Dynamic Neural Fields with Intrinsic Plasticity.
Strub, Claudius; Schöner, Gregor; Wörgötter, Florentin; Sandamirskaya, Yulia
2017-01-01
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, homogeneous, and recurrently connected neural networks based on a mean field approach. Within dynamic field theory, the DNFs have been used as building blocks in architectures to model sensorimotor embedding of cognitive processes. Typically, the parameters of a DNF in an architecture are manually tuned in order to achieve a specific dynamic behavior (e.g., decision making, selection, or working memory) for a given input pattern. This manual parameters search requires expert knowledge and time to find and verify a suited set of parameters. The DNF parametrization may be particular challenging if the input distribution is not known in advance, e.g., when processing sensory information. In this paper, we propose the autonomous adaptation of the DNF resting level and gain by a learning mechanism of intrinsic plasticity (IP). To enable this adaptation, an input and output measure for the DNF are introduced, together with a hyper parameter to define the desired output distribution. The online adaptation by IP gives the possibility to pre-define the DNF output statistics without knowledge of the input distribution and thus, also to compensate for changes in it. The capabilities and limitations of this approach are evaluated in a number of experiments.
Neural Population Dynamics Modeled by Mean-Field Graphs
Kozma, Robert; Puljic, Marko
2011-09-01
In this work we apply random graph theory approach to describe neural population dynamics. There are important advantages of using random graph theory approach in addition to ordinary and partial differential equations. The mathematical theory of large-scale random graphs provides an efficient tool to describe transitions between high- and low-dimensional spaces. Recent advances in studying neural correlates of higher cognition indicate the significance of sudden changes in space-time neurodynamics, which can be efficiently described as phase transitions in the neuropil medium. Phase transitions are rigorously defined mathematically on random graph sequences and they can be naturally generalized to a class of percolation processes called neuropercolation. In this work we employ mean-field graphs with given vertex degree distribution and edge strength distribution. We demonstrate the emergence of collective oscillations in the style of brains.
Dynamic Neural Fields as a Step Towards Cognitive Neuromorphic Architectures
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Yulia eSandamirskaya
2014-01-01
Full Text Available Dynamic Field Theory (DFT is an established framework for modelling embodied cognition. In DFT, elementary cognitive functions such as memory formation, formation of grounded representations, attentional processes, decision making, adaptation, and learning emerge from neuronal dynamics. The basic computational element of this framework is a Dynamic Neural Field (DNF. Under constraints on the time-scale of the dynamics, the DNF is computationally equivalent to a soft winner-take-all (WTA network, which is considered one of the basic computational units in neuronal processing. Recently, it has been shown how a WTA network may be implemented in neuromorphic hardware, such as analogue Very Large Scale Integration (VLSI device. This paper leverages the relationship between DFT and soft WTA networks to systematically revise and integrate established DFT mechanisms that have previously been spread among different architectures. In addition, I also identify some novel computational and architectural mechanisms of DFT which may be implemented in neuromorphic VLSI devices using WTA networks as an intermediate computational layer. These specific mechanisms include the stabilization of working memory, the coupling of sensory systems to motor dynamics, intentionality, and autonomous learning. I further demonstrate how all these elements may be integrated into a unified architecture to generate behavior and autonomous learning.
TUTORIAL: The dynamic neural field approach to cognitive robotics
Erlhagen, Wolfram; Bicho, Estela
2006-09-01
This tutorial presents an architecture for autonomous robots to generate behavior in joint action tasks. To efficiently interact with another agent in solving a mutual task, a robot should be endowed with cognitive skills such as memory, decision making, action understanding and prediction. The proposed architecture is strongly inspired by our current understanding of the processing principles and the neuronal circuitry underlying these functionalities in the primate brain. As a mathematical framework, we use a coupled system of dynamic neural fields, each representing the basic functionality of neuronal populations in different brain areas. It implements goal-directed behavior in joint action as a continuous process that builds on the interpretation of observed movements in terms of the partner's action goal. We validate the architecture in two experimental paradigms: (1) a joint search task; (2) a reproduction of an observed or inferred end state of a grasping-placing sequence. We also review some of the mathematical results about dynamic neural fields that are important for the implementation work. .
A dynamic neural field model of temporal order judgments.
Hecht, Lauren N; Spencer, John P; Vecera, Shaun P
2015-12-01
Temporal ordering of events is biased, or influenced, by perceptual organization-figure-ground organization-and by spatial attention. For example, within a region assigned figural status or at an attended location, onset events are processed earlier (Lester, Hecht, & Vecera, 2009; Shore, Spence, & Klein, 2001), and offset events are processed for longer durations (Hecht & Vecera, 2011; Rolke, Ulrich, & Bausenhart, 2006). Here, we present an extension of a dynamic field model of change detection (Johnson, Spencer, Luck, & Schöner, 2009; Johnson, Spencer, & Schöner, 2009) that accounts for both the onset and offset performance for figural and attended regions. The model posits that neural populations processing the figure are more active, resulting in a peak of activation that quickly builds toward a detection threshold when the onset of a target is presented. This same enhanced activation for some neural populations is maintained when a present target is removed, creating delays in the perception of the target's offset. We discuss the broader implications of this model, including insights regarding how neural activation can be generated in response to the disappearance of information. (c) 2015 APA, all rights reserved).
Behavioral dynamics and neural grounding of a dynamic field theory of multi-object tracking.
Spencer, J P; Barich, K; Goldberg, J; Perone, S
2012-09-01
The ability to dynamically track moving objects in the environment is crucial for efficient interaction with the local surrounds. Here, we examined this ability in the context of the multi-object tracking (MOT) task. Several theories have been proposed to explain how people track moving objects; however, only one of these previous theories is implemented in a real-time process model, and there has been no direct contact between theories of object tracking and the growing neural literature using ERPs and fMRI. Here, we present a neural process model of object tracking that builds from a Dynamic Field Theory of spatial cognition. Simulations reveal that our dynamic field model captures recent behavioral data examining the impact of speed and tracking duration on MOT performance. Moreover, we show that the same model with the same trajectories and parameters can shed light on recent ERP results probing how people distribute attentional resources to targets vs. distractors. We conclude by comparing this new theory of object tracking to other recent accounts, and discuss how the neural grounding of the theory might be effectively explored in future work.
The dynamic brain: from spiking neurons to neural masses and cortical fields.
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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
Bicho, Estela; Louro, Luís; Erlhagen, Wolfram
2010-01-01
How do humans coordinate their intentions, goals and motor behaviors when performing joint action tasks? Recent experimental evidence suggests that resonance processes in the observer's motor system are crucially involved in our ability to understand actions of others’, to infer their goals and even to comprehend their action-related language. In this paper, we present a control architecture for human–robot collaboration that exploits this close perception-action linkage as a means to achieve more natural and efficient communication grounded in sensorimotor experiences. The architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of neural populations that encode in their activation patterns goals, actions and shared task knowledge. We validate the verbal and nonverbal communication skills of the robot in a joint assembly task in which the human–robot team has to construct toy objects from their components. The experiments focus on the robot's capacity to anticipate the user's needs and to detect and communicate unexpected events that may occur during joint task execution. PMID:20725504
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Estela Bicho
2010-05-01
Full Text Available How do humans coordinate their intentions, goals and motor behaviors when performing joint action tasks? Recent experimental evidence suggests that resonance processes in the observer's motor system are crucially involved in our ability to understand actions of others', to infer their goals and even to comprehend their action-related language. In this paper, we present a control architecture for human-robot collaboration that exploits this close perception-action linkage as a means to achieve more natural and efficient communication grounded in sensorimotor experiences. The architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of neural populations that encode in their activation patterns goals, actions and shared task knowledge. We validate the verbal and non-verbal communication skills of the robot in a joint assembly task in which the human-robot team has to construct toy objects from their components. The experiments focus on the robot’s capacity to anticipate the user’s needs and to detect and communicate unexpected events that may occur during joint task execution.
Dynamics of neural cryptography.
Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido
2007-05-01
Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.
Perone, Sammy; Spencer, John P.
2013-01-01
Looking is a fundamental exploratory behavior by which infants acquire knowledge about the world. In theories of infant habituation, however, looking as an exploratory behavior has been deemphasized relative to the reliable nature with which looking indexes active cognitive processing. We present a new theory that connects looking to the dynamics of memory formation and formally implement this theory in a Dynamic Neural Field model that learns autonomously as it actively looks and looks away from a stimulus. We situate this model in a habituation task and illustrate the mechanisms by which looking, encoding, working memory formation, and long-term memory formation give rise to habituation across multiple stimulus and task contexts. We also illustrate how the act of looking and the temporal dynamics of learning affect each other. Finally, we test a new hypothesis about the sources of developmental differences in looking. PMID:23136815
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 ...
Spatiotemporal canards in neural field equations
Avitabile, D.; Desroches, M.; Knobloch, E.
2017-04-01
Canards are special solutions to ordinary differential equations that follow invariant repelling slow manifolds for long time intervals. In realistic biophysical single-cell models, canards are responsible for several complex neural rhythms observed experimentally, but their existence and role in spatially extended systems is largely unexplored. We identify and describe a type of coherent structure in which a spatial pattern displays temporal canard behavior. Using interfacial dynamics and geometric singular perturbation theory, we classify spatiotemporal canards and give conditions for the existence of folded-saddle and folded-node canards. We find that spatiotemporal canards are robust to changes in the synaptic connectivity and firing rate. The theory correctly predicts the existence of spatiotemporal canards with octahedral symmetry in a neural field model posed on the unit sphere.
Foetal ECG recovery using dynamic neural networks.
Camps-Valls, Gustavo; Martínez-Sober, Marcelino; Soria-Olivas, Emilio; Magdalena-Benedito, Rafael; Calpe-Maravilla, Javier; Guerrero-Martínez, Juan
2004-07-01
Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coefficient) and statistical (analysis of variance, ANOVA) measures allows us to select the best recovery model. Finally, finite impulse response (FIR) and gamma neural networks are included in the adaptive noise cancellation (ANC) scheme in order to provide highly non-linear, dynamic capabilities to the recovery model. Neural networks are benchmarked with classical adaptive methods such as the least mean squares (LMS) and the normalized LMS (NLMS) algorithms in simulated and real registers and some conclusions are drawn. For synthetic registers, the most determinant factor in the identification of the models is the foetal-maternal signal-to-noise ratio (SNR). In addition, as the electromyogram contribution becomes more relevant, neural networks clearly outperform the LMS-based algorithm. From the ANOVA test, we found statistical differences between LMS-based models and neural models when complex situations (high foetal-maternal and foetal-noise SNRs) were present. These conclusions were confirmed after doing robustness tests on synthetic registers, visual inspection of the recovered signals and calculation of the recognition rates of foetal R-peaks for real situations. Finally, the best compromise between model complexity and outcomes was provided by the FIR neural network. Both
Dynamic properties of cellular neural networks
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Angela Slavova
1993-01-01
Full Text Available Dynamic behavior of a new class of information-processing systems called Cellular Neural Networks is investigated. In this paper we introduce a small parameter in the state equation of a cellular neural network and we seek for periodic phenomena. New approach is used for proving stability of a cellular neural network by constructing Lyapunov's majorizing equations. This algorithm is helpful for finding a map from initial continuous state space of a cellular neural network into discrete output. A comparison between cellular neural networks and cellular automata is made.
Coordination: Neural, Behavioral and Social Dynamics
Fuchs, Armin
2008-01-01
One of the most striking features of Coordination Dynamics is its interdisciplinary character. The problems we are trying to solve in this field range from behavioral phenomena of interlimb coordination and coordination between stimuli and movements (perception-action tasks) through neural activation patterns that can be observed during these tasks to clinical applications and social behavior. It is not surprising that close collaboration among scientists from different fields as psychology, kinesiology, neurology and even physics are imperative to deal with the enormous difficulties we are facing when we try to understand a system as complex as the human brain. The chapters in this volume are not simply write-ups of the lectures given by the experts at the meeting but are written in a way that they give sufficient introductory information to be comprehensible and useful for all interested scientists and students.
The Complexity of Dynamics in Small Neural Circuits.
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Diego Fasoli
2016-08-01
Full Text Available Mean-field approximations are a powerful tool for studying large neural networks. However, they do not describe well the behavior of networks composed of a small number of neurons. In this case, major differences between the mean-field approximation and the real behavior of the network can arise. Yet, many interesting problems in neuroscience involve the study of mesoscopic networks composed of a few tens of neurons. Nonetheless, mathematical methods that correctly describe networks of small size are still rare, and this prevents us to make progress in understanding neural dynamics at these intermediate scales. Here we develop a novel systematic analysis of the dynamics of arbitrarily small networks composed of homogeneous populations of excitatory and inhibitory firing-rate neurons. We study the local bifurcations of their neural activity with an approach that is largely analytically tractable, and we numerically determine the global bifurcations. We find that for strong inhibition these networks give rise to very complex dynamics, caused by the formation of multiple branching solutions of the neural dynamics equations that emerge through spontaneous symmetry-breaking. This qualitative change of the neural dynamics is a finite-size effect of the network, that reveals qualitative and previously unexplored differences between mesoscopic cortical circuits and their mean-field approximation. The most important consequence of spontaneous symmetry-breaking is the ability of mesoscopic networks to regulate their degree of functional heterogeneity, which is thought to help reducing the detrimental effect of noise correlations on cortical information processing.
Synthesis of recurrent neural networks for dynamical system simulation.
Trischler, Adam P; D'Eleuterio, Gabriele M T
2016-08-01
We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.
Electrokinetic confinement of axonal growth for dynamically configurable neural networks
Honegger, Thibault; Scott, Mark A.; Yanik, Mehmet F.; Voldman, Joel
2013-01-01
Axons in the developing nervous system are directed via guidance cues, whose expression varies both spatially and temporally, to create functional neural circuits. Existing methods to create patterns of neural connectivity in vitro use only static geometries, and are unable to dynamically alter the guidance cues imparted on the cells. We introduce the use of AC electrokinetics to dynamically control axonal growth in cultured rat hippocampal neurons. We find that the application of modest voltages at frequencies on the order of 105 Hz can cause developing axons to be stopped adjacent to the electrodes while axons away from the electric fields exhibit uninhibited growth. By switching electrodes on or off, we can reversibly inhibit or permit axon passage across the electrodes. Our models suggest that dielectrophoresis is the causative AC electrokinetic effect. We make use of our dynamic control over axon elongation to create an axon-diode via an axon-lock system that consists of a pair of electrode `gates' that either permit or prevent axons from passing through. Finally, we developed a neural circuit consisting of three populations of neurons, separated by three axon-locks to demonstrate the assembly of a functional, engineered neural network. Action potential recordings demonstrate that the AC electrokinetic effect does not harm axons, and Ca2+ imaging demonstrated the unidirectional nature of the synaptic connections. AC electrokinetic confinement of axonal growth has potential for creating configurable, directional neural networks. PMID:23314575
Takiyama, Ken
2017-12-01
How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.
Dynamic causal models of neural system dynamics: current state ...
Indian Academy of Sciences (India)
2006-09-28
Sep 28, 2006 ... Keywords. Dynamic causal modelling; EEG; effective connectivity; event-related potentials; fMRI; neural system ... In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing ...
Two-dimensional neural field simulator with parameter interface and 3D visualization
Nichols, Eric; Hutt, Axel
2014-01-01
International audience; A simulator calculating two-dimensional dynamic neural fields with multiple order derivatives is presented in this work. The simulated neural fields are of the type ... where I, L and S are respectively a field's input, spatial delay kernel with axonal transmission speed c and nonlinear firing rate function S = S0 / (1 + exp(-α(V-Θ)). A Fast Fourier Transform in space is used to accelerate the integral calculation. The stochastic differential equation is useful for stu...
A New Neural Dynamic Classification Algorithm.
Rafiei, Mohammad Hossein; Adeli, Hojjat
2017-12-01
The keys for the development of an effective classification algorithm are: 1) discovering feature spaces with large margins between clusters and close proximity of the classmates and 2) discovering the smallest number of the features to perform accurate classification. In this paper, a new supervised classification algorithm, called neural dynamic classification (NDC), is presented with the goal of: 1) discovering the most effective feature spaces and 2) finding the optimum number of features required for accurate classification using the patented robust neural dynamic optimization model of Adeli and Park. The new classification algorithm is compared with the probabilistic neural network (PNN), enhanced PNN (EPNN), and support vector machine using two sets of classification problems. The first set consists of five standard benchmark problems. The second set is a large benchmark problem called Mixed National Institute of Standards and Technology database of handwritten digits. In general, NDC yields the most accurate classification results followed by EPNN. A beauty of the new algorithm is the smoothness of convergence curves which is an indication of robustness and good performance of the algorithm. The main aim is to maximize the prediction accuracy.
Conditional Neural Fields untuk Pengenalan Fase Gerak
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Intan Nurma Yulita
2017-01-01
Full Text Available Pengenalan pola merupakan area informatika yang banyak dikaji hingga saat ini. Hal ini dikarenakan pemanfaatannya yang luas diterapkan dalam kehidupan sehari-hari. Di dalam makalah ini disajikan pengenalan pola untuk gerakan khususnya fase gerak. Secara khusus pengenalan fase gerak di dalam makalah ini menitik beratkan pada pengenalan pola pada data berbentuk sekuensial. Pengenalan ini dapat saja mengabaikan faktor sekuensialnya, namun tentu akan menurunkan akurasi yang akan diperoleh. Oleh karena itu untuk mengatasi tantangan tersebut, maka ditawarkan penggunaan Conditional Neural Fields (CNF. Metode ini merupakan gabungan antara Conditional Random Fields (CRF dan Artifisial Neural Networks (ANN. Representasi ANN disajikan dalam bentuk gate pada lapisan tengah dari CRF. Lapisan ini bertujuan untuk memetakan hubungan non-linear antara input dan output yang terdapat di dalam data. Sebagai hasilnya diperoleh bahwa CNF terbukti lebih efektif dan efisien dibandingkan CRF berdasarkan akurasi dan banyaknya iterasi yang dibutuhkan. Namun penggunaan terlalu banyak gate ternyata tidak efektif dikarenakan konvergensi dari model pengenalan semakin sulit tercapai. Di sisi lain, jika hanya satu gate yang digunakan maka konvergensi tercapai namun akuarsi yang diperoleh rendah. Sehingga diperlukan upaya untuk menemukan banyaknya gate optimal yang diperlukan.
Front Propagation in Stochastic Neural Fields
Bressloff, Paul C.
2012-01-01
We analyze the effects of extrinsic multiplicative noise on front propagation in a scalar neural field with excitatory connections. Using a separation of time scales, we represent the fluctuating front in terms of a diffusive-like displacement (wandering) of the front from its uniformly translating position at long time scales, and fluctuations in the front profile around its instantaneous position at short time scales. One major result of our analysis is a comparison between freely propagating fronts and fronts locked to an externally moving stimulus. We show that the latter are much more robust to noise, since the stochastic wandering of the mean front profile is described by an Ornstein-Uhlenbeck process rather than a Wiener process, so that the variance in front position saturates in the long time limit rather than increasing linearly with time. Finally, we consider a stochastic neural field that supports a pulled front in the deterministic limit, and show that the wandering of such a front is now subdiffusive. © 2012 Society for Industrial and Applied Mathematics.
ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.
Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan
2017-07-20
Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.
Decoding Local Field Potentials for Neural Interfaces.
Jackson, Andrew; Hall, Thomas M
2017-10-01
The stability and frequency content of local field potentials (LFPs) offer key advantages for long-term, low-power neural interfaces. However, interpreting LFPs may require new signal processing techniques which should be informed by a scientific understanding of how these recordings arise from the coordinated activity of underlying neuronal populations. We review current approaches to decoding LFPs for brain-machine interface (BMI) applications, and suggest several directions for future research. To facilitate an improved understanding of the relationship between LFPs and spike activity, we share a dataset of multielectrode recordings from monkey motor cortex, and describe two unsupervised analysis methods we have explored for extracting a low-dimensional feature space that is amenable to biomimetic decoding and biofeedback training.
Natural neural projection dynamics underlying social behavior.
Gunaydin, Lisa A; Grosenick, Logan; Finkelstein, Joel C; Kauvar, Isaac V; Fenno, Lief E; Adhikari, Avishek; Lammel, Stephan; Mirzabekov, Julie J; Airan, Raag D; Zalocusky, Kelly A; Tye, Kay M; Anikeeva, Polina; Malenka, Robert C; Deisseroth, Karl
2014-06-19
Social interaction is a complex behavior essential for many species and is impaired in major neuropsychiatric disorders. Pharmacological studies have implicated certain neurotransmitter systems in social behavior, but circuit-level understanding of endogenous neural activity during social interaction is lacking. We therefore developed and applied a new methodology, termed fiber photometry, to optically record natural neural activity in genetically and connectivity-defined projections to elucidate the real-time role of specified pathways in mammalian behavior. Fiber photometry revealed that activity dynamics of a ventral tegmental area (VTA)-to-nucleus accumbens (NAc) projection could encode and predict key features of social, but not novel object, interaction. Consistent with this observation, optogenetic control of cells specifically contributing to this projection was sufficient to modulate social behavior, which was mediated by type 1 dopamine receptor signaling downstream in the NAc. Direct observation of deep projection-specific activity in this way captures a fundamental and previously inaccessible dimension of mammalian circuit dynamics. Copyright © 2014 Elsevier Inc. All rights reserved.
Dynamic Object Identification with SOM-based neural networks
Directory of Open Access Journals (Sweden)
Aleksey Averkin
2014-03-01
Full Text Available In this article a number of neural networks based on self-organizing maps, that can be successfully used for dynamic object identification, is described. Unique SOM-based modular neural networks with vector quantized associative memory and recurrent self-organizing maps as modules are presented. The structured algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results and comparison with some other neural networks are given.
Numerical continuation of travelling waves and pulses in neural fields
Meijer, Hil Gaétan Ellart; Coombes, Stephen
2013-01-01
We study travelling waves and pulses in neural fields. Neural fields are a macroscopic description of the activity of brain tissue, which mathematically are formulated as integro-differential equations. While linear and weakly nonlinear analysis can describe instabilities and small amplitude
Spatial Dynamics of Multilayer Cellular Neural Networks
Wu, Shi-Liang; Hsu, Cheng-Hsiung
2018-02-01
The purpose of this work is to study the spatial dynamics of one-dimensional multilayer cellular neural networks. We first establish the existence of rightward and leftward spreading speeds of the model. Then we show that the spreading speeds coincide with the minimum wave speeds of the traveling wave fronts in the right and left directions. Moreover, we obtain the asymptotic behavior of the traveling wave fronts when the wave speeds are positive and greater than the spreading speeds. According to the asymptotic behavior and using various kinds of comparison theorems, some front-like entire solutions are constructed by combining the rightward and leftward traveling wave fronts with different speeds and a spatially homogeneous solution of the model. Finally, various qualitative features of such entire solutions are investigated.
An efficient neural network approach to dynamic robot motion planning.
Yang, S X; Meng, M
2000-03-01
In this paper, a biologically inspired neural network approach to real-time collision-free motion planning of mobile robots or robot manipulators in a nonstationary environment is proposed. Each neuron in the topologically organized neural network has only local connections, whose neural dynamics is characterized by a shunting equation. Thus the computational complexity linearly depends on the neural network size. The real-time robot motion is planned through the dynamic activity landscape of the neural network without any prior knowledge of the dynamic environment, without explicitly searching over the free workspace or the collision paths, and without any learning procedures. Therefore it is computationally efficient. The global stability of the neural network is guaranteed by qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.
Neural dynamics of phonological processing in the dorsal auditory stream.
Liebenthal, Einat; Sabri, Merav; Beardsley, Scott A; Mangalathu-Arumana, Jain; Desai, Anjali
2013-09-25
Neuroanatomical models hypothesize a role for the dorsal auditory pathway in phonological processing as a feedforward efferent system (Davis and Johnsrude, 2007; Rauschecker and Scott, 2009; Hickok et al., 2011). But the functional organization of the pathway, in terms of time course of interactions between auditory, somatosensory, and motor regions, and the hemispheric lateralization pattern is largely unknown. Here, ambiguous duplex syllables, with elements presented dichotically at varying interaural asynchronies, were used to parametrically modulate phonological processing and associated neural activity in the human dorsal auditory stream. Subjects performed syllable and chirp identification tasks, while event-related potentials and functional magnetic resonance images were concurrently collected. Joint independent component analysis was applied to fuse the neuroimaging data and study the neural dynamics of brain regions involved in phonological processing with high spatiotemporal resolution. Results revealed a highly interactive neural network associated with phonological processing, composed of functional fields in posterior temporal gyrus (pSTG), inferior parietal lobule (IPL), and ventral central sulcus (vCS) that were engaged early and almost simultaneously (at 80-100 ms), consistent with a direct influence of articulatory somatomotor areas on phonemic perception. Left hemispheric lateralization was observed 250 ms earlier in IPL and vCS than pSTG, suggesting that functional specialization of somatomotor (and not auditory) areas determined lateralization in the dorsal auditory pathway. The temporal dynamics of the dorsal auditory pathway described here offer a new understanding of its functional organization and demonstrate that temporal information is essential to resolve neural circuits underlying complex behaviors.
Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.
Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu
2017-10-01
This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.
Two-photon imaging and analysis of neural network dynamics
Lütcke, Henry; Helmchen, Fritjof
2011-08-01
The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to measure and analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behavior. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so-called 'microcircuits') remains comparably poor. Predominantly, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near-millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.
Two-photon imaging and analysis of neural network dynamics
Energy Technology Data Exchange (ETDEWEB)
Luetcke, Henry; Helmchen, Fritjof [Brain Research Institute, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich (Switzerland)
2011-08-15
The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to measure and analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behavior. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so-called 'microcircuits') remains comparably poor. Predominantly, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near-millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.
Kozma, Robert
2016-01-01
This intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains. The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets). The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields. This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.
Neural field model of memory-guided search
Kilpatrick, Zachary P.; Poll, Daniel B.
2017-12-01
Many organisms can remember locations they have previously visited during a search. Visual search experiments have shown exploration is guided away from these locations, reducing redundancies in the search path before finding a hidden target. We develop and analyze a two-layer neural field model that encodes positional information during a search task. A position-encoding layer sustains a bump attractor corresponding to the searching agent's current location, and search is modeled by velocity input that propagates the bump. A memory layer sustains persistent activity bounded by a wave front, whose edges expand in response to excitatory input from the position layer. Search can then be biased in response to remembered locations, influencing velocity inputs to the position layer. Asymptotic techniques are used to reduce the dynamics of our model to a low-dimensional system of equations that track the bump position and front boundary. Performance is compared for different target-finding tasks.
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
Neural dynamics during repetitive visual stimulation
Tsoneva, Tsvetomira; Garcia-Molina, Gary; Desain, Peter
2015-12-01
Objective. Steady-state visual evoked potentials (SSVEPs), the brain responses to repetitive visual stimulation (RVS), are widely utilized in neuroscience. Their high signal-to-noise ratio and ability to entrain oscillatory brain activity are beneficial for their applications in brain-computer interfaces, investigation of neural processes underlying brain rhythmic activity (steady-state topography) and probing the causal role of brain rhythms in cognition and emotion. This paper aims at analyzing the space and time EEG dynamics in response to RVS at the frequency of stimulation and ongoing rhythms in the delta, theta, alpha, beta, and gamma bands. Approach.We used electroencephalography (EEG) to study the oscillatory brain dynamics during RVS at 10 frequencies in the gamma band (40-60 Hz). We collected an extensive EEG data set from 32 participants and analyzed the RVS evoked and induced responses in the time-frequency domain. Main results. Stable SSVEP over parieto-occipital sites was observed at each of the fundamental frequencies and their harmonics and sub-harmonics. Both the strength and the spatial propagation of the SSVEP response seem sensitive to stimulus frequency. The SSVEP was more localized around the parieto-occipital sites for higher frequencies (>54 Hz) and spread to fronto-central locations for lower frequencies. We observed a strong negative correlation between stimulation frequency and relative power change at that frequency, the first harmonic and the sub-harmonic components over occipital sites. Interestingly, over parietal sites for sub-harmonics a positive correlation of relative power change and stimulation frequency was found. A number of distinct patterns in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz) and beta (15-30 Hz) bands were also observed. The transient response, from 0 to about 300 ms after stimulation onset, was accompanied by increase in delta and theta power over fronto-central and occipital sites, which returned to baseline
Extracting novel information from neuroimaging data using neural fields
Directory of Open Access Journals (Sweden)
Pinotsis Dimitris A
2014-12-01
Full Text Available We showcase three case studies that illustrate how neural fields can be useful in the analysis of neuroimaging data. In particular, we argue that neural fields allow one to: (i compare evidences for alternative hypotheses regarding neurobiological determinants of stimulus-specific response variability; (ii make inferences about between subject variability in cortical function and microstructure using non-invasive data and (iii estimate spatial parameters describing cortical sources, even without spatially resolved data.
Direct Neural Imaging using Ultra-Low Field Magnetic Resonance
Maskaly, Karlene; Espy, Michelle; Flynn, Mark; Gomez, John; Kraus, Robert; Matlashov, Andrei; Mosher, John; Newman, Shaun; Owens, Tuba; Peters, Mark; Sandin, J.; Schultz, Larry; Urbaitis, Algis; Volegov, Petr; Zotev, Vadim
2009-03-01
An enduring challenge in neuroscience is the accurate in vivo mapping of neural activity with high spatial and temporal resolution. A method being developed by our group tries to meet this challenge by using Ultra-Low Field (ULF) MRI. Other groups have attempted direct neural imaging (DNI) using high field MRI. However, the use of ULF presents two advantages. First, the susceptibility artifact at high fields, which masks the DNI signal, is negligible at low fields. Second, the reduced Larmor frequency at ULF may overlap with the frequency spectrum of the neural magnetic field, resonantly enhancing the MRI signal. In this presentation, I will first show our custom-built ULF MRI setups that have successfully produced ULF anatomical images. I will then highlight the numerous studies we have done to investigate the feasibility of DNI with these systems, including both experimental and theoretical studies.
Shaping the learning curve: epigenetic dynamics in neural plasticity.
Bronfman, Zohar Z; Ginsburg, Simona; Jablonka, Eva
2014-01-01
A key characteristic of learning and neural plasticity is state-dependent acquisition dynamics reflected by the non-linear learning curve that links increase in learning with practice. Here we propose that the manner by which epigenetic states of individual cells change during learning contributes to the shape of the neural and behavioral learning curve. We base our suggestion on recent studies showing that epigenetic mechanisms such as DNA methylation, histone acetylation, and RNA-mediated gene regulation are intimately involved in the establishment and maintenance of long-term neural plasticity, reflecting specific learning-histories and influencing future learning. Our model, which is the first to suggest a dynamic molecular account of the shape of the learning curve, leads to several testable predictions regarding the link between epigenetic dynamics at the promoter, gene-network, and neural-network levels. This perspective opens up new avenues for therapeutic interventions in neurological pathologies.
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.
A Neural Network Approach to Fluid Quantity Measurement in Dynamic Environments
Terzic, Edin; Nagarajah, Romesh; Alamgir, Muhammad
2012-01-01
Sloshing causes liquid to fluctuate, making accurate level readings difficult to obtain in dynamic environments. The measurement system described uses a single-tube capacitive sensor to obtain an instantaneous level reading of the fluid surface, thereby accurately determining the fluid quantity in the presence of slosh. A neural network based classification technique has been applied to predict the actual quantity of the fluid contained in a tank under sloshing conditions. In A neural network approach to fluid quantity measurement in dynamic environments, effects of temperature variations and contamination on the capacitive sensor are discussed, and the authors propose that these effects can also be eliminated with the proposed neural network based classification system. To examine the performance of the classification system, many field trials were carried out on a running vehicle at various tank volume levels that range from 5 L to 50 L. The effectiveness of signal enhancement on the neural network base...
Stochastic Neural Field Theory and the System-Size Expansion
Bressloff, Paul C.
2010-01-01
We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.
Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models
DEFF Research Database (Denmark)
Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin
2017-01-01
In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...
Evolution of Neural Dynamics in an Ecological Model
Directory of Open Access Journals (Sweden)
Steven Williams
2017-07-01
Full Text Available What is the optimal level of chaos in a computational system? If a system is too chaotic, it cannot reliably store information. If it is too ordered, it cannot transmit information. A variety of computational systems exhibit dynamics at the “edge of chaos”, the transition between the ordered and chaotic regimes. In this work, we examine the evolved neural networks of Polyworld, an artificial life model consisting of a simulated ecology populated with biologically inspired agents. As these agents adapt to their environment, their initially simple neural networks become increasingly capable of exhibiting rich dynamics. Dynamical systems analysis reveals that natural selection drives these networks toward the edge of chaos until the agent population is able to sustain itself. After this point, the evolutionary trend stabilizes, with neural dynamics remaining on average significantly far from the transition to chaos.
Neural Dynamics Underlying Event-Related Potentials
Shah, Ankoor S.; Bressler, Steven L.; Knuth, Kevin H.; Ding, Ming-Zhou; Mehta, Ashesh D.; Ulbert, Istvan; Schroeder, Charles E.
2003-01-01
There are two opposing hypotheses about the brain mechanisms underlying sensory event-related potentials (ERPs). One holds that sensory ERPs are generated by phase resetting of ongoing electroencephalographic (EEG) activity, and the other that they result from signal averaging of stimulus-evoked neural responses. We tested several contrasting predictions of these hypotheses by direct intracortical analysis of neural activity in monkeys. Our findings clearly demonstrate evoked response contributions to the sensory ERP in the monkey, and they suggest the likelihood that a mixed (Evoked/Phase Resetting) model may account for the generation of scalp ERPs in humans.
Evolvable Block-Based Neural Network Design for Applications in Dynamic Environments
Directory of Open Access Journals (Sweden)
Saumil G. Merchant
2010-01-01
Full Text Available Dedicated hardware implementations of artificial neural networks promise to provide faster, lower-power operation when compared to software implementations executing on microprocessors, but rarely do these implementations have the flexibility to adapt and train online under dynamic conditions. A typical design process for artificial neural networks involves offline training using software simulations and synthesis and hardware implementation of the obtained network offline. This paper presents a design of block-based neural networks (BbNNs on FPGAs capable of dynamic adaptation and online training. Specifically the network structure and the internal parameters, the two pieces of the multiparametric evolution of the BbNNs, can be adapted intrinsically, in-field under the control of the training algorithm. This ability enables deployment of the platform in dynamic environments, thereby significantly expanding the range of target applications, deployment lifetimes, and system reliability. The potential and functionality of the platform are demonstrated using several case studies.
Neural Computations in a Dynamical System with Multiple Time Scales
Mi, Yuanyuan; Lin, Xiaohan; Wu, Si
2016-01-01
Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions. PMID:27679569
Dynamical foundations of the neural circuit for bayesian decision making.
Morita, Kenji
2009-07-01
On the basis of accumulating behavioral and neural evidences, it has recently been proposed that the brain neural circuits of humans and animals are equipped with several specific properties, which ensure that perceptual decision making implemented by the circuits can be nearly optimal in terms of Bayesian inference. Here, I introduce the basic ideas of such a proposal and discuss its implications from the standpoint of biophysical modeling developed in the framework of dynamical systems.
Neural dynamics of the cognitive map in the hippocampus.
Wagatsuma, Hiroaki; Yamaguchi, Yoko
2007-06-01
The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. In the classical theory, the cognitive map has been explained as a consequence of the fact that different spatial regions are assigned to different cell populations in the framework of rate coding. Recently, the relation between place cell firing and local field oscillation theta in terms of theta phase precession was experimentally discovered and suggested as a temporal coding mechanism leading to memory formation of behavioral sequences accompanied with asymmetric Hebbian plasticity. The cognitive map theory is apparently outside of the sequence memory view. Therefore, theoretical analysis is necessary to consider the biological neural dynamics for the sequence encoding of the memory of behavioral sequences, providing the cognitive map formation. In this article, we summarize the theoretical neural dynamics of the real-time sequence encoding by theta phase precession, called theta phase coding, and review a series of theoretical models with the theta phase coding that we previously reported. With respect to memory encoding functions, instantaneous memory formation of one-time experience was first demonstrated, and then the ability of integration of memories of behavioral sequences into a network of the cognitive map was shown. In terms of memory retrieval functions, theta phase coding enables the hippocampus to represent the spatial location in the current behavioral context even with ambiguous sensory input when multiple sequences were coded. Finally, for utilization, retrieved temporal sequences in the hippocampus can be available for action selection, through the process of reverting theta rhythm-dependent activities to information in the behavioral time scale. This theoretical approach allows us to investigate how the behavioral sequences are encoded, updated, retrieved and used in the hippocampus, as the real-time interaction with the external environment. It may
Beyond slots and resources: grounding cognitive concepts in neural dynamics.
Johnson, Jeffrey S; Simmering, Vanessa R; Buss, Aaron T
2014-08-01
Research over the past decade has suggested that the ability to hold information in visual working memory (VWM) may be limited to as few as three to four items. However, the precise nature and source of these capacity limits remains hotly debated. Most commonly, capacity limits have been inferred from studies of visual change detection, in which performance declines systematically as a function of the number of items that participants must remember. According to one view, such declines indicate that a limited number of fixed-resolution representations are held in independent memory "slots." Another view suggests that such capacity limits are more apparent than real, but emerge as limited memory resources are distributed across more to-be-remembered items. Here we argue that, although both perspectives have merit and have generated and explained impressive amounts of empirical data, their central focus on the representations--rather than processes--underlying VWM may ultimately limit continuing progress in this area. As an alternative, we describe a neurally grounded, process-based approach to VWM: the dynamic field theory. Simulations demonstrate that this model can account for key aspects of behavioral performance in change detection, in addition to generating novel behavioral predictions that have been confirmed experimentally. Furthermore, we describe extensions of the model to recall tasks, the integration of visual features, cognitive development, individual differences, and functional imaging studies of VWM. We conclude by discussing the importance of grounding psychological concepts in neural dynamics, as a first step toward understanding the link between brain and behavior.
Discriminating lysosomal membrane protein types using dynamic neural network.
Tripathi, Vijay; Gupta, Dwijendra Kumar
2014-01-01
This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.
Dynamics of gauge field inflation
Energy Technology Data Exchange (ETDEWEB)
Alexander, Stephon; Jyoti, Dhrubo [Center for Cosmic Origins and Department of Physics and Astronomy, 6127 Wilder Laboratory, Dartmouth College, Hanover, NH 03755 (United States); Kosowsky, Arthur [Department of Physics and Astronomy, University of Pittsburgh, 3941 O' Hara Street, Pittsburgh, PA 15260 (United States); Marcianò, Antonino, E-mail: stephon.alexander@dartmouth.edu, E-mail: dhrubo.jyoti@dartmouth.edu, E-mail: kosowsky@pitt.edu, E-mail: marciano@fudan.edu.cn [Center for Field Theory and Particle Physics and Department of Physics, Fudan University, 220 Handan Road, Shanghai (China)
2015-05-01
We analyze the existence and stability of dynamical attractor solutions for cosmological inflation driven by the coupling between fermions and a gauge field. Assuming a spatially homogeneous and isotropic gauge field and fermion current, the interacting fermion equation of motion reduces to that of a free fermion up to a phase shift. Consistency of the model is ensured via the Stückelberg mechanism. We prove the existence of exactly one stable solution, and demonstrate the stability numerically. Inflation arises without fine tuning, and does not require postulating any effective potential or non-standard coupling.
Dynamics of gauge field inflation
Energy Technology Data Exchange (ETDEWEB)
Alexander, Stephon; Jyoti, Dhrubo [Center for Cosmic Origins and Department of Physics and Astronomy, 6127 Wilder Laboratory, Dartmouth College, Hanover, NH 03755 (United States); Kosowsky, Arthur [Department of Physics and Astronomy, University of Pittsburgh, 3941 O’Hara Street, Pittsburgh, PA 15260 (United States); Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (Pitt-PACC), 420 Allen Hall, 3941 O’Hara Street, Pittsburgh, PA 15260 (United States); Marcianò, Antonino [Center for Field Theory and Particle Physics & Department of Physics, Fudan University, 220 Handan Road, Shanghai (China)
2015-05-05
We analyze the existence and stability of dynamical attractor solutions for cosmological inflation driven by the coupling between fermions and a gauge field. Assuming a spatially homogeneous and isotropic gauge field and fermion current, the interacting fermion equation of motion reduces to that of a free fermion up to a phase shift. Consistency of the model is ensured via the Stückelberg mechanism. We prove the existence of exactly one stable solution, and demonstrate the stability numerically. Inflation arises without fine tuning, and does not require postulating any effective potential or non-standard coupling.
Dual adaptive dynamic control of mobile robots using neural networks.
Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato
2009-02-01
This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.
Neural network approaches to dynamic collision-free trajectory generation.
Yang, S X; Meng, M
2001-01-01
In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.
Estimating Neural Signal Dynamics in the Human Brain
Directory of Open Access Journals (Sweden)
Christopher W Tyler
2011-06-01
Full Text Available Although brain imaging methods are highly effective for localizing the effects of neural activation throughout the human brain in terms of the blood oxygenation level dependent (BOLD response, there is currently no way to estimate the underlying neural signal dynamics in generating the BOLD response in each local activation region (except for processes slower than the BOLD time course. Knowledge of the neural signal is critical information if spatial mapping is to progress to the analysis of dynamic information flow through the cortical networks as the brain performs its tasks. We introduce an analytic approach that provides a new level of conceptualization and specificity in the study of brain processing by noninvasive methods. This technique allows us to use brain imaging methods to determine the dynamics of local neural population responses to their native temporal resolution throughout the human brain, with relatively narrow confidence intervals on many response properties. The ability to characterize local neural dynamics in the human brain represents a significant enhancement of brain imaging capabilities, with potential application from general cognitive studies to assessment of neuropathologies.
Weak electric fields detectability in a noisy neural network.
Zhao, Jia; Deng, Bin; Qin, Yingmei; Men, Cong; Wang, Jiang; Wei, Xile; Sun, Jianbing
2017-02-01
We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.
Miconi, Thomas
2017-02-23
Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.
A Dynamic Neural Network Approach to CBM
2011-03-15
Therefore post-processing is needed to extract the time difference between corresponding events from which to calculate the crankshaft rotational speed...potentially already available from existing sensors (such as a crankshaft timing device) and a Neural Network processor to carry out the calculation . As...files are designated with the “_genmod” suffix. These files were the sources for the training and testing sets and made the extraction process easy
Neural stochastic dynamics of perceptual decision making
Martí Ortega, Daniel
2008-01-01
Models computacionals basats en xarxes a gran escala d'inspiració neurobiològica permeten descriure els correlats neurals de la decisió observats en certes àrees corticals com una transició entre atractors de la xarxa cortical. L'estimulació provoca un canvi en el paisatge d'atractors que afavoreix la transició entre l'atractor neutre inicial a un dels atractors associats a les eleccions categòriques. El soroll present en el sistema introdueix indeterminació en les transicions. En aquest treb...
Logic Dynamics for Deductive Inference -- Its Stability and Neural Basis
Tsuda, Ichiro
2014-12-01
We propose a dynamical model that represents a process of deductive inference. We discuss the stability of logic dynamics and a neural basis for the dynamics. We propose a new concept of descriptive stability, thereby enabling a structure of stable descriptions of mathematical models concerning dynamic phenomena to be clarified. The present theory is based on the wider and deeper thoughts of John S. Nicolis. In particular, it is based on our joint paper on the chaos theory of human short-term memories with a magic number of seven plus or minus two.
Can Neural Activity Propagate by Endogenous Electrical Field?
Qiu, Chen; Shivacharan, Rajat S.; Zhang, Mingming
2015-01-01
It is widely accepted that synaptic transmissions and gap junctions are the major governing mechanisms for signal traveling in the neural system. Yet, a group of neural waves, either physiological or pathological, share the same speed of ∼0.1 m/s without synaptic transmission or gap junctions, and this speed is not consistent with axonal conduction or ionic diffusion. The only explanation left is an electrical field effect. We tested the hypothesis that endogenous electric fields are sufficient to explain the propagation with in silico and in vitro experiments. Simulation results show that field effects alone can indeed mediate propagation across layers of neurons with speeds of 0.12 ± 0.09 m/s with pathological kinetics, and 0.11 ± 0.03 m/s with physiologic kinetics, both generating weak field amplitudes of ∼2–6 mV/mm. Further, the model predicted that propagation speed values are inversely proportional to the cell-to-cell distances, but do not significantly change with extracellular resistivity, membrane capacitance, or membrane resistance. In vitro recordings in mice hippocampi produced similar speeds (0.10 ± 0.03 m/s) and field amplitudes (2.5–5 mV/mm), and by applying a blocking field, the propagation speed was greatly reduced. Finally, osmolarity experiments confirmed the model's prediction that cell-to-cell distance inversely affects propagation speed. Together, these results show that despite their weak amplitude, electric fields can be solely responsible for spike propagation at ∼0.1 m/s. This phenomenon could be important to explain the slow propagation of epileptic activity and other normal propagations at similar speeds. SIGNIFICANCE STATEMENT Neural activity (waves or spikes) can propagate using well documented mechanisms such as synaptic transmission, gap junctions, or diffusion. However, the purpose of this paper is to provide an explanation for experimental data showing that neural signals can propagate by means other than synaptic
A Neural Network Model for Dynamics Simulation | Bholoa ...
African Journals Online (AJOL)
University of Mauritius Research Journal. Journal Home · ABOUT · Advanced Search · Current Issue · Archives · Journal Home > Vol 15, No 1 (2009) >. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register. A Neural Network Model for Dynamics Simulation. Ajeevsing ...
Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture
Energy Technology Data Exchange (ETDEWEB)
Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)
2015-01-01
Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.
Slow diffusive dynamics in a chaotic balanced neural network.
Shaham, Nimrod; Burak, Yoram
2017-05-01
It has been proposed that neural noise in the cortex arises from chaotic dynamics in the balanced state: in this model of cortical dynamics, the excitatory and inhibitory inputs to each neuron approximately cancel, and activity is driven by fluctuations of the synaptic inputs around their mean. It remains unclear whether neural networks in the balanced state can perform tasks that are highly sensitive to noise, such as storage of continuous parameters in working memory, while also accounting for the irregular behavior of single neurons. Here we show that continuous parameter working memory can be maintained in the balanced state, in a neural circuit with a simple network architecture. We show analytically that in the limit of an infinite network, the dynamics generated by this architecture are characterized by a continuous set of steady balanced states, allowing for the indefinite storage of a continuous parameter. In finite networks, we show that the chaotic noise drives diffusive motion along the approximate attractor, which gradually degrades the stored memory. We analyze the dynamics and show that the slow diffusive motion induces slowly decaying temporal cross correlations in the activity, which differ substantially from those previously described in the balanced state. We calculate the diffusivity, and show that it is inversely proportional to the system size. For large enough (but realistic) neural population sizes, and with suitable tuning of the network connections, the proposed balanced network can sustain continuous parameter values in memory over time scales larger by several orders of magnitude than the single neuron time scale.
Neural network structure for navigation using potential fields
Plumer, Edward S.
1992-01-01
A hybrid-network method for obstacle avoidance in the truck-backing system of D. Nguyen and B. Widrow (1989) is presented. A neural network technique for vehicle navigation and control in the presence of obstacles has been developed. A potential function which peaks at the surface of obstacles and has its minimum at the proper vehicle destination is computed using a network structure. The field is guaranteed not to have spurious local minima and does not have the property of flattening-out far from the goal. A feedforward neural network is used to control the steering of the vehicle using local field information. The network is trained in an obstacle-free space to follow the negative gradient of the field, after which the network is able to control and navigate the truck to its target destination in a space of obstacles which may be stationary or movable.
Dynamics of macro- and microscopic neural networks
DEFF Research Database (Denmark)
Mikkelsen, Kaare
2014-01-01
GN), which is a class of signals with a non-trivial low-frequency component. It is assumed that certain characteristica about the low-frequency component can yield information about the neural processes behind the signal. The method has been used in a range of different studies over the course of the past 10...... that the method continues to find use, of which examples are presented. In the second part of the thesis, numerical simulations of networks of neurons are described. To simplify the analysis, a relatively simpled neuron model - Leaky Integrate and Fire - is chosen. The strengths of the connections between...... shown that the syncronizing effect of the plasticity disappears when the strengths of the connections are frozen in time. Subsequently, the so-called ``Sisyphus'' mechanism is discussed, which is shown to cause slow fluctuations in the both the network synchronization and the strengths...
