Results for hemispherically endcapped, evacuated steel cylinders obtained in a lake experiment carried out by the NSWC were interpreted in the same fashion.
Scalar resonances as two-quark states
International Nuclear Information System (INIS)
Shabalin, E.P.
1984-01-01
On the base of the theory with U(3)xU(3) symmetric chiral Lagrangian the properties of the two-quark scalar mesons are considered. It is shown, that the scalar resonances delta (980) and K(1240) may be treated as the p-wave states of anti qq system. The properties of the isovector and strange scalar mesons, obtained as a propetrties of the two-quark states, turn out to be very close to the properties of the isovector scalar resonance delta (980) and strange resonance K(1240)
Prescott, Steven A.
1998-01-01
Repetitive stimulation often results in habituation of the elicited response. However, if the stimulus is sufficiently strong, habituation may be preceded by transient sensitization or even replaced by enduring sensitization. In 1970, Groves and Thompson formulated the dual-process theory of plasticity to explain these characteristic behavioral changes on the basis of competition between decremental plasticity (depression) and incremental plasticity (facilitation) occurring within the neural network. Data from both vertebrate and invertebrate systems are reviewed and indicate that the effects of depression and facilitation are not exclusively additive but, rather, that those processes interact in a complex manner. Serial ordering of induction of learning, in which a depressing locus precedes the modulatory system responsible for inducing facilitation, causes the facilitation to wane. The parallel and/or serial expression of depression and waning facilitation within the stimulus–response pathway culminates in the behavioral changes that characterize dual-process learning. A mathematical model is presented to formally express and extend understanding of the interactions between depression and facilitation. PMID:10489261
A systematic review of the neural bases of psychotherapy for anxiety and related disorders.
Brooks, Samantha J; Stein, Dan J
2015-09-01
Brain imaging studies over two decades have delineated the neural circuitry of anxiety and related disorders, particularly regions involved in fear processing and in obsessive-compulsive symptoms. The neural circuitry of fear processing involves the amygdala, anterior cingulate, and insular cortex, while cortico-striatal-thalamic circuitry plays a key role in obsessive-compulsive disorder. More recently, neuroimaging studies have examined how psychotherapy for anxiety and related disorders impacts on these neural circuits. Here we conduct a systematic review of the findings of such work, which yielded 19 functional magnetic resonance imaging studies examining the neural bases of cognitive-behavioral therapy (CBT) in 509 patients with anxiety and related disorders. We conclude that, although each of these related disorders is mediated by somewhat different neural circuitry, CBT may act in a similar way to increase prefrontal control of subcortical structures. These findings are consistent with an emphasis in cognitive-affective neuroscience on the potential therapeutic value of enhancing emotional regulation in various psychiatric conditions.
Workspace and sensorimotor theories : Complementary approaches to experience
Degenaar, J.; Keijzer, F.
A serious difficulty for theories of consciousness is to go beyond mere correlation between physical processes and experience. Currently, neural workspace and sensorimotor contingency theories are two of the most promising approaches to make any headway here. This paper explores the relation between
Efficiency turns the table on neural encoding, decoding and noise.
Deneve, Sophie; Chalk, Matthew
2016-04-01
Sensory neurons are usually described with an encoding model, for example, a function that predicts their response from the sensory stimulus using a receptive field (RF) or a tuning curve. However, central to theories of sensory processing is the notion of 'efficient coding'. We argue here that efficient coding implies a completely different neural coding strategy. Instead of a fixed encoding model, neural populations would be described by a fixed decoding model (i.e. a model reconstructing the stimulus from the neural responses). Because the population solves a global optimization problem, individual neurons are variable, but not noisy, and have no truly invariant tuning curve or receptive field. We review recent experimental evidence and implications for neural noise correlations, robustness and adaptation. Copyright © 2016. Published by Elsevier Ltd.
Altered Synchronizations among Neural Networks in Geriatric Depression.
Wang, Lihong; Chou, Ying-Hui; Potter, Guy G; Steffens, David C
2015-01-01
Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.
The harmonics detection method based on neural network applied ...
African Journals Online (AJOL)
user
Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total ..... Genetic algorithm-based self-learning fuzzy PI controller for shunt active filter, ... Verification of global optimality of the OFC active power filters by means of ...
Energy Technology Data Exchange (ETDEWEB)
Aubrun, J.N. [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires
1964-05-15
Theoretical study of nuclear magnetic resonance in ferromagnetic metals shows the near dependence of ferromagnetic properties and unusual feature of this nuclear resonance. This results from a strong interaction between nuclei and magnetic electrons. They excite the nuclei, and, in Bloch walls, submit them to a RF field much stronger than those directly applied. The parameters of the resonance are determined from wall movement and depend consequently of ferromagnetic constants. The theory is enable to provide quantitatively some peculiar effects, specially those of a continuous magnetic field and of temperature. Experimental study was made on cobalt powders, and is in good agreement with theory. However one must take the skin-effect into consideration and accordingly adjust, the theory. This can explain some observed divergences, as well as the influence at particles size and magnetic field over the line shape. Original informations have been obtained about some typical ferromagnetic properties of cobalt, when studying magnetic field effect, and it has been able to apply this method to other ferromagnetic materials. In consideration of the peculiar characteristics of this nuclear resonance, which occurs without external magnetic field and whose line width is large, new models of spectrographs have been realized and have permitted accurate measures of the line shape. The weak intensity of the signals obtained in some cases, has induced the elaboration of an original method of extraction whose theory and practical uses are described here. The whole of this experiment reveals the nuclear resonance as a strong way for the study of ferromagnetism, which is able to detect microscopic phenomenons, not easily accessible by classical methods. (author) [French] L'etude theorique de la resonance magnetique nucleaire dans les metaux ferromagnetiques revele l'etroite liaison entre les proprietes ferromagnetiques et l'aspect inhabituel de cette resonance. Ceci
Energy Technology Data Exchange (ETDEWEB)
Aubrun, J N [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires
1964-05-15
Theoretical study of nuclear magnetic resonance in ferromagnetic metals shows the near dependence of ferromagnetic properties and unusual feature of this nuclear resonance. This results from a strong interaction between nuclei and magnetic electrons. They excite the nuclei, and, in Bloch walls, submit them to a RF field much stronger than those directly applied. The parameters of the resonance are determined from wall movement and depend consequently of ferromagnetic constants. The theory is enable to provide quantitatively some peculiar effects, specially those of a continuous magnetic field and of temperature. Experimental study was made on cobalt powders, and is in good agreement with theory. However one must take the skin-effect into consideration and accordingly adjust, the theory. This can explain some observed divergences, as well as the influence at particles size and magnetic field over the line shape. Original informations have been obtained about some typical ferromagnetic properties of cobalt, when studying magnetic field effect, and it has been able to apply this method to other ferromagnetic materials. In consideration of the peculiar characteristics of this nuclear resonance, which occurs without external magnetic field and whose line width is large, new models of spectrographs have been realized and have permitted accurate measures of the line shape. The weak intensity of the signals obtained in some cases, has induced the elaboration of an original method of extraction whose theory and practical uses are described here. The whole of this experiment reveals the nuclear resonance as a strong way for the study of ferromagnetism, which is able to detect microscopic phenomenons, not easily accessible by classical methods. (author) [French] L'etude theorique de la resonance magnetique nucleaire dans les metaux ferromagnetiques revele l'etroite liaison entre les proprietes ferromagnetiques et l'aspect inhabituel de cette resonance. Ceci resulte du
Pulse sequences for contrast-enhanced magnetic resonance imaging
International Nuclear Information System (INIS)
Graves, Martin J.
2007-01-01
The theory and application of magnetic resonance imaging (MRI) pulse sequences following the administration of an exogenous contrast agent are discussed. Pulse sequences are categorised according to the contrast agent mechanism: changes in proton density, relaxivity, magnetic susceptibility and resonant frequency shift. Applications in morphological imaging, magnetic resonance angiography, dynamic imaging and cell labelling are described. The importance of optimising the pulse sequence for each application is emphasised
The character of resonant charge exchange involving highly excited atoms
International Nuclear Information System (INIS)
Kosarim, A. V.; Smirnov, B. M.; Capitelli, M.; Laricchiuta, A.
2012-01-01
We study the process of resonant charge exchange involving excited helium atoms with the principal quantum number n = 5 colliding with the helium ion in the ground state in the collision energy range from thermal up to 10 eV. This information may be important for the analysis of planet atmospheres containing helium, in particular, for Jupiter’s atmosphere, but our basic interest is the transition from the quantum to classical description of this process, where, due to large cross sections, evaluations of the cross sections are possible. For the chosen process, quantum theory allows determining the cross section as a result of a tunnel electron transition, while classical theory accounts for over-barrier electron transitions. The classical theory additionally requires effective transitions between states with close energies. The analysis of these transitions for helium with n = 5 shows that electron momenta and their projections are mixed for a part of the states, while for other states, the mixing is absent. A simple criterion to separate such states is given. In addition, the main contribution to the cross section of resonant charge exchange follows from tunnel electron transitions. As a result, the quantum theory is better for calculating the cross sections of resonant charge exchange than the classical one and also allows finding the partial cross sections of resonant charge exchange, while the classical approach gives the cross section of resonant charge exchange in a simple manner with the accuracy of 20%.
Decoding the Brain’s Algorithm for Categorization from its Neural Implementation
Mack, Michael L.; Preston, Alison R.; Love, Bradley C.
2013-01-01
Summary Acts of cognition can be described at different levels of analysis: what behavior should characterize the act, what algorithms and representations underlie the behavior, and how the algorithms are physically realized in neural activity [1]. Theories that bridge levels of analysis offer more complete explanations by leveraging the constraints present at each level [2–4]. Despite the great potential for theoretical advances, few studies of cognition bridge levels of analysis. For example, formal cognitive models of category decisions accurately predict human decision making [5, 6], but whether model algorithms and representations supporting category decisions are consistent with underlying neural implementation remains unknown. This uncertainty is largely due to the hurdle of forging links between theory and brain [7–9]. Here, we tackle this critical problem by using brain response to characterize the nature of mental computations that support category decisions to evaluate two dominant, and opposing, models of categorization. We found that brain states during category decisions were significantly more consistent with latent model representations from exemplar [5] rather than prototype theory [10, 11]. Representations of individual experiences, not the abstraction of experiences, are critical for category decision making. Holding models accountable for behavior and neural implementation provides a means for advancing more complete descriptions of the algorithms of cognition. PMID:24094852
Neural basis of exertional fatigue in the heat: A review of magnetic resonance imaging methods.