Dynamic behaviors of the non-neural ectoderm during mammalian cranial neural tube closure.
Ray, Heather J; Niswander, Lee A
2016-08-15
The embryonic brain and spinal cord initially form through the process of neural tube closure (NTC). NTC is thought to be highly similar between rodents and humans, and studies of mouse genetic mutants have greatly increased our understanding of the molecular basis of NTC with relevance for human neural tube defects. In addition, studies using amphibian and chick embryos have shed light into the cellular and tissue dynamics underlying NTC. However, the dynamics of mammalian NTC has been difficult to study due to in utero development until recently when advances in mouse embryo ex vivo culture techniques along with confocal microscopy have allowed for imaging of mouse NTC in real time. Here, we have performed live imaging of mouse embryos with a particular focus on the non-neural ectoderm (NNE). Previous studies in multiple model systems have found that the NNE is important for proper NTC, but little is known about the behavior of these cells during mammalian NTC. Here we utilized a NNE-specific genetic labeling system to assess NNE dynamics during murine NTC and identified different NNE cell behaviors as the cranial region undergoes NTC. These results bring valuable new insight into regional differences in cellular behavior during NTC that may be driven by different molecular regulators and which may underlie the various positional disruptions of NTC observed in humans with neural tube defects. Copyright © 2016 Elsevier Inc. All rights reserved.
Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.
Directory of Open Access Journals (Sweden)
Andrea Maesani
2015-11-01
Full Text Available The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.
Dynamic Pricing in Electronic Commerce Using Neural Network
Ghose, Tapu Kumar; Tran, Thomas T.
In this paper, we propose an approach where feed-forward neural network is used for dynamically calculating a competitive price of a product in order to maximize sellers’ revenue. In the approach we considered that along with product price other attributes such as product quality, delivery time, after sales service and seller’s reputation contribute in consumers purchase decision. We showed that once the sellers, by using their limited prior knowledge, set an initial price of a product our model adjusts the price automatically with the help of neural network so that sellers’ revenue is maximized.
Neural network dynamics in Parkinson's disease
Lourens, Marcel Antonius Johannes
2013-01-01
Parkinson's disease (PD) is characterized by the cell death of neuronal brain cells producing the signaling molecule dopamine. Due to resulting shortage of dopamine, the dynamics of neuronal cells changes, most notably abnormal synchronization of neuronal activity. Such changes complicate the
Ghrab, Nadya; Kallel, Hichem
2013-01-01
A comparative study between static and dynamic neural networks for robotic systems control is considered. So, two approaches of neural robot control were selected, exposed, and compared. One uses a static neural network; the other uses a dynamic neural network. Both compensate the nonlinear modeling and uncertainties of robotic systems. The first approach is direct; it approximates the nonlinearities and uncertainties by a static neural network. The second approach is indirect; it uses a dyna...
Predicting local field potentials with recurrent neural networks.
Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter
2016-08-01
We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.
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.
Transient dynamics for sequence processing neural networks
Energy Technology Data Exchange (ETDEWEB)
Kawamura, Masaki [Faculty of Science, Yamaguchi University, Yamaguchi (Japan)]. E-mail: kawamura@sci.yamaguchi-u.ac.jp; Okada, Masato [RIKEN BSI, Hirosawa, Wako-shi (Japan)
2002-01-18
An exact solution of the transient dynamics for a sequential associative memory model is discussed through both the path-integral method and the statistical neurodynamics. Although the path-integral method has the ability to give an exact solution of the transient dynamics, only stationary properties have been discussed for the sequential associative memory. We have succeeded in deriving an exact macroscopic description of the transient dynamics by analysing the correlation of crosstalk noise. Surprisingly, the order parameter equations of this exact solution are completely equivalent to those of the statistical neurodynamics, which is an approximation theory that assumes crosstalk noise to obey the Gaussian distribution. In order to examine our theoretical findings, we numerically obtain cumulants of the crosstalk noise. We verify that the third- and fourth-order cumulants are equal to zero, and that the crosstalk noise is normally distributed even in the non-retrieval case. We show that the results obtained by our theory agree with those obtained by computer simulations. We have also found that the macroscopic unstable state completely coincides with the separatrix. (author)
Topological defects control collective dynamics in neural progenitor cell cultures
Kawaguchi, Kyogo; Kageyama, Ryoichiro; Sano, Masaki
2017-04-01
Cultured stem cells have become a standard platform not only for regenerative medicine and developmental biology but also for biophysical studies. Yet, the characterization of cultured stem cells at the level of morphology and of the macroscopic patterns resulting from cell-to-cell interactions remains largely qualitative. Here we report on the collective dynamics of cultured murine neural progenitor cells (NPCs), which are multipotent stem cells that give rise to cells in the central nervous system. At low densities, NPCs moved randomly in an amoeba-like fashion. However, NPCs at high density elongated and aligned their shapes with one another, gliding at relatively high velocities. Although the direction of motion of individual cells reversed stochastically along the axes of alignment, the cells were capable of forming an aligned pattern up to length scales similar to that of the migratory stream observed in the adult brain. The two-dimensional order of alignment within the culture showed a liquid-crystalline pattern containing interspersed topological defects with winding numbers of +1/2 and -1/2 (half-integer due to the nematic feature that arises from the head-tail symmetry of cell-to-cell interaction). We identified rapid cell accumulation at +1/2 defects and the formation of three-dimensional mounds. Imaging at the single-cell level around the defects allowed us to quantify the velocity field and the evolving cell density; cells not only concentrate at +1/2 defects, but also escape from -1/2 defects. We propose a generic mechanism for the instability in cell density around the defects that arises from the interplay between the anisotropic friction and the active force field.
Electric field effects in hyperexcitable neural tissue: A review
Energy Technology Data Exchange (ETDEWEB)
Durand, D.M
2003-07-01
Uniform electric fields applied to neural tissue can modulate neuronal excitability with a threshold value of about 1mV mm{sup -1} in normal physiological conditions. However, electric fields could have a lower threshold in conditions where field sensitivity is enhanced, such as those simulating epilepsy. Uniform electrical fields were applied to hippocampal brain slices exposed to picrotoxin, high potassium or low calcium solutions. The results in the low calcium medium show that neuronal activity can be completely blocked in 10% of the 30 slices tested with a field amplitude of 1mV mm{sup -1}. These results suggest that the threshold for this effect is clearly smaller than 1mV mm{sup -1}. The hypothesis that the extracellular resistance could affect the sensitivity to the electrical fields was tested by measuring the effect of the osmolarity of the extracellular solution on the efficacy of the field. A 10% decrease on osmolarity resulted in a 56% decrease (n=4) in the minimum field required for full suppression. A 14% in osmolarity produced an 81% increase in the minimum field required for full suppression. These results show that the extracellular volume can modulate the efficacy of the field and could lower the threshold field amplitudes to values lower than {approx}1mmV mm{sup -.} (author)
Simulating dynamic plastic continuous neural networks by finite elements.
Joghataie, Abdolreza; Torghabehi, Omid Oliyan
2014-08-01
We introduce dynamic plastic continuous neural network (DPCNN), which is comprised of neurons distributed in a nonlinear plastic medium where wire-like connections of neural networks are replaced with the continuous medium. We use finite element method to model the dynamic phenomenon of information processing within the DPCNNs. During the training, instead of weights, the properties of the continuous material at its different locations and some properties of neurons are modified. Input and output can be vectors and/or continuous functions over lines and/or areas. Delay and feedback from neurons to themselves and from outputs occur in the DPCNNs. We model a simple form of the DPCNN where the medium is a rectangular plate of bilinear material, and the neurons continuously fire a signal, which is a function of the horizontal displacement.
Persistent activity in neural networks with dynamic synapses.
Directory of Open Access Journals (Sweden)
Omri Barak
2007-02-01
Full Text Available Persistent activity states (attractors, observed in several neocortical areas after the removal of a sensory stimulus, are believed to be the neuronal basis of working memory. One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections. A recent experimental study revealed that connections between pyramidal cells in prefrontal cortex exhibit various degrees of synaptic depression and facilitation. Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in interconnected neural networks. We show that different combinations of synaptic depression and facilitation result in qualitatively different network dynamics with respect to the emergence of the attractor states. This analysis raises the possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.
Shaping the dynamics of a bidirectional neural interface.
Directory of Open Access Journals (Sweden)
Alessandro Vato
Full Text Available Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a a motor interface decoding signals from a motor cortical area, and (b a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected
Shaping the Dynamics of a Bidirectional Neural Interface
Vato, Alessandro; Semprini, Marianna; Maggiolini, Emma; Szymanski, Francois D.; Fadiga, Luciano; Panzeri, Stefano; Mussa-Ivaldi, Ferdinando A.
2012-01-01
Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations. PMID
Slow diffusive dynamics in a chaotic balanced neural network.
Directory of Open Access Journals (Sweden)
Nimrod Shaham
2017-05-01
Full Text Available It has been proposed that neural noise in the cortex arises from chaotic dynamics in the balanced state: in this model of cortical dynamics, the excitatory and inhibitory inputs to each neuron approximately cancel, and activity is driven by fluctuations of the synaptic inputs around their mean. It remains unclear whether neural networks in the balanced state can perform tasks that are highly sensitive to noise, such as storage of continuous parameters in working memory, while also accounting for the irregular behavior of single neurons. Here we show that continuous parameter working memory can be maintained in the balanced state, in a neural circuit with a simple network architecture. We show analytically that in the limit of an infinite network, the dynamics generated by this architecture are characterized by a continuous set of steady balanced states, allowing for the indefinite storage of a continuous parameter. In finite networks, we show that the chaotic noise drives diffusive motion along the approximate attractor, which gradually degrades the stored memory. We analyze the dynamics and show that the slow diffusive motion induces slowly decaying temporal cross correlations in the activity, which differ substantially from those previously described in the balanced state. We calculate the diffusivity, and show that it is inversely proportional to the system size. For large enough (but realistic neural population sizes, and with suitable tuning of the network connections, the proposed balanced network can sustain continuous parameter values in memory over time scales larger by several orders of magnitude than the single neuron time scale.
Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks
Zhelavskaya, Irina S.; Shprits, Yuri Y.; Spasojević, Maria
2017-11-01
We present the PINE (Plasma density in the Inner magnetosphere Neural network-based Empirical) model - a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of 1 October 2012 to 1 July 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2≤L≤6 and all local times. We validate and test the model by measuring its performance on independent data sets withheld from the training set and by comparing the model-predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). The optimal model is based on the 96 h time history of Kp, AE, SYM-H, and F10.7 indices. The model successfully reproduces erosion of the plasmasphere on the nightside and plume formation and evolution. We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in situ observations by using machine learning techniques.
Gamma Oscillations and Neural Field DCMs Can Reveal Cortical Excitability and Microstructure
Directory of Open Access Journals (Sweden)
Dimitris Pinotsis
2014-05-01
Full Text Available This paper shows how gamma oscillations can be combined with neural population models and dynamic causal modeling (DCM to distinguish among alternative hypotheses regarding cortical excitability and microstructure. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. Neural field models are used to evaluate model evidence and obtain parameter estimates using invasive and non-invasive gamma recordings. Our overview comprises two parts: in the first part, we use neural fields to simulate neural activity and distinguish the effects of post synaptic filtering on predicted responses in terms of synaptic rate constants that correspond to different timescales and distinct neurotransmitters. We focus on model predictions of conductance and convolution based field models and show that these can yield spectral responses that are sensitive to biophysical properties of local cortical circuits like synaptic kinetics and filtering; we also consider two different mechanisms for this filtering: a nonlinear mechanism involving specific conductances and a linear convolution of afferent firing rates producing post synaptic potentials. In the second part of this paper, we use neural fields quantitatively—to fit empirical data recorded during visual stimulation. We present two studies of spectral responses obtained from the visual cortex during visual perception experiments: in the first study, MEG data were acquired during a task designed to show how activity in the gamma band is related to visual perception, while in the second study, we exploited high density electrocorticographic (ECoG data to study the effect of varying stimulus contrast on cortical excitability and gamma peak frequency.
Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks
Al-Jumeily, Dhiya; Ghazali, Rozaida; Hussain, Abir
2014-01-01
Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques. PMID:25157950
Neural network simulation of the industrial producer price index dynamical series
Soshnikov, L. E.
2013-01-01
This paper is devoted the simulation and forecast of dynamical series of the economical indicators. Multilayer perceptron and Radial basis function neural networks have been used. The neural networks model results are compared with the econometrical modeling.
Dynamical systems, attractors, and neural circuits [version 1; referees: 3 approved
Directory of Open Access Journals (Sweden)
Paul Miller
2016-05-01
Full Text Available Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic—they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.
Bio-Inspired Neural Model for Learning Dynamic Models
Duong, Tuan; Duong, Vu; Suri, Ronald
2009-01-01
A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.
Spatio-Temporal Dynamics in Cellular Neural Networks
Directory of Open Access Journals (Sweden)
Liviu GORAS
2009-07-01
Full Text Available Analog Parallel Architectures like Cellular Neural Networks (CNN’s have been thoroughly studied not only for their potential in high-speed image processing applications but also for their rich and exciting spatio-temporal dynamics. An interesting behavior such architectures can exhibit is spatio-temporal filtering and pattern formation, aspects that will be discussed in this work for a general structure consisting of linear cells locally and homogeneously connected within a specified neighborhood. The results are generalizations of those regarding Turing pattern formation in CNN’s. Using linear cells (or piecewise linear cells working in the central linear part of their characteristic allows the use of the decoupling technique – a powerful technique that gives significant insight into the dynamics of the CNN. The roles of the cell structure as well as that of the connection template are discussed and models for the spatial modes dynamics are made as well.
A neural network approach to dynamic task assignment of multirobots.
Zhu, Anmin; Yang, Simon X
2006-09-01
In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.
Forecasting financial asset processes: stochastic dynamics via learning neural networks.
Giebel, S; Rainer, M
2010-01-01
Models for financial asset dynamics usually take into account their inherent unpredictable nature by including a suitable stochastic component into their process. Unknown (forward) values of financial assets (at a given time in the future) are usually estimated as expectations of the stochastic asset under a suitable risk-neutral measure. This estimation requires the stochastic model to be calibrated to some history of sufficient length in the past. Apart from inherent limitations, due to the stochastic nature of the process, the predictive power is also limited by the simplifying assumptions of the common calibration methods, such as maximum likelihood estimation and regression methods, performed often without weights on the historic time series, or with static weights only. Here we propose a novel method of "intelligent" calibration, using learning neural networks in order to dynamically adapt the parameters of the stochastic model. Hence we have a stochastic process with time dependent parameters, the dynamics of the parameters being themselves learned continuously by a neural network. The back propagation in training the previous weights is limited to a certain memory length (in the examples we consider 10 previous business days), which is similar to the maximal time lag of autoregressive processes. We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts for the EURTRY and EUR-HUF exchange rates each.
Corticothalamic feedback dynamics for neural correlates of auditory selective attention.
Trenado, Carlos; Haab, Lars; Strauss, Daniel J
2009-02-01
Auditory evoked cortical potentials (AECPs) have been consolidated as a diagnostic tool in audiology. Further applications of this technique are in experimental neuropsychology, neuroscience, and psychiatry, e.g., for the attention deficit disorder, schizophrenia, or for studying the tinnitus decompensation. In particular, numerous psychophysiological studies have emphasized their dynamic characteristics in relation to exogenous and endogenous attention. However, the effect of corticothalamic feedback dynamics to neural correlates of focal and nonfocal attention and its large-scale effect reflected in AECPs is far from being understood. To address this issue, we model neural correlates of auditory selective attention reflected in AECPs by using corticothalamic feedback dynamics. In our framework, we make use of a well-known multiscale model of evoked potentials, for which we define for the first time a neurofunctional map of relevant corticothalamic loops to the hearing path. Such loops are in turn are coupled to our proposed probabilistic scheme of auditory selective attention. It is concluded that our model represents a promising approach to gain a deeper understanding of the neurodynamics of auditory attention and might be used as an efficient forward model to support hypotheses that are obtained in experimental paradigms involving AECPs.
Neural ensemble dynamics underlying a long-term associative memory
Grewe, Benjamin F.; Gründemann, Jan; Kitch, Lacey J.; Lecoq, Jerome A.; Parker, Jones G.; Marshall, Jesse D.; Larkin, Margaret C.; Jercog, Pablo E.; Grenier, Francois; Li, Jin Zhong; Lüthi, Andreas; Schnitzer, Mark J.
2017-01-01
The brain’s ability to associate different stimuli is vital to long-term memory, but how neural ensembles encode associative memories is unknown. Here we studied how cell ensembles in the basal and lateral amygdala (BLA) encode associations between conditioned and unconditioned stimuli (CS, US). Using a miniature fluorescence microscope, we tracked BLA ensemble neural Ca2+ dynamics during fear learning and extinction over six days in behaving mice. Fear conditioning induced both up- and down-regulation of individual cells’ CS-evoked responses. This bi-directional plasticity mainly occurred after conditioning and reshaped the CS ensemble neural representation to gain similarity to the US-representation. During extinction training with repetitive CS presentations, the CS-representation became more distinctive without reverting to its original form. Throughout, the strength of the ensemble-encoded CS-US association predicted each mouse’s level of behavioral conditioning. These findings support a supervised learning model in which activation of the US-representation guides the transformation of the CS-representation. PMID:28329757
Neural dynamics of reward probability coding: a Magnetoencephalographic study in humans
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Julie eThomas
2013-11-01
Full Text Available Prediction of future rewards and discrepancy between actual and expected outcomes (prediction error are crucial signals for adaptive behavior. In humans, a number of fMRI studies demonstrated that reward probability modulates these two signals in a large brain network. Yet, the spatio-temporal dynamics underlying the neural coding of reward probability remains unknown. Here, using magnetoencephalography, we investigated the neural dynamics of prediction and reward prediction error computations while subjects learned to associate cues of slot machines with monetary rewards with different probabilities. We showed that event-related magnetic fields (ERFs arising from the visual cortex coded the expected reward value 155 ms after the cue, demonstrating that reward value signals emerge early in the visual stream. Moreover, a prediction error was reflected in ERF peaking 300 ms after the rewarded outcome and showing decreasing amplitude with higher reward probability. This prediction error signal was generated in a network including the anterior and posterior cingulate cortex. These findings pinpoint the spatio-temporal characteristics underlying reward probability coding. Together, our results provide insights into the neural dynamics underlying the ability to learn probabilistic stimuli-reward contingencies.
Research of Recurrent Dynamic Neural Networks for Adaptive Control of Complex Dynamic Systems
2010-07-08
of human brain . Neural Dynamic Associative Memory can be considered as an analogue of mechanisms of brain memory that explains processes of forming...4402.85 UAH. Total, without VAT 13164.30 UAH. Pure VAT 2632.86 UAH. Total with VAT
String Analysis for Dynamic Field Access
DEFF Research Database (Denmark)
Madsen, Magnus; Andreasen, Esben
2014-01-01
In JavaScript, and scripting languages in general, dynamic field access is a commonly used feature. Unfortunately, current static analysis tools either completely ignore dynamic field access or use overly conservative approximations that lead to poor precision and scalability. We present new string...... domains to reason about dynamic field access in a static analysis tool. A key feature of the domains is that the equal, concatenate and join operations take Ο(1) time. Experimental evaluation on four common JavaScript libraries, including jQuery and Prototype, shows that traditional string domains...
A complex-valued neural dynamical optimization approach and its stability analysis.
Zhang, Songchuan; Xia, Youshen; Zheng, Weixing
2015-01-01
In this paper, we propose a complex-valued neural dynamical method for solving a complex-valued nonlinear convex programming problem. Theoretically, we prove that the proposed complex-valued neural dynamical approach is globally stable and convergent to the optimal solution. The proposed neural dynamical approach significantly generalizes the real-valued nonlinear Lagrange network completely in the complex domain. Compared with existing real-valued neural networks and numerical optimization methods for solving complex-valued quadratic convex programming problems, the proposed complex-valued neural dynamical approach can avoid redundant computation in a double real-valued space and thus has a low model complexity and storage capacity. Numerical simulations are presented to show the effectiveness of the proposed complex-valued neural dynamical approach.
Sensorimotor learning biases choice behavior: a learning neural field model for decision making.
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Christian Klaes
Full Text Available According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action
Out-of-equilibrium quantum field dynamics in external fields
Energy Technology Data Exchange (ETDEWEB)
Cao, F.J. [Universidad Complutense de Madrid, Departamento Fisica Atomica, Molecular y Nuclear, Madrid (Spain); LERMA, Observatoire de Paris, Laboratoire Associe au CNRS UMR 8112, Paris (France)
2007-03-15
The quantum dynamics of the symmetry-broken {lambda}({phi} {sup 2}){sup 2} scalar-field theory in the presence of an homogeneous external field is investigated in the large-N limit. We consider an initial thermal state of temperature T for a constant external field J. A subsequent sign flip of the external field, J{yields}-J, gives rise to an out-of-equilibrium nonperturbative quantum field dynamics. We review here the dynamics for the symmetry-broken {lambda}({phi}{sup 2}){sup 2} scalar N component field theory in the large-N limit, with particular stress in the comparison between the results when the initial temperature is zero and when it is finite. The presence of a finite temperature modifies the dynamical effective potential for the expectation value, and also makes that the transition between the two regimes of the early dynamics occurs for lower values of the external field. The two regimes are characterized by the presence or absence of a temporal trapping close to the metastable equilibrium position of the potential. In the cases when the trapping occurs it is shorter for larger initial temperatures. (orig.)
Neural Dynamics and Information Representation in Microcircuits of Motor Cortex
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Yasuhiro eTsubo
2013-05-01
Full Text Available The brain has to analyze and respond to external events that can change rapidly from time to time, suggesting that information processing by the brain may be essentially dynamic rather than static. The dynamical features of neural computation are of significant importance in motor cortex that governs the process of movement generation and learning. In this paper, we discuss these features based primarily on our recent findings on neural dynamics and information coding in the microcircuit of rat motor cortex. In fact, cortical neurons show a variety of dynamical behavior from rhythmic activity in various frequency bands to highly irregular spike firing. Of particular interest are the similarity and dissimilarity of the neuronal response properties in different layers of motor cortex. By conducting electrophysiological recordings in slice preparation, we report the phase response curves of neurons in different cortical layers to demonstrate their layer-dependent synchronization properties. We then study how motor cortex recruits task-related neurons in different layers for voluntary arm movements by simultaneous juxtacellular and multiunit recordings from behaving rats. The results suggest an interesting difference in the spectrum of functional activity between the superficial and deep layers. Furthermore, the task-related activities recorded from various layers exhibited power law distributions of inter-spike intervals (ISIs, in contrast to a general belief that ISIs obey Poisson or Gamma distributions in cortical neurons. We present a theoretical argument that this power law of in vivo neurons may represent the maximization of the entropy of firing rate with limited energy consumption of spike generation. Though further studies are required to fully clarify the functional implications of this coding principle, it may shed new light on information representations by neurons and circuits in motor cortex.
Nonlinear modeling of neural population dynamics for hippocampal prostheses.
Song, Dong; Chan, Rosa H M; Marmarelis, Vasilis Z; Hampson, Robert E; Deadwyler, Sam A; Berger, Theodore W
2009-11-01
Developing a neural prosthesis for the damaged hippocampus requires restoring the transformation of population neural activities performed by the hippocampal circuitry. To bypass a damaged region, output spike trains need to be predicted from the input spike trains and then reinstated through stimulation. We formulate a multiple-input, multiple-output (MIMO) nonlinear dynamic model for the input-output transformation of spike trains. In this approach, a MIMO model comprises a series of physiologically-plausible multiple-input, single-output (MISO) neuron models that consist of five components each: (1) feedforward Volterra kernels transforming the input spike trains into the synaptic potential, (2) a feedback kernel transforming the output spikes into the spike-triggered after-potential, (3) a noise term capturing the system uncertainty, (4) an adder generating the pre-threshold potential, and (5) a threshold function generating output spikes. It is shown that this model is equivalent to a generalized linear model with a probit link function. To reduce model complexity and avoid overfitting, statistical model selection and cross-validation methods are employed to choose the significant inputs and interactions between inputs. The model is applied successfully to the hippocampal CA3-CA1 population dynamics. Such a model can serve as a computational basis for the development of hippocampal prostheses.
Dynamic Constrained Economic/Emission Dispatch Scheduling Using Neural Network
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Farid Benhamida
2013-01-01
Full Text Available In this paper, a Dynamic Economic/Emission Dispatch (DEED problem is obtained by considering both the economy and emission objectives with required constraints dynamically. This paper presents an optimization algorithm for solving constrained combined economic emission dispatch (EED problem and DEED, through the application of neural network, which is a flexible Hopfield neural network (FHNN. The constrained DEED must not only satisfy the system load demand and the spinning reserve capacity, but some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone, are also considered in practical generator operation. The feasibility of the proposed FHNN using to solve DEED is demonstrated using three power systems, and it is compared with the other methods in terms of solution quality and computation efficiency. The simulation results showed that the proposed FHNN method was indeed capable of obtaining higher quality solutions efficiently in constrained DEED and EED problems with a much shorter computation time compared to other methods.
A stochastic-field description of finite-size spiking neural networks.
Dumont, Grégory; Payeur, Alexandre; Longtin, André
2017-08-01
Neural network dynamics are governed by the interaction of spiking neurons. Stochastic aspects of single-neuron dynamics propagate up to the network level and shape the dynamical and informational properties of the population. Mean-field models of population activity disregard the finite-size stochastic fluctuations of network dynamics and thus offer a deterministic description of the system. Here, we derive a stochastic partial differential equation (SPDE) describing the temporal evolution of the finite-size refractory density, which represents the proportion of neurons in a given refractory state at any given time. The population activity-the density of active neurons per unit time-is easily extracted from this refractory density. The SPDE includes finite-size effects through a two-dimensional Gaussian white noise that acts both in time and along the refractory dimension. For an infinite number of neurons the standard mean-field theory is recovered. A discretization of the SPDE along its characteristic curves allows direct simulations of the activity of large but finite spiking networks; this constitutes the main advantage of our approach. Linearizing the SPDE with respect to the deterministic asynchronous state allows the theoretical investigation of finite-size activity fluctuations. In particular, analytical expressions for the power spectrum and autocorrelation of activity fluctuations are obtained. Moreover, our approach can be adapted to incorporate multiple interacting populations and quasi-renewal single-neuron dynamics.
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Ingo eBojak
2015-02-01
Full Text Available Burst suppression in the electroencephalogram (EEG is a well described phenomenon that occurs during deep anaesthesia, as well as in a variety of congenital and acquired brain insults. Classically it is thought of as spatially synchronous, quasi-periodic bursts of high amplitude EEG separated by low amplitude activity. However, its characterisation as a ``global brain state'' has been challenged by recent results obtained with intracranial electrocortigraphy. Not only does it appear that burst suppression activity is highly asynchronous across cortex, but also that it may occur in isolated regions of circumscribed spatial extent. Here we outline a realistic neural field model for burst suppression by adding a slow process of synaptic resource depletion and recovery, which is able to reproduce qualitatively the empirically observed features during general anaesthesia at the whole cortex level. Simulations reveal heterogeneous bursting over the model cortex and complex spatiotemporal dynamics during simulated anaesthetic action, and provide forward predictions of neuroimaging signals for subsequent empirical comparisons and more detailed characterisation.Because burst suppression corresponds to a dynamical end-point of brain activity, theoretically accounting for its spatiotemporal emergence will vitally contribute to efforts aimed at clarifying whether a common physiological trajectory is induced by the actions of general anaesthetic agents. We have taken a first step in this direction by showing that a neural field model can qualitatively match recent experimental data that indicate spatial differentiation of burst suppression activity across cortex.
Multiplex visibility graphs to investigate recurrent neural network dynamics
Bianchi, Filippo Maria; Livi, Lorenzo; Alippi, Cesare; Jenssen, Robert
2017-03-01
A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design principled unsupervised methods to derive hyperparameters configurations yielding maximal ESN performance, expressed in terms of prediction error and memory capacity. In particular, we propose to model time series generated by each neuron activations with a horizontal visibility graph, whose topological properties have been shown to be related to the underlying system dynamics. Successively, horizontal visibility graphs associated with all neurons become layers of a larger structure called a multiplex. We show that topological properties of such a multiplex reflect important features of ESN dynamics that can be used to guide the tuning of its hyperparamers. Results obtained on several benchmarks and a real-world dataset of telephone call data records show the effectiveness of the proposed methods.
Modeling multiple time scale firing rate adaptation in a neural network of local field potentials.
Lundstrom, Brian Nils
2015-02-01
In response to stimulus changes, the firing rates of many neurons adapt, such that stimulus change is emphasized. Previous work has emphasized that rate adaptation can span a wide range of time scales and produce time scale invariant power law adaptation. However, neuronal rate adaptation is typically modeled using single time scale dynamics, and constructing a conductance-based model with arbitrary adaptation dynamics is nontrivial. Here, a modeling approach is developed in which firing rate adaptation, or spike frequency adaptation, can be understood as a filtering of slow stimulus statistics. Adaptation dynamics are modeled by a stimulus filter, and quantified by measuring the phase leads of the firing rate in response to varying input frequencies. Arbitrary adaptation dynamics are approximated by a set of weighted exponentials with parameters obtained by fitting to a desired filter. With this approach it is straightforward to assess the effect of multiple time scale adaptation dynamics on neural networks. To demonstrate this, single time scale and power law adaptation were added to a network model of local field potentials. Rate adaptation enhanced the slow oscillations of the network and flattened the output power spectrum, dampening intrinsic network frequencies. Thus, rate adaptation may play an important role in network dynamics.
Two-Dimensional Bumps in Piecewise Smooth Neural Fields with Synaptic Depression
Bressloff, Paul C.
2011-01-01
We analyze radially symmetric bumps in a two-dimensional piecewise-smooth neural field model with synaptic depression. The continuum dynamics is described in terms of a nonlocal integrodifferential equation, in which the integral kernel represents the spatial distribution of synaptic weights between populations of neurons whose mean firing rate is taken to be a Heaviside function of local activity. Synaptic depression dynamically reduces the strength of synaptic weights in response to increases in activity. We show that in the case of a Mexican hat weight distribution, sufficiently strong synaptic depression can destabilize a stationary bump solution that would be stable in the absence of depression. Numerically it is found that the resulting instability leads to the formation of a traveling spot. The local stability of a bump is determined by solutions to a system of pseudolinear equations that take into account the sign of perturbations around the circular bump boundary. © 2011 Society for Industrial and Applied Mathematics.
Magnetic Field Control of Combustion Dynamics
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Barmina I.
2016-08-01
Full Text Available Experimental studies and mathematical modelling of the effects of magnetic field on combustion dynamics at thermo-chemical conversion of biomass are carried out with the aim of providing control of the processes developing in the reaction zone of swirling flame. The joint research of the magnetic field effect on the combustion dynamics includes the estimation of this effect on the formation of the swirling flame dynamics, flame temperature and composition, providing analysis of the magnetic field effects on the flame characteristics. The results of experiments have shown that the magnetic field exerts the influence on the flow velocity components by enhancing a swirl motion in the flame reaction zone with swirl-enhanced mixing of the axial flow of volatiles with cold air swirl, by cooling the flame reaction zone and by limiting the thermo-chemical conversion of volatiles. Mathematical modelling of magnetic field effect on the formation of the flame dynamics confirms that the electromagnetic force, which is induced by the electric current surrounding the flame, leads to field-enhanced increase of flow vorticity by enhancing mixing of the reactants. The magnetic field effect on the flame temperature and rate of reactions leads to conclusion that field-enhanced increase of the flow vorticity results in flame cooling by limiting the chemical conversion of the reactants.
A place for time: the spatiotemporal structure of neural dynamics during natural audition
Stephens, G.J.; Honey, C.J.; Hasson, U.
2013-01-01
We use functional magnetic resonance imaging (fMRI) to analyze neural responses to natural auditory stimuli. We characterize the fMRI time series through the shape of the voxel power spectrum and find that the timescales of neural dynamics vary along a spatial gradient, with faster dynamics in early
Moving to higher ground: The dynamic field theory and the dynamics of visual cognition.
Johnson, Jeffrey S; Spencer, John P; Schöner, Gregor
2008-08-01
In the present report, we describe a new dynamic field theory that captures the dynamics of visuo-spatial cognition. This theory grew out of the dynamic systems approach to motor control and development, and is grounded in neural principles. The initial application of dynamic field theory to issues in visuo-spatial cognition extended concepts of the motor approach to decision making in a sensori-motor context, and, more recently, to the dynamics of spatial cognition. Here we extend these concepts still further to address topics in visual cognition, including visual working memory for non-spatial object properties, the processes that underlie change detection, and the 'binding problem' in vision. In each case, we demonstrate that the general principles of the dynamic field approach can unify findings in the literature and generate novel predictions. We contend that the application of these concepts to visual cognition avoids the pitfalls of reductionist approaches in cognitive science, and points toward a formal integration of brains, bodies, and behavior.
Spatial interactions in the superior colliculus predict saccade behavior in a neural field model.
Marino, Robert A; Trappenberg, Thomas P; Dorris, Michael; Munoz, Douglas P
2012-02-01
During natural vision, eye movements are dynamically controlled by the combinations of goal-related top-down (TD) and stimulus-related bottom-up (BU) neural signals that map onto objects or locations of interest in the visual world. In primates, both BU and TD signals converge in many areas of the brain, including the intermediate layers of the superior colliculus (SCi), a midbrain structure that contains a retinotopically coded map for saccades. How TD and BU signals combine or interact within the SCi map to influence saccades remains poorly understood and actively debated. It has been proposed that winner-take-all competition between these signals occurs dynamically within this map to determine the next location for gaze. Here, we examine how TD and BU signals interact spatially within an artificial two-dimensional dynamic winner-take-all neural field model of the SCi to influence saccadic RT (SRT). We measured point images (spatially organized population activity on the SC map) physiologically to inform the TD and BU model parameters. In this model, TD and BU signals interacted nonlinearly within the SCi map to influence SRT via changes to the (1) spatial size or extent of individual signals, (2) peak magnitude of individual signals, (3) total number of competing signals, and (4) the total spatial separation between signals in the visual field. This model reproduced previous behavioral studies of TD and BU influences on SRT and accounted for multiple inconsistencies between them. This is achieved by demonstrating how, under different experimental conditions, the spatial interactions of TD and BU signals can lead to either increases or decreases in SRT. Our results suggest that dynamic winner-take-all modeling with local excitation and distal inhibition in two dimensions accurately reflects both the physiological activity within the SCi map and the behavioral changes in SRT that result from BU and TD manipulations.
Hidden Conditional Neural Fields for Continuous Phoneme Speech Recognition
Fujii, Yasuhisa; Yamamoto, Kazumasa; Nakagawa, Seiichi
In this paper, we propose Hidden Conditional Neural Fields (HCNF) for continuous phoneme speech recognition, which are a combination of Hidden Conditional Random Fields (HCRF) and a Multi-Layer Perceptron (MLP), and inherit their merits, namely, the discriminative property for sequences from HCRF and the ability to extract non-linear features from an MLP. HCNF can incorporate many types of features from which non-linear features can be extracted, and is trained by sequential criteria. We first present the formulation of HCNF and then examine three methods to further improve automatic speech recognition using HCNF, which is an objective function that explicitly considers training errors, provides a hierarchical tandem-style feature and includes a deep non-linear feature extractor for the observation function. We show that HCNF can be trained realistically without any initial model and outperforms HCRF and the triphone hidden Markov model trained by the minimum phone error (MPE) manner using experimental results for continuous English phoneme recognition on the TIMIT core test set and Japanese phoneme recognition on the IPA 100 test set.
Spatiotemporal neural network dynamics for the processing of dynamic facial expressions
Sato, Wataru; Kochiyama, Takanori; Uono, Shota
2015-01-01
The dynamic facial expressions of emotion automatically elicit multifaceted psychological activities; however, the temporal profiles and dynamic interaction patterns of brain activities remain unknown. We investigated these issues using magnetoencephalography. Participants passively observed dynamic facial expressions of fear and happiness, or dynamic mosaics. Source-reconstruction analyses utilizing functional magnetic-resonance imaging data revealed higher activation in broad regions of the bilateral occipital and temporal cortices in response to dynamic facial expressions than in response to dynamic mosaics at 150–200 ms and some later time points. The right inferior frontal gyrus exhibited higher activity for dynamic faces versus mosaics at 300–350 ms. Dynamic causal-modeling analyses revealed that dynamic faces activated the dual visual routes and visual–motor route. Superior influences of feedforward and feedback connections were identified before and after 200 ms, respectively. These results indicate that hierarchical, bidirectional neural network dynamics within a few hundred milliseconds implement the processing of dynamic facial expressions. PMID:26206708
A novel neural dynamical approach to convex quadratic program and its efficient applications.
Xia, Youshen; Sun, Changyin
2009-12-01
This paper proposes a novel neural dynamical approach to a class of convex quadratic programming problems where the number of variables is larger than the number of equality constraints. The proposed continuous-time and proposed discrete-time neural dynamical approach are guaranteed to be globally convergent to an optimal solution. Moreover, the number of its neurons is equal to the number of equality constraints. In contrast, the number of neurons in existing neural dynamical methods is at least the number of the variables. Therefore, the proposed neural dynamical approach has a low computational complexity. Compared with conventional numerical optimization methods, the proposed discrete-time neural dynamical approach reduces multiplication operation per iteration and has a large computational step length. Computational examples and two efficient applications to signal processing and robot control further confirm the good performance of the proposed approach.
Neural network based adaptive control for nonlinear dynamic regimes
Shin, Yoonghyun
Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.
Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks
Jorgensen, Charles C.
1997-01-01
A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.
A dynamical systems view of motor preparation: Implications for neural prosthetic system design
Shenoy, Krishna V.; Kaufman, Matthew T.; Sahani, Maneesh; Churchland, Mark M.
2013-01-01
Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached. PMID:21763517
Research on wind field algorithm of wind lidar based on BP neural network and grey prediction
Chen, Yong; Chen, Chun-Li; Luo, Xiong; Zhang, Yan; Yang, Ze-hou; Zhou, Jie; Shi, Xiao-ding; Wang, Lei
2018-01-01
This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.
Dynamic shielding of the magnetic fields
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RAU, M.
2010-11-01
Full Text Available The paper presents a comparative study of the methods used to control and compensate the direct and alternative magnetic fields. Two frequently used methods in the electromagnetic compatibility of the complex biomagnetism installations were analyzed. The two methods refer to the use of inductive magnetic field sensors (only for alternative fields and of fluxgate magnetometers as active transducers which measures both the direct and alternative components of the magnetic field. The applications of the dynamic control of the magnetic field are: control of the magnetic field of the military ships, control of parasite magnetic field produced by power transformers and the electrical networks, protection of the mass spectrometers, electronic microscopes, SQUID and optical pumping magnetometers for applications in biomagnetism.
Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.
Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus
2017-01-01
Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.
A Neural Information Field Approach to Computational Cognition
2016-11-18
of irrelevant information) during the task. The spiking neural model accounts for the probability of first recall, recency effects , primacy effects ...neuron models, allowing the simulated testing of drug effects on cognitive performance; demonstrated a scalable neural model of motor planning... effects of distraction in working memory; shown a hippocampal model able to perform context sensitive sequence encoding and retrieval; proposed what is
Imaging electric field dynamics with graphene optoelectronics.
Horng, Jason; Balch, Halleh B; McGuire, Allister F; Tsai, Hsin-Zon; Forrester, Patrick R; Crommie, Michael F; Cui, Bianxiao; Wang, Feng
2016-12-16
The use of electric fields for signalling and control in liquids is widespread, spanning bioelectric activity in cells to electrical manipulation of microstructures in lab-on-a-chip devices. However, an appropriate tool to resolve the spatio-temporal distribution of electric fields over a large dynamic range has yet to be developed. Here we present a label-free method to image local electric fields in real time and under ambient conditions. Our technique combines the unique gate-variable optical transitions of graphene with a critically coupled planar waveguide platform that enables highly sensitive detection of local electric fields with a voltage sensitivity of a few microvolts, a spatial resolution of tens of micrometres and a frequency response over tens of kilohertz. Our imaging platform enables parallel detection of electric fields over a large field of view and can be tailored to broad applications spanning lab-on-a-chip device engineering to analysis of bioelectric phenomena.
Controlling Josephson dynamics by strong microwave fields
Chesca, B.; Savel'ev, E.; Rakhmanov, A.L.; Smilde, H.J.H.; Hilgenkamp, Johannes W.M.
2008-01-01
We observe several sharp changes in the slope of the current-voltage characteristics (CVCs) of thin-film ramp-edge Josephson junctions between YBa2Cu3O7−delta and Nb when applying strong microwave fields. Such behavior indicates an intriguing Josephson dynamics associated with the switching from a
Sase, Takumi; Katori, Yuichi; Komuro, Motomasa; Aihara, Kazuyuki
2017-01-01
We investigate a discrete-time network model composed of excitatory and inhibitory neurons and dynamic synapses with the aim at revealing dynamical properties behind oscillatory phenomena possibly related to brain functions. We use a stochastic neural network model to derive the corresponding macroscopic mean field dynamics, and subsequently analyze the dynamical properties of the network. In addition to slow and fast oscillations arising from excitatory and inhibitory networks, respectively, we show that the interaction between these two networks generates phase-amplitude cross-frequency coupling (CFC), in which multiple different frequency components coexist and the amplitude of the fast oscillation is modulated by the phase of the slow oscillation. Furthermore, we clarify the detailed properties of the oscillatory phenomena by applying the bifurcation analysis to the mean field model, and accordingly show that the intermittent and the continuous CFCs can be characterized by an aperiodic orbit on a closed curve and one on a torus, respectively. These two CFC modes switch depending on the coupling strength from the excitatory to inhibitory networks, via the saddle-node cycle bifurcation of a one-dimensional torus in map (MT1SNC), and may be associated with the function of multi-item representation. We believe that the present model might have potential for studying possible functional roles of phase-amplitude CFC in the cerebral cortex. PMID:28424606
Field dynamics inference via spectral density estimation
Frank, Philipp; Steininger, Theo; Enßlin, Torsten A.
2017-11-01
Stochastic differential equations are of utmost importance in various scientific and industrial areas. They are the natural description of dynamical processes whose precise equations of motion are either not known or too expensive to solve, e.g., when modeling Brownian motion. In some cases, the equations governing the dynamics of a physical system on macroscopic scales occur to be unknown since they typically cannot be deduced from general principles. In this work, we describe how the underlying laws of a stochastic process can be approximated by the spectral density of the corresponding process. Furthermore, we show how the density can be inferred from possibly very noisy and incomplete measurements of the dynamical field. Generally, inverse problems like these can be tackled with the help of Information Field Theory. For now, we restrict to linear and autonomous processes. To demonstrate its applicability, we employ our reconstruction algorithm on a time-series and spatiotemporal processes.
Directory of Open Access Journals (Sweden)
Hocine Chebi
2016-10-01
Full Text Available Visual analysis of human behavior is a broad field within computer vision. In this field of work, we are interested in dynamic methods in the analysis of crowd behavior which consist in detecting the abnormal entities in a group in a dense scene. These scenes are characterized by the presence of a great number of people in the camera’s field of vision. The major problem is the development of an autonomous approach for the management of a great number of anomalies which is almost impossible to carry out by human operators. We present in this paper a new approach for the detection of dynamic anomalies of very dense scenes measuring the speed of both the individuals and the whole group. The various anomalies are detected by dynamically switching between two approaches: An artificial neural network (ANN for the management of group anomalies of people, and a Density-Based Spatial Clustering of Application with Noise (DBSCAN in the case of entities. For greater robustness and effectiveness, we introduced two routines that serve to eliminate the shades and the management of occlusions. The two latter phases have proven that the results of the simulation are comparable to existing work.
Exploring neural cell dynamics with digital holographic microscopy
Marquet, Pierre
2013-07-11
In this review, we summarize how the new concept of digital optics applied to the field of holographic microscopy has allowed the development of a reliable and flexible digital holographic quantitative phase microscopy (DH-QPM) technique at the nanoscale particularly suitable for cell imaging. Particular emphasis is placed on the original biological ormation provided by the quantitative phase signal. We present the most relevant DH-QPM applications in the field of cell biology, including automated cell counts, recognition, classification, three-dimensional tracking, discrimination between physiological and pathophysiological states, and the study of cell membrane fluctuations at the nanoscale. In the last part, original results show how DH-QPM can address two important issues in the field of neurobiology, namely, multiple-site optical recording of neuronal activity and noninvasive visualization of dendritic spine dynamics resulting from a full digital holographic microscopy tomographic approach. Copyright © 2013 by Annual Reviews.
Sidelnikov, O. S.; Redyuk, A. A.; Sygletos, S.
2017-12-01
We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.
Hoellinger, Thomas; Petieau, Mathieu; Duvinage, Matthieu; Castermans, Thierry; Seetharaman, Karthik; Cebolla, Ana-Maria; Bengoetxea, Ana; Ivanenko, Yuri; Dan, Bernard; Cheron, Guy
2013-01-01
The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.
Research on quasi-dynamic calibration model of plastic sensitive element based on neural networks
Wang, Fang; Kong, Deren; Yang, Lixia; Zhang, Zouzou
2017-08-01
Quasi-dynamic calibration accuracy of the plastic sensitive element depends on the accuracy of the fitting model between pressure and deformation. By using the excellent nonlinear mapping ability of RBF (Radial Basis Function) neural network, a calibration model is established which use the peak pressure as the input and use the deformation of the plastic sensitive element as the output in this paper. The calibration experiments of a batch of copper cylinders are carried out on the quasi-dynamic pressure calibration device, which pressure range is within the range of 200MPa to 700MPa. The experiment data are acquired according to the standard pressure monitoring system. The network train and study are done to quasi dynamic calibration model based on neural network by using MATLAB neural network toolbox. Taking the testing samples as the research object, the prediction accuracy of neural network model is compared with the exponential fitting model and the second-order polynomial fitting model. The results show that prediction of the neural network model is most close to the testing samples, and the accuracy of prediction model based on neural network is better than 0.5%, respectively one order higher than the second-order polynomial fitting model and two orders higher than the exponential fitting model. The quasi-dynamic calibration model between pressure peak and deformation of plastic sensitive element, which is based on neural network, provides important basis for creating higher accuracy quasi-dynamic calibration table.
Synchrony-induced modes of oscillation of a neural field model
Esnaola-Acebes, Jose M.; Roxin, Alex; Avitabile, Daniele; Montbrió, Ernest
2017-11-01
We investigate the modes of oscillation of heterogeneous ring networks of quadratic integrate-and-fire (QIF) neurons with nonlocal, space-dependent coupling. Perturbations of the equilibrium state with a particular wave number produce transient standing waves with a specific temporal frequency, analogously to those in a tense string. In the neuronal network, the equilibrium corresponds to a spatially homogeneous, asynchronous state. Perturbations of this state excite the network's oscillatory modes, which reflect the interplay of episodes of synchronous spiking with the excitatory-inhibitory spatial interactions. In the thermodynamic limit, an exact low-dimensional neural field model describing the macroscopic dynamics of the network is derived. This allows us to obtain formulas for the Turing eigenvalues of the spatially homogeneous state and hence to obtain its stability boundary. We find that the frequency of each Turing mode depends on the corresponding Fourier coefficient of the synaptic pattern of connectivity. The decay rate instead is identical for all oscillation modes as a consequence of the heterogeneity-induced desynchronization of the neurons. Finally, we numerically compute the spectrum of spatially inhomogeneous solutions branching from the Turing bifurcation, showing that similar oscillatory modes operate in neural bump states and are maintained away from onset.
Cellular neural network modelling of soft tissue dynamics for surgical simulation.
Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan
2017-07-20
Currently, the mechanical dynamics of soft tissue deformation is achieved by numerical time integrations such as the explicit or implicit integration; however, the explicit integration is stable only under a small time step, whereas the implicit integration is computationally expensive in spite of the accommodation of a large time step. This paper presents a cellular neural network method for stable simulation of soft tissue deformation dynamics. The non-rigid motion equation is formulated as a cellular neural network with local connectivity of cells, and thus the dynamics of soft tissue deformation is transformed into the neural dynamics of the cellular neural network. Results show that the proposed method can achieve good accuracy at a small time step. It still remains stable at a large time step, while maintaining the computational efficiency of the explicit integration. The proposed method can achieve stable soft tissue deformation with efficiency of explicit integration for surgical simulation.
Identification of Complex Dynamical Systems with Neural Networks (2/2)
CERN. Geneva
2016-01-01
The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...
Identification of Complex Dynamical Systems with Neural Networks (1/2)
CERN. Geneva
2016-01-01
The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...
The Dynamics of Open-Field Corridors
Viall, N. M.; Antiochos, S. K.; Higginson, A. K.; DeVore, C. R.
2016-12-01
The source of the slow solar wind and the origins of its dynamics have long been major problems in solar/heliospheric physics. Due to its observed location in the heliosphere, its plasma composition, and its variability, the slow wind is widely believed to be due to the release of closed-field plasma onto open field lines. In the S-Web model the slow wind is postulated to result from the driving of the open-closed boundary in the corona by the quasi-random photospheric convective motions. A key feature of the model is the topological complexity of the open field regions at the Sun, in other words, the distribution and geometry of coronal holes. In particular, narrow corridors of open field and even singular topologies are required in order to account for the observed angular extent of the slow wind in the heliosphere. We present the first calculations of the dynamics of an open-field corridor driven by photospheric flows. The calculations use our high-resolution MHD code and an isothermal approximation for the coronal and solar wind plasma. We show that the corridor dynamics do, in fact, result in the release of closed field plasma far from the heliospheric current sheet, in agreement with observations and as predicted by the S-Web model. The implications of our results for understanding the corona-heliosphere connection and especially for interpreting observations from the upcoming Solar Orbiter and Solar Probe Plus missions will be discussed. This research was supported by the NASA LWS programs.
Relativistic field theory and chaotic dynamics
Energy Technology Data Exchange (ETDEWEB)
Tanaka, Yosuke
2005-01-01
We have studied the relativistic equations and chaotic motions of gravitational field on the basis of the theory of relativity and chaos. Friedmann equation (the space component) shows the chaotic behaviours in case of the inflation universe (G/G>0) and shows the non-chaotic behaviours in case of the flat and contraction universe (G/G {<=} 0). With the use of Kerr metric, we have discussed the non-diagonal tensor effect on gravitational field and chaotic dynamics. We have also discussed the dimension of the universe on the basis of E infinity theory.
Dynamics of coupled phantom and tachyon fields
Energy Technology Data Exchange (ETDEWEB)
Shahalam, M. [Zhejiang University of Technology, Institute for Advanced Physics and Mathematics, Hangzhou (China); Pathak, S.D.; Li, Shiyuan [Shandong University, School of Physics, Jinan (China); Myrzakulov, R. [Eurasian National University, Department of General and Theoretical Physics, Eurasian International Center for Theoretical Physics, Astana (Kazakhstan); Wang, Anzhong [Zhejiang University of Technology, Institute for Advanced Physics and Mathematics, Hangzhou (China); Baylor University, Department of Physics, GCAP-CASPER, Waco, TX (United States)
2017-10-15
In this paper, we apply the dynamical analysis to a coupled phantom field with scaling potential taking particular forms of the coupling (linear and combination of linear), and present phase space analysis. We investigate if there exists a late time accelerated scaling attractor that has the ratio of dark energy and dark matter densities of the order one. We observe that the scrutinized couplings cannot alleviate the coincidence problem, however, they acquire stable late time accelerated solutions. We also discuss a coupled tachyon field with inverse square potential assuming linear coupling. (orig.)
Ultracold plasma dynamics in a magnetic field
Zhang, Xianli
Plasmas, often called the fourth state of matter and the most common one in the universe, have parameters varying by many orders of magnitude, from temperature of a few hundred kelvin in the Earth's ionosphere to 10 16 K in the magnetosphere of a pulsar. Ultracold plasmas, produced by photoionizing a sample of laser-cooled and trapped atoms near the ionization limit, have extended traditional neutral plasma parameters by many orders of magnitude, to electron temperatures below 1 K and ion temperatures in the tens of muK to a few Kelvin, and densities of 105 cm -3 to 1010 cm-3. These plasmas thus provide a testing ground to study basic plasma theory in a clean and simple system with or without a magnetic field. Previous studies of ultracold plasmas have primarily concentrated on temperature measurements, collective modes and expansion dynamics in the absence of magnetic fields. This thesis presents the first study of ultracold plasma dynamics in a magnetic field. The presence of a magnetic field during the expansion can initiate various phenomena, such as plasma confinement and plasma instabilities. While the electron temperatures are very low in ultracold plasmas, we need only tens of Gauss of magnetic field to observe significant effects on the expansion dynamics. To probe the ultraocold plasma dynamics in a magnetic field, we developed a new diagnostic - projection imaging, which images the ion distribution by extracting the ions with a high voltage pulse onto a position-sensitive detector. Early in the lifetime of the plasma (explosion of the dense ion cloud. For later times, we measure the 2-D Gaussian width of the ion image, obtaining the transverse expansion velocity as a function of magnetic field (up to 70 G), and observe that the transverse expansion velocity scales as B-1/2, explained by a nonlinear ambipolar diffusion model that involes anisotropic diffusion in two different directions. We also present the first observation of a plasma instability in an
Hellyer, Peter J.; Scott, Gregory; Shanahan, Murray; Sharp, David J.
2015-01-01
Current theory proposes that healthy neural dynamics operate in a metastable regime, where brain regions interact to simultaneously maximize integration and segregation. Metastability may confer important behavioral properties, such as cognitive flexibility. It is increasingly recognized that neural dynamics are constrained by the underlying structural connections between brain regions. An important challenge is, therefore, to relate structural connectivity, neural dynamics, and behavior. Traumatic brain injury (TBI) is a pre-eminent structural disconnection disorder whereby traumatic axonal injury damages large-scale connectivity, producing characteristic cognitive impairments, including slowed information processing speed and reduced cognitive flexibility, that may be a result of disrupted metastable dynamics. Therefore, TBI provides an experimental and theoretical model to examine how metastable dynamics relate to structural connectivity and cognition. Here, we use complementary empirical and computational approaches to investigate how metastability arises from the healthy structural connectome and relates to cognitive performance. We found reduced metastability in large-scale neural dynamics after TBI, measured with resting-state functional MRI. This reduction in metastability was associated with damage to the connectome, measured using diffusion MRI. Furthermore, decreased metastability was associated with reduced cognitive flexibility and information processing. A computational model, defined by empirically derived connectivity data, demonstrates how behaviorally relevant changes in neural dynamics result from structural disconnection. Our findings suggest how metastable dynamics are important for normal brain function and contingent on the structure of the human connectome. PMID:26085630
The relevance of network micro-structure for neural dynamics
Directory of Open Access Journals (Sweden)
Volker ePernice
2013-06-01
Full Text Available The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previousstudies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neuronsin recurrent networks. However, typically very simple random network models are considered in such studies. Here weuse a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much morevariable than commonly used network models, and which therefore promise to sample the space of recurrent networks ina more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology insimulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive datasetof networks and neuronal simulations we assess statistical relations between features of the network structure and the spikingactivity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics ofboth single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistentrelations between activity characteristics like spike-train irregularity or correlations and network properties, for example thedistributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that itis possible to estimate structural characteristics of the network from activity data. We also assess higher order correlationsof spiking activity in the various networks considered here, and find that their occurrence strongly depends on the networkstructure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpretspike train recordings from neural circuits.
Hybrid neural network bushing model for vehicle dynamics simulation
Energy Technology Data Exchange (ETDEWEB)
Sohn, Jeong Hyun [Pukyong National University, Busan (Korea, Republic of); Lee, Seung Kyu [Hyosung Corporation, Changwon (Korea, Republic of); Yoo, Wan Suk [Pusan National University, Busan (Korea, Republic of)
2008-12-15
Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers
Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar.
Lomp, Oliver; Richter, Mathis; Zibner, Stephan K U; Schöner, Gregor
2016-01-01
Embodied artificial cognitive systems, such as autonomous robots or intelligent observers, connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT), a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar, which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.
Developing dynamic field theory architectures for embodied cognitive systems with cedar
Directory of Open Access Journals (Sweden)
Oliver Lomp
2016-11-01
Full Text Available Embodied artificial cognitive systems such as autonomous robots or intelligent observers connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT, a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar, which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real-time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.
Cortical geometry as a determinant of brain activity eigenmodes: Neural field analysis
Gabay, Natasha C.; Robinson, P. A.
2017-09-01
Perturbation analysis of neural field theory is used to derive eigenmodes of neural activity on a cortical hemisphere, which have previously been calculated numerically and found to be close analogs of spherical harmonics, despite heavy cortical folding. The present perturbation method treats cortical folding as a first-order perturbation from a spherical geometry. The first nine spatial eigenmodes on a population-averaged cortical hemisphere are derived and compared with previous numerical solutions. These eigenmodes contribute most to brain activity patterns such as those seen in electroencephalography and functional magnetic resonance imaging. The eigenvalues of these eigenmodes are found to agree with the previous numerical solutions to within their uncertainties. Also in agreement with the previous numerics, all eigenmodes are found to closely resemble spherical harmonics. The first seven eigenmodes exhibit a one-to-one correspondence with their numerical counterparts, with overlaps that are close to unity. The next two eigenmodes overlap the corresponding pair of numerical eigenmodes, having been rotated within the subspace spanned by that pair, likely due to second-order effects. The spatial orientations of the eigenmodes are found to be fixed by gross cortical shape rather than finer-scale cortical properties, which is consistent with the observed intersubject consistency of functional connectivity patterns. However, the eigenvalues depend more sensitively on finer-scale cortical structure, implying that the eigenfrequencies and consequent dynamical properties of functional connectivity depend more strongly on details of individual cortical folding. Overall, these results imply that well-established tools from perturbation theory and spherical harmonic analysis can be used to calculate the main properties and dynamics of low-order brain eigenmodes.
Standard representation and unified stability analysis for dynamic artificial neural network models.
Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D
2017-12-02
An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.
Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks
Directory of Open Access Journals (Sweden)
Jose P. Perez
2014-01-01
Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.
Predicting Expressive Dynamics in Piano Performances using Neural Networks
van Herwaarden, Sam; Grachten, Maarten; de Haas, W. Bas
2014-01-01
This paper presents a model for predicting expressive accentuation in piano performances with neural networks. Using Restricted Boltzmann Machines (RBMs), features are learned from performance data, after which these features are used to predict performed loudness. During feature learning, data
Molecular dynamics in high electric fields
Energy Technology Data Exchange (ETDEWEB)
Apostol, M., E-mail: apoma@theory.nipne.ro; Cune, L.C.
2016-06-15
Highlights: • New method for rotation molecular spectra in high electric fields. • Parametric resonances – new features in spectra. • New elementary excitations in polar solids from dipolar interaction (“dipolons”). • Discussion about a possible origin of the ferroelectricity from dipolar interactions. - Abstract: Molecular rotation spectra, generated by the coupling of the molecular electric-dipole moments to an external time-dependent electric field, are discussed in a few particular conditions which can be of some experimental interest. First, the spherical-pendulum molecular model is reviewed, with the aim of introducing an approximate method which consists in the separation of the azimuthal and zenithal motions. Second, rotation spectra are considered in the presence of a static electric field. Two particular cases are analyzed, corresponding to strong and weak fields. In both cases the classical motion of the dipoles consists of rotations and vibrations about equilibrium positions; this motion may exhibit parametric resonances. For strong fields a large macroscopic electric polarization may appear. This situation may be relevant for polar matter (like pyroelectrics, ferroelectrics), or for heavy impurities embedded in a polar solid. The dipolar interaction is analyzed in polar condensed matter, where it is shown that new polarization modes appear for a spontaneous macroscopic electric polarization (these modes are tentatively called “dipolons”); one of the polarization modes is related to parametric resonances. The extension of these considerations to magnetic dipoles is briefly discussed. The treatment is extended to strong electric fields which oscillate with a high frequency, as those provided by high-power lasers. It is shown that the effect of such fields on molecular dynamics is governed by a much weaker, effective, renormalized, static electric field.
Dynamic nuclear polarization at high magnetic fields.
Maly, Thorsten; Debelouchina, Galia T; Bajaj, Vikram S; Hu, Kan-Nian; Joo, Chan-Gyu; Mak-Jurkauskas, Melody L; Sirigiri, Jagadishwar R; van der Wel, Patrick C A; Herzfeld, Judith; Temkin, Richard J; Griffin, Robert G
2008-02-07
Dynamic nuclear polarization (DNP) is a method that permits NMR signal intensities of solids and liquids to be enhanced significantly, and is therefore potentially an important tool in structural and mechanistic studies of biologically relevant molecules. During a DNP experiment, the large polarization of an exogeneous or endogeneous unpaired electron is transferred to the nuclei of interest (I) by microwave (microw) irradiation of the sample. The maximum theoretical enhancement achievable is given by the gyromagnetic ratios (gamma(e)gamma(l)), being approximately 660 for protons. In the early 1950s, the DNP phenomenon was demonstrated experimentally, and intensively investigated in the following four decades, primarily at low magnetic fields. This review focuses on recent developments in the field of DNP with a special emphasis on work done at high magnetic fields (> or =5 T), the regime where contemporary NMR experiments are performed. After a brief historical survey, we present a review of the classical continuous wave (cw) DNP mechanisms-the Overhauser effect, the solid effect, the cross effect, and thermal mixing. A special section is devoted to the theory of coherent polarization transfer mechanisms, since they are potentially more efficient at high fields than classical polarization schemes. The implementation of DNP at high magnetic fields has required the development and improvement of new and existing instrumentation. Therefore, we also review some recent developments in microw and probe technology, followed by an overview of DNP applications in biological solids and liquids. Finally, we outline some possible areas for future developments.
Directory of Open Access Journals (Sweden)
Daniel Durstewitz
2017-06-01
Full Text Available The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast maximum-likelihood estimation framework for PLRNNs that may enable to recover
Durstewitz, Daniel
2017-06-01
The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects
Précis of Neural organization: structure, function, and dynamics.
Arbib, M A; Erdi, P
2000-08-01
NEURAL ORGANIZATION: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. (1) STRUCTURE: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. (2) FUNCTION: Control of eye movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. (3) DYNAMICS: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and (often synchronized) neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai.
Mean field methods for cortical network dynamics
DEFF Research Database (Denmark)
Hertz, J.; Lerchner, Alexander; Ahmadi, M.
2004-01-01
We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases...... with the strength of the synapses in the network and with the value to which the membrane potential is reset after a spike. Generalizing the model to include conductance-based synapses gives insight into the connection between the firing statistics and the high- conductance state observed experimentally in visual...
Cognitive and Neural Modeling of Dynamics of Trust in Competitive Trustees
Hoogendoorn, M.; Jaffry, S.W.Q.; Treur, J.
2012-01-01
Trust dynamics can be modeled in relation to experiences. In this paper two models to represent human trust dynamics are introduced, namely a model on a cognitive level and a neural model. These models include a number of parameters, providing the possibility to express certain relations between
Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems
DEFF Research Database (Denmark)
Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole
2011-01-01
It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is ab...... to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude.......It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is able...
Differential neural activation for camouflage detection task in Field ...
Indian Academy of Sciences (India)
Keywords. Camouflage; Field-Dependece; Field-Independence; MRI; visual perception; visual search. Abstract. It is not clearly known as to why some people identify camouflaged objects with ease compared with others. The literature suggests that Field-Independent individuals detect camouflaged object better than their ...
Dynamics of gamma bursts in local field potentials.
Greenwood, Priscilla E; McDonnell, Mark D; Ward, Lawrence M
2015-01-01
In this letter, we provide a stochastic analysis of, and supporting simulation data for, a stochastic model of the generation of gamma bursts in local field potential (LFP) recordings by interacting populations of excitatory and inhibitory neurons. Our interest is in behavior near a fixed point of the stochastic dynamics of the model. We apply a recent limit theorem of stochastic dynamics to probe into details of this local behavior, obtaining several new results. We show that the stochastic model can be written in terms of a rotation multiplied by a two-dimensional standard Ornstein-Uhlenbeck (OU) process. Viewing the rewritten process in terms of phase and amplitude processes, we are able to proceed further in analysis. We demonstrate that gamma bursts arise in the model as excursions of the modulus of the OU process. The associated pair of stochastic phase and amplitude processes satisfies their own pair of stochastic differential equations, which indicates that large phase slips occur between gamma bursts. This behavior is mirrored in LFP data simulated from the original model. These results suggest that the rewritten model is a valid representation of the behavior near the fixed point for a wide class of models of oscillatory neural processes.
Modelling of word usage frequency dynamics using artificial neural network
Maslennikova, Yu S.; Bochkarev, V. V.; Voloskov, D. S.
2014-03-01
In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models.
Energy Technology Data Exchange (ETDEWEB)
Schuelke, J.S.; Quirein, J.A.; Sarg, J.F.
1998-12-31
This case study shows the benefit of using multiple seismic trace attributes and the pattern recognition capabilities of neural networks to predict reservoir architecture and porosity distribution in the Pegasus Field, West Texas. The study used the power of neural networks to integrate geologic, borehole and seismic data. Illustrated are the improvements between the new neural network approach and the more traditional method of seismic trace inversion for porosity estimation. Comprehensive statistical methods and interpretational/subjective measures are used in the prediction of porosity from seismic attributes. A 3-D volume of seismic derived porosity estimates for the Devonian reservoir provide a very detailed estimate of porosity, both spatially and vertically, for the field. The additional reservoir porosity detail provided, between the well control, allows for optimal placement of horizontal wells and improved field development. 6 refs., 2 figs.
Holographic equilibration under external dynamical electric field
Directory of Open Access Journals (Sweden)
M. Ali-Akbari
2017-10-01
Full Text Available The holographic equilibration of a far-from-equilibrium strongly coupled gauge theory is investigated. In particular, the dynamics of a probe D7-brane in an AdS-Vaidya background is studied in the presence of an external time-dependent electric field. Defining the equilibration times teqc and teqj, at which condensation and current relax to their final equilibrated values, receptively, the smallness of transition time kM or kE is enough to observe a universal behaviour for re-scaled equilibration times kMkE(teqc−2 and kMkE(teqj−2. kM(kE is the time interval in which the temperature (electric field increases from zero to finite value. Moreover, regardless of the values for kM and kE, teqc/teqj also behaves universally for large enough value of the ratio of the final electric field to final temperature. Then a simple discussion of the static case reveals that teqc≤teqj. For an out-of-equilibrium process, our numerical results show that, apart from the cases for which kE is small, the static time-ordering, that is teqc≤teqj, persists.
Field-driven dynamics of nematic microcapillaries
Khayyatzadeh, Pouya; Fu, Fred; Abukhdeir, Nasser Mohieddin
2015-12-01
Polymer-dispersed liquid-crystal (PDLC) composites long have been a focus of study for their unique electro-optical properties which have resulted in various applications such as switchable (transparent or translucent) windows. These composites are manufactured using desirable "bottom-up" techniques, such as phase separation of a liquid-crystal-polymer mixture, which enable production of PDLC films at very large scales. LC domains within PDLCs are typically spheroidal, as opposed to rectangular for an LCD panel, and thus exhibit substantially different behavior in the presence of an external field. The fundamental difference between spheroidal and rectangular nematic domains is that the former results in the presence of nanoscale orientational defects in LC order while the latter does not. Progress in the development and optimization of PDLC electro-optical properties has progressed at a relatively slow pace due to this increased complexity. In this work, continuum simulations are performed in order to capture the complex formation and electric field-driven switching dynamics of approximations of PDLC domains. Using a simplified elliptic cylinder (microcapillary) geometry as an approximation of spheroidal PDLC domains, the effects of geometry (aspect ratio), surface anchoring, and external field strength are studied through the use of the Landau-de Gennes model of the nematic LC phase.
Lebedev, Dmitry V; Steil, Jochen J; Ritter, Helge J
2005-04-01
We introduce a new type of neural network--the dynamic wave expansion neural network (DWENN)--for path generation in a dynamic environment for both mobile robots and robotic manipulators. Our model is parameter-free, computationally efficient, and its complexity does not explicitly depend on the dimensionality of the configuration space. We give a review of existing neural networks for trajectory generation in a time-varying domain, which are compared to the presented model. We demonstrate several representative simulative comparisons as well as the results of long-run comparisons in a number of randomly-generated scenes, which reveal that the proposed model yields dominantly shorter paths, especially in highly-dynamic environments.
Quantum perceptron over a field and neural network architecture selection in a quantum computer.
da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa
2016-04-01
In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator. Copyright © 2016 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Min Wang
2017-01-01
Full Text Available A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF neural network (NN approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.
Stability analysis and the stabilization of a class of discrete-time dynamic neural networks.
Patan, Krzysztof
2007-05-01
This paper deals with problems of stability and the stabilization of discrete-time neural networks. Neural structures under consideration belong to the class of the so-called locally recurrent globally feedforward networks. The single processing unit possesses dynamic behavior. It is realized by introducing into the neuron structure a linear dynamic system in the form of an infinite impulse response filter. In this way, a dynamic neural network is obtained. It is well known that the crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates stability conditions for the analyzed class of neural networks. Moreover, a stabilization problem is defined and solved as a constrained optimization task. In order to tackle this problem two methods are proposed. The first one is based on a gradient projection (GP) and the second one on a minimum distance projection (MDP). It is worth noting that these methods can be easily introduced into the existing learning algorithm as an additional step, and suitable convergence conditions can be developed for them. The efficiency and usefulness of the proposed approaches are justified by using a number of experiments including numerical complexity analysis, stabilization effectiveness, and the identification of an industrial process.
Dynamic pulsed-field-gradient NMR
Sørland, Geir Humborstad
2014-01-01
Dealing with the basics, theory and applications of dynamic pulsed-field-gradient NMR NMR (PFG NMR), this book describes the essential theory behind diffusion in heterogeneous media that can be combined with NMR measurements to extract important information of the system being investigated. This information could be the surface to volume ratio, droplet size distribution in emulsions, brine profiles, fat content in food stuff, permeability/connectivity in porous materials and medical applications currently being developed. Besides theory and applications it will provide the readers with background knowledge on the experimental set-ups, and most important, deal with the pitfalls that are numerously present in work with PFG-NMR. How to analyze the NMR data and some important basic knowledge on the hardware will be explained, too.
Subduction dynamics: Constraints from gravity field observations
Mcadoo, D. C.
1985-01-01
Satellite systems do the best job of resolving the long wavelength components of the Earth's gravity field. Over the oceans, satellite-borne radar altimeters such as SEASAT provide the best resolution observations of the intermediate wavelength components. Satellite observations of gravity contributed to the understanding of the dynamics of subduction. Large, long wavelength geoidal highs generally occur over subduction zones. These highs are attributed to the superposition of two effects of subduction: (1) the positive mass anomalies of subducting slabs themselves; and (2) the surface deformations such as the trenches convectively inducted by these slabs as they sink into the mantle. Models of this subduction process suggest that the mantle behaves as a nonNewtonian fluid, its effective viscosity increases significantly with depth, and that large positive mass anomalies may occur beneath the seismically defined Benioff zones.
Curley, J Lowry; Jennings, Scott R; Moore, Michael J
2011-02-11
Increasingly, patterned cell culture environments are becoming a relevant technique to study cellular characteristics, and many researchers believe in the need for 3D environments to represent in vitro experiments which better mimic in vivo qualities. Studies in fields such as cancer research, neural engineering, cardiac physiology, and cell-matrix interaction have shown cell behavior differs substantially between traditional monolayer cultures and 3D constructs. Hydrogels are used as 3D environments because of their variety, versatility and ability to tailor molecular composition through functionalization. Numerous techniques exist for creation of constructs as cell-supportive matrices, including electrospinning, elastomer stamps, inkjet printing, additive photopatterning, static photomask projection-lithography, and dynamic mask microstereolithography. Unfortunately, these methods involve multiple production steps and/or equipment not readily adaptable to conventional cell and tissue culture methods. The technique employed in this protocol adapts the latter two methods, using a digital micromirror device (DMD) to create dynamic photomasks for crosslinking geometrically specific poly-(ethylene glycol) (PEG) hydrogels, induced through UV initiated free radical polymerization. The resulting "2.5D" structures provide a constrained 3D environment for neural growth. We employ a dual-hydrogel approach, where PEG serves as a cell-restrictive region supplying structure to an otherwise shapeless but cell-permissive self-assembling gel made from either Puramatrix or agarose. The process is a quick simple one step fabrication which is highly reproducible and easily adapted for use with conventional cell culture methods and substrates. Whole tissue explants, such as embryonic dorsal root ganglia (DRG), can be incorporated into the dual hydrogel constructs for experimental assays such as neurite outgrowth. Additionally, dissociated cells can be encapsulated in the
Point process modeling and estimation: Advances in the analysis of dynamic neural spiking data
Deng, Xinyi
2016-08-01
A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in
Nonequilibrium dynamical mean-field theory
Energy Technology Data Exchange (ETDEWEB)
Eckstein, Martin
2009-12-21
The aim of this thesis is the investigation of strongly interacting quantum many-particle systems in nonequilibrium by means of the dynamical mean-field theory (DMFT). An efficient numerical implementation of the nonequilibrium DMFT equations within the Keldysh formalism is provided, as well a discussion of several approaches to solve effective single-site problem to which lattice models such as the Hubbard-model are mapped within DMFT. DMFT is then used to study the relaxation of the thermodynamic state after a sudden increase of the interaction parameter in two different models: the Hubbard model and the Falicov-Kimball model. In the latter case an exact solution can be given, which shows that the state does not even thermalize after infinite waiting times. For a slow change of the interaction, a transition to adiabatic behavior is found. The Hubbard model, on the other hand, shows a very sensitive dependence of the relaxation on the interaction, which may be called a dynamical phase transition. Rapid thermalization only occurs at the interaction parameter which corresponds to this transition. (orig.)
An Artificial Neural Network Based Short-term Dynamic Prediction of Algae Bloom
Directory of Open Access Journals (Sweden)
Yao Junyang
2014-06-01
Full Text Available This paper proposes a method of short-term prediction of algae bloom based on artificial neural network. Firstly, principal component analysis is applied to water environmental factors in algae bloom raceway ponds to get main factors that influence the formation of algae blooms. Then, a model of short-term dynamic prediction based on neural network is built with the current chlorophyll_a values as input and the chlorophyll_a values in the next moment as output to realize short-term dynamic prediction of algae bloom. Simulation results show that the model can realize short-term prediction of algae bloom effectively.
Neural network design for J function approximation in dynamic programming
Pang, X
1998-01-01
This paper shows that a new type of artificial neural network (ANN) -- the Simultaneous Recurrent Network (SRN) -- can, if properly trained, solve a difficult function approximation problem which conventional ANNs -- either feedforward or Hebbian -- cannot. This problem, the problem of generalized maze navigation, is typical of problems which arise in building true intelligent control systems using neural networks. (Such systems are discussed in the chapter by Werbos in K.Pribram, Brain and Values, Erlbaum 1998.) The paper provides a general review of other types of recurrent networks and alternative training techniques, including a flowchart of the Error Critic training design, arguable the only plausible approach to explain how the brain adapts time-lagged recurrent systems in real-time. The C code of the test is appended. As in the first tests of backprop, the training here was slow, but there are ways to do better after more experience using this type of network.
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.
Neural correlates of dynamically evolving interpersonal ties predict prosocial behaviour
Directory of Open Access Journals (Sweden)
Johannes Jacobus Fahrenfort
2012-03-01
Full Text Available There is a growing interest for the determinants of human choice behaviour in social settings. Upon initial contact, investment choices in social settings can be inherently risky, as the degree to which the other person will reciprocate is unknown. Nevertheless, people have been shown to exhibit prosocial behaviour even in one-shot laboratory settings where all interaction has been taken away. A logical step has been to link such behaviour to trait empathy-related neurobiological networks. However, as a social interaction unfolds, the degree of uncertainty with respect to the expected payoff of choice behaviour may change as a function of the interaction. Here we attempt to capture this factor. We show that the interpersonal tie one develops with another person during interaction - rather than trait empathy - motivates investment in a public good that is shared with an anonymous interaction partner. We examined how individual differences in trait empathy and interpersonal ties modulate neural responses to imposed monetary sharing. After, but not before interaction in a public good game, sharing prompted activation of neural systems associated with reward (striatum, empathy (anterior insular cortex [AIC] and anterior cingulate cortex [ACC] as well as altruism and social significance (posterior superior temporal sulcus [pSTS]. Although these activations could be linked to both empathy and interpersonal ties, only tie-related pSTS activation predicted prosocial behaviour during subsequent interaction, suggesting a neural substrate for keeping track of social relevance.
Neural correlates of dynamically evolving interpersonal ties predict prosocial behavior.
Fahrenfort, Johannes J; van Winden, Frans; Pelloux, Benjamin; Stallen, Mirre; Ridderinkhof, K Richard
2012-01-01
There is a growing interest for the determinants of human choice behavior in social settings. Upon initial contact, investment choices in social settings can be inherently risky, as the degree to which the other person will reciprocate is unknown. Nevertheless, people have been shown to exhibit prosocial behavior even in one-shot laboratory settings where all interaction has been taken away. A logical step has been to link such behavior to trait empathy-related neurobiological networks. However, as a social interaction unfolds, the degree of uncertainty with respect to the expected payoff of choice behavior may change as a function of the interaction. Here we attempt to capture this factor. We show that the interpersonal tie one develops with another person during interaction - rather than trait empathy - motivates investment in a public good that is shared with an anonymous interaction partner. We examined how individual differences in trait empathy and interpersonal ties modulate neural responses to imposed monetary sharing. After, but not before interaction in a public good game, sharing prompted activation of neural systems associated with reward (striatum), empathy (anterior insular cortex and anterior cingulate cortex) as well as altruism, and social significance [posterior superior temporal sulcus (pSTS)]. Although these activations could be linked to both empathy and interpersonal ties, only tie-related pSTS activation predicted prosocial behavior during subsequent interaction, suggesting a neural substrate for keeping track of social relevance.
Neural Correlates of Dynamically Evolving Interpersonal Ties Predict Prosocial Behavior
Fahrenfort, Johannes J.; van Winden, Frans; Pelloux, Benjamin; Stallen, Mirre; Ridderinkhof, K. Richard
2011-01-01
There is a growing interest for the determinants of human choice behavior in social settings. Upon initial contact, investment choices in social settings can be inherently risky, as the degree to which the other person will reciprocate is unknown. Nevertheless, people have been shown to exhibit prosocial behavior even in one-shot laboratory settings where all interaction has been taken away. A logical step has been to link such behavior to trait empathy-related neurobiological networks. However, as a social interaction unfolds, the degree of uncertainty with respect to the expected payoff of choice behavior may change as a function of the interaction. Here we attempt to capture this factor. We show that the interpersonal tie one develops with another person during interaction – rather than trait empathy – motivates investment in a public good that is shared with an anonymous interaction partner. We examined how individual differences in trait empathy and interpersonal ties modulate neural responses to imposed monetary sharing. After, but not before interaction in a public good game, sharing prompted activation of neural systems associated with reward (striatum), empathy (anterior insular cortex and anterior cingulate cortex) as well as altruism, and social significance [posterior superior temporal sulcus (pSTS)]. Although these activations could be linked to both empathy and interpersonal ties, only tie-related pSTS activation predicted prosocial behavior during subsequent interaction, suggesting a neural substrate for keeping track of social relevance. PMID:22403524
Stability of bumps in piecewise smooth neural fields with nonlinear adaptation
Kilpatrick, Zachary P.
2010-06-01
We study the linear stability of stationary bumps in piecewise smooth neural fields with local negative feedback in the form of synaptic depression or spike frequency adaptation. The continuum dynamics is described in terms of a nonlocal integrodifferential equation, in which the integral kernel represents the spatial distribution of synaptic weights between populations of neurons whose mean firing rate is taken to be a Heaviside function of local activity. Discontinuities in the adaptation variable associated with a bump solution means that bump stability cannot be analyzed by constructing the Evans function for a network with a sigmoidal gain function and then taking the high-gain limit. In the case of synaptic depression, we show that linear stability can be formulated in terms of solutions to a system of pseudo-linear equations. We thus establish that sufficiently strong synaptic depression can destabilize a bump that is stable in the absence of depression. These instabilities are dominated by shift perturbations that evolve into traveling pulses. In the case of spike frequency adaptation, we show that for a wide class of perturbations the activity and adaptation variables decouple in the linear regime, thus allowing us to explicitly determine stability in terms of the spectrum of a smooth linear operator. We find that bumps are always unstable with respect to this class of perturbations, and destabilization of a bump can result in either a traveling pulse or a spatially localized breather. © 2010 Elsevier B.V. All rights reserved.
Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.
Ly, Cheng
2015-12-01
Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically firing. These observations are captured with the aforementioned reduction method, and furthermore simpler analytic descriptions based on this dimension reduction method are developed. The final analytic descriptions provide compact and descriptive formulas for how the relationship between intrinsic and network heterogeneity determines the firing rate heterogeneity dynamics in various settings.
Robustness analysis of uncertain dynamical neural networks with multiple time delays.
Senan, Sibel
2015-10-01
This paper studies the problem of global robust asymptotic stability of the equilibrium point for the class of dynamical neural networks with multiple time delays with respect to the class of slope-bounded activation functions and in the presence of the uncertainties of system parameters of the considered neural network model. By using an appropriate Lyapunov functional and exploiting the properties of the homeomorphism mapping theorem, we derive a new sufficient condition for the existence, uniqueness and global robust asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays. The obtained stability condition basically relies on testing some relationships imposed on the interconnection matrices of the neural system, which can be easily verified by using some certain properties of matrices. An instructive numerical example is also given to illustrate the applicability of our result and show the advantages of this new condition over the previously reported corresponding results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics.
Sokoloski, Sacha
2017-09-01
In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli that caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the predictions of an internal model. The internal model of the Bayes filter is based on the true stimulus dynamics, and in this note, we present a method for training a theoretical neural circuit to approximately implement a Bayes filter when the stimulus dynamics are unknown. To do this we use the inferential properties of linear probabilistic population codes to compute Bayes' rule and train a neural network to compute approximate predictions by the method of maximum likelihood. In particular, we perform stochastic gradient descent on the negative log-likelihood of the neural network parameters with a novel approximation of the gradient. We demonstrate our methods on a finite-state, a linear, and a nonlinear filtering problem and show how the hidden layer of the neural network develops tuning curves consistent with findings in experimental neuroscience.
Dynamic Encoding of Acoustic Features in Neural Responses to Continuous Speech.
Khalighinejad, Bahar; Cruzatto da Silva, Guilherme; Mesgarani, Nima
2017-02-22
Humans are unique in their ability to communicate using spoken language. However, it remains unclear how the speech signal is transformed and represented in the brain at different stages of the auditory pathway. In this study, we characterized electroencephalography responses to continuous speech by obtaining the time-locked responses to phoneme instances (phoneme-related potential). We showed that responses to different phoneme categories are organized by phonetic features. We found that each instance of a phoneme in continuous speech produces multiple distinguishable neural responses occurring as early as 50 ms and as late as 400 ms after the phoneme onset. Comparing the patterns of phoneme similarity in the neural responses and the acoustic signals confirms a repetitive appearance of acoustic distinctions of phonemes in the neural data. Analysis of the phonetic and speaker information in neural activations revealed that different time intervals jointly encode the acoustic similarity of both phonetic and speaker categories. These findings provide evidence for a dynamic neural transformation of low-level speech features as they propagate along the auditory pathway, and form an empirical framework to study the representational changes in learning, attention, and speech disorders.SIGNIFICANCE STATEMENT We characterized the properties of evoked neural responses to phoneme instances in continuous speech. We show that each instance of a phoneme in continuous speech produces several observable neural responses at different times occurring as early as 50 ms and as late as 400 ms after the phoneme onset. Each temporal event explicitly encodes the acoustic similarity of phonemes, and linguistic and nonlinguistic information are best represented at different time intervals. Finally, we show a joint encoding of phonetic and speaker information, where the neural representation of speakers is dependent on phoneme category. These findings provide compelling new evidence for
Coombes, S.; Venkov, N.A.; Shiau, L.; Bojak, I.; Liley, D.T.; Laing, C.R.
2007-01-01
Neural field models of firing rate activity typically take the form of integral equations with space-dependent axonal delays. Under natural assumptions on the synaptic connectivity we show how one can derive an equivalent partial differential equation (PDE) model that properly treats the axonal
Kang, Hongki; Kim, Jee-Yeon; Choi, Yang-Kyu; Nam, Yoonkey
2017-01-01
In this research, a high performance silicon nanowire field-effect transistor (transconductance as high as 34 µS and sensitivity as 84 nS/mV) is extensively studied and directly compared with planar passive microelectrode arrays for neural recording application. Electrical and electrochemical characteristics are carefully characterized in a very well-controlled manner. We especially focused on the signal amplification capability and intrinsic noise of the transistors. A neural recording system using both silicon nanowire field-effect transistor-based active-type microelectrode array and platinum black microelectrode-based passive-type microelectrode array are implemented and compared. An artificial neural spike signal is supplied as input to both arrays through a buffer solution and recorded simultaneously. Recorded signal intensity by the silicon nanowire transistor was precisely determined by an electrical characteristic of the transistor, transconductance. Signal-to-noise ratio was found to be strongly dependent upon the intrinsic 1/f noise of the silicon nanowire transistor. We found how signal strength is determined and how intrinsic noise of the transistor determines signal-to-noise ratio of the recorded neural signals. This study provides in-depth understanding of the overall neural recording mechanism using silicon nanowire transistors and solid design guideline for further improvement and development. PMID:28350370
Parameter estimation of breast tumour using dynamic neural network from thermal pattern
Directory of Open Access Journals (Sweden)
Elham Saniei
2016-11-01
Full Text Available This article presents a new approach for estimating the depth, size, and metabolic heat generation rate of a tumour. For this purpose, the surface temperature distribution of a breast thermal image and the dynamic neural network was used. The research consisted of two steps: forward and inverse. For the forward section, a finite element model was created. The Pennes bio-heat equation was solved to find surface and depth temperature distributions. Data from the analysis, then, were used to train the dynamic neural network model (DNN. Results from the DNN training/testing confirmed those of the finite element model. For the inverse section, the trained neural network was applied to estimate the depth temperature distribution (tumour position from the surface temperature profile, extracted from the thermal image. Finally, tumour parameters were obtained from the depth temperature distribution. Experimental findings (20 patients were promising in terms of the model’s potential for retrieving tumour parameters.
Directory of Open Access Journals (Sweden)
ZHANG Yongzhi
2016-10-01
Full Text Available A dynamic fuzzy RBF neural network model was built to predict the mechanical properties of welded joints, and the purpose of the model was to overcome the shortcomings of static neural networks including structural identification, dynamic sample training and learning algorithm. The structure and parameters of the model are no longer head of default, dynamic adaptive adjustment in the training, suitable for dynamic sample data for learning, learning algorithm introduces hierarchical learning and fuzzy rule pruning strategy, to accelerate the training speed of model and make the model more compact. Simulation of the model was carried out by using three kinds of thickness and different process TC4 titanium alloy TIG welding test data. The results show that the model has higher prediction accuracy, which is suitable for predicting the mechanical properties of welded joints, and has opened up a new way for the on-line control of the welding process.
A dynamic programming approach to missing data estimation using neural networks
CSIR Research Space (South Africa)
Nelwamondo, FV
2013-01-01
Full Text Available This paper develops and presents a novel technique for missing data estimation using a combination of dynamic programming, neural networks and genetic algorithms (GA) on suitable subsets of the input data. The method proposed here is well suited...
The influence of mental fatigue and motivation on neural network dynamics; an EEG coherence study
Lorist, Monicque M.; Bezdan, Eniko; Caat, Michael ten; Span, Mark M.; Roerdink, Jos B.T.M.; Maurits, Natasha M.
2009-01-01
The purpose of the present study is to examine the effects of mental fatigue and motivation on neural network dynamics activated during task switching. Mental fatigue was induced by 2 h of continuous performance; after which subjects were motivated by using social comparison and monetary reward as
A Neural Network Model of the Structure and Dynamics of Human Personality
Read, Stephen J.; Monroe, Brian M.; Brownstein, Aaron L.; Yang, Yu; Chopra, Gurveen; Miller, Lynn C.
2010-01-01
We present a neural network model that aims to bridge the historical gap between dynamic and structural approaches to personality. The model integrates work on the structure of the trait lexicon, the neurobiology of personality, temperament, goal-based models of personality, and an evolutionary analysis of motives. It is organized in terms of two…
Hoppensteadt, F C; Izhikevich, E M
1996-08-01
We study weakly connected networks of neural oscillators near multiple Andronov-Hopf bifurcation points. We analyze relationships between synaptic organizations (anatomy) of the networks and their dynamical properties (function). Our principal assumptions are: (1) Each neural oscillator comprises two populations of neurons; excitatory and inhibitory ones; (2) activity of each population of neurons is described by a scalar (one-dimensional) variable; (3) each neural oscillator is near a nondegenerate supercritical Andronov-Hopf bifurcation point; (4) the synaptic connections between the neural oscillators are weak. All neural networks satisfying these hypotheses are governed by the same dynamical system, which we call the canonical model. Studying the canonical model shows that: (1) A neural oscillator can communicate only with those oscillators which have roughly the same natural frequency. That is, synaptic connections between a pair of oscillators having different natural frequencies are functionally insignificant. (2) Two neural oscillators having the same natural frequencies might not communicate if the connections between them are from among a class of pathological synaptic configurations. In both cases the anatomical presence of synaptic connections between neural oscillators does not necessarily guarantee that the connections are functionally significant. (3) There can be substantial phase differences (time delays) between the neural oscillators, which result from the synaptic organization of the network, not from the transmission delays. Using the canonical model we can illustrate self-ignition and autonomous quiescence (oscillator death) phenomena. That is, a network of passive elements can exhibit active properties and vice versa. We also study how Dale's principle affects dynamics of the networks, in particular, the phase differences that the network can reproduce. We present a complete classification of all possible synaptic organizations from this
Real-time collision-free motion planning of a mobile robot using a Neural Dynamics-based approach.
Yang, S X; Meng, M H
2003-01-01
A neural dynamics based approach is proposed for real-time motion planning with obstacle avoidance of a mobile robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. The real-time collision-free robot motion is planned through the dynamic neural activity landscape of the neural network without any learning procedures and without any local collision-checking procedures at each step of the robot movement. Therefore the model algorithm is computationally simple. There are only local connections among neurons. The computational complexity linearly depends on the neural network size. The stability of the proposed neural network system is proved by qualitative analysis and a Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.
Exploring Neural Cell Dynamics with Digital Holographic Microscopy
Marquet, Pierre
2013-04-21
In this talk, I will present how digital holographic microscopy, as a powerful quantitative phase technique, can non-invasively measure cell dynamics and especially resolve local neuronal network activity through simultaneous multiple site optical recording.
Amozegar, M; Khorasani, K
2016-04-01
In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. Copyright © 2016 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Mattia Rigotti
2010-10-01
Full Text Available Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics, the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding. A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.
Reconstructing neural dynamics using data assimilation with multiple models
Hamilton, Franz; Cressman, John; Peixoto, Nathalia; Sauer, Timothy
2014-09-01
Assimilation of data with models of physical processes is a critical component of modern scientific analysis. In recent years, nonlinear versions of Kalman filtering have been developed, in addition to methods that estimate model parameters in parallel with the system state. We propose a substantial extension of these tools to deal with the specific case of unmodeled variables, when training data from the variable is avaiable. The method uses a stack of several, nonidentical copies of a physical model to jointly reconstruct the variable in question. We demonstrate the ability of this technique to accurately recover an unmodeled experimental quantity, such as an ion concentration, from a single voltage trace after the training period is completed. The method is applied to reconstruct the potassium concentration in a neural culture from multielectrode array voltage measurements.
Dynamic neural processing of linguistic cues related to death.
Directory of Open Access Journals (Sweden)
Xi Liu
Full Text Available Behavioral studies suggest that humans evolve the capacity to cope with anxiety induced by the awareness of death's inevitability. However, the neurocognitive processes that underlie online death-related thoughts remain unclear. Our recent functional MRI study found that the processing of linguistic cues related to death was characterized by decreased neural activity in human insular cortex. The current study further investigated the time course of neural processing of death-related linguistic cues. We recorded event-related potentials (ERP to death-related, life-related, negative-valence, and neutral-valence words in a modified Stroop task that required color naming of words. We found that the amplitude of an early frontal/central negativity at 84-120 ms (N1 decreased to death-related words but increased to life-related words relative to neutral-valence words. The N1 effect associated with death-related and life-related words was correlated respectively with individuals' pessimistic and optimistic attitudes toward life. Death-related words also increased the amplitude of a frontal/central positivity at 124-300 ms (P2 and of a frontal/central positivity at 300-500 ms (P3. However, the P2 and P3 modulations were observed for both death-related and negative-valence words but not for life-related words. The ERP results suggest an early inverse coding of linguistic cues related to life and death, which is followed by negative emotional responses to death-related information.
Dynamic neural processing of linguistic cues related to death.
Liu, Xi; Shi, Zhenhao; Ma, Yina; Qin, Jungang; Han, Shihui
2013-01-01
Behavioral studies suggest that humans evolve the capacity to cope with anxiety induced by the awareness of death's inevitability. However, the neurocognitive processes that underlie online death-related thoughts remain unclear. Our recent functional MRI study found that the processing of linguistic cues related to death was characterized by decreased neural activity in human insular cortex. The current study further investigated the time course of neural processing of death-related linguistic cues. We recorded event-related potentials (ERP) to death-related, life-related, negative-valence, and neutral-valence words in a modified Stroop task that required color naming of words. We found that the amplitude of an early frontal/central negativity at 84-120 ms (N1) decreased to death-related words but increased to life-related words relative to neutral-valence words. The N1 effect associated with death-related and life-related words was correlated respectively with individuals' pessimistic and optimistic attitudes toward life. Death-related words also increased the amplitude of a frontal/central positivity at 124-300 ms (P2) and of a frontal/central positivity at 300-500 ms (P3). However, the P2 and P3 modulations were observed for both death-related and negative-valence words but not for life-related words. The ERP results suggest an early inverse coding of linguistic cues related to life and death, which is followed by negative emotional responses to death-related information.
Dynamic Neural Processing of Linguistic Cues Related to Death
Ma, Yina; Qin, Jungang; Han, Shihui
2013-01-01
Behavioral studies suggest that humans evolve the capacity to cope with anxiety induced by the awareness of death’s inevitability. However, the neurocognitive processes that underlie online death-related thoughts remain unclear. Our recent functional MRI study found that the processing of linguistic cues related to death was characterized by decreased neural activity in human insular cortex. The current study further investigated the time course of neural processing of death-related linguistic cues. We recorded event-related potentials (ERP) to death-related, life-related, negative-valence, and neutral-valence words in a modified Stroop task that required color naming of words. We found that the amplitude of an early frontal/central negativity at 84–120 ms (N1) decreased to death-related words but increased to life-related words relative to neutral-valence words. The N1 effect associated with death-related and life-related words was correlated respectively with individuals’ pessimistic and optimistic attitudes toward life. Death-related words also increased the amplitude of a frontal/central positivity at 124–300 ms (P2) and of a frontal/central positivity at 300–500 ms (P3). However, the P2 and P3 modulations were observed for both death-related and negative-valence words but not for life-related words. The ERP results suggest an early inverse coding of linguistic cues related to life and death, which is followed by negative emotional responses to death-related information. PMID:23840787
Directory of Open Access Journals (Sweden)
Svitlana Volkova
Full Text Available This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs units capable of nowcasting (predicting in "real-time" and forecasting (predicting the future ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus
Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D
2017-01-01
This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from
An implantable wireless neural interface for recording cortical circuit dynamics in moving primates.
Borton, David A; Yin, Ming; Aceros, Juan; Nurmikko, Arto
2013-04-01
Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile patient use, have the potential for wider diagnosis of
Directory of Open Access Journals (Sweden)
Mohammad S. Islam
2017-01-01
Full Text Available Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs for robust movement decoding of Parkinson’s disease (PD and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value at about 0.729±0.16 for decoding movement from the resting state and about 0.671±0.14 for decoding left and right visually cued movements.
Pair production by three fields dynamically assisted Schwinger process
Sitiwaldi, Ibrahim; Xie, Bai-Song
2018-02-01
The dynamically assisted Schwinger mechanism for vacuum pair production from two fields to three fields is proposed and examined. Numerical results for enhanced electron-positron pair production in the combination of three fields with different time scales are obtained using the quantum Vlasov equation. The significance of the combination of three fields in the regime of super low field strength is verified. Although the strengths of each of the three fields are far below the critical field strength, we obtain a significant enhancement of the production rate and a considerable yields in this combination, where the nonperturbative field is dynamically assisted by two oscillating fields. The number density depending on field parameters are also investigated. It is shown that the field threshold to detect the Schwinger effect can be lowered significantly if the configuration of three fields with different time scales are chosen carefully.