Tan, X R; Low, I C C; Stephenson, M C; Soong, T W; Lee, J K W
2018-03-01
The central nervous system, specifically the brain, is implicated in the development of exertional fatigue under a hot environment. Diverse neuroimaging techniques have been used to visualize the brain activity during or after exercise. Notably, the use of magnetic resonance imaging (MRI) has become prevalent due to its excellent spatial resolution and versatility. This review evaluates the significance and limitations of various brain MRI techniques in exercise studies-brain volumetric analysis, functional MRI, functional connectivity MRI, and arterial spin labeling. The review aims to provide a summary on the neural basis of exertional fatigue and proposes future directions for brain MRI studies. A systematic literature search was performed where a total of thirty-seven brain MRI studies associated with exercise, fatigue, or related physiological factors were reviewed. The findings suggest that with moderate dehydration, there is a decrease in total brain volume accompanied with expansion of ventricular volume. With exercise fatigue, there is increased activation of sensorimotor and cognitive brain areas, increased thalamo-insular activation and decreased interhemispheric connectivity in motor cortex. Under passive hyperthermia, there are regional changes in cerebral perfusion, a reduction in local connectivity in functional brain networks and an impairment to executive function. Current literature suggests that the brain structure and function are influenced by exercise, fatigue, and related physiological perturbations. However, there is still a dearth of knowledge and it is hoped that through understanding of MRI advantages and limitations, future studies will shed light on the central origin of exertional fatigue in the heat. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Properties of spiral resonators
International Nuclear Information System (INIS)
Haeuser, J.
1989-10-01
The present thesis deals with the calculation and the study of the application possibilities of single and double spiral resonators. The main aim was the development and the construction of reliable and effective high-power spiral resonators for the UNILAC of the GSI in Darmstadt and the H - -injector for the storage ring HERA of DESY in Hamburg. After the presentation of the construction and the properties of spiral resonators and their description by oscillating-circuit models the theoretical foundations of the bunching are presented and some examples of a rebuncher and debuncher and their influence on the longitudinal particle dynamics are shown. After the description of the characteristic accelerator quantities by means of an oscillating-circuit model and the theory of an inhomogeneous λ/4 line it is shown, how the resonance frequency and the efficiency of single and double spiral resonators can be calculated from the geometrical quantities of the structure. In the following the dependence of the maximal reachable resonator voltage in dependence on the gap width and the surface of the drift tubes is studied. Furthermore the high-power resonators are presented, which were built for the different applications for the GSI in Darmstadt, DESY in Hamburg, and for the FOM Institute in Amsterdam. (orig./HSI) [de
Siegel, Edward
2011-10-01
Numbers: primality/indivisibility/non-factorization versus compositeness/divisibility /factor-ization, often in tandem but not always, provocatively close analogy to nuclear-physics: (2 + 1)=(fusion)=3; (3+1)=(fission)=4[=2 × 2]; (4+1)=(fusion)=5; (5 +1)=(fission)=6[=2 × 3]; (6 + 1)=(fusion)=7; (7+1)=(fission)=8[= 2 × 4 = 2 × 2 × 2]; (8 + 1) =(non: fission nor fusion)= 9[=3 × 3]; then ONLY composites' Islands of fusion-INstability: 8, 9, 10; then 14, 15, 16,... Could inter-digit Feshbach-resonances exist??? Applications to: quantum-information/computing non-Shore factorization, millennium-problem Riemann-hypotheses proof as Goodkin BEC intersection with graph-theory ``short-cut'' method: Rayleigh(1870)-Polya(1922)-``Anderson'' (1958)-localization, Goldbach-conjecture, financial auditing/accounting as quantum-statistical-physics;... abound!!!
Coherence Phenomena in Coupled Optical Resonators
Smith, D. D.; Chang, H.
2004-01-01
We predict a variety of photonic coherence phenomena in passive and active coupled ring resonators. Specifically, the effective dispersive and absorptive steady-state response of coupled resonators is derived, and used to determine the conditions for coupled-resonator-induced transparency and absorption, lasing without gain, and cooperative cavity emission. These effects rely on coherent photon trapping, in direct analogy with coherent population trapping phenomena in atomic systems. We also demonstrate that the coupled-mode equations are formally identical to the two-level atom Schrodinger equation in the rotating-wave approximation, and use this result for the analysis of coupled-resonator photon dynamics. Notably, because these effects are predicted directly from coupled-mode theory, they are not unique to atoms, but rather are fundamental to systems of coherently coupled resonators.
Wang, Dongshu; Huang, Lihong
2014-03-01
In this paper, we investigate the periodic dynamical behaviors for a class of general Cohen-Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and the fixed point theorem of multi-valued maps, the existence of periodic solutions for the neural networks is obtained. After that, we derive some sufficient conditions for the global exponential stability and convergence of the neural networks, in terms of nonsmooth analysis theory with generalized Lyapunov approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, our results will also be valid. Moreover, our results extend previous works not only on discrete time-varying and distributed delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete time-varying and distributed delayed neural networks with discontinuous activations. We give some numerical examples to show the applicability and effectiveness of our main results. Copyright © 2013 Elsevier Ltd. All rights reserved.
Deshpande, Gopikrishna; Wang, Peng; Rangaprakash, D; Wilamowski, Bogdan
2015-12-01
Automated recognition and classification of brain diseases are of tremendous value to society. Attention deficit hyperactivity disorder (ADHD) is a diverse spectrum disorder whose diagnosis is based on behavior and hence will benefit from classification utilizing objective neuroimaging measures. Toward this end, an international competition was conducted for classifying ADHD using functional magnetic resonance imaging data acquired from multiple sites worldwide. Here, we consider the data from this competition as an example to illustrate the utility of fully connected cascade (FCC) artificial neural network (ANN) architecture for performing classification. We employed various directional and nondirectional brain connectivity-based methods to extract discriminative features which gave better classification accuracy compared to raw data. Our accuracy for distinguishing ADHD from healthy subjects was close to 90% and between the ADHD subtypes was close to 95%. Further, we show that, if properly used, FCC ANN performs very well compared to other classifiers such as support vector machines in terms of accuracy, irrespective of the feature used. Finally, the most discriminative connectivity features provided insights about the pathophysiology of ADHD and showed reduced and altered connectivity involving the left orbitofrontal cortex and various cerebellar regions in ADHD.
Delayed development of neural language organization in very preterm born children.
Mürner-Lavanchy, Ines; Steinlin, Maja; Kiefer, Claus; Weisstanner, Christian; Ritter, Barbara Catherine; Perrig, Walter; Everts, Regula
2014-01-01
This study investigates neural language organization in very preterm born children compared to control children and examines the relationship between language organization, age, and language performance. Fifty-six preterms and 38 controls (7-12 y) completed a functional magnetic resonance imaging language task. Lateralization and signal change were computed for language-relevant brain regions. Younger preterms showed a bilateral language network whereas older preterms revealed left-sided language organization. No age-related differences in language organization were observed in controls. Results indicate that preterms maintain atypical bilateral language organization longer than term born controls. This might reflect a delay of neural language organization due to very premature birth.
Periodic bidirectional associative memory neural networks with distributed delays
Chen, Anping; Huang, Lihong; Liu, Zhigang; Cao, Jinde
2006-05-01
Some sufficient conditions are obtained for the existence and global exponential stability of a periodic solution to the general bidirectional associative memory (BAM) neural networks with distributed delays by using the continuation theorem of Mawhin's coincidence degree theory and the Lyapunov functional method and the Young's inequality technique. These results are helpful for designing a globally exponentially stable and periodic oscillatory BAM neural network, and the conditions can be easily verified and be applied in practice. An example is also given to illustrate our results.
International Nuclear Information System (INIS)
Queen, N.M.
1978-01-01
This series of lectures on basic scattering theory were given as part of a course for postgraduate high energy physicists and were designed to acquaint the student with some of the basic language and formalism used for the phenomenological description of nuclear reactions and decay processes used for the study of elementary particle interactions. Well established and model independent aspects of scattering theory, which are the basis of S-matrix theory, are considered. The subject is considered under the following headings; the S-matrix, cross sections and decay rates, phase space, relativistic kinematics, the Mandelstam variables, the flux factor, two-body phase space, Dalitz plots, other kinematic plots, two-particle reactions, unitarity, the partial-wave expansion, resonances (single-channel case), multi-channel resonances, analyticity and crossing, dispersion relations, the one-particle exchange model, the density matrix, mathematical properties of the density matrix, the density matrix in scattering processes, the density matrix in decay processes, and the helicity formalism. Some exercises for the students are included. (U.K.)
Neural networks and applications tutorial
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.
Measurement of optical Feshbach resonances in an ideal gas.
Blatt, S; Nicholson, T L; Bloom, B J; Williams, J R; Thomsen, J W; Julienne, P S; Ye, J
2011-08-12
Using a narrow intercombination line in alkaline earth atoms to mitigate large inelastic losses, we explore the optical Feshbach resonance effect in an ultracold gas of bosonic (88)Sr. A systematic measurement of three resonances allows precise determinations of the optical Feshbach resonance strength and scaling law, in agreement with coupled-channel theory. Resonant enhancement of the complex scattering length leads to thermalization mediated by elastic and inelastic collisions in an otherwise ideal gas. Optical Feshbach resonance could be used to control atomic interactions with high spatial and temporal resolution.
Measurement of Optical Feshbach Resonances in an Ideal Gas
International Nuclear Information System (INIS)
Blatt, S.; Nicholson, T. L.; Bloom, B. J.; Williams, J. R.; Thomsen, J. W.; Ye, J.; Julienne, P. S.
2011-01-01
Using a narrow intercombination line in alkaline earth atoms to mitigate large inelastic losses, we explore the optical Feshbach resonance effect in an ultracold gas of bosonic 88 Sr. A systematic measurement of three resonances allows precise determinations of the optical Feshbach resonance strength and scaling law, in agreement with coupled-channel theory. Resonant enhancement of the complex scattering length leads to thermalization mediated by elastic and inelastic collisions in an otherwise ideal gas. Optical Feshbach resonance could be used to control atomic interactions with high spatial and temporal resolution.
Identification of generalized state transfer matrix using neural networks
International Nuclear Information System (INIS)
Zhu Changchun
2001-01-01
The research is introduced on identification of generalized state transfer matrix of linear time-invariant (LTI) system by use of neural networks based on LM (Levenberg-Marquart) algorithm. Firstly, the generalized state transfer matrix is defined. The relationship between the identification of state transfer matrix of structural dynamics and the identification of the weight matrix of neural networks has been established in theory. A singular layer neural network is adopted to obtain the structural parameters as a powerful tool that has parallel distributed processing ability and the property of adaptation or learning. The constraint condition of weight matrix of the neural network is deduced so that the learning and training of the designed network can be more effective. The identified neural network can be used to simulate the structural response excited by any other signals. In order to cope with its further application in practical problems, some noise (5% and 10%) is expected to be present in the response measurements. Results from computer simulation studies show that this method is valid and feasible
Wigner-Eisenbud-Smith photoionization time delay due to autoioinization resonances
Deshmukh, P. C.; Kumar, A.; Varma, H. R.; Banerjee, S.; Manson, Steven T.; Dolmatov, V. K.; Kheifets, A. S.
2018-03-01
An empirical ansatz for the complex photoionization amplitude and Wigner-Eisenbud-Smith time delay in the vicinity of a Fano autoionization resonance are proposed to evaluate and interpret the time delay in the resonant region. The utility of this expression is evaluated in comparison with accurate numerical calculations employing the ab initio relativistic random phase approximation and relativistic multichannel quantum defect theory. The indisputably good qualitative agreement (and semiquantitative agreement) between corresponding results of the proposed model and results produced by the ab initio theories proves the usability of the model. In addition, the phenomenology of the time delay in the vicinity of multichannel autoionizing resonances is detailed.
International Nuclear Information System (INIS)
Delvecchio, Giuseppe; Frangou, Sophia; Fossati, Philippe; Boyer, Patrice; Brambilla, Paolo; Falkai, Peter; Gruber, Olivier; Hietala, Jarmo; Lawrie, Stephen M.; Martinot, Jean-Luc; McIntosh, Andrew M.; Meisenzahl, Eva
2012-01-01
Neuroimaging studies have consistently shown functional brain abnormalities in patients with Bipolar Disorder (BD) and Major Depressive Disorder (MDD). However, the extent to which these two disorders are associated with similar or distinct neural changes remains unclear. We conducted a systematic review of functional magnetic resonance imaging studies comparing BD and MDD patients to healthy participants using facial affect processing paradigms. Relevant spatial coordinates from twenty original studies were subjected to quantitative Activation Likelihood Estimation meta-analyses based on 168 BD and 189 MDD patients and 344 healthy controls. We identified common and distinct patterns of neural engagement for BD and MDD within the facial affect processing network. Both disorders were associated with increased engagement of limbic regions. Diagnosis-specific differences were observed in cortical, thalamic and striatal regions. Decreased ventro-lateral prefrontal cortical engagement was associated with BD while relative hypo-activation of the sensorimotor cortices was seen in MDD. Increased responsiveness in the thalamus and basal ganglia were associated with BD. These findings were modulated by stimulus valence. These data suggest that whereas limbic over-activation is reported consistently in patients with mood disorders, future research should consider the relevance of a wider network of regions in formulating conceptual models of BD and MDD. (authors)
Neural underpinnings of divergent production of rules in numerical analogical reasoning.
Wu, Xiaofei; Jung, Rex E; Zhang, Hao
2016-05-01
Creativity plays an important role in numerical problem solving. Although the neural underpinnings of creativity have been studied over decades, very little is known about neural mechanisms of the creative process that relates to numerical problem solving. In the present study, we employed a numerical analogical reasoning task with functional Magnetic Resonance Imaging (fMRI) to investigate the neural correlates of divergent production of rules in numerical analogical reasoning. Participants performed two tasks: a multiple solution analogical reasoning task and a single solution analogical reasoning task. Results revealed that divergent production of rules involves significant activations at Brodmann area (BA) 10 in the right middle frontal cortex, BA 40 in the left inferior parietal lobule, and BA 8 in the superior frontal cortex. The results suggest that right BA 10 and left BA 40 are involved in the generation of novel rules, and BA 8 is associated with the inhibition of initial rules in numerical analogical reasoning. The findings shed light on the neural mechanisms of creativity in numerical processing. Copyright © 2016 Elsevier B.V. All rights reserved.
Chen, Chien-Chang; Juan, Hung-Hui; Tsai, Meng-Yuan; Lu, Henry Horng-Shing
2018-01-11
By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher's iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.
Resonance – Journal of Science Education | Indian Academy of ...
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education. Rajkumar Radder. Articles written in Resonance – Journal of Science Education. Volume 11 Issue 4 April 2006 pp 100-105 Classroom. On Teaching the Theory of Evolution · Rajkumar Radder · More Details Fulltext PDF ...
Milne, Bruce F; Norman, Patrick
2015-05-28
The first-order hyperpolarizability, β, has been calculated for a group of marine natural products, the makaluvamines. These compounds possess a common cationic pyrroloiminoquinone structure that is substituted to varying degrees. Calculations at the MP2 level indicate that makaluvamines possessing phenolic side chains conjugated with the pyrroloiminoquinone moiety display large β values, while breaking this conjugation leads to a dramatic decrease in the calculated hyperpolarizability. This is consistent with a charge-transfer donor-π-acceptor (D-π-A) structure type, characteristic of nonlinear optical chromophores. Dynamic hyperpolarizabilities calculated using resonance-convergent time-dependent density functional theory coupled to polarizable continuum model (PCM) solvation suggest that significant resonance enhancement effects can be expected for incident radiation with wavelengths around 800 nm. The results of the current work suggest that the pyrroloiminoquinone moiety represents a potentially useful new chromophore subunit, in particular for the development of molecular probes for biological imaging. The introduction of solvent-solute interactions in the theory is conventionally made in a density matrix formalism, and the present work will provide detailed account of the approximations that need to be introduced in wave function theory and our program implementation. The program implementation as such is achieved by a mere combination of existing modules from previous developments, and it is here only briefly reviewed.
Marchetti, Antonella; Baglio, Francesca; Costantini, Isa; Dipasquale, Ottavia; Savazzi, Federica; Nemni, Raffaello; Sangiuliano Intra, Francesca; Tagliabue, Semira; Valle, Annalisa; Massaro, Davide; Castelli, Ilaria
2015-01-01
A topic of common interest to psychologists and philosophers is the spontaneous flow of thoughts when the individual is awake but not involved in cognitive demands. This argument, classically referred to as the "stream of consciousness" of James, is now known in the psychological literature as "Mind-Wandering." Although of great interest, this construct has been scarcely investigated so far. Diaz et al. (2013) created the Amsterdam Resting State Questionnaire (ARSQ), composed of 27 items, distributed in seven factors: discontinuity of mind, theory of mind (ToM), self, planning, sleepiness, comfort, and somatic awareness. The present study aims at: testing psychometric properties of the ARSQ in a sample of 670 Italian subjects; exploring the neural correlates of a subsample of participants (N = 28) divided into two groups on the basis of the scores obtained in the ToM factor. Results show a satisfactory reliability of the original factional structure in the Italian sample. In the subjects with a high mean in the ToM factor compared to low mean subjects, functional MRI revealed: a network (48 nodes) with higher functional connectivity (FC) with a dominance of the left hemisphere; an increased within-lobe FC in frontal and insular lobes. In both neural and behavioral terms, our results support the idea that the mind, which does not rest even when explicitly asked to do so, has various and interesting mentalistic-like contents.
Binelli, C; Subirà, S; Batalla, A; Muñiz, A; Sugranyés, G; Crippa, J A; Farré, M; Pérez-Jurado, L; Martín-Santos, R
2014-11-01
Social Anxiety Disorder (SAD) and Williams-Beuren Syndrome (WS) are two conditions which seem to be at opposite ends in the continuum of social fear but show compromised abilities in some overlapping areas, including some social interactions, gaze contact and processing of facial emotional cues. The increase in the number of neuroimaging studies has greatly expanded our knowledge of the neural bases of facial emotion processing in both conditions. However, to date, SAD and WS have not been compared. We conducted a systematic review of functional magnetic resonance imaging (fMRI) studies comparing SAD and WS cases to healthy control participants (HC) using facial emotion processing paradigms. Two researchers conducted comprehensive PubMed/Medline searches to identify all fMRI studies of facial emotion processing in SAD and WS. The following search key-words were used: "emotion processing"; "facial emotion"; "social anxiety"; "social phobia"; "Williams syndrome"; "neuroimaging"; "functional magnetic resonance"; "fMRI" and their combinations, as well as terms specifying individual facial emotions. We extracted spatial coordinates from each study and conducted two separate voxel-wise activation likelihood estimation meta-analyses, one for SAD and one for WS. Twenty-two studies met the inclusion criteria: 17 studies of SAD and five of WS. We found evidence for both common and distinct patterns of neural activation. Limbic engagement was common to SAD and WS during facial emotion processing, although we observed opposite patterns of activation for each disorder. Compared to HC, SAD cases showed hyperactivation of the amygdala, the parahippocampal gyrus and the globus pallidus. Compared to controls, participants with WS showed hypoactivation of these regions. Differential activation in a number of regions specific to either condition was also identified: SAD cases exhibited greater activation of the insula, putamen, the superior temporal gyrus, medial frontal regions and
Application of two neural network paradigms to the study of voluntary employee turnover.
Somers, M J
1999-04-01
Two neural network paradigms--multilayer perceptron and learning vector quantization--were used to study voluntary employee turnover with a sample of 577 hospital employees. The objectives of the study were twofold. The 1st was to assess whether neural computing techniques offered greater predictive accuracy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies offered new insights into turnover processes. When compared with logistic regression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to the correct classification of leavers. In addition, these neural network paradigms captured nonlinear relationships that are relevant for theory development. Results are discussed in terms of their implications for future research.
International Nuclear Information System (INIS)
Adler, S.L.; Wilczek, F.