Neural substrate of dynamic Bayesian inference in the cerebral cortex.
Funamizu, Akihiro; Kuhn, Bernd; Doya, Kenji
2016-12-01
Dynamic Bayesian inference allows a system to infer the environmental state under conditions of limited sensory observation. Using a goal-reaching task, we found that posterior parietal cortex (PPC) and adjacent posteromedial cortex (PM) implemented the two fundamental features of dynamic Bayesian inference: prediction of hidden states using an internal state transition model and updating the prediction with new sensory evidence. We optically imaged the activity of neurons in mouse PPC and PM layers 2, 3 and 5 in an acoustic virtual-reality system. As mice approached a reward site, anticipatory licking increased even when sound cues were intermittently presented; this was disturbed by PPC silencing. Probabilistic population decoding revealed that neurons in PPC and PM represented goal distances during sound omission (prediction), particularly in PPC layers 3 and 5, and prediction improved with the observation of cue sounds (updating). Our results illustrate how cerebral cortex realizes mental simulation using an action-dependent dynamic model.
Degradation Prediction Model Based on a Neural Network with Dynamic Windows
Directory of Open Access Journals (Sweden)
Xinghui Zhang
2015-03-01
Full Text Available Tracking degradation of mechanical components is very critical for effective maintenance decision making. Remaining useful life (RUL estimation is a widely used form of degradation prediction. RUL prediction methods when enough run-to-failure condition monitoring data can be used have been fully researched, but for some high reliability components, it is very difficult to collect run-to-failure condition monitoring data, i.e., from normal to failure. Only a certain number of condition indicators in certain period can be used to estimate RUL. In addition, some existing prediction methods have problems which block RUL estimation due to poor extrapolability. The predicted value converges to a certain constant or fluctuates in certain range. Moreover, the fluctuant condition features also have bad effects on prediction. In order to solve these dilemmas, this paper proposes a RUL prediction model based on neural network with dynamic windows. This model mainly consists of three steps: window size determination by increasing rate, change point detection and rolling prediction. The proposed method has two dominant strengths. One is that the proposed approach does not need to assume the degradation trajectory is subject to a certain distribution. The other is it can adapt to variation of degradation indicators which greatly benefits RUL prediction. Finally, the performance of the proposed RUL prediction model is validated by real field data and simulation data.
Dynamic control of ROV`s making use of the neural network concept
Energy Technology Data Exchange (ETDEWEB)
Ooi, Tadashi; Yoshida, Yuki; Takahashi, Yoshiaki; Kidoushi, Hideki [Ishikawajima-Harima Heavy Industries Co., Ltd., Tokyo (Japan)
1994-12-31
An attempt is made to combine the classical controller with the concept of neural network, the result of which is a control system that they have named the Robust Adaptive Neural-net Controller (RANC). The RANC identifies the dynamic characteristics of the remotely operated vehicle (ROV) including its ambient environment involving cyclic disturbances such as forces induced by waves, and organizes automatically an optimized controller. A tank experiment is described in which the RANC is set to maintain a model ROV at a prescribed depth of water under artificially generated wave disturbance.
Study of the neural dynamics for understanding communication in terms of complex hetero systems.
Tsuda, Ichiro; Yamaguchi, Yoko; Hashimoto, Takashi; Okuda, Jiro; Kawasaki, Masahiro; Nagasaka, Yasuo
2015-01-01
The purpose of the research project was to establish a new research area named "neural information science for communication" by elucidating its neural mechanism. The research was performed in collaboration with applied mathematicians in complex-systems science and experimental researchers in neuroscience. The project included measurements of brain activity during communication with or without languages and analyses performed with the help of extended theories for dynamical systems and stochastic systems. The communication paradigm was extended to the interactions between human and human, human and animal, human and robot, human and materials, and even animal and animal. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.
Neural population dynamics in human motor cortex during movements in people with ALS.
Pandarinath, Chethan; Gilja, Vikash; Blabe, Christine H; Nuyujukian, Paul; Sarma, Anish A; Sorice, Brittany L; Eskandar, Emad N; Hochberg, Leigh R; Henderson, Jaimie M; Shenoy, Krishna V
2015-06-23
The prevailing view of motor cortex holds that motor cortical neural activity represents muscle or movement parameters. However, recent studies in non-human primates have shown that neural activity does not simply represent muscle or movement parameters; instead, its temporal structure is well-described by a dynamical system where activity during movement evolves lawfully from an initial pre-movement state. In this study, we analyze neuronal ensemble activity in motor cortex in two clinical trial participants diagnosed with Amyotrophic Lateral Sclerosis (ALS). We find that activity in human motor cortex has similar dynamical structure to that of non-human primates, indicating that human motor cortex contains a similar underlying dynamical system for movement generation.
Dynamically constrained pipeline for tracking neural progenitor cells
DEFF Research Database (Denmark)
Vestergaard, Jacob Schack; Dahl, Anders; Holm, Peter
2013-01-01
. A mitosis detector constructed from empirical observations of cells in a pre-mitotic state interacts with the graph formulation to dynamically allow for cell mitosis when appropriate. Track consistency is ensured by introducing pragmatic constraints and the notion of blob states. We validate the proposed...
Active Control of Complex Systems via Dynamic (Recurrent) Neural Networks
1992-05-30
present in the underlying fluid dynamics, biology , chemistry, economics, etc. of observed processes. Robert May and others have demonstrated that process...3 _ ___ 2b__ 2’__ ___ ’T (miec) Wk=100 Figure___ 5.2_ (cotined) Firt-Ode Kernel CoptdbIeebr- aqa Aloih sn5DfeetSothn eat I. ____ _E__30 O(28I x(t) x(t
Singularity and dynamics on discontinuous vector fields
Luo, Albert CJ
2006-01-01
This book discussed fundamental problems in dynamics, which extensively exist in engineering, natural and social sciences. The book presented a basic theory for the interactions among many dynamical systems and for a system whose motions are constrained naturally or artificially. The methodology and techniques presented in this book are applicable to discontinuous dynamical systems in physics, engineering and control. In addition, they may provide useful tools to solve non-traditional dynamics in biology, stock market and internet network et al, which cannot be easily solved by the traditional
Direct field measurement of the dynamic amplification in a bridge
Carey, Ciarán; OBrien, Eugene J.; Malekjafarian, Abdollah; Lydon, Myra; Taylor, Su
2017-02-01
In this paper, the level of dynamics, as described by the Assessment Dynamic Ratio (ADR), is measured directly through a field test on a bridge in the United Kingdom. The bridge was instrumented using fiber optic strain sensors and piezo-polymer weigh-in-motion sensors were installed in the pavement on the approach road. Field measurements of static and static-plus-dynamic strains were taken over 45 days. The results show that, while dynamic amplification is large for many loading events, these tend not to be the critical events. ADR, the allowance that should be made for dynamics in an assessment of safety, is small.
Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.
Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong
2015-03-01
This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.
Xu, Bin; Yang, Chenguang; Pan, Yongping
2015-10-01
This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.
Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks
Directory of Open Access Journals (Sweden)
Nasser Talebi
2014-01-01
Full Text Available Occurrence of faults in wind energy conversion systems (WECSs is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS is required. Recurrent neural networks (RNNs have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.
Robust fault detection of wind energy conversion systems based on dynamic neural networks.
Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad
2014-01-01
Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.
Neural network architecture for cognitive navigation in dynamic environments.
Villacorta-Atienza, José Antonio; Makarov, Valeri A
2013-12-01
Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essential tasks: 1) understand the environment in terms of what may happen and how I can deal with this and 2) learn successful experiences for their further use in an automatic subconscious way. The recently introduced concept of compact internal representation (CIR) provides the ground for both the tasks. CIR is a specific cognitive map that compacts time-evolving situations into static structures containing information necessary for navigation. It belongs to the class of global approaches, i.e., it finds trajectories to a target when they exist but also detects situations when no solution can be found. Here we extend the concept of situations with mobile targets. Then using CIR as a core, we propose a closed-loop neural network architecture consisting of conscious and subconscious pathways for efficient decision-making. The conscious pathway provides solutions to novel situations if the default subconscious pathway fails to guide the agent to a target. Employing experiments with roving robots and numerical simulations, we show that the proposed architecture provides the robot with cognitive abilities and enables reliable and flexible navigation in realistic time-evolving environments. We prove that the subconscious pathway is robust against uncertainty in the sensory information. Thus if a novel situation is similar but not identical to the previous experience (because of, e.g., noisy perception) then the subconscious pathway is able to provide an effective solution.
The neural dynamics of reward value and risk coding in the human orbitofrontal cortex.
Li, Yansong; Vanni-Mercier, Giovanna; Isnard, Jean; Mauguière, François; Dreher, Jean-Claude
2016-04-01
The orbitofrontal cortex is known to carry information regarding expected reward, risk and experienced outcome. Yet, due to inherent limitations in lesion and neuroimaging methods, the neural dynamics of these computations has remained elusive in humans. Here, taking advantage of the high temporal definition of intracranial recordings, we characterize the neurophysiological signatures of the intact orbitofrontal cortex in processing information relevant for risky decisions. Local field potentials were recorded from the intact orbitofrontal cortex of patients suffering from drug-refractory partial epilepsy with implanted depth electrodes as they performed a probabilistic reward learning task that required them to associate visual cues with distinct reward probabilities. We observed three successive signals: (i) around 400 ms after cue presentation, the amplitudes of the local field potentials increased with reward probability; (ii) a risk signal emerged during the late phase of reward anticipation and during the outcome phase; and (iii) an experienced value signal appeared at the time of reward delivery. Both the medial and lateral orbitofrontal cortex encoded risk and reward probability while the lateral orbitofrontal cortex played a dominant role in coding experienced value. The present study provides the first evidence from intracranial recordings that the human orbitofrontal cortex codes reward risk both during late reward anticipation and during the outcome phase at a time scale of milliseconds. Our findings offer insights into the rapid mechanisms underlying the ability to learn structural relationships from the environment. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Directed Migration of Embryonic Stem Cell-derived Neural Cells In An Applied Electric Field
Li, Yongchao; Weiss, Mark; Yao, Li
2014-01-01
Spinal cord injury or diseases, such as amyotrophic lateral sclerosis, can cause the loss of motor neurons and therefore results in the paralysis of muscles. Stem cells may improve functional recovery by promoting endogenous regeneration, or by directly replacing neurons. Effective directional migration of grafted neural cells to reconstruct functional connections is crucial in the process. Steady direct current electric fields (EFs) play an important role in the development of the central ne...
Utilizing neural networks in magnetic media modeling and field computation: A review
Amr A. Adly; Abd-El-Hafiz, Salwa K.
2013-01-01
Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs) have been utilized in ma...
Mean-field Dynamics and Fisher Information in matterwave Interferometry
Haine, Simon A.
2015-01-01
There has been considerable recent interest in the mean-field dynamics of various atom-interferometry schemes designed for precision sensing. In the field of quantum metrology, the standard tools for evaluating metrological sensitivity are the classical- and quantum-Fisher information. In this letter, we show how these tools can be adapted to evaluate the sensitivity when the behaviour is dominated by mean-field dynamics. As an example, we compare the behaviour of four recent theoretical prop...
Dynamic neural networking as a basis for plasticity in the control of heart rate.
Kember, G; Armour, J A; Zamir, M
2013-01-21
A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. Copyright © 2012 Elsevier Ltd. All rights reserved.
Hotson, Guy; Smith, Ryan J; Rouse, Adam G; Schieber, Marc H; Thakor, Nitish V; Wester, Brock A
2016-07-01
Brain-machine interfaces (BMIs) are a rapidly progressing technology with the potential to restore function to victims of severe paralysis via neural control of robotic systems. Great strides have been made in directly mapping a user's cortical activity to control of the individual degrees of freedom of robotic end-effectors. While BMIs have yet to achieve the level of reliability desired for widespread clinical use, environmental sensors (e.g. RGB-D cameras for object detection) and prior knowledge of common movement trajectories hold great potential for improving system performance. Here we present a novel sensor fusion paradigm for BMIs that capitalizes on information able to be extracted from the environment to greatly improve the performance of control. This was accomplished by using dynamic movement primitives to model the 3D endpoint trajectories of manipulating various objects. We then used a switching unscented Kalman filter to continuously arbitrate between the 3D endpoint kinematics predicted by the dynamic movement primitives and control derived from neural signals. We experimentally validated our system by decoding 3D endpoint trajectories executed by a non-human primate manipulating four different objects at various locations. Performance using our system showed a dramatic improvement over using neural signals alone, with median distance between actual and decoded trajectories decreasing from 31.1 cm to 9.9 cm, and mean correlation increasing from 0.80 to 0.98. Our results indicate that our sensor fusion framework can dramatically increase the fidelity of neural prosthetic trajectory decoding.
The effects of noise on binocular rivalry waves: a stochastic neural field model
Webber, Matthew A
2013-03-12
We analyze the effects of extrinsic noise on traveling waves of visual perception in a competitive neural field model of binocular rivalry. The model consists of two one-dimensional excitatory neural fields, whose activity variables represent the responses to left-eye and right-eye stimuli, respectively. The two networks mutually inhibit each other, and slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We first show how, in the absence of any noise, the system supports a propagating composite wave consisting of an invading activity front in one network co-moving with a retreating front in the other network. Using a separation of time scales and perturbation methods previously developed for stochastic reaction-diffusion equations, we then show how extrinsic noise in the activity variables leads to a diffusive-like displacement (wandering) of the composite wave from its uniformly translating position at long time scales, and fluctuations in the wave profile around its instantaneous position at short time scales. We use our analysis to calculate the first-passage-time distribution for a stochastic rivalry wave to travel a fixed distance, which we find to be given by an inverse Gaussian. Finally, we investigate the effects of noise in the depression variables, which under an adiabatic approximation lead to quenched disorder in the neural fields during propagation of a wave. © 2013 IOP Publishing Ltd and SISSA Medialab srl.
The effects of noise on binocular rivalry waves: a stochastic neural field model
Webber, Matthew A.; Bressloff, Paul C.
2013-03-01
We analyze the effects of extrinsic noise on traveling waves of visual perception in a competitive neural field model of binocular rivalry. The model consists of two one-dimensional excitatory neural fields, whose activity variables represent the responses to left-eye and right-eye stimuli, respectively. The two networks mutually inhibit each other, and slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We first show how, in the absence of any noise, the system supports a propagating composite wave consisting of an invading activity front in one network co-moving with a retreating front in the other network. Using a separation of time scales and perturbation methods previously developed for stochastic reaction-diffusion equations, we then show how extrinsic noise in the activity variables leads to a diffusive-like displacement (wandering) of the composite wave from its uniformly translating position at long time scales, and fluctuations in the wave profile around its instantaneous position at short time scales. We use our analysis to calculate the first-passage-time distribution for a stochastic rivalry wave to travel a fixed distance, which we find to be given by an inverse Gaussian. Finally, we investigate the effects of noise in the depression variables, which under an adiabatic approximation lead to quenched disorder in the neural fields during propagation of a wave.
Dynamic recurrent neural networks for stable adaptive control of wing rock motion
Kooi, Steven Boon-Lam
Wing rock is a self-sustaining limit cycle oscillation (LCO) which occurs as the result of nonlinear coupling between the dynamic response of the aircraft and the unsteady aerodynamic forces. In this thesis, dynamic recurrent RBF (Radial Basis Function) network control methodology is proposed to control the wing rock motion. The concept based on the properties of the Presiach hysteresis model is used in the design of dynamic neural networks. The structure and memory mechanism in the Preisach model is analogous to the parallel connectivity and memory formation in the RBF neural networks. The proposed dynamic recurrent neural network has a feature for adding or pruning the neurons in the hidden layer according to the growth criteria based on the properties of ensemble average memory formation of the Preisach model. The recurrent feature of the RBF network deals with the dynamic nonlinearities and endowed temporal memories of the hysteresis model. The control of wing rock is a tracking problem, the trajectory starts from non-zero initial conditions and it tends to zero as time goes to infinity. In the proposed neural control structure, the recurrent dynamic RBF network performs identification process in order to approximate the unknown non-linearities of the physical system based on the input-output data obtained from the wing rock phenomenon. The design of the RBF networks together with the network controllers are carried out in discrete time domain. The recurrent RBF networks employ two separate adaptation schemes where the RBF's centre and width are adjusted by the Extended Kalman Filter in order to give a minimum networks size, while the outer networks layer weights are updated using the algorithm derived from Lyapunov stability analysis for the stable closed loop control. The issue of the robustness of the recurrent RBF networks is also addressed. The effectiveness of the proposed dynamic recurrent neural control methodology is demonstrated through simulations to
Global dynamic evolution of the cold plasma inferred with neural networks
Zhelavskaya, Irina; Shprits, Yuri; Spasojevic, Maria
2017-04-01
The electron number density is a fundamental parameter of plasmas and is critical for the wave-particle interactions. Despite its global importance, the distribution of cold plasma and its dynamic dependence on solar wind conditions remains poorly quantified. Existing empirical models present statistical averages based on static geomagnetic parameters, but cannot reflect the dynamics of the highly structured and quickly varying plasmasphere environment, especially during times of high geomagnetic activity. Global imaging provides insights on the dynamics but quantitative inversion to electron number density has been lacking. We propose an empirical model for reconstruction of global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. We develop a neural network that is capable of globally reconstructing the dynamics of the cold plasma density distribution for L shells from 2 to 6 and all local times. We utilize the density database obtained using the NURD algorithm [Zhelavskaya et al., 2016] in conjunction with solar wind data and geomagnetic indices to train the neural network. This study demonstrates how the global dynamics can be reconstructed from local in-situ observations by using machine learning tools. We describe aspects of the validation process in detail and discuss the selected inputs to the model and their physical implication.
The Dynamical Recollection of Interconnected Neural Networks Using Meta-heuristics
Kuremoto, Takashi; Watanabe, Shun; Kobayashi, Kunikazu; Feng, Laing-Bing; Obayashi, Masanao
The interconnected recurrent neural networks are well-known with their abilities of associative memory of characteristic patterns. For example, the traditional Hopfield network (HN) can recall stored pattern stably, meanwhile, Aihara's chaotic neural network (CNN) is able to realize dynamical recollection of a sequence of patterns. In this paper, we propose to use meta-heuristic (MH) methods such as the particle swarm optimization (PSO) and the genetic algorithm (GA) to improve traditional associative memory systems. Using PSO or GA, for CNN, optimal parameters are found to accelerate the recollection process and raise the rate of successful recollection, and for HN, optimized bias current is calculated to improve the network with dynamical association of a series of patterns. Simulation results of binary pattern association showed effectiveness of the proposed methods.
Gravitation Field Dynamics in Jeans Theory
Indian Academy of Sciences (India)
Closed system of time equations for nonrelativistic gravitation field and hydrodynamic medium was obtained by taking into account binary correlations of the field, which is the generalization of Jeans theory. Distribution function of the systemwas built on the basis of the Bogolyubov reduced description method. Calculations ...
Goldstone bosons and a dynamical Higgs field
Mooij, S.; Postma, M.
2011-01-01
Higgs inflation uses the gauge variant Higgs field as the inflaton. During inflation the Higgs field is displaced from its minimum, which results in associated Goldstone bosons that are apparently massive. Working in a minimally coupled U(1) toy model, we use the closed-time-path formalism to show
scalar field dynamics on a brane
Indian Academy of Sciences (India)
power-law potential V ~φa is investigated. We describe solutions for which the scalar field energy density scales as a power-law of the scale factor. We also describe solutions existing in regions of the parameter space where these scaling solutions are unstable or do not exist. Keywords. Brane; scalar field; scaling solution.
Global dynamic evolution of the cold plasma inferred with neural networks
Zhelavskaya, I. S.; Shprits, Y. Y.; Spasojevic, M.
2016-12-01
The electron number density is a fundamental parameter of plasmas and a critical parameter in the wave-particle interactions. However, the distribution of cold plasma and its dynamic dependence on solar wind conditions remains poorly quantified. Existing empirical models provide us with statistical averages based on static geomagnetic parameters, but cannot reflect the dynamics of the highly structured and quickly varying plasmasphere environment, especially during times of high geomagnetic activity. Global imaging provides insights on the dynamics but does not provide quantitative estimates of number density. Accurately calculating the evolving distribution from first principles has also proven elusive due to the sheer number of physical processes involved.In this study, we propose an empirical model for reconstruction of global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. We develop a neural network that is capable of globally reconstructing the dynamics of the cold plasma density distribution for L shells from 2 to 6 and all local times. First, we derive a plasma density database by using the NURD algorithm to identify the upper hybrid resonance band in plasma wave observations from Van Allen Probes [Zhelavskaya et al., 2016]. Then, we utilize the density database in conjunction with solar wind data and geomagnetic indices to train the neural network. To validate and test the model, we choose validation and test sets independently from the density database. We validate and test the neural network by measuring its performance on these sets and also by comparing the model predicted global evolution with global images of the He+ distribution in the Earth's plasmasphere from the IMAGE extreme ultraviolet (EUV) instrument.The present study demonstrates how we can reconstruct the global dynamics from local in-situ observations by using machine learning tools. We describe aspects of the validation process in
National Research Council Canada - National Science Library
Miconi, Thomas; VanRullen, Rufin
2016-01-01
Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space...
Energy Technology Data Exchange (ETDEWEB)
Rong Bao, E-mail: rongbao_nust@sina.com; Rui Xiaoting [Nanjing University of Science and Technology, Institute of Launch Dynamics (China); Tao Ling [Chinese Academy of Sciences (ASIPP), Institute of Plasma Physics (China)
2012-11-15
In this paper, a dynamic modeling method and an active vibration control scheme for a smart flexible four-bar linkage mechanism featuring piezoelectric actuators and strain gauge sensors are presented. The dynamics of this smart mechanism is described by the Discrete Time Transfer Matrix Method of Multibody System (MS-DTTMM). Then a nonlinear fuzzy neural network control is employed to suppress the vibration of this smart mechanism. For improving the dynamic performance of the fuzzy neural network, a genetic algorithm based on the MS-DTTMM is designed offline to tune the initial parameters of the fuzzy neural network. The MS-DTTMM avoids the global dynamics equations of the system, which results in the matrices involved are always very small, so the computational efficiency of the dynamic analysis and control system optimization can be greatly improved. Formulations of the method as well as a numerical simulation are given to demonstrate the proposed dynamic method and control scheme.
Wang, Shang; Garcia, Monica D; Lopez, Andrew L; Overbeek, Paul A; Larin, Kirill V; Larina, Irina V
2017-01-01
Neural tube closure is a critical feature of central nervous system morphogenesis during embryonic development. Failure of this process leads to neural tube defects, one of the most common forms of human congenital defects. Although molecular and genetic studies in model organisms have provided insights into the genes and proteins that are required for normal neural tube development, complications associated with live imaging of neural tube closure in mammals limit efficient morphological analyses. Here, we report the use of optical coherence tomography (OCT) for dynamic imaging and quantitative assessment of cranial neural tube closure in live mouse embryos in culture. Through time-lapse imaging, we captured two neural tube closure mechanisms in different cranial regions, zipper-like closure of the hindbrain region and button-like closure of the midbrain region. We also used OCT imaging for phenotypic characterization of a neural tube defect in a mouse mutant. These results suggest that the described approach is a useful tool for live dynamic analysis of normal neural tube closure and neural tube defects in the mouse model.
Dynamics of Scalar field in a Brane World
Mizuno, Shuntaro; Maeda, Kei-ichi; Yamamoto, Kohta
2002-01-01
We study the dynamics of a scalar field in the brane cosmology. We assume that a scalar field is confined in our 4-dimensional world. As for the potential of the scalar field, we discuss three typical models: (1) a power-law potential, (2) an inverse-power-law potential, and (3) an exponential potential. We show that the behavior of the scalar field is very different from a conventional cosmology when the energy density square term is dominated.
Nonequilibrium Dynamical Mean-Field Theory for Bosonic Lattice Models
Directory of Open Access Journals (Sweden)
Hugo U. R. Strand
2015-03-01
Full Text Available We develop the nonequilibrium extension of bosonic dynamical mean-field theory and a Nambu real-time strong-coupling perturbative impurity solver. In contrast to Gutzwiller mean-field theory and strong-coupling perturbative approaches, nonequilibrium bosonic dynamical mean-field theory captures not only dynamical transitions but also damping and thermalization effects at finite temperature. We apply the formalism to quenches in the Bose-Hubbard model, starting from both the normal and the Bose-condensed phases. Depending on the parameter regime, one observes qualitatively different dynamical properties, such as rapid thermalization, trapping in metastable superfluid or normal states, as well as long-lived or strongly damped amplitude oscillations. We summarize our results in nonequilibrium “phase diagrams” that map out the different dynamical regimes.
Field measurements of cloud droplet dynamics
Molacek, Jan; Bagheri, Gholamhossein; Bertens, Augustinus; Xu, Haitao; Bodenschatz, Eberhard
2017-11-01
We present an in-situ experiment investigating the dynamics of cloud droplets and its dependence on the turbulent flow properties. This dynamics plays a major role in the rate of growth of cloud particles by coalescence and the resulting precipitation rate. The experiment takes place at a mountain research station at an altitude of 2650m, and will make use of a movable platform that can travel with the mean wind velocity. Here we present preliminary results using a stationary setup. Simultaneous measurements of other variables such as droplet size distribution and humidity fluctuations are done in order to develop a more complete picture of the microphysical conditions within clouds. We thank the Bavarian State Ministry of the Environment and Consumer Protection for their generous financial support. We also acknowledge funding from European Union Horizon 2020 Programme via the COMPLETE project.
Temporal and spatial neural dynamics in the perception of basic emotions from complex scenes.
Costa, Tommaso; Cauda, Franco; Crini, Manuella; Tatu, Mona-Karina; Celeghin, Alessia; de Gelder, Beatrice; Tamietto, Marco
2014-11-01
The different temporal dynamics of emotions are critical to understand their evolutionary role in the regulation of interactions with the surrounding environment. Here, we investigated the temporal dynamics underlying the perception of four basic emotions from complex scenes varying in valence and arousal (fear, disgust, happiness and sadness) with the millisecond time resolution of Electroencephalography (EEG). Event-related potentials were computed and each emotion showed a specific temporal profile, as revealed by distinct time segments of significant differences from the neutral scenes. Fear perception elicited significant activity at the earliest time segments, followed by disgust, happiness and sadness. Moreover, fear, disgust and happiness were characterized by two time segments of significant activity, whereas sadness showed only one long-latency time segment of activity. Multidimensional scaling was used to assess the correspondence between neural temporal dynamics and the subjective experience elicited by the four emotions in a subsequent behavioral task. We found a high coherence between these two classes of data, indicating that psychological categories defining emotions have a close correspondence at the brain level in terms of neural temporal dynamics. Finally, we localized the brain regions of time-dependent activity for each emotion and time segment with the low-resolution brain electromagnetic tomography. Fear and disgust showed widely distributed activations, predominantly in the right hemisphere. Happiness activated a number of areas mostly in the left hemisphere, whereas sadness showed a limited number of active areas at late latency. The present findings indicate that the neural signature of basic emotions can emerge as the byproduct of dynamic spatiotemporal brain networks as investigated with millisecond-range resolution, rather than in time-independent areas involved uniquely in the processing one specific emotion. © The Author (2013
Caldesmon regulates actin dynamics to influence cranial neural crest migration in Xenopus.
Nie, Shuyi; Kee, Yun; Bronner-Fraser, Marianne
2011-09-01
Caldesmon (CaD) is an important actin modulator that associates with actin filaments to regulate cell morphology and motility. Although extensively studied in cultured cells, there is little functional information regarding the role of CaD in migrating cells in vivo. Here we show that nonmuscle CaD is highly expressed in both premigratory and migrating cranial neural crest cells of Xenopus embryos. Depletion of CaD with antisense morpholino oligonucleotides causes cranial neural crest cells to migrate a significantly shorter distance, prevents their segregation into distinct migratory streams, and later results in severe defects in cartilage formation. Demonstrating specificity, these effects are rescued by adding back exogenous CaD. Interestingly, CaD proteins with mutations in the Ca(2+)-calmodulin-binding sites or ErK/Cdk1 phosphorylation sites fail to rescue the knockdown phenotypes, whereas mutation of the PAK phosphorylation site is able to rescue them. Analysis of neural crest explants reveals that CaD is required for the dynamic arrangements of actin and, thus, for cell shape changes and process formation. Taken together, these results suggest that the actin-modulating activity of CaD may underlie its critical function and is regulated by distinct signaling pathways during normal neural crest migration.
Distributed dynamical computation in neural circuits with propagating coherent activity patterns.
Directory of Open Access Journals (Sweden)
Pulin Gong
2009-12-01
Full Text Available Activity in neural circuits is spatiotemporally organized. Its spatial organization consists of multiple, localized coherent patterns, or patchy clusters. These patterns propagate across the circuits over time. This type of collective behavior has ubiquitously been observed, both in spontaneous activity and evoked responses; its function, however, has remained unclear. We construct a spatially extended, spiking neural circuit that generates emergent spatiotemporal activity patterns, thereby capturing some of the complexities of the patterns observed empirically. We elucidate what kind of fundamental function these patterns can serve by showing how they process information. As self-sustained objects, localized coherent patterns can signal information by propagating across the neural circuit. Computational operations occur when these emergent patterns interact, or collide with each other. The ongoing behaviors of these patterns naturally embody both distributed, parallel computation and cascaded logical operations. Such distributed computations enable the system to work in an inherently flexible and efficient way. Our work leads us to propose that propagating coherent activity patterns are the underlying primitives with which neural circuits carry out distributed dynamical computation.
Andrade, Andre; Costa, Marcelo; Paolucci, Leopoldo; Braga, Antônio; Pires, Flavio; Ugrinowitsch, Herbert; Menzel, Hans-Joachim
2015-01-01
The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.
De Geeter, N.; Crevecoeur, G.; Leemans, A.; Dupré, L.
2015-01-01
In transcranial magnetic stimulation (TMS), an applied alternating magnetic field induces an electric field in the brain that can interact with the neural system. It is generally assumed that this induced electric field is the crucial effect exciting a certain region of the brain. More specifically,
Laser fields in dynamically ionized plasma structures for coherent acceleration
Luu-Thanh, Ph.; Pukhov, A.; Kostyukov, I.
2015-01-01
With the emergence of the CAN (Coherent Amplification Network) laser technology, a new scheme for direct particle acceleration in periodic plasma structures has been proposed. By using our full electromagnetic relativistic particle-in-cell (PIC) simulation code equipped with ionisation module, we simulate the laser fields dynamics in the periodic structures of different materials. We study how the dynamic ionization influences the field structure.
Dynamical Mean Field Theory and Electronic Structure Calculations
Chitra, R.; Kotliar, G.
1999-01-01
We formulate the dynamical mean field theory directly in the continuum. For a given definition of the local Green's function, we show the existence of a unique functional, whose stationary point gives the physical local Green's function of the solid. We present the diagrammatic rules to calculate it perturbatively in the interaction. Inspired by the success of dynamical mean field calculations for model Hamiltonian systems, we present approximations to the exact saddle point equations which m...
An implantable wireless neural interface for recording cortical circuit dynamics in moving primates
Borton, David A.; Yin, Ming; Aceros, Juan; Nurmikko, Arto
2013-04-01
Objective. Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. Approach. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Main results. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile
Elastic and dynamic properties of membrane phase-field models.
Lázaro, Guillermo R; Pagonabarraga, Ignacio; Hernández-Machado, Aurora
2017-09-01
Phase-field models have been extensively used to study interfacial phenomena, from solidification to vesicle dynamics. In this article, we analyze a phase-field model that captures the relevant physical features that characterize biological membranes. We show that the Helfrich theory of elasticity of membranes can be applied to phase-field models, allowing to derive the expressions of the stress tensor, lateral stress profile and elastic moduli. We discuss the relevance and interpretations of these magnitudes from a phase-field perspective. Taking the sharp-interface limit we show that the membrane macroscopic equilibrium equation can be derived from the equilibrium condition of the phase-field interface. We also study two dynamic models that describe the behaviour of a membrane. From the study of the relaxational behaviour of the membrane we characterize the relevant dynamics of each model, and discuss their applications.
Directory of Open Access Journals (Sweden)
C. K. Kwong
2013-01-01
Full Text Available Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1 the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS failed to run due to a large number of inputs; (2 the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.
Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael
2013-01-01
Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.
Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P
2017-03-01
In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Žigman, Mihaela; Laumann-Lipp, Nico; Titus, Tom; Postlethwait, John; Moens, Cecilia B.
2014-01-01
Hox genes are classically ascribed to function in patterning the anterior-posterior axis of bilaterian animals; however, their role in directing molecular mechanisms underlying morphogenesis at the cellular level remains largely unstudied. We unveil a non-classical role for the zebrafish hoxb1b gene, which shares ancestral functions with mammalian Hoxa1, in controlling progenitor cell shape and oriented cell division during zebrafish anterior hindbrain neural tube morphogenesis. This is likely distinct from its role in cell fate acquisition and segment boundary formation. We show that, without affecting major components of apico-basal or planar cell polarity, Hoxb1b regulates mitotic spindle rotation during the oriented neural keel symmetric mitoses that are required for normal neural tube lumen formation in the zebrafish. This function correlates with a non-cell-autonomous requirement for Hoxb1b in regulating microtubule plus-end dynamics in progenitor cells in interphase. We propose that Hox genes can influence global tissue morphogenesis by control of microtubule dynamics in individual cells in vivo. PMID:24449840
Directory of Open Access Journals (Sweden)
Lars Buesing
2011-11-01
Full Text Available The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.
Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang
2011-11-01
The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.
Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.
Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi
2017-01-01
Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.
DO DYNAMIC NEURAL NETWORKS STAND A BETTER CHANCE IN FRACTIONALLY INTEGRATED PROCESS FORECASTING?
Directory of Open Access Journals (Sweden)
Majid Delavari
2013-04-01
Full Text Available The main purpose of the present study was to investigate the capabilities of two generations of models such as those based on dynamic neural network (e.g., Nonlinear Neural network Auto Regressive or NNAR model and a regressive (Auto Regressive Fractionally Integrated Moving Average model which is based on Fractional Integration Approach in forecasting daily data related to the return index of Tehran Stock Exchange (TSE. In order to compare these models under similar conditions, Mean Square Error (MSE and also Root Mean Square Error (RMSE were selected as criteria for the models’ simulated out-of-sample forecasting performance. Besides, fractal markets hypothesis was examined and according to the findings, fractal structure was confirmed to exist in the time series under investigation. Another finding of the study was that dynamic artificial neural network model had the best performance in out-of-sample forecasting based on the criteria introduced for calculating forecasting error in comparison with the ARFIMA model.
Kim, Junkyeong; Lee, Chaggil; Park, Seunghee
2017-06-07
Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.
Random field estimation approach to robot dynamics
Rodriguez, Guillermo
1990-01-01
The difference equations of Kalman filtering and smoothing recursively factor and invert the covariance of the output of a linear state-space system driven by a white-noise process. Here it is shown that similar recursive techniques factor and invert the inertia matrix of a multibody robot system. The random field models are based on the assumption that all of the inertial (D'Alembert) forces in the system are represented by a spatially distributed white-noise model. They are easier to describe than the models based on classical mechanics, which typically require extensive derivation and manipulation of equations of motion for complex mechanical systems. With the spatially random models, more primitive locally specified computations result in a global collective system behavior equivalent to that obtained with deterministic models. The primary goal of applying random field estimation is to provide a concise analytical foundation for solving robot control and motion planning problems.
Dynamically orthogonal field equations for stochastic flows and particle dynamics
2011-02-01
hypotheses on the spectrum of the orthogonal complement of the stochastic subspace. Note that we restrict ourselves to the ‘internal’ adaptation, i.e...square sense ([87]). For the case where the integrands are deterministic the mean square integral is reduced to the 40 classical Riemann integral. In...exact, closed set of equa- tions that determine the evolution of continuous stochastic fields described by a SPDE. By hypothesizing a finite order
Brunton, Bingni W; Johnson, Lise A; Ojemann, Jeffrey G; Kutz, J Nathan
2016-01-30
There is a broad need in neuroscience to understand and visualize large-scale recordings of neural activity, big data acquired by tens or hundreds of electrodes recording dynamic brain activity over minutes to hours. Such datasets are characterized by coherent patterns across both space and time, yet existing computational methods are typically restricted to analysis either in space or in time separately. Here we report the adaptation of dynamic mode decomposition (DMD), an algorithm originally developed for studying fluid physics, to large-scale neural recordings. DMD is a modal decomposition algorithm that describes high-dimensional dynamic data using coupled spatial-temporal modes. The algorithm is robust to variations in noise and subsampling rate; it scales easily to very large numbers of simultaneously acquired measurements. We first validate the DMD approach on sub-dural electrode array recordings from human subjects performing a known motor task. Next, we combine DMD with unsupervised clustering, developing a novel method to extract spindle networks during sleep. We uncovered several distinct sleep spindle networks identifiable by their stereotypical cortical distribution patterns, frequency, and duration. DMD is closely related to principal components analysis (PCA) and discrete Fourier transform (DFT). We may think of DMD as a rotation of the low-dimensional PCA space such that each basis vector has coherent dynamics. The resulting analysis combines key features of performing PCA in space and power spectral analysis in time, making it particularly suitable for analyzing large-scale neural recordings. Copyright © 2015 Elsevier B.V. All rights reserved.
Neural dynamics of speech act comprehension: an MEG study of naming and requesting.
Egorova, Natalia; Pulvermüller, Friedemann; Shtyrov, Yury
2014-05-01
The neurobiological basis and temporal dynamics of communicative language processing pose important yet unresolved questions. It has previously been suggested that comprehension of the communicative function of an utterance, i.e. the so-called speech act, is supported by an ensemble of neural networks, comprising lexico-semantic, action and mirror neuron as well as theory of mind circuits, all activated in concert. It has also been demonstrated that recognition of the speech act type occurs extremely rapidly. These findings however, were obtained in experiments with insufficient spatio-temporal resolution, thus possibly concealing important facets of the neural dynamics of the speech act comprehension process. Here, we used magnetoencephalography to investigate the comprehension of Naming and Request actions performed with utterances controlled for physical features, psycholinguistic properties and the probability of occurrence in variable contexts. The results show that different communicative actions are underpinned by a dynamic neural network, which differentiates between speech act types very early after the speech act onset. Within 50-90 ms, Requests engaged mirror-neuron action-comprehension systems in sensorimotor cortex, possibly for processing action knowledge and intentions. Still, within the first 200 ms of stimulus onset (100-150 ms), Naming activated brain areas involved in referential semantic retrieval. Subsequently (200-300 ms), theory of mind and mentalising circuits were activated in medial prefrontal and temporo-parietal areas, possibly indexing processing of intentions and assumptions of both communication partners. This cascade of stages of processing information about actions and intentions, referential semantics, and theory of mind may underlie dynamic and interactive speech act comprehension.
Sato, Naoyuki
2015-02-01
Local field potentials (LFPs) are thought to integrate neuronal processes within the range of a few millimeters of radius, which corresponds to the scale of multiple columns. In this study, the model of LFP in the visual cortex proposed by Mazzoni et al. (2008) was adapted to organize a network of two cortical areas, in which pyramidal neurons were divided into two sub-population modeling columns with spatially organized connections to neurons in other areas. Using the model enabled the relationship between neural firing and LFP to be evaluated, in addition to the LFP coherence between the two areas. Results showed that: (1) neurons in a particular sub-population generated the LFP in the area; (2) the spatial consistency of neural firing in the two areas was strongly correlated with LFP coherence; and (3) this consistency was capable of regulating LFP coherence in a lower frequency band, which was originally introduced to neurons in a particular sub-population. These results were derived from a winner-take-all operation in the columnar structure; thus, they are expected to be common in the cortex. It is suggested that the spatial consistency of neural firing is essential for regulating long-range LFP synchronization, which would facilitate neuronal integration processes over multiple cortical areas. Copyright © 2014 Elsevier Ltd. All rights reserved.
Porosity Estimation By Artificial Neural Networks Inversion . Application to Algerian South Field
Eladj, Said; Aliouane, Leila; Ouadfeul, Sid-Ali
2017-04-01
One of the main geophysicist's current challenge is the discovery and the study of stratigraphic traps, this last is a difficult task and requires a very fine analysis of the seismic data. The seismic data inversion allows obtaining lithological and stratigraphic information for the reservoir characterization . However, when solving the inverse problem we encounter difficult problems such as: Non-existence and non-uniqueness of the solution add to this the instability of the processing algorithm. Therefore, uncertainties in the data and the non-linearity of the relationship between the data and the parameters must be taken seriously. In this case, the artificial intelligence techniques such as Artificial Neural Networks(ANN) is used to resolve this ambiguity, this can be done by integrating different physical properties data which requires a supervised learning methods. In this work, we invert the acoustic impedance 3D seismic cube using the colored inversion method, then, the introduction of the acoustic impedance volume resulting from the first step as an input of based model inversion method allows to calculate the Porosity volume using the Multilayer Perceptron Artificial Neural Network. Application to an Algerian South hydrocarbon field clearly demonstrate the power of the proposed processing technique to predict the porosity for seismic data, obtained results can be used for reserves estimation, permeability prediction, recovery factor and reservoir monitoring. Keywords: Artificial Neural Networks, inversion, non-uniqueness , nonlinear, 3D porosity volume, reservoir characterization .
Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems
Directory of Open Access Journals (Sweden)
Cong-Hui Huang
2014-12-01
Full Text Available This paper presents modified neural network for dynamic control and operation of a hybrid generation systems. PV and wind power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. The simulation model of the hybrid system was developed using MATLAB Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN and an modified Elman Neural Network (ENN for maximum power point tracking (MPPT. The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC I DC boost converters to achieve the MPPT. And the results show the hybrid generation system can effectively extract the maximum power from the PV and wind energy sources.
Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games
Directory of Open Access Journals (Sweden)
Emmanuel García
2014-01-01
Full Text Available This paper deals with the problem of identifying and filtering a class of continuous-time nonlinear dynamic games (nonlinear differential games subject to additive and undesired deterministic perturbations. Moreover, the mathematical model of this class is completely unknown with the exception of the control actions of each player, and even though the deterministic noises are known, their power (or their effect is not. Therefore, two differential neural networks are designed in order to obtain a feedback (perfect state information pattern for the mentioned class of games. In this way, the stability conditions for two state identification errors and for a filtering error are established, the upper bounds of these errors are obtained, and two new learning laws for each neural network are suggested. Finally, an illustrating example shows the applicability of this approach.
A phase-field description of dynamic brittle fracture
2011-05-01
Landau-Ginzburg type phase-field evolution equations, e.g., Karma , Kessler, and Levine (2001). However, we favor the phase-field formulation of the...Proceedings of the International Conference on Impact Loading and Dynamic Behavior of Materials, volume 1, pages 185–195, 1987. A. Karma , D. A. Kessler, and
Steering Micro-Robotic Swarm by Dynamic Actuating Fields
Chao, Q.; Yu, J; Dai, C.; Xu, T; Zhang, L.; Wang, C.C.; Jin, X.
2016-01-01
We present a general solution for steering microrobotic
swarm by dynamic actuating fields. In our approach, the
motion of micro-robots is controlled by changing the actuating
direction of a field applied to them. The time-series sequence
of actuating field’s directions can be
Neely, Kristina A.; Coombes, Stephen A.; Planetta, Peggy J.; Vaillancourt, David E.
2011-01-01
A central topic in sensorimotor neuroscience is the static-dynamic dichotomy that exists throughout the nervous system. Previous work examining motor unit synchronization reports that the activation strategy and timing of motor units differ for static and dynamic tasks. However, it remains unclear whether segregated or overlapping blood-oxygen-level-dependent (BOLD) activity exists in the brain for static and dynamic motor control. This study compared the neural circuits associated with the production of static force to those associated with the production of dynamic force pulses. To that end, healthy young adults (n = 17) completed static and dynamic precision grip force tasks during functional magnetic resonance imaging (fMRI). Both tasks activated core regions within the visuomotor network, including primary and sensory motor cortices, premotor cortices, multiple visual areas, putamen, and cerebellum. Static force was associated with unique activity in a right-lateralized cortical network including inferior parietal lobe, ventral premotor cortex, and dorsolateral prefrontal cortex. In contrast, dynamic force was associated with unique activity in left-lateralized and midline cortical regions, including supplementary motor area, superior parietal lobe, fusiform gyrus, and visual area V3. These findings provide the first neuroimaging evidence supporting a lateralized pattern of brain activity for the production of static and dynamic precision grip force. PMID:22109998
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.
Dynamically important magnetic fields near accreting supermassive black holes.
Zamaninasab, M; Clausen-Brown, E; Savolainen, T; Tchekhovskoy, A
2014-06-05
Accreting supermassive black holes at the centres of active galaxies often produce 'jets'--collimated bipolar outflows of relativistic particles. Magnetic fields probably play a critical role in jet formation and in accretion disk physics. A dynamically important magnetic field was recently found near the Galactic Centre black hole. If this is common and if the field continues to near the black hole event horizon, disk structures will be affected, invalidating assumptions made in standard models. Here we report that jet magnetic field and accretion disk luminosity are tightly correlated over seven orders of magnitude for a sample of 76 radio-loud active galaxies. We conclude that the jet-launching regions of these radio-loud galaxies are threaded by dynamically important fields, which will affect the disk properties. These fields obstruct gas infall, compress the accretion disk vertically, slow down the disk rotation by carrying away its angular momentum in an outflow and determine the directionality of jets.
High-field spin dynamics of antiferromagnetic quantum spin chains
DEFF Research Database (Denmark)
Enderle, M.; Regnault, L.P.; Broholm, C.
2000-01-01
The characteristic internal order of macroscopic quantum ground states in one-dimensional spin systems is usually not directly accessible, but reflected in the spin dynamics and the field dependence of the magnetic excitations. In high magnetic fields quantum phase transitions are expected. We...... present recent work on the high-field spin dynamics of the S = I antiferromagnetic Heisenberg chains NENP (Haldane ground state) and CsNiCl3 (quasi-1D HAF close to the quantum critical point), the uniform S = 1/2 chain CTS, and the spin-Peierls system CuGeO3. (C) 2000 Elsevier Science B,V. All rights...
Dynamics of classical and quantum fields an introduction
Setlur, Girish S
2014-01-01
Dynamics of Classical and Quantum Fields: An Introduction focuses on dynamical fields in non-relativistic physics. Written by a physicist for physicists, the book is designed to help readers develop analytical skills related to classical and quantum fields at the non-relativistic level, and think about the concepts and theory through numerous problems. In-depth yet accessible, the book presents new and conventional topics in a self-contained manner that beginners would find useful. A partial list of topics covered includes: Geometrical meaning of Legendre transformation in classical mechanics Dynamical symmetries in the context of Noether's theorem The derivation of the stress energy tensor of the electromagnetic field, the expression for strain energy in elastic bodies, and the Navier Stokes equation Concepts of right and left movers in case of a Fermi gas explained Functional integration is interpreted as a limit of a sequence of ordinary integrations Path integrals for one and two quantum particles and for...
Wang, Yanan; Geng, Xinyi; Huang, Yongzhi; Wang, Shouyan
2016-02-01
The dysfunction of subthalamic nucleus is the main cause of Parkinson's disease. Local field potentials in human subthalamic nucleus contain rich physiological information. The present study aimed to quantify the oscillatory and dynamic characteristics of local field potentials of subthalamic nucleus, and their modulation by the medication therapy for Parkinson's disease. The subthalamic nucleus local field potentials were recorded from patients with Parkinson's disease at the states of on and off medication. The oscillatory features were characterised with the power spectral analysis. Furthermore, the dynamic features were characterised with time-frequency analysis and the coefficient of variation measure of the time-variant power at each frequency. There was a dominant peak at low beta-band with medication off. The medication significantly suppressed the low beta component and increased the theta component. The amplitude fluctuation of neural oscillations was measured by the coefficient of variation. The coefficient of variation in 4-7 Hz and 60-66 Hz was increased by medication. These effects proved that medication had significant modulation to subthalamic nucleus neural oscillatory synchronization and dynamic features. The subthalamic nucleus neural activities tend towards stable state under medication. The findings would provide quantitative biomarkers for studying the mechanisms of Parkinson's disease and clinical treatments of medication or deep brain stimulation.