1992-11-01
Members of the Institute have worked on a number of problems including the following: acceleration algorithms for the Monte Carlo analysis of lattice field, and gauge and spin theories, based on changes of variables specific to lattices of dimension 2 ell ; construction of quaternionic generalizations of complex quantum mechanics and field theory; wave functions for paired Hall states; black hole quantum mechanics; generalized target-space duality in curved string backgrounds; gauge symnmetry algebra of the N = 2 string; two-dimensional quantum gravity and associated string theories; organizing principles from which the signal processing of neural networks in the retina and cortex can be deduced; integrable systems of KdV type; and a theory for Kondo insulators
Robust fixed-time synchronization of delayed Cohen-Grossberg neural networks.
Wan, Ying; Cao, Jinde; Wen, Guanghui; Yu, Wenwu
2016-01-01
The fixed-time master-slave synchronization of Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays is investigated. Compared with finite-time synchronization where the convergence time relies on the initial synchronization errors, the settling time of fixed-time synchronization can be adjusted to desired values regardless of initial conditions. Novel synchronization control strategy for the slave neural network is proposed. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, some sufficient schemes are provided for selecting the control parameters to ensure synchronization with required convergence time and in the presence of parameter uncertainties. Corresponding criteria for tuning control inputs are also derived for the finite-time synchronization. Finally, two numerical examples are given to illustrate the validity of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Right hemisphere neural activations in the recall of waking fantasies and of dreams.
Benedetti, Francesco; Poletti, Sara; Radaelli, Daniele; Ranieri, Rebecca; Genduso, Valeria; Cavallotti, Simone; Castelnovo, Anna; Smeraldi, Enrico; Scarone, Silvio; D'Agostino, Armando
2015-10-01
The story-like organization of dreams is characterized by a pervasive bizarreness of events and actions that resembles psychotic thought, and largely exceeds that observed in normal waking fantasies. Little is known about the neural correlates of the confabulatory narrative construction of dreams. In this study, dreams, fantasies elicited by ambiguous pictorial stimuli, and non-imaginative first- and third-person narratives from healthy participants were recorded, and were then studied for brain blood oxygen level-dependent functional magnetic resonance imaging on a 3.0-Tesla scanner while listening to their own narrative reports and attempting a retrieval of the corresponding experience. In respect to non-bizarre reports of daytime activities, the script-driven recall of dreams and fantasies differentially activated a right hemisphere network including areas in the inferior frontal gyrus, and superior and middle temporal gyrus. Neural responses were significantly greater for fantasies than for dreams in all regions, and inversely proportional to the degree of bizarreness observed in narrative reports. The inferior frontal gyrus, superior and middle temporal gyrus have been implicated in the semantic activation, integration and selection needed to build a coherent story representation and to resolve semantic ambiguities; in deductive and inferential reasoning; in self- and other-perspective taking, theory of mind, moral and autobiographical reasoning. Their degree of activation could parallel the level of logical robustness or inconsistency experienced when integrating information and mental representations in the process of building fantasy and dream narratives. © 2015 European Sleep Research Society.
Wang, Dongshu; Huang, Lihong; Tang, Longkun
2015-08-01
This paper is concerned with the synchronization dynamical behaviors for a class of delayed neural networks with discontinuous neuron activations. Continuous and discontinuous state feedback controller are designed such that the neural networks model can realize exponential complete synchronization in view of functional differential inclusions theory, Lyapunov functional method and inequality technique. The new proposed results here are very easy to verify and also applicable to neural networks with continuous activations. Finally, some numerical examples show the applicability and effectiveness of our main results.
Neural mechanisms underlying the induction and relief of perceptual curiosity
Directory of Open Access Journals (Sweden)
Marieke eJepma
2012-02-01
Full Text Available Curiosity is one of the most basic biological drives in both animals and humans, and has been identified as a key motive for learning and discovery. Despite the importance of curiosity and related behaviors, the topic has been largely neglected in human neuroscience; hence little is known about the neurobiological mechanisms underlying curiosity. We used functional magnetic resonance imaging (fMRI to investigate what happens in our brain during the induction and subsequent relief of perceptual curiosity. Our core findings were that (i the induction of perceptual curiosity, through the presentation of ambiguous visual input, activated the anterior insula and anterior cingulate cortex, brain regions sensitive to conflict and arousal; (ii the relief of perceptual curiosity, through visual disambiguation, activated regions of the striatum that have been related to reward processing; and (iii the relief of perceptual curiosity was associated with hippocampal activation and enhanced incidental memory. These findings provide the first demonstration of the neural basis of human perceptual curiosity. Our results provide neurobiological support for a classic psychological theory of curiosity, which holds that curiosity is an aversive condition of increased arousal whose termination is rewarding and facilitates memory.
Losin, Elizabeth A Reynolds; Dapretto, Mirella; Iacoboni, Marco
2009-01-01
Cultural neuroscience, the study of how cultural experience shapes the brain, is an emerging subdiscipline in the neurosciences. Yet, a foundational question to the study of culture and the brain remains neglected by neuroscientific inquiry: "How does cultural information get into the brain in the first place?" Fortunately, the tools needed to explore the neural architecture of cultural learning - anthropological theories and cognitive neuroscience methodologies - already exist; they are merely separated by disciplinary boundaries. Here we review anthropological theories of cultural learning derived from fieldwork and modeling; since cultural learning theory suggests that sophisticated imitation abilities are at the core of human cultural learning, we focus our review on cultural imitative learning. Accordingly we proceed to discuss the neural underpinnings of imitation and other mechanisms important for cultural learning: learning biases, mental state attribution, and reinforcement learning. Using cultural neuroscience theory and cognitive neuroscience research as our guides, we then propose a preliminary model of the neural architecture of cultural learning. Finally, we discuss future studies needed to test this model and fully explore and explain the neural underpinnings of cultural imitative learning.
Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks
Directory of Open Access Journals (Sweden)
Zhisheng Zhang
2016-01-01
Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.
Attractor neural networks with resource-efficient synaptic connectivity
Pehlevan, Cengiz; Sengupta, Anirvan
Memories are thought to be stored in the attractor states of recurrent neural networks. Here we explore how resource constraints interplay with memory storage function to shape synaptic connectivity of attractor networks. We propose that given a set of memories, in the form of population activity patterns, the neural circuit choses a synaptic connectivity configuration that minimizes a resource usage cost. We argue that the total synaptic weight (l1-norm) in the network measures the resource cost because synaptic weight is correlated with synaptic volume, which is a limited resource, and is proportional to neurotransmitter release and post-synaptic current, both of which cost energy. Using numerical simulations and replica theory, we characterize optimal connectivity profiles in resource-efficient attractor networks. Our theory explains several experimental observations on cortical connectivity profiles, 1) connectivity is sparse, because synapses are costly, 2) bidirectional connections are overrepresented and 3) are stronger, because attractor states need strong recurrence.
A Possible Neural Representation of Mathematical Group Structures.
Pomi, Andrés
2016-09-01
Every cognitive activity has a neural representation in the brain. When humans deal with abstract mathematical structures, for instance finite groups, certain patterns of activity are occurring in the brain that constitute their neural representation. A formal neurocognitive theory must account for all the activities developed by our brain and provide a possible neural representation for them. Associative memories are neural network models that have a good chance of achieving a universal representation of cognitive phenomena. In this work, we present a possible neural representation of mathematical group structures based on associative memory models that store finite groups through their Cayley graphs. A context-dependent associative memory stores the transitions between elements of the group when multiplied by each generator of a given presentation of the group. Under a convenient election of the vector basis mapping the elements of the group in the neural activity, the input of a vector corresponding to a generator of the group collapses the context-dependent rectangular matrix into a virtual square permutation matrix that is the matrix representation of the generator. This neural representation corresponds to the regular representation of the group, in which to each element is assigned a permutation matrix. This action of the generator on the memory matrix can also be seen as the dissection of the corresponding monochromatic subgraph of the Cayley graph of the group, and the adjacency matrix of this subgraph is the permutation matrix corresponding to the generator.
DWI-Based Neural Fingerprinting Technology: A Preliminary Study on Stroke Analysis
Directory of Open Access Journals (Sweden)
Chenfei Ye
2014-01-01
Full Text Available Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI and diffusion tensor imaging (DTI. Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.
Personality traits modulate neural responses to emotions expressed in music.
Park, Mona; Hennig-Fast, Kristina; Bao, Yan; Carl, Petra; Pöppel, Ernst; Welker, Lorenz; Reiser, Maximilian; Meindl, Thomas; Gutyrchik, Evgeny
2013-07-26
Music communicates and evokes emotions. The number of studies on the neural correlates of musical emotion processing is increasing but few have investigated the factors that modulate these neural activations. Previous research has shown that personality traits account for individual variability of neural responses. In this study, we used functional magnetic resonance imaging (fMRI) to investigate how the dimensions Extraversion and Neuroticism are related to differences in brain reactivity to musical stimuli expressing the emotions happiness, sadness and fear. 12 participants (7 female, M=20.33 years) completed the NEO-Five Factor Inventory (NEO-FFI) and were scanned while performing a passive listening task. Neurofunctional analyses revealed significant positive correlations between Neuroticism scores and activations in bilateral basal ganglia, insula and orbitofrontal cortex in response to music expressing happiness. Extraversion scores were marginally negatively correlated with activations in the right amygdala in response to music expressing fear. Our findings show that subjects' personality may have a predictive power in the neural correlates of musical emotion processing and should be considered in the context of experimental group homogeneity. Copyright © 2013 Elsevier B.V. All rights reserved.
Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding
Sun, Zheng; Liu, Jiaqi; Zhang, Zewang; Chen, Jingwen; Huo, Zhao; Lee, Ching Hua; Zhang, Xiao
2016-01-01
Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research. Despite recent successes of long-short term memory (LSTM) recurrent neural networks (RNNs) in sequential learning, LSTM neural networks have not, by themselves, been able to generate natural-sounding music conforming to music theory. To transcend this inadequacy, we put forward a novel method for music composition that combines the LSTM with Grammars motivated by mus...
Exponential stability of neural networks with asymmetric connection weights
International Nuclear Information System (INIS)
Yang Jinxiang; Zhong Shouming
2007-01-01
This paper investigates the exponential stability of a class of neural networks with asymmetric connection weights. By dividing the network state variables into various parts according to the characters of the neural networks, some new sufficient conditions of exponential stability are derived via constructing a Lyapunov function and using the method of the variation of constant. The new conditions are associated with the initial values and are described by some blocks of the interconnection matrix, and do not depend on other blocks. Examples are given to further illustrate the theory
Park, Gyeong-Moon; Yoo, Yong-Ho; Kim, Deok-Hwa; Kim, Jong-Hwan
2017-06-26
Robots are expected to perform smart services and to undertake various troublesome or difficult tasks in the place of humans. Since these human-scale tasks consist of a temporal sequence of events, robots need episodic memory to store and retrieve the sequences to perform the tasks autonomously in similar situations. As episodic memory, in this paper we propose a novel Deep adaptive resonance theory (ART) neural model and apply it to the task performance of the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST. Deep ART has a deep structure to learn events, episodes, and even more like daily episodes. Moreover, it can retrieve the correct episode from partial input cues robustly. To demonstrate the effectiveness and applicability of the proposed Deep ART, experiments are conducted with the humanoid robot, Mybot, for performing the three tasks of arranging toys, making cereal, and disposing of garbage.
Novel mathematical neural models for visual attention
DEFF Research Database (Denmark)
Li, Kang
for the visual attention theories and spiking neuron models for single spike trains. Statistical inference and model selection are performed and various numerical methods are explored. The designed methods also give a framework for neural coding under visual attention theories. We conduct both analysis on real......Visual attention has been extensively studied in psychology, but some fundamental questions remain controversial. We focus on two questions in this study. First, we investigate how a neuron in visual cortex responds to multiple stimuli inside the receptive eld, described by either a response...... system, supported by simulation study. Finally, we present the decoding of multiple temporal stimuli under these visual attention theories, also in a realistic biophysical situation with simulations....
Stability analysis for stochastic BAM nonlinear neural network with delays
Lv, Z. W.; Shu, H. S.; Wei, G. L.
2008-02-01
In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associative memory neural networks are globally asymptotically stable in the mean square if there are solutions to some linear matrix inequalities(LMIs). Hence, the global asymptotic stability of the stochastic bidirectional associative memory neural networks can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed global asymptotic stability criteria.
Stability analysis for stochastic BAM nonlinear neural network with delays
International Nuclear Information System (INIS)
Lv, Z W; Shu, H S; Wei, G L
2008-01-01
In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associative memory neural networks are globally asymptotically stable in the mean square if there are solutions to some linear matrix inequalities(LMIs). Hence, the global asymptotic stability of the stochastic bidirectional associative memory neural networks can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed global asymptotic stability criteria
An information integration theory of consciousness
Directory of Open Access Journals (Sweden)
Tononi Giulio
2004-11-01
concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness. Implications of the hypothesis The theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts.
Resonance Analysis of High-Frequency Electrohydraulic Exciter Controlled by 2D Valve
Directory of Open Access Journals (Sweden)
Guojun Pan
2015-01-01
Full Text Available The resonant characteristic of hydraulic system has not been described yet because it is necessarily restricted by linear assumptions in classical fluid theory. A way of the resonance analysis is presented for an electrohydraulic exciter controlled by 2D valve. The block diagram of this excitation system is established by extracting nonlinear parts from the traditional linearization analysis; as a result the resonant frequency is obtained. According to input energy from oil source which is equal to the reverse energy to oil source, load pressure and load flow are solved analytically as the working frequency reaches the natural frequency. The analytical expression of resonant peak is also derived without damping. Finally, the experimental system is built to verify the theoretical analysis. The initial research on resonant characteristic will lay theoretical foundation and make useful complement for resonance phenomena of classical fluid theory in hydraulic system.
The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.
Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun
2018-01-01
Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.
Neural activity predicts attitude change in cognitive dissonance.
van Veen, Vincent; Krug, Marie K; Schooler, Jonathan W; Carter, Cameron S
2009-11-01
When our actions conflict with our prior attitudes, we often change our attitudes to be more consistent with our actions. This phenomenon, known as cognitive dissonance, is considered to be one of the most influential theories in psychology. However, the neural basis of this phenomenon is unknown. Using a Solomon four-group design, we scanned participants with functional MRI while they argued that the uncomfortable scanner environment was nevertheless a pleasant experience. We found that cognitive dissonance engaged the dorsal anterior cingulate cortex and anterior insula; furthermore, we found that the activation of these regions tightly predicted participants' subsequent attitude change. These effects were not observed in a control group. Our findings elucidate the neural representation of cognitive dissonance, and support the role of the anterior cingulate cortex in detecting cognitive conflict and the neural prediction of attitude change.
The neural basis of event simulation: an FMRI study.
Directory of Open Access Journals (Sweden)
Yukihito Yomogida
Full Text Available Event simulation (ES is the situational inference process in which perceived event features such as objects, agents, and actions are associated in the brain to represent the whole situation. ES provides a common basis for various cognitive processes, such as perceptual prediction, situational understanding/prediction, and social cognition (such as mentalizing/trait inference. Here, functional magnetic resonance imaging was used to elucidate the neural substrates underlying important subdivisions within ES. First, the study investigated whether ES depends on different neural substrates when it is conducted explicitly and implicitly. Second, the existence of neural substrates specific to the future-prediction component of ES was assessed. Subjects were shown contextually related object pictures implying a situation and performed several picture-word-matching tasks. By varying task goals, subjects were made to infer the implied situation implicitly/explicitly or predict the future consequence of that situation. The results indicate that, whereas implicit ES activated the lateral prefrontal cortex and medial/lateral parietal cortex, explicit ES activated the medial prefrontal cortex, posterior cingulate cortex, and medial/lateral temporal cortex. Additionally, the left temporoparietal junction plays an important role in the future-prediction component of ES. These findings enrich our understanding of the neural substrates of the implicit/explicit/predictive aspects of ES-related cognitive processes.
Parton Theory of Magnetic Polarons: Mesonic Resonances and Signatures in Dynamics
Grusdt, F.; Kánasz-Nagy, M.; Bohrdt, A.; Chiu, C. S.; Ji, G.; Greiner, M.; Greif, D.; Demler, E.
2018-01-01
When a mobile hole is moving in an antiferromagnet it distorts the surrounding Néel order and forms a magnetic polaron. Such interplay between hole motion and antiferromagnetism is believed to be at the heart of high-temperature superconductivity in cuprates. In this article, we study a single hole described by the t -Jz model with Ising interactions between the spins in two dimensions. This situation can be experimentally realized in quantum gas microscopes with Mott insulators of Rydberg-dressed bosons or fermions, or using polar molecules. We work at strong couplings, where hole hopping is much larger than couplings between the spins. In this regime we find strong theoretical evidence that magnetic polarons can be understood as bound states of two partons, a spinon and a holon carrying spin and charge quantum numbers, respectively. Starting from first principles, we introduce a microscopic parton description which is benchmarked by comparison with results from advanced numerical simulations. Using this parton theory, we predict a series of excited states that are invisible in the spectral function and correspond to rotational excitations of the spinon-holon pair. This is reminiscent of mesonic resonances observed in high-energy physics, which can be understood as rotating quark-antiquark pairs carrying orbital angular momentum. Moreover, we apply the strong-coupling parton theory to study far-from-equilibrium dynamics of magnetic polarons observable in current experiments with ultracold atoms. Our work supports earlier ideas that partons in a confining phase of matter represent a useful paradigm in condensed-matter physics and in the context of high-temperature superconductivity in particular. While direct observations of spinons and holons in real space are impossible in traditional solid-state experiments, quantum gas microscopes provide a new experimental toolbox. We show that, using this platform, direct observations of partons in and out of equilibrium are
Stability Analysis and Application for Delayed Neural Networks Driven by Fractional Brownian Noise.
Zhou, Wuneng; Zhou, Xianghui; Yang, Jun; Zhou, Jun; Tong, Dongbing
2018-05-01
This paper deals with two types of the stability problem for the delayed neural networks driven by fractional Brownian noise (FBN). The existence and the uniqueness of the solution to the main system with respect to FBN are proved via fixed point theory. Based on Hilbert-Schmidt operator theory and analytic semigroup principle, the mild solution of the stochastic neural networks is obtained. By applying the stochastic analytic technique and some well-known inequalities, the asymptotic stability criteria and the exponential stability condition are established. Both numerical example and practical application for synchronization control of multiagent system are provided to illustrate the effectiveness and potential of the proposed techniques.
Pinning synchronization of memristor-based neural networks with time-varying delays.
Yang, Zhanyu; Luo, Biao; Liu, Derong; Li, Yueheng
2017-09-01
In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.
Resonance – Journal of Science Education | Indian Academy of ...
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 14; Issue 8. Complex Systems: An Introduction - Information Theory, Chaos Theory and Computational Complexity. V K Wadhawan. General Article Volume 14 Issue 8 August 2009 pp 761-781 ...
Neural bases of ingroup altruistic motivation in soccer fans.
Bortolini, Tiago; Bado, Patrícia; Hoefle, Sebastian; Engel, Annerose; Zahn, Roland; de Oliveira Souza, Ricardo; Dreher, Jean-Claude; Moll, Jorge
2017-11-23
Humans have a strong need to belong to social groups and a natural inclination to benefit ingroup members. Although the psychological mechanisms behind human prosociality have extensively been studied, the specific neural systems bridging group belongingness and altruistic motivation remain to be identified. Here, we used soccer fandom as an ecological framing of group membership to investigate the neural mechanisms underlying ingroup altruistic behaviour in male fans using event-related functional magnetic resonance. We designed an effort measure based on handgrip strength to assess the motivation to earn money (i) for oneself, (ii) for anonymous ingroup fans, or (iii) for a neutral group of anonymous non-fans. While overlapping valuation signals in the medial orbitofrontal cortex (mOFC) were observed for the three conditions, the subgenual cingulate cortex (SCC) exhibited increased functional connectivity with the mOFC as well as stronger hemodynamic responses for ingroup versus outgroup decisions. These findings indicate a key role for the SCC, a region previously implicated in altruistic decisions and group affiliation, in dovetailing altruistic motivations with neural valuation systems in real-life ingroup behaviour.