Hasegawa, Mikio; Tran, Ha Nguyen; Miyamoto, Goh; Murata, Yoshitoshi; Harada, Hiroshi; Kato, Shuzo
We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.
Voytek, Bradley; Knight, Robert T
2015-06-15
Perception, cognition, and social interaction depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at multiple timescales. These networks are built upon a structural scaffolding with intrinsic neuroplasticity that changes with development, aging, disease, and personal experience. In this article, we begin from the perspective that successful interregional communication relies upon the transient synchronization between distinct low-frequency (communication via phase-coordinated local neuronal spiking. From this, we construct a theoretical framework for dynamic network communication, arguing that these networks reflect a balance between oscillatory coupling and local population spiking activity and that these two levels of activity interact. We theorize that when oscillatory coupling is too strong, spike timing within the local neuronal population becomes too synchronous; when oscillatory coupling is too weak, spike timing is too disorganized. Each results in specific disruptions to neural communication. These alterations in communication dynamics may underlie cognitive changes associated with healthy development and aging, in addition to neurological and psychiatric disorders. A number of neurological and psychiatric disorders-including Parkinson's disease, autism, depression, schizophrenia, and anxiety-are associated with abnormalities in oscillatory activity. Although aging, psychiatric and neurological disease, and experience differ in the biological changes to structural gray or white matter, neurotransmission, and gene expression, our framework suggests that any resultant cognitive and behavioral changes in normal or disordered states or their treatment are a product of how these physical processes affect dynamic network communication. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
McDermott, Timothy J; Badura-Brack, Amy S; Becker, Katherine M; Ryan, Tara J; Khanna, Maya M; Heinrichs-Graham, Elizabeth; Wilson, Tony W
2016-06-01
Posttraumatic stress disorder (PTSD) is associated with executive functioning deficits, including disruptions in working memory. In this study, we examined the neural dynamics of working memory processing in veterans with PTSD and a matched healthy control sample using magnetoencephalography (MEG). Our sample of recent combat veterans with PTSD and demographically matched participants without PTSD completed a working memory task during a 306-sensor MEG recording. The MEG data were preprocessed and transformed into the time-frequency domain. Significant oscillatory brain responses were imaged using a beamforming approach to identify spatiotemporal dynamics. Fifty-one men were included in our analyses: 27 combat veterans with PTSD and 24 controls. Across all participants, a dynamic wave of neural activity spread from posterior visual cortices to left frontotemporal regions during encoding, consistent with a verbal working memory task, and was sustained throughout maintenance. Differences related to PTSD emerged during early encoding, with patients exhibiting stronger α oscillatory responses than controls in the right inferior frontal gyrus (IFG). Differences spread to the right supramarginal and temporal cortices during later encoding where, along with the right IFG, they persisted throughout the maintenance period. This study focused on men with combat-related PTSD using a verbal working memory task. Future studies should evaluate women and the impact of various traumatic experiences using diverse tasks. Posttraumatic stress disorder is associated with neurophysiological abnormalities during working memory encoding and maintenance. Veterans with PTSD engaged a bilateral network, including the inferior prefrontal cortices and supramarginal gyri. Right hemispheric neural activity likely reflects compensatory processing, as veterans with PTSD work to maintain accurate performance despite known cognitive deficits associated with the disorder.
A stochastic phase-field model determined from molecular dynamics
von Schwerin, Erik
2010-03-17
The dynamics of dendritic growth of a crystal in an undercooled melt is determined by macroscopic diffusion-convection of heat and by capillary forces acting on the nanometer scale of the solid-liquid interface width. Its modelling is useful for instance in processing techniques based on casting. The phase-field method is widely used to study evolution of such microstructural phase transformations on a continuum level; it couples the energy equation to a phenomenological Allen-Cahn/Ginzburg-Landau equation modelling the dynamics of an order parameter determining the solid and liquid phases, including also stochastic fluctuations to obtain the qualitatively correct result of dendritic side branching. This work presents a method to determine stochastic phase-field models from atomistic formulations by coarse-graining molecular dynamics. It has three steps: (1) a precise quantitative atomistic definition of the phase-field variable, based on the local potential energy; (2) derivation of its coarse-grained dynamics model, from microscopic Smoluchowski molecular dynamics (that is Brownian or over damped Langevin dynamics); and (3) numerical computation of the coarse-grained model functions. The coarse-grained model approximates Gibbs ensemble averages of the atomistic phase-field, by choosing coarse-grained drift and diffusion functions that minimize the approximation error of observables in this ensemble average. © EDP Sciences, SMAI, 2010.
Automatic Estimation of the Dynamics of Channel Conductance Using a Recurrent Neural Network
Directory of Open Access Journals (Sweden)
Masaaki Takahashi
2009-01-01
Full Text Available In order to simulate neuronal electrical activities, we must estimate the dynamics of channel conductances from physiological experimental data. However, this approach requires the formulation of differential equations that express the time course of channel conductance. On the other hand, if the dynamics are automatically estimated, neuronal activities can be easily simulated. By using a recurrent neural network (RNN, it is possible to estimate the dynamics of channel conductances without formulating the differential equations. In the present study, we estimated the dynamics of the Na+ and K+ conductances of a squid giant axon using two different fully connected RNNs and were able to reproduce various neuronal activities of the axon. The reproduced activities were an action potential, a threshold, a refractory phenomenon, a rebound action potential, and periodic action potentials with a constant stimulation. RNNs can be trained using channels other than the Na+ and K+ channels. Therefore, using our RNN estimation method, the dynamics of channel conductance can be automatically estimated and the neuronal activities can be simulated using the channel RNNs. An RNN can be a useful tool to estimate the dynamics of the channel conductance of a neuron, and by using the method presented here, it is possible to simulate neuronal activities more easily than by using the previous methods.
Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network
Directory of Open Access Journals (Sweden)
Hongshan Yu
2014-01-01
Full Text Available Hammerstein model has been popularly applied to identify the nonlinear systems. In this paper, a Hammerstein-type neural network (HTNN is derived to formulate the well-known Hammerstein model. The HTNN consists of a nonlinear static gain in cascade with a linear dynamic part. First, the Lipschitz criterion for order determination is derived. Second, the backpropagation algorithm for updating the network weights is presented, and the stability analysis is also drawn. Finally, simulation results show that HTNN identification approach demonstrated identification performances.
Passivation and control of partially known SISO nonlinear systems via dynamic neural networks
Directory of Open Access Journals (Sweden)
Reyes-Reyes J.
2000-01-01
Full Text Available In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN, containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback controller, serving for wide class of nonlinear systems with an a priori incomplete model description, is designed. Two typical examples illustrate the effectiveness of the suggested approach.
A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach
Directory of Open Access Journals (Sweden)
Choon Ki Ahn
2010-01-01
Full Text Available A new robust training law, which is called an input/output-to-state stable training law (IOSSTL, is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.
Traveling pulses in a stochastic neural field model of direction selectivity.
Bressloff, Paul C; Wilkerson, Jeremy
2012-01-01
We analyze the effects of extrinsic noise on traveling pulses in a neural field model of direction selectivity. The model consists of a one-dimensional scalar neural field with an asymmetric weight distribution consisting of an offset Mexican hat function. We first show how, in the absence of any noise, the system supports spontaneously propagating traveling pulses that can lock to externally moving stimuli. Using a separation of time-scales and perturbation methods previously developed for stochastic reaction-diffusion equations, we then show how extrinsic noise in the activity variables leads to a diffusive-like displacement (wandering) of the wave from its uniformly translating position at long time-scales, and fluctuations in the wave profile around its instantaneous position at short time-scales. In the case of freely propagating pulses, the wandering is characterized by pure Brownian motion, whereas in the case of stimulus-locked pulses, it is given by an Ornstein-Uhlenbeck process. This establishes that stimulus-locked pulses are more robust to noise.
Traveling pulses in a stochastic neural field model of direction selectivity
Directory of Open Access Journals (Sweden)
Paul C Bressloff
2012-10-01
Full Text Available We analyze the effects of extrinsic noise on traveling pulses in a neural field model of direction selectivity. The model consists of a one-dimensional scalar neural field with an asymmetric weight distribution consisting of an offset Mexican hat function. We first show how, in the absence of any noise, the system supports spontaneously propagating traveling pulses that can lock to externally moving stimuli. Using a separation of time scales and perturbation methods previously developed for stochastic reaction-diffusion equations, we then show how extrinsic noise in the activity variables leads to a diffusive-like displacement (wandering of the wave from its uniformly translating position at long time scales, and fluctuations in the wave profile around its instantaneous position at short time scales. In the case of freely propagating pulses, the wandering is characterized by pure Brownian motion, whereas in the case of stimulus-locked pulses, it is given by an Ornstein-Uhlenbeck process. This establishes that stimulus-locked pulses are more robust to noise.
Directed Migration of Embryonic Stem Cell-derived Neural Cells In An Applied Electric Field
Weiss, Mark; Yao, Li
2014-01-01
Spinal cord injury or diseases, such as amyotrophic lateral sclerosis, can cause the loss of motor neurons and therefore results in the paralysis of muscles. Stem cells may improve functional recovery by promoting endogenous regeneration, or by directly replacing neurons. Effective directional migration of grafted neural cells to reconstruct functional connections is crucial in the process. Steady direct current electric fields (EFs) play an important role in the development of the central nervous system. A strong biological effect of EFs is the induction of directional cell migration. In this study, we investigated the guided migration of embryonic stem cell (ESC) derived presumptive motor neurons in an applied EF. The dissociated mouse ESC derived presumptive motor neurons or embryoid bodies were subjected to EFs stimulation and the cell migration was studied. We found that the migration of neural precursors from embryoid bodies was toward cathode pole of applied EFs. Single motor neurons migrated to the cathode of the EFs and reversal of EFs poles reversed their migration direction. The directedness and displacement of cathodal migration became more significant when the field strength was increased from 50 mV/mm to 100 mV/mm. EFs stimulation did not influence the cell migration velocity. Our work suggests that EFs may serve as a guidance cue to direct grafted cell migration in vivo. PMID:24804615
Wilson, M T; Fung, P K; Robinson, P A; Shemmell, J; Reynolds, J N J
2016-08-01
The calcium dependent plasticity (CaDP) approach to the modeling of synaptic weight change is applied using a neural field approach to realistic repetitive transcranial magnetic stimulation (rTMS) protocols. A spatially-symmetric nonlinear neural field model consisting of populations of excitatory and inhibitory neurons is used. The plasticity between excitatory cell populations is then evaluated using a CaDP approach that incorporates metaplasticity. The direction and size of the plasticity (potentiation or depression) depends on both the amplitude of stimulation and duration of the protocol. The breaks in the inhibitory theta-burst stimulation protocol are crucial to ensuring that the stimulation bursts are potentiating in nature. Tuning the parameters of a spike-timing dependent plasticity (STDP) window with a Monte Carlo approach to maximize agreement between STDP predictions and the CaDP results reproduces a realistically-shaped window with two regions of depression in agreement with the existing literature. Developing understanding of how TMS interacts with cells at a network level may be important for future investigation.
Directed migration of embryonic stem cell-derived neural cells in an applied electric field.
Li, Yongchao; Weiss, Mark; Yao, Li
2014-10-01
Spinal cord injury or diseases, such as amyotrophic lateral sclerosis, can cause the loss of motor neurons and therefore results in the paralysis of muscles. Stem cells may improve functional recovery by promoting endogenous regeneration, or by directly replacing neurons. Effective directional migration of grafted neural cells to reconstruct functional connections is crucial in the process. Steady direct current electric fields (EFs) play an important role in the development of the central nervous system. A strong biological effect of EFs is the induction of directional cell migration. In this study, we investigated the guided migration of embryonic stem cell (ESC) derived presumptive motor neurons in an applied EF. The dissociated mouse ESC derived presumptive motor neurons or embryoid bodies were subjected to EFs stimulation and the cell migration was studied. We found that the migration of neural precursors from embryoid bodies was toward cathode pole of applied EFs. Single motor neurons migrated to the cathode of the EFs and reversal of EFs poles reversed their migration direction. The directedness and displacement of cathodal migration became more significant when the field strength was increased from 50 mV/mm to 100 mV/mm. EFs stimulation did not influence the cell migration velocity. Our work suggests that EFs may serve as a guidance cue to direct grafted cell migration in vivo.
DEFF Research Database (Denmark)
Riis, Louise Claudius; Kjær, Inger; Mølsted, Kirsten
2014-01-01
OBJECTIVE: To analyze dental deviations in three cleft groups and relate findings to embryological neural crest fields (frontonasal, maxillary, and palatal). The overall purpose was to evaluate how fields are involved in different cleft types. DESIGN: Retrospective audit of clinical photographs...... seen significantly more often in cleft palate. Combined cleft lip and palate: Number and type of dental deviations differed significantly from deviations in other cleft types, e.g. significantly more ageneses. CONCLUSIONS: Cleft lip seems to be caused by a disorder in neural crest migration...... to the frontonasal field and cleft palate by a disorder in neural crest migration to the maxillary and palatal fields. Combined cleft lip and palate seems to be caused by a primary early defect in the cranial course and function of the notochord. The dentition was significantly different in the different cleft types...
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
A Dynamic Model of Mercury's Magnetospheric Magnetic Field
Korth, Haje; Johnson, Catherine L.; Philpott, Lydia; Tsyganenko, Nikolai A.; Anderson, Brian J.
2017-10-01
Mercury's solar wind and interplanetary magnetic field environment is highly dynamic, and variations in these external conditions directly control the current systems and magnetic fields inside the planetary magnetosphere. We update our previous static model of Mercury's magnetic field by incorporating variations in the magnetospheric current systems, parameterized as functions of Mercury's heliocentric distance and magnetic activity. The new, dynamic model reproduces the location of the magnetopause current system as a function of systematic pressure variations encountered during Mercury's eccentric orbit, as well as the increase in the cross-tail current intensity with increasing magnetic activity. Despite the enhancements in the external field parameterization, the residuals between the observed and modeled magnetic field inside the magnetosphere indicate that the dynamic model achieves only a modest overall improvement over the previous static model. The spatial distribution of the residuals in the magnetic field components shows substantial improvement of the model accuracy near the dayside magnetopause. Elsewhere, the large-scale distribution of the residuals is similar to those of the static model. This result implies either that magnetic activity varies much faster than can be determined from the spacecraft's passage through the magnetosphere or that the residual fields are due to additional external current systems not represented in the model or both. Birkeland currents flowing along magnetic field lines between the magnetosphere and planetary high-latitude regions have been identified as one such contribution.
A Dynamic Model of Mercury's Magnetospheric Magnetic Field.
Korth, Haje; Johnson, Catherine L; Philpott, Lydia; Tsyganenko, Nikolai A; Anderson, Brian J
2017-10-28
Mercury's solar wind and interplanetary magnetic field environment is highly dynamic, and variations in these external conditions directly control the current systems and magnetic fields inside the planetary magnetosphere. We update our previous static model of Mercury's magnetic field by incorporating variations in the magnetospheric current systems, parameterized as functions of Mercury's heliocentric distance and magnetic activity. The new, dynamic model reproduces the location of the magnetopause current system as a function of systematic pressure variations encountered during Mercury's eccentric orbit, as well as the increase in the cross-tail current intensity with increasing magnetic activity. Despite the enhancements in the external field parameterization, the residuals between the observed and modeled magnetic field inside the magnetosphere indicate that the dynamic model achieves only a modest overall improvement over the previous static model. The spatial distribution of the residuals in the magnetic field components shows substantial improvement of the model accuracy near the dayside magnetopause. Elsewhere, the large-scale distribution of the residuals is similar to those of the static model. This result implies either that magnetic activity varies much faster than can be determined from the spacecraft's passage through the magnetosphere or that the residual fields are due to additional external current systems not represented in the model or both. Birkeland currents flowing along magnetic field lines between the magnetosphere and planetary high-latitude regions have been identified as one such contribution.
A Dynamic Model of Mercury's Magnetospheric Magnetic Field
Korth, H.; Johnson, C. L.; Philpott, L. C.; Tsyganenko, N. A.; Anderson, B. J.
2017-09-01
Mercury's solar wind and interplanetary magnetic field environment is highly dynamic, and variations in these external conditions directly control the current systems and magnetic fields inside the planetary magnetosphere. We update our previous static model of Mercury's magnetic field [Korth et al., 2015] by incorporating variations in the magnetospheric current systems, parameterized as functions of Mercury's heliocentric distance and magnetic activity [Anderson et al., 2013]. The new, dynamic model reproduces the location of the magnetopause current system as a function of systematic pressure variations encountered during Mercury's eccentric orbit, as well as the increase in the cross-tail current intensity with increasing magnetic activity. Despite the enhancements in the external field parameterization, the residuals between the observed and modeled magnetic field inside the magnetosphere indicate that the dynamic model achieves only a modest overall improvement over the previous static model. The spatial distribution of the residuals in the magnetic field components shows substantial improvement of the model accuracy near the dayside magnetopause. Elsewhere, the large-scale distribution of the residuals is similar to those of the static model. This result implies either that magnetic activity varies much faster than can be determined from the spacecraft's passage through the magnetosphere or that the residual fields are due to additional external current systems not represented in the model or both. Birkeland currents flowing along magnetic field lines between the magnetosphere and planetary high latitude regions have been identified as one such contribution.
Zhang, Dandan; Kou, Kit Ian; Liu, Yang; Cao, Jinde
2017-10-01
In this paper, the global exponential stability for recurrent neural networks (QVNNs) with asynchronous time delays is investigated in quaternion field. Due to the non-commutativity of quaternion multiplication resulting from Hamilton rules: ij=-ji=k, jk=-kj=i, ki=-ik=j, ijk=i(2)=j(2)=k(2)=-1, the QVNN is decomposed into four real-valued systems, which are studied separately. The exponential convergence is proved directly accompanied with the existence and uniqueness of the equilibrium point to the consider systems. Combining with the generalized ∞-norm and Cauchy convergence property in the quaternion field, some sufficient conditions to guarantee the stability are established without using any Lyapunov-Krasovskii functional and linear matrix inequality. Finally, a numerical example is given to demonstrate the effectiveness of the results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Utilizing neural networks in magnetic media modeling and field computation: A review.
Adly, Amr A; Abd-El-Hafiz, Salwa K
2014-11-01
Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs) have been utilized in many applications to perform miscellaneous tasks such as identification, approximation, optimization, classification and forecasting. The purpose of this review article is to give an account of the utilization of ANNs in modeling as well as field computation involving complex magnetic materials. Mostly used ANN types in magnetics, advantages of this usage, detailed implementation methodologies as well as numerical examples are given in the paper.
Utilizing neural networks in magnetic media modeling and field computation: A review
Directory of Open Access Journals (Sweden)
Amr A. Adly
2014-11-01
Full Text Available Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs have been utilized in many applications to perform miscellaneous tasks such as identification, approximation, optimization, classification and forecasting. The purpose of this review article is to give an account of the utilization of ANNs in modeling as well as field computation involving complex magnetic materials. Mostly used ANN types in magnetics, advantages of this usage, detailed implementation methodologies as well as numerical examples are given in the paper.
The Dynamic Local Field Correction of Yukawa Plasmas
Choi, Yongjun; Dharuman, Gautham; Murillo, Michael
2017-10-01
The mean-field approximation is the cornerstone of modern statistical mechanics; therefore, unknowns are ``beyond mean field'' (BMF). Being tantamount to solving the complete many-body problem, however, few accurate BMF models exist even for simplified systems. Knowing the exact representation for the dynamics of a model system provides an important constraint on model validation and an exact limit. The dynamic local field correction (DLFC) is a complex function of wave vector and frequency in which all BMF information is contained. All collisional (e.g., wave damping, transport, equation of state, etc.) information is contained in the DLFC, since it represents the exact solution of the many-body problem. From these two functions (real and imaginary parts) we can validate theoretical models and compute many physical properties (e.g., wave dispersions). In this research, the DLFC will be obtained through molecular dynamics simulations on the Yukawa plasmas. The study covers full range of coupling and screening regimes.
Electron Dynamics in Nanostructures in Strong Laser Fields
Energy Technology Data Exchange (ETDEWEB)
Kling, Matthias
2014-09-11
The goal of our research was to gain deeper insight into the collective electron dynamics in nanosystems in strong, ultrashort laser fields. The laser field strengths will be strong enough to extract and accelerate electrons from the nanoparticles and to transiently modify the materials electronic properties. We aimed to observe, with sub-cycle resolution reaching the attosecond time domain, how collective electronic excitations in nanoparticles are formed, how the strong field influences the optical and electrical properties of the nanomaterial, and how the excitations in the presence of strong fields decay.
Fractional dynamics of charged particles in magnetic fields
Coronel-Escamilla, A.; Gómez-Aguilar, J. F.; Alvarado-Méndez, E.; Guerrero-Ramírez, G. V.; Escobar-Jiménez, R. F.
2016-02-01
In many physical applications the electrons play a relevant role. For example, when a beam of electrons accelerated to relativistic velocities is used as an active medium to generate Free Electron Lasers (FEL), the electrons are bound to atoms, but move freely in a magnetic field. The relaxation time, longitudinal effects and transverse variations of the optical field are parameters that play an important role in the efficiency of this laser. The electron dynamics in a magnetic field is a means of radiation source for coupling to the electric field. The transverse motion of the electrons leads to either gain or loss energy from or to the field, depending on the position of the particle regarding the phase of the external radiation field. Due to the importance to know with great certainty the displacement of charged particles in a magnetic field, in this work we study the fractional dynamics of charged particles in magnetic fields. Newton’s second law is considered and the order of the fractional differential equation is (0;1]. Based on the Grünwald-Letnikov (GL) definition, the discretization of fractional differential equations is reported to get numerical simulations. Comparison between the numerical solutions obtained on Euler’s numerical method for the classical case and the GL definition in the fractional approach proves the good performance of the numerical scheme applied. Three application examples are shown: constant magnetic field, ramp magnetic field and harmonic magnetic field. In the first example the results obtained show bistability. Dissipative effects are observed in the system and the standard dynamic is recovered when the order of the fractional derivative is 1.
Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai
2013-09-01
In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
Investigation of neural-net based control strategies for improved power system dynamic performance
Energy Technology Data Exchange (ETDEWEB)
Sobajic, D.J. [Electric Power Research Institute, Palo Alto, CA (United States)
1995-12-31
The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net base system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.
Plasticity and dislocation dynamics in a phase field crystal model.
Chan, Pak Yuen; Tsekenis, Georgios; Dantzig, Jonathan; Dahmen, Karin A; Goldenfeld, Nigel
2010-07-02
The critical dynamics of dislocation avalanches in plastic flow is examined using a phase field crystal model. In the model, dislocations are naturally created, without any ad hoc creation rules, by applying a shearing force to the perfectly periodic ground state. These dislocations diffuse, interact and annihilate with one another, forming avalanche events. By data collapsing the event energy probability density function for different shearing rates, a connection to interface depinning dynamics is confirmed. The relevant critical exponents agree with mean field theory predictions.
Dynamic electrophoresis of charged colloids in an oscillating electric field.
Shih, Chunyu; Yamamoto, Ryoichi
2014-06-01
The dynamics of charged colloids in an electrolyte solution is studied using direct numerical simulations via the smoothed profile method. We calculated the complex electrophoretic mobility μ(ω) of the charged colloids under an oscillating electric field of frequency ω. We show the existence of three dynamically distinct regimes, determined by the momentum diffusion and ionic diffusion time scales. The present results agree well with approximate theories based on the cell model in dilute suspensions; however, systematic deviations between the simulation results and theoretical predictions are observed as the volume fraction of colloids is increased, similar to the case of constant electric fields.
Quantum dynamics of trapped ions in a dynamic field gradient using dressed states
Wölk, Sabine; Wunderlich, Christof
2017-08-01
Novel ion traps that provide either a static or a dynamic magnetic gradient field allow for the use of radio-frequency radiation for coupling internal and motional states of ions, which is essential for conditional quantum logic. We show that the Hamiltonian describing this coupling in the presence of a resonant dynamic gradient, is identical, in a dressed state basis, to the Hamiltonian in the case of a static gradient. The coupling strength is in both cases described by the same effective Lamb-Dicke parameter. This insight can be used to overcome demanding experimental requirements when using a dynamic gradient field for state-of-the-art experiments with trapped ions, for example, in quantum information science. At the same time, this insight opens new experimental perspectives by way of using a single resonant or detuned dynamic gradient field, inducing long-range coupling, for conditional multi-qubit dynamics.
The effects of dynamical synapses on firing rate activity: a spiking neural network model.
Khalil, Radwa; Moftah, Marie Z; Moustafa, Ahmed A
2017-11-01
Accumulating evidence relates the fine-tuning of synaptic maturation and regulation of neural network activity to several key factors, including GABA A signaling and a lateral spread length between neighboring neurons (i.e., local connectivity). Furthermore, a number of studies consider short-term synaptic plasticity (STP) as an essential element in the instant modification of synaptic efficacy in the neuronal network and in modulating responses to sustained ranges of external Poisson input frequency (IF). Nevertheless, evaluating the firing activity in response to the dynamical interaction between STP (triggered by ranges of IF) and these key parameters in vitro remains elusive. Therefore, we designed a spiking neural network (SNN) model in which we incorporated the following parameters: local density of arbor essences and a lateral spread length between neighboring neurons. We also created several network scenarios based on these key parameters. Then, we implemented two classes of STP: (1) short-term synaptic depression (STD) and (2) short-term synaptic facilitation (STF). Each class has two differential forms based on the parametric value of its synaptic time constant (either for depressing or facilitating synapses). Lastly, we compared the neural firing responses before and after the treatment with STP. We found that dynamical synapses (STP) have a critical differential role on evaluating and modulating the firing rate activity in each network scenario. Moreover, we investigated the impact of changing the balance between excitation (E) and inhibition (I) on stabilizing this firing activity. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Chapin, Heather; Jantzen, Kelly; Kelso, J A Scott; Steinberg, Fred; Large, Edward
2010-12-16
Apart from its natural relevance to cognition, music provides a window into the intimate relationships between production, perception, experience, and emotion. Here, emotional responses and neural activity were observed as they evolved together with stimulus parameters over several minutes. Participants listened to a skilled music performance that included the natural fluctuations in timing and sound intensity that musicians use to evoke emotional responses. A mechanical performance of the same piece served as a control. Before and after fMRI scanning, participants reported real-time emotional responses on a 2-dimensional rating scale (arousal and valence) as they listened to each performance. During fMRI scanning, participants listened without reporting emotional responses. Limbic and paralimbic brain areas responded to the expressive dynamics of human music performance, and both emotion and reward related activations during music listening were dependent upon musical training. Moreover, dynamic changes in timing predicted ratings of emotional arousal, as well as real-time changes in neural activity. BOLD signal changes correlated with expressive timing fluctuations in cortical and subcortical motor areas consistent with pulse perception, and in a network consistent with the human mirror neuron system. These findings show that expressive music performance evokes emotion and reward related neural activations, and that music's affective impact on the brains of listeners is altered by musical training. Our observations are consistent with the idea that music performance evokes an emotional response through a form of empathy that is based, at least in part, on the perception of movement and on violations of pulse-based temporal expectancies.
Directory of Open Access Journals (Sweden)
Heather Chapin
2010-12-01
Full Text Available Apart from its natural relevance to cognition, music provides a window into the intimate relationships between production, perception, experience, and emotion. Here, emotional responses and neural activity were observed as they evolved together with stimulus parameters over several minutes. Participants listened to a skilled music performance that included the natural fluctuations in timing and sound intensity that musicians use to evoke emotional responses. A mechanical performance of the same piece served as a control. Before and after fMRI scanning, participants reported real-time emotional responses on a 2-dimensional rating scale (arousal and valence as they listened to each performance. During fMRI scanning, participants listened without reporting emotional responses. Limbic and paralimbic brain areas responded to the expressive dynamics of human music performance, and both emotion and reward related activations during music listening were dependent upon musical training. Moreover, dynamic changes in timing predicted ratings of emotional arousal, as well as real-time changes in neural activity. BOLD signal changes correlated with expressive timing fluctuations in cortical and subcortical motor areas consistent with pulse perception, and in a network consistent with the human mirror neuron system. These findings show that expressive music performance evokes emotion and reward related neural activations, and that music's affective impact on the brains of listeners is altered by musical training. Our observations are consistent with the idea that music performance evokes an emotional response through a form of empathy that is based, at least in part, on the perception of movement and on violations of pulse-based temporal expectancies.
Hoppensteadt, F C; Izhikevich, E M
1996-08-01
This is the second of two articles devoted to analyzing the relationship between synaptic organizations (anatomy) and dynamical properties (function) of networks of neural oscillators near multiple supercritical Andronov-Hopf bifurcation points. Here we analyze learning processes in such networks. Regarding learning dynamics, we assume (1) learning is local (i.e. synaptic modification depends on pre- and postsynaptic neurons but not on others), (2) synapses modify slowly relative to characteristic neuron response times, (3) in the absence of either pre- or postsynaptic activity, the synapse weakens (forgets). Our major goal is to analyze all synaptic organizations of oscillatory neural networks that can memorize and retrieve phase information or time delays. We show that such network have the following attributes: (1) the rate of synaptic plasticity connected with learning is determined locally by the presynaptic neurons, (2) the excitatory neurons must be long-axon relay neurons capable of forming distant connections with other excitatory and inhibitory neurons, (3) if inhibitory neurons have long axons, then the network can learn, passively forget and actively unlearn information by adjusting synaptic plasticity rates.
Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.
Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng
2016-02-01
This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.
A Neural-Dynamic Architecture for Concurrent Estimation of Object Pose and Identity
Directory of Open Access Journals (Sweden)
Oliver Lomp
2017-04-01
Full Text Available Handling objects or interacting with a human user about objects on a shared tabletop requires that objects be identified after learning from a small number of views and that object pose be estimated. We present a neurally inspired architecture that learns object instances by storing features extracted from a single view of each object. Input features are color and edge histograms from a localized area that is updated during processing. The system finds the best-matching view for the object in a novel input image while concurrently estimating the object’s pose, aligning the learned view with current input. The system is based on neural dynamics, computationally operating in real time, and can handle dynamic scenes directly off live video input. In a scenario with 30 everyday objects, the system achieves recognition rates of 87.2% from a single training view for each object, while also estimating pose quite precisely. We further demonstrate that the system can track moving objects, and that it can segment the visual array, selecting and recognizing one object while suppressing input from another known object in the immediate vicinity. Evaluation on the COIL-100 dataset, in which objects are depicted from different viewing angles, revealed recognition rates of 91.1% on the first 30 objects, each learned from four training views.
A Neural-Dynamic Architecture for Concurrent Estimation of Object Pose and Identity.
Lomp, Oliver; Faubel, Christian; Schöner, Gregor
2017-01-01
Handling objects or interacting with a human user about objects on a shared tabletop requires that objects be identified after learning from a small number of views and that object pose be estimated. We present a neurally inspired architecture that learns object instances by storing features extracted from a single view of each object. Input features are color and edge histograms from a localized area that is updated during processing. The system finds the best-matching view for the object in a novel input image while concurrently estimating the object's pose, aligning the learned view with current input. The system is based on neural dynamics, computationally operating in real time, and can handle dynamic scenes directly off live video input. In a scenario with 30 everyday objects, the system achieves recognition rates of 87.2% from a single training view for each object, while also estimating pose quite precisely. We further demonstrate that the system can track moving objects, and that it can segment the visual array, selecting and recognizing one object while suppressing input from another known object in the immediate vicinity. Evaluation on the COIL-100 dataset, in which objects are depicted from different viewing angles, revealed recognition rates of 91.1% on the first 30 objects, each learned from four training views.
A nonlinear dynamics for the scalar field in Randers spacetime
Energy Technology Data Exchange (ETDEWEB)
Silva, J.E.G. [Universidade Federal do Cariri (UFCA), Instituto de formação de professores, Rua Olegário Emídio de Araújo, Brejo Santo, CE, 63.260.000 (Brazil); Maluf, R.V. [Universidade Federal do Ceará (UFC), Departamento de Física, Campus do Pici, Fortaleza, CE, C.P. 6030, 60455-760 (Brazil); Almeida, C.A.S., E-mail: carlos@fisica.ufc.br [Universidade Federal do Ceará (UFC), Departamento de Física, Campus do Pici, Fortaleza, CE, C.P. 6030, 60455-760 (Brazil)
2017-03-10
We investigate the properties of a real scalar field in the Finslerian Randers spacetime, where the local Lorentz violation is driven by a geometrical background vector. We propose a dynamics for the scalar field by a minimal coupling of the scalar field and the Finsler metric. The coupling is intrinsically defined on the Randers spacetime, and it leads to a non-canonical kinetic term for the scalar field. The nonlinear dynamics can be split into a linear and nonlinear regimes, which depend perturbatively on the even and odd powers of the Lorentz-violating parameter, respectively. We analyze the plane-waves solutions and the modified dispersion relations, and it turns out that the spectrum is free of tachyons up to second-order.
Ultrafast carrier dynamics in graphene under a high electric field.
Tani, Shuntaro; Blanchard, François; Tanaka, Koichiro
2012-10-19
We investigated ultrafast carrier dynamics in graphene with near-infrared transient absorption measurement after intense half-cycle terahertz pulse excitation. The terahertz electric field efficiently drives the carriers, inducing large transparency in the near-infrared region. Theoretical calculations using the Boltzmann transport equation quantitatively reproduce the experimental findings. This good agreement suggests that the intense terahertz field should promote a remarkable impact ionization process and increase the carrier density.
Steering Micro-Robotic Swarm by Dynamic Actuating Fields
Chao, Q.; Yu, J.; Dai, C; Xu, T.; Zhang, L.; Wang, C. C.; Jin, X.
2016-01-01
We present a general solution for steering microroboticswarm by dynamic actuating fields. In our approach, themotion of micro-robots is controlled by changing the actuatingdirection of a field applied to them. The time-series sequenceof actuating field’s directions can be computed automatically.Given a target position in the domain of swarm, a governingfield is first constructed to provide optimal moving directions atevery points. Following these directions, a robot can be drivento the target...
Dynamics of Radiation and Atoms in Ultrahigh Intensity Laser Fields
2013-12-31
excitation in strong and ultrastrong optical frequency fields. Advances in laser technology continue to push the boundaries of this interaction in...possible ultrastrong magnetic fields and the electron cyclotron frequency in the bound state can create dynamics, such as is the case for `cycloatoms...promise of increasing the returning rescattering electron energy led to advances in the production of HHG. In addition to (e,2e) and HHG rescattering
Hysteretic dynamics of active particles in a periodic orienting field.
Romensky, Maksym; Scholz, Dimitri; Lobaskin, Vladimir
2015-07-06
Active motion of living organisms and artificial self-propelling particles has been an area of intense research at the interface of biology, chemistry and physics. Significant progress in understanding these phenomena has been related to the observation that dynamic self-organization in active systems has much in common with ordering in equilibrium condensed matter such as spontaneous magnetization in ferromagnets. The velocities of active particles may behave similar to magnetic dipoles and develop global alignment, although interactions between the individuals might be completely different. In this work, we show that the dynamics of active particles in external fields can also be described in a way that resembles equilibrium condensed matter. It follows simple general laws, which are independent of the microscopic details of the system. The dynamics is revealed through hysteresis of the mean velocity of active particles subjected to a periodic orienting field. The hysteresis is measured in computer simulations and experiments on unicellular organisms. We find that the ability of the particles to follow the field scales with the ratio of the field variation period to the particles' orientational relaxation time, which, in turn, is related to the particle self-propulsion power and the energy dissipation rate. The collective behaviour of the particles due to aligning interactions manifests itself at low frequencies via increased persistence of the swarm motion when compared with motion of an individual. By contrast, at high field frequencies, the active group fails to develop the alignment and tends to behave like a set of independent individuals even in the presence of interactions. We also report on asymptotic laws for the hysteretic dynamics of active particles, which resemble those in magnetic systems. The generality of the assumptions in the underlying model suggests that the observed laws might apply to a variety of dynamic phenomena from the motion of
Ross, Muriel D.; Cutler, Lynn; Meyer, Glenn; Lam, Tony; Vaziri, Parshaw
1990-01-01
Computer-assisted, 3-dimensional reconstructions of macular receptive fields and of their linkages into a neural network have revealed new information about macular functional organization. Both type I and type II hair cells are included in the receptive fields. The fields are rounded, oblong, or elongated, but gradations between categories are common. Cell polarizations are divergent. Morphologically, each calyx of oblong and elongated fields appears to be an information processing site. Intrinsic modulation of information processing is extensive and varies with the kind of field. Each reconstructed field differs in detail from every other, suggesting that an element of randomness is introduced developmentally and contributes to endorgan adaptability.
Song, J.; Garner, A. L.; Joshi, R. P.
2017-02-01
The use of nanosecond-duration-pulsed voltages with high-intensity electric fields (˜100 kV /cm ) is a promising development with many biomedical applications. Electroporation occurs in this regime, and has been attributed to the high fields. However, here we focus on temperature gradients. Our numerical simulations based on molecular dynamics predict the formation of nanopores and water nanowires, but only in the presence of a temperature gradient. Our results suggest a far greater role of temperature gradients in enhancing biophysical responses, including possible neural stimulation by infrared lasers.
Dynamics of atomic clusters in intense optical fields of ultrashort ...
Indian Academy of Sciences (India)
Atomic clusters; Coulomb explosion; few-cycle laser pulses; strong fields; cluster dynamics. 1. Introduction. A number of scientific and technological developments are responsible for the resurgence of interest in studi- es of light-matter interactions, particularly of how very intense light interacts with matter. The interest stems.
Field and Laboratory Evaluation of Dynamics in Soil Properties of ...
African Journals Online (AJOL)
Dynamics in properties of soils of three land use types (Fallow, Pineapple and Cassava) in Owerri, Southeastern Nigeria were evaluated under field and laboratory incubation conditions. Soil properties varied with time within land use types, with chemical more significantly than physical properties under both conditions.
Dynamical mean-field theory from a quantum chemical perspective.
Zgid, Dominika; Chan, Garnet Kin-Lic
2011-03-07
We investigate the dynamical mean-field theory (DMFT) from a quantum chemical perspective. Dynamical mean-field theory offers a formalism to extend quantum chemical methods for finite systems to infinite periodic problems within a local correlation approximation. In addition, quantum chemical techniques can be used to construct new ab initio Hamiltonians and impurity solvers for DMFT. Here, we explore some ways in which these things may be achieved. First, we present an informal overview of dynamical mean-field theory to connect to quantum chemical language. Next, we describe an implementation of dynamical mean-field theory where we start from an ab initio Hartree-Fock Hamiltonian that avoids double counting issues present in many applications of DMFT. We then explore the use of the configuration interaction hierarchy in DMFT as an approximate solver for the impurity problem. We also investigate some numerical issues of convergence within DMFT. Our studies are carried out in the context of the cubic hydrogen model, a simple but challenging test for correlation methods. Finally, we finish with some conclusions for future directions. © 2011 American Institute of Physics.
Dynamic Incentive Effects of Relative Performance Pay: A Field Experiment
J. Delfgaauw (Josse); A.J. Dur (Robert); J.A. Non (Arjan); W.J.M.I. Verbeke (Willem)
2010-01-01
textabstractWe conduct a field experiment among 189 stores of a retail chain to study dynamic incentive effects of relative performance pay. Employees in the randomly selected treatment stores could win a bonus by outperforming three comparable stores from the control group over the course of four
Strong-field short-pulse nondipole dynamics
DEFF Research Database (Denmark)
Dimitrovski, Darko; Førre, Morten; Madsen, Lars Bojer
2009-01-01
We present a quantitative investigation of strong-field short-pulse nondipole dynamics in laser-matter interactions. We find excellent agreement between ab initio numerical and analytic results obtained using the Magnus expansion. We show that in the short-pulse limit, ultrafast transfer...
Dynamics measured in a non-Archimedean field
Kool, J.
2012-01-01
We study dynamical systems using measures taking values in a non-Archimedean field. The underlying space for such measure is a zero-dimensional topological space. In this paper we elaborate on the natural translation of several notions, e.g., probability measures, isomorphic transformations,
Inflationary dynamics of kinetically-coupled gauge fields
DEFF Research Database (Denmark)
Ferreira, Ricardo J. Z.; Ganc, Jonathan
2015-01-01
We investigate the inflationary dynamics of two kinetically-coupled massless U(1) gauge fields with time-varying kinetic-term coefficients. Ensuring that the system does not have strongly coupled regimes shrinks the parameter space. Also, we further restrict ourselves to systems that can be quant...
Quantum emitters dynamically coupled to a quantum field
Energy Technology Data Exchange (ETDEWEB)
Acevedo, O. L.; Quiroga, L.; Rodríguez, F. J. [Departamento de Física, Universidad de los Andes, A.A. 4976, Bogotá (Colombia); Johnson, N. F. [Department of Physics, University of Miami, Coral Gables, Miami, FL 33124 (United States)
2013-12-04
We study theoretically the dynamical response of a set of solid-state quantum emitters arbitrarily coupled to a single-mode microcavity system. Ramping the matter-field coupling strength in round trips, we quantify the hysteresis or irreversible quantum dynamics. The matter-field system is modeled as a finite-size Dicke model which has previously been used to describe equilibrium (including quantum phase transition) properties of systems such as quantum dots in a microcavity. Here we extend this model to address non-equilibrium situations. Analyzing the system’s quantum fidelity, we find that the near-adiabatic regime exhibits the richest phenomena, with a strong asymmetry in the internal collective dynamics depending on which phase is chosen as the starting point. We also explore signatures of the crossing of the critical points on the radiation subsystem by monitoring its Wigner function; then, the subsystem can exhibit the emergence of non-classicality and complexity.
Dynamics of Deformable Active Particles under External Flow Field
Tarama, Mitsusuke
2017-10-01
In most practical situations, active particles are affected by their environment, for example, by a chemical concentration gradient, light intensity, gravity, or confinement. In particular, the effect of an external flow field is important for particles swimming in a solvent fluid. For deformable active particles such as self-propelled liquid droplets and active vesicles, as well as microorganisms such as euglenas and neutrophils, a general description has been developed by focusing on shape deformation. In this review, we present our recent studies concerning the dynamics of a single active deformable particle under an external flow field. First, a set of model equations of active deformable particles including the effect of a general external flow is introduced. Then, the dynamics under two specific flow profiles is discussed: a linear shear flow, as the simplest example, and a swirl flow. In the latter case, the scattering dynamics of the active deformable particles by the swirl flow is also considered.
Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems
Directory of Open Access Journals (Sweden)
Vandana Sakhre
2015-01-01
Full Text Available Fuzzy Counter Propagation Neural Network (FCPN controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL. FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN and Back Propagation Network (BPN on the basis of Mean Absolute Error (MAE, Mean Square Error (MSE, Best Fit Rate (BFR, and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO and a single input and single output (SISO gas furnace Box-Jenkins time series data.
Park, Gibeom; Tani, Jun
2015-12-01
The current study presents neurorobotics experiments on acquisition of skills for "communicable congruence" with human via learning. A dynamic neural network model which is characterized by its multiple timescale dynamics property was utilized as a neuromorphic model for controlling a humanoid robot. In the experimental task, the humanoid robot was trained to generate specific sequential movement patterns as responding to various sequences of imperative gesture patterns demonstrated by the human subjects by following predefined compositional semantic rules. The experimental results showed that (1) the adopted MTRNN can achieve generalization by learning in the lower feature perception level by using a limited set of tutoring patterns, (2) the MTRNN can learn to extract compositional semantic rules with generalization in its higher level characterized by slow timescale dynamics, (3) the MTRNN can develop another type of cognitive capability for controlling the internal contextual processes as situated to on-going task sequences without being provided with cues for explicitly indicating task segmentation points. The analysis on the dynamic property developed in the MTRNN via learning indicated that the aforementioned cognitive mechanisms were achieved by self-organization of adequate functional hierarchy by utilizing the constraint of the multiple timescale property and the topological connectivity imposed on the network configuration. These results of the current research could contribute to developments of socially intelligent robots endowed with cognitive communicative competency similar to that of human. Copyright © 2015 Elsevier Ltd. All rights reserved.
Nonlinear dynamics of semiconductors in strong THz electric fields
DEFF Research Database (Denmark)
Tarekegne, Abebe Tilahun
weak THz and near infrared pulses as probes. Firstly, an intense THz pulse is used to study THz-induced impact ionization (IMI) dynamics in silicon. Local field enhancement by metallic dipole antenna arrays has been used to generate strong electric fields of several MV/cm in the hot spots near...... uniquely. Finally it is demonstrated for the first time that SiC can be tailored to have extremely fast THz-induced nonlinear behavior in moderate THz electric fields by addition of appropriate dopants. A 4H-SiC sample with high concentrations of nitrogen and boron dopants shows a nonlinear THz...
Phase-Field Model of Mode III Dynamic Fracture
Karma, Alain; Kessler, David A.; Levine, Herbert
2001-07-01
We introduce a phenomenological continuum model for the mode III dynamic fracture that is based on the phase-field methodology used extensively to model interfacial pattern formation. We couple a scalar field, which distinguishes between ``broken'' and ``unbroken'' states of the system, to the displacement field in a way that consistently includes both macroscopic elasticity and a simple rotationally invariant short-scale description of breaking. We report two-dimensional simulations that yield steady-state crack motion in a strip geometry above the Griffith threshold.
Ensemble of Neural Network Conditional Random Fields for Self-Paced Brain Computer Interfaces
Directory of Open Access Journals (Sweden)
Hossein Bashashati
2017-07-01
Full Text Available Classification of EEG signals in self-paced Brain Computer Interfaces (BCI is an extremely challenging task. The main diﬃculty stems from the fact that start time of a control task is not defined. Therefore it is imperative to exploit the characteristics of the EEG data to the extent possible. In sensory motor self-paced BCIs, while performing the mental task, the user’s brain goes through several well-defined internal state changes. Applying appropriate classifiers that can capture these state changes and exploit the temporal correlation in EEG data can enhance the performance of the BCI. In this paper, we propose an ensemble learning approach for self-paced BCIs. We use Bayesian optimization to train several different classifiers on different parts of the BCI hyper- parameter space. We call each of these classifiers Neural Network Conditional Random Field (NNCRF. NNCRF is a combination of a neural network and conditional random field (CRF. As in the standard CRF, NNCRF is able to model the correlation between adjacent EEG samples. However, NNCRF can also model the nonlinear dependencies between the input and the output, which makes it more powerful than the standard CRF. We compare the performance of our algorithm to those of three popular sequence labeling algorithms (Hidden Markov Models, Hidden Markov Support Vector Machines and CRF, and to two classical classifiers (Logistic Regression and Support Vector Machines. The classifiers are compared for the two cases: when the ensemble learning approach is not used and when it is. The data used in our studies are those from the BCI competition IV and the SM2 dataset. We show that our algorithm is considerably superior to the other approaches in terms of the Area Under the Curve (AUC of the BCI system.
Phase-space dynamics of runaway electrons in magnetic fields
Guo, Zehua; McDevitt, Christopher J.; Tang, Xian-Zhu
2017-04-01
Dynamics of runaway electrons in magnetic fields are governed by the competition of three dominant physics: parallel electric field acceleration, Coulomb collision, and synchrotron radiation. Examination of the energy and pitch-angle flows reveals that the presence of local vortex structure and global circulation is crucial to the saturation of primary runaway electrons. Models for the vortex structure, which has an O-point to X-point connection, and the bump of runaway electron distribution in energy space have been developed and compared against the simulation data. Identification of these velocity-space structures opens a new venue to re-examine the conventional understanding of runaway electron dynamics in magnetic fields.
Quantum dynamics of charge state in silicon field evaporation
Directory of Open Access Journals (Sweden)
Elena P. Silaeva
2016-08-01
Full Text Available The charge state of an ion field-evaporating from a silicon-atom cluster is analyzed using time-dependent density functional theory coupled to molecular dynamics. The final charge state of the ion is shown to increase gradually with increasing external electrostatic field in agreement with the average charge state of silicon ions detected experimentally. When field evaporation is triggered by laser-induced electronic excitations the charge state also increases with increasing intensity of the laser pulse. At the evaporation threshold, the charge state of the evaporating ion does not depend on the electrostatic field due to the strong contribution of laser excitations to the ionization process both at low and high laser energies. A neutral silicon atom escaping the cluster due to its high initial kinetic energy is shown to be eventually ionized by external electrostatic field.