An analytic approach to probability tables for the unresolved resonance region
Brown, David; Kawano, Toshihiko
2017-09-01
The Unresolved Resonance Region (URR) connects the fast neutron region with the Resolved Resonance Region (RRR). The URR is problematic since resonances are not resolvable experimentally yet the fluctuations in the neutron cross sections play a discernible and technologically important role: the URR in a typical nucleus is in the 100 keV - 2 MeV window where the typical fission spectrum peaks. The URR also represents the transition between R-matrix theory used to described isolated resonances and Hauser-Feshbach theory which accurately describes the average cross sections. In practice, only average or systematic features of the resonances in the URR are known and are tabulated in evaluations in a nuclear data library such as ENDF/B-VII.1. Codes such as AMPX and NJOY can compute the probability distribution of the cross section in the URR under some assumptions using Monte Carlo realizations of sets of resonances. These probability distributions are stored in the so-called PURR tables. In our work, we begin to develop a scheme for computing the covariance of the cross section probability distribution analytically. Our approach offers the possibility of defining the limits of applicability of Hauser-Feshbach theory and suggests a way to calculate PURR tables directly from systematics for nuclei whose RRR is unknown, provided one makes appropriate assumptions about the shape of the cross section probability distribution.
The price of your soul: neural evidence for the non-utilitarian representation of sacred values.
Berns, Gregory S; Bell, Emily; Capra, C Monica; Prietula, Michael J; Moore, Sara; Anderson, Brittany; Ginges, Jeremy; Atran, Scott
2012-03-05
Sacred values, such as those associated with religious or ethnic identity, underlie many important individual and group decisions in life, and individuals typically resist attempts to trade off their sacred values in exchange for material benefits. Deontological theory suggests that sacred values are processed based on rights and wrongs irrespective of outcomes, while utilitarian theory suggests that they are processed based on costs and benefits of potential outcomes, but which mode of processing an individual naturally uses is unknown. The study of decisions over sacred values is difficult because outcomes cannot typically be realized in a laboratory, and hence little is known about the neural representation and processing of sacred values. We used an experimental paradigm that used integrity as a proxy for sacredness and which paid real money to induce individuals to sell their personal values. Using functional magnetic resonance imaging (fMRI), we found that values that people refused to sell (sacred values) were associated with increased activity in the left temporoparietal junction and ventrolateral prefrontal cortex, regions previously associated with semantic rule retrieval. This suggests that sacred values affect behaviour through the retrieval and processing of deontic rules and not through a utilitarian evaluation of costs and benefits.
Algebraic and structural automata theory
Mikolajczak, B
1991-01-01
Automata Theory is part of computability theory which covers problems in computer systems, software, activity of nervous systems (neural networks), and processes of live organisms development.The result of over ten years of research, this book presents work in the following areas of Automata Theory: automata morphisms, time-varying automata, automata realizations and relationships between automata and semigroups.Aimed at those working in discrete mathematics and computer science, parts of the book are suitable for use in graduate courses in computer science, electronics, telecommunications, and control engineering. It is assumed that the reader is familiar with the basic concepts of algebra and graph theory.
Clinical magnetic resonance: imaging and spectroscopy
International Nuclear Information System (INIS)
Andrew, E.R.; Bydder, Graeme; Griffiths, John; Iles, Richard; Styles, Peter
1990-01-01
This book begins with a readable, comprehensive but non-mathematical introduction to the basic underlying principles of magnetic resonance. Further chapters include information on the theory and principles of MRI and MRS, the interpretation of MR images, the clinical applications and scope of MRI and MRS, practical aspects of spectroscopy and magnetic resonance, and also the practical problems associated with the siting, safety and operation of large MRI and MRS equipment. (author)
Generalized Projective Synchronization between Two Different Neural Networks with Mixed Time Delays
Directory of Open Access Journals (Sweden)
Xuefei Wu
2012-01-01
Full Text Available The generalized projective synchronization (GPS between two different neural networks with nonlinear coupling and mixed time delays is considered. Several kinds of nonlinear feedback controllers are designed to achieve GPS between two different such neural networks. Some results for GPS of these neural networks are proved theoretically by using the Lyapunov stability theory and the LaSalle invariance principle. Moreover, by comparison, we determine an optimal nonlinear controller from several ones and provide an adaptive update law for it. Computer simulations are provided to show the effectiveness and feasibility of the proposed methods.
Motivational orientation modulates the neural response to reward.
Linke, Julia; Kirsch, Peter; King, Andrea V; Gass, Achim; Hennerici, Michael G; Bongers, André; Wessa, Michèle
2010-02-01
Motivational orientation defines the source of motivation for an individual to perform a particular action and can either originate from internal desires (e.g., interest) or external compensation (e.g., money). To this end, motivational orientation should influence the way positive or negative feedback is processed during learning situations and this might in turn have an impact on the learning process. In the present study, we thus investigated whether motivational orientation, i.e., extrinsic and intrinsic motivation modulates the neural response to reward and punishment as well as learning from reward and punishment in 33 healthy individuals. To assess neural responses to reward, punishment and learning of reward contingencies we employed a probabilistic reversal learning task during functional magnetic resonance imaging. Extrinsic and intrinsic motivation were assessed with a self-report questionnaire. Rewarding trials fostered activation in the medial orbitofrontal cortex and anterior cingulate gyrus (ACC) as well as the amygdala and nucleus accumbens, whereas for punishment an increased neural response was observed in the medial and inferior prefrontal cortex, the superior parietal cortex and the insula. High extrinsic motivation was positively correlated to increased neural responses to reward in the ACC, amygdala and putamen, whereas a negative relationship between intrinsic motivation and brain activation in these brain regions was observed. These findings show that motivational orientation indeed modulates the responsiveness to reward delivery in major components of the human reward system and therefore extends previous results showing a significant influence of individual differences in reward-related personality traits on the neural processing of reward. Copyright (c) 2009 Elsevier Inc. All rights reserved.
Physics of pitch angle scattering and velocity diffusion. I - Theory
Karimabadi, H.; Krauss-Varban, D.; Terasawa, T.
1992-01-01
A general theory for the pitch angle scattering and velocity diffusion of particles in the field of a spectrum of waves in a magnetized plasma is presented. The test particle theory is used to analyze the particle motion. The form of diffusion surfaces is examined, and analytical expressions are given for the resonance width and bounce frequency. The resonance widths are found to vary strongly as a function of harmonic number. The resulting diffusion can be quite asymmetric with respect to pitch angle of 90 deg. The conditions for the onset of pitch angle scattering and energy diffusion are explained in detail. Some of the known shortcomings of the standard quasi-linear theory are also addressed, and ways to overcome them are shown. In particular, the often stated quasi-linear gap at 90 deg is found to exist only under very special cases. For instance, oblique wave propagation can easily remove the gap. The conditions for the existence of the gap are described in great detail. A new diffusion equation which takes into account the finite resonance widths is also discussed. The differences between this new theory and the standard resonance broadening theory is explained.
Germ layers, the neural crest and emergent organization in development and evolution.
Hall, Brian K
2018-04-10
Discovered in chick embryos by Wilhelm His in 1868 and named the neural crest by Arthur Milnes Marshall in 1879, the neural crest cells that arise from the neural folds have since been shown to differentiate into almost two dozen vertebrate cell types and to have played major roles in the evolution of such vertebrate features as bone, jaws, teeth, visceral (pharyngeal) arches, and sense organs. I discuss the discovery that ectodermal neural crest gave rise to mesenchyme and the controversy generated by that finding; the germ layer theory maintained that only mesoderm could give rise to mesenchyme. A second topic of discussion is germ layers (including the neural crest) as emergent levels of organization in animal development and evolution that facilitated major developmental and evolutionary change. The third topic is gene networks, gene co-option, and the evolution of gene-signaling pathways as key to developmental and evolutionary transitions associated with the origin and evolution of the neural crest and neural crest cells. © 2018 Wiley Periodicals, Inc.
Neural reactivation links unconscious thought to decision-making performance
Creswell, John David; Bursley, James K.; Satpute, Ajay B.
2013-01-01
Brief periods of unconscious thought (UT) have been shown to improve decision making compared with making an immediate decision (ID). We reveal a neural mechanism for UT in decision making using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Participants (N = 33) encoded information on a set of consumer products (e.g. 48 attributes describing four different cars), and we manipulated whether participants (i) consciously thought about this information (conscious thou...
Genetic optimization of neural network architecture
International Nuclear Information System (INIS)
Harp, S.A.; Samad, T.
1994-03-01
Neural networks are now a popular technology for a broad variety of application domains, including the electric utility industry. Yet, as the technology continues to gain increasing acceptance, it is also increasingly apparent that the power that neural networks provide is not an unconditional blessing. Considerable care must be exercised during application development if the full benefit of the technology is to be realized. At present, no fully general theory or methodology for neural network design is available, and application development is a trial-and-error process that is time-consuming and expertise-intensive. Each application demands appropriate selections of the network input space, the network structure, and values of learning algorithm parameters-design choices that are closely coupled in ways that largely remain a mystery. This EPRI-funded exploratory research project was initiated to take the key next step in this research program: the validation of the approach on a realistic problem. We focused on the problem of modeling the thermal performance of the TVA Sequoyah nuclear power plant (units 1 and 2)
Lamp, Gemma; Alexander, Bonnie; Laycock, Robin; Crewther, David P; Crewther, Sheila G
2016-01-01
Mapping of the underlying neural mechanisms of visuo-spatial working memory (WM) has been shown to consistently elicit activity in right hemisphere dominant fronto-parietal networks. However to date, the bulk of neuroimaging literature has focused largely on the maintenance aspect of visuo-spatial WM, with a scarcity of research into the aspects of WM involving manipulation of information. Thus, this study aimed to compare maintenance-only with maintenance and manipulation of visuo-spatial stimuli (3D cube shapes) utilizing a 1-back task while functional magnetic resonance imaging (fMRI) scans were acquired. Sixteen healthy participants (9 women, M = 23.94 years, SD = 2.49) were required to perform the 1-back task with or without mentally rotating the shapes 90° on a vertical axis. When no rotation was required (maintenance-only condition), a right hemispheric lateralization was revealed across fronto-parietal areas. However, when the task involved maintaining and manipulating the same stimuli through 90° rotation, activation was primarily seen in the bilateral parietal lobe and left fusiform gyrus. The findings confirm that the well-established right lateralized fronto-parietal networks are likely to underlie simple maintenance of visuo-spatial stimuli. The results also suggest that the added demand of manipulation of information maintained online appears to require further neural recruitment of functionally related areas. In particular mental rotation of visuospatial stimuli required bilateral parietal areas, and the left fusiform gyrus potentially to maintain a categorical or object representation. It can be concluded that WM is a complex neural process involving the interaction of an increasingly large network.
On the kinetic theory of parametric resonance in relativistic plasma
International Nuclear Information System (INIS)
El-Ashry, M.Y.
1982-08-01
The instability of relativistic hot plasma located in high-frequency external electric field is studied. The dispersion relation, in the case when the plasma electrons have relativistic oscillatory motion, is obtained. It is shown that if the electron Deby's radius is less than the wave length of plasma oscillation and far from the resonance on the overtones of the external field frequency, the oscillation build-up is possible. It is also shown that taking into account the relativistic motion of electrons leads to a considerable decrease in the frequency at which the parametric resonance takes place. (author)
S-wave Kπ scattering in chiral perturbation theory with resonances
International Nuclear Information System (INIS)
Jamin, Matthias; Oller, Jose Antonio; Pich, Antonio
2000-01-01
We present a detailed analysis of S-wave Kπ scattering up to 2 GeV, making use of the resonance chiral Lagrangian predictions together with a suitable unitarisation method. Our approach incorporates known theoretical constraints at low and high energies. The present experimental status, with partly conflicting data from different experiments, is discussed. Our analysis allows to resolve some experimental ambiguities, but better data are needed in order to determine the cross-section in the higher-energy range. Our best fits are used to determine the masses and widths of the relevant scalar resonances in this energy region
On the Elementary Neural Forms of Micro-Interactional Rituals
DEFF Research Database (Denmark)
Heinskou, Marie Bruvik; Liebst, Lasse Suonperä
2016-01-01
of the neural basis for rhythmic entrainment. The polyvagal theory furthermore challenges IR theory to reconsider the importance of individual biological differences ritual success may not merely be ascribed to interactional eﬀects, but also to reciprocal causality between situations and neurobiological......Randall Collins’s interaction ritual (IR) theory suggests social solidarity as hardwired in the human neurological capacity for rhythmic entrainment. Yet, this article suggests that IR theory may beneﬁt from being tied more ﬁrmly to recent neurobiological research, speciﬁcally Stephen W. Porges......’s polyvagal theory that proposes autonomic nervous system functioning as a basis for emotions and social behavior. In this perspective, IR theory does not suﬃciently acknowledge the human nervous system as a system involving a phylogenetically ordered response hierarchy, of which only one subsystem supports...
Elnaggar, Sameh Y.; Tervo, Richard; Mattar, Saba M.
2014-01-01
Probes consisting of a dielectric resonator (DR) inserted in a cavity are important integral components of electron paramagnetic resonance (EPR) spectrometers because of their high signal-to-noise ratio. This article studies the behavior of this system, based on the coupling between its dielectric and cavity modes. Coupled-mode theory (CMT) is used to determine the frequencies and electromagnetic fields of this coupled system. General expressions for the frequencies and field distributions are derived for both the resulting symmetric and anti-symmetric modes. These expressions are applicable to a wide range of frequencies (from MHz to THz). The coupling of cavities and DRs of various sizes and their resonant frequencies are studied in detail. Since the DR is situated within the cavity then the coupling between them is strong. In some cases the coupling coefficient, κ, is found to be as high as 0.4 even though the frequency difference between the uncoupled modes is large. This is directly attributed to the strong overlap between the fields of the uncoupled DR and cavity modes. In most cases, this improves the signal to noise ratio of the spectrometer. When the DR and the cavity have the same frequency, the coupled electromagnetic fields are found to contain equal contributions from the fields of the two uncoupled modes. This situation is ideal for the excitation of the probe through an iris on the cavity wall. To verify and validate the results, finite element simulations are carried out. This is achieved by simulating the coupling between a cylindrical cavity's TE011 and the dielectric insert's TE01δ modes. Coupling between the modes of higher order is also investigated and discussed. Based on CMT, closed form expressions for the fields of the coupled system are proposed. These expressions are crucial in the analysis of the probe's performance.
Meaning Finds a Way: Chaos (Theory) and Composition
Kyburz, Bonnie Lenore
2004-01-01
The explanatory power provided by the chaos theory is explored. A dynamic and reciprocal relationship between culture and chaos theory indicates that the progressive cultural work may be formed by the cross-disciplinary resonance of chaos theory.
Bejanin, Alexandre; Chételat, Gaël; Laisney, Mickael; Pélerin, Alice; Landeau, Brigitte; Merck, Catherine; Belliard, Serge; de La Sayette, Vincent; Eustache, Francis; Desgranges, Béatrice
2017-06-01
Using structural MRI, we investigated the brain substrates of both affective and cognitive theory of mind (ToM) in 19 patients with semantic dementia. We also ran intrinsic connectivity analyses to identify the networks to which the substrates belong and whether they are functionally disturbed in semantic dementia. In line with previous studies, we observed a ToM impairment in patients with semantic dementia even when semantic memory was regressed out. Our results also highlighted different neural bases according to the nature (affective or cognitive) of the representations being inferred. The affective ToM deficit was associated with atrophy in the amygdala, suggesting the involvement of emotion-processing deficits in this impairment. By contrast, cognitive ToM performances were correlated with the volume of medial prefrontal and parietal regions, as well as the right frontal operculum. Intrinsic connectivity analyses revealed decreased functional connectivity, mainly between midline cortical regions and temporal regions. They also showed that left medial temporal regions were functionally isolated, a further possible hindrance to normal social cognitive functioning in semantic dementia. Overall, this study addressed for the first time the neuroanatomical substrates of both cognitive and affective ToM disruption in semantic dementia, highlighting disturbed connectivity within the networks that sustain these abilities.
Automatic Speech Recognition from Neural Signals: A Focused Review
Directory of Open Access Journals (Sweden)
Christian Herff
2016-09-01
Full Text Available Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e.~patients suffering from locked-in syndrome. For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people.This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography. As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the emph{Brain-to-text} system.
Low, Yin Fen; Trenado, Carlos; Delb, Wolfgang; Corona-Strauss, Farah I; Strauss, Daniel J
2007-01-01
Large-scale neural correlates of the tinnitus decompensation have been identified by using wavelet phase stability criteria of single sweep sequences of auditory late responses (ALRs). The suggested measure provided an objective quantification of the tinnitus decompensation and allowed for a reliable discrimination between a group of compensated and decompensated tinnitus patients. By interpreting our results with an oscillatory tinnitus model, our synchronization stability measure of ALRs can be linked to the focus of attention on the tinnitus signal. In the following study, we examined in detail the correlates of this attentional mechanism in healthy subjects. The results support our previous findings of the phase synchronization stability measure that reflected neural correlates of the fixation of attention to the tinnitus signal. In this case, enabling the differentiation between the attended and unattended conditions. It is concluded that the wavelet phase synchronization stability of ALRs single sweeps can be used as objective tinnitus decompensation measure and can be interpreted in the framework of the Jastreboff tinnitus model and adaptive resonance theory. Our studies confirm that the synchronization stability in ALR sequences is linked to attention. This measure is not only able to serve as objective quantification of the tinnitus decompensation, but also can be applied in all online and real time neurofeedback therapeutic approach where a direct stimulus locked attention monitoring is compulsory as if it based on a single sweeps processing.
Theory of laser-assisted autoionization by attosecond light pulses
International Nuclear Information System (INIS)
Zhao, Z.X.; Lin, C.D.
2005-01-01
We present a quantum theory of the decay of an autoionizing state created in the attosecond xuv (extreme ultraviolet) pump and laser probe measurements within the strong field approximation employing resonance parameters from Fano's theory. From the electron spectra versus the pump-probe time delay, we show how the lifetimes of the resonances can be extracted directly from the time domain measurements
International Nuclear Information System (INIS)
Moon, Sang Ki; Chang, Soon Heung
1994-01-01
A new method to predict the critical heat flux (CHF) is proposed, based on the fuzzy clustering and artificial neural network. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulting clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanism. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. ((orig.))
Neural substrates of male parochial altruism are modulated by testosterone and behavioral strategy.
Reimers, Luise; Büchel, Christian; Diekhof, Esther K
2017-08-01
Parochial altruism refers to ingroup favoritism and outgroup hostility and has recently been linked to testosterone. Here, we investigated the neurobiological mechanism of parochial altruism in male soccer fans playing the ultimatum game (UG) against ingroup and outgroup members (i.e., fans of the favorite or of a rivalling team) using functional magnetic resonance imaging. Our results suggest that individual differences in altruistic tendency influence the tendency for parochialism. While altruistic subjects rejected unfair offers independent of team membership, the more self-oriented 'pro-selfs' displayed a stronger ingroup bias and rejected outgroup offers more often. However, during a second session that introduced a team competition the altruists adapted to this parochial pattern. Behavioral strategy was further characterized by dissociable and context-dependent correlations between endogenous testosterone and neural responses in the anterior insula and the ventromedial prefrontal cortex. In sum, the present findings indicate that parochial altruism is shaped by individual differences in testosterone and behavioral strategy. In that way the results are in line with evolutionary theories of both individual and group selection. Copyright © 2017 Elsevier Inc. All rights reserved.
Stark resonances: asymptotics and distributional Borel sum
International Nuclear Information System (INIS)
Caliceti, E.; Grecchi, V.; Maioli, M.
1993-01-01
We prove that the Stark effect perturbation theory of a class of bound states uniquely determines the position and the width of the resonances by Distributional Borel Sum. In particular the small field asymptotics of the width is uniquely related to the large order asymptotics of the perturbation coefficients. Similar results apply to all the ''resonances'' of the anharmonic and double well oscillators. (orig.)
Magnetic Resonance Imaging Studies of Postpartum Depression: An Overview
Directory of Open Access Journals (Sweden)
Marco Fiorelli
2015-01-01
Full Text Available Postpartum depression is a frequent and disabling condition whose pathophysiology is still unclear. In recent years, the study of the neural correlates of mental disorders has been increasingly approached using magnetic resonance techniques. In this review we synthesize the results from studies on postpartum depression in the context of structural, functional, and spectroscopic magnetic resonance studies of major depression as a whole. Compared to the relative wealth of data available for major depression, magnetic resonance studies of postpartum depression are limited in number and design. A systematic literature search yielded only eleven studies conducted on about one hundred mothers with postpartum depression overall. Brain magnetic resonance findings in postpartum depression appear to replicate those obtained in major depression, with minor deviations that are not sufficient to delineate a distinct neurobiological profile for this condition, due to the small samples used and the lack of direct comparisons with subjects with major depression. However, it seems reasonable to expect that studies conducted in larger populations, and using a larger variety of brain magnetic resonance techniques than has been done so far, might allow for the identification of neuroimaging signatures for postpartum depression.