Quantum dynamics of charge state in silicon field evaporation
Energy Technology Data Exchange (ETDEWEB)
Silaeva, Elena P.; Uchida, Kazuki; Watanabe, Kazuyuki, E-mail: kazuyuki@rs.kagu.tus.ac.jp [Department of Physics, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku, Tokyo 162-8601 (Japan)
2016-08-15
The charge state of an ion field-evaporating from a silicon-atom cluster is analyzed using time-dependent density functional theory coupled to molecular dynamics. The final charge state of the ion is shown to increase gradually with increasing external electrostatic field in agreement with the average charge state of silicon ions detected experimentally. When field evaporation is triggered by laser-induced electronic excitations the charge state also increases with increasing intensity of the laser pulse. At the evaporation threshold, the charge state of the evaporating ion does not depend on the electrostatic field due to the strong contribution of laser excitations to the ionization process both at low and high laser energies. A neutral silicon atom escaping the cluster due to its high initial kinetic energy is shown to be eventually ionized by external electrostatic field.
Water response to intense electric fields: A molecular dynamics study.
Marracino, Paolo; Liberti, Micaela; d'Inzeo, Guglielmo; Apollonio, Francesca
2015-07-01
This paper investigated polarization properties of water molecules in close proximity to an ionic charge in the presence of external electric fields by using an approach based on simulations at the atomic level. We chose sodium and chloride ions in water as examples of dilute ionic solutions and used molecular dynamics simulations to systematically investigate the influence of an external static electric field on structural, dipolar, and polarization properties of water near charged ions. Results showed that a threshold electric field higher than 10(8) V/m is needed to affect water polarization and increase mean dipole moment of water molecules close to the ion. A similar threshold holds for water permittivity profiles, although a field 10× higher is needed to ensure that water permittivity is almost constant independently of the position close to the ion. Electric fields of such intensities can greatly enhance polarizability of water in hydration shells around ions. © 2015 Wiley Periodicals, Inc.
Directory of Open Access Journals (Sweden)
Jing-Peng Fu
2016-08-01
Full Text Available Abstract Living organisms are exposed to the geomagnetic field (GMF throughout their lifespan. Elimination of the GMF, resulting in a hypogeomagnetic field (HMF, leads to central nervous system dysfunction and abnormal development in animals. However, the cellular mechanisms underlying these effects have not been identified so far. Here, we show that exposure to an HMF (<200 nT, produced by a magnetic field shielding chamber, promotes the proliferation of neural progenitor/stem cells (NPCs/NSCs from C57BL/6 mice. Following seven-day HMF-exposure, the primary neurospheres (NSs were significantly larger in size, and twice more NPCs/NSCs were harvested from neonatal NSs, when compared to the GMF controls. The self-renewal capacity and multipotency of the NSs were maintained, as HMF-exposed NSs were positive for NSC markers (Nestin and Sox2, and could differentiate into neurons and astrocyte/glial cells and be passaged continuously. In addition, adult mice exposed to the HMF for one month were observed to have a greater number of proliferative cells in the subventricular zone. These findings indicate that continuous HMF-exposure increases the proliferation of NPCs/NSCs, in vitro and in vivo. HMF-disturbed NPCs/NSCs production probably affects brain development and function, which provides a novel clue for elucidating the cellular mechanisms of the bio-HMF response.
Dose uncertainty due to aperture effects in dynamic fields.
Higgins, P D; Alaei, P
2006-07-01
Dosimetry of intensity modulated radiation therapy requires accurate modeling of the beamlets that comprise each treatment segment. Planning systems such as Varian Eclipse and Philips Pinnacle recommend measuring dose distributions and output factors for fields as small as possible, generally down to at least 2 x 2 cm2. Conventionally, we perform these measurements for regular fields, defined by the secondary collimators. In practice, it is the multileaf collimation system (MLC) that defines the intensity map and provides dynamic dose modulation in either a moving window or segmented step-and-shoot mode. For this review we have only considered the latter delivery mode. Using this method, we have studied aperture motion effects on the dynamic collimator scatter (S(c)), total scatter (S(c,p)), and phantom scatter (S(p)) factors for various combinations of collimator settings (4 x 4-14 x 40 cm2) and dynamically stepped leaf gaps (0.1 to 1.0 cm) in comparison with those for static field factors. For two different Varian linear accelerators, we found similar results in a systematic dependence of collimator scatter on gap width and collimator setting. As the gap increases from 0.1 to 1.0 cm the dynamic collimator scatter factors converge from a maximum difference of about 30% toward the static field values. At the same time, there is no measurable difference between dynamic field phantom scatter factors and those conventionally obtained for static fields. Second, we evaluated the two planning systems as to how well they account for collimator scatter by attempting to mimic the dynamic apertures used above by planning and measuring dose distributions to several small, cylindrical targets for a similar range of fixed collimator settings. We found that the ratio of measured-to-planned doses as a function of target size were similar to the measured, dynamic S(c) data for the Varian Eclipse planning system, indicating underestimation of dose for targets smaller than 1 cm
Sang, Dong; Lv, Bin; He, Huiguang; He, Jiping; Wang, Feiyue
2010-01-01
In this work, we took the analysis of neural interaction based on the data recorded from the motor cortex of a monkey, when it was trained to complete multi-targets reach-to-grasp tasks. As a recently proved effective tool, Dynamic Bayesian Network (DBN) was applied to model and infer interactions of dependence between neurons. In the results, the gained networks of neural interactions, which correspond to different tasks with different directions and orientations, indicated that the target information was not encoded in simple ways by neuronal networks. We also explored the difference of neural interactions between delayed period and peri-movement period during reach-to-grasp task. We found that the motor control process always led to relatively more complex neural interaction networks than the plan thinking process.
Directory of Open Access Journals (Sweden)
Caroline M. Whiting
2013-11-01
Full Text Available Rapid and automatic processing of grammatical complexity is argued to take place during speech comprehension, engaging a left-lateralised fronto-temporal language network. Here we address how neural activity in these regions is modulated by the grammatical properties of spoken words. We used combined magneto- and electroencephalography (MEG, EEG to delineate the spatiotemporal patterns of activity that support the recognition of morphologically complex words in English with inflectional (-s and derivational (-er affixes (e.g. bakes, baker. The mismatch negativity (MMN, an index of linguistic memory traces elicited in a passive listening paradigm, was used to examine the neural dynamics elicited by morphologically complex words. Results revealed an initial peak 130-180 ms after the deviation point with a major source in left superior temporal cortex. The localisation of this early activation showed a sensitivity to two grammatical properties of the stimuli: 1 the presence of morphological complexity, with affixed words showing increased left-laterality compared to non-affixed words; and 2 the grammatical category, with affixed verbs showing greater left-lateralisation in inferior frontal gyrus compared to affixed nouns (bakes vs. beaks. This automatic brain response was additionally sensitive to semantic coherence (the meaning of the stem vs. the meaning of the whole form in fronto-temporal regions. These results demonstrate that the spatiotemporal pattern of neural activity in spoken word processing is modulated by the presence of morphological structure, predominantly engaging the left-hemisphere’s fronto-temporal language network, and does not require focused attention on the linguistic input.
Roellig, Daniela; Tan-Cabugao, Johanna; Esaian, Sevan; Bronner, Marianne E
2017-03-29
The 'neural plate border' of vertebrate embryos contains precursors of neural crest and placode cells, both defining vertebrate characteristics. How these lineages segregate from neural and epidermal fates has been a matter of debate. We address this by performing a fine-scale quantitative temporal analysis of transcription factor expression in the neural plate border of chick embryos. The results reveal significant overlap of transcription factors characteristic of multiple lineages in individual border cells from gastrula through neurula stages. Cell fate analysis using a Sox2 (neural) enhancer reveals that cells that are initially Sox2+ cells can contribute not only to neural tube but also to neural crest and epidermis. Moreover, modulating levels of Sox2 or Pax7 alters the apportionment of neural tube versus neural crest fates. Our results resolve a long-standing question and suggest that many individual border cells maintain ability to contribute to multiple ectodermal lineages until or beyond neural tube closure.
Orbital effect of the magnetic field in dynamical mean-field theory
Acheche, S.; Arsenault, L.-F.; Tremblay, A.-M. S.
2017-12-01
The availability of large magnetic fields at international facilities and of simulated magnetic fields that can reach the flux-quantum-per-unit-area level in cold atoms calls for systematic studies of orbital effects of the magnetic field on the self-energy of interacting systems. Here we demonstrate theoretically that orbital effects of magnetic fields can be treated within single-site dynamical mean-field theory with a translationally invariant quantum impurity problem. As an example, we study the one-band Hubbard model on the square lattice using iterated perturbation theory as an impurity solver. We recover the expected quantum oscillations in the scattering rate, and we show that the magnetic fields allow the interaction-induced effective mass to be measured through the single-particle density of states accessible in tunneling experiments. The orbital effect of magnetic fields on scattering becomes particularly important in the Hofstadter butterfly regime.
McDermott, Timothy J; Badura-Brack, Amy S; Becker, Katherine M; Ryan, Tara J; Bar-Haim, Yair; Pine, Daniel S; Khanna, Maya M; Heinrichs-Graham, Elizabeth; Wilson, Tony W
2016-12-01
Posttraumatic stress disorder (PTSD) is associated with executive functioning deficits, including disruptions in working memory (WM). Recent studies suggest that attention training reduces PTSD symptomatology, but the underlying neural mechanisms are unknown. We used high-density magnetoencephalography (MEG) to evaluate whether attention training modulates brain regions serving WM processing in PTSD. Fourteen veterans with PTSD completed a WM task during a 306-sensor MEG recording before and after 8 sessions of attention training treatment. A matched comparison sample of 12 combat-exposed veterans without PTSD completed the same WM task during a single MEG session. To identify the spatiotemporal dynamics, each group's data were transformed into the time-frequency domain, and significant oscillatory brain responses were imaged using a beamforming approach. All participants exhibited activity in left hemispheric language areas consistent with a verbal WM task. Additionally, veterans with PTSD and combat-exposed healthy controls each exhibited oscillatory responses in right hemispheric homologue regions (e.g., right Broca's area); however, these responses were in opposite directions. Group differences in oscillatory activity emerged in the theta band (4-8 Hz) during encoding and in the alpha band (9-12 Hz) during maintenance and were significant in right prefrontal and right supramarginal and inferior parietal regions. Importantly, following attention training, these significant group differences were reduced or eliminated. This study provides initial evidence that attention training improves aberrant neural activity in brain networks serving WM processing.
Self: an adaptive pressure arising from self-organization, chaotic dynamics, and neural Darwinism.
Bruzzo, Angela Alessia; Vimal, Ram Lakhan Pandey
2007-12-01
In this article, we establish a model to delineate the emergence of "self" in the brain making recourse to the theory of chaos. Self is considered as the subjective experience of a subject. As essential ingredients of subjective experiences, our model includes wakefulness, re-entry, attention, memory, and proto-experiences. The stability as stated by chaos theory can potentially describe the non-linear function of "self" as sensitive to initial conditions and can characterize it as underlying order from apparently random signals. Self-similarity is discussed as a latent menace of a pathological confusion between "self" and "others". Our test hypothesis is that (1) consciousness might have emerged and evolved from a primordial potential or proto-experience in matter, such as the physical attractions and repulsions experienced by electrons, and (2) "self" arises from chaotic dynamics, self-organization and selective mechanisms during ontogenesis, while emerging post-ontogenically as an adaptive pressure driven by both volume and synaptic-neural transmission and influencing the functional connectivity of neural nets (structure).
Hybrid Generalised Additive Type-2 Fuzzy-Wavelet-Neural Network in Dynamic Data Mining
Directory of Open Access Journals (Sweden)
Bodyanskiy Yevgeniy
2015-12-01
Full Text Available In the paper, a new hybrid system of computational intelligence is proposed. This system combines the advantages of neuro-fuzzy system of Takagi-Sugeno-Kang, type-2 fuzzy logic, wavelet neural networks and generalised additive models of Hastie-Tibshirani. The proposed system has universal approximation properties and learning capability based on the experimental data sets which pertain to the neural networks and neuro-fuzzy systems; interpretability and transparency of the obtained results due to the soft computing systems and, first of all, due to type-2 fuzzy systems; possibility of effective description of local signal and process features due to the application of systems based on wavelet transform; simplicity and speed of learning process due to generalised additive models. The proposed system can be used for solving a wide class of dynamic data mining tasks, which are connected with non-stationary, nonlinear stochastic and chaotic signals. Such a system is sufficiently simple in numerical implementation and is characterised by a high speed of learning and information processing.
Ni, Jianjun; Wu, Liuying; Shi, Pengfei; Yang, Simon X
2017-01-01
Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.
Neural substrates and behavioral profiles of romantic jealousy and its temporal dynamics.
Sun, Yan; Yu, Hongbo; Chen, Jie; Liang, Jie; Lu, Lin; Zhou, Xiaolin; Shi, Jie
2016-06-07
Jealousy is not only a way of experiencing love but also a stabilizer of romantic relationships, although morbid romantic jealousy is maladaptive. Being engaged in a formal romantic relationship can tune one's romantic jealousy towards a specific target. Little is known about how the human brain processes romantic jealousy by now. Here, by combining scenario-based imagination and functional MRI, we investigated the behavioral and neural correlates of romantic jealousy and their development across stages (before vs. after being in a formal relationship). Romantic jealousy scenarios elicited activations primarily in the basal ganglia (BG) across stages, and were significantly higher after the relationship was established in both the behavioral rating and BG activation. The intensity of romantic jealousy was related to the intensity of romantic happiness, which mainly correlated with ventral medial prefrontal cortex activation. The increase in jealousy across stages was associated with the tendency for interpersonal aggression. These results bridge the gap between the theoretical conceptualization of romantic jealousy and its neural correlates and shed light on the dynamic changes in jealousy.
Fractional Dynamics Applications of Fractional Calculus to Dynamics of Particles, Fields and Media
Tarasov, Vasily E
2010-01-01
"Fractional Dynamics: Applications of Fractional Calculus to Dynamics of Particles, Fields and Media" presents applications of fractional calculus, integral and differential equations of non-integer orders in describing systems with long-time memory, non-local spatial and fractal properties. Mathematical models of fractal media and distributions, generalized dynamical systems and discrete maps, non-local statistical mechanics and kinetics, dynamics of open quantum systems, the hydrodynamics and electrodynamics of complex media with non-local properties and memory are considered. This book is intended to meet the needs of scientists and graduate students in physics, mechanics and applied mathematics who are interested in electrodynamics, statistical and condensed matter physics, quantum dynamics, complex media theories and kinetics, discrete maps and lattice models, and nonlinear dynamics and chaos. Dr. Vasily E. Tarasov is a Senior Research Associate at Nuclear Physics Institute of Moscow State University and...
On the Construction and Dynamics of Knotted Fields
Kedia, Hridesh
Representing a physical field in terms of its field lines has often enabled a deeper understanding of complex physical phenomena, from Faraday's law of magnetic induction, to the Helmholtz laws of vortex motion, to the free energy density of liquid crystals in terms of the distortions of the lines of the director field. At the same time, the application of ideas from topology--the study of properties that are invariant under continuous deformations--has led to robust insights into the nature of complex physical systems from defects in crystal structures, to the earth's magnetic field, to topological conservation laws. The study of knotted fields, physical fields in which the field lines encode knots, emerges naturally from the application of topological ideas to the investigation of the physical phenomena best understood in terms of the lines of a field. A knot--a closed loop tangled with itself which can not be untangled without cutting the loop--is the simplest topologically non-trivial object constructed from a line. Remarkably, knots in the vortex (magnetic field) lines of a dissipationless fluid (plasma), persist forever as they are transported by the flow, stretching and rotating as they evolve. Moreover, deeply entwined with the topology-preserving dynamics of dissipationless fluids and plasmas, is an additional conserved quantity--helicity, a measure of the average linking of the vortex (magnetic field) lines in a fluid (plasma)--which has had far-reaching consequences for fluids and plasmas. Inspired by the persistence of knots in dissipationless flows, and their far-reaching physical consequences, we seek to understand the interplay between the dynamics of a field and the topology of its field lines in a variety of systems. While it is easy to tie a knot in a shoelace, tying a knot in the the lines of a space-filling field requires contorting the lines everywhere to match the knotted region. The challenge of analytically constructing knotted field
Sustained neural activity to gaze and emotion perception in dynamic social scenes.
Ulloa, José Luis; Puce, Aina; Hugueville, Laurent; George, Nathalie
2014-03-01
To understand social interactions, we must decode dynamic social cues from seen faces. Here, we used magnetoencephalography (MEG) to study the neural responses underlying the perception of emotional expressions and gaze direction changes as depicted in an interaction between two agents. Subjects viewed displays of paired faces that first established a social scenario of gazing at each other (mutual attention) or gazing laterally together (deviated group attention) and then dynamically displayed either an angry or happy facial expression. The initial gaze change elicited a significantly larger M170 under the deviated than the mutual attention scenario. At around 400 ms after the dynamic emotion onset, responses at posterior MEG sensors differentiated between emotions, and between 1000 and 2200 ms, left posterior sensors were additionally modulated by social scenario. Moreover, activity on right anterior sensors showed both an early and prolonged interaction between emotion and social scenario. These results suggest that activity in right anterior sensors reflects an early integration of emotion and social attention, while posterior activity first differentiated between emotions only, supporting the view of a dual route for emotion processing. Altogether, our data demonstrate that both transient and sustained neurophysiological responses underlie social processing when observing interactions between others.
Using motor imagery to study the neural substrates of dynamic balance.
Directory of Open Access Journals (Sweden)
Murielle Ursulla Ferraye
Full Text Available This study examines the cerebral structures involved in dynamic balance using a motor imagery (MI protocol. We recorded cerebral activity with functional magnetic resonance imaging while subjects imagined swaying on a balance board along the sagittal plane to point a laser at target pairs of different sizes (small, large. We used a matched visual imagery (VI control task and recorded imagery durations during scanning. MI and VI durations were differentially influenced by the sway accuracy requirement, indicating that MI of balance is sensitive to the increased motor control necessary to point at a smaller target. Compared to VI, MI of dynamic balance recruited additional cortical and subcortical portions of the motor system, including frontal cortex, basal ganglia, cerebellum and mesencephalic locomotor region, the latter showing increased effective connectivity with the supplementary motor area. The regions involved in MI of dynamic balance were spatially distinct but contiguous to those involved in MI of gait (Bakker et al., 2008; Snijders et al., 2011; Crémers et al., 2012, in a pattern consistent with existing somatotopic maps of the trunk (for balance and legs (for gait. These findings validate a novel, quantitative approach for studying the neural control of balance in humans. This approach extends previous reports on MI of static stance (Jahn et al., 2004, 2008, and opens the way for studying gait and balance impairments in patients with neurodegenerative disorders.
Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S.
2017-05-01
We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au147), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au147, and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au147 is performed, and it is concluded that Au147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.
Using motor imagery to study the neural substrates of dynamic balance.
Ferraye, Murielle Ursulla; Debû, Bettina; Heil, Lieke; Carpenter, Mark; Bloem, Bastiaan Roelof; Toni, Ivan
2014-01-01
This study examines the cerebral structures involved in dynamic balance using a motor imagery (MI) protocol. We recorded cerebral activity with functional magnetic resonance imaging while subjects imagined swaying on a balance board along the sagittal plane to point a laser at target pairs of different sizes (small, large). We used a matched visual imagery (VI) control task and recorded imagery durations during scanning. MI and VI durations were differentially influenced by the sway accuracy requirement, indicating that MI of balance is sensitive to the increased motor control necessary to point at a smaller target. Compared to VI, MI of dynamic balance recruited additional cortical and subcortical portions of the motor system, including frontal cortex, basal ganglia, cerebellum and mesencephalic locomotor region, the latter showing increased effective connectivity with the supplementary motor area. The regions involved in MI of dynamic balance were spatially distinct but contiguous to those involved in MI of gait (Bakker et al., 2008; Snijders et al., 2011; Crémers et al., 2012), in a pattern consistent with existing somatotopic maps of the trunk (for balance) and legs (for gait). These findings validate a novel, quantitative approach for studying the neural control of balance in humans. This approach extends previous reports on MI of static stance (Jahn et al., 2004, 2008), and opens the way for studying gait and balance impairments in patients with neurodegenerative disorders.
Post-Traumatic Stress Constrains the Dynamic Repertoire of Neural Activity.
Mišić, Bratislav; Dunkley, Benjamin T; Sedge, Paul A; Da Costa, Leodante; Fatima, Zainab; Berman, Marc G; Doesburg, Sam M; McIntosh, Anthony R; Grodecki, Richard; Jetly, Rakesh; Pang, Elizabeth W; Taylor, Margot J
2016-01-13
Post-traumatic stress disorder (PTSD) is an anxiety disorder arising from exposure to a traumatic event. Although primarily defined in terms of behavioral symptoms, the global neurophysiological effects of traumatic stress are increasingly recognized as a critical facet of the human PTSD phenotype. Here we use magnetoencephalographic recordings to investigate two aspects of information processing: inter-regional communication (measured by functional connectivity) and the dynamic range of neural activity (measured in terms of local signal variability). We find that both measures differentiate soldiers diagnosed with PTSD from soldiers without PTSD, from healthy civilians, and from civilians with mild traumatic brain injury, which is commonly comorbid with PTSD. Specifically, soldiers with PTSD display inter-regional hypersynchrony at high frequencies (80-150 Hz), as well as a concomitant decrease in signal variability. The two patterns are spatially correlated and most pronounced in a left temporal subnetwork, including the hippocampus and amygdala. We hypothesize that the observed hypersynchrony may effectively constrain the expression of local dynamics, resulting in less variable activity and a reduced dynamic repertoire. Thus, the re-experiencing phenomena and affective sequelae in combat-related PTSD may result from functional networks becoming "stuck" in configurations reflecting memories, emotions, and thoughts originating from the traumatizing experience. The present study investigates the effects of post-traumatic stress disorder (PTSD) in combat-exposed soldiers. We find that soldiers with PTSD exhibit hypersynchrony in a circuit of temporal lobe areas associated with learning and memory function. This rigid functional architecture is associated with a decrease in signal variability in the same areas, suggesting that the observed hypersynchrony may constrain the expression of local dynamics, resulting in a reduced dynamic range. Our findings suggest that
Dynamic model of a PEM electrolyser based on artificial neural networks
Energy Technology Data Exchange (ETDEWEB)
Chavez-Ramirez, A.U.; Munoz-Guerrero, R.; Sanchez-Huerta, V.; Ramirez-Arredondo, Juan M.; Ornelas, R.; Arriaga, L.G.; Siracusano, S.; Brunaccini, G.; Napoli, G.; Antonucci, V.; Arico, A.S.
2011-01-15
Hydrogen production by electrolysis is emerging as a promising way to meet future fuel demand, and developing models capable of simulating the operation of electrolysis devices is indispensable to efficiently design power generation systems, reduce manufacturing costs and save resources. The nonlinear nature of the Artificial Neural Network (ANN) plays a key role in developing models predicting the performance of complex systems. The behaviour of a Polymer Electrolyte Membrane (PEM) Electrolyser of three cell stack was modelled successfully using a Multilayer Perceptron Network (MLP). This dynamic model was trained to learn the internal relationships of this electrolysis device and predict its behaviour without physical equations. Electric current supply and operation temperature were used as input vector able to predict each cell voltage behaviour. An accuracy (< 2%) was reached after comparing the single cell performance of the real electrolyser versus the ANN based model. This predictive model can be used as a virtual device into a more complex energy system.
Using System Dynamic Model and Neural Network Model to Analyse Water Scarcity in Sudan
Li, Y.; Tang, C.; Xu, L.; Ye, S.
2017-07-01
Many parts of the world are facing the problem of Water Scarcity. Analysing Water Scarcity quantitatively is an important step to solve the problem. Water scarcity in a region is gauged by WSI (water scarcity index), which incorporate water supply and water demand. To get the WSI, Neural Network Model and SDM (System Dynamic Model) that depict how environmental and social factors affect water supply and demand are developed to depict how environmental and social factors affect water supply and demand. The uneven distribution of water resource and water demand across a region leads to an uneven distribution of WSI within this region. To predict WSI for the future, logistic model, Grey Prediction, and statistics are applied in predicting variables. Sudan suffers from severe water scarcity problem with WSI of 1 in 2014, water resource unevenly distributed. According to the result of modified model, after the intervention, Sudan’s water situation will become better.
Mandal, Sumantra; Sivaprasad, P. V.; Dube, R. K.
2007-12-01
An artificial neural network (ANN) model was developed to predict the microstructural evolution of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel (Alloy D9) during dynamic recrystallization (DRX). The input parameters were strain, strain rate, and temperature whereas microstructural features namely, %DRX and average grain size were the output parameters. The ANN was trained with the database obtained from various industrial scale metal-forming operations like forge hammer, hydraulic press, and rolling carried out in the temperature range 1173-1473 K to various strain levels. The performance of the model was evaluated using a wide variety of statistical indices and the predictability of the model was found to be good. The combined influence of temperature and strain on microstructural features has been simulated employing the developed model. The results were found to be consistent with the relevant fundamental metallurgical phenomena.
Dutt-Mazumder, Aviroop; Button, Chris; Robins, Anthony; Bartlett, Roger
2011-12-01
Recent studies have explored the organization of player movements in team sports using a range of statistical tools. However, the factors that best explain the performance of association football teams remain elusive. Arguably, this is due to the high-dimensional behavioural outputs that illustrate the complex, evolving configurations typical of team games. According to dynamical system analysts, movement patterns in team sports exhibit nonlinear self-organizing features. Nonlinear processing tools (i.e. Artificial Neural Networks; ANNs) are becoming increasingly popular to investigate the coordination of participants in sports competitions. ANNs are well suited to describing high-dimensional data sets with nonlinear attributes, however, limited information concerning the processes required to apply ANNs exists. This review investigates the relative value of various ANN learning approaches used in sports performance analysis of team sports focusing on potential applications for association football. Sixty-two research sources were summarized and reviewed from electronic literature search engines such as SPORTDiscus, Google Scholar, IEEE Xplore, Scirus, ScienceDirect and Elsevier. Typical ANN learning algorithms can be adapted to perform pattern recognition and pattern classification. Particularly, dimensionality reduction by a Kohonen feature map (KFM) can compress chaotic high-dimensional datasets into low-dimensional relevant information. Such information would be useful for developing effective training drills that should enhance self-organizing coordination among players. We conclude that ANN-based qualitative analysis is a promising approach to understand the dynamical attributes of association football players.
Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...
Directory of Open Access Journals (Sweden)
Wilten eNicola
2016-02-01
Full Text Available A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF. The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks
Neural dynamics necessary and sufficient for transition into pre-sleep induced by EEG neurofeedback.
Kinreich, Sivan; Podlipsky, Ilana; Jamshy, Shahar; Intrator, Nathan; Hendler, Talma
2014-08-15
The transition from being fully awake to pre-sleep occurs daily just before falling asleep; thus its disturbance might be detrimental. Yet, the neuronal correlates of the transition remain unclear, mainly due to the difficulty in capturing its inherent dynamics. We used an EEG theta/alpha neurofeedback to rapidly induce the transition into pre-sleep and simultaneous fMRI to reveal state-dependent neural activity. The relaxed mental state was verified by the corresponding enhancement in the parasympathetic response. Neurofeedback sessions were categorized as successful or unsuccessful, based on the known EEG signature of theta power increases over alpha, temporally marked as a distinct "crossover" point. The fMRI activation was considered before and after this point. During successful transition into pre-sleep the period before the crossover was signified by alpha modulation that corresponded to decreased fMRI activity mainly in sensory gating related regions (e.g. medial thalamus). In parallel, although not sufficient for the transition, theta modulation corresponded with increased activity in limbic and autonomic control regions (e.g. hippocampus, cerebellum vermis, respectively). The post-crossover period was designated by alpha modulation further corresponding to reduced fMRI activity within the anterior salience network (e.g. anterior cingulate cortex, anterior insula), and in contrast theta modulation corresponded to the increased variance in the posterior salience network (e.g. posterior insula, posterior cingulate cortex). Our findings portray multi-level neural dynamics underlying the mental transition from awake to pre-sleep. To initiate the transition, decreased activity was required in external monitoring regions, and to sustain the transition, opposition between the anterior and posterior parts of the salience network was needed, reflecting shifting from extra- to intrapersonal based processing, respectively. Copyright © 2014 Elsevier Inc. All rights
Mamun, K A; Mace, M; Lutman, M E; Stein, J; Liu, X; Aziz, T; Vaidyanathan, R; Wang, S
2015-10-01
Correlating electrical activity within the human brain to movement is essential for developing and refining interventions (e.g. deep brain stimulation (DBS)) to treat central nervous system disorders. It also serves as a basis for next generation brain-machine interfaces (BMIs). This study highlights a new decoding strategy for capturing movement and its corresponding laterality from deep brain local field potentials (LFPs). LFPs were recorded with surgically implanted electrodes from the subthalamic nucleus or globus pallidus interna in twelve patients with Parkinson's disease or dystonia during a visually cued finger-clicking task. We introduce a method to extract frequency dependent neural synchronization and inter-hemispheric connectivity features based upon wavelet packet transform (WPT) and Granger causality approaches. A novel weighted sequential feature selection algorithm has been developed to select optimal feature subsets through a feature contribution measure. This is particularly useful when faced with limited trials of high dimensionality data as it enables estimation of feature importance during the decoding process. This novel approach was able to accurately and informatively decode movement related behaviours from the recorded LFP activity. An average accuracy of 99.8% was achieved for movement identification, whilst subsequent laterality classification was 81.5%. Feature contribution analysis highlighted stronger contralateral causal driving between the basal ganglia hemispheres compared to ipsilateral driving, with causality measures considerably improving laterality discrimination. These findings demonstrate optimally selected neural synchronization alongside causality measures related to inter-hemispheric connectivity can provide an effective control signal for augmenting adaptive BMIs. In the case of DBS patients, acquiring such signals requires no additional surgery whilst providing a relatively stable and computationally inexpensive control
Magnetic field lines, Hamiltonian dynamics, and nontwist systems
Energy Technology Data Exchange (ETDEWEB)
Morrison, P. J. [Department of Physics and Institute for Fusion Studies, University of Texas at Austin, Austin, Texas 78712 (United States)
2000-06-01
Magnetic field lines typically do not behave as described in the symmetrical situations treated in conventional physics textbooks. Instead, they behave in a chaotic manner; in fact, magnetic field lines are trajectories of Hamiltonian systems. Consequently the quest for fusion energy has interwoven, for 50 years, the study of magnetic field configurations and Hamiltonian systems theory. The manner in which invariant tori breakup in symplectic twist maps, maps that embody one and a half degree-of-freedom Hamiltonian systems in general and describe magnetic field lines in tokamaks in particular, will be reviewed, including symmetry methods for finding periodic orbits and Greene's residue criterion. In nontwist maps, which describe, e.g., reverse shear tokamaks and zonal flows in geophysical fluid dynamics, a new theory is required for describing tori breakup. The new theory is discussed and comments about renormalization are made. (c) 2000 American Institute of Physics.
Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong
2016-02-01
We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.
Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong
2016-02-11
We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.
Dipole-Quadrupole dynamics during magnetic field reversals
Gissinger, Christophe
2010-01-01
The shape and the dynamics of reversals of the magnetic field in a turbulent dynamo experiment are investigated. We report the evolution of the dipolar and the quadrupolar parts of the magnetic field in the VKS experiment, and show that the experimental results are in good agreement with the predictions of a recent model of reversals: when the dipole reverses, part of the magnetic energy is transferred to the quadrupole, reversals begin with a slow decay of the dipole and are followed by a fast recovery, together with an overshoot of the dipole. Random reversals are observed at the borderline between stationary and oscillatory dynamos.
Post-Newtonian celestial dynamics in cosmology: Field equations
Kopeikin, Sergei M.; Petrov, Alexander N.
2013-02-01
Post-Newtonian celestial dynamics is a relativistic theory of motion of massive bodies and test particles under the influence of relatively weak gravitational forces. The standard approach for development of this theory relies upon the key concept of the isolated astronomical system supplemented by the assumption that the background spacetime is flat. The standard post-Newtonian theory of motion was instrumental in the explanation of the existing experimental data on binary pulsars, satellite, and lunar laser ranging, and in building precise ephemerides of planets in the Solar System. Recent studies of the formation of large-scale structures in our Universe indicate that the standard post-Newtonian mechanics fails to describe more subtle dynamical effects in motion of the bodies comprising the astronomical systems of larger size—galaxies and clusters of galaxies—where the Riemann curvature of the expanding Friedmann-Lemaître-Robertson-Walker universe interacts with the local gravitational field of the astronomical system and, as such, cannot be ignored. The present paper outlines theoretical principles of the post-Newtonian mechanics in the expanding Universe. It is based upon the gauge-invariant theory of the Lagrangian perturbations of cosmological manifold caused by an isolated astronomical N-body system (the Solar System, a binary star, a galaxy, and a cluster of galaxies). We postulate that the geometric properties of the background manifold are described by a homogeneous and isotropic Friedmann-Lemaître-Robertson-Walker metric governed by two primary components—the dark matter and the dark energy. The dark matter is treated as an ideal fluid with the Lagrangian taken in the form of pressure along with the scalar Clebsch potential as a dynamic variable. The dark energy is associated with a single scalar field with a potential which is hold unspecified as long as the theory permits. Both the Lagrangians of the dark matter and the scalar field are
Dynamic measurement of near-field radiative heat transfer
Lang, S.; G. Sharma; Molesky, S.; Kränzien, P. U.; Jalas, T.; Z. Jacob; Petrov, A. Yu.; Eich, M.
2017-01-01
Super-Planckian near-field radiative heat transfer allows effective heat transfer between a hot and a cold body to increase beyond the limits long known for black bodies. Until present, experimental techniques to measure the radiative heat flow relied on steady-state systems. Here, we present a dynamic measurement approach based on the transient plane source technique, which extracts thermal properties from a temperature transient caused by a step input power function. Using this versatile me...
Magnetization dynamics under electromagnetic fields in the wavepacket methods
Xiong, Bangguo; Chen, Hua; Li, Xiao; Niu, Qian
In this work we try to understand the magnetization dynamics in magnetic materials with electrons described by the semiclasscial wavepacket methods. Using the Lagrangian of electron wavepackets under slowly varying magnetization, we can explicitly write down the dynamic equations for both electrons and magnetization order, where the mutual interplay between the two presents itself naturally. It turns out that, more general than LLG equation, the magnetization dynamics is written as a first order differential equation as for a general vector, which allows a detailed discussion on physical process studied before, such as spin transfer torque, spin orbital torque and damping mechanism, and also gives the vortex-like torques that can pump energy into the system. Since electrons are easy to control by electromagnetic fields, we expect a theory that electromagnetic fields through coupling to electrons can be used to manipulate the magnetization. It is interesting that this formalism on magnetization dynamics can be used to study the electromagnetic response of bulk electrons, from which the current and magnetization expressions are extracted that match well with previous studies.
Phase-Field Model of Dynamic Crack Instability
Karma, Alain
2002-03-01
Steady-state propagation of brittle cracks is known to end abruptly when the tip speed exceeds a fraction of the Rayleigh wave speed, at which point crack branching is typically observed in both real experiments and molecular dynamic simulations. There have been several theoretical attempts to understand the origin of this dynamic instability over the last decades, but no clear picture has yet emerged. We have recently introduced a phenomenological continuum model of dynamic fracture [Karma et al., Phys. Rev. Lett., Vol 87, 045501 (2001)] that is based on the phase-field methodology used extensively to model interfacial pattern formation. This model couples a scalar field, which distinguishes between ``broken'' and ``unbroken'' states of the system, to the displacement field in a way that consistently includes both macroscopic elasticity and a simple rotationally invariant short scale description of breaking. This talk will report the results of a numerical simulation study of this model done in collaboration with Alex Lobkovsky that sheds new light on the origin of this elusive instability.
Dynamics of paramagnetic squares in uniform magnetic fields
Energy Technology Data Exchange (ETDEWEB)
Du, Di; He, Peng; Zeng, Yongchao; Biswal, Sibani Lisa, E-mail: biswal@rice.edu
2016-11-01
The magnetic forces between paramagnetic squares cannot be calculated using a classic dipolar model because the magnetic field distribution is not uniform within square particles. Here, we present the calculation of magnetic forces and torques on paramagnetic squares in a uniform 2-D magnetic field using a Laplace's equation solver. With these calculations, we simulate the variations in equilibrium configurations as a function of number of interacting squares. For example, a single square orients with its diagonal directed to the external field while a system of multiple squares will assemble into chain-like structures with their edges directed to the external field. Unlike chains of spherical magnetic particles, that easily stagger themselves to aggregate, chains consisting of magnetic squares are unable to aggregate due to interchain repulsion. - Highlights: • Numerical calculations demonstrate that the orientation dynamics of a magnetic square or rectangle is highly dependent on the magnetic field distribution within the particle and its interactions with neighboring particles. • A paramagnetic square acquires an asymmetric field distribution that results in a torque that rotates it so that its diagonal aligns with the magnetic field. • Chains of magnetic square particles will not combine into bundles as observed in chains of magnetic disk particles.
Guarneri, Paolo; Rocca, Gianpiero; Gobbi, Massimiliano
2008-09-01
This paper deals with the simulation of the tire/suspension dynamics by using recurrent neural networks (RNNs). RNNs are derived from the multilayer feedforward neural networks, by adding feedback connections between output and input layers. The optimal network architecture derives from a parametric analysis based on the optimal tradeoff between network accuracy and size. The neural network can be trained with experimental data obtained in the laboratory from simulated road profiles (cleats). The results obtained from the neural network demonstrate good agreement with the experimental results over a wide range of operation conditions. The NN model can be effectively applied as a part of vehicle system model to accurately predict elastic bushings and tire dynamics behavior. Although the neural network model, as a black-box model, does not provide a good insight of the physical behavior of the tire/suspension system, it is a useful tool for assessing vehicle ride and noise, vibration, harshness (NVH) performance due to its good computational efficiency and accuracy.
Directory of Open Access Journals (Sweden)
Eli eShlizerman
2014-08-01
Full Text Available The antennal lobe (AL, olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units, and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (i design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (ii characterize scent recognition, i.e., decision-making based on olfactory signals and (iii infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.
Shlizerman, Eli; Riffell, Jeffrey A; Kutz, J Nathan
2014-01-01
The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.
De Geeter, N; Crevecoeur, G; Leemans, A; Dupré, L
2015-01-21
In transcranial magnetic stimulation (TMS), an applied alternating magnetic field induces an electric field in the brain that can interact with the neural system. It is generally assumed that this induced electric field is the crucial effect exciting a certain region of the brain. More specifically, it is the component of this field parallel to the neuron's local orientation, the so-called effective electric field, that can initiate neuronal stimulation. Deeper insights on the stimulation mechanisms can be acquired through extensive TMS modelling. Most models study simple representations of neurons with assumed geometries, whereas we embed realistic neural trajectories computed using tractography based on diffusion tensor images. This way of modelling ensures a more accurate spatial distribution of the effective electric field that is in addition patient and case specific. The case study of this paper focuses on the single pulse stimulation of the left primary motor cortex with a standard figure-of-eight coil. Including realistic neural geometry in the model demonstrates the strong and localized variations of the effective electric field between the tracts themselves and along them due to the interplay of factors such as the tract's position and orientation in relation to the TMS coil, the neural trajectory and its course along the white and grey matter interface. Furthermore, the influence of changes in the coil orientation is studied. Investigating the impact of tissue anisotropy confirms that its contribution is not negligible. Moreover, assuming isotropic tissues lead to errors of the same size as rotating or tilting the coil with 10 degrees. In contrast, the model proves to be less sensitive towards the not well-known tissue conductivity values.
Bamford, Simeon A; Hogri, Roni; Giovannucci, Andrea; Taub, Aryeh H; Herreros, Ivan; Verschure, Paul F M J; Mintz, Matti; Del Giudice, Paolo
2012-07-01
A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals.
Trenado, Carlos; Haab, Lars; Strauss, Daniel J
2007-01-01
Auditory evoked cortical potentials (AECP) are well established as diagnostic tool in audiology and gain more and more impact in experimental neuropsychology, neuro-science, and psychiatry, e.g., for the attention deficit disorder, schizophrenia, or for studying the tinnitus decompensation. The modulation of AECP due to exogenous and endogenous attention plays a major role in many clinical applications and has experimentally been studied in neuropsychology. However the relation of corticothalamic feedback dynamics to focal and non-focal attention and its large-scale effect reflected in AECPs is far from being understood. In this paper, we model neural correlates of auditory attention reflected in AECPs using corticothalamic feedback dynamics. We present a mapping of a recently developed multiscale model of evoked potentials to the hearing path and discuss for the first time its neurofunctionality in terms of corticothalamic feedback loops related to focal and non-focal attention. Our model reinforced recent experimental results related to online attention monitoring using AECPs with application as objective tinnitus decompensation measure. It is concluded that our model presents a promising approach to gain a deeper understanding of the neurodynamics of auditory attention and might be use as an efficient forward model to reinforce hypotheses that are obtained from experimental paradigms involving AECPs.
Injury to the Spinal Cord Niche Alters the Engraftment Dynamics of Human Neural Stem Cells
Directory of Open Access Journals (Sweden)
Christopher J. Sontag
2014-05-01
Full Text Available The microenvironment is a critical mediator of stem cell survival, proliferation, migration, and differentiation. The majority of preclinical studies involving transplantation of neural stem cells (NSCs into the CNS have focused on injured or degenerating microenvironments, leaving a dearth of information as to how NSCs differentially respond to intact versus damaged CNS. Furthermore, single, terminal histological endpoints predominate, providing limited insight into the spatiotemporal dynamics of NSC engraftment and migration. We investigated the early and long-term engraftment dynamics of human CNS stem cells propagated as neurospheres (hCNS-SCns following transplantation into uninjured versus subacutely injured spinal cords of immunodeficient NOD-scid mice. We stereologically quantified engraftment, survival, proliferation, migration, and differentiation at 1, 7, 14, 28, and 98 days posttransplantation, and identified injury-dependent alterations. Notably, the injured microenvironment decreased hCNS-SCns survival, delayed and altered the location of proliferation, influenced both total and fate-specific migration, and promoted oligodendrocyte maturation.
Shen, Lin; Yang, Weitao
2018-02-13
Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of chemical reactions in complex environment but very time consuming. The computational cost on QM/MM calculations during MD simulations can be reduced significantly using semiempirical QM/MM methods with lower accuracy. To achieve higher accuracy at the ab initio QM/MM level, a correction on the existing semiempirical QM/MM model is an attractive way. Recently, we reported a neural network (NN) method as QM/MM-NN to predict the potential energy difference between semiempirical and ab initio QM/MM approaches. The high-level results can be obtained using neural network based on semiempirical QM/MM MD simulations, but the lack of direct MD samplings at the ab initio QM/MM level is still a deficiency that limits the applications of QM/MM-NN. In the present paper, we developed a dynamic scheme of QM/MM-NN for direct MD simulations on the NN-predicted potential energy surface to approximate ab initio QM/MM MD. Since some configurations excluded from the database for NN training were encountered during simulations, which may cause some difficulties on MD samplings, an adaptive procedure inspired by the selection scheme reported by Behler was employed with some adaptions to update NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN MD method to the free energy calculation and transition path optimization on chemical reactions in water. The results at the ab initio QM/MM level can be well reproduced using this method after 2-4 iteration cycles. The saving in computational cost is about 2 orders of magnitude. It demonstrates that the QM/MM-NN with direct MD simulations has great potentials not only for the calculation of thermodynamic properties but also for the characterization of reaction dynamics, which provides a useful tool to study chemical or biochemical systems in solution or enzymes.
Jupiter's Equatorially Antisymmetric Gravitational Field and its Interior Dynamics
Zhang, Keke; Kong, Dali; Schubert, Gerald; Anderson, John D.
2017-10-01
The equatorially anti-symmetric gravitational field of Jupiter is nearly unaffected by its rotational distortion and,hence, it provides a direct window into the equatorially anti-symmetric fluid motion taking place in Jupiter's interior.We present a new accurate approach, based on the thermal-gravitational wind equation in spherical geometry(a two-dimensional kernel integral equation with the Green's function in its integrand), for estimating the location/structure/amplitude of the Jovian equatorially antisymmetric zonal flow of Jupiter via its equatorially anti-symmetric gravitational field and understanding the dynamics of Jupiter's deep interior. The mathematical and numerical difficulties in computing the equatorially anti-symmetric gravitational field are discussed.
Johnson, Cameron; Venayagamoorthy, Ganesh Kumar; Mitra, Pinaki
2009-01-01
The application of a spiking neural network (SNN) and a multi-layer perceptron (MLP) for online identification of generator dynamics in a multimachine power system are compared in this paper. An integrate-and-fire model of an SNN which communicates information via the inter-spike interval is applied. The neural network identifiers are used to predict the speed and terminal voltage deviations one time-step ahead of generators in a multimachine power system. The SNN is developed in two steps: (i) neuron centers determined by offline k-means clustering and (ii) output weights obtained by online training. The sensitivity of the SNN to the neuron centers determined in the first step is evaluated on generators of different ratings and parameters. Performances of the SNN and MLP are compared to evaluate robustness on the identification of generator dynamics under small and large disturbances, and to illustrate that SNNs are capable of learning nonlinear dynamics of complex systems.
Striatal Activity and Reward Relativity: Neural Signals Encoding Dynamic Outcome Valuation.
Webber, Emily S; Mankin, David E; Cromwell, Howard C
2016-01-01
The striatum is a key brain region involved in reward processing. Striatal activity has been linked to encoding reward magnitude and integrating diverse reward outcome information. Recent work has supported the involvement of striatum in the valuation of outcomes. The present work extends this idea by examining striatal activity during dynamic shifts in value that include different levels and directions of magnitude disparity. A novel task was used to produce diverse relative reward effects on a chain of instrumental action. Rats (Rattus norvegicus) were trained to respond to cues associated with specific outcomes varying by food pellet magnitude. Animals were exposed to single-outcome sessions followed by mixed-outcome sessions, and neural activity was compared among identical outcome trials from the different behavioral contexts. Results recording striatal activity show that neural responses to different task elements reflect incentive contrast as well as other relative effects that involve generalization between outcomes or possible influences of outcome variety. The activity that was most prevalent was linked to food consumption and post-food consumption periods. Relative encoding was sensitive to magnitude disparity. A within-session analysis showed strong contrast effects that were dependent upon the outcome received in the immediately preceding trial. Significantly higher numbers of responses were found in ventral striatum linked to relative outcome effects. Our results support the idea that relative value can incorporate diverse relationships, including comparisons from specific individual outcomes to general behavioral contexts. The striatum contains these diverse relative processes, possibly enabling both a higher information yield concerning value shifts and a greater behavioral flexibility.
Wei, Qikang; Chen, Tao; Xu, Ruifeng; He, Yulan; Gui, Lin
2016-01-01
The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V. Database URL: http://219.223.252.210:8080/SS/cdr.html PMID:27777244
Polymer Dynamics Studied by Field-Cycling NMR Relaxometry
Hofmann, Marius; Kresse, Benjamin; Privalov, Alexei; Fujara, Franz; Fatkullin, Nail; Roessler, Ernst
We apply Field-Cycling (FC) 1H NMR relaxometry to study translational as well as reorientational segmental dynamics in linear polymer melts. Assuming frequency-temperature superposition the fluctuation spectrum of the dipole-dipole interaction J(ω) is probed over ten decades in frequency, covering the local, Rouse and entanglement dynamics at high M. Fourier transformation yields the dipolar correlation function CDD(t), which turns out to be generic for different polymers with comparable M. Using the isotope dilution technique CDD(t) =Cintra(t) +Cinter(t) is separated into an intra- and an intermolecular component. While Cintra(t) =C2(t) reflects reorientational motion in terms of the l =2 Legendre polynomial, Cinter(t) is related to translation, specifically to the segmental mean square displacement. The found transition from Rouse to constrained Rouse dynamics is probed, and the data agrees with such of neutron scattering well. Combining FC and field-gradient NMR all four power-law regimes of the tube-reptation (TR) model are reproduced. Concerning reorientation, however, C2(t) doesn't conform to the TR model, a result which is also verified by FC 2H relaxometry. Based on our findings the return-to-origin hypothesis is challenged.
Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya
2016-01-01
To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language–behavior relationships and the temporal patterns of interaction. Here, “internal dynamics” refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human’s linguistic instruction. After learning, the network actually formed the attractor structure representing both language–behavior relationships and the task’s temporal pattern in its internal dynamics. In the dynamics, language–behavior mapping was achieved by the branching structure. Repetition of human’s instruction and robot’s behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases. PMID:27471463
Hamshaw, S. D.; Underwood, K.; Wemple, B. C.; Rizzo, D.
2016-12-01
Sediment transport can be an immensely complex process, yet plays a vital role in the transport of substances and nutrients that can impact receiving waters. Advancements in the use of sensors for indirect measurement of suspended sediments have allowed access to high frequency sediment data. This has promoted the use of more advanced computational tools to identify patterns in sediment data to improve our understanding of physical processes occurring in the watershed. In this study, a network of weather stations and in-stream turbidity sensors were deployed to capture more than three years of sediment dynamics and meteorological data in the Mad River watershed in central Vermont. Monitoring sites were located along the main stem of the the Mad River and on five tributaries. Separate storm events were identified from the data at each site to study event sediment dynamics associated with erosion and deposition over space and time. Two types of artificial neural networks (ANNs), a self-organizing map (SOM) and a radial basis function (RBF), were used to cluster the storm event data based on hydrometeorological metrics and were subsequently compared to traditional classes of hysteresis patterns in suspended sediment concentration - discharge (SSC-Q) relationships. Hysteresis patterns were also directly used as inputs to both ANNs to identify distinct patterns and test the applicability of performing pattern recognition on hysteresis patterns. The results of this study will be used to gain insight into the dynamic physical processes (both spatial and temporal) occurring in the watershed based on patterns observed in SSQ-Q data.
Wang, Bin; Yan, Tianyi; Wu, Jinglong; Chen, Kewei; Imajyo, Satoshi; Ohno, Seiichiro; Kanazawa, Susumu
2013-01-01
In human visual cortex, the primary visual cortex (V1) is considered to be essential for visual information processing; the fusiform face area (FFA) and parahippocampal place area (PPA) are considered as face-selective region and places-selective region, respectively. Recently, a functional magnetic resonance imaging (fMRI) study showed that the neural activity ratios between V1 and FFA were constant as eccentricities increasing in central visual field. However, in wide visual field, the neural activity relationships between V1 and FFA or V1 and PPA are still unclear. In this work, using fMRI and wide-view present system, we tried to address this issue by measuring neural activities in V1, FFA and PPA for the images of faces and houses aligning in 4 eccentricities and 4 meridians. Then, we further calculated ratio relative to V1 (RRV1) as comparing the neural responses amplitudes in FFA or PPA with those in V1. We found V1, FFA, and PPA showed significant different neural activities to faces and houses in 3 dimensions of eccentricity, meridian, and region. Most importantly, the RRV1s in FFA and PPA also exhibited significant differences in 3 dimensions. In the dimension of eccentricity, both FFA and PPA showed smaller RRV1s at central position than those at peripheral positions. In meridian dimension, both FFA and PPA showed larger RRV1s at upper vertical positions than those at lower vertical positions. In the dimension of region, FFA had larger RRV1s than PPA. We proposed that these differential RRV1s indicated FFA and PPA might have different processing strategies for encoding the wide field visual information from V1. These different processing strategies might depend on the retinal position at which faces or houses are typically observed in daily life. We posited a role of experience in shaping the information processing strategies in the ventral visual cortex.
Andreon, S.; Gargiulo, G.; Longo, G.; Tagliaferri, R.; Capuano, N.
2000-12-01
Astronomical wide-field imaging performed with new large-format CCD detectors poses data reduction problems of unprecedented scale, which are difficult to deal with using traditional interactive tools. We present here NExt (Neural Extractor), a new neural network (NN) based package capable of detecting objects and performing both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first distinguished from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold; they are then classified as stars or as galaxies through diagnostic diagrams having variables chosen according to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of `what an object is' (i.e. it keeps all structures composed of more than one pixel) and performs the detection via an unsupervised NN, approaching detection as a clustering problem that has been thoroughly studied in the artificial intelligence literature. The first part of the NExt procedure consists of an optimal compression of the redundant information contained in the pixels via a mapping from pixel intensities to a subspace individualized through principal component analysis. At magnitudes fainter than the completeness limit, stars are usually almost indistinguishable from galaxies, and therefore the parameters characterizing the two classes do not lie in disconnected subspaces, thus preventing the use of unsupervised methods. We therefore adopted a supervised NN (i.e. a NN that first finds the rules to classify objects from examples and then applies them to the whole data set). In practice, each object is classified depending on its membership of the regions mapping the input feature space in the training set. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features
Cai, Zuowei; Huang, Lihong
2014-05-01
In this paper, we formulate and investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, the viability and dissipativity of solutions for functional differential inclusions and memristive BAM neural networks can be guaranteed by the matrix measure approach and generalized Halanay inequalities. Then, a new method involving the application of set-valued version of Krasnoselskii' fixed point theorem in a cone is successfully employed to derive the existence of the positive periodic solution. The dynamic analysis in this paper utilizes the theory of set-valued maps and functional differential equations with discontinuous right-hand sides of Filippov type. The obtained results extend and improve some previous works on conventional BAM neural networks. Finally, numerical examples are given to demonstrate the theoretical results via computer simulations.
Effect of electrostatic field on dynamic friction coefficient of pistachio
Directory of Open Access Journals (Sweden)
M. H Aghkhani
2016-04-01
Full Text Available Introduction: Separation and grading of agricultural products from the production to supply, has notable importance. The separation can be done based on physical, electrical, magnetic, optical properties and etc. It is necessary for any development of new systems to study enough on the properties and behavior of agricultural products. Some characteristics for separation are size (length, width and thickness, hardness, shape, density, surface roughness, color, speed limit, aerodynamic properties, electrical conductivity, elasticity and coefficient of static friction point. So far, the friction properties of agricultural products used in the separating process, but the effect of electrostatic charging on static and dynamic coefficients of friction for separation had little attention. The aim of this study was to find out the interactions between electrostatic and friction properties to find a way to separate products that separation is not possible with conventional methods or not sufficiently accurate. In this paper, the separation of close and smiley pistachios by electrostatic charging was investigated. Materials and Methods: Kallehghoochi pistachio cultivar has the top rank in production in Iran. Therefore, it was used as a sample. The experimental design that used in this study, had moisture content at three levels (24.2, 14.5 and 8.1 percent, electric field intensity at three levels (zero, 4000 and 7000 V, speed of movement on the surface at three levels (1300, 2500 and 3300 mm per minute, friction surface (galvanized sheet iron, aluminum and flat rubber and pistachio type at two levels (filled splits and closed that was measured and analyzed in completely randomized factorial design. A friction measuring device (built in Ferdowsi University of Mashhad used to measure the friction force. It has a removable table that can move in two directions with adjustable speed. The test sample put into the vessel with internal dimensions of 300 × 150
Localization of vector field on dynamical domain wall
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Masafumi Higuchi
2017-03-01
Full Text Available In the previous works (arXiv:1202.5375 and arXiv:1402.1346, the dynamical domain wall, where the four dimensional FRW universe is embedded in the five dimensional space–time, has been realized by using two scalar fields. In this paper, we consider the localization of vector field in three formulations. The first formulation was investigated in the previous paper (arXiv:1510.01099 for the U(1 gauge field. In the second formulation, we investigate the Dvali–Shifman mechanism (arXiv:hep-th/9612128, where the non-abelian gauge field is confined in the bulk but the gauge symmetry is spontaneously broken on the domain wall. In the third formulation, we investigate the Kaluza–Klein modes coming from the five dimensional graviton. In the Randall–Sundrum model, the graviton was localized on the brane. We show that the (5,μ components (μ=0,1,2,3 of the graviton are also localized on the domain wall and can be regarded as the vector field on the domain wall. There are, however, some corrections coming from the bulk extra dimension if the domain wall universe is expanding.
Samarasinghe, S; Ling, H
In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced
Reducing Visual Discomfort with HMDs Using Dynamic Depth of Field.
Carnegie, Kieran; Rhee, Taehyun
2015-01-01
Although head-mounted displays (HMDs) are ideal devices for personal viewing of immersive stereoscopic content, exposure to VR applications on them results in significant discomfort for the majority of people, with symptoms including eye fatigue, headaches, nausea, and sweating. A conflict between accommodation and vergence depth cues on stereoscopic displays is a significant cause of visual discomfort. This article describes the results of an evaluation used to judge the effectiveness of dynamic depth-of-field (DoF) blur in an effort to reduce discomfort caused by exposure to stereoscopic content on HMDs. Using a commercial game engine implementation, study participants report a reduction of visual discomfort on a simulator sickness questionnaire when DoF blurring is enabled. The study participants reported a decrease in symptom severity caused by HMD exposure, indicating that dynamic DoF can effectively reduce visual discomfort.
Complex dynamics of a particle in an oscillating potential field
Pal, Barnali; Dutta, Debjit; Poria, Swarup
2017-08-01
In this paper, the classical problem of the motion of a particle in one dimension with an external time-dependent field is studied from the point of view of the dynamical system. The dynamical equations of motion of the particle are formulated. Equilibrium points of the non-oscillating systems are found and their local stability natures are analysed. Effect of oscillating potential barrier is analysed through numerical simulations. Phase diagrams, bifurcation diagrams and variations of largest Lyapunov exponents are presented to show the existence of a wide range of nonlinear phenomena such as limit cycle, quasiperiodic and chaotic oscillations in the system. Effects of nonlinear damping in the model are also reported. Analysis of the physically interesting cases where damping is proportional to higher powers of velocity are presented for the sake of generalizing our findings and establishing firm conclusion.
Aero-acoustic Measurement and Monitoring of Dynamic Pressure Fields Project
National Aeronautics and Space Administration — This innovative and practical measurement and monitoring system optimally defines dynamic pressure fields, including sound fields. It is based on passive acoustic...
Characterization of Molecular Dynamics in Ultrashort Laser Fields
Feuerstein, B.; Ergler, T.; Rudenko, A.; Moshammer, R.; Ullrich, J.; Niederhausen, T.; Thumm, U.
2008-05-01
Reaction Microscope-based, complete, and time-resolved Coulomb explosion imaging of vibrating and dissociating D2^2+ molecules with femtosecond time-resolution allowed us to perform an internuclear distance (R-)dependent Fourier analysis of the corresponding wave packets. Our wave packet propagation calculations demonstrate that the obtained two-dimensional R-dependent frequency spectra enable the complete characterization of the wave packet dynamics and directly visualize the field-modified molecular potential curves in intense, ultrashort laser pulses, cf., Phys. Rev. Lett. 99 153002 (2007).
Species and dynamics of floating weed seeds in paddy field
Ranling Zuo; Sheng Qiang
2008-01-01
In order to explore effective methods for weed control in paddy fields, we investigated the dynamics of weed seeds in Nanjing from June to November of 2005. A total of 24 weed species representing 15families were found before seedling transplanting and at late growth stage of rice, while during irrigation stage, 26 species of 17 families were identified from floating weed seeds. The two stages shared 18 weed species, accounting for 56.25% of the total weeds. Most of them belonged to Gramineae...
Dynamic measurement of near-field radiative heat transfer.
Lang, S; Sharma, G; Molesky, S; Kränzien, P U; Jalas, T; Jacob, Z; Petrov, A Yu; Eich, M
2017-10-24
Super-Planckian near-field radiative heat transfer allows effective heat transfer between a hot and a cold body to increase beyond the limits long known for black bodies. Until present, experimental techniques to measure the radiative heat flow relied on steady-state systems. Here, we present a dynamic measurement approach based on the transient plane source technique, which extracts thermal properties from a temperature transient caused by a step input power function. Using this versatile method, that requires only single sided contact, we measure enhanced radiative conduction up to 16 times higher than the blackbody limit on centimeter sized glass samples without any specialized sample preparation or nanofabrication.
Pancreatic cancer study based on full field OCT and dynamic full field OCT (Conference Presentation)
Apelian, Clement; Camus, Marine; Prat, Frederic; Boccara, A. Claude
2017-02-01
Pancreatic cancer is one of the most feared cancer types due to high death rates and the difficulty to perform surgery. This cancer outcome could benefit from recent technological developments for diagnosis. We used a combination of standard Full Field OCT and Dynamic Full Field OCT to capture both morphological features and metabolic functions of rodents pancreas in normal and cancerous conditions with and without chemotherapy. Results were compared to histology to evaluate the performances and the specificities of the method. The comparison highlighted the importance of a number of endogenous markers like immune cells, fibrous development, architecture and more.
Roellig, Daniela; Tan-Cabugao, Johanna; Esaian, Sevan; Bronner, Marianne E
2017-01-01
The ‘neural plate border’ of vertebrate embryos contains precursors of neural crest and placode cells, both defining vertebrate characteristics. How these lineages segregate from neural and epidermal fates has been a matter of debate. We address this by performing a fine-scale quantitative temporal analysis of transcription factor expression in the neural plate border of chick embryos. The results reveal significant overlap of transcription factors characteristic of multiple lineages in individual border cells from gastrula through neurula stages. Cell fate analysis using a Sox2 (neural) enhancer reveals that cells that are initially Sox2+ cells can contribute not only to neural tube but also to neural crest and epidermis. Moreover, modulating levels of Sox2 or Pax7 alters the apportionment of neural tube versus neural crest fates. Our results resolve a long-standing question and suggest that many individual border cells maintain ability to contribute to multiple ectodermal lineages until or beyond neural tube closure. DOI: http://dx.doi.org/10.7554/eLife.21620.001 PMID:28355135
Superconducting circuits for quantum simulation of dynamical gauge fields.
Marcos, D; Rabl, P; Rico, E; Zoller, P
2013-09-13
We describe a superconducting-circuit lattice design for the implementation and simulation of dynamical lattice gauge theories. We illustrate our proposal by analyzing a one-dimensional U(1) quantum-link model, where superconducting qubits play the role of matter fields on the lattice sites and the gauge fields are represented by two coupled microwave resonators on each link between neighboring sites. A detailed analysis of a minimal experimental protocol for probing the physics related to string breaking effects shows that, despite the presence of decoherence in these systems, distinctive phenomena from condensed-matter and high-energy physics can be visualized with state-of-the-art technology in small superconducting-circuit arrays.
Dynamics of nanoparticle concentration in nanofluids under laser light field
Livashvili, A. I.; Krishtop, V. V.; Vinogradova, P. V.; Kostina, G. V.; Bryukhanova, T. N.
2017-12-01
The dynamics of the concentration of nanofluids placed in a light field with a Gaussian intensity profile is studied theoretically. The investigation is based on the analytical and numerical solutions of the system of linearized heat conduction and convection-diffusion equations. The convection-diffusion equation contains terms that correspond both to the Soret effect and to the transfer of nanoparticles, caused by the action of a light field on them (electrostriction). The dependence of the coefficient of thermal conductivity of the medium on the concentration is taken into account. It is shown that under these conditions single travelling waves appear in the medium, the velocity of which depends not only on the thermal physical parameters of the medium, but also on the polarization of the particles. Conditions under which the stratification of the medium is possible are found.
Downscaling Transpiration from the Field to the Tree Scale using the Neural Network Approach
Hopmans, J. W.
2015-12-01
Estimating actual evapotranspiration (ETa) spatial variability in orchards is key when trying to quantify water (and associated nutrients) leaching, both with the mass balance and inverse modeling methods. ETa measurements however generally occur at larger scales (e.g. Eddy-covariance method) or have a limited quantitative accuracy. In this study we propose to establish a statistical relation between field ETa and field averaged variables known to be closely related to it, such as stem water potential (WP), soil water storage (WS) and ETc. For that we use 4 years of soil and almond trees water status data to train artificial neural networks (ANNs) predicting field scale ETa and downscale the relation to the individual tree scale. ANNs composed of only two neurons in a hidden layer (11 parameters on total) proved to be the most accurate (overall RMSE = 0.0246 mm/h, R2 = 0.944), seemingly because adding more neurons generated overfitting of noise in the training dataset. According to the optimized weights in the best ANNs, the first hidden neuron could be considered in charge of relaying the ETc information while the other one would deal with the water stress response to stem WP, soil WS, and ETc. As individual trees had specific signatures for combinations of these variables, variability was generated in their ETa responses. The relative canopy cover was the main source of variability of ETa while stem WP was the most influent factor for the ETa / ETc ratio. Trees on drip-irrigated side of the orchard appeared to be less affected by low estimated soil WS in the root zone than on the fanjet micro-sprinklers side, possibly due to a combination of (i) more substantial root biomass increasing the plant hydraulic conductance, (ii) bias in the soil WS estimation due to soil moisture heterogeneity on the drip-side, and (iii) the access to deeper water resource. Tree scale ETa responses are in good agreement with soil-plant water relations reported in the literature, and
Zhang, Zhijun; Li, Zhijun; Zhang, Yunong; Luo, Yamei; Li, Yuanqing
2015-12-01
We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-varying constraints, called the time-varying-constrained DACMG (TVC-DACMG) scheme. The scheme includes the kinematic motion equations of two arms and the time-varying joint limits. The scheme can not only generate the cyclic motion of two arms for a humanoid robot but also control the arms to move to the desired position. In addition, the scheme considers the physical limit avoidance. To solve the QP problem, a recurrent neural network is presented and used to obtain the optimal solutions. Computer simulations and physical experiments demonstrate the effectiveness and the accuracy of such a TVC-DACMG scheme and the neural network solver.
Directory of Open Access Journals (Sweden)
Mario Collotta
2014-07-01
Full Text Available Heating, ventilating and air-conditioning (HVAC systems are typical non-linear time-variable multivariate systems with disturbances and uncertainties. In this paper, an approach based on a combined neuro-fuzzy model for dynamic and automatic regulation of indoor temperature is proposed. The proposed artificial neural network performs indoor temperatures forecasts that are used to feed a fuzzy logic control unit in order to manage the on/off switching of the HVAC system and the regulation of the inlet air speed. Moreover, the used neural network is optimized by the analytical calculation of the embedding parameters, and the goodness of this approach is tested through MATLAB. The fuzzy controller is driven by the indoor temperature forecasted by the neural network module and is able to adjust the membership functions dynamically, since thermal comfort is a very subjective factor and may vary even in the same subject. The paper shows some experimental results, through a real implementation in an embedded prototyping board, of the proposed approach in terms of the evolution of the inlet air speed injected by the fan coils, the indoor air temperature forecasted by the neural network model and the adjusting of the membership functions after receiving user feedback.
Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics
Freeman, Walter J
2008-01-01
Neural activity patterns related to behavior occur at many scales in time and space from the atomic and molecular to the whole brain. Here we explore the feasibility of interpreting neurophysiological data in the context of many-body physics by using tools that physicists have devised to analyze comparable hierarchies in other fields of science. We focus on a mesoscopic level that offers a multi-step pathway between the microscopic functions of neurons and the macroscopic functions of brain systems revealed by hemodynamic imaging. We use electroencephalographic (EEG) records collected from high-density electrode arrays fixed on the epidural surfaces of primary sensory and limbic areas in rabbits and cats trained to discriminate conditioned stimuli (CS) in the various modalities. High temporal resolution of EEG signals with the Hilbert transform gives evidence for diverse intermittent spatial patterns of amplitude (AM) and phase modulations (PM) of carrier waves that repeatedly re-synchronize in the beta and g...
Spectrotemporal dynamics of auditory cortical synaptic receptive field plasticity.
Froemke, Robert C; Martins, Ana Raquel O
2011-09-01
The nervous system must dynamically represent sensory information in order for animals to perceive and operate within a complex, changing environment. Receptive field plasticity in the auditory cortex allows cortical networks to organize around salient features of the sensory environment during postnatal development, and then subsequently refine these representations depending on behavioral context later in life. Here we review the major features of auditory cortical receptive field plasticity in young and adult animals, focusing on modifications to frequency tuning of synaptic inputs. Alteration in the patterns of acoustic input, including sensory deprivation and tonal exposure, leads to rapid adjustments of excitatory and inhibitory strengths that collectively determine the suprathreshold tuning curves of cortical neurons. Long-term cortical plasticity also requires co-activation of subcortical neuromodulatory control nuclei such as the cholinergic nucleus basalis, particularly in adults. Regardless of developmental stage, regulation of inhibition seems to be a general mechanism by which changes in sensory experience and neuromodulatory state can remodel cortical receptive fields. We discuss recent findings suggesting that the microdynamics of synaptic receptive field plasticity unfold as a multi-phase set of distinct phenomena, initiated by disrupting the balance between excitation and inhibition, and eventually leading to wide-scale changes to many synapses throughout the cortex. These changes are coordinated to enhance the representations of newly-significant stimuli, possibly for improved signal processing and language learning in humans. Copyright © 2011 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Orlando Arévalo
Full Text Available In everyday life, humans interact with a dynamic environment often requiring rapid adaptation of visual perception and motor control. In particular, new visuo-motor mappings must be learned while old skills have to be kept, such that after adaptation, subjects may be able to quickly change between two different modes of generating movements ('dual-adaptation'. A fundamental question is how the adaptation schedule determines the acquisition speed of new skills. Given a fixed number of movements in two different environments, will dual-adaptation be faster if switches ('phase changes' between the environments occur more frequently? We investigated the dynamics of dual-adaptation under different training schedules in a virtual pointing experiment. Surprisingly, we found that acquisition speed of dual visuo-motor mappings in a pointing task is largely independent of the number of phase changes. Next, we studied the neuronal mechanisms underlying this result and other key phenomena of dual-adaptation by relating model simulations to experimental data. We propose a simple and yet biologically plausible neural model consisting of a spatial mapping from an input layer to a pointing angle which is subjected to a global gain modulation. Adaptation is performed by reinforcement learning on the model parameters. Despite its simplicity, the model provides a unifying account for a broad range of experimental data: It quantitatively reproduced the learning rates in dual-adaptation experiments for both direct effect, i.e. adaptation to prisms, and aftereffect, i.e. behavior after removal of prisms, and their independence on the number of phase changes. Several other phenomena, e.g. initial pointing errors that are far smaller than the induced optical shift, were also captured. Moreover, the underlying mechanisms, a local adaptation of a spatial mapping and a global adaptation of a gain factor, explained asymmetric spatial transfer and generalization of prism
Bengoetxea, Ana; Leurs, Françoise; Hoellinger, Thomas; Cebolla, Ana M; Dan, Bernard; McIntyre, Joseph; Cheron, Guy
2014-01-01
In this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.
Directory of Open Access Journals (Sweden)
Ana eBengoetxea
2014-09-01
Full Text Available In this study we employed a dynamic recurrent neural network (DRNN in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane. We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others patterns of reciprocal activation operating in orthogonal
Learning from adaptive neural dynamic surface control of strict-feedback systems.
Wang, Min; Wang, Cong
2015-06-01
Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.
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Rodrigues, Serafim [Department of Mathematical Sciences, Loughborough University, Leicestershire, LE11 3TU (United Kingdom); Terry, John R. [Department of Mathematical Sciences, Loughborough University, Leicestershire, LE11 3TU (United Kingdom)]. E-mail: j.r.terry@lboro.ac.uk; Breakspear, Michael [Black Dog Institute, Randwick, NSW 2031 (Australia); School of Psychiatry, UNSW, NSW 2030 (Australia)
2006-07-10
In this Letter, the genesis of spike-wave activity-a hallmark of many generalized epileptic seizures-is investigated in a reduced mean-field model of human neural activity. Drawing upon brain modelling and dynamical systems theory, we demonstrate that the thalamic circuitry of the system is crucial for the generation of these abnormal rhythms, observing that the combination of inhibition from reticular nuclei and excitation from the cortical signal, interplay to generate the spike-wave oscillation. The mechanism revealed provides an explanation of why approaches based on linear stability and Heaviside approximations to the activation function have failed to explain the phenomena of spike-wave behaviour in mean-field models. A mathematical understanding of this transition is a crucial step towards relating spiking network models and mean-field approaches to human brain modelling.
Directory of Open Access Journals (Sweden)
Marlen Knobloch
2014-11-01
Full Text Available Proliferation of neural stem/progenitor cells (NSPCs in the adult brain is tightly controlled to prevent exhaustion and to ensure proper neurogenesis. Several extrinsic stimuli affect NSPC regulation. However, the lack of unique markers led to controversial results regarding the in vivo behavior of NSPCs to different stimuli. We recently identified SPOT14, which controls NSPC proliferation through regulation of de novo lipogenesis, selectively in low-proliferating NSPCs. Whether SPOT14-expressing (SPOT14+ NSPCs react in vivo to neurogenic regulators is not known. We show that aging is accompanied by a marked disappearance of SPOT14+ NSPCs, whereas running, a positive neurogenic stimulus, increases proliferation of SPOT14+ NSPCs. Furthermore, transient depletion of highly proliferative cells recruits SPOT14+ NSPCs into the proliferative pool. Additionally, we have established endogenous SPOT14 protein staining, reflecting transgenic SPOT14-GFP expression. Thus, our data identify SPOT14 as a potent marker for adult NSPCs that react dynamically to positive and negative neurogenic regulators.
Dynamic frame resizing with convolutional neural network for efficient video compression
Kim, Jaehwan; Park, Youngo; Choi, Kwang Pyo; Lee, JongSeok; Jeon, Sunyoung; Park, JeongHoon
2017-09-01
In the past, video codecs such as vc-1 and H.263 used a technique to encode reduced-resolution video and restore original resolution from the decoder for improvement of coding efficiency. The techniques of vc-1 and H.263 Annex Q are called dynamic frame resizing and reduced-resolution update mode, respectively. However, these techniques have not been widely used due to limited performance improvements that operate well only under specific conditions. In this paper, video frame resizing (reduced/restore) technique based on machine learning is proposed for improvement of coding efficiency. The proposed method features video of low resolution made by convolutional neural network (CNN) in encoder and reconstruction of original resolution using CNN in decoder. The proposed method shows improved subjective performance over all the high resolution videos which are dominantly consumed recently. In order to assess subjective quality of the proposed method, Video Multi-method Assessment Fusion (VMAF) which showed high reliability among many subjective measurement tools was used as subjective metric. Moreover, to assess general performance, diverse bitrates are tested. Experimental results showed that BD-rate based on VMAF was improved by about 51% compare to conventional HEVC. Especially, VMAF values were significantly improved in low bitrate. Also, when the method is subjectively tested, it had better subjective visual quality in similar bit rate.
Directory of Open Access Journals (Sweden)
Xiaoyan Liao
2017-06-01
Full Text Available This study used event-related potentials (ERPs to investigate the effects of age on neural temporal dynamics of processing task-relevant facial expressions and their relationship to cognitive functions. Negative (sad, afraid, angry, and disgusted, positive (happy, and neutral faces were presented to 30 older and 31 young participants who performed a facial emotion categorization task. Behavioral and ERP indices of facial emotion processing were analyzed. An enhanced N170 for negative faces, in addition to intact right-hemispheric N170 for positive faces, was observed in older adults relative to their younger counterparts. Moreover, older adults demonstrated an attenuated within-group N170 laterality effect for neutral faces, while younger adults showed the opposite pattern. Furthermore, older adults exhibited sustained temporo-occipital negativity deflection over the time range of 200–500 ms post-stimulus, while young adults showed posterior positivity and subsequent emotion-specific frontal negativity deflections. In older adults, decreased accuracy for labeling negative faces was positively correlated with Montreal Cognitive Assessment Scores, and accuracy for labeling neutral faces was negatively correlated with age. These findings suggest that older people may exert more effort in structural encoding for negative faces and there are different response patterns for the categorization of different facial emotions. Cognitive functioning may be related to facial emotion categorization deficits observed in older adults. This may not be attributable to positivity effects: it may represent a selective deficit for the processing of negative facial expressions in older adults.
El-Nagar, Ahmad M
2017-10-31
In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
GEMAN, O.
2014-02-01
Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.
Principal dynamic mode analysis of neural mass model for the identification of epileptic states
Cao, Yuzhen; Jin, Liu; Su, Fei; Wang, Jiang; Deng, Bin
2016-11-01
The detection of epileptic seizures in Electroencephalography (EEG) signals is significant for the diagnosis and treatment of epilepsy. In this paper, in order to obtain characteristics of various epileptiform EEGs that may differentiate different states of epilepsy, the concept of Principal Dynamic Modes (PDMs) was incorporated to an autoregressive model framework. First, the neural mass model was used to simulate the required intracerebral EEG signals of various epileptiform activities. Then, the PDMs estimated from the nonlinear autoregressive Volterra models, as well as the corresponding Associated Nonlinear Functions (ANFs), were used for the modeling of epileptic EEGs. The efficient PDM modeling approach provided physiological interpretation of the system. Results revealed that the ANFs of the 1st and 2nd PDMs for the auto-regressive input exhibited evident differences among different states of epilepsy, where the ANFs of the sustained spikes' activity encountered at seizure onset or during a seizure were the most differentiable from that of the normal state. Therefore, the ANFs may be characteristics for the classification of normal and seizure states in the clinical detection of seizures and thus provide assistance for the diagnosis of epilepsy.
Dynamic mass redistribution assay decodes differentiation of a neural progenitor stem cell.
Pai, Sadashiva; Verrier, Florence; Sun, Haiyan; Hu, Haibei; Ferrie, Ann M; Eshraghi, Azita; Fang, Ye
2012-10-01
Stem cells hold great potential in drug discovery and development. However, challenges remain to quantitatively measure the functions of stem cells and their differentiated products. Here, we applied fluorescent imaging, quantitative real-time PCR, and label-free dynamic mass redistribution (DMR) assays to characterize the differentiation process of the ReNcell VM human neural progenitor stem cell. Immunofluorescence imaging showed that after growth factor withdrawal, the neuroprogenitor stem cell was differentiated into dopaminergic neurons, astrocytes, and oligodendrocytes, thus creating a neuronal cell system. High-performance liquid chromatography analysis showed that the differentiated cell system released dopamine upon depolarization with KCl. In conjunction with quantitative real-time PCR, DMR assays using a G-protein-coupled receptor agonist library revealed that a subset of receptors, including dopamine D(1) and D(4) receptors, underwent marked alterations in both receptor expression and signaling pathway during the differentiation process. These findings suggest that DMR assays can decode the differentiation process of stem cells at the cell system level.
Topological dynamics in spike-timing dependent plastic model neural networks
Directory of Open Access Journals (Sweden)
David B. Stone
2013-04-01
Full Text Available Spike-timing dependent plasticity (STDP is a biologically constrained unsupervised form of learning that potentiates or depresses synaptic connections based on the precise timing of pre-synaptic and post-synaptic firings. The effects of on-going STDP on the topology of evolving model neural networks were assessed in 50 unique simulations which modeled two hours of activity. After a period of stabilization, a number of global and local topological features were monitored periodically to quantify on-going changes in network structure. Global topological features included the total number of remaining synapses, average synaptic strengths, and average number of synapses per neuron (degree. Under a range of different input regimes and initial network configurations, each network maintained a robust and highly stable global structure across time. Local topology was monitored by assessing state changes of all three-neuron subgraphs (triads present in the networks. Overall counts and the range of triad configurations varied little across the simulations; however, a substantial set of individual triads continued to undergo rapid state changes and revealed a dynamic local topology. In addition, specific small-world properties also fluctuated across time. These findings suggest that on-going STDP provides an efficient means of selecting and maintaining a stable yet flexible network organization.
Directory of Open Access Journals (Sweden)
Shaohua Luo
2014-01-01
Full Text Available This paper is concerned with the problem of the nonlinear dynamic surface control (DSC of chaos based on the minimum weights of RBF neural network for the permanent magnet synchronous motor system (PMSM wherein the unknown parameters, disturbances, and chaos are presented. RBF neural network is used to approximate the nonlinearities and an adaptive law is employed to estimate unknown parameters. Then, a simple and effective controller is designed by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed controller is testified through simulation results.
DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields.
Shao, Mingfu; Ma, Jianzhu; Wang, Sheng
2017-07-15
Reconstructing the full-length expressed transcripts ( a.k.a. the transcript assembly problem) from the short sequencing reads produced by RNA-seq protocol plays a central role in identifying novel genes and transcripts as well as in studying gene expressions and gene functions. A crucial step in transcript assembly is to accurately determine the splicing junctions and boundaries of the expressed transcripts from the reads alignment. In contrast to the splicing junctions that can be efficiently detected from spliced reads, the problem of identifying boundaries remains open and challenging, due to the fact that the signal related to boundaries is noisy and weak. We present DeepBound, an effective approach to identify boundaries of expressed transcripts from RNA-seq reads alignment. In its core DeepBound employs deep convolutional neural fields to learn the hidden distributions and patterns of boundaries. To accurately model the transition probabilities and to solve the label-imbalance problem, we novelly incorporate the AUC (area under the curve) score into the optimizing objective function. To address the issue that deep probabilistic graphical models requires large number of labeled training samples, we propose to use simulated RNA-seq datasets to train our model. Through extensive experimental studies on both simulation datasets of two species and biological datasets, we show that DeepBound consistently and significantly outperforms the two existing methods. DeepBound is freely available at https://github.com/realbigws/DeepBound . mingfu.shao@cs.cmu.edu or realbigws@gmail.com.
Yiran Yuan; Leung, Ada W. S.; Hongxia Duan; Liang Zhang; Kan Zhang; Jianhui Wu; Shaozheng Qin
2016-01-01
This study examined the neural dynamics of working memory (WM) processing under long-term stress. Forty participants who had been exposed to a long period of major exam preparation (six months) and twenty-one control participants performed a numerical n-back task (n?=?1,?2) while electroencephalograms were recorded. Psychological and endocrinal measurements confirmed significantly higher levels of long-term stress for participants in the exam group. The exam group showed significantly increas...
Park, Jeong-Eun; Seo, Young-Kwon; Yoon, Hee-Hoon; Kim, Chan-Wha; Park, Jung-Keug; Jeon, Songhee
2013-03-01
Even though the inducing effect of electromagnetic fields (EMF) on the neural differentiation of human bone marrow mesenchymal stem cells (hBM-MSCs) is a distinctive, the underlying mechanism of differentiation remains unclear. To find out the signaling pathways involved in the neural differentiation of BM-MSCs by EMF, we examined the CREB phosphorylation and Akt or ERK activation as an upstream of CREB. In hBM-MSCs treated with ELF-EMF (50 Hz, 1 mT), the expression of neural markers such as NF-L, MAP2, and NeuroD1 increased at 6 days and phosphorylation of Akt and CREB but not ERK increased at 90 min in BM-MSCs. Moreover, EMF increased phosphorylation of epidermal growth factor receptor (EGFR) as an upstream receptor tyrosine kinase of PI3K/Akt at 90 min. It has been well documented that ELF-MF exposure may alter cellular processes by increasing intracellular reactive oxygen species (ROS) concentrations. Thus, we examined EMF-induced ROS production in BM-MSCs. Moreover, pretreatment with a ROS scavenger, N-acetylcystein, and an EGFR inhibitor, AG-1478, prevented the phosphorylation of EGFR and downstream molecules. These results suggest that EMF induce neural differentiation through activation of EGFR signaling and mild generation of ROS. Copyright © 2013 Elsevier Ltd. All rights reserved.
Nitzan, Erez; Avraham, Oshri; Kahane, Nitza; Ofek, Shai; Kumar, Deepak; Kalcheim, Chaya
2016-03-24
The dorsal midline region of the neural tube that results from closure of the neural folds is generally termed the roof plate (RP). However, this domain is highly dynamic and complex, and is first transiently inhabited by prospective neural crest (NC) cells that sequentially emigrate from the neuroepithelium. It only later becomes the definitive RP, the dorsal midline cells of the spinal cord. We previously showed that at the trunk level of the axis, prospective RP progenitors originate ventral to the premigratory NC and progressively reach the dorsal midline following NC emigration. However, the molecular mechanisms underlying the end of NC production and formation of the definitive RP remain virtually unknown. Based on distinctive cellular and molecular traits, we have defined an initial NC and a subsequent RP stage, allowing us to investigate the mechanisms responsible for the transition between the two phases. We demonstrate that in spite of the constant production of BMP4 in the dorsal tube at both stages, RP progenitors only transiently respond to the ligand and lose competence shortly before they arrive at their final location. In addition, exposure of dorsal tube cells at the NC stage to high levels of BMP signaling induces premature RP traits, such as Hes1/Hairy1, while concomitantly inhibiting NC production. Reciprocally, early inhibition of BMP signaling prevents Hairy1 mRNA expression at the RP stage altogether, suggesting that BMP is both necessary and sufficient for the development of this RP-specific trait. Furthermore, when Hes1/Hairy1 is misexpressed at the NC stage, it inhibits BMP signaling and downregulates BMPR1A/Alk3 mRNA expression, transcription of BMP targets such as Foxd3, cell-cycle progression, and NC emigration. Reciprocally, Foxd3 inhibits Hairy1, suggesting that repressive cross-interactions at the level of, and downstream from, BMP ensure the temporal separation between both lineages. Together, our data suggest that BMP signaling is
Miconi, Thomas; VanRullen, Rufin
2016-02-01
Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space. Several existing models of attention suggest that these effects arise from selective modulation of neural inputs. However, anatomical and physiological observations suggest that attentional modulation targets higher levels of the visual system (such as V4 or MT) rather than input areas (such as V1). Here we propose a simple mechanism that explains how a top-down attentional modulation, falling on higher visual areas, can produce the observed effects of attention on neural responses. Our model requires only the existence of modulatory feedback connections between areas, and short-range lateral inhibition within each area. Feedback connections redistribute the top-down modulation to lower areas, which in turn alters the inputs of other higher-area cells, including those that did not receive the initial modulation. This produces firing rate modulations and receptive field shifts. Simultaneously, short-range lateral inhibition between neighboring cells produce competitive effects that are automatically scaled to receptive field size in any given area. Our model reproduces the observed attentional effects on response rates (response gain, input gain, biased competition automatically scaled to receptive field size) and receptive field structure (shifts and resizing of receptive fields both spatially and in complex feature space), without modifying model parameters. Our model also makes the novel prediction that attentional effects on response curves should shift from response gain to contrast gain as the spatial focus of attention drifts away from the studied cell.
Liu, Xiaosong; Shan, Zebiao; Li, Yuanchun
2017-04-01
Pinpoint landing is a critical step in some asteroid exploring missions. This paper is concerned with the descent trajectory control for soft touching down on a small irregularly-shaped asteroid. A dynamic boundary layer based neural network quasi-sliding mode control law is proposed to track a desired descending path. The asteroid's gravitational acceleration acting on the spacecraft is described by the polyhedron method. Considering the presence of input constraint and unmodeled acceleration, the dynamic equation of relative motion is presented first. The desired descending path is planned using cubic polynomial method, and a collision detection algorithm is designed. To perform trajectory tracking, a neural network sliding mode control law is given first, where the sliding mode control is used to ensure the convergence of system states. Two radial basis function neural networks (RBFNNs) are respectively used as an approximator for the unmodeled term and a compensator for the difference between the actual control input with magnitude constraint and nominal control. To improve the chattering induced by the traditional sliding mode control and guarantee the reachability of the system, a specific saturation function with dynamic boundary layer is proposed to replace the sign function in the preceding control law. Through the Lyapunov approach, the reachability condition of the control system is given. The improved control law can guarantee the system state move within a gradually shrinking quasi-sliding mode band. Numerical simulation results demonstrate the effectiveness of the proposed control strategy.
Yamada, Kazunori D
2018-01-01
A profile-comparison method with position-specific scoring matrix (PSSM) is among the most accurate alignment methods. Currently, cosine similarity and correlation coefficients are used as scoring functions of dynamic programming to calculate similarity between PSSMs. However, it is unclear whether these functions are optimal for profile alignment methods. By definition, these functions cannot capture nonlinear relationships between profiles. Therefore, we attempted to discover a novel scoring function, which was more suitable for the profile-comparison method than existing functions, using neural networks. Although neural networks required derivative-of-cost functions, the problem being addressed in this study lacked them. Therefore, we implemented a novel derivative-free neural network by combining a conventional neural network with an evolutionary strategy optimization method used as a solver. Using this novel neural network system, we optimized the scoring function to align remote sequence pairs. Our results showed that the pairwise-profile aligner using the novel scoring function significantly improved both alignment sensitivity and precision relative to aligners using existing functions. We developed and implemented a novel derivative-free neural network and aligner (Nepal) for optimizing sequence alignments. Nepal improved alignment quality by adapting to remote sequence alignments and increasing the expressiveness of similarity scores. Additionally, this novel scoring function can be realized using a simple matrix operation and easily incorporated into other aligners. Moreover our scoring function could potentially improve the performance of homology detection and/or multiple-sequence alignment of remote homologous sequences. The goal of the study was to provide a novel scoring function for profile alignment method and develop a novel learning system capable of addressing derivative-free problems. Our system is capable of optimizing the performance of other
National Research Council Canada - National Science Library
Thomas Miconi; Rufin VanRullen
2016-01-01
Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space...
Plumer, Edward S.
1991-01-01
A technique is developed for vehicle navigation and control in the presence of obstacles. A potential function was devised that peaks at the surface of obstacles and has its minimum at the proper vehicle destination. This function is computed using a systolic array and is guaranteed not to have local minima. A feedfoward neural network is then used to control the steering of the vehicle using local potential field information. In this case, the vehicle is a trailer truck backing up. Previous work has demonstrated the capability of a neural network to control steering of such a trailer truck backing to a loading platform, but without obstacles. Now, the neural network was able to learn to navigate a trailer truck around obstacles while backing toward its destination. The network is trained in an obstacle free space to follow the negative gradient of the field, after which the network is able to control and navigate the truck to its target destination in a space of obstacles which may be stationary or movable.
Ziv, Omer; Zaritsky, Assaf; Yaffe, Yakey; Mutukula, Naresh; Edri, Reuven; Elkabetz, Yechiel
2015-10-01
Neural stem cells (NSCs) are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture) and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM)--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII) reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.
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Omer Ziv
2015-10-01
Full Text Available Neural stem cells (NSCs are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.
Hopf bifurcation in a nonlocal nonlinear transport equation stemming from stochastic neural dynamics
Drogoul, Audric; Veltz, Romain
2017-02-01
In this work, we provide three different numerical evidences for the occurrence of a Hopf bifurcation in a recently derived [De Masi et al., J. Stat. Phys. 158, 866-902 (2015) and Fournier and löcherbach, Ann. Inst. H. Poincaré Probab. Stat. 52, 1844-1876 (2016)] mean field limit of a stochastic network of excitatory spiking neurons. The mean field limit is a challenging nonlocal nonlinear transport equation with boundary conditions. The first evidence relies on the computation of the spectrum of the linearized equation. The second stems from the simulation of the full mean field. Finally, the last evidence comes from the simulation of the network for a large number of neurons. We provide a "recipe" to find such bifurcation which nicely complements the works in De Masi et al. [J. Stat. Phys. 158, 866-902 (2015)] and Fournier and löcherbach [Ann. Inst. H. Poincaré Probab. Stat. 52, 1844-1876 (2016)]. This suggests in return to revisit theoretically these mean field equations from a dynamical point of view. Finally, this work shows how the noise level impacts the transition from asynchronous activity to partial synchronization in excitatory globally pulse-coupled networks.
The dynamics of coupled atom and field assisted by continuous external pumping
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Burlak, G.; Hernandez, J.A. [Centro de Investigacion en Ingenieria y Ciencias Aplicadas, Universidad Autonoma de Morelos, Cuernavaca, Morelos (Mexico); Starostenko, O. [Departamento de Fisica, Electronica, Sistemas y Mecatronica, Universidad de las Americas, 72820 Puebla (Mexico)
2006-07-01
The dynamics of a coupled system comprising a two-level atom and cavity field assisted by a continuous external classical field (driving Jaynes-Cummings model) is studied. When the initial field is prepared in a coherent state, the dynamics strongly depends on the algebraic sum of both fields. If this sum is zero (the compensative case) in the system, only the vacuum Rabi oscillations occur. The results with dissipation and external field detuning from the cavity field are also discussed. (Author)
Can strong-field ionization prepare attosecond dynamics?
Pabst, Stefan
2015-01-01
Strong-field ionization (SFI) has been shown to prepare wave packets with few-femtosecond periods. Here, we explore whether this technique can be extended to the attosecond time scale. We introduce an intuitive model for predicting the bandwidth of ionic states that can be coherently prepared by SFI. This bandwidth is given by the Fourier-transformed sub-cycle SFI rate and decreases considerably with increasing central wavelength of the ionizing pulse. Many-body calculations based on time-dependent configuration-interaction singles (TDCIS) quantitatively support this result and reveal an additional decrease of the bandwidth as a consequence of channel interactions and non-adiabatic dynamics. Our results further predict that multi-cycle femtosecond pulses can coherently prepare attosecond wave packets with higher selectivity and versatility compared to single-cycle pulses.
Mean-field games with logistic population dynamics
Gomes, Diogo A.
2013-12-01
In its standard form, a mean-field game can be defined by coupled system of equations, a Hamilton-Jacobi equation for the value function of agents and a Fokker-Planck equation for the density of agents. Traditionally, the latter equation is adjoint to the linearization of the former. Since the Fokker-Planck equation models a population dynamic, we introduce natural features such as seeding and birth, and nonlinear death rates. In this paper we analyze a stationary meanfield game in one dimension, illustrating various techniques to obtain regularity of solutions in this class of systems. In particular we consider a logistic-type model for birth and death of the agents which is natural in problems where crowding affects the death rate of the agents. The introduction of these new terms requires a number of new ideas to obtain wellposedness. In a forthcoming publication we will address higher dimensional models. ©2013 IEEE.
Directory of Open Access Journals (Sweden)
Laura Dempere-Marco
Full Text Available The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1 the presence of a visually salient item reduces the number of items that can be held in working memory, and 2 visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC in contrast to the maximal upper capacity limit only reached under ideal conditions.
Health impact on Economy by Artificial Neural Network and Dynamic Ordinary Least Squares
Directory of Open Access Journals (Sweden)
Marziyeh Sadat Safe
2017-10-01
Full Text Available Introduction: Achievement of economic growth, as one of the most important macroeconomic variables, depends on the precise understanding of potential routes and the factors affecting on it. The aim of this study was to evaluate the health care sector’s effect on Iran Gross Domestic Product (GDP, as the status of economy. Method: Artificial Neural Network (ANN and Dynamic Ordinary Least Squares (DOLS were performed according to Iran GDP as the output variable and the input variables of life expectancy at birth, under five mortality rates, public health expenditures, the number of doctors and hospital beds during 1961-2012 in Iran. Data were collected from the Statistical Center of Iran, the Central Bank of the Islamic Republic of Iran, the World Health Organization and the World Bank databases. Data management and analysis were performed using Eviewes 7, stata 11 and also Mathlab. MSE, MAE and R2 were calculated to assess and compare the models. Results: One percent reduction in deaths of children under 5-years could improve Iran GDP as much as 1.9%. Additionally, one percent increment in the number of doctors, hospital beds or health expenditure would increase GDP by 0.37%, 0.27% and 0.29%, respectively. Mean Absolute Error (MAE demonstrated the superiority of DOLS in the model estimation. Conclusion: The lack of sufficient considerations and excellent models in the health care sector is the main reason for underestimating the effect of this sector on economy. This limitation leads to neglecting the resource allocation to the health care sector, as the great potential motivation of the economic growth.
Short-term EEG dynamics and neural generators evoked by navigational images.
Leroy, Axelle; Cevallos, Carlos; Cebolla, Ana-Maria; Caharel, Stéphanie; Dan, Bernard; Cheron, Guy
2017-01-01
The ecological environment offered by virtual reality is primarily supported by visual information. The different image contents and their rhythmic presentation imply specific bottom-up and top-down processing. Because these processes already occur during passive observation we studied the brain responses evoked by the presentation of specific 3D virtual tunnels with respect to 2D checkerboard. For this, we characterized electroencephalograhy dynamics (EEG), the evoked potentials and related neural generators involved in various visual paradigms. Time-frequency analysis showed modulation of alpha-beta oscillations indicating the presence of stronger prediction and after-effects of the 3D-tunnel with respect to the checkerboard. Whatever the presented image, the generators of the P100 were situated bilaterally in the occipital cortex (BA18, BA19) and in the right inferior temporal cortex (BA20). In checkerboard but not 3D-tunnel presentation, the left fusiform gyrus (BA37) was additionally recruited. P200 generators were situated in the temporal cortex (BA21) and the cerebellum (lobule VI/Crus I) specifically for the checkerboard while the right parahippocampal gyrus (BA36) and the cerebellum (lobule IV/V and IX/X) were involved only during the 3D-tunnel presentation. For both type of image, P300 generators were localized in BA37 but also in BA19, the right BA21 and the cerebellar lobule VI for only the checkerboard and the left BA20-BA21 for only the 3D-tunnel. Stronger P300 delta-theta oscillations recorded in this later situation point to a prevalence of the effect of changing direction over the proper visual content of the 3D-tunnel. The parahippocampal gyrus (BA36) implicated in navigation was also identified when the 3D-tunnel was compared to their scrambled versions, highlighting an action-oriented effect linked to navigational content.
Short-term EEG dynamics and neural generators evoked by navigational images.
Directory of Open Access Journals (Sweden)
Axelle Leroy
Full Text Available The ecological environment offered by virtual reality is primarily supported by visual information. The different image contents and their rhythmic presentation imply specific bottom-up and top-down processing. Because these processes already occur during passive observation we studied the brain responses evoked by the presentation of specific 3D virtual tunnels with respect to 2D checkerboard. For this, we characterized electroencephalograhy dynamics (EEG, the evoked potentials and related neural generators involved in various visual paradigms. Time-frequency analysis showed modulation of alpha-beta oscillations indicating the presence of stronger prediction and after-effects of the 3D-tunnel with respect to the checkerboard. Whatever the presented image, the generators of the P100 were situated bilaterally in the occipital cortex (BA18, BA19 and in the right inferior temporal cortex (BA20. In checkerboard but not 3D-tunnel presentation, the left fusiform gyrus (BA37 was additionally recruited. P200 generators were situated in the temporal cortex (BA21 and the cerebellum (lobule VI/Crus I specifically for the checkerboard while the right parahippocampal gyrus (BA36 and the cerebellum (lobule IV/V and IX/X were involved only during the 3D-tunnel presentation. For both type of image, P300 generators were localized in BA37 but also in BA19, the right BA21 and the cerebellar lobule VI for only the checkerboard and the left BA20-BA21 for only the 3D-tunnel. Stronger P300 delta-theta oscillations recorded in this later situation point to a prevalence of the effect of changing direction over the proper visual content of the 3D-tunnel. The parahippocampal gyrus (BA36 implicated in navigation was also identified when the 3D-tunnel was compared to their scrambled versions, highlighting an action-oriented effect linked to navigational content.
Dempere-Marco, Laura; Melcher, David P.; Deco, Gustavo
2012-01-01
The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions. PMID:22952608
Dempere-Marco, Laura; Melcher, David P; Deco, Gustavo
2012-01-01
The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions.
Directory of Open Access Journals (Sweden)
T. R. Sun
2012-08-01
Full Text Available We performed global MHD simulations of the geosynchronous magnetic field in response to fast solar wind dynamic pressure (Pd enhancements. Taking three Pd enhancement events in 2000 as examples, we found that the main features of the total field B and the dominant component Bz can be efficiently predicted by the MHD model. The predicted B and Bz varies with local time, with the highest level near noon and a slightly lower level around mid-night. However, it is more challenging to accurately predict the responses of the smaller component at the geosynchronous orbit (i.e., Bx and By. In contrast, the limitations of T01 model in predicting responses to fast Pd enhancements are presented.
Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory
DEFF Research Database (Denmark)
Lüders, Benno; Schläger, Mikkel; Korach, Aleksandra
2017-01-01
Training neural networks to quickly learn new skills without forgetting previously learned skills is an important open challenge in machine learning. A common problem for adaptive networks that can learn during their lifetime is that the weights encoding a particular task are often overridden when...... a new task is learned. This paper takes a step in overcoming this limitation by building on the recently proposed Evolving Neural Turing Machine (ENTM) approach. In the ENTM, neural networks are augmented with an external memory component that they can write to and read from, which allows them to store...
Dynamics of Uncertain Discrete-Time Neural Network with Delay and Impulses
Directory of Open Access Journals (Sweden)
Xuehui Mei
2015-01-01
Full Text Available The stability of discrete-time impulsive delay neural networks with and without uncertainty is investigated. First, by using Razumikhin-type theorem, a new less conservative condition for the exponential stability of discrete-time neural network with delay and impulse is proposed. Moreover, some new sufficient conditions are derived to guarantee the stability of uncertain discrete-time neural network with delay and impulse by using Lyapunov function and linear matrix inequality (LMI. Finally, several examples with numerical simulation are presented to demonstrate the effectiveness of the obtained results.
Non-local correlations within dynamical mean field theory
Energy Technology Data Exchange (ETDEWEB)
Li, Gang
2009-03-15
The contributions from the non-local fluctuations to the dynamical mean field theory (DMFT) were studied using the recently proposed dual fermion approach. Straight forward cluster extensions of DMFT need the solution of a small cluster, where all the short-range correlations are fully taken into account. All the correlations beyond the cluster scope are treated in the mean-field level. In the dual fermion method, only a single impurity problem needs to be solved. Both the short and long-range correlations could be considered on equal footing in this method. The weak-coupling nature of the dual fermion ensures the validity of the finite order diagram expansion. The one and two particle Green's functions calculated from the dual fermion approach agree well with the Quantum Monte Carlo solutions, and the computation time is considerably less than with the latter method. The access of the long-range order allows us to investigate the collective behavior of the electron system, e.g. spin wave excitations. (orig.)
Controlled nonperturbative dynamics of quantum fields out of equilibrium
Energy Technology Data Exchange (ETDEWEB)
Berges, Juergen E-mail: j.berges@thphys.uni-heidelberg.de
2002-03-11
We compute the nonequilibrium real-time evolution of an O(N)-symmetric scalar quantum field theory from a systematic 1/N expansion of the 2PI effective action to next-to-leading order, which includes scattering and memory effects. In contrast to the standard 1/N expansion of the 1PI effective action, the next-to-leading-order expansion in presence of a possible expectation value for the composite operator leads to a bounded-time evolution where the truncation error may be controlled by higher powers in 1/N. We present a detailed comparison with the leading-order results and determine the range of validity of standard mean-field-type approximations. We investigate 'quench' and 'tsunami' initial conditions frequently used to mimic idealized far-from-equilibrium pion dynamics in the context of heavy-ion collisions. For spatially homogeneous initial conditions, we find three generic regimes, characterized by an early-time exponential damping, a parametrically slow (power-law) behavior at intermediate times, and a late-time exponential approach to thermal equilibrium. The different time scales are obtained from a numerical solution of the time-reversal invariant equations in 1+1 dimensions without further approximations. We discuss in detail the out-of-equilibrium behavior of the nontrivial n-point correlation functions as well as the evolution of a particle number distribution and inverse slope parameter.
Quantum mean-field theory of collective dynamics and tunneling
Energy Technology Data Exchange (ETDEWEB)
Negele, J.W.
1981-01-01
A fundamental problem in quantum many-body theory is formulation of a microscopic theory of collective motion. For self-bound, saturating systems like finite nuclei described in the context of nonrelativistic quantum mechanics with static interactions, the essential problem is how to formulate a systematic quantal theory in which the relevant collective variables and their dynamics arise directly and naturally from the Hamiltonian and the system under consideration. Significant progress has been made recently in formulating the quantum many-body problem in terms of an expansion about solutions to time-dependent mean-field equations. The essential ideas, principal results, and illustrative examples are summarized. An exact expression for an observable of interest is written using a functional integral representation for the evolution operator, and tractable time-dependent mean field equations are obtained by application of the stationary-phase approximation (SPA) to the functional integral. Corrections to the lowest-order theory may be systematically enumerated. 6 figures. (RWR)
Phenotyping for the dynamics of field wheat root system architecture
Chen, Xinxin; Ding, Qishuo; Błaszkiewicz, Zbigniew; Sun, Jiuai; Sun, Qian; He, Ruiyin; Li, Yinian
2017-01-01
We investigated a method to quantify field-state wheat RSA in a phenotyping way, depicting the 3D topology of wheat RSA in 14d periods. The phenotyping procedure, proposed for understanding the spatio-temporal variations of root-soil interaction and the RSA dynamics in the field, is realized with a set of indices of mm scale precision, illustrating the gradients of both wheat root angle and elongation rate along soil depth, as well as the foraging potential along the side directions. The 70d was identified as the shifting point distinguishing the linear root length elongation from power-law development. Root vertical angle in the 40 mm surface soil layer was the largest, but steadily decreased along the soil depth. After 98d, larger root vertical angle appeared in the deep soil layers. PAC revealed a stable root foraging potential in the 0-70d period, which increased rapidly afterwards (70-112d). Root foraging potential, explained by MaxW/MaxD ratio, revealed an enhanced gravitropism in 14d period. No-till post-paddy wheat RLD decreased exponentially in both depth and circular directions, with 90% roots concentrated within the top 20 cm soil layer. RER along soil depth was either positive or negative, depending on specific soil layers and the sampling time.
Pulsed DC Electric Field-Induced Differentiation of Cortical Neural Precursor Cells.
Directory of Open Access Journals (Sweden)
Hui-Fang Chang
Full Text Available We report the differentiation of neural stem and progenitor cells solely induced by direct current (DC pulses stimulation. Neural stem and progenitor cells in the adult mammalian brain are promising candidates for the development of therapeutic neuroregeneration strategies. The differentiation of neural stem and progenitor cells depends on various in vivo environmental factors, such as nerve growth factor and endogenous EF. In this study, we demonstrated that the morphologic and phenotypic changes of mouse neural stem and progenitor cells (mNPCs could be induced solely by exposure to square-wave DC pulses (magnitude 300 mV/mm at frequency of 100-Hz. The DC pulse stimulation was conducted for 48 h, and the morphologic changes of mNPCs were monitored continuously. The length of primary processes and the amount of branching significantly increased after stimulation by DC pulses for 48 h. After DC pulse treatment, the mNPCs differentiated into neurons, astrocytes, and oligodendrocytes simultaneously in stem cell maintenance medium. Our results suggest that simple DC pulse treatment could control the fate of NPCs. With further studies, DC pulses may be applied to manipulate NPC differentiation and may be used for the development of therapeutic strategies that employ NPCs to treat nervous system disorders.
Pulsed DC Electric Field-Induced Differentiation of Cortical Neural Precursor Cells.
Chang, Hui-Fang; Lee, Ying-Shan; Tang, Tang K; Cheng, Ji-Yen
2016-01-01
We report the differentiation of neural stem and progenitor cells solely induced by direct current (DC) pulses stimulation. Neural stem and progenitor cells in the adult mammalian brain are promising candidates for the development of therapeutic neuroregeneration strategies. The differentiation of neural stem and progenitor cells depends on various in vivo environmental factors, such as nerve growth factor and endogenous EF. In this study, we demonstrated that the morphologic and phenotypic changes of mouse neural stem and progenitor cells (mNPCs) could be induced solely by exposure to square-wave DC pulses (magnitude 300 mV/mm at frequency of 100-Hz). The DC pulse stimulation was conducted for 48 h, and the morphologic changes of mNPCs were monitored continuously. The length of primary processes and the amount of branching significantly increased after stimulation by DC pulses for 48 h. After DC pulse treatment, the mNPCs differentiated into neurons, astrocytes, and oligodendrocytes simultaneously in stem cell maintenance medium. Our results suggest that simple DC pulse treatment could control the fate of NPCs. With further studies, DC pulses may be applied to manipulate NPC differentiation and may be used for the development of therapeutic strategies that employ NPCs to treat nervous system disorders.
Vomweg, T W; Teifke, A; Kauczor, H U; Achenbach, T; Rieker, O; Schreiber, W G; Heitmann, K R; Beier, T; Thelen, M
2005-05-01
Investigation and statistical evaluation of "Self-Organizing Maps," a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically. A well balanced neural network achieved a sensitivity of 90.5 % and a specificity of 72.2 % in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a "typical malignant" signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map. Self-Organizing Maps are capable of classifying a dynamic signal/time curve as "typical benign" or "typical malignant." Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future.
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…
Shakouri, Khosrow; Behler, Jörg; Meyer, Jörg; Kroes, Geert-Jan
2017-05-18
Ab initio molecular dynamics (AIMD) simulations enable the accurate description of reactive molecule-surface scattering especially if energy transfer involving surface phonons is important. However, presently, the computational expense of AIMD rules out its application to systems where reaction probabilities are smaller than about 1%. Here we show that this problem can be overcome by a high-dimensional neural network fit of the molecule-surface interaction potential, which also incorporates the dependence on phonons by taking into account all degrees of freedom of the surface explicitly. As shown for N2 + Ru(0001), which is a prototypical case for highly activated dissociative chemisorption, the method allows an accurate description of the coupling of molecular and surface atom motion and accurately accounts for vibrational properties of the employed slab model of Ru(0001). The neural network potential allows reaction probabilities as low as 10-5 to be computed, showing good agreement with experimental results.
Directory of Open Access Journals (Sweden)
Simon Sándor
2013-03-01
Full Text Available The evolution of information society, globalisation, made great changes concerning the human-computer relationship. Mobile technology gives new perspectives for the administration of enterprises and decision making. Microsoft Dynamics NAV is not only a software capable to model the various activities of a firm through the desktop platform, but with a properly developed user interface which is optimised for a mobile device, the possibilities of the use of this ERP software can be broadened with workflows characterised with great distances. In this study I show how a field sales workflow can be modelled and managed by me with the software environment “NAV Anywhere Framework”. The survey gives a closer look at both a suggestible administrative process for an imagined workflow and its technical management on a mobile device. For my development creates specialised and dynamic web pages for a mobile device, it can be accessible from a lot of types of smart phones and tablet computers.
Caldesmon regulates actin dynamics to influence cranial neural crest migration in Xenopus
Nie, Shuyi; Kee, Yun; Bronner-Fraser, Marianne
2011-01-01
Caldesmon (CaD) is an important actin modulator that associates with actin filaments to regulate cell morphology and motility. Although extensively studied in cultured cells, there is little functional information regarding the role of CaD in migrating cells in vivo. Here we show that nonmuscle CaD is highly expressed in both premigratory and migrating cranial neural crest cells of Xenopus embryos. Depletion of CaD with antisense morpholino oligonucleotides causes cranial neural crest cells t...
Casey, M
1996-08-15
Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure.
Dynamical downscaling of wind fields for wind power applications
Mengelkamp, H.-T.; Huneke, S.; Geyer, J.
2010-09-01
Dynamical downscaling of wind fields for wind power applications H.-T. Mengelkamp*,**, S. Huneke**, J, Geyer** *GKSS Research Center Geesthacht GmbH **anemos Gesellschaft für Umweltmeteorologie mbH Investments in wind power require information on the long-term mean wind potential and its temporal variations on daily to annual and decadal time scales. This information is rarely available at specific wind farm sites. Short-term on-site measurements usually are only performed over a 12 months period. These data have to be set into the long-term perspective through correlation to long-term consistent wind data sets. Preliminary wind information is often asked for to select favourable wind sites over regional and country wide scales. Lack of high-quality wind measurements at weather stations was the motivation to start high resolution wind field simulations The simulations are basically a refinement of global scale reanalysis data by means of high resolution simulations with an atmospheric mesoscale model using high-resolution terrain and land-use data. The 3-dimensional representation of the atmospheric state available every six hours at 2.5 degree resolution over the globe, known as NCAR/NCEP reanalysis data, forms the boundary conditions for continuous simulations with the non-hydrostatic atmospheric mesoscale model MM5. MM5 is nested in itself down to a horizontal resolution of 5 x 5 km². The simulation is performed for different European countries and covers the period 2000 to present and is continuously updated. Model variables are stored every 10 minutes for various heights. We have analysed the wind field primarily. The wind data set is consistent in space and time and provides information on the regional distribution of the long-term mean wind potential, the temporal variability of the wind potential, the vertical variation of the wind potential, and the temperature, and pressure distribution (air density). In the context of wind power these data are used
Djurabekova, Flyura; Pohjonen, Aarne; Nordlund, Kai
2011-01-01
The effect of electric fields on metal surfaces is fairly well studied, resulting in numerous analytical models developed to understand the mechanisms of ionization of surface atoms observed at very high electric fields, as well as the general behavior of a metal surface in this condition. However, the derivation of analytical models does not include explicitly the structural properties of metals, missing the link between the instantaneous effects owing to the applied field and the consequent response observed in the metal surface as a result of an extended application of an electric field. In the present work, we have developed a concurrent electrodynamic–molecular dynamic model for the dynamical simulation of an electric-field effect and subsequent modification of a metal surface in the framework of an atomistic molecular dynamics (MD) approach. The partial charge induced on the surface atoms by the electric field is assessed by applying the classical Gauss law. The electric forces acting on the partially...
Kashina, M. A.; Alabuzhev, A. A.
2017-12-01
The dynamics of the incompressible fluid drop under the non-uniform electric field are considered. The drop is bounded axially by two parallel solid planes and the case of heterogeneous plates is investigated. The external electric field acts as an external force that causes motion of the contact line. We assume that the electric current is alternative current and the AC filed amplitude is a spatially non-uniform function. In equilibrium, the drop has the form of a circular cylinder. The equilibrium contact angle is 0.5π. In order to describe this contact line motion the modified Hocking boundary condition is applied: the velocity of the contact line is proportional to the deviation of the contact angle and the speed of the fast relaxation processes, which frequency is proportional to twice the frequency of the electric field. The Hocking parameter depends on the polar angle, i.e. the coefficient of the interaction between the plate and the fluid (the contact line) is a function of the plane coordinates. This function is expanded in a series of the Laplace operator eigenfunctions.
Chang, Victor; Basheer, Azam; Baumer, Timothy; Oravec, Daniel; McDonald, Colin P; Bey, Michael J; Bartol, Stephen; Yeni, Yener N
2017-10-01
Neural foraminal dimensions are considered important in nerve root compression and development of cervical radiculopathy, but baseline data regarding their range during normal motion are not available. An in vivo study of cervical foraminal motion was conducted to characterize normal 3D dynamic foraminal dimensions during physiological neck motion and compare between different tasks and intervertebral segments. Biplane X-ray imaging and computed tomography-based markerless tracking were used to measure foraminal height (FH) and width (FW) from five asymptomatic subjects during neck axial rotation and extension. FH and FW were quantified as the minimum (SI.Min and AP.Min), range (SI.Range and AP.Range), and median (SI.Med and AP.Med) of superoinferior (SI) and anteroposterior (AP) dimensions for each trial and as the coefficient of variation of these variables from three trials (SI.Med.CV and AP.Med.CV, SI.Range.CV and AP.Range.CV) at C3-4 through C6-7 levels for each subject. Differences were analyzed using mixed model ANOVA. AP.Range and AP.Med.CV were greater (P < 0.0001) while AP.Min and AP.Range.CV were smaller (P < 0.0006 and P < 0.0005) during neck extension than rotation. SI.Range and SI.Med.CV were greater for extension than rotation at C5-6 (P < 0.002 and P < 0.03), whereas SI.Med.CV was greater for rotation than extension at C3-4 (P < 0.03). AP.Range (P < 0.02), AP.Med.CV (P < 0.05), SI.Range (P < 0.0004), and SI.Med.CV (P < 0.02) were different between cervical levels, the latter two being during extension only. Patterns of FH and FW during normal motion are different between tasks and cervical levels. These findings are expected to provide a basis for future studies of spinal degeneration and surgical efficacy.
Liebenthal, Einat; Silbersweig, David A; Stern, Emily
2016-01-01
Rapid assessment of emotions is important for detecting and prioritizing salient input. Emotions are conveyed in spoken words via verbal and non-verbal channels that are mutually informative and unveil in parallel over time, but the neural dynamics and interactions of these processes are not well understood. In this paper, we review the literature on emotion perception in faces, written words, and voices, as a basis for understanding the functional organization of emotion perception in spoken words. The characteristics of visual and auditory routes to the amygdala-a subcortical center for emotion perception-are compared across these stimulus classes in terms of neural dynamics, hemispheric lateralization, and functionality. Converging results from neuroimaging, electrophysiological, and lesion studies suggest the existence of an afferent route to the amygdala and primary visual cortex for fast and subliminal processing of coarse emotional face cues. We suggest that a fast route to the amygdala may also function for brief non-verbal vocalizations (e.g., laugh, cry), in which emotional category is conveyed effectively by voice tone and intensity. However, emotional prosody which evolves on longer time scales and is conveyed by fine-grained spectral cues appears to be processed via a slower, indirect cortical route. For verbal emotional content, the bulk of current evidence, indicating predominant left lateralization of the amygdala response and timing of emotional effects attributable to speeded lexical access, is more consistent with an indirect cortical route to the amygdala. Top-down linguistic modulation may play an important role for prioritized perception of emotions in words. Understanding the neural dynamics and interactions of emotion and language perception is important for selecting potent stimuli and devising effective training and/or treatment approaches for the alleviation of emotional dysfunction across a range of neuropsychiatric states.
Directory of Open Access Journals (Sweden)
Daqi Zhu
2014-03-01
Full Text Available In this paper a biologically inspired neural dynamics and map planning based approach are simultaneously proposed for AUV (Autonomous Underwater Vehicle path planning and obstacle avoidance in an unknown dynamic environment. Firstly the readings of an ultrasonic sensor are fused into the map using the D-S (Dempster-Shafer inference rule and a two-dimensional occupancy grid map is built. Secondly the dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation. The AUV path is autonomously generated from the dynamic activity landscape of the neural network and previous AUV location. Finally, simulation results show high quality path optimization and obstacle avoidance behaviour for the AUV.
DEFF Research Database (Denmark)
Jensen, Dan Børge; Kristensen, Anders Ringgaard
2016-01-01
, can provide early and automatic detection of diarrhea. To determine the best approach to achieve this goal, we compared 36 different strategies for combining a multivariate dynamic linear model (DLM) with an artificial neural network (ANN). We used data collected in 16 pens between November 2013...... (SP), and the sensitivity (SE). The best performance was seen when using a training window with a total of 42 hours for the numerical forecast errors, which produced an error rate=0.16, a specificity=0.88, and a sensitivity=0.80. For the other tested strategies, the ranges of error rates...
Suzuki, Makoto; Sato, Masanao; Koyama, Hiroshi; Hara, Yusuke; Hayashi, Kentaro; Yasue, Naoko; Imamura, Hiromi; Fujimori, Toshihiko; Nagai, Takeharu; Campbell, Robert E; Ueno, Naoto
2017-04-01
Early in the development of the central nervous system, progenitor cells undergo a shape change, called apical constriction, that triggers the neural plate to form a tubular structure. How apical constriction in the neural plate is controlled and how it contributes to tissue morphogenesis are not fully understood. In this study, we show that intracellular calcium ions (Ca(2+)) are required for Xenopus neural tube formation and that there are two types of Ca(2+)-concentration changes, a single-cell and a multicellular wave-like fluctuation, in the developing neural plate. Quantitative imaging analyses revealed that transient increases in Ca(2+) concentration induced cortical F-actin remodeling, apical constriction and accelerations of the closing movement of the neural plate. We also show that extracellular ATP and N-cadherin (cdh2) participate in the Ca(2+)-induced apical constriction. Furthermore, our mathematical model suggests that the effect of Ca(2+) fluctuations on tissue morphogenesis is independent of fluctuation frequency and that fluctuations affecting individual cells are more efficient than those at the multicellular level. We propose that distinct Ca(2+) signaling patterns differentially modulate apical constriction for efficient epithelial folding and that this mechanism has a broad range of physiological outcomes. © 2017. Published by The Company of Biologists Ltd.
Low-dimensional attractor for neural activity from local field potentials in optogenetic mice.
Oprisan, Sorinel A; Lynn, Patrick E; Tompa, Tamas; Lavin, Antonieta
2015-01-01
We used optogenetic mice to investigate possible nonlinear responses of the medial prefrontal cortex (mPFC) local network to light stimuli delivered by a 473 nm laser through a fiber optics. Every 2 s, a brief 10 ms light pulse was applied and the local field potentials (LFPs) were recorded with a 10 kHz sampling rate. The experiment was repeated 100 times and we only retained and analyzed data from six animals that showed stable and repeatable response to optical stimulations. The presence of nonlinearity in our data was checked using the null hypothesis that the data were linearly correlated in the temporal domain, but were random otherwise. For each trail, 100 surrogate data sets were generated and both time reversal asymmetry and false nearest neighbor (FNN) were used as discriminating statistics for the null hypothesis. We found that nonlinearity is present in all LFP data. The first 0.5 s of each 2 s LFP recording were dominated by the transient response of the networks. For each trial, we used the last 1.5 s of steady activity to measure the phase resetting induced by the brief 10 ms light stimulus. After correcting the LFPs for the effect of phase resetting, additional preprocessing was carried out using dendrograms to identify "similar" groups among LFP trials. We found that the steady dynamics of mPFC in response to light stimuli could be reconstructed in a three-dimensional phase space with topologically similar "8"-shaped attractors across different animals. Our results also open the possibility of designing a low-dimensional model for optical stimulation of the mPFC local network.
Field theory of bicritical and tetracritical points. II. Relaxational dynamics.
Folk, R; Holovatch, Yu; Moser, G
2008-10-01
We calculate the relaxational dynamical critical behavior of systems of O(n_||)(plus sign in circle)O(n_perpendicular) symmetry by renormalization group method within the minimal subtraction scheme in two-loop order. The three different bicritical static universality classes previously found for such systems correspond to three different dynamical universality classes within the static borderlines. The Heisenberg and the biconical fixed point lead to strong dynamic scaling whereas in the region of stability of the decoupled fixed point weak dynamic scaling holds. Due to the neighborhood of the stability border between the strong and the weak scaling dynamic fixed point to the dynamical stable fixed point a very small dynamic transient exponent of omega(Beta)_(v) =0.0044 is present in the dynamics for the physically important case n_|| =1 and n_perpendicular =2 in d=3 .
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Hanlu Ma
Full Text Available For humans and animals, the ability to discriminate speech and conspecific vocalizations is an important physiological assignment of the auditory system. To reveal the underlying neural mechanism, many electrophysiological studies have investigated the neural responses of the auditory cortex to conspecific vocalizations in monkeys. The data suggest that vocalizations may be hierarchically processed along an anterior/ventral stream from the primary auditory cortex (A1 to the ventral prefrontal cortex. To date, the organization of vocalization processing has not been well investigated in the auditory cortex of other mammals. In this study, we examined the spike activities of single neurons in two early auditory cortical regions with different anteroposterior locations: anterior auditory field (AAF and posterior auditory field (PAF in awake cats, as the animals were passively listening to forward and backward conspecific calls (meows and human vowels. We found that the neural response patterns in PAF were more complex and had longer latency than those in AAF. The selectivity for different vocalizations based on the mean firing rate was low in both AAF and PAF, and not significantly different between them; however, more vocalization information was transmitted when the temporal response profiles were considered, and the maximum transmitted information by PAF neurons was higher than that by AAF neurons. Discrimination accuracy based on the activities of an ensemble of PAF neurons was also better than that of AAF neurons. Our results suggest that AAF and PAF are similar with regard to which vocalizations they represent but differ in the way they represent these vocalizations, and there may be a complex processing stream between them.
Malherbe, T K; Hanekom, T; Hanekom, J J
2015-09-01
The resistivity of bone is the most variable of all the tissues in the human body, ranging from 312 Ω cm to 84,745 Ω cm. Volume conduction models of cochlear implants have generally used a resistivity value of 641 Ω cm for the bone surrounding the cochlea. This study investigated the effect that bone resistivity has on modelled neural thresholds and intracochlear potentials using user-specific volume conduction models of implanted cochleae applying monopolar stimulation. The complexity of the description of the head volume enveloping the cochlea was varied between a simple infinite bone volume and a detailed skull containing a brain volume, scalp and accurate return electrode position. It was found that, depending on the structure of the head model and implementation of the return electrode, different bone resistivity values are necessary to match model predictions to data from literature. Modelled forward-masked spatial tuning curve (fmSTC) widths and slopes and intracochlear electric field profile length constants were obtained for a range of bone resistivity values for the various head models. The predictions were compared to measurements found in literature. It was concluded that, depending on the head model, a bone resistivity value between 3500 Ω cm and 10,500 Ω cm allows prediction of neural and electrical responses that match measured data. A general recommendation is made to use a resistivity value of approximately 10,000 Ω cm for bone volumes in conduction models of the implanted cochlea when neural excitation is predicted and a value of approximately 6500 Ω cm when predicting electric fields inside the cochlear duct. Copyright © 2015 Elsevier B.V. All rights reserved.
Vlasov simulations of electron hole dynamics in inhomogeneous magnetic field
Kuzichev, Ilya; Vasko, Ivan; Agapitov, Oleksiy; Mozer, Forrest; Artemyev, Anton
2017-04-01
Electron holes (EHs) or phase space vortices are solitary electrostatic waves existing due to electrons trapped within EH electrostatic potential. Since the first direct observation [1], EHs have been widely observed in the Earth's magnetosphere: in reconnecting current sheets [2], injection fronts [3], auroral region [4], and many other space plasma systems. EHs have typical spatial scales up to tens of Debye lengths, electric field amplitudes up to hundreds of mV/m and propagate along magnetic field lines with velocities of about electron thermal velocity [5]. The role of EHs in energy dissipation and supporting of large-scale potential drops is under active investigation. The accurate interpretation of spacecraft observations requires understanding of EH evolution in inhomogeneous plasma. The critical role of plasma density gradients in EH evolution was demonstrated in [6] using PIC simulations. Interestingly, up to date no studies have addressed a role of magnetic field gradients in EH evolution. In this report, we use 1.5D gyrokinetic Vlasov code to demonstrate the critical role of magnetic field gradients in EH dynamics. We show that EHs propagating into stronger (weaker) magnetic field are decelerated (accelerated) with deceleration (acceleration) rate dependent on the magnetic field gradient. Remarkably, the reflection points of decelerating EHs are independent of the average magnetic field gradient in the system and depend only on the EH parameters. EHs are decelerated (accelerated) faster than would follow from the "quasi-particle" concept assuming that EH is decelerated (accelerated) entirely due to the mirror force acting on electrons trapped within EH. We demonstrate that EH propagation in inhomogeneous magnetic fields results in development of a net potential drop along an EH, which depends on the magnetic field gradient. The revealed features will be helpful for interpreting spacecraft observations and results of advanced particle simulations. In
Robb, Daniel T.; Ostrander, Aaron
2014-02-01
We present numerical evidence for an extended order parameter and conjugate field for the dynamic phase transition in a Ginzburg-Landau mean-field model driven by an oscillating field. The order parameter, previously taken to be the time-averaged magnetization, comprises the deviations of the Fourier components of the magnetization from their values at the critical period. The conjugate field, previously taken to be the time-averaged magnetic field, comprises the even Fourier components of the field. The scaling exponents β and δ associated with the extended order parameter and conjugate field are shown numerically to be consistent with their values in the equilibrium mean-field model.
Dorado-Moreno, Manuel; Pérez-Ortiz, María; Gutiérrez, Pedro A; Ciria, Rubén; Briceño, Javier; Hervás-Martínez, César
2017-03-01
Create an efficient decision-support model to assist medical experts in the process of organ allocation in liver transplantation. The mathematical model proposed here uses different sources of information to predict the probability of organ survival at different thresholds for each donor-recipient pair considered. Currently, this decision is mainly based on the Model for End-stage Liver Disease, which depends only on the severity of the recipient and obviates donor-recipient compatibility. We therefore propose to use information concerning the donor, the recipient and the surgery, with the objective of allocating the organ correctly. The database consists of information concerning transplants conducted in 7 different Spanish hospitals and the King's College Hospital (United Kingdom). The state of the patients is followed up for 12 months. We propose to treat the problem as an ordinal classification one, where we predict the organ survival at different thresholds: less than 15 days, between 15 and 90 days, between 90 and 365 days and more than 365 days. This discretization is intended to produce finer-grain survival information (compared with the common binary approach). However, it results in a highly imbalanced dataset in which more than 85% of cases belong to the last class. To solve this, we combine two approaches, a cost-sensitive evolutionary ordinal artificial neural network (ANN) (in which we propose to incorporate dynamic weights to make more emphasis on the worst classified classes) and an ordinal over-sampling technique (which adds virtual patterns to the minority classes and thus alleviates the imbalanced nature of the dataset). The results obtained by our proposal are promising and satisfactory, considering the overall accuracy, the ordering of the classes and the sensitivity of minority classes. In this sense, both the dynamic costs and the over-sampling technique improve the base results of the considered ANN-based method. Comparing our model with
Kamau, Edwin Ngugi
2016-01-01
The generation and manipulation of electromagnetic field distributions plays an essential role in physics in general, and particularly in the vast field of physical optics. In the current state of the art, one of the most convenient methods of performing this task is provided by either static or dynamic diffractive as well as holographic optical elements. Currently available dynamic optical elements, such as spatial light modulators, do offer on the one hand high temporal flexibility. They ho...
Zhao, Qingbai; Zhou, Zhijin; Xu, Haibo; Chen, Shi; Xu, Fang; Fan, Wenliang; Han, Lei
2013-01-01
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. PMID:23555020
Barrett, Frederick S; Preller, Katrin H; Herdener, Marcus; Janata, Petr; Vollenweider, Franz X
2017-09-28
Classic psychedelic drugs (serotonin 2A, or 5HT2A, receptor agonists) have notable effects on music listening. In the current report, blood oxygen level-dependent (BOLD) signal was collected during music listening in 25 healthy adults after administration of placebo, lysergic acid diethylamide (LSD), and LSD pretreated with the 5HT2A antagonist ketanserin, to investigate the role of 5HT2A receptor signaling in the neural response to the time-varying tonal structure of music. Tonality-tracking analysis of BOLD data revealed that 5HT2A receptor signaling alters the neural response to music in brain regions supporting basic and higher-level musical and auditory processing, and areas involved in memory, emotion, and self-referential processing. This suggests a critical role of 5HT2A receptor signaling in supporting the neural tracking of dynamic tonal structure in music, as well as in supporting the associated increases in emotionality, connectedness, and meaningfulness in response to music that are commonly observed after the administration of LSD and other psychedelics. Together, these findings inform the neuropsychopharmacology of music perception and cognition, meaningful music listening experiences, and altered perception of music during psychedelic experiences. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Cui, Yiqian; Shi, Junyou; Wang, Zili
2015-11-01
Quantum Neural Networks (QNN) models have attracted great attention since it innovates a new neural computing manner based on quantum entanglement. However, the existing QNN models are mainly based on the real quantum operations, and the potential of quantum entanglement is not fully exploited. In this paper, we proposes a novel quantum neuron model called Complex Quantum Neuron (CQN) that realizes a deep quantum entanglement. Also, a novel hybrid networks model Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) is proposed based on Complex Quantum Neuron (CQN). CRQDNN is a three layer model with both CQN and classical neurons. An infinite impulse response (IIR) filter is embedded in the Networks model to enable the memory function to process time series inputs. The Levenberg-Marquardt (LM) algorithm is used for fast parameter learning. The networks model is developed to conduct time series predictions. Two application studies are done in this paper, including the chaotic time series prediction and electronic remaining useful life (RUL) prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Lin Lu
2016-01-01
Full Text Available We investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponentially stable. Moreover, the theoretical findings of this paper on the almost periodic solution are applied to prove the existence and stability of periodic solution for memristor-based shunting inhibitory cellular neural networks with leakage delays and periodic coefficients. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies of Wu (2011 and Chen and Cao (2002.
Elman neural network for modeling and predictive control of delayed dynamic systems
Directory of Open Access Journals (Sweden)
Wysocki Antoni
2016-03-01
Full Text Available The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.
Pashandi, Zaiddodine; Molakarimi, Maryam; Mohseni, Ammar; Sajedi, Reza H; Taghdir, Majid; Naderi-Manesh, Hossein
2017-08-19
Photoinactivation is a common phenomenon in bioluminescence ctenophore photoproteins (e.g mnemiopsin, berovin and BfosPP) with still unknown mechanism. The activity of coelenterate photoproteins (e.g aequorin), which has high structural similarity with ctenophore photoproteins, is not affected by light. Recently, we have characterized the effects of light on ctenophore photoprotein mnemiopsin, in different conformations, which has demonstrated light induced structural changes, uniquely secondary structures, of both apo and holo mnemiopsin. This paper is further expansion of our previous work, by applying molecular dynamics simulations to investigate photoinactivation related dynamics of berovin at atomistic level, in comparison with aequorin, under the influence of electric component of electromagnetic field. The results have indicated that the intense electric filed could influence structure of both berovin and aequorin but in different manner, whereas moderate electric field only effects on berovin's structure remarkably. In this case, increased helicity of residues E180-M193 and decreased helical contents of L38-D46 and L125-D138 segments are considerable in berovin as well as flexibility elevation of calcium binding loops. These changes cause structural expansion of berovin, especially at N-terminal domain, in direction of electric field. In conclusion, the induced structural changes of mentioned helical parts together with elevated fluctuation of their adjacent segments, N26-D46 and M193-Y206, indicate the influence of light on substrate stabilizing residues, Arg41 and Y204. This condition could presumably leads to inactivation of bioluminescence reaction due to separation of substrate from the cavity of the protein. Copyright © 2017 Elsevier Inc. All rights reserved.
Capone, Cristiano; Mattia, Maurizio
2017-01-01
Neural field models are powerful tools to investigate the richness of spatiotemporal activity patterns like waves and bumps, emerging from the cerebral cortex. Understanding how spontaneous and evoked activity is related to the structure of underlying networks is of central interest to unfold how information is processed by these systems. Here we focus on the interplay between local properties like input-output gain function and recurrent synaptic self-excitation of cortical modules, and nonlocal intermodular synaptic couplings yielding to define a multiscale neural field. In this framework, we work out analytic expressions for the wave speed and the stochastic diffusion of propagating fronts uncovering the existence of an optimal balance between local and nonlocal connectivity which minimizes the fluctuations of the activation front propagation. Incorporating an activity-dependent adaptation of local excitability further highlights the independent role that local and nonlocal connectivity play in modulating the speed of propagation of the activation and silencing wavefronts, respectively. Inhomogeneities in space of local excitability give raise to a novel hysteresis phenomenon such that the speed of waves traveling in opposite directions display different velocities in the same location. Taken together these results provide insights on the multiscale organization of brain slow-waves measured during deep sleep and anesthesia.
DEFF Research Database (Denmark)
Ahmadi, Seyed Hamid; Sepaskhah, A R; Andersen, Mathias Neumann
2014-01-01
Root length density (RLD) is a highly wanted parameter for use in crop growth modeling but difficult to measure under field conditions. Therefore, artificial neural networks (ANNs) were implemented to predict the RLD of field grown potatoes that were subject to three irrigation strategies and three...... soil textures with different soil water status and soil densities. The objectives of the study were to test whether soil textural information, soil water status, and soil density might be used by ANN to simulate RLD at harvest. In the study 63 data pairs were divided into data sets of training (80......% of the data) and testing (20% of the data). A feed forward three-layer perceptron network and the sigmoid, hyperbolic tangent, and linear transfer functions were used for the ANN modeling. The RLDs (target variable) in different soil layers were predicted by nine ANNs representing combinations (models...
A Case Study on Neural Inspired Dynamic Memory Management Strategies for High Performance Computing.
Energy Technology Data Exchange (ETDEWEB)
Vineyard, Craig Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Verzi, Stephen Joseph [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-09-01
As high performance computing architectures pursue more computational power there is a need for increased memory capacity and bandwidth as well. A multi-level memory (MLM) architecture addresses this need by combining multiple memory types with different characteristics as varying levels of the same architecture. How to efficiently utilize this memory infrastructure is an unknown challenge, and in this research we sought to investigate whether neural inspired approaches can meaningfully help with memory management. In particular we explored neurogenesis inspired re- source allocation, and were able to show a neural inspired mixed controller policy can beneficially impact how MLM architectures utilize memory.
Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin
2015-11-01
In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.
Energy Technology Data Exchange (ETDEWEB)
Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin, E-mail: xmli@cqu.edu.cn [Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044 (China); College of Automation, Chongqing University, Chongqing 400044 (China)
2015-11-15
In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.
Advances in dynamic and mean field games theory, applications, and numerical methods
Viscolani, Bruno
2017-01-01
This contributed volume considers recent advances in dynamic games and their applications, based on presentations given at the 17th Symposium of the International Society of Dynamic Games, held July 12-15, 2016, in Urbino, Italy. Written by experts in their respective disciplines, these papers cover various aspects of dynamic game theory including mean-field games, stochastic and pursuit-evasion games, and computational methods for dynamic games. Topics covered include Pedestrian flow in crowded environments Models for climate change negotiations Nash Equilibria for dynamic games involving Volterra integral equations Differential games in healthcare markets Linear-quadratic Gaussian dynamic games Aircraft control in wind shear conditions Advances in Dynamic and Mean-Field Games presents state-of-the-art research in a wide spectrum of areas. As such, it serves as a testament to the continued vitality and growth of the field of dynamic games and their applications. It will be of interest to an interdisciplinar...
Dynamics and stress field of the Eurasian plate
Warners-Ruckstuhl, Karin; Govers, Rob; Wortel, Rinus
2013-04-01
We address the connection between forces on the Eurasian plate, the plate's motion and the intraplate stress field. Resistive forces along convergent plate boundaries have a major impact on surface deformation, most visibly at collisional plate boundaries. Although quantification of these forces is key to understanding the evolution and present state of mountain belts, they remain highly uncertain due to the complexity of plate boundary structures and rheologies. In this study we analyse the forces along the southern boundary of the Eurasian plate, presently the most prominent suture zone on Earth, resulting from the closure of the Neo-Tethys ocean. We address the dynamics of the Eurasian plate as a whole. This enables us to base our analysis on mechanical equilibrium of a tectonic plate and to evaluate the force distribution along the Tethyan boundary as part of an internally consistent set of forces driving and deforming Eurasia. We evaluate force distributions obeying this mechanical law on the basis of their ability to reproduce observed stress orientations. We incorporate tractions from convective mantle flow modelling in a lithospheric model in which edge and lithospheric body forces are modelled explicitly and compute resulting stresses in a homogeneous elastic thin shell. Our investigation is structured according to two research objectives, pursued in a corresponding step-wise approach: (1) a detailed understanding of the sensitivity of Eurasia's stress field to the distribution of all acting forces; and (2) a quantification of collision-related forces along the southern boundary of Eurasia, including their relation to observed plate boundary structure, in particular plateau height. Intraplate stress observations as compiled in the World Stress Map project are used to constrain the distribution of forces acting on Eurasia. Eurasia's stress field turns out to be sensitive to the distribution of collision forces on the plate's southern margin and, to a lesser
Self-similarity and quasi-idempotence in neural networks and related dynamical systems
Minati, Ludovico; Winkel, Julia; Bifone, Angelo; Oświecimka, Paweł; Jovicich, Jorge
2017-04-01
Self-similarity across length scales is pervasively observed in natural systems. Here, we investigate topological self-similarity in complex networks representing diverse forms of connectivity in the brain and some related dynamical systems, by considering the correlation between edges directly connecting any two nodes in a network and indirect connection between the same via all triangles spanning the rest of the network. We note that this aspect of self-similarity, which is distinct from hierarchically nested connectivity (coarse-grain similarity), is closely related to idempotence of the matrix representing the graph. We introduce two measures, ι ( 1 ) and ι ( ∞ ) , which represent the element-wise correlation coefficients between the initial matrix and the ones obtained after squaring it once or infinitely many times, and term the matrices which yield large values of these parameters "quasi-idempotent". These measures delineate qualitatively different forms of "shallow" and "deep" quasi-idempotence, which are influenced by nodal strength heterogeneity. A high degree of quasi-idempotence was observed for partially synchronized mean-field Kuramoto oscillators with noise, electronic chaotic oscillators, and cultures of dissociated neurons, wherein the expression of quasi-idempotence correlated strongly with network maturity. Quasi-idempotence was also detected for macro-scale brain networks representing axonal connectivity, synchronization of slow activity fluctuations during idleness, and co-activation across experimental tasks, and preliminary data indicated that quasi-idempotence of structural connectivity may decrease with ageing. This initial study highlights that the form of network self-similarity indexed by quasi-idempotence is detectable in diverse dynamical systems, and draws attention to it as a possible basis for measures representing network "collectivity" and pattern formation.
Directory of Open Access Journals (Sweden)
T. R. Sun
2012-08-01
Full Text Available We performed global MHD simulations of the geosynchronous magnetic field in response to fast solar wind dynamic pressure (P_{d} enhancements. Taking three P_{d} enhancement events in 2000 as examples, we found that the main features of the total field B and the dominant component B_{z} can be efficiently predicted by the MHD model. The predicted B and B_{z} varies with local time, with the highest level near noon and a slightly lower level around mid-night. However, it is more challenging to accurately predict the responses of the smaller component at the geosynchronous orbit (i.e., B_{x} and B_{y}. In contrast, the limitations of T01 model in predicting responses to fast P_{d} enhancements are presented.
Education and Education Research: Moribund Fields or Dynamic Interacting Systems?
Reddy, C.
2011-01-01
The complex field of education is often depicted as a static field governed by technocratic approaches to activities that characterise the field. Education change is equally viewed in such limited and positivistic ways and linear means-end processes (Hoban 2002). In such orientations to the field, educational research therefore, is about finding…
Neural Network Based Reactive Navigation for Mobile Robot in Dynamic Environment
Czech Academy of Sciences Publication Activity Database
Krejsa, Jiří; Věchet, S.; Ripel, T.
2013-01-01
Roč. 198, č. 2013 (2013), s. 108-113 ISSN 1012-0394 Institutional research plan: CEZ:AV0Z20760514 Institutional support: RVO:61388998 Keywords : mobile robot * reactive navigation * artificial neural networks Subject RIV: JD - Computer Applications, Robot ics
Neural estimation of kinetic rate constants from dynamic PET-scans
DEFF Research Database (Denmark)
Fog, Torben L.; Nielsen, Lars Hupfeldt; Hansen, Lars Kai
1994-01-01
A feedforward neural net is trained to invert a simple three compartment model describing the tracer kinetics involved in the metabolism of [18F]fluorodeoxyglucose in the human brain. The network can estimate rate constants from positron emission tomography sequences and is about 50 times faster...... than direct fitting of rate constants using the parametrized transients of the compartment model...
Directory of Open Access Journals (Sweden)
Evangelos Stromatias
2017-06-01
Full Text Available This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77% and Poker-DVS (100% real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.
Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé
2017-01-01
This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.
Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang
2014-06-01
This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.
Bu, Xiangwei; He, Guangjun; Wang, Ke
2018-02-16
This study considers the design of a new back-stepping control approach for air-breathing hypersonic vehicle (AHV) non-affine models via neural approximation. The AHV's non-affine dynamics is decomposed into velocity subsystem and altitude subsystem to be controlled separately, and robust adaptive tracking control laws are developed using improved back-stepping designs. Neural networks are applied to estimate the unknown non-affine dynamics, which guarantees the addressed controllers with satisfactory robustness against uncertainties. In comparison with the existing control methodologies, the special contributions are that the non-affine issue is handled by constructing two low-pass filters based on model transformations, and virtual controllers are treated as intermediate variables such that they aren't needed for back-stepping designs any more. Lyapunov techniques are employed to show the uniformly ultimately boundedness of all closed-loop signals. Finally, simulation results are presented to verify the tracking performance and superiorities of the investigated control strategy. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Landscape agronomy : a new field for addressing agricultural landscape dynamics
Marraccini, Élisa; Moonen, Anna Camilla; Galli, Mariassunta; Lardon, Sylvie; Rapey, Hélène; Thenail, Claudine; Bonari, Enrico
2012-01-01
Landscape dynamics increasingly challenge agronomists to explain how and why agricultural landscapes are designed and managed by farmers. Nevertheless, agronomy is rarely included in the wide range of disciplines involved in landscape research. In this paper, we describe how landscape agronomy can help explain the relationship between farming systems and agricultural landscape dynamics. For this, we propose a conceptual model of agricultural landscape dynamics that illustrates the specific co...
Neural Networks: Implementations and Applications
Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.
1996-01-01
Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas
Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias
2008-12-01
We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.
An Exploration of the System Dynamics Field : A Model-Based Policy Analysis
Rose, A.C.
2014-01-01
This report presents a first look study at the field of System Dynamics. The objective of the study is to perform a model-based policy analysis in order to investigate the future advancement of the System Dynamics field. The aim of this investigation is to determine what this advancement should look
Local field corrections in the lattice dynamics of chromium | Ndukwe ...
African Journals Online (AJOL)
s – d hybridizing effects constitute the major part of local field effects in transition metals. Therefore local field corrections to phonon frequencies in transition metals are taken to be mainly as a result of these hybridizing effects. This work extends the inclusion of local field corrections in the calculation of the phonon dispersion ...