Resonance charge exchange processes
International Nuclear Information System (INIS)
Duman, E.L.; Evseev, A.V.; Eletskij, A.V.; Radtsig, A.A.; Smirnov, B.M.
1979-01-01
The calculation results for the resonance charge exchange cross sections for positive and negative atomic and molecular ions are given. The calculations are performed on the basis of the asymptotic theory. The factors affecting the calculation accuracy are analysed. The calculation data for 28 systems are compared with the experiment
Multi-stability and almost periodic solutions of a class of recurrent neural networks
International Nuclear Information System (INIS)
Liu Yiguang; You Zhisheng
2007-01-01
This paper studies multi-stability, existence of almost periodic solutions of a class of recurrent neural networks with bounded activation functions. After introducing a sufficient condition insuring multi-stability, many criteria guaranteeing existence of almost periodic solutions are derived using Mawhin's coincidence degree theory. All the criteria are constructed without assuming the activation functions are smooth, monotonic or Lipschitz continuous, and that the networks contains periodic variables (such as periodic coefficients, periodic inputs or periodic activation functions), so all criteria can be easily extended to fit many concrete forms of neural networks such as Hopfield neural networks, or cellular neural networks, etc. Finally, all kinds of simulations are employed to illustrate the criteria
Electrothermally Tunable Arch Resonator
Hajjaj, Amal Z.
2017-03-18
This paper demonstrates experimentally, theoretically, and numerically a wide-range tunability of electrothermally actuated microelectromechanical arch beams. The beams are made of silicon and are intentionally fabricated with some curvature as in-plane shallow arches. An electrothermal voltage is applied between the anchors of the beam generating a current that controls the axial stress caused by thermal expansion. When the electrothermal voltage increases, the compressive stress increases inside the arch beam. This leads to an increase in its curvature, thereby increasing its resonance frequencies. We show here that the first resonance frequency can increase monotonically up to twice its initial value. We show also that after some electrothermal voltage load, the third resonance frequency starts to become more sensitive to the axial thermal stress, while the first resonance frequency becomes less sensitive. These results can be used as guidelines to utilize arches as wide-range tunable resonators. Analytical results based on the nonlinear Euler Bernoulli beam theory are generated and compared with the experimental data and the results of a multi-physics finite-element model. A good agreement is found among all the results. [2016-0291
Resonant scattering in the presence of an electromagnetic field
International Nuclear Information System (INIS)
Rosenberg, L.
1983-01-01
The theory of resonant reactions, in the projection-operator formulation of Feshbach, is generalized to account for the presence of an external electromagnetic field. The theory is used as the basis for the construction of low-frequency approximations for the transition amplitude. Results obtained here for scattering in a laser field confirm earlier versions of the low-frequency approximation when the resonances are isolated. However, if there are several closely spaced resonances additional terms must be included (their importance magnified by the appearance of near singularities) which account for the effect of radiative transitions between pairs of nearly degenerate resonant states. The weak-field limit of this result yields a low-frequency approximation for single-photon spontaneous bremsstrahlung which, through the inclusion of correction terms associated with closely spaced resonances, provides an improvement over the Feshbach-Yennie version derived some time ago. A separate treatment is required to deal with the limiting case of a static external field and this is worked out here in the context of a time-dependent formulation of the scattering problem. Linear and quadratic Stark splitting of the resonance positions, and resonance broadening due to the tunneling mechanism, are expected to play a significant role in the static limit and these effects are included in the approximation derived here for the transition amplitude
Neural Responses to Heartbeats in the Default Network Encode the Self in Spontaneous Thoughts
Babo-Rebelo, Mariana; Richter, Craig G.
2016-01-01
The default network (DN) has been consistently associated with self-related cognition, but also to bodily state monitoring and autonomic regulation. We hypothesized that these two seemingly disparate functional roles of the DN are functionally coupled, in line with theories proposing that selfhood is grounded in the neural monitoring of internal organs, such as the heart. We measured with magnetoencephalograhy neural responses evoked by heartbeats while human participants freely mind-wandered. When interrupted by a visual stimulus at random intervals, participants scored the self-relatedness of the interrupted thought. They evaluated their involvement as the first-person perspective subject or agent in the thought (“I”), and on another scale to what degree they were thinking about themselves (“Me”). During the interrupted thought, neural responses to heartbeats in two regions of the DN, the ventral precuneus and the ventromedial prefrontal cortex, covaried, respectively, with the “I” and the “Me” dimensions of the self, even at the single-trial level. No covariation between self-relatedness and peripheral autonomic measures (heart rate, heart rate variability, pupil diameter, electrodermal activity, respiration rate, and phase) or alpha power was observed. Our results reveal a direct link between selfhood and neural responses to heartbeats in the DN and thus directly support theories grounding selfhood in the neural monitoring of visceral inputs. More generally, the tight functional coupling between self-related processing and cardiac monitoring observed here implies that, even in the absence of measured changes in peripheral bodily measures, physiological and cognitive functions have to be considered jointly in the DN. SIGNIFICANCE STATEMENT The default network (DN) has been consistently associated with self-processing but also with autonomic regulation. We hypothesized that these two functions could be functionally coupled in the DN, inspired by
[Functional magnetic resonance imaging in psychiatry and psychotherapy].
Derntl, B; Habel, U; Schneider, F
2010-01-01
technical improvements, functional magnetic resonance imaging (fMRI) has become the most popular and versatile imaging method in psychiatric research. The scope of this manuscript is to briefly introduce the basics of MR physics, the blood oxygenation level-dependent (BOLD) contrast as well as the principles of MR study design and functional data analysis. The presentation of exemplary studies on emotion recognition and empathy in schizophrenia patients will highlight the importance of MR methods in psychiatry. Finally, we will demonstrate insights into new developments that will further boost MR techniques in clinical research and will help to gain more insight into dysfunctional neural networks underlying cognitive and emotional deficits in psychiatric patients. Moreover, some techniques such as neurofeedback seem promising for evaluation of therapy effects on a behavioral and neural level.
P-wave Feshbach resonances of ultracold 6Li
International Nuclear Information System (INIS)
Zhang, J.; Kempen, E.G.M. van; Bourdel, T.; Cubizolles, J.; Chevy, F.; Teichmann, M.; Tarruell, L.; Salomon, C.; Khaykovich, L.; Kokkelmans, S.J.J.M.F.
2004-01-01
We report the observation of three p-wave Feshbach resonances of 6 Li atoms in the lowest hyperfine state f=1/2. The positions of the resonances are in good agreement with theory. We study the lifetime of the cloud in the vicinity of the Feshbach resonances and show that, depending on the spin states, two- or three-body mechanisms are at play. In the case of dipolar losses, we observe a nontrivial temperature dependence that is well explained by a simple model
Saggar, Manish; Shelly, Elizabeth Walter; Lepage, Jean-Francois; Hoeft, Fumiko; Reiss, Allan L
2014-01-01
Understanding the intentions and desires of those around us is vital for adapting to a dynamic social environment. In this paper, a novel event-related functional Magnetic Resonance Imaging (fMRI) paradigm with dynamic and natural stimuli (2s video clips) was developed to directly examine the neural networks associated with processing of gestures with social intent as compared to nonsocial intent. When comparing social to nonsocial gestures, increased activation in both the mentalizing (or theory of mind) and amygdala networks was found. As a secondary aim, a factor of actor-orientation was included in the paradigm to examine how the neural mechanisms differ with respect to personal engagement during a social interaction versus passively observing an interaction. Activity in the lateral occipital cortex and precentral gyrus was found sensitive to actor-orientation during social interactions. Lastly, by manipulating face-visibility we tested whether facial information alone is the primary driver of neural activation differences observed between social and nonsocial gestures. We discovered that activity in the posterior superior temporal sulcus (pSTS) and fusiform gyrus (FFG) was partially driven by observing facial expressions during social gestures. Altogether, using multiple factors associated with processing of natural social interaction, we conceptually advance our understanding of how social stimuli is processed in the brain and discuss the application of this paradigm to clinical populations where atypical social cognition is manifested as a key symptom. © 2013.
Saggar, Manish; Shelly, Elizabeth Walter; Lepage, Jean-Francois; Hoeft, Fumiko; Reiss, Allan L.
2013-01-01
Understanding the intentions and desires of those around us is vital for adapting to a dynamic social environment. In this paper, a novel event-related functional Magnetic Resonance Imaging (fMRI) paradigm with dynamic and natural stimuli (2s video clips) was developed to directly examine the neural networks associated with processing of gestures with social intent as compared to nonsocial intent. When comparing social to nonsocial gestures, increased activation in both the mentalizing (or theory of mind) and amygdala networks were found. As a secondary aim, a factor of actor-orientation was included in the paradigm to examine how the neural mechanisms differ with respect to personal engagement during a social interaction versus passively observing an interaction. Activity in the lateral occipital cortex and precentral gyrus were found sensitive to actor-orientation during social interactions. Lastly, by manipulating face-visibility we tested whether facial information alone is the primary driver of neural activation differences observed between social and nonsocial gestures. We discovered that activity in the posterior superior temporal sulcus (pSTS) and fusiform gyrus (FFG) were partially driven by observing facial expressions during social gestures. Altogether, using multiple factors associated with processing of natural social interaction, we conceptually advance our understanding of how social stimuli is processed in the brain and discuss the application of this paradigm to clinical populations where atypical social cognition is manifested as a key symptom. PMID:24084068
Voos, Avery C.; Pelphrey, Kevin A.; Tirrell, Jonathan; Bolling, Danielle Z.; Vander Wyk, Brent; Kaiser, Martha D.; McPartland, James C.; Volkmar, Fred R.; Ventola, Pamela
2013-01-01
Pivotal response treatment (PRT) is an empirically validated behavioral treatment that has widespread positive effects on communication, behavior, and social skills in young children with autism spectrum disorder (ASD). For the first time, functional magnetic resonance imaging was used to identify the neural correlates of successful response to…